From 6f4de1b53fb7ab0e67880e8920375f55db6ac05c Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Fri, 16 Jun 2017 12:31:06 +0200 Subject: [PATCH 01/72] [SW-466] Separate DEVEL.md into multiple files and convert them to rst - also make the documentation up-to-date - fix few typos --- DEVEL.md | 211 +--------------------------------------- README.rst | 2 + doc/datasource.rst | 163 +++++++++++++++++++++++++++++++ doc/internal_backend_tuning.rst | 79 +++++++++++++++ doc/zeppelin.rst | 48 +++++++++ 5 files changed, 297 insertions(+), 206 deletions(-) create mode 100644 doc/datasource.rst create mode 100644 doc/internal_backend_tuning.rst create mode 100644 doc/zeppelin.rst diff --git a/DEVEL.md b/DEVEL.md index f2ee20bc3..5309c8a2f 100644 --- a/DEVEL.md +++ b/DEVEL.md @@ -18,7 +18,7 @@ - [Configuration](#Config) - [Build Environment](#BuildEnv) - [Run Environment](#RunEnv) - - [Sparkling Water Configuration Properties](#Properties) + - [Sparkling Water Configuration Properties](doc/configuration_properties.rst) - [Running Sparkling Water](#RunSW) - [Starting H2O Services](#StartH2O) - [Memory Allocation](#MemorySetup) @@ -40,12 +40,9 @@ - [Integration Tests Example](#IntegExample) - [Troubleshooting and Log Locations](#Logging) - [Sparkling Shell Console Output](#log4j) -- [H2O Frame as Spark's Data Source](#DataSource) - - [Usage in Python - pySparkling](#DataSourcePython) - - [Usage in Scala](#DataSourceScala) - - [Specifying Saving Mode](#SavingMode) -- [Sparkling Water Tuning](#SparklingWaterTuning) -- [Sparkling Water and Zeppelin](#SparklingWaterZeppelin) +- [H2O Frame as Spark's Data Source](doc/datasource.rst) +- [Sparkling Water Tuning](doc/internal_backend_tuning.rst) +- [Sparkling Water and Zeppelin](doc/zeppelin.rst) --- @@ -211,11 +208,6 @@ The environment must contain the property `SPARK_HOME` that points to the Spark --- - -### Sparkling Water Configuration Properties - -All available Sparkling Water configuration properties are listed at [Sparkling Water Properties](doc/configuration_properties.rst). - # Running Sparkling Water @@ -705,197 +697,4 @@ to: ... ``` ---- - - -## H2O Frame as Spark's Data Source - The way how H2O Frame can be used as Spark's Data Source differs a little bit in Python and Scala. - -### Usage in Python - pySparkling - -#### Reading from H2O Frame -Let's suppose we have H2OFrame `frame`. - -There are two ways how dataframe can be loaded from H2OFrame in pySparkling: - -``` -df = sqlContext.read.format("h2o").option("key",frame.frame_id).load() -``` - -or - -``` -df = sqlContext.read.format("h2o").load(frame.frame_id) -``` - -#### Saving to H2O Frame -Let's suppose we have DataFrame `df`. - -There are two ways how dataframe can be saved as H2OFrame in pySparkling: - -``` -df.write.format("h2o").option("key","new_key").save() -``` - -or - -``` -df.write.format("h2o").save("new_key") -``` - -Both variants save dataframe as H2OFrame with key "new_key". They won't succeed if the H2OFrame with the same key already exists. - -#### Loading & Saving Options -If the key is specified as 'key' option and also in the load/save method, the option 'key' is preferred - -``` -df = sqlContext.read.from("h2o").option("key","key_one").load("key_two") -``` - -or - -``` -df = sqlContext.read.from("h2o").option("key","key_one").save("key_two") -``` - -In both examples, "key_one" is used. - - -### Usage in Scala - -#### Reading from H2O Frame -Let's suppose we have H2OFrame `frame` - -The shortest way how dataframe can be loaded from H2OFrame with default settings is: - -``` -val df = sqlContext.read.h2o(frame.key) -``` - -There are two more ways how dataframe can be loaded from H2OFrame allowing us to specify additional options: - -``` -val df = sqlContext.read.format("h2o").option("key",frame.key.toString).load() -``` - -or - -``` -val df = sqlContext.read.format("h2o").load(frame.key.toString) -``` - -#### Saving to H2O Frame -Let's suppose we have DataFrame `df` - -The shortest way how dataframe can be saved as H2O Frame with default settings is: - -``` -df.write.h2o("new_key") -``` - -There are two more ways how dataframe can be saved as H2OFrame allowing us to specify additional options: - -``` -df.write.format("h2o").option("key","new_key").save() -``` - -or - -``` -df.write.format("h2o").save("new_key") -``` - -All three variants save dataframe as H2OFrame with key "new_key". They won't succeed if the H2O Frame with the same key already exists - -#### Loading & Saving Options -If the key is specified as 'key' option and also in the load/save method, the option 'key' is preferred - -``` -val df = sqlContext.read.from("h2o").option("key","key_one").load("key_two") -``` - -or - -``` -val df = sqlContext.read.from("h2o").option("key","key_one").save("key_two") -``` - -In both examples, "key_one" is used. - - -### Specifying Saving Mode -There are four save modes available when saving data using Data Source API- see http://spark.apache.org/docs/latest/sql-programming-guide.html#save-modes - -If "append" mode is used, an existing H2OFrame with the same key is deleted and new one containing union of -all rows from original H2O Frame and appended Data Frame is created with the same key. - -If "overwrite" mode is used, an existing H2OFrame with the same key is deleted and new one with the new rows is created with the same key. - -If "error" mode is used and a H2OFrame with the specified key already exists, exception is thrown. - -if "ignore" mode is used and a H2OFrame with the specified key already exists, no data are changed. - - -## Sparkling Water Tuning -For running Sparkling Water general recommendation are: - - increase available memory in driver and executors (options `spark.driver.memory` resp., `spark.yarn.am.memory` and `spark.executor.memory`), - - make cluster homogeneous - use the same value for driver and executor memory - - increase PermGen size if you are running on top of Java7 (options `spark.driver.extraJavaOptions` resp., `spark.yarn.am.extraJavaOptions` and `spark.executor.extraJavaOptions`) - - in rare cases, it helps to increase `spark.yarn.driver.memoryOverhead`, `spark.yarn.am.memoryOverhead`, or `spark.yarn.executor.memoryOverhead` - -For running Sparkling Water on top of Yarn: - - make sure that Yarn provides stable containers, do not use preemptive Yarn scheduler - - make sure that Spark application manager has enough memory and increase PermGen size - - in case of a container failure Yarn should not restart container and application should gracefully terminate - - -Furthermore, we recommend to configure the following Spark properties to speedup and stabilize creation of H2O services on top of Spark cluster: - -| Property | Context | Value | Explanation | -|----------|---------|-------|-------------| -| `spark.locality.wait` | all | `3000` | Number of seconds to wait for task launch on data-local node. We recommend to increase since we would like to make sure that H2O tasks are processed locally with data.| -| `spark.scheduler.minRegisteredResourcesRatio` | all| `1` | Make sure that Spark starts scheduling when it sees 100% of resources. | -| `spark.task.maxFailures` | all | `1`| Do not try to retry failed tasks. | -| `spark...extraJavaOptions` | all| `-XX:MaxPermSize=384m` | Increase PermGen size if you are running on Java7. Make sure to configure it on driver/executor/Yarn application manager. | -| `spark.yarn.....memoryOverhead` | yarn | increase | Increase memoryOverhead if it is necessary. | -| `spark.yarn.max.executor.failures` | yarn | `1` | Do not try restart executors after failure and directly fail computation. | -| `spark.executor.heartbeatInterval` | all | `10s` | Interval between each executor's heartbeats to the driver. This property should be significantly less than spark.network.timeout. | - - -## Sparkling Water and Zeppelin -Since Sparkling Water exposes Scala API, it is possible to access it directly from the Zeppelin's notebook cell marked by `%spark` tag. - -### Launch Zeppelin with Sparkling Water -Using Sparkling Water from Zeppelin is easy since Sparkling Water is distributed as a Spark package. -In this case, before launching Zeppelin addition shell variable is needed: - -```bash -export SPARK_HOME=...# Spark 2.0 home -export SPARK_SUBMIT_OPTIONS="--packages ai.h2o:sparkling-water-examples_2.11:2.0.0" -bin/zeppelin.sh -Pspark-2.0 -``` - -The command is using Spark 2.0 version and corresponding Sparkling Water package. - -### Using Zeppelin -The use of Sparkling Water package is directly driven by Sparkling Water API. For example, getting `H2OContext` is straightforward: - -```scala -%spark -import org.apache.spark.h2o._ -val hc = H2OContext.getOrCreate(sc) -``` - -Creating `H2OFrame` from Spark `DataFrame`: -```scala -%spark -val df = sc.parallelize(1 to 1000).toDF -val hf = hc.asH2OFrame(df) -``` - -Creating Spark `DataFrame` from `H2OFrame`: -```scala -%spark -val df = hc.asDataFrame(hf) -``` - +--- \ No newline at end of file diff --git a/README.rst b/README.rst index dc5fdcd79..cccdd31ed 100644 --- a/README.rst +++ b/README.rst @@ -168,6 +168,8 @@ List of all Frequently Asked Questions is available at `FAQ `__. Development ----------- +Complete development documentation is available at `Development Documentation `__. + Build Sparkling Water ~~~~~~~~~~~~~~~~~~~~~ diff --git a/doc/datasource.rst b/doc/datasource.rst new file mode 100644 index 000000000..a6483fbdb --- /dev/null +++ b/doc/datasource.rst @@ -0,0 +1,163 @@ +H2O Frame as Spark's Data Source +-------------------------------- + +The way how H2O Frame can be used as Spark's Data Source differs a +little bit in Python and Scala. + +Quick links: + - `Usage in Python - PySparkling`_ + - `Usage in Scala`_ + - `Specifying Saving Mode`_ + +Usage in Python - PySparkling +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Reading from H2O Frame +^^^^^^^^^^^^^^^^^^^^^^ + +Let's suppose we have H2OFrame ``frame``. + +There are two ways how dataframe can be loaded from H2OFrame in +PySparkling: + +.. code:: python + + df = spark.read.format("h2o").option("key", frame.frame_id).load() + +or + +.. code:: python + + df = spark.read.format("h2o").load(frame.frame_id) + +Saving to H2O Frame +^^^^^^^^^^^^^^^^^^^ + +Let's suppose we have DataFrame ``df``. + +There are two ways how dataframe can be saved as H2OFrame in +PySparkling: + +.. code:: python + + df.write.format("h2o").option("key", "new_key").save() + +or + +.. code:: python + + df.write.format("h2o").save("new_key") + +Both variants save dataframe as H2OFrame with key ``new_key``. They +don't succeed if the H2OFrame with the same key already exists. + +Loading & Saving Options +^^^^^^^^^^^^^^^^^^^^^^^^ + +If the key is specified as ``key`` option and also in the ``load/save`` +method, the option ``key`` is preferred + +.. code:: python + + df = spark.read.from("h2o").option("key", "key_one").load("key_two") + +or + +.. code:: python + + df = spark.read.from("h2o").option("key", "key_one").save("key_two") + +In both examples, ``key_one`` is used. + +Usage in Scala +~~~~~~~~~~~~~~ + +Reading from H2O Frame +^^^^^^^^^^^^^^^^^^^^^^ + +Let's suppose we have H2OFrame ``frame``. + +The shortest way how dataframe can be loaded from H2OFrame with default +settings is: + +.. code:: scala + + val df = spark.read.h2o(frame.key) + +There are two more ways how dataframe can be loaded from H2OFrame. These calls allow +us to specify additional options: + +.. code:: scala + + val df = spark.read.format("h2o").option("key", frame.key.toString).load() + +or + +.. code:: scala + + val df = spark.read.format("h2o").load(frame.key.toString) + +Saving to H2O Frame +^^^^^^^^^^^^^^^^^^^ + +Let's suppose we have DataFrame ``df``. + +The shortest way how dataframe can be saved as H2O Frame with default +settings is: + +.. code:: scala + + df.write.h2o("new_key") + +There are two more ways how dataframe can be saved as H2OFrame. These calls allow +us to specify additional options: + +.. code:: scala + + df.write.format("h2o").option("key", "new_key").save() + +or + +.. code:: scala + + df.write.format("h2o").save("new_key") + +All three variants save dataframe as H2OFrame with key ``new_key``. They +don't succeed if the H2OFrame with the same key already exists. + +Loading & Saving Options +^^^^^^^^^^^^^^^^^^^^^^^^ + +If the key is specified as ``key`` option and also in the ``load/save`` +method, the option ``key`` is preferred + +.. code:: scala + + val df = spark.read.from("h2o").option("key", "key_one").load("key_two") + +or + +.. code:: scala + + val df = spark.read.from("h2o").option("key", "key_one").save("key_two") + +In both examples, ``key_one`` is used. + +Specifying Saving Mode +~~~~~~~~~~~~~~~~~~~~~~ + +There are 4 save modes available when saving data using Data Source +API - ``append``, ``overwrite``, ``error`` and ``ignore``. The full description is available at `Spark Save Modes `__. + +- If ``append`` mode is used, an existing H2OFrame with the same key is + deleted and new one containing union of all rows from original H2O Frame + and appended Data Frame is created with the same key. + +- If ``overwrite`` mode is used, an existing H2OFrame with the same key is + deleted and new one with the new rows is created with the same key. + +- If ``error`` mode is used and a H2OFrame with the specified key already + exists, exception is thrown. + +- If ``ignore`` mode is used and a H2OFrame with the specified key already + exists, no data is changed. diff --git a/doc/internal_backend_tuning.rst b/doc/internal_backend_tuning.rst new file mode 100644 index 000000000..4bfa32356 --- /dev/null +++ b/doc/internal_backend_tuning.rst @@ -0,0 +1,79 @@ +Sparkling Water Tuning +---------------------- + +For running Sparkling Water general recommendation are: + - Increase available memory in driver and executors (options ``spark.driver.memory`` resp., ``spark.yarn.am.memory`` and ``spark.executor.memory``). + - Make cluster homogeneous - use the same value for driver and executor memory. + - Increase PermGen size if you are running on top of Java7 (options ``spark.driver.extraJavaOptions`` resp., ``spark.yarn.am.extraJavaOptions`` and ``spark.executor.extraJavaOptions``). + - In rare cases, it helps to increase ``spark.yarn.driver.memoryOverhead``, ``spark.yarn.am.memoryOverhead``, or ``spark.yarn.executor.memoryOverhead``. + +For running Sparkling Water on top of Yarn: + - Make sure that Yarn provides stable containers, do not use preemptive Yarn scheduler. + - Make sure that Spark application manager has enough memory and increase PermGen size. + - In case of a container failure, Yarn should not restart container and application should gracefully terminate. + +Furthermore, we recommend to configure the following Spark properties to +speedup and stabilize creation of H2O services on top of Spark cluster: + ++-------------------------------------------------+--------------------------+----------------------------+ +| Property | Value | Explanation | ++=================================================+==========================+============================+ +| **All environments (YARN/Standalone/Local)** | | | ++-------------------------------------------------+--------------------------+----------------------------+ +| ``spark.locality.wait`` | ``3000`` | Number of seconds to wait | +| | | for task launched on | +| | | data-local node. We | +| | | recommend to increase | +| | | since we would like to | +| | | make sure that H2O tasks | +| | | are processed locally | +| | | with data. | ++-------------------------------------------------+--------------------------+----------------------------+ +| ``spark.scheduler.minRegisteredResourcesRatio`` | ``1`` | Make sure that Spark | +| | | starts scheduling when it | +| | | sees 100% of resources. | ++-------------------------------------------------+--------------------------+----------------------------+ +| ``spark.task.maxFailures`` | ``1`` | Do not try to retry | +| | | failed tasks. | ++-------------------------------------------------+--------------------------+----------------------------+ +| ``spark.driver.extraJavaOptions`` | ``-XX:MaxPermSize=384m`` | Increase PermGem if you | +| | | are running in Java7 on | +| | | the Spark driver. | ++-------------------------------------------------+--------------------------+----------------------------+ +| ``spark.executor.extraJavaOptions`` | ``-XX:MaxPermSize=384m`` | Increase PermGem if you | +| | | are running in Java7 on | +| | | the Spark executor. | ++-------------------------------------------------+--------------------------+----------------------------+ +| ``spark.executor.heartbeatInterval`` | ``10s`` | Interval between each | +| | | executor heartbeats to | +| | | the driver. This property | +| | | should be significantly | +| | | less than | +| | | ``spark.network.timeout``. | ++-------------------------------------------------+--------------------------+----------------------------+ +| **YARN environment** | | | ++-------------------------------------------------+--------------------------+----------------------------+ +| ``spark.yarn.am.extraJavaOptions`` | ``-XX:MaxPermSize=384m`` | Increase PermGem if you | +| | | are running in Java7 on | +| | | the Yarn application | +| | | master. | ++-------------------------------------------------+--------------------------+----------------------------+ +| ``spark.yarn.driver.memoryOverhead`` | increase | Increase memory overhead | +| | | if it's necessary of the | +| | | container with | +| | | driver node. | ++-------------------------------------------------+--------------------------+----------------------------+ +| ``spark.yarn.executor.memoryOverhead`` | increase | Increase memory overhead | +| | | if it's necessary of the | +| | | containers with | +| | | executor nodes. | ++-------------------------------------------------+--------------------------+----------------------------+ +| ``spark.yarn.am.memoryOverhead`` | increase | Increase memory overhead | +| | | if it's necessary of the | +| | | Yarn application master. | ++-------------------------------------------------+--------------------------+----------------------------+ +| ``spark.yarn.max.executor.failures`` | ``1`` | Do not try to restart | +| | | executors after failure | +| | | and directly fail the | +| | | computation. | ++-------------------------------------------------+--------------------------+----------------------------+ diff --git a/doc/zeppelin.rst b/doc/zeppelin.rst new file mode 100644 index 000000000..741fdf199 --- /dev/null +++ b/doc/zeppelin.rst @@ -0,0 +1,48 @@ +Sparkling Water and Zeppelin +---------------------------- + +Since Sparkling Water exposes Scala API, it is possible to access it +directly from the Zeppelin's notebook cell marked by ``%spark`` tag. + +Launch Zeppelin with Sparkling Water +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Using Sparkling Water from Zeppelin is easy since Sparkling Water is +distributed as a Spark package. In this case, before launching Zeppelin +addition shell variable is needed: + +.. code:: bash + + export SPARK_HOME=...# Spark 2.1 home + export SPARK_SUBMIT_OPTIONS="--packages ai.h2o:sparkling-water-examples_2.11:2.1.0" + bin/zeppelin.sh -Pspark-2.1 + +The command is using Spark 2.1 version and corresponding Sparkling Water +package. + +Using Zeppelin +~~~~~~~~~~~~~~ + +The use of Sparkling Water package is directly driven by Sparkling Water +API. For example, getting ``H2OContext`` is straightforward: + +.. code:: scala + + %spark + import org.apache.spark.h2o._ + val hc = H2OContext.getOrCreate(spark) + +Creating ``H2OFrame`` from Spark ``DataFrame``: + +.. code:: scala + + %spark + val df = sc.parallelize(1 to 1000).toDF + val hf = hc.asH2OFrame(df) + +Creating Spark ``DataFrame`` from ``H2OFrame``: + +.. code:: scala + + %spark + val df = hc.asDataFrame(hf) From 74e31bd70469a3c843c70c1a32fc3cf02092eb3c Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Mon, 19 Jun 2017 12:18:29 +0200 Subject: [PATCH 02/72] Extract doc for changing the log level --- DEVEL.md | 31 ++----------------------------- doc/change_log_level.rst | 29 +++++++++++++++++++++++++++++ 2 files changed, 31 insertions(+), 29 deletions(-) create mode 100644 doc/change_log_level.rst diff --git a/DEVEL.md b/DEVEL.md index 5309c8a2f..cf1173a99 100644 --- a/DEVEL.md +++ b/DEVEL.md @@ -39,7 +39,7 @@ - [Testing Scenarios](#TestCases) - [Integration Tests Example](#IntegExample) - [Troubleshooting and Log Locations](#Logging) -- [Sparkling Shell Console Output](#log4j) +- [Change Sparkling Shell Logging Level](doc/change_log_level.rst) - [H2O Frame as Spark's Data Source](doc/datasource.rst) - [Sparkling Water Tuning](doc/internal_backend_tuning.rst) - [Sparkling Water and Zeppelin](doc/zeppelin.rst) @@ -670,31 +670,4 @@ the application master which is also the Spark master. Then run to get the yarn ``` --- - - -## Sparkling Shell Console Output -The console output for Sparkling Shell by default will show a verbose Spark output as well as H2O logs. If you would like to switch the output to -only warnings from Spark, you will need to change it in the log4j properities file in Spark's configuration directory. To do this: - - ``` - cd $SPARK_HOME/conf - cp log4j.properties.template log4j.properties - ``` - -Then either in a text editor or vim to change the contents of the log4j.properties file from: - - ``` - #Set everything to be logged to the console - log4j.rootCategory=INFO, console - ... - ``` - -to: - - ``` - #Set everything to be logged to the console - log4j.rootCategory=WARN, console - ... - ``` - ---- \ No newline at end of file + \ No newline at end of file diff --git a/doc/change_log_level.rst b/doc/change_log_level.rst new file mode 100644 index 000000000..53a3e1397 --- /dev/null +++ b/doc/change_log_level.rst @@ -0,0 +1,29 @@ +Change Sparkling Shell Logging Level +------------------------------------ + +The console output for Sparkling Water Shell by default shows verbose +Spark output as well as H2O logs. If you would like to switch the output +to only warnings from Spark, you need to change it in the log4j +properties file in the Spark's configuration directory. To do this: + +.. code:: shell + + cd $SPARK_HOME/conf + cp log4j.properties.template log4j.properties + +Then either in a text editor or vim change the contents of the +log4j.properties file from: + +.. code:: shell + + #Set everything to be logged to the console + log4j.rootCategory=INFO, console + ... + +to: + +.. code:: shell + + #Set everything to be logged to the console + log4j.rootCategory=WARN, console + ... From 799598ec86bad583bbd365f4b870948564a691c3 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Mon, 19 Jun 2017 12:34:28 +0200 Subject: [PATCH 03/72] Extract docs for log location --- DEVEL.md | 34 ++-------------------------------- doc/log_location.rst | 34 ++++++++++++++++++++++++++++++++++ 2 files changed, 36 insertions(+), 32 deletions(-) create mode 100644 doc/log_location.rst diff --git a/DEVEL.md b/DEVEL.md index cf1173a99..d98a9ca5d 100644 --- a/DEVEL.md +++ b/DEVEL.md @@ -38,7 +38,7 @@ - [Testing Environment](#TestEnv) - [Testing Scenarios](#TestCases) - [Integration Tests Example](#IntegExample) -- [Troubleshooting and Log Locations](#Logging) +- [Sparkling Water Log Locations](doc/log_location.rst) - [Change Sparkling Shell Logging Level](doc/change_log_level.rst) - [H2O Frame as Spark's Data Source](doc/datasource.rst) - [Sparkling Water Tuning](doc/internal_backend_tuning.rst) @@ -640,34 +640,4 @@ Spark 1.6.0+ is required. val train = prostateRDD.map( v => Vectors.dense(v.CAPSULE.get*1.0, v.AGE.get*1.0, v.DPROS.get*1.0,v.DCAPS.get*1.0, v.GLEASON.get*1.0)) val clusters = KMeans.train(train, 5, 20) ``` - ---- - - -## Troubleshooting and Log Locations -In the event you hit a bug or find that Sparkling Water is not reacting the way it is suppose to, help us improve the product by sending the -[H2O.ai team](support@h2o.ai) the logs. Depending on how you launched H2O there are a couple of ways to obtain the logs. - - -### Logs for Standalone Sparkling Water -By default Spark sets SPARK_LOG_DIR is set to $SPARK_HOME/work/ and if logging needs to be enabled. So when launching Sparkling Shell run: - - ``` - bin/sparkling-shell.sh --conf spark.logConf=true - ``` - -Zip up the log files in $SPARK_HOME/work/ and the directory should contain the assembly jar file and stdout and stderr for -each node in the cluster. - - - -### Logs for Sparkling Water on YARN -When launching Sparkling Water on YARN, you can find the application id for the Yarn job on the resource manager where you can also find -the application master which is also the Spark master. Then run to get the yarn logs: - - ``` - yarn logs -applicationId - ``` - ---- - \ No newline at end of file + \ No newline at end of file diff --git a/doc/log_location.rst b/doc/log_location.rst new file mode 100644 index 000000000..c743ce8dd --- /dev/null +++ b/doc/log_location.rst @@ -0,0 +1,34 @@ +Sparkling Water Log Location +---------------------------- + +Depending on how you launched H2O there are a couple of ways to obtain +the logs. + +Logs for Sparkling Water on YARN +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +When launching Sparkling Water on YARN, you can find the application id +for the Yarn job on the resource manager where you can also find the +application master which is also the Spark master. Following command prints +the yarn logs to the console: + +.. code:: shell + + yarn logs -applicationId + + +Logs for Standalone Sparkling Water +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +By default, Spark property ``SPARK_LOG_DIR`` is set to +``$SPARK_HOME/work/``. To also log the configuration with which the +Spark is started, start Sparkling Water with the following +configuration: + +.. code:: shell + + bin/sparkling-shell.sh --conf spark.logConf=true + +The logs for the particular application are located at +``$SPARK_HOME/work/``. The directory contains also +stdout and stderr for each node in the cluster. From 93c73531118dd68bfd23edd85a980f833b9540ab Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Mon, 19 Jun 2017 14:36:06 +0200 Subject: [PATCH 04/72] Convert integ_tests --- DEVEL.md | 170 +----------------------------------------- doc/integ_tests.rst | 208 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 209 insertions(+), 169 deletions(-) create mode 100644 doc/integ_tests.rst diff --git a/DEVEL.md b/DEVEL.md index d98a9ca5d..7b0b16a0d 100644 --- a/DEVEL.md +++ b/DEVEL.md @@ -34,10 +34,7 @@ - [Creating H2OFrame from an existing key](#CreateDF) - [Calling H2O Algorithms](#CallAlgos) - [Running Unit Tests](#UnitTest) -- [Integration Tests](#IntegTest) - - [Testing Environment](#TestEnv) - - [Testing Scenarios](#TestCases) - - [Integration Tests Example](#IntegExample) +- [Integration Tests](doc/integ_tests.rst) - [Sparkling Water Log Locations](doc/log_location.rst) - [Change Sparkling Shell Logging Level](doc/change_log_level.rst) - [H2O Frame as Spark's Data Source](doc/datasource.rst) @@ -476,168 +473,3 @@ You can find Sparkling Water self-contained application skeleton in [Droplet rep - TODO: used datasources, how data is moved to spark - TODO: platform testing - mesos, SIMR - - ---- - -# Integration Tests - ---- - -## Testing Environments - * Local - corresponds to setting Spark `MASTER` variable to one of `local`, or `local[*]`, or `local-cluster[_,_,_]` values - * Standalone cluster - the `MASTER` variable points to existing standalone Spark cluster `spark://...` - * ad-hoc build cluster - * CDH5.3 provided cluster - * YARN cluster - the `MASTER variable contains `yarn-client` or `yarn-cluster` values - ---- - - -## Testing Scenarios - 1. Initialize H2O on top of Spark by running `H2OContext.getOrCreate(sc)` and verifying that H2O was properly initialized on all Spark nodes. - 2. Load data with help from the H2O API from various data sources: - * local disk - * HDFS - * S3N - 3. Convert from `RDD[T]` to `H2OFrame` - 4. Convert from `DataFrame` to `H2OFrame` - 5. Convert from `H2OFrame` to `RDD` - 6. Convert from `H2OFrame` to `DataFrame` - 7. Integrate with H2O Algorithms using RDD as algorithm input - 8. Integrate with MLlib Algorithms using H2OFrame as algorithm input (KMeans) - 9. Integrate with MLlib pipelines (TBD) - ---- - - -## Integration Tests Example - -The following code reflects the use cases listed above. The code is executed in all testing environments (if applicable): - * local - * standalone cluster - * YARN -Spark 1.6.0+ is required. - -1. Initialize H2O: - - ```scala - import org.apache.spark.h2o._ - val sc = new SparkContext(conf) - val h2oContext = H2OContext.getOrCreate(sc) - import h2oContext._ - ``` -2. Load data: - * From the local disk: - - ```scala - val sc = new SparkContext(conf) - import org.apache.spark.h2o._ - val h2oContext = H2OContext.getOrCreate(sc) - import java.io.File - val df: H2OFrame = new H2OFrame(new File("examples/smalldata/allyears2k_headers.csv.gz")) - ``` - > Note: The file must be present on all nodes. - - * From HDFS: - - ```scala - val sc = new SparkContext(conf) - import org.apache.spark.h2o._ - val h2oContext = H2OContext.getOrCreate(sc) - val path = "hdfs://mr-0xd6.0xdata.loc/datasets/airlines_all.csv" - val uri = new java.net.URI(path) - val airlinesHF = new H2OFrame(uri) - ``` - * From S3N: - - ```scala - val sc = new SparkContext(conf) - import org.apache.spark.h2o._ - val h2oContext = H2OContext.getOrCreate(sc) - val path = "s3n://h2o-airlines-unpacked/allyears2k.csv" - val uri = new java.net.URI(path) - val airlinesHF = new H2OFrame(uri) - ``` - > Spark/H2O needs to know the AWS credentials specified in `core-site.xml`. The credentials are passed via `HADOOP_CONF_DIR` that points to a configuration directory with `core-site.xml`. -3. Convert from `RDD[T]` to `H2oFrame`: - - ```scala - val sc = new SparkContext(conf) - import org.apache.spark.h2o._ - val h2oContext = H2OContext.getOrCreate(sc) - val rdd = sc.parallelize(1 to 1000, 100).map( v => IntHolder(Some(v))) - val hf: H2OFrame = h2oContext.asH2OFrame(rdd) - ``` -4. Convert from `DataFrame` to `H2OFrame`: - - ```scala - val sc = new SparkContext(conf) - import org.apache.spark.h2o._ - val h2oContext = H2OContext.getOrCreate(sc) - import org.apache.spark.sql._ - val sqlContext = new SQLContext(sc) - import sqlContext.implicits._ - val df: DataFrame = sc.parallelize(1 to 1000, 100).map(v => IntHolder(Some(v))).toDF - val hf = h2oContext.asH2OFrame(df) - ``` -5. Convert from `H2OFrame` to `RDD[T]`: - - ```scala - val sc = new SparkContext(conf) - import org.apache.spark.h2o._ - val h2oContext = H2OContext.getOrCreate(sc) - val rdd = sc.parallelize(1 to 1000, 100).map(v => IntHolder(Some(v))) - val hf: H2OFrame = h2oContext.asH2OFrame(rdd) - val newRdd = h2oContext.asRDD[IntHolder](hf) - ``` -6. Convert from `H2OFrame` to `DataFrame`: - - ```scala - val sc = new SparkContext(conf) - import org.apache.spark.h2o._ - val h2oContext = H2OContext.getOrCreate(sc) - import org.apache.spark.sql._ - val sqlContext = new SQLContext(sc) - import sqlContext.implicits._ - val df: DataFrame = sc.parallelize(1 to 1000, 100).map(v => IntHolder(Some(v))).toDF - val hf = h2oContext.asH2OFrame(df) - val newRdd = h2oContext.asDataFrame(hf)(sqlContext) - ``` -7. Integrate with H2O Algorithms using RDD as algorithm input: - - ```scala - val sc = new SparkContext(conf) - import org.apache.spark.h2o._ - import org.apache.spark.examples.h2o._ - val h2oContext = H2OContext.getOrCreate(sc) - val path = "examples/smalldata/prostate.csv" - val prostateText = sc.textFile(path) - val prostateRDD = prostateText.map(_.split(",")).map(row => ProstateParse(row)) - import _root_.hex.tree.gbm.GBM - import _root_.hex.tree.gbm.GBMModel.GBMParameters - import h2oContext._ - val train: H2OFrame = prostateRDD - val gbmParams = new GBMParameters() - gbmParams._train = train - gbmParams._response_column = 'CAPSULE - gbmParams._ntrees = 10 - val gbmModel = new GBM(gbmParams).trainModel.get - ``` -8. Integrate with MLlib algorithms: - - ```scala - val sc = new SparkContext(conf) - import org.apache.spark.h2o._ - import org.apache.spark.examples.h2o._ - import java.io.File - val h2oContext = H2OContext.getOrCreate(sc) - val path = "examples/smalldata/prostate.csv" - val prostateHF = new H2OFrame(new File(path)) - val prostateRDD = h2oContext.asRDD[Prostate](prostateHF) - import org.apache.spark.mllib.clustering.KMeans - import org.apache.spark.mllib.linalg.Vectors - val train = prostateRDD.map( v => Vectors.dense(v.CAPSULE.get*1.0, v.AGE.get*1.0, v.DPROS.get*1.0,v.DCAPS.get*1.0, v.GLEASON.get*1.0)) - val clusters = KMeans.train(train, 5, 20) - ``` - \ No newline at end of file diff --git a/doc/integ_tests.rst b/doc/integ_tests.rst new file mode 100644 index 000000000..6b8afcea9 --- /dev/null +++ b/doc/integ_tests.rst @@ -0,0 +1,208 @@ +Integration Tests +----------------- + +This documentation paper describes what is tested in terms of +integration tests as part of the Sparkling Water project. + +The tests are perform for both Sparkling Water backend types. Please see `Sparkling Water Backends `__ +for more information about the backends. + +Quick links: + - `Testing Environments`_ + - `Testing Scenarios`_ + - `Integration Tests Example`_ + +Testing Environments +-------------------- + +- Local - corresponds to setting Spark ``MASTER`` variable to one of + ``local``, or ``local[*]``, or ``local-cluster[_,_,_]`` values +- Standalone cluster - the ``MASTER`` variable points to existing + standalone Spark cluster ``spark://...`` +- YARN cluster - the ``MASTER`` variable contains ``yarn-client`` or + ``yarn-cluster`` values + +-------------- + +Testing Scenarios +----------------- + +1. Initialize H2O on top of Spark by running + ``H2OContext.getOrCreate(spark)`` and verifying that H2O was properly + initialized. +2. Load data with help from the H2O API from various data sources: + +- local disk +- HDFS +- S3N + +3. Convert from ``RDD[T]`` to ``H2OFrame`` +4. Convert from ``DataFrame`` to ``H2OFrame`` +5. Convert from ``H2OFrame`` to ``RDD`` +6. Convert from ``H2OFrame`` to ``DataFrame`` +7. Integrate with H2O Algorithms using RDD as algorithm input +8. Integrate with MLlib Algorithms using H2OFrame as algorithm input + (KMeans) +9. Integrate with MLlib pipelines + +-------------- + +Integration Tests Example +------------------------- + +The following code reflects the use cases listed above. The code is +executed in all testing environments (if applicable). Spark 2.0+ required: +- local +- standalone cluster +- YARN + +1. Initialize H2O: + +.. code:: scala + + import org.apache.spark.sql.SparkSession + val spark = SparkSession.builder().getOrCreate() + import org.apache.spark.h2o._ + val h2oContext = H2OContext.getOrCreate(spark) + import h2oContext.implicits._ + +2. Load data: + +- From the local disk: + + .. code:: scala + + import org.apache.spark.sql.SparkSession + val spark = SparkSession.builder().getOrCreate() + import org.apache.spark.h2o._ + val h2oContext = H2OContext.getOrCreate(spark) + + import java.io.File + val df: H2OFrame = new H2OFrame(new File("examples/smalldata/allyears2k_headers.csv.gz")) + + Note: The file must be present on all nodes. In case of Sparkling Water internal backend, on all nodes with Spark. In case + of Sparkling Water external backend, on all nodes with H2O. + + +- From HDFS: + + .. code:: scala + + import org.apache.spark.sql.SparkSession + val spark = SparkSession.builder().getOrCreate() + import org.apache.spark.h2o._ + val h2oContext = H2OContext.getOrCreate(spark) + + val path = "hdfs://mr-0xd6.0xdata.loc/datasets/airlines_all.csv" + val uri = new java.net.URI(path) + val airlinesHF = new H2OFrame(uri) + +- From S3N: + + .. code:: scala + + import org.apache.spark.sql.SparkSession + val spark = SparkSession.builder().getOrCreate() + import org.apache.spark.h2o._ + val h2oContext = H2OContext.getOrCreate(spark) + + val path = "s3n://h2o-airlines-unpacked/allyears2k.csv" + val uri = new java.net.URI(path) + val airlinesHF = new H2OFrame(uri) + + Note: Spark/H2O needs to know the AWS credentials specified in ``core-site.xml``. The credentials are passed via ``HADOOP_CONF_DIR`` + that points to a configuration directory with ``core-site.xml``. + +3. Convert from ``RDD[T]`` to ``H2OFrame``: + +.. code:: scala + + import org.apache.spark.sql.SparkSession + val spark = SparkSession.builder().getOrCreate() + import org.apache.spark.h2o._ + val h2oContext = H2OContext.getOrCreate(spark) + + val rdd = sc.parallelize(1 to 1000, 100).map( v => IntHolder(Some(v))) + val hf: H2OFrame = h2oContext.asH2OFrame(rdd) + +4. Convert from ``DataFrame`` to ``H2OFrame``: + +.. code:: scala + + import org.apache.spark.sql.SparkSession + val spark = SparkSession.builder().getOrCreate() + import org.apache.spark.h2o._ + val h2oContext = H2OContext.getOrCreate(spark) + + import spark.implicits._ + val df = spark.sparkContext.parallelize(1 to 1000, 100).map(v => IntHolder(Some(v))).toDF + val hf = h2oContext.asH2OFrame(df) + +5. Convert from ``H2OFrame`` to ``RDD[T]``: + +.. code:: scala + + import org.apache.spark.sql.SparkSession + val spark = SparkSession.builder().getOrCreate() + import org.apache.spark.h2o._ + val h2oContext = H2OContext.getOrCreate(spark) + + val rdd = spark.sparkContext.parallelize(1 to 1000, 100).map(v => IntHolder(Some(v))) + val hf: H2OFrame = h2oContext.asH2OFrame(rdd) + val newRdd = h2oContext.asRDD[IntHolder](hf) + +6. Convert from ``H2OFrame`` to ``DataFrame``: + +.. code:: scala + + import org.apache.spark.sql.SparkSession + val spark = SparkSession.builder().getOrCreate() + import org.apache.spark.h2o._ + val h2oContext = H2OContext.getOrCreate(spark) + + import spark.implicits._ + val df = spark.sparkContext.parallelize(1 to 1000, 100).map(v => IntHolder(Some(v))).toDF + val hf = h2oContext.asH2OFrame(df) + val newRdd = h2oContext.asDataFrame(hf)(spark.sqlContext) + +7. Integrate with H2O Algorithms using RDD as algorithm input: + +.. code:: scala + + import org.apache.spark.sql.SparkSession + val spark = SparkSession.builder().getOrCreate() + import org.apache.spark.h2o._ + val h2oContext = H2OContext.getOrCreate(spark) + import h2oContext.implicits._ + import org.apache.spark.examples.h2o._ + + val path = "examples/smalldata/prostate.csv" + val prostateText = spark.sparkContext.textFile(path) + val prostateRDD = prostateText.map(_.split(",")).map(row => ProstateParse(row)) + import _root_.hex.tree.gbm.GBM + import _root_.hex.tree.gbm.GBMModel.GBMParameters + val train: H2OFrame = prostateRDD + val gbmParams = new GBMParameters() + gbmParams._train = train + gbmParams._response_column = "CAPSULE" + gbmParams._ntrees = 10 + val gbmModel = new GBM(gbmParams).trainModel.get + +8. Integrate with MLlib algorithms: + +.. code:: scala + + import org.apache.spark.sql.SparkSession + val spark = SparkSession.builder().getOrCreate() + import org.apache.spark.h2o._ + val h2oContext = H2OContext.getOrCreate(spark) + import org.apache.spark.examples.h2o._ + + import java.io.File + val path = "examples/smalldata/prostate.csv" + val prostateHF = new H2OFrame(new File(path)) + val prostateRDD = h2oContext.asRDD[Prostate](prostateHF) + import org.apache.spark.mllib.clustering.KMeans + import org.apache.spark.mllib.linalg.Vectors + val train = prostateRDD.map( v => Vectors.dense(v.CAPSULE.get*1.0, v.AGE.get*1.0, v.DPROS.get*1.0,v.DCAPS.get*1.0, v.GLEASON.get*1.0)) + val clusters = KMeans.train(train, 5, 20) \ No newline at end of file From c29cdab629f573573ca4159a2fdc6806690de301 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Mon, 19 Jun 2017 15:53:54 +0200 Subject: [PATCH 05/72] Unit test doc to rst --- DEVEL.md | 20 +++----------------- doc/unit_tests.rst | 13 +++++++++++++ 2 files changed, 16 insertions(+), 17 deletions(-) create mode 100644 doc/unit_tests.rst diff --git a/DEVEL.md b/DEVEL.md index 7b0b16a0d..0e80702aa 100644 --- a/DEVEL.md +++ b/DEVEL.md @@ -33,8 +33,9 @@ - [Example](#Example4) - [Creating H2OFrame from an existing key](#CreateDF) - [Calling H2O Algorithms](#CallAlgos) - - [Running Unit Tests](#UnitTest) -- [Integration Tests](doc/integ_tests.rst) +- Testing + - [Running Unit Tests](doc/unit_tests.rst) + - [Integration Tests](doc/integ_tests.rst) - [Sparkling Water Log Locations](doc/log_location.rst) - [Change Sparkling Shell Logging Level](doc/change_log_level.rst) - [H2O Frame as Spark's Data Source](doc/datasource.rst) @@ -458,18 +459,3 @@ As is specified in the table, Sparkling Water provides support for transforming ```scala val gbmModel = gbm.trainModel.get ``` ---- - -## Running Unit Tests -To invoke tests, the following JVM options are required: - - `-Dspark.testing=true` - - `-Dspark.test.home=/Users/michal/Tmp/spark/spark-1.5.1-bin-cdh4/` - - -## Application Development -You can find Sparkling Water self-contained application skeleton in [Droplet repository](https://github.com/h2oai/h2o-droplets/tree/master/sparkling-water-droplet). - -## Sparkling Water configuration - - - TODO: used datasources, how data is moved to spark - - TODO: platform testing - mesos, SIMR diff --git a/doc/unit_tests.rst b/doc/unit_tests.rst new file mode 100644 index 000000000..5dcb8bafc --- /dev/null +++ b/doc/unit_tests.rst @@ -0,0 +1,13 @@ +Running Unit Tests +------------------ + +To invoke tests for example from IntelliJ Idea, the following JVM +options are required: + +- ``-Dspark.testing=true`` + +To invoke unit tests from gradle, run: + +.. code:: shell + + ./gradlew build -x integTest -x scriptTest \ No newline at end of file From c47dc4ff7bc5387278fcda83789e7904ef8ad292 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Mon, 19 Jun 2017 15:59:11 +0200 Subject: [PATCH 06/72] Convert calling h2o algos --- DEVEL.md | 31 +------------------------------ doc/calling_h2o_algos.rst | 29 +++++++++++++++++++++++++++++ 2 files changed, 30 insertions(+), 30 deletions(-) create mode 100644 doc/calling_h2o_algos.rst diff --git a/DEVEL.md b/DEVEL.md index 0e80702aa..c38c6f57c 100644 --- a/DEVEL.md +++ b/DEVEL.md @@ -32,7 +32,7 @@ - [Converting DataFrame into H2OFrame](#ConvertSchematoDF) - [Example](#Example4) - [Creating H2OFrame from an existing key](#CreateDF) - - [Calling H2O Algorithms](#CallAlgos) + - [Calling H2O Algorithms](doc/calling_h2o_algos.rst) - Testing - [Running Unit Tests](doc/unit_tests.rst) - [Integration Tests](doc/integ_tests.rst) @@ -430,32 +430,3 @@ As type T we support following types: |org.apache.spark.mllib.regression.LabeledPoint| As is specified in the table, Sparkling Water provides support for transforming arbitrary scala class extending `Product`, which are for example all case classes. - - - ---- - - - -### Calling H2O Algorithms - - 1. Create the parameters object that holds references to input data and parameters specific for the algorithm: - ```scala - val train: RDD = ... - val valid: H2OFrame = ... - - val gbmParams = new GBMParameters() - gbmParams._train = train - gbmParams._valid = valid - gbmParams._response_column = 'bikes - gbmParams._ntrees = 500 - gbmParams._max_depth = 6 - ``` - 2. Create a model builder: - ```scala - val gbm = new GBM(gbmParams) - ``` - 3. Invoke the model build job and block until the end of computation (`trainModel` is an asynchronous call by default): - ```scala - val gbmModel = gbm.trainModel.get - ``` diff --git a/doc/calling_h2o_algos.rst b/doc/calling_h2o_algos.rst new file mode 100644 index 000000000..a0a3d3d29 --- /dev/null +++ b/doc/calling_h2o_algos.rst @@ -0,0 +1,29 @@ +Calling H2O Algorithms +---------------------- + +1. Create the parameters object that holds references to input data and + parameters specific for the algorithm: + +.. code:: scala + + val train: RDD = ... + val valid: H2OFrame = ... + + val gbmParams = new GBMParameters() + gbmParams._train = train + gbmParams._valid = valid + gbmParams._response_column = "bikes" + gbmParams._ntrees = 500 + gbmParams._max_depth = 6 + +2. Create a model builder: + +.. code:: scala + + val gbm = new GBM(gbmParams) + +3. Invoke the model build job and block until the end of computation (``trainModel`` is an asynchronous call by default): + +.. code:: scala + + val gbmModel = gbm.trainModel.get From 471b71a415d0567c0e5d65dee81c0f4a9edb0809 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Mon, 19 Jun 2017 16:42:18 +0200 Subject: [PATCH 07/72] extract security.rst --- DEVEL.md | 43 +------------------------------------------ doc/security.rst | 47 +++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 48 insertions(+), 42 deletions(-) create mode 100644 doc/security.rst diff --git a/DEVEL.md b/DEVEL.md index c38c6f57c..c013fecb1 100644 --- a/DEVEL.md +++ b/DEVEL.md @@ -22,7 +22,7 @@ - [Running Sparkling Water](#RunSW) - [Starting H2O Services](#StartH2O) - [Memory Allocation](#MemorySetup) - - [Security](#Security) + - [Security](doc/security.rst) - [Converting H2OFrame into RDD](#ConvertDF) - [Example](#Example) - [Converting H2OFrame into DataFrame](#ConvertSchema) @@ -237,48 +237,7 @@ H2O resides in the same executor JVM as Spark. The memory provided for H2O is co * For JVMs that require a large amount of memory, we strongly recommend configuring the maximum amount of memory available for individual mappers. For information on how to do this using Yarn, refer to http://docs.h2o.ai/deployment/hadoop_yarn.html --- - -### Security -Both Spark and H2O support basic node authentication and data encryption. In H2O's case we encrypt all the data sent between server nodes and between client -and server nodes. This feature does not support H2O's UDP feature, only data sent via TCP is encrypted. - -Currently only encryption based on Java's key pair is supported (more in-depth explanation can be found in H2O's documentation linked below). - -To enable security for Spark methods please check [their documentation](http://spark.apache.org/docs/latest/security.html). - -Security for data exchanged between H2O instances can be enabled manually by generating all necessary files and distributing them to all worker nodes as -described in [H2O's documentation](https://github.com/h2oai/h2o-3/blob/master/h2o-docs/src/product/security.rst#ssl-internode-security) and passing the "spark.ext.h2o -.internal_security_conf" to spark submit: - -```scala -bin/sparkling-shell / ---conf "spark.ext.h2o.internal_security_conf=ssl.properties" -``` - -We also provide utility methods which will automatically generate all necessary files and enable security on all H2O nodes: - -``` -import org.apache.spark.network.Security -import org.apache.spark.h2o._ -Security.enableSSL(sc) // generate properties file, key pairs and set appropriate H2O parameters -val hc = H2OContext.getOrCreate(sc) // start the H2O cloud -``` - -Or if you plan on passing your own H2OConf then please use: - -``` -import org.apache.spark.network.Security -import org.apache.spark.h2o._ -val conf: H2OConf = // generate H2OConf file -Security.enableSSL(sc, conf) // generate properties file, key pairs and set appropriate H2O parameters -val hc = H2OContext.getOrCreate(sc, conf) // start the H2O cloud -``` - -This method will generate all files and distribute them via YARN or Spark methods to all worker nodes. This communication will be secure if you configured -YARN/Spark security. - ---- ### Converting H2OFrame into RDD[T] The `H2OContext` class provides the explicit conversion, `asRDD`, which creates an RDD-like wrapper around the provided H2O's H2OFrame: diff --git a/doc/security.rst b/doc/security.rst new file mode 100644 index 000000000..dbc27fd99 --- /dev/null +++ b/doc/security.rst @@ -0,0 +1,47 @@ +Security +-------- + +Both Spark and H2O support basic node authentication and data +encryption. In H2O's case we encrypt all the data sent between server +nodes and between client and server nodes. This feature does not support +H2O's UDP feature, only data sent via TCP is encrypted. + +Currently only encryption based on Java's key pair is supported (more +in-depth explanation can be found in H2O's documentation linked below). + +To enable security for Spark methods please check `their +documentation `__. + +Security for data exchanged between H2O instances can be enabled +manually by generating all necessary files and distributing them to all +worker nodes as described in `H2O's +documentation `__ +and passing the ``spark.ext.h2o.internal_security_conf`` to spark +submit: + +.. code:: shell + + bin/sparkling-shell --conf "spark.ext.h2o.internal_security_conf=ssl.properties" + +We also provide utility methods which automatically generate all +necessary files and enable security on all H2O nodes: + +.. code:: scala + + import org.apache.spark.network.Security + import org.apache.spark.h2o._ + Security.enableSSL(spark) // generate properties file, key pairs and set appropriate H2O parameters + val hc = H2OContext.getOrCreate(spark) // start the H2O cloud + +Or if you plan on passing your own H2OConf then please use: + +.. code:: scala + + import org.apache.spark.network.Security + import org.apache.spark.h2o._ + val conf: H2OConf = // generate H2OConf file + Security.enableSSL(spark, conf) // generate properties file, key pairs and set appropriate H2O parameters + val hc = H2OContext.getOrCreate(spark, conf) // start the H2O cloud + +This method generates all files and distributes them via YARN or +Spark methods to all worker nodes. This communication is secure in case of configured YARN/Spark security. From 5f31642d8de3402c91c1885614c653689b86ca0c Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Mon, 19 Jun 2017 21:16:28 +0200 Subject: [PATCH 08/72] Convert spark <--> h2o mapping --- DEVEL.md | 43 ++-------------------------- doc/spark_h2o_mapping.rst | 73 +++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 75 insertions(+), 41 deletions(-) create mode 100644 doc/spark_h2o_mapping.rst diff --git a/DEVEL.md b/DEVEL.md index c013fecb1..aae1c100d 100644 --- a/DEVEL.md +++ b/DEVEL.md @@ -33,6 +33,7 @@ - [Example](#Example4) - [Creating H2OFrame from an existing key](#CreateDF) - [Calling H2O Algorithms](doc/calling_h2o_algos.rst) + - [Spark Frame - H2O Frame Mapping](doc/spark_h2o_mapping.rst) - Testing - [Running Unit Tests](doc/unit_tests.rst) - [Integration Tests](doc/integ_tests.rst) @@ -348,44 +349,4 @@ If the H2O cluster already contains a loaded `H2OFrame` referenced by the key `t to reference it from Sparkling Water by creating a proxy `H2OFrame` instance using the key as the input: ```scala val trainHF = new H2OFrame("train.hex") -``` - -### Type mapping between H2O H2OFrame types and Spark DataFrame types - -For all primitive Scala types or Spark SQL (see `org.apache.spark.sql.types`) types which can be part of Spark RDD/DataFrame we provide mapping into H2O vector types (numeric, categorical, string, time, UUID - see `water.fvec.Vec`): - -| Scala type | SQL type | H2O type | -|------------|------------| ---------| -| _NA_ | BinaryType | Numeric | -| Byte | ByteType | Numeric | -| Short | ShortType | Numeric | -|Integer | IntegerType| Numeric | -|Long | LongType | Numeric | -|Float | FloatType | Numeric | -|Double | DoubleType | Numeric | -|String | StringType | String | -|Boolean | BooleanType| Numeric | -|java.sql.Timestamp| TimestampType | Time| - ---- - -### Type mapping between H2O H2OFrame types and RDD\[T\] types - -As type T we support following types: - -| T | -|------------| -| _NA_ | -| Byte | -| Short | -|Integer | -|Long | -|Float | -|Double | -|String | -|Boolean | -|java.sql.Timestamp | -|Any scala class extending scala `Product` | -|org.apache.spark.mllib.regression.LabeledPoint| - -As is specified in the table, Sparkling Water provides support for transforming arbitrary scala class extending `Product`, which are for example all case classes. +``` \ No newline at end of file diff --git a/doc/spark_h2o_mapping.rst b/doc/spark_h2o_mapping.rst new file mode 100644 index 000000000..99f58a938 --- /dev/null +++ b/doc/spark_h2o_mapping.rst @@ -0,0 +1,73 @@ +Spark Frame - H2O Frame Mapping +------------------------------- + +Type mapping between H2O H2OFrame types and Spark DataFrame types +----------------------------------------------------------------- + +For all primitive Scala types or Spark SQL (see +``org.apache.spark.sql.types``) types which can be part of Spark +RDD/DataFrame we provide mapping into H2O vector types (numeric, +categorical, string, time, UUID - see ``water.fvec.Vec``): + ++----------------------+-----------------+------------+ +| Scala type | SQL type | H2O type | ++======================+=================+============+ +| *NA* | BinaryType | Numeric | ++----------------------+-----------------+------------+ +| Byte | ByteType | Numeric | ++----------------------+-----------------+------------+ +| Short | ShortType | Numeric | ++----------------------+-----------------+------------+ +| Integer | IntegerType | Numeric | ++----------------------+-----------------+------------+ +| Long | LongType | Numeric | ++----------------------+-----------------+------------+ +| Float | FloatType | Numeric | ++----------------------+-----------------+------------+ +| Double | DoubleType | Numeric | ++----------------------+-----------------+------------+ +| String | StringType | String | ++----------------------+-----------------+------------+ +| Boolean | BooleanType | Numeric | ++----------------------+-----------------+------------+ +| java.sql.Timestamp | TimestampType | Time | ++----------------------+-----------------+------------+ + +-------------- + +Type mapping between H2O H2OFrame types and RDD[T] types +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +As type ``T`` we support following types: + ++--------------------------------------------------+ +| T | ++==================================================+ +| *NA* | ++--------------------------------------------------+ +| Byte | ++--------------------------------------------------+ +| Short | ++--------------------------------------------------+ +| Integer | ++--------------------------------------------------+ +| Long | ++--------------------------------------------------+ +| Float | ++--------------------------------------------------+ +| Double | ++--------------------------------------------------+ +| String | ++--------------------------------------------------+ +| Boolean | ++--------------------------------------------------+ +| java.sql.Timestamp | ++--------------------------------------------------+ +| Any scala class extending scala ``Product`` | ++--------------------------------------------------+ +| org.apache.spark.mllib.regression.LabeledPoint | ++--------------------------------------------------+ + +As is specified in the table, Sparkling Water provides support for +transforming arbitrary scala class extending ``Product``, which are, for +example, all case classes. From 3ad1fba3e374f068d3407b22993d53ab81084cc0 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Mon, 19 Jun 2017 21:24:32 +0200 Subject: [PATCH 09/72] convert doc for creating frame from key --- DEVEL.md | 12 +----------- doc/h2o_frame_from_key.rst | 11 +++++++++++ 2 files changed, 12 insertions(+), 11 deletions(-) create mode 100644 doc/h2o_frame_from_key.rst diff --git a/DEVEL.md b/DEVEL.md index aae1c100d..4a14bbede 100644 --- a/DEVEL.md +++ b/DEVEL.md @@ -31,7 +31,7 @@ - [Example](#Example3) - [Converting DataFrame into H2OFrame](#ConvertSchematoDF) - [Example](#Example4) - - [Creating H2OFrame from an existing key](#CreateDF) + - [Creating H2OFrame from an existing key](doc/h2o_frame_from_key.rst) - [Calling H2O Algorithms](doc/calling_h2o_algos.rst) - [Spark Frame - H2O Frame Mapping](doc/spark_h2o_mapping.rst) - Testing @@ -340,13 +340,3 @@ val hf: H2OFrame = df // Explicit call of H2Context API with name for resulting H2O frame val hfNamed: H2OFrame = h2oContext.asH2OFrame(df, Some("h2oframe")) ``` ---- - - -### Creating H2OFrame from an existing Key - -If the H2O cluster already contains a loaded `H2OFrame` referenced by the key `train.hex`, it is possible -to reference it from Sparkling Water by creating a proxy `H2OFrame` instance using the key as the input: -```scala -val trainHF = new H2OFrame("train.hex") -``` \ No newline at end of file diff --git a/doc/h2o_frame_from_key.rst b/doc/h2o_frame_from_key.rst new file mode 100644 index 000000000..c3b3d3cd6 --- /dev/null +++ b/doc/h2o_frame_from_key.rst @@ -0,0 +1,11 @@ +Creating H2OFrame from an existing Key +-------------------------------------- + +If the H2O cluster already contains a loaded ``H2OFrame`` referenced by +the key ``train.hex``, it is possible to reference it from Sparkling +Water by creating a proxy ``H2OFrame`` instance using the key as the +input: + +.. code:: scala + + val trainHF = new H2OFrame("train.hex") From caf4b7f0308e04352d88b6ce29f347b05b4fb5e2 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Tue, 20 Jun 2017 10:25:51 +0200 Subject: [PATCH 10/72] Convert doc for conversions --- DEVEL.md | 112 +-------------------------------- doc/spark_h2o_conversions.rst | 140 ++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 141 insertions(+), 111 deletions(-) create mode 100644 doc/spark_h2o_conversions.rst diff --git a/DEVEL.md b/DEVEL.md index 4a14bbede..af197735d 100644 --- a/DEVEL.md +++ b/DEVEL.md @@ -23,14 +23,7 @@ - [Starting H2O Services](#StartH2O) - [Memory Allocation](#MemorySetup) - [Security](doc/security.rst) - - [Converting H2OFrame into RDD](#ConvertDF) - - [Example](#Example) - - [Converting H2OFrame into DataFrame](#ConvertSchema) - - [Example](#Example2) - - [Converting RDD into H2OFrame](#ConvertRDD) - - [Example](#Example3) - - [Converting DataFrame into H2OFrame](#ConvertSchematoDF) - - [Example](#Example4) + - [Spark Frame - H2O Frame Conversions](doc/spark_h2o_conversions.rst) - [Creating H2OFrame from an existing key](doc/h2o_frame_from_key.rst) - [Calling H2O Algorithms](doc/calling_h2o_algos.rst) - [Spark Frame - H2O Frame Mapping](doc/spark_h2o_mapping.rst) @@ -237,106 +230,3 @@ H2O resides in the same executor JVM as Spark. The memory provided for H2O is co * For JVMs that require a large amount of memory, we strongly recommend configuring the maximum amount of memory available for individual mappers. For information on how to do this using Yarn, refer to http://docs.h2o.ai/deployment/hadoop_yarn.html ---- - - -### Converting H2OFrame into RDD[T] -The `H2OContext` class provides the explicit conversion, `asRDD`, which creates an RDD-like wrapper around the provided H2O's H2OFrame: -```scala -def asRDD[A <: Product: TypeTag: ClassTag](fr : H2OFrame) : RDD[A] -``` - -The call expects the type `A` to create a correctly-typed RDD. -The conversion requires type `A` to be bound by `Product` interface. -The relationship between the columns of H2OFrame and the attributes of class `A` is based on name matching. - - -#### Example -```scala -val df: H2OFrame = ... -val rdd = asRDD[Weather](df) - -``` ---- - - -### Converting H2OFrame into DataFrame -The `H2OContext` class provides the explicit conversion, `asDataFrame`, which creates a DataFrame-like wrapper -around the provided H2O H2OFrame. Technically, it provides the `RDD[sql.Row]` RDD API: -```scala -def asDataFrame(fr : H2OFrame)(implicit sqlContext: SQLContext) : DataFrame -``` - -This call does not require any type of parameters, but since it creates `DataFrame` instances, it requires access to an instance of `SQLContext`. In this case, the instance is provided as an implicit parameter of the call. The parameter can be passed in two ways: as an explicit parameter or by introducing an implicit variable into the current context. - -The schema of the created instance of the `DataFrame` is derived from the column name and the types of `H2OFrame` specified. - - -#### Example - -Using an explicit parameter in the call to pass sqlContext: -```scala -val sqlContext = new SQLContext(sc) -val schemaRDD = asDataFrame(h2oFrame)(sqlContext) -``` -or as implicit variable provided by actual environment: -```scala -implicit val sqlContext = new SQLContext(sc) -val schemaRDD = asDataFrame(h2oFrame) -``` ---- - - -### Converting RDD[T] into H2OFrame -The `H2OContext` provides **implicit** conversion from the specified `RDD[A]` to `H2OFrame`. As with conversion in the opposite direction, the type `A` has to satisfy the upper bound expressed by the type `Product`. The conversion will create a new `H2OFrame`, transfer data from the specified RDD, and save it to the H2O K/V data store. - - -```scala -implicit def asH2OFrame[A <: Product : TypeTag](rdd : RDD[A]) : H2OFrame -``` - -The API also provides explicit version which allows for specifying name for resulting -H2OFrame. - -```scala -def asH2OFrame[A <: Product : TypeTag](rdd : RDD[A], frameName: Option[String]) : H2OFrame -``` - - -#### Example -```scala -val rdd: RDD[Weather] = ... -import h2oContext._ -// implicit call of H2OContext.asH2OFrame[Weather](rdd) is used -val hf: H2OFrame = rdd -// Explicit call of of H2OContext API with name for resulting H2O frame -val hfNamed: H2OFrame = h2oContext.asH2OFrame(rdd, Some("h2oframe")) -``` - - ---- - -### Converting DataFrame into H2OFrame -The `H2OContext` provides **implicit** conversion from the specified `DataFrame` to `H2OFrame`. The conversion will create a new `H2OFrame`, transfer data from the specified `DataFrame`, and save it to the H2O K/V data store. - -```scala -implicit def asH2OFrame(rdd : DataFrame) : H2OFrame -``` - -The API also provides explicit version which allows for specifying name for resulting -H2OFrame. - -```scala -def asH2OFrame(rdd : DataFrame, frameName: Option[String]) : H2OFrame -``` - - -#### Example -```scala -val df: DataFrame = ... -import h2oContext._ -// Implicit call of H2OContext.asH2OFrame(srdd) is used -val hf: H2OFrame = df -// Explicit call of H2Context API with name for resulting H2O frame -val hfNamed: H2OFrame = h2oContext.asH2OFrame(df, Some("h2oframe")) -``` diff --git a/doc/spark_h2o_conversions.rst b/doc/spark_h2o_conversions.rst new file mode 100644 index 000000000..919b32423 --- /dev/null +++ b/doc/spark_h2o_conversions.rst @@ -0,0 +1,140 @@ +Spark Frame <--> H2O Frame Conversions +-------------------------------------- + +Quick links: + - `Converting H2OFrame into RDD[T]`_ + - `Converting H2OFrame into DataFrame`_ + - `Converting RDD[T] into H2OFrame`_ + - `Converting DataFrame into H2OFrame`_ + +Converting H2OFrame into RDD[T] +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The ``H2OContext`` class provides the explicit conversion, ``asRDD``, +which creates an RDD-like wrapper around the provided H2O's H2OFrame: + +.. code:: scala + + def asRDD[A <: Product: TypeTag: ClassTag](fr : H2OFrame) : RDD[A] + +The call expects the type ``A`` to create a correctly-typed RDD. The +conversion requires type ``A`` to be bound by ``Product`` interface. The +relationship between the columns of H2OFrame and the attributes of class +``A`` is based on name matching. + +Example +^^^^^^^ + +.. code:: scala + + val df: H2OFrame = ... + val rdd = asRDD[Weather](df) + +-------------- + +Converting H2OFrame into DataFrame +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The ``H2OContext`` class provides the explicit conversion, +``asDataFrame``, which creates a DataFrame-like wrapper around the +provided H2O H2OFrame. Technically, it provides the ``RDD[sql.Row]`` RDD +API: + +.. code:: scala + + def asDataFrame(fr : H2OFrame)(implicit sqlContext: SQLContext) : DataFrame + +This call does not require any type of parameters, but since it creates +``DataFrame`` instances, it requires access to an instance of +``SQLContext``. In this case, the instance is provided as an implicit +parameter of the call. The parameter can be passed in two ways: as an +explicit parameter or by introducing an implicit variable into the +current context. + +The schema of the created instance of the ``DataFrame`` is derived from +the column name and the types of ``H2OFrame`` specified. + +Example +^^^^^^^ + +Using an explicit parameter in the call to pass sqlContext: + +.. code:: scala + + val sqlContext = new SQLContext(sc) + val schemaRDD = asDataFrame(h2oFrame)(sqlContext) + +or as implicit variable provided by actual environment: + +.. code:: scala + + implicit val sqlContext = new SQLContext(sc) + val schemaRDD = asDataFrame(h2oFrame) + +-------------- + +Converting RDD[T] into H2OFrame +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The ``H2OContext`` provides **implicit** conversion from the specified +``RDD[A]`` to ``H2OFrame``. As with conversion in the opposite +direction, the type ``A`` has to satisfy the upper bound expressed by +the type ``Product``. The conversion will create a new ``H2OFrame``, +transfer data from the specified RDD, and save it to the H2O K/V data +store. + +.. code:: scala + + implicit def asH2OFrame[A <: Product : TypeTag](rdd : RDD[A]) : H2OFrame + +The API also provides explicit version which allows for specifying name +for resulting H2OFrame. + +.. code:: scala + + def asH2OFrame[A <: Product : TypeTag](rdd : RDD[A], frameName: Option[String]) : H2OFrame + +Example +^^^^^^^ + +.. code:: scala + + val rdd: RDD[Weather] = ... + import h2oContext.implicits._ + // implicit call of H2OContext.asH2OFrame[Weather](rdd) is used + val hf: H2OFrame = rdd + // Explicit call of of H2OContext API with name for resulting H2O frame + val hfNamed: H2OFrame = h2oContext.asH2OFrame(rdd, Some("h2oframe")) + +-------------- + +Converting DataFrame into H2OFrame +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The ``H2OContext`` provides **implicit** conversion from the specified +``DataFrame`` to ``H2OFrame``. The conversion will create a new +``H2OFrame``, transfer data from the specified ``DataFrame``, and save +it to the H2O K/V data store. + +.. code:: scala + + implicit def asH2OFrame(rdd : DataFrame) : H2OFrame + +The API also provides explicit version which allows for specifying name +for resulting H2OFrame. + +.. code:: scala + + def asH2OFrame(rdd : DataFrame, frameName: Option[String]) : H2OFrame + +Example +^^^^^^^ + +.. code:: scala + + val df: DataFrame = ... + import h2oContext.implicits._ + // Implicit call of H2OContext.asH2OFrame(srdd) is used + val hf: H2OFrame = df + // Explicit call of H2Context API with name for resulting H2O frame + val hfNamed: H2OFrame = h2oContext.asH2OFrame(df, Some("h2oframe")) From 5bf6a587defa7acc41cb80136b416d4af1c6da5b Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Tue, 20 Jun 2017 10:27:30 +0200 Subject: [PATCH 11/72] capitals --- DEVEL.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DEVEL.md b/DEVEL.md index af197735d..6ad87c4a4 100644 --- a/DEVEL.md +++ b/DEVEL.md @@ -24,7 +24,7 @@ - [Memory Allocation](#MemorySetup) - [Security](doc/security.rst) - [Spark Frame - H2O Frame Conversions](doc/spark_h2o_conversions.rst) - - [Creating H2OFrame from an existing key](doc/h2o_frame_from_key.rst) + - [Create H2OFrame From an Existing Key](doc/h2o_frame_from_key.rst) - [Calling H2O Algorithms](doc/calling_h2o_algos.rst) - [Spark Frame - H2O Frame Mapping](doc/spark_h2o_mapping.rst) - Testing From e5aa47919acbf4479eb8a51e688488cf5f99d606 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Tue, 20 Jun 2017 10:31:16 +0200 Subject: [PATCH 12/72] convert doc for memory setup --- DEVEL.md | 23 ++--------------------- doc/memory_setup.rst | 34 ++++++++++++++++++++++++++++++++++ 2 files changed, 36 insertions(+), 21 deletions(-) create mode 100644 doc/memory_setup.rst diff --git a/DEVEL.md b/DEVEL.md index 6ad87c4a4..81c0b91af 100644 --- a/DEVEL.md +++ b/DEVEL.md @@ -21,7 +21,7 @@ - [Sparkling Water Configuration Properties](doc/configuration_properties.rst) - [Running Sparkling Water](#RunSW) - [Starting H2O Services](#StartH2O) - - [Memory Allocation](#MemorySetup) + - [Memory Allocation](doc/memory_setup.rst) - [Security](doc/security.rst) - [Spark Frame - H2O Frame Conversions](doc/spark_h2o_conversions.rst) - [Create H2OFrame From an Existing Key](doc/h2o_frame_from_key.rst) @@ -210,23 +210,4 @@ The environment must contain the property `SPARK_HOME` that points to the Spark ```scala val sc:SparkContext = ... val hc = H2OContext.getOrCreate(sc) -``` - ---- - -### Memory Allocation - -H2O resides in the same executor JVM as Spark. The memory provided for H2O is configured via Spark; refer to [Spark configuration](http://spark.apache.org/docs/1.4.0/configuration.html) for more details. - -#### Generic configuration - * Configure the Executor memory (i.e., memory available for H2O) via the Spark configuration property `spark.executor.memory` . - > For example, `bin/sparkling-shell --conf spark.executor.memory=5g` or configure the property in `$SPARK_HOME/conf/spark-defaults.conf` - - * Configure the Driver memory (i.e., memory available for H2O client running inside Spark driver) via the Spark configuration property `spark.driver.memory` - > For example, `bin/sparkling-shell --conf spark.driver.memory=4g` or configure the property in `$SPARK_HOME/conf/spark-defaults.conf` - -#### Yarn specific configuration -* Refer to the [Spark documentation](http://spark.apache.org/docs/1.4.0/running-on-yarn.html) - -* For JVMs that require a large amount of memory, we strongly recommend configuring the maximum amount of memory available for individual mappers. For information on how to do this using Yarn, refer to http://docs.h2o.ai/deployment/hadoop_yarn.html - +``` \ No newline at end of file diff --git a/doc/memory_setup.rst b/doc/memory_setup.rst new file mode 100644 index 000000000..3d1048d19 --- /dev/null +++ b/doc/memory_setup.rst @@ -0,0 +1,34 @@ +Memory Allocation +----------------- + +H2O resides in the same executor JVM as Spark. The memory provided for +H2O is configured via Spark; refer to `Spark +configuration `__ +for more details. + +Generic configuration +~~~~~~~~~~~~~~~~~~~~~ + +- Configure the Executor memory (i.e., memory available for H2O) via + the Spark configuration property ``spark.executor.memory`` . + + > For example, ``bin/sparkling-shell --conf spark.executor.memory=5g`` or + configure the property in ``$SPARK_HOME/conf/spark-defaults.conf`` + +- Configure the Driver memory (i.e., memory available for H2O client + running inside Spark driver) via the Spark configuration property + ``spark.driver.memory`` + + > For example, ``bin/sparkling-shell --conf spark.driver.memory=4g`` or configure + the property in ``$SPARK_HOME/conf/spark-defaults.conf`` + +Yarn specific configuration +~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +- Refer to the `Spark + documentation `__ + +- For JVMs that require a large amount of memory, we strongly recommend + configuring the maximum amount of memory available for individual + mappers. For information on how to do this using Yarn, refer to + http://docs.h2o.ai/deployment/hadoop\_yarn.html From de936f24032572cbc67f3270447e3aeeb46d96b6 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Tue, 20 Jun 2017 11:01:48 +0200 Subject: [PATCH 13/72] Convert and polish running sparkling water --- DEVEL.md | 115 +++--------------------------------------------------------- doc/run.md | 62 ++++++++++++++++++++++++++++++++ doc/run.rst | 83 +++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 149 insertions(+), 111 deletions(-) create mode 100644 doc/run.md create mode 100644 doc/run.rst diff --git a/DEVEL.md b/DEVEL.md index 81c0b91af..72bb6ad29 100644 --- a/DEVEL.md +++ b/DEVEL.md @@ -1,6 +1,7 @@ # Sparkling Water Development Documentation -## Table of Contents +This documentation acts only as the table of content and provides links to the actual documentation documents. + - [Typical Use Case](#UseCase) - [Requirements](#Req) - [Design](#Design) @@ -9,16 +10,8 @@ - [Supported Data Formats](#DataFormat) - [Data Sharing](#DataShare) - [Provided Primitives](#ProvPrim) -- [Running on Select Target Platforms](#TargetPlatforms) - - [Local](#Local) - - [Standalone](#Standalone) - - [YARN](#YARN) - - [Mesos](#Mesos) -- [H2O Initialization Sequence](#H2OInit) - - [Configuration](#Config) - - [Build Environment](#BuildEnv) - - [Run Environment](#RunEnv) - - [Sparkling Water Configuration Properties](doc/configuration_properties.rst) +- [Running Sparkling Water](doc/run.rst) +- [Sparkling Water Configuration Properties](doc/configuration_properties.rst) - [Running Sparkling Water](#RunSW) - [Starting H2O Services](#StartH2O) - [Memory Allocation](doc/memory_setup.rst) @@ -38,20 +31,6 @@ --- - -## Typical Use-Case -Sparkling Water excels in leveraging existing Spark-based workflows that need to call advanced machine learning algorithms. A typical example involves data munging with help of Spark API, where a prepared table is passed to the H2O DeepLearning algorithm. The constructed DeepLearning model estimates different metrics based on the testing data, which can be used in the rest of the Spark workflow. - ---- - - -## Requirements - - Linux or Mac OSX platform - - Java 1.7+ - - [Spark 1.6.0+](http://spark.apache.org/downloads.html) - ---- - ## Design @@ -125,89 +104,3 @@ The Sparkling Water provides following primitives, which are the basic classes u | H2O H2OFrame | `water.fvec.H2OFrame` | H2OFrame is the H2O data structure that represents a table of values. The table is column-based and provides column and row accessors. | | H2O Algorithms | package `hex` | Represents the H2O machine learning algorithms library, including DeepLearning, GBM, RandomForest. | - ---- - - -# Running on Select Target Platforms - -Sparkling Water can run on top of Spark in the various ways described in the following sections. - - -## Local -In this case Sparkling Water runs as a local cluster (Spark master variable points to one of values `local`, `local[*]`, or `local-cluster[...]` - - -## Standalone -[Spark documentation - running Standalone cluster](http://spark.apache.org/docs/latest/spark-standalone.html) - - -## YARN -[Spark documentation - running Spark Application on YARN](http://spark.apache.org/docs/latest/running-on-yarn.html) - -When submitting Sparkling Water application to CHD or Apache Hadoop cluster, the command to submit may look like: -``` -./spark-submit --master=yarn-client --class water.SparklingWaterDriver --conf "spark.yarn.am.extraJavaOptions=-XX:MaxPermSize=384m -Dhdp.version=current" ---driver-memory=8G --num-executors=3 --executor-memory=3G --conf "spark.executor.extraClassPath=-XX:MaxPermSize=384m -Dhdp.version=current" -sparkling-water-assembly-1.5.11-all.jar -``` - -When submitting sparkling water application to HDP Cluster, the command to submit may look like: -``` -./spark-submit --master=yarn-client --class water.SparklingWaterDriver --conf "spark.yarn.am.extraJavaOptions=-XX:MaxPermSize=384m -Dhdp.version=current" ---driver-memory=8G --num-executors=3 --executor-memory=3G --conf "spark.executor.extraClassPath=-XX:MaxPermSize=384m -Dhdp.version=current" -sparkling-water-assembly-1.5.11-all.jar -``` -Apart from the typical spark configuration it is necessary to add `-XX:MaxPermSize=384m` (or higher, but 384m is minimum) to both `spark.executor.extraClassPath` and `spark.yarn.am.extraJavaOptions` (or for client mode, `spark.driver.extraJavaOptions` for cluster mode) configuration properties in order to run Sparkling Water correctly. - -The only difference between HDP cluster and both CDH and Apache hadoop clusters is that we need to add `-Dhdp.version=current` to both `spark.executor.extraClassPath` and `spark.yarn.am.extraJavaOptions` (resp., `spark.driver.extraJavaOptions`) configuration properties in the HDP case. - - -## Mesos -[Spark documentation - running Spark Application on Mesos](http://spark.apache.org/docs/latest/running-on-mesos.html) - - ---- - -# H2O Initialization Sequence -If `SparkContext` is available, initialize and start H2O context: -```scala -val sc:SparkContext = ... -val hc = H2OContext.getOrCreate(sc) -``` - -The call will: - 1. Collect the number and host names of the executors (worker nodes) in the Spark cluster - 2. Launch H2O services on each detected executor - 3. Create a cloud for H2O services based on the list of executors - 4. Verify the H2O cloud status - -The former variant is preferred, because it initiates and starts H2O Context in one call and also can be used to obtain already existing H2OContext, but it does semantically the same as the latter variant. - ---- - -## Configuration - - -### Build Environment -The environment must contain the property `SPARK_HOME` that points to the Spark distribution. - ---- - - -### Run Environment -The environment must contain the property `SPARK_HOME` that points to the Spark distribution. - ---- - - -# Running Sparkling Water - ---- - - -### Starting H2O Services -```scala -val sc:SparkContext = ... -val hc = H2OContext.getOrCreate(sc) -``` \ No newline at end of file diff --git a/doc/run.md b/doc/run.md new file mode 100644 index 000000000..7554a91eb --- /dev/null +++ b/doc/run.md @@ -0,0 +1,62 @@ +# Running Sparkling Water + +In order to run Sparkling Water, the environment must contain the property `SPARK_HOME` that +points to the Spark distribution. + +## Starting H2OContext + +H2O on Spark can be started in the Spark Shell or in the Spark application as: + +```scala +val hc = H2OContext.getOrCreate(spark) +``` + +The semantic of the call depends on the configured Sparkling Water backend. For more information +about the backends, please see [Sparkling Water Backends](backends.rst). + +In internal backend mode, the call will: + 1. Collect the number and host names of the executors (worker nodes) in the Spark cluster + 2. Launch H2O services on each detected executor + 3. Create a cloud for H2O services based on the list of executors + 4. Verify the H2O cloud status + +In external backend mode, the call will: + 1. Start H2O in client mode on the Spark driver + 2. Start separated H2O cluster on the configured YARN queue + 3. Connects to the external cluster from the H2O client + + +Sparkling Water can run on top of Spark in the various ways, however starting +Sparkling Water requires different configuration on different environments: +- Local + + In this case Sparkling Water runs as a local cluster (Spark master variable points to one of values `local`, `local[*]`, or `local-cluster[...]` + +- Standalone Spark Cluster + + [Spark documentation - running Standalone cluster](http://spark.apache.org/docs/latest/spark-standalone.html) + +- YARN + + [Spark documentation - running Spark Application on YARN](http://spark.apache.org/docs/latest/running-on-yarn.html) + + When submitting Sparkling Water application to CHD or Apache Hadoop cluster, the command to submit may look like: + ``` + ./spark-submit --master=yarn-client --class water.SparklingWaterDriver --conf "spark.yarn.am.extraJavaOptions=-XX:MaxPermSize=384m -Dhdp.version=current" + --driver-memory=8G --num-executors=3 --executor-memory=3G --conf "spark.executor.extraClassPath=-XX:MaxPermSize=384m -Dhdp.version=current" + sparkling-water-assembly-1.5.11-all.jar + ``` + + When submitting sparkling water application to HDP Cluster, the command to submit may look like: + ``` + ./spark-submit --master=yarn-client --class water.SparklingWaterDriver --conf "spark.yarn.am.extraJavaOptions=-XX:MaxPermSize=384m -Dhdp.version=current" + --driver-memory=8G --num-executors=3 --executor-memory=3G --conf "spark.executor.extraClassPath=-XX:MaxPermSize=384m -Dhdp.version=current" + sparkling-water-assembly-1.5.11-all.jar + ``` + Apart from the typical spark configuration it is necessary to add `-XX:MaxPermSize=384m` (or higher, but 384m is minimum) to both `spark.executor.extraClassPath` and `spark.yarn.am.extraJavaOptions` (or for client mode, `spark.driver.extraJavaOptions` for cluster mode) configuration properties in order to run Sparkling Water correctly. + + The only difference between HDP cluster and both CDH and Apache hadoop clusters is that we need to add `-Dhdp.version=current` to both `spark.executor.extraClassPath` and `spark.yarn.am.extraJavaOptions` (resp., `spark.driver.extraJavaOptions`) configuration properties in the HDP case. + +- Mesos + + [Spark documentation - running Spark Application on Mesos](http://spark.apache.org/docs/latest/running-on-mesos.html) diff --git a/doc/run.rst b/doc/run.rst new file mode 100644 index 000000000..2e8b3ae53 --- /dev/null +++ b/doc/run.rst @@ -0,0 +1,83 @@ +Running Sparkling Water +======================= + +In order to run Sparkling Water, the environment must contain the +property ``SPARK_HOME`` that points to the Spark distribution. + +Starting H2OContext +------------------- + +H2O on Spark can be started in the Spark Shell or in the Spark +application as: + +.. code:: scala + + val hc = H2OContext.getOrCreate(spark) + +The semantic of the call depends on the configured Sparkling Water +backend. For more information about the backends, please see `Sparkling +Water Backends `__. + +In internal backend mode, the call will: 1. Collect the number and host +names of the executors (worker nodes) in the Spark cluster 2. Launch H2O +services on each detected executor 3. Create a cloud for H2O services +based on the list of executors 4. Verify the H2O cloud status + +In external backend mode, the call will: 1. Start H2O in client mode on +the Spark driver 2. Start separated H2O cluster on the configured YARN +queue 3. Connects to the external cluster from the H2O client + +Sparkling Water can run on top of Spark in the various ways, however +starting Sparkling Water requires different configuration on different +environments: + +- Local + + In this case Sparkling Water runs as a local cluster (Spark master variable points to one of values `local`, `local[*]`, or `local-cluster[...]` + +- Standalone Spark Cluster + + `Spark documentation - running Standalone + cluster `__ + +- YARN + + `Spark documentation - running Spark Application on + YARN `__ + + When submitting Sparkling Water application to CHD or Apache Hadoop + cluster, the command to submit may look like: + + .. code:: bash + + ./spark-submit --master=yarn-client --class water.SparklingWaterDriver --conf "spark.yarn.am.extraJavaOptions=-XX:MaxPermSize=384m -Dhdp.version=current" + --driver-memory=8G --num-executors=3 --executor-memory=3G --conf "spark.executor.extraClassPath=-XX:MaxPermSize=384m -Dhdp.version=current" + sparkling-water-assembly-2.1.9-all.jar + + When submitting sparkling water application to HDP Cluster, the + command to submit may look like: + + .. code:: bash + + ./spark-submit --master=yarn-client --class water.SparklingWaterDriver --conf "spark.yarn.am.extraJavaOptions=-XX:MaxPermSize=384m -Dhdp.version=current" + --driver-memory=8G --num-executors=3 --executor-memory=3G --conf "spark.executor.extraClassPath=-XX:MaxPermSize=384m -Dhdp.version=current" + sparkling-water-assembly-2.1.9-all.jar + + Apart from the typical spark configuration it is necessary to add + ``-XX:MaxPermSize=384m`` (or higher, but 384m is minimum) to both + ``spark.executor.extraClassPath`` and + ``spark.yarn.am.extraJavaOptions`` (or for client mode, + ``spark.driver.extraJavaOptions`` for cluster mode) configuration + properties in order to run Sparkling Water correctly. + + The only difference between HDP cluster and both CDH and Apache + hadoop clusters is that we need to add ``-Dhdp.version=current`` to + both ``spark.executor.extraClassPath`` and + ``spark.yarn.am.extraJavaOptions`` (resp., + ``spark.driver.extraJavaOptions``) configuration properties in the + HDP case. + +- Mesos + + `Spark documentation - running Spark Application on + Mesos `__ From 6e08159b987d1490d87cac9fb109a048df7349dc Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Tue, 20 Jun 2017 11:09:32 +0200 Subject: [PATCH 14/72] change structure a bit --- doc/run.md | 51 ++++++++++++++++---------------- doc/run.rst | 97 ++++++++++++++++++++++++++++++++++--------------------------- 2 files changed, 80 insertions(+), 68 deletions(-) diff --git a/doc/run.md b/doc/run.md index 7554a91eb..5f35f9e37 100644 --- a/doc/run.md +++ b/doc/run.md @@ -28,35 +28,36 @@ In external backend mode, the call will: Sparkling Water can run on top of Spark in the various ways, however starting Sparkling Water requires different configuration on different environments: -- Local + +### Local - In this case Sparkling Water runs as a local cluster (Spark master variable points to one of values `local`, `local[*]`, or `local-cluster[...]` +In this case Sparkling Water runs as a local cluster (Spark master variable points to one of values `local`, `local[*]`, or `local-cluster[...]` -- Standalone Spark Cluster +### Standalone Spark Cluster - [Spark documentation - running Standalone cluster](http://spark.apache.org/docs/latest/spark-standalone.html) +[Spark documentation - running Standalone cluster](http://spark.apache.org/docs/latest/spark-standalone.html) -- YARN +### YARN - [Spark documentation - running Spark Application on YARN](http://spark.apache.org/docs/latest/running-on-yarn.html) - - When submitting Sparkling Water application to CHD or Apache Hadoop cluster, the command to submit may look like: - ``` - ./spark-submit --master=yarn-client --class water.SparklingWaterDriver --conf "spark.yarn.am.extraJavaOptions=-XX:MaxPermSize=384m -Dhdp.version=current" - --driver-memory=8G --num-executors=3 --executor-memory=3G --conf "spark.executor.extraClassPath=-XX:MaxPermSize=384m -Dhdp.version=current" - sparkling-water-assembly-1.5.11-all.jar - ``` - - When submitting sparkling water application to HDP Cluster, the command to submit may look like: - ``` - ./spark-submit --master=yarn-client --class water.SparklingWaterDriver --conf "spark.yarn.am.extraJavaOptions=-XX:MaxPermSize=384m -Dhdp.version=current" - --driver-memory=8G --num-executors=3 --executor-memory=3G --conf "spark.executor.extraClassPath=-XX:MaxPermSize=384m -Dhdp.version=current" - sparkling-water-assembly-1.5.11-all.jar - ``` - Apart from the typical spark configuration it is necessary to add `-XX:MaxPermSize=384m` (or higher, but 384m is minimum) to both `spark.executor.extraClassPath` and `spark.yarn.am.extraJavaOptions` (or for client mode, `spark.driver.extraJavaOptions` for cluster mode) configuration properties in order to run Sparkling Water correctly. - - The only difference between HDP cluster and both CDH and Apache hadoop clusters is that we need to add `-Dhdp.version=current` to both `spark.executor.extraClassPath` and `spark.yarn.am.extraJavaOptions` (resp., `spark.driver.extraJavaOptions`) configuration properties in the HDP case. +[Spark documentation - running Spark Application on YARN](http://spark.apache.org/docs/latest/running-on-yarn.html) + +When submitting Sparkling Water application to CHD or Apache Hadoop cluster, the command to submit may look like: +``` +./spark-submit --master=yarn-client --class water.SparklingWaterDriver --conf "spark.yarn.am.extraJavaOptions=-XX:MaxPermSize=384m -Dhdp.version=current" +--driver-memory=8G --num-executors=3 --executor-memory=3G --conf "spark.executor.extraClassPath=-XX:MaxPermSize=384m -Dhdp.version=current" +sparkling-water-assembly-1.5.11-all.jar +``` + +When submitting sparkling water application to HDP Cluster, the command to submit may look like: +``` +./spark-submit --master=yarn-client --class water.SparklingWaterDriver --conf "spark.yarn.am.extraJavaOptions=-XX:MaxPermSize=384m -Dhdp.version=current" +--driver-memory=8G --num-executors=3 --executor-memory=3G --conf "spark.executor.extraClassPath=-XX:MaxPermSize=384m -Dhdp.version=current" +sparkling-water-assembly-1.5.11-all.jar +``` +Apart from the typical spark configuration it is necessary to add `-XX:MaxPermSize=384m` (or higher, but 384m is minimum) to both `spark.executor.extraClassPath` and `spark.yarn.am.extraJavaOptions` (or for client mode, `spark.driver.extraJavaOptions` for cluster mode) configuration properties in order to run Sparkling Water correctly. + +The only difference between HDP cluster and both CDH and Apache hadoop clusters is that we need to add `-Dhdp.version=current` to both `spark.executor.extraClassPath` and `spark.yarn.am.extraJavaOptions` (resp., `spark.driver.extraJavaOptions`) configuration properties in the HDP case. -- Mesos +### Mesos - [Spark documentation - running Spark Application on Mesos](http://spark.apache.org/docs/latest/running-on-mesos.html) +[Spark documentation - running Spark Application on Mesos](http://spark.apache.org/docs/latest/running-on-mesos.html) diff --git a/doc/run.rst b/doc/run.rst index 2e8b3ae53..8b2bb52d1 100644 --- a/doc/run.rst +++ b/doc/run.rst @@ -18,66 +18,77 @@ The semantic of the call depends on the configured Sparkling Water backend. For more information about the backends, please see `Sparkling Water Backends `__. -In internal backend mode, the call will: 1. Collect the number and host -names of the executors (worker nodes) in the Spark cluster 2. Launch H2O -services on each detected executor 3. Create a cloud for H2O services -based on the list of executors 4. Verify the H2O cloud status - -In external backend mode, the call will: 1. Start H2O in client mode on -the Spark driver 2. Start separated H2O cluster on the configured YARN -queue 3. Connects to the external cluster from the H2O client +In internal backend mode, the call will: +1. Collect the number and host +names of the executors (worker nodes) in the Spark cluster +2. Launch H2O +services on each detected executor +3. Create a cloud for H2O services +based on the list of executors +4. Verify the H2O cloud status + +In external backend mode, the call will: +1. Start H2O in client mode on +the Spark driver +2. Start separated H2O cluster on the configured YARN +queue +3. Connects to the external cluster from the H2O client Sparkling Water can run on top of Spark in the various ways, however starting Sparkling Water requires different configuration on different environments: -- Local +Local +~~~~~ - In this case Sparkling Water runs as a local cluster (Spark master variable points to one of values `local`, `local[*]`, or `local-cluster[...]` +In this case Sparkling Water runs as a local cluster (Spark master +variable points to one of values ``local``, ``local[*]``, or +``local-cluster[...]`` -- Standalone Spark Cluster +Standalone Spark Cluster +~~~~~~~~~~~~~~~~~~~~~~~~ - `Spark documentation - running Standalone - cluster `__ +`Spark documentation - running Standalone +cluster `__ -- YARN +YARN +~~~~ - `Spark documentation - running Spark Application on - YARN `__ +`Spark documentation - running Spark Application on +YARN `__ - When submitting Sparkling Water application to CHD or Apache Hadoop - cluster, the command to submit may look like: +When submitting Sparkling Water application to CHD or Apache Hadoop +cluster, the command to submit may look like: - .. code:: bash +.. code:: bash - ./spark-submit --master=yarn-client --class water.SparklingWaterDriver --conf "spark.yarn.am.extraJavaOptions=-XX:MaxPermSize=384m -Dhdp.version=current" - --driver-memory=8G --num-executors=3 --executor-memory=3G --conf "spark.executor.extraClassPath=-XX:MaxPermSize=384m -Dhdp.version=current" - sparkling-water-assembly-2.1.9-all.jar + ./spark-submit --master=yarn-client --class water.SparklingWaterDriver --conf "spark.yarn.am.extraJavaOptions=-XX:MaxPermSize=384m -Dhdp.version=current" + --driver-memory=8G --num-executors=3 --executor-memory=3G --conf "spark.executor.extraClassPath=-XX:MaxPermSize=384m -Dhdp.version=current" + sparkling-water-assembly-1.5.11-all.jar - When submitting sparkling water application to HDP Cluster, the - command to submit may look like: +When submitting sparkling water application to HDP Cluster, the command +to submit may look like: - .. code:: bash +.. code:: bash - ./spark-submit --master=yarn-client --class water.SparklingWaterDriver --conf "spark.yarn.am.extraJavaOptions=-XX:MaxPermSize=384m -Dhdp.version=current" - --driver-memory=8G --num-executors=3 --executor-memory=3G --conf "spark.executor.extraClassPath=-XX:MaxPermSize=384m -Dhdp.version=current" - sparkling-water-assembly-2.1.9-all.jar + ./spark-submit --master=yarn-client --class water.SparklingWaterDriver --conf "spark.yarn.am.extraJavaOptions=-XX:MaxPermSize=384m -Dhdp.version=current" + --driver-memory=8G --num-executors=3 --executor-memory=3G --conf "spark.executor.extraClassPath=-XX:MaxPermSize=384m -Dhdp.version=current" + sparkling-water-assembly-1.5.11-all.jar - Apart from the typical spark configuration it is necessary to add - ``-XX:MaxPermSize=384m`` (or higher, but 384m is minimum) to both - ``spark.executor.extraClassPath`` and - ``spark.yarn.am.extraJavaOptions`` (or for client mode, - ``spark.driver.extraJavaOptions`` for cluster mode) configuration - properties in order to run Sparkling Water correctly. +Apart from the typical spark configuration it is necessary to add +``-XX:MaxPermSize=384m`` (or higher, but 384m is minimum) to both +``spark.executor.extraClassPath`` and ``spark.yarn.am.extraJavaOptions`` +(or for client mode, ``spark.driver.extraJavaOptions`` for cluster mode) +configuration properties in order to run Sparkling Water correctly. - The only difference between HDP cluster and both CDH and Apache - hadoop clusters is that we need to add ``-Dhdp.version=current`` to - both ``spark.executor.extraClassPath`` and - ``spark.yarn.am.extraJavaOptions`` (resp., - ``spark.driver.extraJavaOptions``) configuration properties in the - HDP case. +The only difference between HDP cluster and both CDH and Apache hadoop +clusters is that we need to add ``-Dhdp.version=current`` to both +``spark.executor.extraClassPath`` and ``spark.yarn.am.extraJavaOptions`` +(resp., ``spark.driver.extraJavaOptions``) configuration properties in +the HDP case. -- Mesos +Mesos +~~~~~ - `Spark documentation - running Spark Application on - Mesos `__ +`Spark documentation - running Spark Application on +Mesos `__ From da3034e28afcb62cec2d2ff98e1b628e1f198fe6 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Tue, 20 Jun 2017 11:11:46 +0200 Subject: [PATCH 15/72] enumerated list --- doc/run.rst | 2 ++ 1 file changed, 2 insertions(+) diff --git a/doc/run.rst b/doc/run.rst index 8b2bb52d1..af45989b9 100644 --- a/doc/run.rst +++ b/doc/run.rst @@ -19,6 +19,7 @@ backend. For more information about the backends, please see `Sparkling Water Backends `__. In internal backend mode, the call will: + 1. Collect the number and host names of the executors (worker nodes) in the Spark cluster 2. Launch H2O @@ -28,6 +29,7 @@ based on the list of executors 4. Verify the H2O cloud status In external backend mode, the call will: + 1. Start H2O in client mode on the Spark driver 2. Start separated H2O cluster on the configured YARN From fe46b46a38bf17b942e2786a9cae0f4cf3082d6e Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Tue, 20 Jun 2017 11:15:56 +0200 Subject: [PATCH 16/72] separate plaforms and init --- DEVEL.md | 1 + doc/run.md | 63 ----------------------------------- doc/run.rst | 81 ++++----------------------------------------- doc/supported_platforms.rst | 61 ++++++++++++++++++++++++++++++++++ 4 files changed, 69 insertions(+), 137 deletions(-) delete mode 100644 doc/run.md create mode 100644 doc/supported_platforms.rst diff --git a/DEVEL.md b/DEVEL.md index 72bb6ad29..0909e666f 100644 --- a/DEVEL.md +++ b/DEVEL.md @@ -10,6 +10,7 @@ This documentation acts only as the table of content and provides links to the a - [Supported Data Formats](#DataFormat) - [Data Sharing](#DataShare) - [Provided Primitives](#ProvPrim) +- [Supported Platforms](doc/supported_platforms.rst) - [Running Sparkling Water](doc/run.rst) - [Sparkling Water Configuration Properties](doc/configuration_properties.rst) - [Running Sparkling Water](#RunSW) diff --git a/doc/run.md b/doc/run.md deleted file mode 100644 index 5f35f9e37..000000000 --- a/doc/run.md +++ /dev/null @@ -1,63 +0,0 @@ -# Running Sparkling Water - -In order to run Sparkling Water, the environment must contain the property `SPARK_HOME` that -points to the Spark distribution. - -## Starting H2OContext - -H2O on Spark can be started in the Spark Shell or in the Spark application as: - -```scala -val hc = H2OContext.getOrCreate(spark) -``` - -The semantic of the call depends on the configured Sparkling Water backend. For more information -about the backends, please see [Sparkling Water Backends](backends.rst). - -In internal backend mode, the call will: - 1. Collect the number and host names of the executors (worker nodes) in the Spark cluster - 2. Launch H2O services on each detected executor - 3. Create a cloud for H2O services based on the list of executors - 4. Verify the H2O cloud status - -In external backend mode, the call will: - 1. Start H2O in client mode on the Spark driver - 2. Start separated H2O cluster on the configured YARN queue - 3. Connects to the external cluster from the H2O client - - -Sparkling Water can run on top of Spark in the various ways, however starting -Sparkling Water requires different configuration on different environments: - -### Local - -In this case Sparkling Water runs as a local cluster (Spark master variable points to one of values `local`, `local[*]`, or `local-cluster[...]` - -### Standalone Spark Cluster - -[Spark documentation - running Standalone cluster](http://spark.apache.org/docs/latest/spark-standalone.html) - -### YARN - -[Spark documentation - running Spark Application on YARN](http://spark.apache.org/docs/latest/running-on-yarn.html) - -When submitting Sparkling Water application to CHD or Apache Hadoop cluster, the command to submit may look like: -``` -./spark-submit --master=yarn-client --class water.SparklingWaterDriver --conf "spark.yarn.am.extraJavaOptions=-XX:MaxPermSize=384m -Dhdp.version=current" ---driver-memory=8G --num-executors=3 --executor-memory=3G --conf "spark.executor.extraClassPath=-XX:MaxPermSize=384m -Dhdp.version=current" -sparkling-water-assembly-1.5.11-all.jar -``` - -When submitting sparkling water application to HDP Cluster, the command to submit may look like: -``` -./spark-submit --master=yarn-client --class water.SparklingWaterDriver --conf "spark.yarn.am.extraJavaOptions=-XX:MaxPermSize=384m -Dhdp.version=current" ---driver-memory=8G --num-executors=3 --executor-memory=3G --conf "spark.executor.extraClassPath=-XX:MaxPermSize=384m -Dhdp.version=current" -sparkling-water-assembly-1.5.11-all.jar -``` -Apart from the typical spark configuration it is necessary to add `-XX:MaxPermSize=384m` (or higher, but 384m is minimum) to both `spark.executor.extraClassPath` and `spark.yarn.am.extraJavaOptions` (or for client mode, `spark.driver.extraJavaOptions` for cluster mode) configuration properties in order to run Sparkling Water correctly. - -The only difference between HDP cluster and both CDH and Apache hadoop clusters is that we need to add `-Dhdp.version=current` to both `spark.executor.extraClassPath` and `spark.yarn.am.extraJavaOptions` (resp., `spark.driver.extraJavaOptions`) configuration properties in the HDP case. - -### Mesos - -[Spark documentation - running Spark Application on Mesos](http://spark.apache.org/docs/latest/running-on-mesos.html) diff --git a/doc/run.rst b/doc/run.rst index af45989b9..37276e838 100644 --- a/doc/run.rst +++ b/doc/run.rst @@ -1,12 +1,9 @@ Running Sparkling Water -======================= +----------------------- In order to run Sparkling Water, the environment must contain the property ``SPARK_HOME`` that points to the Spark distribution. -Starting H2OContext -------------------- - H2O on Spark can be started in the Spark Shell or in the Spark application as: @@ -20,77 +17,13 @@ Water Backends `__. In internal backend mode, the call will: -1. Collect the number and host -names of the executors (worker nodes) in the Spark cluster -2. Launch H2O -services on each detected executor -3. Create a cloud for H2O services -based on the list of executors +1. Collect the number and host names of the executors (worker nodes) in the Spark cluster +2. Launch H2O services on each detected executor +3. Create a cloud for H2O services based on the list of executors 4. Verify the H2O cloud status In external backend mode, the call will: -1. Start H2O in client mode on -the Spark driver -2. Start separated H2O cluster on the configured YARN -queue -3. Connects to the external cluster from the H2O client - -Sparkling Water can run on top of Spark in the various ways, however -starting Sparkling Water requires different configuration on different -environments: - -Local -~~~~~ - -In this case Sparkling Water runs as a local cluster (Spark master -variable points to one of values ``local``, ``local[*]``, or -``local-cluster[...]`` - -Standalone Spark Cluster -~~~~~~~~~~~~~~~~~~~~~~~~ - -`Spark documentation - running Standalone -cluster `__ - -YARN -~~~~ - -`Spark documentation - running Spark Application on -YARN `__ - -When submitting Sparkling Water application to CHD or Apache Hadoop -cluster, the command to submit may look like: - -.. code:: bash - - ./spark-submit --master=yarn-client --class water.SparklingWaterDriver --conf "spark.yarn.am.extraJavaOptions=-XX:MaxPermSize=384m -Dhdp.version=current" - --driver-memory=8G --num-executors=3 --executor-memory=3G --conf "spark.executor.extraClassPath=-XX:MaxPermSize=384m -Dhdp.version=current" - sparkling-water-assembly-1.5.11-all.jar - -When submitting sparkling water application to HDP Cluster, the command -to submit may look like: - -.. code:: bash - - ./spark-submit --master=yarn-client --class water.SparklingWaterDriver --conf "spark.yarn.am.extraJavaOptions=-XX:MaxPermSize=384m -Dhdp.version=current" - --driver-memory=8G --num-executors=3 --executor-memory=3G --conf "spark.executor.extraClassPath=-XX:MaxPermSize=384m -Dhdp.version=current" - sparkling-water-assembly-1.5.11-all.jar - -Apart from the typical spark configuration it is necessary to add -``-XX:MaxPermSize=384m`` (or higher, but 384m is minimum) to both -``spark.executor.extraClassPath`` and ``spark.yarn.am.extraJavaOptions`` -(or for client mode, ``spark.driver.extraJavaOptions`` for cluster mode) -configuration properties in order to run Sparkling Water correctly. - -The only difference between HDP cluster and both CDH and Apache hadoop -clusters is that we need to add ``-Dhdp.version=current`` to both -``spark.executor.extraClassPath`` and ``spark.yarn.am.extraJavaOptions`` -(resp., ``spark.driver.extraJavaOptions``) configuration properties in -the HDP case. - -Mesos -~~~~~ - -`Spark documentation - running Spark Application on -Mesos `__ +1. Start H2O in client mode on the Spark driver +2. Start separated H2O cluster on the configured YARN queue +3. Connects to the external cluster from the H2O client \ No newline at end of file diff --git a/doc/supported_platforms.rst b/doc/supported_platforms.rst new file mode 100644 index 000000000..11a2459b2 --- /dev/null +++ b/doc/supported_platforms.rst @@ -0,0 +1,61 @@ +Supported platforms +------------------- + +Sparkling Water can run on top of Spark in the various ways, however +starting Sparkling Water requires different configuration on different +environments: + +Local +~~~~~ + +In this case Sparkling Water runs as a local cluster (Spark master +variable points to one of values ``local``, ``local[*]``, or +``local-cluster[...]`` + +Standalone Spark Cluster +~~~~~~~~~~~~~~~~~~~~~~~~ + +`Spark documentation - running Standalone +cluster `__ + +YARN +~~~~ + +`Spark documentation - running Spark Application on +YARN `__ + +When submitting Sparkling Water application to CHD or Apache Hadoop +cluster, the command to submit may look like: + +.. code:: bash + + ./spark-submit --master=yarn-client --class water.SparklingWaterDriver --conf "spark.yarn.am.extraJavaOptions=-XX:MaxPermSize=384m -Dhdp.version=current" + --driver-memory=8G --num-executors=3 --executor-memory=3G --conf "spark.executor.extraClassPath=-XX:MaxPermSize=384m -Dhdp.version=current" + sparkling-water-assembly-2.1.9-all.jar + +When submitting sparkling water application to HDP Cluster, the command +to submit may look like: + +.. code:: bash + + ./spark-submit --master=yarn-client --class water.SparklingWaterDriver --conf "spark.yarn.am.extraJavaOptions=-XX:MaxPermSize=384m -Dhdp.version=current" + --driver-memory=8G --num-executors=3 --executor-memory=3G --conf "spark.executor.extraClassPath=-XX:MaxPermSize=384m -Dhdp.version=current" + sparkling-water-assembly-2.1.9-all.jar + +Apart from the typical spark configuration it is necessary to add +``-XX:MaxPermSize=384m`` (or higher, but 384m is minimum) to both +``spark.executor.extraClassPath`` and ``spark.yarn.am.extraJavaOptions`` +(or for client mode, ``spark.driver.extraJavaOptions`` for cluster mode) +configuration properties in order to run Sparkling Water correctly. + +The only difference between HDP cluster and both CDH and Apache hadoop +clusters is that we need to add ``-Dhdp.version=current`` to both +``spark.executor.extraClassPath`` and ``spark.yarn.am.extraJavaOptions`` +(resp., ``spark.driver.extraJavaOptions``) configuration properties in +the HDP case. + +Mesos +~~~~~ + +`Spark documentation - running Spark Application on +Mesos `__ From d782ee3d86860b37e4a0d515888aeb1fae3cb36a Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Tue, 20 Jun 2017 11:26:44 +0200 Subject: [PATCH 17/72] Improve devel.md --- DEVEL.md | 30 ++++++++++++++++-------------- doc/log_location.rst | 4 ++-- doc/security.rst | 4 ++-- 3 files changed, 20 insertions(+), 18 deletions(-) diff --git a/DEVEL.md b/DEVEL.md index 0909e666f..d72efbaa6 100644 --- a/DEVEL.md +++ b/DEVEL.md @@ -5,29 +5,31 @@ This documentation acts only as the table of content and provides links to the a - [Typical Use Case](#UseCase) - [Requirements](#Req) - [Design](#Design) + - [Supported Platforms](doc/supported_platforms.rst) + - [Spark Frame - H2O Frame Mapping](doc/spark_h2o_mapping.rst) - [Features](#Features) - [Supported Data Sources](#DataSource) - [Supported Data Formats](#DataFormat) - [Data Sharing](#DataShare) - [Provided Primitives](#ProvPrim) -- [Supported Platforms](doc/supported_platforms.rst) - [Running Sparkling Water](doc/run.rst) -- [Sparkling Water Configuration Properties](doc/configuration_properties.rst) -- [Running Sparkling Water](#RunSW) - - [Starting H2O Services](#StartH2O) - - [Memory Allocation](doc/memory_setup.rst) - - [Security](doc/security.rst) - - [Spark Frame - H2O Frame Conversions](doc/spark_h2o_conversions.rst) - - [Create H2OFrame From an Existing Key](doc/h2o_frame_from_key.rst) - - [Calling H2O Algorithms](doc/calling_h2o_algos.rst) - - [Spark Frame - H2O Frame Mapping](doc/spark_h2o_mapping.rst) +- Sparkling Water Configuration + - [Sparkling Water Configuration Properties](doc/configuration_properties.rst) + - [Memory Allocation](doc/memory_setup.rst) +- Tutorials + - [Enabling Security](doc/security.rst) + - [Calling H2O Algorithms](doc/calling_h2o_algos.rst) + - Frames Conversions & Creation + - [Spark Frame - H2O Frame Conversions](doc/spark_h2o_conversions.rst) + - [H2O Frame as Spark's Data Source](doc/datasource.rst) + - [Create H2OFrame From an Existing Key](doc/h2o_frame_from_key.rst) + - Logging + - [Change Sparkling Shell Logging Level](doc/change_log_level.rst) + - [Obtain Sparkling Water Logs](doc/log_location.rst) - Testing - [Running Unit Tests](doc/unit_tests.rst) - [Integration Tests](doc/integ_tests.rst) -- [Sparkling Water Log Locations](doc/log_location.rst) -- [Change Sparkling Shell Logging Level](doc/change_log_level.rst) -- [H2O Frame as Spark's Data Source](doc/datasource.rst) -- [Sparkling Water Tuning](doc/internal_backend_tuning.rst) +- [Sparkling Water Internal Backend Tuning](doc/internal_backend_tuning.rst) - [Sparkling Water and Zeppelin](doc/zeppelin.rst) --- diff --git a/doc/log_location.rst b/doc/log_location.rst index c743ce8dd..ad376c882 100644 --- a/doc/log_location.rst +++ b/doc/log_location.rst @@ -1,5 +1,5 @@ -Sparkling Water Log Location ----------------------------- +Obtain Sparkling Water Logs +--------------------------- Depending on how you launched H2O there are a couple of ways to obtain the logs. diff --git a/doc/security.rst b/doc/security.rst index dbc27fd99..87c1065f8 100644 --- a/doc/security.rst +++ b/doc/security.rst @@ -1,5 +1,5 @@ -Security --------- +Enabling Security +----------------- Both Spark and H2O support basic node authentication and data encryption. In H2O's case we encrypt all the data sent between server From a493ee8b5063dd4c83857cf59b1898319d9954d2 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Tue, 20 Jun 2017 11:28:32 +0200 Subject: [PATCH 18/72] Improve devel.md --- DEVEL.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/DEVEL.md b/DEVEL.md index d72efbaa6..9ebd95c91 100644 --- a/DEVEL.md +++ b/DEVEL.md @@ -13,23 +13,23 @@ This documentation acts only as the table of content and provides links to the a - [Data Sharing](#DataShare) - [Provided Primitives](#ProvPrim) - [Running Sparkling Water](doc/run.rst) -- Sparkling Water Configuration +- **Sparkling Water Configuration** - [Sparkling Water Configuration Properties](doc/configuration_properties.rst) - [Memory Allocation](doc/memory_setup.rst) -- Tutorials + - [Sparkling Water Internal Backend Tuning](doc/internal_backend_tuning.rst) +- **Tutorials** - [Enabling Security](doc/security.rst) - [Calling H2O Algorithms](doc/calling_h2o_algos.rst) - - Frames Conversions & Creation + - **Frames Conversions & Creation** - [Spark Frame - H2O Frame Conversions](doc/spark_h2o_conversions.rst) - [H2O Frame as Spark's Data Source](doc/datasource.rst) - [Create H2OFrame From an Existing Key](doc/h2o_frame_from_key.rst) - - Logging + - **Logging** - [Change Sparkling Shell Logging Level](doc/change_log_level.rst) - [Obtain Sparkling Water Logs](doc/log_location.rst) -- Testing +- **Testing** - [Running Unit Tests](doc/unit_tests.rst) - [Integration Tests](doc/integ_tests.rst) -- [Sparkling Water Internal Backend Tuning](doc/internal_backend_tuning.rst) - [Sparkling Water and Zeppelin](doc/zeppelin.rst) --- From a03ebf7d9b7b5c07c1da7ba1d8a927f173534c79 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Tue, 20 Jun 2017 11:29:01 +0200 Subject: [PATCH 19/72] Improve devel.md --- DEVEL.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/DEVEL.md b/DEVEL.md index 9ebd95c91..732f88902 100644 --- a/DEVEL.md +++ b/DEVEL.md @@ -20,11 +20,11 @@ This documentation acts only as the table of content and provides links to the a - **Tutorials** - [Enabling Security](doc/security.rst) - [Calling H2O Algorithms](doc/calling_h2o_algos.rst) - - **Frames Conversions & Creation** + - Frames Conversions & Creation - [Spark Frame - H2O Frame Conversions](doc/spark_h2o_conversions.rst) - [H2O Frame as Spark's Data Source](doc/datasource.rst) - [Create H2OFrame From an Existing Key](doc/h2o_frame_from_key.rst) - - **Logging** + - Logging - [Change Sparkling Shell Logging Level](doc/change_log_level.rst) - [Obtain Sparkling Water Logs](doc/log_location.rst) - **Testing** From 587699db1f923458d76172a0140f21e254e633c0 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Tue, 20 Jun 2017 11:34:22 +0200 Subject: [PATCH 20/72] remove presentation from design-docs as it just contains the image SparklingWaterCluster.png --- DEVEL.md | 17 +----- .../images/src/sparkling_cluster_diagram.pptx | Bin 1027535 -> 0 bytes doc/basic_primitives.rst | 65 +++++++++++++++++++++ 3 files changed, 66 insertions(+), 16 deletions(-) delete mode 100644 design-doc/images/src/sparkling_cluster_diagram.pptx create mode 100644 doc/basic_primitives.rst diff --git a/DEVEL.md b/DEVEL.md index 732f88902..bc6b18889 100644 --- a/DEVEL.md +++ b/DEVEL.md @@ -5,13 +5,13 @@ This documentation acts only as the table of content and provides links to the a - [Typical Use Case](#UseCase) - [Requirements](#Req) - [Design](#Design) + - [Basic Primitives](doc/basic_primitives.rst) - [Supported Platforms](doc/supported_platforms.rst) - [Spark Frame - H2O Frame Mapping](doc/spark_h2o_mapping.rst) - [Features](#Features) - [Supported Data Sources](#DataSource) - [Supported Data Formats](#DataFormat) - [Data Sharing](#DataShare) - - [Provided Primitives](#ProvPrim) - [Running Sparkling Water](doc/run.rst) - **Sparkling Water Configuration** - [Sparkling Water Configuration Properties](doc/configuration_properties.rst) @@ -92,18 +92,3 @@ Sparkling Water enables transformation between different types of Spark `RDD` an When converting from `H2OFrame` to `RDD`, a wrapper is created around the `H2OFrame` to provide an RDD-like API. In this case, no data is duplicated; instead, the data is served directly from then underlying `H2OFrame`. Converting in the opposite direction (i.e, from Spark `RDD`/`DataFrame` to `H2OFrame`) needs evaluation of data stored in Spark `RDD` and transfer them from RDD storage into `H2OFrame`. However, data stored in `H2OFrame` is heavily compressed. - - ----- - -### Provided Primitives -The Sparkling Water provides following primitives, which are the basic classes used by Spark components: - - -| Concept | Implementation class | Description | -|----------------|-----------------------------------|-------------| -| H2O context | `org.apache.spark.h2o.H2OContext` | H2O context that holds H2O state and provides primitives to transfer RDD into H2OFrame and vice versa. It follows design principles of Spark primitives such as `SparkContext` or `SQLContext` | -| H2O entry point| `water.H2O` | Represents the entry point for accessing H2O services. 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The table is column-based and provides column and row accessors. | -| H2O Algorithms | package `hex` | Represents the H2O machine learning algorithms library, including DeepLearning, GBM, RandomForest. | - diff --git a/design-doc/images/src/sparkling_cluster_diagram.pptx b/design-doc/images/src/sparkling_cluster_diagram.pptx deleted file mode 100644 index 5d157559b239eba7779c15ee31bc2a612f73d4fc..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1027535 zcmeFZV~}lawk4c-(#||-+qP}nwsF$7ZQHhO+qQYqIEgRot=nC7`&LAMH==)4_xiD8 zFYF&{#(c&cbBy_HIY}U3BmhtVZ~y=Re1Px_r%vD>004oo004*p;6F44ZEYNlZ5(x# z-0X}Uv}s(et?=@Je~{$@d_VO6j{ln@FqtxCzfK1~a+7t&>FRi;$G=`|4wfLuEAuf5 zeFjAK^uxkfoRFHS(@WLgH~7~t@aaWVJ!r@enE??R1HA zw?6M}Z*;A?$%5G=IT69W<=wT%!=s4Xt9AdGQO!U8)bLke(_U@98ZVn_vrG(()hJ@c z84i@=I|i`t25g;&MRVX7U}y%lV=su4g9`djuh}@ZVxw8cxU64LWsoG5%JoQMxm=NV zB0ah&$6(R*uwe&$4KrDbU^M2{N!73ON#L_d;fN@F> zUjR+*X^dR9!;ZP{=9$if?lRd3)3k+;HO>1Uxyml}{+A+b?=%+ishSa7S<>E~=-GHv 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zFM}$Hfd9P2_2>-IArrim djYSdapZOvUWgIX!0DvF-J3$Np*$Lna@L$LSZ;t=~ diff --git a/doc/basic_primitives.rst b/doc/basic_primitives.rst new file mode 100644 index 000000000..a4b663318 --- /dev/null +++ b/doc/basic_primitives.rst @@ -0,0 +1,65 @@ +Provided Primitives +------------------- + +The Sparkling Water provides following primitives, which are the basic +classes used by Spark components: + ++-------------------+--------------------------------------+----------------+ +| Concept | Implementation class | Description | ++===================+======================================+================+ +| H2O context | ``org.apache.spark.h2o.H2OContext`` | H2O context | +| | | that holds H2O | +| | | state and | +| | | provides | +| | | primitives to | +| | | transfer RDD | +| | | into H2OFrame | +| | | and vice | +| | | versa. It | +| | | follows design | +| | | principles of | +| | | Spark | +| | | primitives | +| | | such as | +| | | ``SparkContext | +| | | `` | +| | | or | +| | | ``SQLContext`` | ++-------------------+--------------------------------------+----------------+ +| H2O entry point | ``water.H2O`` | Represents the | +| | | entry point | +| | | for accessing | +| | | H2O services. | +| | | It holds | +| | | information | +| | | about the | +| | | actual H2O | +| | | cluster, | +| | | including a | +| | | list of nodes | +| | | and the status | +| | | of distributed | +| | | K/V datastore. | ++-------------------+--------------------------------------+----------------+ +| H2O H2OFrame | ``water.fvec.H2OFrame`` | H2OFrame is | +| | | the H2O data | +| | | structure that | +| | | represents a | +| | | table of | +| | | values. The | +| | | table is | +| | | column-based | +| | | and provides | +| | | column and row | +| | | accessors. | ++-------------------+--------------------------------------+----------------+ +| H2O Algorithms | package ``hex`` | Represents the | +| | | H2O machine | +| | | learning | +| | | algorithms | +| | | library, | +| | | including | +| | | DeepLearning, | +| | | GBM, | +| | | RandomForest. | ++-------------------+--------------------------------------+----------------+ From 63b4b960ddeeb227651f6594e5db6b4b791030fb Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Tue, 20 Jun 2017 11:41:03 +0200 Subject: [PATCH 21/72] move rest_api_scala_endpoints --- DEVEL.md | 2 + doc/rest_api_scala_endpoints.md | 46 +++++++++++++++++++++++ doc/rest_api_scala_endpoints.rst | 81 ++++++++++++++++++++++++++++++++++++++++ 3 files changed, 129 insertions(+) create mode 100644 doc/rest_api_scala_endpoints.md create mode 100644 doc/rest_api_scala_endpoints.rst diff --git a/DEVEL.md b/DEVEL.md index bc6b18889..c2970ef10 100644 --- a/DEVEL.md +++ b/DEVEL.md @@ -30,6 +30,8 @@ This documentation acts only as the table of content and provides links to the a - **Testing** - [Running Unit Tests](doc/unit_tests.rst) - [Integration Tests](doc/integ_tests.rst) +- **Sparkling Water Rest API** + - [Scala Interpreter REST API Overview](doc/rest_api_scala_endpoints.md) - [Sparkling Water and Zeppelin](doc/zeppelin.rst) --- diff --git a/doc/rest_api_scala_endpoints.md b/doc/rest_api_scala_endpoints.md new file mode 100644 index 000000000..deaf736a6 --- /dev/null +++ b/doc/rest_api_scala_endpoints.md @@ -0,0 +1,46 @@ +# Scala interpreter available through REST API + +## Basic design +There is a simple pool of interpreters created at the start. Once an scala interpreter is associated with +the session, new interpreter is added to the pool. Each interpreter is deleted when it's not used for some +fixed time + +## Example usage of scala interpreter using REST API +Here, you can find some basic calls to access scala interpreter behind REST API using curl + +### init interpreter and obtain session ID +curl --data-urlencode '' http://192.168.0.10:54321/3/scalaint + +### destroy session and interpreter associated with the session +curl -X DELETE http://192.168.0.10:54321/3/scalaint/512ef484-e21a-48f9-979e-2879f63a779e + +### get all active sessions +curl http://192.168.0.10:54321/3/scalaint + +### try to interpret the incomplete code, status is error +curl --data-urlencode code='sc.' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 + +### try to interpret the code, status is error (function does not exist) +curl --data-urlencode code='foo()' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 + +### try to interpret the code, result is success +curl --data-urlencode code='21+21' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 + +### try to interpret the code with the spark context, use semicolon to separate commands +curl --data-urlencode code='val data = Array(1, 2, 3, 4, 5); val distData = sc.parallelize(data); val result = distData.map(s => s+10)' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 + +### try to interpret the code with the spark context, use new lines to separate commands +curl --data-urlencode code=' +val data = Array(1, 2, 3, 4, 5) +val distData = sc.parallelize(data) +val result = distData.map(s => s+10)' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 + +### declare class and use it in the next call +curl --data-urlencode code=' +case class A(number: Int)' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 + +curl --data-urlencode code=' +val data = Array(1, 2, 3, 4, 5) +val distData = sc.parallelize(data) +val result = distData.map(s => A(s))' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 + diff --git a/doc/rest_api_scala_endpoints.rst b/doc/rest_api_scala_endpoints.rst new file mode 100644 index 000000000..dc953f490 --- /dev/null +++ b/doc/rest_api_scala_endpoints.rst @@ -0,0 +1,81 @@ +Scala interpreter available through REST API +============================================ + +Basic design +------------ + +There is a simple pool of interpreters created at the start. Once an +scala interpreter is associated with the session, new interpreter is +added to the pool. Each interpreter is deleted when it's not used for +some fixed time + +Example usage of scala interpreter using REST API +------------------------------------------------- + +Here, you can find some basic calls to access scala interpreter behind +REST API using curl. + +Init interpreter and obtain session ID +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code:: bash + + curl --data-urlencode '' http://192.168.0.10:54321/3/scalaint + +Destroy session and interpreter associated with the session +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code:: bash + + curl -X DELETE http://192.168.0.10:54321/3/scalaint/512ef484-e21a-48f9-979e-2879f63a779e + +Get all active sessions +~~~~~~~~~~~~~~~~~~~~~~~ + +.. code:: bash + + curl http://192.168.0.10:54321/3/scalaint + +Try to interpret the incomplete code, status is error +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code:: bash + + curl --data-urlencode code='sc.' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 + +Try to interpret the code, status is error (function does not exist) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code:: bash + + curl --data-urlencode code='foo()' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 + +Try to interpret the code, result is success +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code:: bash + + curl --data-urlencode code='21+21' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 + +Try to interpret the code with the spark context, use semicolon to separate commands +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code:: bash + + curl --data-urlencode code='val data = Array(1, 2, 3, 4, 5); val distData = sc.parallelize(data); val result = distData.map(s => s+10)' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 + +Try to interpret the code with the spark context, use new lines to separate commands +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code:: bash + + curl --data-urlencode code=' val data = Array(1, 2, 3, 4, 5) val distData = sc.parallelize(data) val result = distData.map(s => s+10)' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 + +Declare class and use it in the next call +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code:: bash + + curl --data-urlencode code=' case class A(number: Int)' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 + + curl --data-urlencode code=' val data = Array(1, 2, 3, 4, 5) val distData = sc.parallelize(data) val result = distData.map(s => A(s))' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 From 604a8a1df6658ad1a928c9009a44b7b310d8d854 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Tue, 20 Jun 2017 11:45:17 +0200 Subject: [PATCH 22/72] finish the convertion of rest api --- design-doc/rest-api-scala-endpoints.md | 46 ------------------------ doc/rest_api_scala_endpoints.md | 46 ------------------------ doc/rest_api_scala_endpoints.rst | 65 +++++++++++++++++++--------------- 3 files changed, 37 insertions(+), 120 deletions(-) delete mode 100644 design-doc/rest-api-scala-endpoints.md delete mode 100644 doc/rest_api_scala_endpoints.md diff --git a/design-doc/rest-api-scala-endpoints.md b/design-doc/rest-api-scala-endpoints.md deleted file mode 100644 index deaf736a6..000000000 --- a/design-doc/rest-api-scala-endpoints.md +++ /dev/null @@ -1,46 +0,0 @@ -# Scala interpreter available through REST API - -## Basic design -There is a simple pool of interpreters created at the start. Once an scala interpreter is associated with -the session, new interpreter is added to the pool. Each interpreter is deleted when it's not used for some -fixed time - -## Example usage of scala interpreter using REST API -Here, you can find some basic calls to access scala interpreter behind REST API using curl - -### init interpreter and obtain session ID -curl --data-urlencode '' http://192.168.0.10:54321/3/scalaint - -### destroy session and interpreter associated with the session -curl -X DELETE http://192.168.0.10:54321/3/scalaint/512ef484-e21a-48f9-979e-2879f63a779e - -### get all active sessions -curl http://192.168.0.10:54321/3/scalaint - -### try to interpret the incomplete code, status is error -curl --data-urlencode code='sc.' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 - -### try to interpret the code, status is error (function does not exist) -curl --data-urlencode code='foo()' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 - -### try to interpret the code, result is success -curl --data-urlencode code='21+21' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 - -### try to interpret the code with the spark context, use semicolon to separate commands -curl --data-urlencode code='val data = Array(1, 2, 3, 4, 5); val distData = sc.parallelize(data); val result = distData.map(s => s+10)' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 - -### try to interpret the code with the spark context, use new lines to separate commands -curl --data-urlencode code=' -val data = Array(1, 2, 3, 4, 5) -val distData = sc.parallelize(data) -val result = distData.map(s => s+10)' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 - -### declare class and use it in the next call -curl --data-urlencode code=' -case class A(number: Int)' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 - -curl --data-urlencode code=' -val data = Array(1, 2, 3, 4, 5) -val distData = sc.parallelize(data) -val result = distData.map(s => A(s))' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 - diff --git a/doc/rest_api_scala_endpoints.md b/doc/rest_api_scala_endpoints.md deleted file mode 100644 index deaf736a6..000000000 --- a/doc/rest_api_scala_endpoints.md +++ /dev/null @@ -1,46 +0,0 @@ -# Scala interpreter available through REST API - -## Basic design -There is a simple pool of interpreters created at the start. Once an scala interpreter is associated with -the session, new interpreter is added to the pool. Each interpreter is deleted when it's not used for some -fixed time - -## Example usage of scala interpreter using REST API -Here, you can find some basic calls to access scala interpreter behind REST API using curl - -### init interpreter and obtain session ID -curl --data-urlencode '' http://192.168.0.10:54321/3/scalaint - -### destroy session and interpreter associated with the session -curl -X DELETE http://192.168.0.10:54321/3/scalaint/512ef484-e21a-48f9-979e-2879f63a779e - -### get all active sessions -curl http://192.168.0.10:54321/3/scalaint - -### try to interpret the incomplete code, status is error -curl --data-urlencode code='sc.' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 - -### try to interpret the code, status is error (function does not exist) -curl --data-urlencode code='foo()' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 - -### try to interpret the code, result is success -curl --data-urlencode code='21+21' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 - -### try to interpret the code with the spark context, use semicolon to separate commands -curl --data-urlencode code='val data = Array(1, 2, 3, 4, 5); val distData = sc.parallelize(data); val result = distData.map(s => s+10)' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 - -### try to interpret the code with the spark context, use new lines to separate commands -curl --data-urlencode code=' -val data = Array(1, 2, 3, 4, 5) -val distData = sc.parallelize(data) -val result = distData.map(s => s+10)' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 - -### declare class and use it in the next call -curl --data-urlencode code=' -case class A(number: Int)' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 - -curl --data-urlencode code=' -val data = Array(1, 2, 3, 4, 5) -val distData = sc.parallelize(data) -val result = distData.map(s => A(s))' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 - diff --git a/doc/rest_api_scala_endpoints.rst b/doc/rest_api_scala_endpoints.rst index dc953f490..9b026bf4b 100644 --- a/doc/rest_api_scala_endpoints.rst +++ b/doc/rest_api_scala_endpoints.rst @@ -1,8 +1,8 @@ Scala interpreter available through REST API -============================================ +-------------------------------------------- Basic design ------------- +~~~~~~~~~~~~ There is a simple pool of interpreters created at the start. Once an scala interpreter is associated with the session, new interpreter is @@ -10,72 +10,81 @@ added to the pool. Each interpreter is deleted when it's not used for some fixed time Example usage of scala interpreter using REST API -------------------------------------------------- +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Here, you can find some basic calls to access scala interpreter behind -REST API using curl. +REST API using curl Init interpreter and obtain session ID -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. code:: bash curl --data-urlencode '' http://192.168.0.10:54321/3/scalaint Destroy session and interpreter associated with the session -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. code:: bash - curl -X DELETE http://192.168.0.10:54321/3/scalaint/512ef484-e21a-48f9-979e-2879f63a779e + curl -X DELETE + http://192.168.0.10:54321/3/scalaint/512ef484-e21a-48f9-979e-2879f63a779e Get all active sessions -~~~~~~~~~~~~~~~~~~~~~~~ +^^^^^^^^^^^^^^^^^^^^^^^ .. code:: bash curl http://192.168.0.10:54321/3/scalaint -Try to interpret the incomplete code, status is error -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Interpret the incomplete code, status is error +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. code:: bash - curl --data-urlencode code='sc.' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 + curl --data-urlencode code='sc.' + http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 -Try to interpret the code, status is error (function does not exist) -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +try to interpret the code, status is error (function does not exist) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. code:: bash - curl --data-urlencode code='foo()' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 + curl --data-urlencode code='foo()' + http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 -Try to interpret the code, result is success -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Interpret the code, result is success +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. code:: bash - curl --data-urlencode code='21+21' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 + curl --data-urlencode code='21+21' + http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 -Try to interpret the code with the spark context, use semicolon to separate commands -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Interpret the code with the spark context, use semicolon to separate commands +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. code:: bash - - curl --data-urlencode code='val data = Array(1, 2, 3, 4, 5); val distData = sc.parallelize(data); val result = distData.map(s => s+10)' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 +curl --data-urlencode code='val data = Array(1, 2, 3, 4, 5); val +distData = sc.parallelize(data); val result = distData.map(s => s+10)' +http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 -Try to interpret the code with the spark context, use new lines to separate commands -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Interpret the code with the spark context, use new lines to separate commands +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. code:: bash - curl --data-urlencode code=' val data = Array(1, 2, 3, 4, 5) val distData = sc.parallelize(data) val result = distData.map(s => s+10)' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 + curl --data-urlencode code=' val data = Array(1, 2, 3, 4, 5) val + distData = sc.parallelize(data) val result = distData.map(s => s+10)' + http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 Declare class and use it in the next call -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. code:: bash - curl --data-urlencode code=' case class A(number: Int)' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 + curl --data-urlencode code=' case class A(number: Int)' + http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 - curl --data-urlencode code=' val data = Array(1, 2, 3, 4, 5) val distData = sc.parallelize(data) val result = distData.map(s => A(s))' http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 + curl --data-urlencode code=' val data = Array(1, 2, 3, 4, 5) val + distData = sc.parallelize(data) val result = distData.map(s => A(s))' + http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 From f365c95ee14ff8b5b02694f1cbdf1b55c81c30bc Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Tue, 20 Jun 2017 11:46:03 +0200 Subject: [PATCH 23/72] missing code block --- doc/rest_api_scala_endpoints.rst | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/doc/rest_api_scala_endpoints.rst b/doc/rest_api_scala_endpoints.rst index 9b026bf4b..f125db7bc 100644 --- a/doc/rest_api_scala_endpoints.rst +++ b/doc/rest_api_scala_endpoints.rst @@ -64,9 +64,11 @@ Interpret the code, result is success Interpret the code with the spark context, use semicolon to separate commands ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -curl --data-urlencode code='val data = Array(1, 2, 3, 4, 5); val -distData = sc.parallelize(data); val result = distData.map(s => s+10)' -http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 +.. code:: bash + + curl --data-urlencode code='val data = Array(1, 2, 3, 4, 5); val + distData = sc.parallelize(data); val result = distData.map(s => s+10)' + http://192.168.0.10:54321/3/scalaint/c3e5ea38-0b7e-4136-9ba3-21615ea2d298 Interpret the code with the spark context, use new lines to separate commands ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ From f48bf30f394645d24edbd9d5f0111d70de8a61a1 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Tue, 20 Jun 2017 12:06:13 +0200 Subject: [PATCH 24/72] restructure --- DEVEL.md | 50 ++++++++------------- design-doc/images/Topology.png | Bin 360157 -> 0 bytes .../configuration_properties.rst | 0 .../internal_backend_tuning.rst | 0 doc/{ => configuration}/memory_setup.rst | 0 doc/{ => design}/basic_primitives.rst | 0 doc/design/data_sharing.rst | 33 ++++++++++++++ doc/{ => design}/spark_h2o_mapping.rst | 0 doc/{ => design}/supported_platforms.rst | 0 doc/{ => devel}/build.rst | 0 doc/{ => devel}/integ_tests.rst | 0 doc/{ => devel}/rest_api_scala_endpoints.rst | 0 doc/{ => devel}/running_examples.rst | 0 doc/{ => devel}/unit_tests.rst | 0 .../images/Sparkling Water cluster.png | Bin .../images/internal_backend_data_sharing.png | Bin doc/{ => tutorials}/calling_h2o_algos.rst | 0 doc/{ => tutorials}/change_log_level.rst | 0 doc/{ => tutorials}/h2o_frame_from_key.rst | 0 .../h2oframe_as_data_source.rst} | 0 .../obtaining_logs.rst} | 0 .../run_on_windows.rst} | 0 doc/{run.rst => tutorials/run_sparkling_water.rst} | 0 doc/{ => tutorials}/security.rst | 0 doc/{ => tutorials}/spark_h2o_conversions.rst | 0 .../use_as_spark_package.rst} | 0 .../use_on_zeppelin.rst} | 0 27 files changed, 52 insertions(+), 31 deletions(-) delete mode 100644 design-doc/images/Topology.png rename doc/{ => configuration}/configuration_properties.rst (100%) rename doc/{ => configuration}/internal_backend_tuning.rst (100%) rename doc/{ => configuration}/memory_setup.rst (100%) rename doc/{ => design}/basic_primitives.rst (100%) create mode 100644 doc/design/data_sharing.rst rename doc/{ => design}/spark_h2o_mapping.rst (100%) rename doc/{ => design}/supported_platforms.rst (100%) rename doc/{ => devel}/build.rst (100%) rename doc/{ => devel}/integ_tests.rst (100%) rename doc/{ => devel}/rest_api_scala_endpoints.rst (100%) rename doc/{ => devel}/running_examples.rst (100%) rename doc/{ => devel}/unit_tests.rst (100%) rename {design-doc => doc}/images/Sparkling Water cluster.png (100%) rename design-doc/images/DataShare.png => doc/images/internal_backend_data_sharing.png (100%) rename doc/{ => tutorials}/calling_h2o_algos.rst (100%) rename doc/{ => tutorials}/change_log_level.rst (100%) rename doc/{ => tutorials}/h2o_frame_from_key.rst (100%) rename doc/{datasource.rst => tutorials/h2oframe_as_data_source.rst} (100%) rename doc/{log_location.rst => tutorials/obtaining_logs.rst} (100%) rename doc/{windows_manual.rst => tutorials/run_on_windows.rst} (100%) rename doc/{run.rst => tutorials/run_sparkling_water.rst} (100%) rename doc/{ => tutorials}/security.rst (100%) rename doc/{ => tutorials}/spark_h2o_conversions.rst (100%) rename doc/{spark_package.rst => tutorials/use_as_spark_package.rst} (100%) rename doc/{zeppelin.rst => tutorials/use_on_zeppelin.rst} (100%) diff --git a/DEVEL.md b/DEVEL.md index c2970ef10..18467848b 100644 --- a/DEVEL.md +++ b/DEVEL.md @@ -5,34 +5,33 @@ This documentation acts only as the table of content and provides links to the a - [Typical Use Case](#UseCase) - [Requirements](#Req) - [Design](#Design) - - [Basic Primitives](doc/basic_primitives.rst) - - [Supported Platforms](doc/supported_platforms.rst) - - [Spark Frame - H2O Frame Mapping](doc/spark_h2o_mapping.rst) -- [Features](#Features) + - [Basic Primitives](doc/design/basic_primitives.rst) + - [Supported Platforms](doc/design/supported_platforms.rst) + - [Spark Frame - H2O Frame Mapping](doc/design/spark_h2o_mapping.rst) + - [Data Sharing Between H2O and Spark](doc/design/data_sharing.rst) - [Supported Data Sources](#DataSource) - [Supported Data Formats](#DataFormat) - - [Data Sharing](#DataShare) -- [Running Sparkling Water](doc/run.rst) +- [Running Sparkling Water](doc/tutorials/run_sparkling_water.rst) - **Sparkling Water Configuration** - - [Sparkling Water Configuration Properties](doc/configuration_properties.rst) - - [Memory Allocation](doc/memory_setup.rst) - - [Sparkling Water Internal Backend Tuning](doc/internal_backend_tuning.rst) + - [Sparkling Water Configuration Properties](doc/configuration/configuration_properties.rst) + - [Memory Allocation](doc/configuration/memory_setup.rst) + - [Sparkling Water Internal Backend Tuning](doc/configuration/internal_backend_tuning.rst) - **Tutorials** - - [Enabling Security](doc/security.rst) - - [Calling H2O Algorithms](doc/calling_h2o_algos.rst) + - [Enabling Security](doc/tutorials/security.rst) + - [Calling H2O Algorithms](doc/tutorials/calling_h2o_algos.rst) - Frames Conversions & Creation - - [Spark Frame - H2O Frame Conversions](doc/spark_h2o_conversions.rst) - - [H2O Frame as Spark's Data Source](doc/datasource.rst) - - [Create H2OFrame From an Existing Key](doc/h2o_frame_from_key.rst) + - [Spark Frame - H2O Frame Conversions](doc/tutorials/spark_h2o_conversions.rst) + - [H2O Frame as Spark's Data Source](doc/tutorials/h2oframe_as_data_source.rst) + - [Create H2OFrame From an Existing Key](doc/tutorials/h2o_frame_from_key.rst) - Logging - - [Change Sparkling Shell Logging Level](doc/change_log_level.rst) - - [Obtain Sparkling Water Logs](doc/log_location.rst) + - [Change Sparkling Shell Logging Level](doc/tutorials/change_log_level.rst) + - [Obtain Sparkling Water Logs](doc/tutorials/obtaining_logs.rst) - **Testing** - - [Running Unit Tests](doc/unit_tests.rst) - - [Integration Tests](doc/integ_tests.rst) + - [Running Unit Tests](doc/devel/unit_tests.rst) + - [Integration Tests](doc/devel/integ_tests.rst) - **Sparkling Water Rest API** - [Scala Interpreter REST API Overview](doc/rest_api_scala_endpoints.md) -- [Sparkling Water and Zeppelin](doc/zeppelin.rst) +- [Sparkling Water and Zeppelin](doc/tutorials/use_on_zeppelin.rst) --- @@ -48,7 +47,7 @@ inside a Spark executor, which is created after application submission. At this point, H2O starts services, including distributed KV store and memory manager, and orchestrates them into a cloud. The topology of the created cloud matches the topology of the underlying Spark cluster exactly. - ![Topology](design-doc/images/Sparkling Water cluster.png) + ![Topology](doc/images/Sparkling Water cluster.png) When H2O services are running, it is possible to create H2O data structures, call H2O algorithms, and transfer values from/to RDD. @@ -83,14 +82,3 @@ Sparkling Water can read data stored in the following formats: - SVMLight - ARFF - ---- - -### Data Sharing -Sparkling Water enables transformation between different types of Spark `RDD` and H2O's `H2OFrame`, and vice versa. - - ![Data Sharing](design-doc/images/DataShare.png) - -When converting from `H2OFrame` to `RDD`, a wrapper is created around the `H2OFrame` to provide an RDD-like API. In this case, no data is duplicated; instead, the data is served directly from then underlying `H2OFrame`. - -Converting in the opposite direction (i.e, from Spark `RDD`/`DataFrame` to `H2OFrame`) needs evaluation of data stored in Spark `RDD` and transfer them from RDD storage into `H2OFrame`. 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uBMe6$b;68a1d%!ZAB_4pPzA*_q9|K>K)ZKcUiDL43?0ZLjbgQDVgCc2eRWd+ diff --git a/doc/configuration_properties.rst b/doc/configuration/configuration_properties.rst similarity index 100% rename from doc/configuration_properties.rst rename to doc/configuration/configuration_properties.rst diff --git a/doc/internal_backend_tuning.rst b/doc/configuration/internal_backend_tuning.rst similarity index 100% rename from doc/internal_backend_tuning.rst rename to doc/configuration/internal_backend_tuning.rst diff --git a/doc/memory_setup.rst b/doc/configuration/memory_setup.rst similarity index 100% rename from doc/memory_setup.rst rename to doc/configuration/memory_setup.rst diff --git a/doc/basic_primitives.rst b/doc/design/basic_primitives.rst similarity index 100% rename from doc/basic_primitives.rst rename to doc/design/basic_primitives.rst diff --git a/doc/design/data_sharing.rst b/doc/design/data_sharing.rst new file mode 100644 index 000000000..37d553749 --- /dev/null +++ b/doc/design/data_sharing.rst @@ -0,0 +1,33 @@ +Data Sharing +------------ + +Sparkling Water enables transformation between different types of Spark +``RDD`` and H2O's ``H2OFrame``, and vice versa. + +Conversion Design +~~~~~~~~~~~~~~~~~ + +When converting from ``H2OFrame`` to ``RDD``, a wrapper is created +around the ``H2OFrame`` to provide an RDD-like API. In this case, no +data is duplicated; instead, the data is served directly from the +underlying ``H2OFrame``. + +Converting in the opposite direction (i.e, from Spark +``RDD``/``DataFrame`` to ``H2OFrame``) needs evaluation of data stored +in Spark ``RDD`` and transfer them from RDD storage into ``H2OFrame``. +However, data stored in ``H2OFrame`` is heavily compressed. + +Exchanging the Data +~~~~~~~~~~~~~~~~~~~ + +The way how data is transferred between Spark and H2O differs on the used +Sparkling Water backend. + +In the Internal Sparkling Water Backend, Spark and H2O share the same +JVM as is depicted on the following figure. |Data Sharing| + +In the External Sparkling Water Backend, Spark and H2O are separated +clusters and data has to be send between these cluster over the network. + +.. |Data Sharing| image:: doc/images/internal_backend_data_sharing.png + diff --git a/doc/spark_h2o_mapping.rst b/doc/design/spark_h2o_mapping.rst similarity index 100% rename from doc/spark_h2o_mapping.rst rename to doc/design/spark_h2o_mapping.rst diff --git a/doc/supported_platforms.rst b/doc/design/supported_platforms.rst similarity index 100% rename from doc/supported_platforms.rst rename to doc/design/supported_platforms.rst diff --git a/doc/build.rst b/doc/devel/build.rst similarity index 100% rename from doc/build.rst rename to doc/devel/build.rst diff --git a/doc/integ_tests.rst b/doc/devel/integ_tests.rst similarity index 100% rename from doc/integ_tests.rst rename to doc/devel/integ_tests.rst diff --git a/doc/rest_api_scala_endpoints.rst b/doc/devel/rest_api_scala_endpoints.rst similarity index 100% rename from doc/rest_api_scala_endpoints.rst rename to doc/devel/rest_api_scala_endpoints.rst diff --git a/doc/running_examples.rst b/doc/devel/running_examples.rst similarity index 100% rename from doc/running_examples.rst rename to doc/devel/running_examples.rst diff --git a/doc/unit_tests.rst b/doc/devel/unit_tests.rst similarity index 100% rename from doc/unit_tests.rst rename to doc/devel/unit_tests.rst diff --git a/design-doc/images/Sparkling Water cluster.png b/doc/images/Sparkling Water cluster.png similarity index 100% rename from design-doc/images/Sparkling Water cluster.png rename to doc/images/Sparkling Water cluster.png diff --git a/design-doc/images/DataShare.png b/doc/images/internal_backend_data_sharing.png similarity index 100% rename from design-doc/images/DataShare.png rename to doc/images/internal_backend_data_sharing.png diff --git a/doc/calling_h2o_algos.rst b/doc/tutorials/calling_h2o_algos.rst similarity index 100% rename from doc/calling_h2o_algos.rst rename to doc/tutorials/calling_h2o_algos.rst diff --git a/doc/change_log_level.rst b/doc/tutorials/change_log_level.rst similarity index 100% rename from doc/change_log_level.rst rename to doc/tutorials/change_log_level.rst diff --git a/doc/h2o_frame_from_key.rst b/doc/tutorials/h2o_frame_from_key.rst similarity index 100% rename from doc/h2o_frame_from_key.rst rename to doc/tutorials/h2o_frame_from_key.rst diff --git a/doc/datasource.rst b/doc/tutorials/h2oframe_as_data_source.rst similarity index 100% rename from doc/datasource.rst rename to doc/tutorials/h2oframe_as_data_source.rst diff --git a/doc/log_location.rst b/doc/tutorials/obtaining_logs.rst similarity index 100% rename from doc/log_location.rst rename to doc/tutorials/obtaining_logs.rst diff --git a/doc/windows_manual.rst b/doc/tutorials/run_on_windows.rst similarity index 100% rename from doc/windows_manual.rst rename to doc/tutorials/run_on_windows.rst diff --git a/doc/run.rst b/doc/tutorials/run_sparkling_water.rst similarity index 100% rename from doc/run.rst rename to doc/tutorials/run_sparkling_water.rst diff --git a/doc/security.rst b/doc/tutorials/security.rst similarity index 100% rename from doc/security.rst rename to doc/tutorials/security.rst diff --git a/doc/spark_h2o_conversions.rst b/doc/tutorials/spark_h2o_conversions.rst similarity index 100% rename from doc/spark_h2o_conversions.rst rename to doc/tutorials/spark_h2o_conversions.rst diff --git a/doc/spark_package.rst b/doc/tutorials/use_as_spark_package.rst similarity index 100% rename from doc/spark_package.rst rename to doc/tutorials/use_as_spark_package.rst diff --git a/doc/zeppelin.rst b/doc/tutorials/use_on_zeppelin.rst similarity index 100% rename from doc/zeppelin.rst rename to doc/tutorials/use_on_zeppelin.rst From e262a759c9fa50db06ec6a49e5af95de270398c5 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Tue, 20 Jun 2017 12:39:35 +0200 Subject: [PATCH 25/72] Cleaning up/ almost there --- DEVEL.md | 73 ++++++--------------------------- README.rst | 8 +--- doc/configuration/configuration.rst | 7 ++++ doc/design/design.rst | 57 +++++++++++++++++++++++++ doc/design/supported_data_formats.rst | 8 ++++ doc/design/supported_data_sources.rst | 10 +++++ doc/images/Sparkling Water cluster.png | Bin 114086 -> 0 bytes doc/images/external_backend.png | Bin 0 -> 290397 bytes doc/images/internal_backend.png | Bin 0 -> 260774 bytes doc/requirements.rst | 8 ++++ doc/{ => tutorials}/backends.rst | 0 doc/tutorials/run_sparkling_water.rst | 9 +++- doc/typical_use_case.rst | 9 ++++ 13 files changed, 121 insertions(+), 68 deletions(-) create mode 100644 doc/configuration/configuration.rst create mode 100644 doc/design/design.rst create mode 100644 doc/design/supported_data_formats.rst create mode 100644 doc/design/supported_data_sources.rst delete mode 100644 doc/images/Sparkling Water cluster.png create mode 100644 doc/images/external_backend.png create mode 100644 doc/images/internal_backend.png create mode 100644 doc/requirements.rst rename doc/{ => tutorials}/backends.rst (100%) create mode 100644 doc/typical_use_case.rst diff --git a/DEVEL.md b/DEVEL.md index 18467848b..630168352 100644 --- a/DEVEL.md +++ b/DEVEL.md @@ -1,22 +1,23 @@ # Sparkling Water Development Documentation -This documentation acts only as the table of content and provides links to the actual documentation documents. +This page acts only as the table of content and provides links to the actual documentation documents. -- [Typical Use Case](#UseCase) -- [Requirements](#Req) -- [Design](#Design) +- [Typical Use Case](doc/typical_use_case.rst) +- [Requirements](doc/requirements.rst) +- [Design](doc/design/design.rst) - [Basic Primitives](doc/design/basic_primitives.rst) - [Supported Platforms](doc/design/supported_platforms.rst) - [Spark Frame - H2O Frame Mapping](doc/design/spark_h2o_mapping.rst) - [Data Sharing Between H2O and Spark](doc/design/data_sharing.rst) - - [Supported Data Sources](#DataSource) - - [Supported Data Formats](#DataFormat) -- [Running Sparkling Water](doc/tutorials/run_sparkling_water.rst) -- **Sparkling Water Configuration** + - [Supported Data Sources](doc/design/supported_data_sources.rst) + - [Supported Data Formats](doc/design/supported_data_formats.rst) +- [Sparkling Water Configuration](doc/configuration/configuration.rst) - [Sparkling Water Configuration Properties](doc/configuration/configuration_properties.rst) - [Memory Allocation](doc/configuration/memory_setup.rst) - [Sparkling Water Internal Backend Tuning](doc/configuration/internal_backend_tuning.rst) -- **Tutorials** +- **Tutorials** + - [Running Sparkling Water](doc/tutorials/run_sparkling_water.rst) + - [Sparkling Water Backends Run & Configuration](doc/tutorials/backends.rst) - [Enabling Security](doc/tutorials/security.rst) - [Calling H2O Algorithms](doc/tutorials/calling_h2o_algos.rst) - Frames Conversions & Creation @@ -26,59 +27,11 @@ This documentation acts only as the table of content and provides links to the a - Logging - [Change Sparkling Shell Logging Level](doc/tutorials/change_log_level.rst) - [Obtain Sparkling Water Logs](doc/tutorials/obtaining_logs.rst) + - [Sparkling Water and Zeppelin](doc/tutorials/use_on_zeppelin.rst) + - [Use Sparkling Water as Spark Package](doc/tutorials/use_as_spark_package.rst) - **Testing** - [Running Unit Tests](doc/devel/unit_tests.rst) - [Integration Tests](doc/devel/integ_tests.rst) - **Sparkling Water Rest API** - [Scala Interpreter REST API Overview](doc/rest_api_scala_endpoints.md) -- [Sparkling Water and Zeppelin](doc/tutorials/use_on_zeppelin.rst) - ---- - - -## Design - -Sparkling Water is designed to be executed as a regular Spark application. -It provides a way to initialize H2O services on each node in the Spark cluster and access -data stored in data structures of Spark and H2O. - -Since Sparkling Water is designed as Spark application, it is launched -inside a Spark executor, which is created after application submission. -At this point, H2O starts services, including distributed KV store and memory manager, -and orchestrates them into a cloud. The topology of the created cloud matches the topology of the underlying Spark cluster exactly. - - ![Topology](doc/images/Sparkling Water cluster.png) - -When H2O services are running, it is possible to create H2O data structures, call H2O algorithms, and transfer values from/to RDD. - ---- - - -## Features - -Sparkling Water provides transparent integration for the H2O engine and its machine learning -algorithms into the Spark platform, enabling: - * use of H2O algorithms in Spark workflow - * transformation between H2O and Spark data structures - * use of Spark RDDs as input for H2O algorithms - * transparent execution of Sparkling Water applications on top of Spark - - - -### Supported Data Sources -Currently, Sparkling Water can use the following data source types: - - standard RDD API to load data and transform them into `H2OFrame` - - H2O API to load data directly into `H2OFrame` from: - - local file(s) - - HDFS file(s) - - S3 file(s) - ---- - -### Supported Data Formats -Sparkling Water can read data stored in the following formats: - - - CSV - - SVMLight - - ARFF - + \ No newline at end of file diff --git a/README.rst b/README.rst index cccdd31ed..0c338f3f0 100644 --- a/README.rst +++ b/README.rst @@ -31,13 +31,7 @@ Please, switch to the right branch: branch includes the latest changes for the latest Spark version. They are back-ported into older Sparkling Water versions. -Requirements -~~~~~~~~~~~~ - -- Linux/OS X/Windows -- Java 7+ -- Python 2.6+ For Python version of Sparkling Water (PySparkling) -- `Spark 1.6+ `__ and ``SPARK_HOME`` shell variable must point to your local Spark installation +.. include:: doc/requirements.txt Download Binaries diff --git a/doc/configuration/configuration.rst b/doc/configuration/configuration.rst new file mode 100644 index 000000000..be92ff8e5 --- /dev/null +++ b/doc/configuration/configuration.rst @@ -0,0 +1,7 @@ +Configuration +------------- + +- `Sparkling Water Configuration Properties `__ +- `Memory Allocation `__ +- `Sparkling Water Internal Backend Tuning `__ +- `Sparkling Water Backends Configuration `__ diff --git a/doc/design/design.rst b/doc/design/design.rst new file mode 100644 index 000000000..2f673ac97 --- /dev/null +++ b/doc/design/design.rst @@ -0,0 +1,57 @@ +Design +------ + +Basic introduction +~~~~~~~~~~~~~~~~~~ + +Sparkling Water is designed to be executed as a regular Spark +application. It provides a way to initialize H2O services on each node +in the Spark cluster and access data stored in data structures of Spark +and H2O. + +Sparkling Water provides transparent integration for the H2O engine +and its machine learning algorithms into the Spark platform, enabling: + +- Use of H2O algorithms in Spark workflow. +- Transformation between H2O and Spark data structures. +- Use of Spark RDDs as input for H2O algorithms. +- Transparent execution of Sparkling Water applications on top of Spark. + +Sparkling Water supports to type of backends. In the internal backend, +Sparkling Water is launched inside a Spark executor, which is created +after application submission. At this point, H2O starts services, +including distributed KV store and memory manager, and orchestrates them +into a cloud. The topology of the created cloud matches the topology of +the underlying Spark cluster exactly. + +.. figure:: doc/images/internal_backend.png + :alt: Internal Sparkling Water Cluster Topology + + Internal Sparkling Water Cluster Topology + +In external backend, the H2O cluster is started separately and we +connect to it from the Spark driver. + +.. figure:: doc/images/external_backend.png + :alt: External Sparkling Water Cluster Topology + + External Sparkling Water Cluster Topology + +To read more about the backends, please visit `Sparkling Water +Backends `__ + +When H2O services are running, it is possible to create H2O data +structures, call H2O algorithms, and transfer values from/to RDD. + +More materials +~~~~~~~~~~~~~~ + +To read more about Sparkling Water design, you can visit on of the links +bellow: + +- `Basic Primitives `__ +- `Supported Platforms `__ +- `Spark Frame - H2O Frame Mapping `__ +- `Data Sharing Between H2O and Spark `__ +- `Supported Data Sources `__ +- `Supported Data Formats `__ diff --git a/doc/design/supported_data_formats.rst b/doc/design/supported_data_formats.rst new file mode 100644 index 000000000..9bb2f5006 --- /dev/null +++ b/doc/design/supported_data_formats.rst @@ -0,0 +1,8 @@ +Supported Data Formats +---------------------- + +Sparkling Water can read data stored in the following formats: + +- CSV +- SVMLight +- ARFF diff --git a/doc/design/supported_data_sources.rst b/doc/design/supported_data_sources.rst new file mode 100644 index 000000000..e3ae1f658 --- /dev/null +++ b/doc/design/supported_data_sources.rst @@ -0,0 +1,10 @@ +Supported Data Sources +---------------------- + +Currently, Sparkling Water can use the following data source types: + +- standard RDD API to load data and transform them into ``H2OFrame`` +- H2OAPI to load data directly into ``H2OFrame`` from: + - local file(s) + - HDFS file(s) + - S3 file(s) diff --git a/doc/images/Sparkling Water cluster.png b/doc/images/Sparkling Water cluster.png deleted file 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z8p^m`5bu%vaf;!b>q3l^&M%Yt9Nkw#HFV4qyv#X=``rqE2GXe|Horw7cwRHziS8*y#spke!R9Dk1U zc>X%JT-UV$hBgzFvkbD(;tMAi$yTW2i(CSutK z{Df8c5xU9383>eLF@&?t34i5-UUQlpKF~7{VJH3o*KjF^aH4Oq5-xEg%rFTTV;~-q zTMJ5JVBN%@;xm6ZQ+E=7Q5O?>ssWFw0)9&mFG6XC?j%g`p&UMtm;Cv>kw3)l=NEC* zfJ@wT34FO${K?ZX#_}{K5`>m2#uz3 z$LR?-aRz@&oHoy=GB4tCOAtywn=?+0kw3?TUKkk5u+Fs*Ka(J(lGD+1!vSqrXDptp zIVWt)pZH@=!!YM0UIjqt@ag$IxUhSAB=H7X&~mM=uz4!MaCm+?wjA(dz6qzW=a}#l z2F7BjaW61l@yESG4zU2S1}F2Ui69j*`jX$f_Mg@})e6YCSF{pN*nhSm&(9z5Z1M-J zZ~$Zz;0Nf$?}m0}<81QGaDusq4ns7JIiDJjp|eAu1V;LN*4i|mN<4@wy>c*C@CBJM z;Ahy|{-fS;As0d_R;$Y=<7ytBhq0)MOQh!rvalt&)oiwP{v4y@_ZYbr#MZdQg3=mr z2||}9yuxJuSiaUwR?Wc89N~~<=g%i!n!kwOU0>VG}0KHxL@PSP7SS6Mn>yj`7SF`__ and ``SPARK_HOME`` shell variable must point to your local Spark installation + diff --git a/doc/backends.rst b/doc/tutorials/backends.rst similarity index 100% rename from doc/backends.rst rename to doc/tutorials/backends.rst diff --git a/doc/tutorials/run_sparkling_water.rst b/doc/tutorials/run_sparkling_water.rst index 37276e838..953907246 100644 --- a/doc/tutorials/run_sparkling_water.rst +++ b/doc/tutorials/run_sparkling_water.rst @@ -7,6 +7,10 @@ property ``SPARK_HOME`` that points to the Spark distribution. H2O on Spark can be started in the Spark Shell or in the Spark application as: +.. code:: bash + + ./bin/sparkling-shell + .. code:: scala val hc = H2OContext.getOrCreate(spark) @@ -26,4 +30,7 @@ In external backend mode, the call will: 1. Start H2O in client mode on the Spark driver 2. Start separated H2O cluster on the configured YARN queue -3. Connects to the external cluster from the H2O client \ No newline at end of file +3. Connects to the external cluster from the H2O client + + +To see how to run Sparkling Water on Windows, please visit `Run on Windows `__. \ No newline at end of file diff --git a/doc/typical_use_case.rst b/doc/typical_use_case.rst new file mode 100644 index 000000000..3004cf270 --- /dev/null +++ b/doc/typical_use_case.rst @@ -0,0 +1,9 @@ +Typical Use Case +---------------- + +Sparkling Water excels in leveraging existing Spark-based workflows that +need to call advanced machine learning algorithms. A typical example +involves data munging with help of Spark API, where a prepared table is +passed to the H2O DeepLearning algorithm. The constructed DeepLearning +model estimates different metrics based on the testing data, which can +be used in the rest of the Spark workflow. From dfa24269024bc5b68314c30d66f3a8352cb45f64 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Tue, 20 Jun 2017 12:42:51 +0200 Subject: [PATCH 26/72] Fix readme --- README.rst | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/README.rst b/README.rst index 0c338f3f0..a17e82003 100644 --- a/README.rst +++ b/README.rst @@ -31,7 +31,16 @@ Please, switch to the right branch: branch includes the latest changes for the latest Spark version. They are back-ported into older Sparkling Water versions. -.. include:: doc/requirements.txt +.. The Requirements section is copied from doc/requirements.rst as github does not support include directive of +.. reStructuredText + +Requirements +~~~~~~~~~~~~ + +- Linux/OS X/Windows +- Java 7+ +- Python 2.6+ For Python version of Sparkling Water (PySparkling) +- `Spark 1.6+ `__ and ``SPARK_HOME`` shell variable must point to your local Spark installation Download Binaries From c3b42da347f95d95a226f8c0c96f428c6e9c86eb Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Tue, 20 Jun 2017 12:44:50 +0200 Subject: [PATCH 27/72] fix image location --- doc/design/design.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/design/design.rst b/doc/design/design.rst index 2f673ac97..837d22cb3 100644 --- a/doc/design/design.rst +++ b/doc/design/design.rst @@ -24,7 +24,7 @@ including distributed KV store and memory manager, and orchestrates them into a cloud. The topology of the created cloud matches the topology of the underlying Spark cluster exactly. -.. figure:: doc/images/internal_backend.png +.. figure:: ../images/internal_backend.png :alt: Internal Sparkling Water Cluster Topology Internal Sparkling Water Cluster Topology @@ -32,7 +32,7 @@ the underlying Spark cluster exactly. In external backend, the H2O cluster is started separately and we connect to it from the Spark driver. -.. figure:: doc/images/external_backend.png +.. figure:: ../images/external_backend.png :alt: External Sparkling Water Cluster Topology External Sparkling Water Cluster Topology From f2b25c7f633ed47a8288b3112d1a3ea2bd183332 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Tue, 20 Jun 2017 12:47:32 +0200 Subject: [PATCH 28/72] fix links --- doc/configuration/configuration.rst | 8 ++++---- doc/design/design.rst | 24 ++++++++++++------------ 2 files changed, 16 insertions(+), 16 deletions(-) diff --git a/doc/configuration/configuration.rst b/doc/configuration/configuration.rst index be92ff8e5..1a0f1e9df 100644 --- a/doc/configuration/configuration.rst +++ b/doc/configuration/configuration.rst @@ -1,7 +1,7 @@ Configuration ------------- -- `Sparkling Water Configuration Properties `__ -- `Memory Allocation `__ -- `Sparkling Water Internal Backend Tuning `__ -- `Sparkling Water Backends Configuration `__ +- `Sparkling Water Configuration Properties `__ +- `Memory Allocation `__ +- `Sparkling Water Internal Backend Tuning `__ +- `Sparkling Water Backends Configuration <../tutorials/backends.rst>`__ diff --git a/doc/design/design.rst b/doc/design/design.rst index 837d22cb3..5861d697d 100644 --- a/doc/design/design.rst +++ b/doc/design/design.rst @@ -22,23 +22,23 @@ Sparkling Water is launched inside a Spark executor, which is created after application submission. At this point, H2O starts services, including distributed KV store and memory manager, and orchestrates them into a cloud. The topology of the created cloud matches the topology of -the underlying Spark cluster exactly. +the underlying Spark cluster exactly. The following figure represents the Internal +Sparkling Water cluster. .. figure:: ../images/internal_backend.png :alt: Internal Sparkling Water Cluster Topology - Internal Sparkling Water Cluster Topology - In external backend, the H2O cluster is started separately and we -connect to it from the Spark driver. +connect to it from the Spark driver. The following figure represents the External +Sparkling Water cluster. + .. figure:: ../images/external_backend.png :alt: External Sparkling Water Cluster Topology - External Sparkling Water Cluster Topology To read more about the backends, please visit `Sparkling Water -Backends `__ +Backends `__. When H2O services are running, it is possible to create H2O data structures, call H2O algorithms, and transfer values from/to RDD. @@ -49,9 +49,9 @@ More materials To read more about Sparkling Water design, you can visit on of the links bellow: -- `Basic Primitives `__ -- `Supported Platforms `__ -- `Spark Frame - H2O Frame Mapping `__ -- `Data Sharing Between H2O and Spark `__ -- `Supported Data Sources `__ -- `Supported Data Formats `__ +- `Basic Primitives `__ +- `Supported Platforms `__ +- `Spark Frame - H2O Frame Mapping `__ +- `Data Sharing Between H2O and Spark `__ +- `Supported Data Sources `__ +- `Supported Data Formats `__ From da90c83a4740dcd96f383634272355482371338c Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Tue, 20 Jun 2017 12:55:53 +0200 Subject: [PATCH 29/72] Fixes --- doc/design/basic_primitives.rst | 87 +++++++++++++---------------------------- doc/design/data_sharing.rst | 2 +- doc/design/design.rst | 2 +- 3 files changed, 30 insertions(+), 61 deletions(-) diff --git a/doc/design/basic_primitives.rst b/doc/design/basic_primitives.rst index a4b663318..4447d1fa4 100644 --- a/doc/design/basic_primitives.rst +++ b/doc/design/basic_primitives.rst @@ -4,62 +4,31 @@ Provided Primitives The Sparkling Water provides following primitives, which are the basic classes used by Spark components: -+-------------------+--------------------------------------+----------------+ -| Concept | Implementation class | Description | -+===================+======================================+================+ -| H2O context | ``org.apache.spark.h2o.H2OContext`` | H2O context | -| | | that holds H2O | -| | | state and | -| | | provides | -| | | primitives to | -| | | transfer RDD | -| | | into H2OFrame | -| | | and vice | -| | | versa. It | -| | | follows design | -| | | principles of | -| | | Spark | -| | | primitives | -| | | such as | -| | | ``SparkContext | -| | | `` | -| | | or | -| | | ``SQLContext`` | -+-------------------+--------------------------------------+----------------+ -| H2O entry point | ``water.H2O`` | Represents the | -| | | entry point | -| | | for accessing | -| | | H2O services. | -| | | It holds | -| | | information | -| | | about the | -| | | actual H2O | -| | | cluster, | -| | | including a | -| | | list of nodes | -| | | and the status | -| | | of distributed | -| | | K/V datastore. | -+-------------------+--------------------------------------+----------------+ -| H2O H2OFrame | ``water.fvec.H2OFrame`` | H2OFrame is | -| | | the H2O data | -| | | structure that | -| | | represents a | -| | | table of | -| | | values. The | -| | | table is | -| | | column-based | -| | | and provides | -| | | column and row | -| | | accessors. | -+-------------------+--------------------------------------+----------------+ -| H2O Algorithms | package ``hex`` | Represents the | -| | | H2O machine | -| | | learning | -| | | algorithms | -| | | library, | -| | | including | -| | | DeepLearning, | -| | | GBM, | -| | | RandomForest. | -+-------------------+--------------------------------------+----------------+ ++-------------------+--------------------------------------+--------------------------------------+ +| Concept | Implementation class | Description | ++===================+======================================+======================================+ +| H2O context | ``org.apache.spark.h2o.H2OContext`` | H2O Context that holds state and | +| | | provides primitives to transfer | +| | | RDD/DataFrames/Datasets into | +| | | H2OFrame and vice versa. It follows | +| | | design principles of Spark | +| | | primitives such as ``SparkSession``, | +| | | ``SparkContext`` and ``SQLContext`` | ++-------------------+--------------------------------------+--------------------------------------+ +| H2O entry point | ``water.H2O`` | Represents the entry point for | +| | | for accessing H2O services. It holds | +| | | information about the actual H2O | +| | | cluster, including a list of nodes | +| | | and the status of distributed K/V | +| | | datastore. | ++-------------------+--------------------------------------+--------------------------------------+ +| H2O H2OFrame | ``water.fvec.H2OFrame`` | H2OFrame is the H2O data structure | +| | | that represents a table of values. | +| | | The table is column-based and | +| | | provides column and row accessors. | ++-------------------+--------------------------------------+--------------------------------------+ +| H2O Algorithms | package ``hex`` | Represents the H2O machine learning | +| | | algorithms library, including, for | +| | | example, DeepLearning, GBM or +| | | RandomForest. | ++-------------------+--------------------------------------+--------------------------------------+ diff --git a/doc/design/data_sharing.rst b/doc/design/data_sharing.rst index 37d553749..f8f947bd3 100644 --- a/doc/design/data_sharing.rst +++ b/doc/design/data_sharing.rst @@ -29,5 +29,5 @@ JVM as is depicted on the following figure. |Data Sharing| In the External Sparkling Water Backend, Spark and H2O are separated clusters and data has to be send between these cluster over the network. -.. |Data Sharing| image:: doc/images/internal_backend_data_sharing.png +.. |Data Sharing| image:: ../images/internal_backend_data_sharing.png diff --git a/doc/design/design.rst b/doc/design/design.rst index 5861d697d..3d0dd0af2 100644 --- a/doc/design/design.rst +++ b/doc/design/design.rst @@ -38,7 +38,7 @@ Sparkling Water cluster. To read more about the backends, please visit `Sparkling Water -Backends `__. +Backends <../tutorials/backends.rst>`__. When H2O services are running, it is possible to create H2O data structures, call H2O algorithms, and transfer values from/to RDD. From 30d9e932d4a4716d0aeac9ccd8bb431a98f0177e Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Tue, 20 Jun 2017 12:59:31 +0200 Subject: [PATCH 30/72] more fixes --- doc/design/basic_primitives.rst | 2 +- doc/design/spark_h2o_mapping.rst | 6 +++--- doc/design/supported_data_sources.rst | 6 +++--- 3 files changed, 7 insertions(+), 7 deletions(-) diff --git a/doc/design/basic_primitives.rst b/doc/design/basic_primitives.rst index 4447d1fa4..d9fef6e9c 100644 --- a/doc/design/basic_primitives.rst +++ b/doc/design/basic_primitives.rst @@ -29,6 +29,6 @@ classes used by Spark components: +-------------------+--------------------------------------+--------------------------------------+ | H2O Algorithms | package ``hex`` | Represents the H2O machine learning | | | | algorithms library, including, for | -| | | example, DeepLearning, GBM or +| | | example, DeepLearning, GBM or | | | | RandomForest. | +-------------------+--------------------------------------+--------------------------------------+ diff --git a/doc/design/spark_h2o_mapping.rst b/doc/design/spark_h2o_mapping.rst index 99f58a938..1d0556724 100644 --- a/doc/design/spark_h2o_mapping.rst +++ b/doc/design/spark_h2o_mapping.rst @@ -1,8 +1,8 @@ -Spark Frame - H2O Frame Mapping -------------------------------- +Spark - H2O Frame Mapping +------------------------- Type mapping between H2O H2OFrame types and Spark DataFrame types ------------------------------------------------------------------ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ For all primitive Scala types or Spark SQL (see ``org.apache.spark.sql.types``) types which can be part of Spark diff --git a/doc/design/supported_data_sources.rst b/doc/design/supported_data_sources.rst index e3ae1f658..7b683769c 100644 --- a/doc/design/supported_data_sources.rst +++ b/doc/design/supported_data_sources.rst @@ -5,6 +5,6 @@ Currently, Sparkling Water can use the following data source types: - standard RDD API to load data and transform them into ``H2OFrame`` - H2OAPI to load data directly into ``H2OFrame`` from: - - local file(s) - - HDFS file(s) - - S3 file(s) + - local file(s) + - HDFS file(s) + - S3 file(s) From 2f4155759fd9063b03edd21fbd6b45632a5a3fe6 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Tue, 20 Jun 2017 12:59:54 +0200 Subject: [PATCH 31/72] missing space --- doc/design/supported_data_sources.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/doc/design/supported_data_sources.rst b/doc/design/supported_data_sources.rst index 7b683769c..e180f1ed9 100644 --- a/doc/design/supported_data_sources.rst +++ b/doc/design/supported_data_sources.rst @@ -5,6 +5,7 @@ Currently, Sparkling Water can use the following data source types: - standard RDD API to load data and transform them into ``H2OFrame`` - H2OAPI to load data directly into ``H2OFrame`` from: + - local file(s) - HDFS file(s) - S3 file(s) From 1f5241dffc78223e78c8d7ba92022d87325678ea Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Tue, 20 Jun 2017 13:01:59 +0200 Subject: [PATCH 32/72] wrong space --- doc/design/supported_data_sources.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/doc/design/supported_data_sources.rst b/doc/design/supported_data_sources.rst index e180f1ed9..5ce81b10e 100644 --- a/doc/design/supported_data_sources.rst +++ b/doc/design/supported_data_sources.rst @@ -6,6 +6,6 @@ Currently, Sparkling Water can use the following data source types: - standard RDD API to load data and transform them into ``H2OFrame`` - H2OAPI to load data directly into ``H2OFrame`` from: - - local file(s) - - HDFS file(s) - - S3 file(s) + - local file(s) + - HDFS file(s) + - S3 file(s) From a7772a6fafde641d42aac244208e0541dbfb09eb Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Tue, 20 Jun 2017 13:13:37 +0200 Subject: [PATCH 33/72] done --- DEVEL.md | 14 +++++++++----- doc/design/basic_primitives.rst | 2 +- doc/devel/devel.rst | 7 +++++++ doc/rest_api/rest_api.rst | 4 ++++ .../scala_interpreter_endpoints.rst} | 0 doc/tutorials/backends.rst | 3 +++ doc/tutorials/tutorials.rst | 20 ++++++++++++++++++++ 7 files changed, 44 insertions(+), 6 deletions(-) create mode 100644 doc/devel/devel.rst create mode 100644 doc/rest_api/rest_api.rst rename doc/{devel/rest_api_scala_endpoints.rst => rest_api/scala_interpreter_endpoints.rst} (100%) create mode 100644 doc/tutorials/tutorials.rst diff --git a/DEVEL.md b/DEVEL.md index 630168352..fcd49c787 100644 --- a/DEVEL.md +++ b/DEVEL.md @@ -15,13 +15,13 @@ This page acts only as the table of content and provides links to the actual doc - [Sparkling Water Configuration Properties](doc/configuration/configuration_properties.rst) - [Memory Allocation](doc/configuration/memory_setup.rst) - [Sparkling Water Internal Backend Tuning](doc/configuration/internal_backend_tuning.rst) -- **Tutorials** +- [Tutorials](doc/tutorials/tutorials.rst) - [Running Sparkling Water](doc/tutorials/run_sparkling_water.rst) - [Sparkling Water Backends Run & Configuration](doc/tutorials/backends.rst) - [Enabling Security](doc/tutorials/security.rst) - [Calling H2O Algorithms](doc/tutorials/calling_h2o_algos.rst) - Frames Conversions & Creation - - [Spark Frame - H2O Frame Conversions](doc/tutorials/spark_h2o_conversions.rst) + - [Spark - H2O Frame Conversions](doc/tutorials/spark_h2o_conversions.rst) - [H2O Frame as Spark's Data Source](doc/tutorials/h2oframe_as_data_source.rst) - [Create H2OFrame From an Existing Key](doc/tutorials/h2o_frame_from_key.rst) - Logging @@ -29,9 +29,13 @@ This page acts only as the table of content and provides links to the actual doc - [Obtain Sparkling Water Logs](doc/tutorials/obtaining_logs.rst) - [Sparkling Water and Zeppelin](doc/tutorials/use_on_zeppelin.rst) - [Use Sparkling Water as Spark Package](doc/tutorials/use_as_spark_package.rst) -- **Testing** +- [Development](doc/devel/devel.rst) + - [Building](doc/devel/build.rst) + - [Running Sparkling Water Examples](doc/devel/running_examples.rst) - [Running Unit Tests](doc/devel/unit_tests.rst) - [Integration Tests](doc/devel/integ_tests.rst) -- **Sparkling Water Rest API** - - [Scala Interpreter REST API Overview](doc/rest_api_scala_endpoints.md) +- [Sparkling Water Rest API](doc/rest_api/rest_api.rst) + - [Scala Interpreter REST API](doc/rest_api_scala_endpoints.md) + + \ No newline at end of file diff --git a/doc/design/basic_primitives.rst b/doc/design/basic_primitives.rst index d9fef6e9c..d7559a39a 100644 --- a/doc/design/basic_primitives.rst +++ b/doc/design/basic_primitives.rst @@ -13,7 +13,7 @@ classes used by Spark components: | | | H2OFrame and vice versa. It follows | | | | design principles of Spark | | | | primitives such as ``SparkSession``, | -| | | ``SparkContext`` and ``SQLContext`` | +| | | ``SparkContext`` and ``SQLContext``. | +-------------------+--------------------------------------+--------------------------------------+ | H2O entry point | ``water.H2O`` | Represents the entry point for | | | | for accessing H2O services. It holds | diff --git a/doc/devel/devel.rst b/doc/devel/devel.rst new file mode 100644 index 000000000..cb9033b18 --- /dev/null +++ b/doc/devel/devel.rst @@ -0,0 +1,7 @@ +Development +----------- + +- `Building `__ +- `Running Sparkling Water Examples `__ +- `Running Unit Tests `__ +- `Integration Tests `__ diff --git a/doc/rest_api/rest_api.rst b/doc/rest_api/rest_api.rst new file mode 100644 index 000000000..465870b20 --- /dev/null +++ b/doc/rest_api/rest_api.rst @@ -0,0 +1,4 @@ +Sparkling Water REST API +------------------------ + +- `Scala Interpreter REST API `__ diff --git a/doc/devel/rest_api_scala_endpoints.rst b/doc/rest_api/scala_interpreter_endpoints.rst similarity index 100% rename from doc/devel/rest_api_scala_endpoints.rst rename to doc/rest_api/scala_interpreter_endpoints.rst diff --git a/doc/tutorials/backends.rst b/doc/tutorials/backends.rst index ae4ab8aeb..39c686c33 100644 --- a/doc/tutorials/backends.rst +++ b/doc/tutorials/backends.rst @@ -1,3 +1,6 @@ +Sparkling Water Backends +======================== + Internal backend ---------------- diff --git a/doc/tutorials/tutorials.rst b/doc/tutorials/tutorials.rst new file mode 100644 index 000000000..9af10e51f --- /dev/null +++ b/doc/tutorials/tutorials.rst @@ -0,0 +1,20 @@ +Tutorials +--------- + +- `Running Sparkling Water `__ +- `Sparkling Water Backends Run & Configuration `__ +- `Enabling Security `__ +- `Calling H2O Algorithms `__ +- Frames Conversions & Creation + + - `Spark - H2O Frame Conversions `__ + - `H2O Frame as Spark's DataSource `__ + - `Create H2OFrame From an Existing Key `__ + +- Logging + + - `Change Sparkling Shell Logging Level `__ + - `Obtain Sparkling Water Logs `__ + +- `Sparkling Water and Zeppelin `__ +- `Use Sparkling Water as Spark Package `__ From cc519bbc08d63e790a9657c622e01ff3d48a531f Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Tue, 20 Jun 2017 13:17:54 +0200 Subject: [PATCH 34/72] convert DEVEL.md to DEVEL.rst --- DEVEL.md | 41 ----------------------------------------- README.rst | 2 +- doc/DEVEL.rst | 54 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 55 insertions(+), 42 deletions(-) delete mode 100644 DEVEL.md create mode 100644 doc/DEVEL.rst diff --git a/DEVEL.md b/DEVEL.md deleted file mode 100644 index fcd49c787..000000000 --- a/DEVEL.md +++ /dev/null @@ -1,41 +0,0 @@ -# Sparkling Water Development Documentation - -This page acts only as the table of content and provides links to the actual documentation documents. - -- [Typical Use Case](doc/typical_use_case.rst) -- [Requirements](doc/requirements.rst) -- [Design](doc/design/design.rst) - - [Basic Primitives](doc/design/basic_primitives.rst) - - [Supported Platforms](doc/design/supported_platforms.rst) - - [Spark Frame - H2O Frame Mapping](doc/design/spark_h2o_mapping.rst) - - [Data Sharing Between H2O and Spark](doc/design/data_sharing.rst) - - [Supported Data Sources](doc/design/supported_data_sources.rst) - - [Supported Data Formats](doc/design/supported_data_formats.rst) -- [Sparkling Water Configuration](doc/configuration/configuration.rst) - - [Sparkling Water Configuration Properties](doc/configuration/configuration_properties.rst) - - [Memory Allocation](doc/configuration/memory_setup.rst) - - [Sparkling Water Internal Backend Tuning](doc/configuration/internal_backend_tuning.rst) -- [Tutorials](doc/tutorials/tutorials.rst) - - [Running Sparkling Water](doc/tutorials/run_sparkling_water.rst) - - [Sparkling Water Backends Run & Configuration](doc/tutorials/backends.rst) - - [Enabling Security](doc/tutorials/security.rst) - - [Calling H2O Algorithms](doc/tutorials/calling_h2o_algos.rst) - - Frames Conversions & Creation - - [Spark - H2O Frame Conversions](doc/tutorials/spark_h2o_conversions.rst) - - [H2O Frame as Spark's Data Source](doc/tutorials/h2oframe_as_data_source.rst) - - [Create H2OFrame From an Existing Key](doc/tutorials/h2o_frame_from_key.rst) - - Logging - - [Change Sparkling Shell Logging Level](doc/tutorials/change_log_level.rst) - - [Obtain Sparkling Water Logs](doc/tutorials/obtaining_logs.rst) - - [Sparkling Water and Zeppelin](doc/tutorials/use_on_zeppelin.rst) - - [Use Sparkling Water as Spark Package](doc/tutorials/use_as_spark_package.rst) -- [Development](doc/devel/devel.rst) - - [Building](doc/devel/build.rst) - - [Running Sparkling Water Examples](doc/devel/running_examples.rst) - - [Running Unit Tests](doc/devel/unit_tests.rst) - - [Integration Tests](doc/devel/integ_tests.rst) -- [Sparkling Water Rest API](doc/rest_api/rest_api.rst) - - [Scala Interpreter REST API](doc/rest_api_scala_endpoints.md) - - - \ No newline at end of file diff --git a/README.rst b/README.rst index a17e82003..3188955a6 100644 --- a/README.rst +++ b/README.rst @@ -171,7 +171,7 @@ List of all Frequently Asked Questions is available at `FAQ `__. Development ----------- -Complete development documentation is available at `Development Documentation `__. +Complete development documentation is available at `Development Documentation `__. Build Sparkling Water ~~~~~~~~~~~~~~~~~~~~~ diff --git a/doc/DEVEL.rst b/doc/DEVEL.rst new file mode 100644 index 000000000..508c76a0f --- /dev/null +++ b/doc/DEVEL.rst @@ -0,0 +1,54 @@ +Sparkling Water Development Documentation +========================================= + +This page acts only as the table of content and provides links to the +actual documentation documents. + +- `Typical Use Case `__ +- `Requirements `__ +- `Design `__ + + - `Basic Primitives `__ + - `Supported Platforms `__ + - `Spark Frame - H2O Frame Mapping `__ + - `Data Sharing Between H2O and + Spark `__ + - `Supported Data Sources `__ + - `Supported Data Formats `__ + +- `Sparkling Water Configuration `__ + + - `Sparkling Water Configuration Properties `__ + - `Memory Allocation `__ + - `Sparkling Water Internal Backend Tuning `__ + +- `Tutorials `__ + + - `Running Sparkling Water `__ + - `Sparkling Water Backends Run & Configuration `__ + - `Enabling Security `__ + - `Calling H2O Algorithms `__ + - Frames Conversions & Creation + + - `Spark - H2O Frame Conversions `__ + - `H2O Frame as Spark's Data Source `__ + - `Create H2OFrame From an Existing Key `__ + + - Logging + + - `Change Sparkling Shell Logging Level `__ + - `Obtain Sparkling Water Logs `__ + + - `Sparkling Water and Zeppelin `__ + - `Use Sparkling Water as Spark Package `__ + +- `Development `__ + + - `Building `__ + - `Running Sparkling Water Examples `__ + - `Running Unit Tests `__ + - `Integration Tests `__ + +- `Sparkling Water Rest API `__ + + - `Scala Interpreter REST API `__ From d83d69a9eae07aa962153f6028a817036659b13a Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Tue, 20 Jun 2017 13:19:13 +0200 Subject: [PATCH 35/72] fix links --- doc/DEVEL.rst | 64 +++++++++++++++++++++++++++++------------------------------ 1 file changed, 32 insertions(+), 32 deletions(-) diff --git a/doc/DEVEL.rst b/doc/DEVEL.rst index 508c76a0f..62e4a4bf6 100644 --- a/doc/DEVEL.rst +++ b/doc/DEVEL.rst @@ -4,51 +4,51 @@ Sparkling Water Development Documentation This page acts only as the table of content and provides links to the actual documentation documents. -- `Typical Use Case `__ -- `Requirements `__ -- `Design `__ +- `Typical Use Case `__ +- `Requirements `__ +- `Design `__ - - `Basic Primitives `__ - - `Supported Platforms `__ - - `Spark Frame - H2O Frame Mapping `__ + - `Basic Primitives `__ + - `Supported Platforms `__ + - `Spark Frame - H2O Frame Mapping `__ - `Data Sharing Between H2O and - Spark `__ - - `Supported Data Sources `__ - - `Supported Data Formats `__ + Spark `__ + - `Supported Data Sources `__ + - `Supported Data Formats `__ -- `Sparkling Water Configuration `__ +- `Sparkling Water Configuration `__ - - `Sparkling Water Configuration Properties `__ - - `Memory Allocation `__ - - `Sparkling Water Internal Backend Tuning `__ + - `Sparkling Water Configuration Properties `__ + - `Memory Allocation `__ + - `Sparkling Water Internal Backend Tuning `__ -- `Tutorials `__ +- `Tutorials `__ - - `Running Sparkling Water `__ - - `Sparkling Water Backends Run & Configuration `__ - - `Enabling Security `__ - - `Calling H2O Algorithms `__ + - `Running Sparkling Water `__ + - `Sparkling Water Backends Run & Configuration `__ + - `Enabling Security `__ + - `Calling H2O Algorithms `__ - Frames Conversions & Creation - - `Spark - H2O Frame Conversions `__ - - `H2O Frame as Spark's Data Source `__ - - `Create H2OFrame From an Existing Key `__ + - `Spark - H2O Frame Conversions `__ + - `H2O Frame as Spark's Data Source `__ + - `Create H2OFrame From an Existing Key `__ - Logging - - `Change Sparkling Shell Logging Level `__ - - `Obtain Sparkling Water Logs `__ + - `Change Sparkling Shell Logging Level `__ + - `Obtain Sparkling Water Logs `__ - - `Sparkling Water and Zeppelin `__ - - `Use Sparkling Water as Spark Package `__ + - `Sparkling Water and Zeppelin `__ + - `Use Sparkling Water as Spark Package `__ -- `Development `__ +- `Development `__ - - `Building `__ - - `Running Sparkling Water Examples `__ - - `Running Unit Tests `__ - - `Integration Tests `__ + - `Building `__ + - `Running Sparkling Water Examples `__ + - `Running Unit Tests `__ + - `Integration Tests `__ -- `Sparkling Water Rest API `__ +- `Sparkling Water Rest API `__ - - `Scala Interpreter REST API `__ + - `Scala Interpreter REST API `__ From ee0f3204395c2c6b8b97b1042ab5093db79b4dd1 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Tue, 20 Jun 2017 13:23:07 +0200 Subject: [PATCH 36/72] remove pysparkling.md in design doc as it contains ideas how to implement pysparkling. Proper and up-to-date documentation is availalbe at py/README.rst --- design-doc/pysparkling.md | 107 ---------------------------------------------- 1 file changed, 107 deletions(-) delete mode 100644 design-doc/pysparkling.md diff --git a/design-doc/pysparkling.md b/design-doc/pysparkling.md deleted file mode 100644 index cd436bd31..000000000 --- a/design-doc/pysparkling.md +++ /dev/null @@ -1,107 +0,0 @@ -# pySparkling - -## Goal -Provide transparent user experience of using Sparkling Water from Python. -It includes: - - support creation of H2OContext - - support data transfers - from H2OFrame to DataFrame/RDD and back - - transparent use of H2OFrames and RDDs - -## Usage - -Command to launch pyspark with Sparkling Water: - ``` -PYSPARK_PYTHON=ipython $SPARK_HOME/bin/pyspark --packages ai.h2o:sparkling-water-core_2.10:1.5.2,ai.h2o:sparkling-water-examples_2.10:1.5.2 -``` - -Command to launch Python script `script.py` via `spark-submit`: -``` -$SPARK_HOME/bin/spark-submit --packages ai.h2o:sparkling-water-core_2.10:1.5.2,ai.h2o:sparkling-water-examples_2.10:1.5.2 script.py -``` - -Creating H2O Context: -``` -hc = H2OContext(sc) -``` - - -Testing - in sparkling-water/py directory: -``` -import sys -sys.path.append(".") -import pysparkling.context -hc = pysparkling.context.H2OContext(sc) -hc.start() -``` - -## Technical details - -Use Py4J bundled with PySpark to access JVM classes - -``` -from py4j.java_gateway import java_import -java_import(sc._jvm, "org.apache.spark.h2o.*") -jvm = sc._jvm -gw = sc._gateway -hc_klazz = jvm.java.lang.Thread.currentThread().getContextClassLoader().loadClass("org.apache.spark.h2o.H2OContext") -ctor_def = gw.new_array(jvm.Class, 1) -ctor_def[0] = sc._jsc.getClass() -hc_ctor = hc_klazz.getConstructor(ctor_def) -ctor_params = gw.new_array(jvm.Object, 1) -ctor_params[0] = sc._jsc -hc = hc_ctor.newInstance(ctor_params) -hc.start() - -``` - -# Running and debugging - -## From Python notebook -Start script: - -``` -#!/usr/bin/env bash -export PYTHONPATH=$H2O_HOME/h2o-py:$SPARKLING_HOME/py:$PYTHONPATH -export SPARK_CLASSPATH=$SPARK_CLASSPATH:$SPARKLING_HOME/assembly/build/libs/sparkling-water-assembly-0.2.17-SNAPSHOT-all.jar -echo $SPARK_CLASSPATH -IPYTHON_OPTS="notebook" $SPARK_HOME/bin/pyspark -``` - -where: - - `H2O_HOME` points to H2O project which contains right python package - - `SPARKLING_HOME` points to Sparkling project git repository - - before run, Sparkling Water has to be build: `./gradlew build -x test` - -# Testing - -## Development testing -``` - export PYTHONPATH=$SPARK_HOME/python:$SPARK_HOME/python/build:$SPARK_HOME/python/lib/py4j-0.8.2.1-src.zip:./:$PYTHONPATH - export SPARK_CLASSPATH=./sparkling-water/assembly/build/libs/sparkling-water-assembly-0.2.17-SNAPSHOT-all.jar - python -m unittest pysparkling/tests.py -``` -## Testing from Gradle -Needs: - - SPARK_HOME - - H2O_HOME (dev, downloadeded) - you can test against different H2O versions - - python - - python packages for H2O - -## Notes -For Python applications, simply pass a .py file in the place of instead of a JAR, and add Python .zip, .egg or .py files to the search path with --py-files. - -## Problems - -Spark Issues - - https://issues.apache.org/jira/browse/SPARK-5185 - `--jars` packages are not appended to driver path - * Solution: `sc._jvm.java.lang.Thread.currentThread().getContextClassLoader().loadClass("org.apache.spark.h2o.H2OContext").newInstance()` - * ``` - We've been setting SPARK_SUBMIT_CLASSPATH as a workaround to this issue, but as of https://github.com/apache/spark/commit/517975d89d40a77c7186f488547eed11f79c1e97 this variable no longer exists. We're now setting SPARK_CLASSPATH as a workaround. - ``` - - Related issue: - - https://issues.apache.org/jira/browse/SPARK-6047 - - - # Neat - - https://github.com/clojure/clojure/blob/master/src/jvm/clojure/lang/DynamicClassLoader.java From dcaef704210589d49f4233550437234f29ae086b Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Tue, 20 Jun 2017 13:23:33 +0200 Subject: [PATCH 37/72] fix formating --- py/README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/py/README.rst b/py/README.rst index 501791ceb..2de1e18cb 100644 --- a/py/README.rst +++ b/py/README.rst @@ -182,7 +182,7 @@ To leverage H2O's algorithms, data in Spark cluster, stored as an RDD, needs to What is PySparkling? --------------------------- +-------------------- PySparkling is an integration of Python with Sparkling Water. It allows user to start H2O services on a Spark cluster from Python API. From 76055950aa34a29e6359c1f8576aca12fe7f825b Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Tue, 20 Jun 2017 22:39:43 +0200 Subject: [PATCH 38/72] fix quick links --- doc/tutorials/h2oframe_as_data_source.rst | 1 + doc/tutorials/spark_h2o_conversions.rst | 1 + 2 files changed, 2 insertions(+) diff --git a/doc/tutorials/h2oframe_as_data_source.rst b/doc/tutorials/h2oframe_as_data_source.rst index a6483fbdb..097b6e46d 100644 --- a/doc/tutorials/h2oframe_as_data_source.rst +++ b/doc/tutorials/h2oframe_as_data_source.rst @@ -5,6 +5,7 @@ The way how H2O Frame can be used as Spark's Data Source differs a little bit in Python and Scala. Quick links: + - `Usage in Python - PySparkling`_ - `Usage in Scala`_ - `Specifying Saving Mode`_ diff --git a/doc/tutorials/spark_h2o_conversions.rst b/doc/tutorials/spark_h2o_conversions.rst index 919b32423..a3bead0f8 100644 --- a/doc/tutorials/spark_h2o_conversions.rst +++ b/doc/tutorials/spark_h2o_conversions.rst @@ -2,6 +2,7 @@ Spark Frame <--> H2O Frame Conversions -------------------------------------- Quick links: + - `Converting H2OFrame into RDD[T]`_ - `Converting H2OFrame into DataFrame`_ - `Converting RDD[T] into H2OFrame`_ From 48d4c06695e4727d02f5e5437c9a939f838a2fc3 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Wed, 21 Jun 2017 00:34:02 +0200 Subject: [PATCH 39/72] fix link to rest_api --- doc/DEVEL.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/DEVEL.rst b/doc/DEVEL.rst index 62e4a4bf6..dbcd2ec22 100644 --- a/doc/DEVEL.rst +++ b/doc/DEVEL.rst @@ -49,6 +49,6 @@ actual documentation documents. - `Running Unit Tests `__ - `Integration Tests `__ -- `Sparkling Water Rest API `__ +- `Sparkling Water REST API `__ - - `Scala Interpreter REST API `__ + - `Scala Interpreter REST API `__ From dc23a084ae58390631e9b1e8af750b746e84068f Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Wed, 21 Jun 2017 00:35:59 +0200 Subject: [PATCH 40/72] fix link to rest_api --- doc/DEVEL.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/DEVEL.rst b/doc/DEVEL.rst index dbcd2ec22..0a4564ef0 100644 --- a/doc/DEVEL.rst +++ b/doc/DEVEL.rst @@ -51,4 +51,4 @@ actual documentation documents. - `Sparkling Water REST API `__ - - `Scala Interpreter REST API `__ + - `Scala Interpreter REST API `__ From 9c750b4e46a38a445a066d221aed44e9f8c644d1 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Wed, 21 Jun 2017 00:36:41 +0200 Subject: [PATCH 41/72] fix link to rest_api, finally --- doc/DEVEL.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/DEVEL.rst b/doc/DEVEL.rst index 0a4564ef0..c04b2278e 100644 --- a/doc/DEVEL.rst +++ b/doc/DEVEL.rst @@ -51,4 +51,4 @@ actual documentation documents. - `Sparkling Water REST API `__ - - `Scala Interpreter REST API `__ + - `Scala Interpreter REST API `__ From ba31da69f57ffe687ee62088f5f13cb89f184a8f Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Wed, 21 Jun 2017 00:38:14 +0200 Subject: [PATCH 42/72] better intro to code --- doc/tutorials/run_sparkling_water.rst | 2 ++ 1 file changed, 2 insertions(+) diff --git a/doc/tutorials/run_sparkling_water.rst b/doc/tutorials/run_sparkling_water.rst index 953907246..9abbfc033 100644 --- a/doc/tutorials/run_sparkling_water.rst +++ b/doc/tutorials/run_sparkling_water.rst @@ -11,6 +11,8 @@ application as: ./bin/sparkling-shell +Sparkling Water ( H2O on Spark) can be initiated using the following call: + .. code:: scala val hc = H2OContext.getOrCreate(spark) From 7bae1ef92d5ed1c39eede397c673e8c6ba344a39 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Wed, 21 Jun 2017 00:39:18 +0200 Subject: [PATCH 43/72] fix link to run on windows --- doc/tutorials/run_sparkling_water.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/tutorials/run_sparkling_water.rst b/doc/tutorials/run_sparkling_water.rst index 9abbfc033..6c237f499 100644 --- a/doc/tutorials/run_sparkling_water.rst +++ b/doc/tutorials/run_sparkling_water.rst @@ -35,4 +35,4 @@ In external backend mode, the call will: 3. Connects to the external cluster from the H2O client -To see how to run Sparkling Water on Windows, please visit `Run on Windows `__. \ No newline at end of file +To see how to run Sparkling Water on Windows, please visit `Run on Windows `__. \ No newline at end of file From e2b23d03763fcb15f7d278438441303777f1a4f1 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Wed, 21 Jun 2017 09:34:47 +0200 Subject: [PATCH 44/72] remove extra spaces --- doc/tutorials/h2oframe_as_data_source.rst | 6 +++--- doc/tutorials/spark_h2o_conversions.rst | 8 ++++---- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/doc/tutorials/h2oframe_as_data_source.rst b/doc/tutorials/h2oframe_as_data_source.rst index 097b6e46d..6962f08cf 100644 --- a/doc/tutorials/h2oframe_as_data_source.rst +++ b/doc/tutorials/h2oframe_as_data_source.rst @@ -6,9 +6,9 @@ little bit in Python and Scala. Quick links: - - `Usage in Python - PySparkling`_ - - `Usage in Scala`_ - - `Specifying Saving Mode`_ +- `Usage in Python - PySparkling`_ +- `Usage in Scala`_ +- `Specifying Saving Mode`_ Usage in Python - PySparkling ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/doc/tutorials/spark_h2o_conversions.rst b/doc/tutorials/spark_h2o_conversions.rst index a3bead0f8..cb57841ed 100644 --- a/doc/tutorials/spark_h2o_conversions.rst +++ b/doc/tutorials/spark_h2o_conversions.rst @@ -3,10 +3,10 @@ Spark Frame <--> H2O Frame Conversions Quick links: - - `Converting H2OFrame into RDD[T]`_ - - `Converting H2OFrame into DataFrame`_ - - `Converting RDD[T] into H2OFrame`_ - - `Converting DataFrame into H2OFrame`_ +- `Converting H2OFrame into RDD[T]`_ +- `Converting H2OFrame into DataFrame`_ +- `Converting RDD[T] into H2OFrame`_ +- `Converting DataFrame into H2OFrame`_ Converting H2OFrame into RDD[T] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ From a38d29f4fe6efe8dd4c97c4bcc75a79b3df09dc9 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Thu, 22 Jun 2017 14:50:09 +0200 Subject: [PATCH 45/72] capital M --- doc/design/design.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/design/design.rst b/doc/design/design.rst index 3d0dd0af2..7b732e97b 100644 --- a/doc/design/design.rst +++ b/doc/design/design.rst @@ -43,7 +43,7 @@ Backends <../tutorials/backends.rst>`__. When H2O services are running, it is possible to create H2O data structures, call H2O algorithms, and transfer values from/to RDD. -More materials +More Materials ~~~~~~~~~~~~~~ To read more about Sparkling Water design, you can visit on of the links From f9b0128d146516573bb2591ddc6412bb88a24a8d Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Thu, 22 Jun 2017 14:51:04 +0200 Subject: [PATCH 46/72] capital I --- doc/design/design.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/design/design.rst b/doc/design/design.rst index 7b732e97b..0416210d3 100644 --- a/doc/design/design.rst +++ b/doc/design/design.rst @@ -1,7 +1,7 @@ Design ------ -Basic introduction +Basic Introduction ~~~~~~~~~~~~~~~~~~ Sparkling Water is designed to be executed as a regular Spark From 3376ffc31c1e67bf96fba2254446aec156c958a6 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Fri, 23 Jun 2017 09:38:25 +0200 Subject: [PATCH 47/72] dots --- doc/rest_api/scala_interpreter_endpoints.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/rest_api/scala_interpreter_endpoints.rst b/doc/rest_api/scala_interpreter_endpoints.rst index f125db7bc..1d5bf89bc 100644 --- a/doc/rest_api/scala_interpreter_endpoints.rst +++ b/doc/rest_api/scala_interpreter_endpoints.rst @@ -7,13 +7,13 @@ Basic design There is a simple pool of interpreters created at the start. Once an scala interpreter is associated with the session, new interpreter is added to the pool. Each interpreter is deleted when it's not used for -some fixed time +some fixed time. Example usage of scala interpreter using REST API ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Here, you can find some basic calls to access scala interpreter behind -REST API using curl +REST API using curl. Init interpreter and obtain session ID ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ From a8633d07fae720db6ba8d8d6ca7587fb23e7ac67 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Fri, 23 Jun 2017 09:40:29 +0200 Subject: [PATCH 48/72] minor fixes --- doc/devel/build.rst | 7 ++++--- doc/devel/integ_tests.rst | 2 ++ 2 files changed, 6 insertions(+), 3 deletions(-) diff --git a/doc/devel/build.rst b/doc/devel/build.rst index 4e647e43d..5713c932d 100644 --- a/doc/devel/build.rst +++ b/doc/devel/build.rst @@ -7,9 +7,10 @@ Download Spark installation and point environment variable Then use the provided ``gradlew`` to build project: In order to build the whole project including PySparkling, one of the -following properties needs to be set: \* ``H2O_HOME`` - should point to -location of the local H2O project directory \* ``H2O_PYTHON_WHEEL`` - -should point to H2O Python Wheel. +following properties needs to be set: + +- ``H2O_HOME`` - should point to location of the local H2O project directory. +- ``H2O_PYTHON_WHEEL`` - should point to H2O Python Wheel. If you are not sure which property to set, just run diff --git a/doc/devel/integ_tests.rst b/doc/devel/integ_tests.rst index 6b8afcea9..2a8afe014 100644 --- a/doc/devel/integ_tests.rst +++ b/doc/devel/integ_tests.rst @@ -8,6 +8,7 @@ The tests are perform for both Sparkling Water backend types. Please see `Sparkl for more information about the backends. Quick links: + - `Testing Environments`_ - `Testing Scenarios`_ - `Integration Tests Example`_ @@ -52,6 +53,7 @@ Integration Tests Example The following code reflects the use cases listed above. The code is executed in all testing environments (if applicable). Spark 2.0+ required: + - local - standalone cluster - YARN From 22d35234d25e6fb682244df0c07fbe0676aad757 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Fri, 23 Jun 2017 09:43:25 +0200 Subject: [PATCH 49/72] fix links --- doc/devel/integ_tests.rst | 6 +++--- doc/devel/running_examples.rst | 8 ++++---- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/doc/devel/integ_tests.rst b/doc/devel/integ_tests.rst index 2a8afe014..74db9e581 100644 --- a/doc/devel/integ_tests.rst +++ b/doc/devel/integ_tests.rst @@ -9,9 +9,9 @@ for more information about the backends. Quick links: - - `Testing Environments`_ - - `Testing Scenarios`_ - - `Integration Tests Example`_ +- `Testing Environments`_ +- `Testing Scenarios`_ +- `Integration Tests Example`_ Testing Environments -------------------- diff --git a/doc/devel/running_examples.rst b/doc/devel/running_examples.rst index e6c19e8ec..b801497a9 100644 --- a/doc/devel/running_examples.rst +++ b/doc/devel/running_examples.rst @@ -1,8 +1,8 @@ -Running Sparkling Water examples +Running Sparkling Water Examples -------------------------------- The Sparkling Water distribution includes also a set of examples. You -can find their implementation in `examples directory `__. You +can find their implementation in `examples directory <../../examples/>`__. You can run them in the following way: 1. Build a package that can be submitted to Spark cluster: @@ -29,8 +29,8 @@ can run them in the following way: For more details about examples, please see the -`README `__ file in the `examples directory `__. +`README <../../examples/README.md>`__ file in the `examples directory <../../examples/>`__. Additional Examples ~~~~~~~~~~~~~~~~~~~ -Additional examples are available at `examples folder `__. +Additional examples are available at `examples folder <../../examples/>`__. From 0102f154bc2d96c5707b562a2283fbd2aa95e53a Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Fri, 23 Jun 2017 09:44:31 +0200 Subject: [PATCH 50/72] F --- doc/devel/integ_tests.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/devel/integ_tests.rst b/doc/devel/integ_tests.rst index 74db9e581..db0d7a0e6 100644 --- a/doc/devel/integ_tests.rst +++ b/doc/devel/integ_tests.rst @@ -4,7 +4,7 @@ Integration Tests This documentation paper describes what is tested in terms of integration tests as part of the Sparkling Water project. -The tests are perform for both Sparkling Water backend types. Please see `Sparkling Water Backends `__ +The tests are perform for both Sparkling Water backend types. Please see `Sparkling Water Backends <../tutorials/backends.rst>`__ for more information about the backends. Quick links: From c5258c72ccd98b37e7d84291dc7f404737059914 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Fri, 23 Jun 2017 09:49:59 +0200 Subject: [PATCH 51/72] more minor fixes --- doc/configuration/internal_backend_tuning.rst | 2 ++ doc/tutorials/h2o_frame_from_key.rst | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/doc/configuration/internal_backend_tuning.rst b/doc/configuration/internal_backend_tuning.rst index 4bfa32356..8ad327fb8 100644 --- a/doc/configuration/internal_backend_tuning.rst +++ b/doc/configuration/internal_backend_tuning.rst @@ -2,12 +2,14 @@ Sparkling Water Tuning ---------------------- For running Sparkling Water general recommendation are: + - Increase available memory in driver and executors (options ``spark.driver.memory`` resp., ``spark.yarn.am.memory`` and ``spark.executor.memory``). - Make cluster homogeneous - use the same value for driver and executor memory. - Increase PermGen size if you are running on top of Java7 (options ``spark.driver.extraJavaOptions`` resp., ``spark.yarn.am.extraJavaOptions`` and ``spark.executor.extraJavaOptions``). - In rare cases, it helps to increase ``spark.yarn.driver.memoryOverhead``, ``spark.yarn.am.memoryOverhead``, or ``spark.yarn.executor.memoryOverhead``. For running Sparkling Water on top of Yarn: + - Make sure that Yarn provides stable containers, do not use preemptive Yarn scheduler. - Make sure that Spark application manager has enough memory and increase PermGen size. - In case of a container failure, Yarn should not restart container and application should gracefully terminate. diff --git a/doc/tutorials/h2o_frame_from_key.rst b/doc/tutorials/h2o_frame_from_key.rst index c3b3d3cd6..4af9f5ad0 100644 --- a/doc/tutorials/h2o_frame_from_key.rst +++ b/doc/tutorials/h2o_frame_from_key.rst @@ -1,4 +1,4 @@ -Creating H2OFrame from an existing Key +Creating H2OFrame from an Existing Key -------------------------------------- If the H2O cluster already contains a loaded ``H2OFrame`` referenced by From b66fb31fb5480dab8d146ce0cb03f80e04a6d043 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Fri, 23 Jun 2017 09:50:36 +0200 Subject: [PATCH 52/72] no indent --- doc/configuration/internal_backend_tuning.rst | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/doc/configuration/internal_backend_tuning.rst b/doc/configuration/internal_backend_tuning.rst index 8ad327fb8..f884fcee5 100644 --- a/doc/configuration/internal_backend_tuning.rst +++ b/doc/configuration/internal_backend_tuning.rst @@ -3,16 +3,16 @@ Sparkling Water Tuning For running Sparkling Water general recommendation are: - - Increase available memory in driver and executors (options ``spark.driver.memory`` resp., ``spark.yarn.am.memory`` and ``spark.executor.memory``). - - Make cluster homogeneous - use the same value for driver and executor memory. - - Increase PermGen size if you are running on top of Java7 (options ``spark.driver.extraJavaOptions`` resp., ``spark.yarn.am.extraJavaOptions`` and ``spark.executor.extraJavaOptions``). - - In rare cases, it helps to increase ``spark.yarn.driver.memoryOverhead``, ``spark.yarn.am.memoryOverhead``, or ``spark.yarn.executor.memoryOverhead``. +- Increase available memory in driver and executors (options ``spark.driver.memory`` resp., ``spark.yarn.am.memory`` and ``spark.executor.memory``). +- Make cluster homogeneous - use the same value for driver and executor memory. +- Increase PermGen size if you are running on top of Java7 (options ``spark.driver.extraJavaOptions`` resp., ``spark.yarn.am.extraJavaOptions`` and ``spark.executor.extraJavaOptions``). +- In rare cases, it helps to increase ``spark.yarn.driver.memoryOverhead``, ``spark.yarn.am.memoryOverhead``, or ``spark.yarn.executor.memoryOverhead``. For running Sparkling Water on top of Yarn: - - Make sure that Yarn provides stable containers, do not use preemptive Yarn scheduler. - - Make sure that Spark application manager has enough memory and increase PermGen size. - - In case of a container failure, Yarn should not restart container and application should gracefully terminate. +- Make sure that Yarn provides stable containers, do not use preemptive Yarn scheduler. +- Make sure that Spark application manager has enough memory and increase PermGen size. +- In case of a container failure, Yarn should not restart container and application should gracefully terminate. Furthermore, we recommend to configure the following Spark properties to speedup and stabilize creation of H2O services on top of Spark cluster: From 3cbaf183f268b21bb8abf92002be3d1b122d0901 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Fri, 23 Jun 2017 09:56:05 +0200 Subject: [PATCH 53/72] more fixes --- doc/DEVEL.rst | 5 +++-- doc/configuration/memory_setup.rst | 2 +- doc/tutorials/run_on_windows.rst | 2 +- doc/tutorials/tutorials.rst | 1 + 4 files changed, 6 insertions(+), 4 deletions(-) diff --git a/doc/DEVEL.rst b/doc/DEVEL.rst index c04b2278e..f19e5be13 100644 --- a/doc/DEVEL.rst +++ b/doc/DEVEL.rst @@ -11,8 +11,7 @@ actual documentation documents. - `Basic Primitives `__ - `Supported Platforms `__ - `Spark Frame - H2O Frame Mapping `__ - - `Data Sharing Between H2O and - Spark `__ + - `Data Sharing Between H2O and Spark `__ - `Supported Data Sources `__ - `Supported Data Formats `__ @@ -25,6 +24,8 @@ actual documentation documents. - `Tutorials `__ - `Running Sparkling Water `__ + - `Running Sparkling Water on Windows `__ + - `Sparkling Water Backends Run & Configuration `__ - `Enabling Security `__ - `Calling H2O Algorithms `__ diff --git a/doc/configuration/memory_setup.rst b/doc/configuration/memory_setup.rst index 3d1048d19..df2024c4b 100644 --- a/doc/configuration/memory_setup.rst +++ b/doc/configuration/memory_setup.rst @@ -31,4 +31,4 @@ Yarn specific configuration - For JVMs that require a large amount of memory, we strongly recommend configuring the maximum amount of memory available for individual mappers. For information on how to do this using Yarn, refer to - http://docs.h2o.ai/deployment/hadoop\_yarn.html + http://docs.h2o.ai/h2o/latest-stable/h2o-docs/faq/hadoop.html diff --git a/doc/tutorials/run_on_windows.rst b/doc/tutorials/run_on_windows.rst index c44a9a2df..5e46cab8b 100644 --- a/doc/tutorials/run_on_windows.rst +++ b/doc/tutorials/run_on_windows.rst @@ -1,4 +1,4 @@ -Use Sparkling Water in Windows environments +Use Sparkling Water in Windows Environments ------------------------------------------- The Windows environments require several additional steps to make Spark diff --git a/doc/tutorials/tutorials.rst b/doc/tutorials/tutorials.rst index 9af10e51f..b3434c768 100644 --- a/doc/tutorials/tutorials.rst +++ b/doc/tutorials/tutorials.rst @@ -2,6 +2,7 @@ Tutorials --------- - `Running Sparkling Water `__ +- `Running Sparkling Water on Windows `__ - `Sparkling Water Backends Run & Configuration `__ - `Enabling Security `__ - `Calling H2O Algorithms `__ From 5b66bdff4dd1c3c42d244abc3a6a582d07ca6c72 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Fri, 23 Jun 2017 09:59:55 +0200 Subject: [PATCH 54/72] list minor fixes --- doc/DEVEL.rst | 1 + doc/design/supported_data_sources.rst | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/doc/DEVEL.rst b/doc/DEVEL.rst index f19e5be13..701dae0cd 100644 --- a/doc/DEVEL.rst +++ b/doc/DEVEL.rst @@ -20,6 +20,7 @@ actual documentation documents. - `Sparkling Water Configuration Properties `__ - `Memory Allocation `__ - `Sparkling Water Internal Backend Tuning `__ + - `Sparkling Water Backends Configuration `__ - `Tutorials `__ diff --git a/doc/design/supported_data_sources.rst b/doc/design/supported_data_sources.rst index 5ce81b10e..44ffd7f64 100644 --- a/doc/design/supported_data_sources.rst +++ b/doc/design/supported_data_sources.rst @@ -3,7 +3,7 @@ Supported Data Sources Currently, Sparkling Water can use the following data source types: -- standard RDD API to load data and transform them into ``H2OFrame`` +- Standard RDD API to load data and transform them into ``H2OFrame`` - H2OAPI to load data directly into ``H2OFrame`` from: - local file(s) From 3d730ec01d61c4bac7c68a661c514ed4a7583456 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Fri, 23 Jun 2017 10:30:46 +0200 Subject: [PATCH 55/72] merging examples.md and running_examples.rst --- doc/devel/running_examples.rst | 36 ++++++++++++++++++++++++++++++++---- 1 file changed, 32 insertions(+), 4 deletions(-) diff --git a/doc/devel/running_examples.rst b/doc/devel/running_examples.rst index b801497a9..3e5bc1a3b 100644 --- a/doc/devel/running_examples.rst +++ b/doc/devel/running_examples.rst @@ -3,7 +3,7 @@ Running Sparkling Water Examples The Sparkling Water distribution includes also a set of examples. You can find their implementation in `examples directory <../../examples/>`__. You -can run them in the following way: +can build and run them in the following way: 1. Build a package that can be submitted to Spark cluster: @@ -11,8 +11,7 @@ can run them in the following way: ./gradlew build -x check -2. Set the configuration of the demo Spark cluster (for example, - ``local[*]`` or ``local-cluster[3,2,1024]``) +2. Set the configuration of the demo Spark cluster (for example, ``local[*]`` or ``local-cluster[3,2,1024]``) .. code:: bash @@ -21,11 +20,40 @@ can run them in the following way: In this example, the description ``local[*]`` causes creation of a single node local cluster. + 3. And run the example: +- On Local Cluster + + The cluster is defined by MASTER address local-cluster[3,2,3072] which means that cluster + contains 3 worker nodes, each having 2 CPU cores and 3GB of memory: + .. code:: bash - bin/run-example.sh + bin/run-example.sh + +- On a Spark Cluster: + + - Run the Spark cluster, for example via: + + .. code:: bash + + bin/launch-spark-cloud.sh + + - Verify that Spark is running: The Spark UI on http://localhost:8080/ should show 3 worker nodes + - Export ``MASTER`` address of Spark master using: + + .. code:: bash + + export MASTER="spark://localhost:7077" + + - Run example: + + .. code:: bash + + bin/run-example.sh + + - Observe status of the application via Spark UI on http://localhost:8080/ For more details about examples, please see the From 5aeae067d8a06bfab790e1e5fdd47dfabfcc6efb Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Fri, 23 Jun 2017 10:35:33 +0200 Subject: [PATCH 56/72] formating --- doc/devel/running_examples.rst | 10 +- examples/README.md | 15 +-- examples/README.rst | 243 +++++++++++++++++++++++++++++++++++++++++ 3 files changed, 253 insertions(+), 15 deletions(-) create mode 100644 examples/README.rst diff --git a/doc/devel/running_examples.rst b/doc/devel/running_examples.rst index 3e5bc1a3b..21eb465c2 100644 --- a/doc/devel/running_examples.rst +++ b/doc/devel/running_examples.rst @@ -25,14 +25,14 @@ can build and run them in the following way: - On Local Cluster - The cluster is defined by MASTER address local-cluster[3,2,3072] which means that cluster - contains 3 worker nodes, each having 2 CPU cores and 3GB of memory: +The cluster is defined by ``MASTER`` address ``local-cluster[3,2,3072]`` which means that cluster +contains 3 worker nodes, each having 2 CPU cores and 3GB of memory: - .. code:: bash +.. code:: bash - bin/run-example.sh + bin/run-example.sh -- On a Spark Cluster: +- On a Spark Standalone Cluster: - Run the Spark cluster, for example via: diff --git a/examples/README.md b/examples/README.md index a330ba3ff..65c7ab650 100644 --- a/examples/README.md +++ b/examples/README.md @@ -1,4 +1,5 @@ -#Sparkling Water Table of Contents +# Sparkling Water Table of Contents + - [Compiling examples](#CompileExample) - [Running examples](#RunExample) - [Running on a local cluster](#LocalCluster) @@ -40,24 +41,20 @@ ----- - ## Compiling Examples To compile, use top-level `gradlew`: ``` -./gradlew assemble +./gradlew build -x check ``` --- - ## Running Examples - ### On a Simple Local Cluster Run a given example on local cluster. The cluster is defined by `MASTER` address `local-cluster[3,2,3072]` which means that cluster contains 3 worker nodes, each having 2CPUs and 3GB of memory: * Run `bin/run-example.sh ` --- - ### On a Spark Cluster * Run the Spark cluster, for example via `bin/launch-spark-cloud.sh` * Verify that Spark is running: The Spark UI on `http://localhost:8080/` should show 3 worker nodes @@ -66,7 +63,6 @@ To compile, use top-level `gradlew`: * Observe status of the application via Spark UI on `http://localhost:8080/` --- - ## Configuring Sparkling Water Variables You can configure Sparkling Water using the following variables: @@ -75,7 +71,6 @@ You can configure Sparkling Water using the following variables: * `spark.h2o.preserve.executors` - do not kill executors via calling `sc.stop()` call --- - ## Step-by-Step Weather Data Example 1. Run Sparkling shell with an embedded cluster: @@ -90,7 +85,7 @@ You can configure Sparkling Water using the following variables: 3. Initialize H2O services on top of Spark cluster: ```scala import org.apache.spark.h2o._ - val h2oContext = H2OContext.getOrCreate(sc) + val h2oContext = H2OContext.getOrCreate(spark) import h2oContext._ import h2oContext.implicits._ ``` @@ -99,7 +94,7 @@ You can configure Sparkling Water using the following variables: ```scala import org.apache.spark.examples.h2o._ val weatherDataFile = "examples/smalldata/Chicago_Ohare_International_Airport.csv" - val wrawdata = sc.textFile(weatherDataFile,3).cache() + val wrawdata = spark.sparkContext.textFile(weatherDataFile,3).cache() val weatherTable = wrawdata.map(_.split(",")).map(row => WeatherParse(row)).filter(!_.isWrongRow()) ``` diff --git a/examples/README.rst b/examples/README.rst new file mode 100644 index 000000000..cb342a3db --- /dev/null +++ b/examples/README.rst @@ -0,0 +1,243 @@ +Sparkling Water Table of Contents +================================= + +- `Compiling examples <#CompileExample>`__ +- `Running examples <#RunExample>`__ +- `Running on a local cluster <#LocalCluster>`__ +- `Running on a Spark cluster <#SparkCluster>`__ +- `Configuring variables <#ConfigVar>`__ +- `Step-by-step weather example <#WeatherExample>`__ +- `Running Sparkling Water on Hadoop <#Hadoop>`__ +- `Importing data from HDFS <#ImportData>`__ + +Sparkling Water Examples +======================== + +Available Demos And Applications +-------------------------------- + +- ```CraigslistJobTitlesStreamingApp`` `__ + - **stream** application - it predicts job category based on incoming + job description +- ```CraigslistJobTitlesApp`` `__ + - predict job category based on posted job description +- ```ChicagoCrimeAppSmall`` `__ + - builds a model predicting a probability of arrest for given crime + in Chicago using data in ```smalldata`` directory `__ +- ```ChicagoCrimeApp`` `__ + - implementation of Chicago Crime demo with setup for data stored on + HDFS +- ```CitiBikeSharingDemo`` `__ + - predicts occupancy of Citi bike stations in NYC +- ```HamOrSpamDemo`` `__ + - shows Spam detector with Spark and H2O's DeepLearning +- ```ProstateDemo`` `__ + - running K-means on prostate dataset (see *smalldata/prostate.csv*) +- ```DeepLearningDemo`` `__ + - running DeepLearning on a subset of airlines dataset (see + *smalldata/allyears2k\_headers.csv.gz*) +- ```AirlinesWithWeatherDemo`` `__ + - joining flights data with weather data and running Deep Learning +- ```AirlinesWithWeatherDemo2`` `__ + - new iteration of ``AirlinesWithWeatherDemo`` + + Run examples by typing ``bin/run-example.sh `` or + follow text below. + +Available Demos for Sparkling Shell +----------------------------------- + +- ```chicagoCrimeSmallShell.script.scala`` `__ + - demo showing full source code of predicting arrest probability for + a given crime. It covers whole machine learning process from loading + and transforming data, building models, scoring incoming events. +- ```chicagoCrimeSmall.script.scala`` `__ + - example of using + `ChicagoCrimeApp `__ + - creating application and using it for scoring individual crime + events. +- ```mlconf_2015_hamSpam.script.scala`` `__ + - HamOrSpam application which detectes Spam messages. Presented at + MLConf 2015 NYC. +- ```strata2015_demo.scala`` `__ - NYC + CitiBike demo presented at Strata 2015 in San Jose. +- ```StrataAirlines.scala`` `__ - example + of using flights and weather data to predict delay of a flight + + Run examples by typing + ``bin/sparkling-shell -i `` + +-------------- + +Compiling Examples +------------------ + +To compile, use top-level ``gradlew``: + +:: + + ./gradlew build -x check + +On a Spark Cluster +~~~~~~~~~~~~~~~~~~ + +- Run the Spark cluster, for example via ``bin/launch-spark-cloud.sh`` + + - Verify that Spark is running: The Spark UI on + ``http://localhost:8080/`` should show 3 worker nodes + +- Export ``MASTER`` address of Spark master using + ``export MASTER="spark://localhost:7077"`` +- Run ``bin/run-example.sh `` +- Observe status of the application via Spark UI on + ``http://localhost:8080/`` + +Configuring Sparkling Water Variables +------------------------------------- + +Please see `Available Sparkling Water Configuration Properties <../doc/configuration/configuration_properties>`__. + +Step-by-Step Weather Data Example +--------------------------------- + +1. Run Sparkling shell with an embedded cluster: + +.. code:: bash + + export SPARK_HOME="/path/to/spark/installation" export MASTER="local[*]" bin/sparkling-shell + +2. To see the Sparkling shell (i.e., Spark driver) status, go to http://localhost:4040/. + +3. Initialize H2O services on top of Spark cluster: + +.. code:: scala + + import org.apache.spark.h2o._ + val h2oContext = H2OContext.getOrCreate(spark) + import h2oContext._ + import h2oContext.implicits._ + +4. Load weather data for Chicago international airport (ORD), with help + from the RDD API: + +.. code:: scala + + import org.apache.spark.examples.h2o._ + val weatherDataFile = "examples/smalldata/Chicago_Ohare_International_Airport.csv" + val wrawdata = spark.sparkContext.textFile(weatherDataFile,3).cache() + val weatherTable = wrawdata.map(_.split(",")).map(row => WeatherParse(row)).filter(!_.isWrongRow()) + +5. Load airlines data using the H2O parser: + +.. code:: scala + + import java.io.File + val dataFile = "examples/smalldata/allyears2k_headers.csv.gz" + val airlinesData = new H2OFrame(new File(dataFile)) + +6. Select flights destined for Chicago (ORD): + +.. code:: scala + + val airlinesTable : RDD[Airlines] = asRDD[Airlines](airlinesData) + val flightsToORD = airlinesTable.filter(f => f.Dest==Some("ORD")) + +7. Compute the number of these flights: + +.. code:: scala + + flightsToORD.count + +8. Use Spark SQL to join the flight data with the weather data: + +.. code:: scala + + implicit val sqlContext = spark.sqlContext + import sqlContext.implicits._ + flightsToORD.toDF.createOrReplaceTempView("FlightsToORD") + weatherTable.toDF.createOrReplaceTempView("WeatherORD") + +9. Perform SQL JOIN on both tables: + +.. code:: scala + + val bigTable = sqlContext.sql( + """SELECT + |f.Year,f.Month,f.DayofMonth, + |f.CRSDepTime,f.CRSArrTime,f.CRSElapsedTime, + |f.UniqueCarrier,f.FlightNum,f.TailNum, + |f.Origin,f.Distance, + |w.TmaxF,w.TminF,w.TmeanF,w.PrcpIn,w.SnowIn,w.CDD,w.HDD,w.GDD, + |f.ArrDelay + |FROM FlightsToORD f + |JOIN WeatherORD w + |ON f.Year=w.Year AND f.Month=w.Month AND f.DayofMonth=w.Day""".stripMargin) + +10. Transform the first 3 columns containing date information into enum columns: + +.. code:: scala + + val bigDataFrame: H2OFrame = h2oContext.asH2OFrame(bigTable) + for( i <- 0 to 2) bigDataFrame.replace(i, bigDataFrame.vec(i).toCategoricalVec) + bigDataFrame.update() + +11. Run deep learning to produce a model estimating arrival delay: + +.. code:: scala + + import _root_.hex.deeplearning.DeepLearning + import _root_.hex.deeplearning.DeepLearningModel.DeepLearningParameters + import _root_.hex.deeplearning.DeepLearningModel.DeepLearningParameters.Activation + val dlParams = new DeepLearningParameters() + dlParams._train = bigDataFrame + dlParams._response_column = "ArrDelay" + dlParams._epochs = 5 + dlParams._activation = Activation.RectifierWithDropout + dlParams._hidden = Array[Int](100, 100) + + // Create a job + val dl = new DeepLearning(dlParams) + val dlModel = dl.trainModel.get + + +12. Use the model to estimate the delay on the training data: + +.. code:: scala + + val predictionH2OFrame = dlModel.score(bigTable)("predict") + val predictionsFromModel = asDataFrame(predictionH2OFrame)(sqlContext).collect.map{ + row => if (row.isNullAt(0)) Double.NaN else row(0) + } + +13. Generate an R-code producing residual plot: + +.. code:: scala + + import org.apache.spark.examples.h2o.AirlinesWithWeatherDemo2.residualPlotRCode + residualPlotRCode(predictionH2OFrame, "predict", bigTable, "ArrDelay", h2oContext) + +14. Execute generated R-code in RStudio: + +.. code:: R + + # + # R script for residual plot + # + # Import H2O library + library(h2o) + # Initialize H2O R-client + h2o.init() + # Fetch prediction and actual data, use remembered keys + pred = h2o.getFrame("dframe_b5f449d0c04ee75fda1b9bc865b14a69") + act = h2o.getFrame ("frame_rdd_14_b429e8b43d2d8c02899ccb61b72c4e57") + # Select right columns + predDelay = pred$predict + actDelay = act$ArrDelay + # Make sure that number of rows is same + nrow(actDelay) == nrow(predDelay) + # Compute residuals + residuals = predDelay - actDelay + # Plot residuals + compare = cbind (as.data.frame(actDelay$ArrDelay), as.data.frame(residuals$predict)) + nrow(compare) + plot( compare[,1:2] ) \ No newline at end of file From d48b20011369b5716a5e31b84ad6b858d3355209 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Fri, 23 Jun 2017 10:36:06 +0200 Subject: [PATCH 57/72] test --- doc/devel/running_examples.rst | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/doc/devel/running_examples.rst b/doc/devel/running_examples.rst index 21eb465c2..875893dc4 100644 --- a/doc/devel/running_examples.rst +++ b/doc/devel/running_examples.rst @@ -25,12 +25,12 @@ can build and run them in the following way: - On Local Cluster -The cluster is defined by ``MASTER`` address ``local-cluster[3,2,3072]`` which means that cluster -contains 3 worker nodes, each having 2 CPU cores and 3GB of memory: + The cluster is defined by ``MASTER`` address ``local-cluster[3,2,3072]`` which means that cluster + contains 3 worker nodes, each having 2 CPU cores and 3GB of memory: -.. code:: bash + .. code:: bash - bin/run-example.sh + bin/run-example.sh - On a Spark Standalone Cluster: From 480ee26b27af26b9a28bd42f0097124f9e6661ab Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Fri, 23 Jun 2017 10:36:48 +0200 Subject: [PATCH 58/72] test --- doc/devel/running_examples.rst | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/doc/devel/running_examples.rst b/doc/devel/running_examples.rst index 875893dc4..edb6247f3 100644 --- a/doc/devel/running_examples.rst +++ b/doc/devel/running_examples.rst @@ -23,10 +23,9 @@ can build and run them in the following way: 3. And run the example: -- On Local Cluster +- On Local Cluster: - The cluster is defined by ``MASTER`` address ``local-cluster[3,2,3072]`` which means that cluster - contains 3 worker nodes, each having 2 CPU cores and 3GB of memory: + The cluster is defined by ``MASTER`` address ``local-cluster[3,2,3072]`` which means that cluster contains 3 worker nodes, each having 2 CPU cores and 3GB of memory: .. code:: bash From fbba0125e213c3048b1170480b773398e8fb3187 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Fri, 23 Jun 2017 10:38:30 +0200 Subject: [PATCH 59/72] test --- doc/devel/running_examples.rst | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/doc/devel/running_examples.rst b/doc/devel/running_examples.rst index edb6247f3..fe5a421ef 100644 --- a/doc/devel/running_examples.rst +++ b/doc/devel/running_examples.rst @@ -35,22 +35,25 @@ can build and run them in the following way: - Run the Spark cluster, for example via: - .. code:: bash + .. code:: bash + + bin/launch-spark-cloud.sh - bin/launch-spark-cloud.sh - Verify that Spark is running: The Spark UI on http://localhost:8080/ should show 3 worker nodes - Export ``MASTER`` address of Spark master using: - .. code:: bash + .. code:: bash + + export MASTER="spark://localhost:7077" - export MASTER="spark://localhost:7077" - Run example: - .. code:: bash + .. code:: bash + + bin/run-example.sh - bin/run-example.sh - Observe status of the application via Spark UI on http://localhost:8080/ From 849119aad4602c33daa0175a1fc9aadd96ac23ed Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Fri, 23 Jun 2017 11:02:09 +0200 Subject: [PATCH 60/72] add examples readme.rst --- examples/README.rst | 107 +++++++++++++++++----------------------------------- 1 file changed, 34 insertions(+), 73 deletions(-) diff --git a/examples/README.rst b/examples/README.rst index cb342a3db..5b6c50910 100644 --- a/examples/README.rst +++ b/examples/README.rst @@ -1,101 +1,62 @@ -Sparkling Water Table of Contents -================================= - -- `Compiling examples <#CompileExample>`__ -- `Running examples <#RunExample>`__ -- `Running on a local cluster <#LocalCluster>`__ -- `Running on a Spark cluster <#SparkCluster>`__ -- `Configuring variables <#ConfigVar>`__ -- `Step-by-step weather example <#WeatherExample>`__ -- `Running Sparkling Water on Hadoop <#Hadoop>`__ -- `Importing data from HDFS <#ImportData>`__ - Sparkling Water Examples ======================== Available Demos And Applications -------------------------------- -- ```CraigslistJobTitlesStreamingApp`` `__ - - **stream** application - it predicts job category based on incoming - job description -- ```CraigslistJobTitlesApp`` `__ +- `CraigslistJobTitlesStreamingApp `__ + - **stream** application - it predicts job category based on incoming job description +- `CraigslistJobTitlesApp `__ - predict job category based on posted job description -- ```ChicagoCrimeAppSmall`` `__ - - builds a model predicting a probability of arrest for given crime - in Chicago using data in ```smalldata`` directory `__ -- ```ChicagoCrimeApp`` `__ - - implementation of Chicago Crime demo with setup for data stored on - HDFS -- ```CitiBikeSharingDemo`` `__ +- `ChicagoCrimeAppSmall `__ + - builds a model predicting a probability of arrest for given crime in Chicago using data in `smalldata directory `__ +- `ChicagoCrimeApp `__ + - implementation of Chicago Crime demo with setup for data stored on HDFS +- `CitiBikeSharingDemo `__ - predicts occupancy of Citi bike stations in NYC -- ```HamOrSpamDemo`` `__ +- `HamOrSpamDemo `__ - shows Spam detector with Spark and H2O's DeepLearning -- ```ProstateDemo`` `__ +- `ProstateDemo `__ - running K-means on prostate dataset (see *smalldata/prostate.csv*) -- ```DeepLearningDemo`` `__ - - running DeepLearning on a subset of airlines dataset (see - *smalldata/allyears2k\_headers.csv.gz*) -- ```AirlinesWithWeatherDemo`` `__ +- `DeepLearningDemo `__ + - running DeepLearning on a subset of `airlines dataset `__ +- `AirlinesWithWeatherDemo `__ - joining flights data with weather data and running Deep Learning -- ```AirlinesWithWeatherDemo2`` `__ +- `AirlinesWithWeatherDemo2 `__ - new iteration of ``AirlinesWithWeatherDemo`` - Run examples by typing ``bin/run-example.sh `` or - follow text below. + Run examples by typing ``bin/run-example.sh `` or follow text below. Available Demos for Sparkling Shell ----------------------------------- -- ```chicagoCrimeSmallShell.script.scala`` `__ - - demo showing full source code of predicting arrest probability for - a given crime. It covers whole machine learning process from loading - and transforming data, building models, scoring incoming events. -- ```chicagoCrimeSmall.script.scala`` `__ - - example of using - `ChicagoCrimeApp `__ - - creating application and using it for scoring individual crime - events. -- ```mlconf_2015_hamSpam.script.scala`` `__ - - HamOrSpam application which detectes Spam messages. Presented at - MLConf 2015 NYC. -- ```strata2015_demo.scala`` `__ - NYC - CitiBike demo presented at Strata 2015 in San Jose. -- ```StrataAirlines.scala`` `__ - example - of using flights and weather data to predict delay of a flight - - Run examples by typing - ``bin/sparkling-shell -i `` - --------------- - -Compiling Examples ------------------- +- `chicagoCrimeSmallShell.script.scala `__ + - demo showing full source code of predicting arrest probability for a given crime. It covers whole machine learning process from loading and transforming data, building models, scoring incoming events. +- `chicagoCrimeSmall.script.scala `__ + - example of using `ChicagoCrimeApp `__ + - creating application and using it for scoring individual crime events. +- `mlconf_2015_hamSpam.script.scala `__ + - HamOrSpam application which detects Spam messages. Presented at MLConf 2015 NYC. +- `strata2015_demo.scala `__ + - NYC CitiBike demo presented at Strata 2015 in San Jose. +- `StrataAirlines.scala `__ + - example of using flights and weather data to predict delay of a flight -To compile, use top-level ``gradlew``: + Run examples by typing ``bin/sparkling-shell -i `` -:: - - ./gradlew build -x check - -On a Spark Cluster -~~~~~~~~~~~~~~~~~~ - -- Run the Spark cluster, for example via ``bin/launch-spark-cloud.sh`` +-------------- - - Verify that Spark is running: The Spark UI on - ``http://localhost:8080/`` should show 3 worker nodes +Building and Running Examples +----------------------------- -- Export ``MASTER`` address of Spark master using - ``export MASTER="spark://localhost:7077"`` -- Run ``bin/run-example.sh `` -- Observe status of the application via Spark UI on - ``http://localhost:8080/`` +Please see `Running Sparkling Water Examples <../doc/devel/running_examples.rst>`__ for more information how to build +and run examples. Configuring Sparkling Water Variables ------------------------------------- -Please see `Available Sparkling Water Configuration Properties <../doc/configuration/configuration_properties>`__. +Please see `Available Sparkling Water Configuration Properties <../doc/configuration/configuration_properties>`__ for +more information about possible Sparkling Water configurations. Step-by-Step Weather Data Example --------------------------------- From 57718e896ff54fc24cff6dcb16adf30550e2f747 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Fri, 23 Jun 2017 11:15:40 +0200 Subject: [PATCH 61/72] test link --- examples/README.rst | 31 ++++++++++++++++++++----------- 1 file changed, 20 insertions(+), 11 deletions(-) diff --git a/examples/README.rst b/examples/README.rst index 5b6c50910..b9c22b956 100644 --- a/examples/README.rst +++ b/examples/README.rst @@ -4,26 +4,32 @@ Sparkling Water Examples Available Demos And Applications -------------------------------- ++---------------------------------------------------------------------+----------------------------------------+ +| Example | Description | ++=====================================================================+========================================+ +| `CraigslistJobTitlesStreamingApp`_ ++---------------------------------------------------------------------+----------------------------------------+ + - `CraigslistJobTitlesStreamingApp `__ - - **stream** application - it predicts job category based on incoming job description + - **stream** application - it predicts job category based on incoming job description. - `CraigslistJobTitlesApp `__ - - predict job category based on posted job description + - predict job category based on posted job description. - `ChicagoCrimeAppSmall `__ - - builds a model predicting a probability of arrest for given crime in Chicago using data in `smalldata directory `__ + - builds a model predicting a probability of arrest for given crime in Chicago using data in `smalldata directory `__. - `ChicagoCrimeApp `__ - - implementation of Chicago Crime demo with setup for data stored on HDFS + - implementation of Chicago Crime demo with setup for data stored on HDFS. - `CitiBikeSharingDemo `__ - - predicts occupancy of Citi bike stations in NYC + - predicts occupancy of Citi bike stations in NYC. - `HamOrSpamDemo `__ - - shows Spam detector with Spark and H2O's DeepLearning + - shows Spam detector with Spark and H2O's DeepLearning. - `ProstateDemo `__ - - running K-means on prostate dataset (see *smalldata/prostate.csv*) + - running K-means on `prostate dataset `__. - `DeepLearningDemo `__ - - running DeepLearning on a subset of `airlines dataset `__ + - running DeepLearning on a subset of `airlines dataset `__. - `AirlinesWithWeatherDemo `__ - - joining flights data with weather data and running Deep Learning + - joining flights data with weather data and running Deep Learning. - `AirlinesWithWeatherDemo2 `__ - - new iteration of ``AirlinesWithWeatherDemo`` + - new iteration of ``AirlinesWithWeatherDemo``. Run examples by typing ``bin/run-example.sh `` or follow text below. @@ -201,4 +207,7 @@ Step-by-Step Weather Data Example # Plot residuals compare = cbind (as.data.frame(actDelay$ArrDelay), as.data.frame(residuals$predict)) nrow(compare) - plot( compare[,1:2] ) \ No newline at end of file + plot( compare[,1:2] ) + + +:: _CraigslistJobTitlesStreamingApp: src/main/scala/org/apache/spark/examples/h2o/CraigslistJobTitlesStreamingApp.scala From 30ec4021e69463a22f09ae65b2d61e2f91273bef Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Fri, 23 Jun 2017 11:16:48 +0200 Subject: [PATCH 62/72] fix table --- examples/README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/README.rst b/examples/README.rst index b9c22b956..c9c388fe2 100644 --- a/examples/README.rst +++ b/examples/README.rst @@ -7,7 +7,7 @@ Available Demos And Applications +---------------------------------------------------------------------+----------------------------------------+ | Example | Description | +=====================================================================+========================================+ -| `CraigslistJobTitlesStreamingApp`_ +| `CraigslistJobTitlesStreamingApp`_ | | +---------------------------------------------------------------------+----------------------------------------+ - `CraigslistJobTitlesStreamingApp `__ From 99528f9b9cc3e8d1b26605060e36dd325db253a3 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Fri, 23 Jun 2017 11:19:25 +0200 Subject: [PATCH 63/72] experiment substitution --- examples/README.rst | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/examples/README.rst b/examples/README.rst index c9c388fe2..9c3faf836 100644 --- a/examples/README.rst +++ b/examples/README.rst @@ -7,7 +7,7 @@ Available Demos And Applications +---------------------------------------------------------------------+----------------------------------------+ | Example | Description | +=====================================================================+========================================+ -| `CraigslistJobTitlesStreamingApp`_ | | +| |CraigslistJobTitlesStreamingApp| | | +---------------------------------------------------------------------+----------------------------------------+ - `CraigslistJobTitlesStreamingApp `__ @@ -209,5 +209,4 @@ Step-by-Step Weather Data Example nrow(compare) plot( compare[,1:2] ) - -:: _CraigslistJobTitlesStreamingApp: src/main/scala/org/apache/spark/examples/h2o/CraigslistJobTitlesStreamingApp.scala +.. |CraigslistJobTitlesStreamingApp| replace:: `CraigslistJobTitlesStreamingApp `__ \ No newline at end of file From 39f57ac8ad6dec1b5d985afa964b6b4abfa69a4a Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Fri, 23 Jun 2017 11:38:37 +0200 Subject: [PATCH 64/72] finish table --- examples/README.rst | 99 +++++++++++++++++++++++++++++++++-------------------- 1 file changed, 61 insertions(+), 38 deletions(-) diff --git a/examples/README.rst b/examples/README.rst index 9c3faf836..c2ec7ffdf 100644 --- a/examples/README.rst +++ b/examples/README.rst @@ -4,49 +4,55 @@ Sparkling Water Examples Available Demos And Applications -------------------------------- -+---------------------------------------------------------------------+----------------------------------------+ -| Example | Description | -+=====================================================================+========================================+ -| |CraigslistJobTitlesStreamingApp| | | -+---------------------------------------------------------------------+----------------------------------------+ - -- `CraigslistJobTitlesStreamingApp `__ - - **stream** application - it predicts job category based on incoming job description. -- `CraigslistJobTitlesApp `__ - - predict job category based on posted job description. -- `ChicagoCrimeAppSmall `__ - - builds a model predicting a probability of arrest for given crime in Chicago using data in `smalldata directory `__. -- `ChicagoCrimeApp `__ - - implementation of Chicago Crime demo with setup for data stored on HDFS. -- `CitiBikeSharingDemo `__ - - predicts occupancy of Citi bike stations in NYC. -- `HamOrSpamDemo `__ - - shows Spam detector with Spark and H2O's DeepLearning. -- `ProstateDemo `__ - - running K-means on `prostate dataset `__. -- `DeepLearningDemo `__ - - running DeepLearning on a subset of `airlines dataset `__. -- `AirlinesWithWeatherDemo `__ - - joining flights data with weather data and running Deep Learning. -- `AirlinesWithWeatherDemo2 `__ - - new iteration of ``AirlinesWithWeatherDemo``. ++-----------------------------------+--------------------------------------------------------------------------+ +| Example | Description | ++===================================+==========================================================================+ +| |CraigslistJobTitlesStreamingApp| | **Stream** application - it predicts job category based on incoming job | +| | description. | ++-----------------------------------+--------------------------------------------------------------------------+ +| |CraigslistJobTitlesApp| | Predict job category based on posted job description. | ++-----------------------------------+--------------------------------------------------------------------------+ +| |ChicagoCrimeAppSmall| | Builds a model predicting a probability of arrest for given crime in | +| | Chicago using data in `smalldata directory `__. | ++-----------------------------------+--------------------------------------------------------------------------+ +| |ChicagoCrimeApp| | Implementation of Chicago Crime demo with setup for data stored on HDFS. | ++-----------------------------------+--------------------------------------------------------------------------+ +| |CitiBikeSharingDemo| | Predicts occupancy of Citi bike stations in NYC. | ++-----------------------------------+--------------------------------------------------------------------------+ +| |HamOrSpamDemo| | Shows Spam detector with Spark and H2O's DeepLearning. | ++-----------------------------------+--------------------------------------------------------------------------+ +| |ProstateDemo| | Running K-means on `prostate dataset `__. | ++-----------------------------------+--------------------------------------------------------------------------+ +| |DeepLearningDemo| | Running DeepLearning on a subset of | +| | `airlines dataset `__. | ++-----------------------------------+--------------------------------------------------------------------------+ +| |AirlinesWithWeatherDemo| | Joining flights data with weather data and running Deep Learning. | ++-----------------------------------+--------------------------------------------------------------------------+ +| |AirlinesWithWeatherDemo2| | New iteration of ``AirlinesWithWeatherDemo``. | ++-----------------------------------+--------------------------------------------------------------------------+ Run examples by typing ``bin/run-example.sh `` or follow text below. Available Demos for Sparkling Shell ----------------------------------- -- `chicagoCrimeSmallShell.script.scala `__ - - demo showing full source code of predicting arrest probability for a given crime. It covers whole machine learning process from loading and transforming data, building models, scoring incoming events. -- `chicagoCrimeSmall.script.scala `__ - - example of using `ChicagoCrimeApp `__ - - creating application and using it for scoring individual crime events. -- `mlconf_2015_hamSpam.script.scala `__ - - HamOrSpam application which detects Spam messages. Presented at MLConf 2015 NYC. -- `strata2015_demo.scala `__ - - NYC CitiBike demo presented at Strata 2015 in San Jose. -- `StrataAirlines.scala `__ - - example of using flights and weather data to predict delay of a flight ++---------------------------------------+--------------------------------------------------------------------------+ +| Example | Description | ++=======================================+==========================================================================+ +| |chicagoCrimeSmallShellScriptScala| | Demo showing full source code of predicting arrest probability for a | +| | given crime. It covers whole machine learning process from loading and | +| | transforming data, building models, scoring incoming events. | ++---------------------------------------+--------------------------------------------------------------------------+ +| |chicagoCrimeSmallScriptScala| | Example of using |ChicagoCrimeApp|. Creating application and using it | +| | for scoring individual crime events. | ++---------------------------------------+--------------------------------------------------------------------------+ +| |mlconf2015HamSpamScriptScala| | HamOrSpam application which detects Spam messages. Presented at | +| | MLConf 2015 NYC. | ++---------------------------------------+--------------------------------------------------------------------------+ +| |strata2015DemoScala| | NYC CitiBike demo presented at Strata 2015 in San Jose. | ++---------------------------------------+--------------------------------------------------------------------------+ +| |StrataAirlinesScala| | Example of using flights and weather data to predict delay of a flight. | ++---------------------------------------+--------------------------------------------------------------------------+ Run examples by typing ``bin/sparkling-shell -i `` @@ -209,4 +215,21 @@ Step-by-Step Weather Data Example nrow(compare) plot( compare[,1:2] ) -.. |CraigslistJobTitlesStreamingApp| replace:: `CraigslistJobTitlesStreamingApp `__ \ No newline at end of file + +.. Links to the examples + +.. |CraigslistJobTitlesStreamingApp| replace:: `CraigslistJobTitlesStreamingApp `__ +.. |CraigslistJobTitlesApp| replace:: `CraigslistJobTitlesApp `__ +.. |ChicagoCrimeAppSmall| replace:: `ChicagoCrimeAppSmall `__ +.. |ChicagoCrimeApp| replace:: `ChicagoCrimeApp `__ +.. |CitiBikeSharingDemo| replace:: `CitiBikeSharingDemo `__ +.. |HamOrSpamDemo| replace:: `HamOrSpamDemo `__ +.. |ProstateDemo| replace:: `ProstateDemo `__ +.. |DeepLearningDemo| replace:: `DeepLearningDemo `__ +.. |AirlinesWithWeatherDemo| replace:: `AirlinesWithWeatherDemo `__ +.. |AirlinesWithWeatherDemo2| replace:: `AirlinesWithWeatherDemo2 `__ +.. |chicagoCrimeSmallShellScriptScala| replace:: `chicagoCrimeSmallShell.script.scala `__ +.. |chicagoCrimeSmallScriptScala| replace:: `chicagoCrimeSmall.script.scala `__ +.. |mlconf2015HamSpamScriptScala| replace:: `mlconf_2015_hamSpam.script.scala `__ +.. |strata2015DemoScala| replace:: `strata2015_demo.scala `__ +.. |StrataAirlinesScala| replace:: `StrataAirlines.scala `__ From d534d0a0eeaceeb846fbe13565b1329f797d3af1 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Fri, 23 Jun 2017 11:43:05 +0200 Subject: [PATCH 65/72] remove md and update link --- doc/devel/running_examples.rst | 2 +- examples/README.md | 201 ----------------------------------------- 2 files changed, 1 insertion(+), 202 deletions(-) delete mode 100644 examples/README.md diff --git a/doc/devel/running_examples.rst b/doc/devel/running_examples.rst index fe5a421ef..82e03b9d2 100644 --- a/doc/devel/running_examples.rst +++ b/doc/devel/running_examples.rst @@ -59,7 +59,7 @@ can build and run them in the following way: For more details about examples, please see the -`README <../../examples/README.md>`__ file in the `examples directory <../../examples/>`__. +`README <../../examples/README.rst>`__ file in the `examples directory <../../examples/>`__. Additional Examples ~~~~~~~~~~~~~~~~~~~ diff --git a/examples/README.md b/examples/README.md deleted file mode 100644 index 65c7ab650..000000000 --- a/examples/README.md +++ /dev/null @@ -1,201 +0,0 @@ -# Sparkling Water Table of Contents - -- [Compiling examples](#CompileExample) -- [Running examples](#RunExample) - - [Running on a local cluster](#LocalCluster) - - [Running on a Spark cluster](#SparkCluster) -- [Configuring variables](#ConfigVar) -- [Step-by-step weather example](#WeatherExample) -- [Running Sparkling Water on Hadoop](#Hadoop) -- [Importing data from HDFS](#ImportData) - - -# Sparkling Water Examples - -## Available Demos And Applications - * [`CraigslistJobTitlesStreamingApp`](src/main/scala/org/apache/spark/examples/h2o/CraigslistJobTitlesStreamingApp.scala) - **stream** application - it predicts job category based on incoming job description - * [`CraigslistJobTitlesApp`](src/main/scala/org/apache/spark/examples/h2o/CraigslistJobTitlesApp.scala) - predict job category based on posted job description - * [`ChicagoCrimeAppSmall`](src/main/scala/org/apache/spark/examples/h2o/ChicagoCrimeAppSmall.scala) - builds a model predicting a probability of arrest for given crime in Chicago using data in [`smalldata` directory](smalldata/) - * [`ChicagoCrimeApp`](src/main/scala/org/apache/spark/examples/h2o/ChicagoCrimeApp.scala) - implementation of Chicago Crime demo with setup for data stored on HDFS - * [`CitiBikeSharingDemo`](src/main/scala/org/apache/spark/examples/h2o/CitiBikeSharingDemo.scala) - predicts occupancy of Citi bike stations in NYC - * [`HamOrSpamDemo`](src/main/scala/org/apache/spark/examples/h2o/HamOrSpamDemo.scala) - shows Spam detector with Spark and H2O's DeepLearning - * [`ProstateDemo`](src/main/scala/org/apache/spark/examples/h2o/ProstateDemo.scala) - running K-means on prostate dataset (see - _smalldata/prostate.csv_) - * [`DeepLearningDemo`](src/main/scala/org/apache/spark/examples/h2o/DeepLearningDemo.scala) - running DeepLearning on a subset of airlines dataset (see - _smalldata/allyears2k\_headers.csv.gz_) - * [`AirlinesWithWeatherDemo`](src/main/scala/org/apache/spark/examples/h2o/AirlinesWithWeatherDemo.scala) - joining flights data with weather data and running - Deep Learning - * [`AirlinesWithWeatherDemo2`](src/main/scala/org/apache/spark/examples/h2o/AirlinesWithWeatherDemo2.scala) - new iteration of `AirlinesWithWeatherDemo` - - -> Run examples by typing `bin/run-example.sh ` or follow text below. - -## Available Demos for Sparkling Shell - * [`chicagoCrimeSmallShell.script.scala`](scripts/chicagoCrimeSmallShell.script.scala) - demo showing full source code of predicting arrest probability for a given crime. It covers whole machine learning process from loading and transforming data, building models, scoring incoming events. - * [`chicagoCrimeSmall.script.scala`](scripts/chicagoCrimeSmall.script.scala) - example of using [ChicagoCrimeApp](src/main/scala/org/apache/spark/examples/h2o/ChicagoCrimeApp.scala) - creating application and using it for scoring individual crime events. - * [`mlconf_2015_hamSpam.script.scala`](scripts/mlconf_2015_hamSpam.script.scala) - HamOrSpam application which detectes Spam messages. Presented at MLConf 2015 NYC. - * [`strata2015_demo.scala`](scripts/strata2015_demo.scala) - NYC CitiBike demo presented at Strata 2015 in San Jose. - * [`StrataAirlines.scala`](scripts/StrataAirlines.scala) - example of using flights and weather data to predict delay of a flight - -> Run examples by typing `bin/sparkling-shell -i ` - ------ - -## Compiling Examples -To compile, use top-level `gradlew`: -``` -./gradlew build -x check -``` ---- -## Running Examples - -### On a Simple Local Cluster - - Run a given example on local cluster. The cluster is defined by `MASTER` address `local-cluster[3,2,3072]` which means that cluster contains 3 worker nodes, each having 2CPUs and 3GB of memory: - * Run `bin/run-example.sh ` - ---- -### On a Spark Cluster - * Run the Spark cluster, for example via `bin/launch-spark-cloud.sh` - * Verify that Spark is running: The Spark UI on `http://localhost:8080/` should show 3 worker nodes - * Export `MASTER` address of Spark master using `export MASTER="spark://localhost:7077"` - * Run `bin/run-example.sh ` - * Observe status of the application via Spark UI on `http://localhost:8080/` - ---- -## Configuring Sparkling Water Variables - -You can configure Sparkling Water using the following variables: - * `spark.h2o.cloud.timeout` - number of milliseconds to wait for cloud formation - * `spark.h2o.workers` - number of expected H2O workers; it should be same as number of Spark workers - * `spark.h2o.preserve.executors` - do not kill executors via calling `sc.stop()` call - ---- -## Step-by-Step Weather Data Example - -1. Run Sparkling shell with an embedded cluster: - ``` - export SPARK_HOME="/path/to/spark/installation" - export MASTER="local[*]" - bin/sparkling-shell - ``` - -2. To see the Sparkling shell (i.e., Spark driver) status, go to [http://localhost:4040/](http://localhost:4040/). - -3. Initialize H2O services on top of Spark cluster: - ```scala - import org.apache.spark.h2o._ - val h2oContext = H2OContext.getOrCreate(spark) - import h2oContext._ - import h2oContext.implicits._ - ``` - -4. Load weather data for Chicago international airport (ORD), with help from the RDD API: - ```scala - import org.apache.spark.examples.h2o._ - val weatherDataFile = "examples/smalldata/Chicago_Ohare_International_Airport.csv" - val wrawdata = spark.sparkContext.textFile(weatherDataFile,3).cache() - val weatherTable = wrawdata.map(_.split(",")).map(row => WeatherParse(row)).filter(!_.isWrongRow()) - ``` - -5. Load airlines data using the H2O parser: - ```scala - import java.io.File - val dataFile = "examples/smalldata/allyears2k_headers.csv.gz" - val airlinesData = new H2OFrame(new File(dataFile)) - ``` - -6. Select flights destined for Chicago (ORD): - ```scala - val airlinesTable : RDD[Airlines] = asRDD[Airlines](airlinesData) - val flightsToORD = airlinesTable.filter(f => f.Dest==Some("ORD")) - ``` - -7. Compute the number of these flights: - ```scala - flightsToORD.count - ``` - -8. Use Spark SQL to join the flight data with the weather data: - ```scala - implicit val sqlContext = spark.sqlContext - import sqlContext.implicits._ - flightsToORD.toDF.createOrReplaceTempView("FlightsToORD") - weatherTable.toDF.createOrReplaceTempView("WeatherORD") - ``` - -9. Perform SQL JOIN on both tables: - ```scala - val bigTable = sqlContext.sql( - """SELECT - |f.Year,f.Month,f.DayofMonth, - |f.CRSDepTime,f.CRSArrTime,f.CRSElapsedTime, - |f.UniqueCarrier,f.FlightNum,f.TailNum, - |f.Origin,f.Distance, - |w.TmaxF,w.TminF,w.TmeanF,w.PrcpIn,w.SnowIn,w.CDD,w.HDD,w.GDD, - |f.ArrDelay - |FROM FlightsToORD f - |JOIN WeatherORD w - |ON f.Year=w.Year AND f.Month=w.Month AND f.DayofMonth=w.Day""".stripMargin) - ``` - -10. Transform the first 3 columns containing date information into enum columns: - ```scala - val bigDataFrame: H2OFrame = h2oContext.asH2OFrame(bigTable) - for( i <- 0 to 2) bigDataFrame.replace(i, bigDataFrame.vec(i).toCategoricalVec) - bigDataFrame.update() - ``` - -11. Run deep learning to produce a model estimating arrival delay: - ```scala - import _root_.hex.deeplearning.DeepLearning - import _root_.hex.deeplearning.DeepLearningModel.DeepLearningParameters - import _root_.hex.deeplearning.DeepLearningModel.DeepLearningParameters.Activation - val dlParams = new DeepLearningParameters() - dlParams._train = bigDataFrame - dlParams._response_column = 'ArrDelay - dlParams._epochs = 5 - dlParams._activation = Activation.RectifierWithDropout - dlParams._hidden = Array[Int](100, 100) - - // Create a job - val dl = new DeepLearning(dlParams) - val dlModel = dl.trainModel.get - ``` - -12. Use the model to estimate the delay on the training data: - ```scala - val predictionH2OFrame = dlModel.score(bigTable)('predict) - val predictionsFromModel = asDataFrame(predictionH2OFrame)(sqlContext).collect.map(row => if (row.isNullAt(0)) Double.NaN else row(0)) - ``` - -13. Generate an R-code producing residual plot: - ```scala - import org.apache.spark.examples.h2o.AirlinesWithWeatherDemo2.residualPlotRCode - residualPlotRCode(predictionH2OFrame, 'predict, bigTable, 'ArrDelay, h2oContext) - ``` - -14. Execute generated R-code in RStudio: - ```R - # - # R script for residual plot - # - # Import H2O library - library(h2o) - # Initialize H2O R-client - h2o.init() - # Fetch prediction and actual data, use remembered keys - pred = h2o.getFrame("dframe_b5f449d0c04ee75fda1b9bc865b14a69") - act = h2o.getFrame ("frame_rdd_14_b429e8b43d2d8c02899ccb61b72c4e57") - # Select right columns - predDelay = pred$predict - actDelay = act$ArrDelay - # Make sure that number of rows is same - nrow(actDelay) == nrow(predDelay) - # Compute residuals - residuals = predDelay - actDelay - # Plot residuals - compare = cbind (as.data.frame(actDelay$ArrDelay), as.data.frame(residuals$predict)) - nrow(compare) - plot( compare[,1:2] ) - ``` From aaea4e0b53947538c15fa86e7dac5ac5c61de9c0 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Fri, 23 Jun 2017 11:47:52 +0200 Subject: [PATCH 66/72] update make-dist --- make-dist.sh | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/make-dist.sh b/make-dist.sh index 4418f4e1a..2e5365071 100755 --- a/make-dist.sh +++ b/make-dist.sh @@ -23,7 +23,7 @@ cat > "$TOPDIR/demofiles.list" < Date: Fri, 23 Jun 2017 11:50:16 +0200 Subject: [PATCH 67/72] convert docker readme to rst --- docker/README.md | 38 -------------------------------------- docker/README.rst | 50 ++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 50 insertions(+), 38 deletions(-) delete mode 100644 docker/README.md create mode 100644 docker/README.rst diff --git a/docker/README.md b/docker/README.md deleted file mode 100644 index 237313736..000000000 --- a/docker/README.md +++ /dev/null @@ -1,38 +0,0 @@ -# Docker Support - -## Create DockerFile - -Docker file can be created by calling ```./gradlew createDockerFile -PsparkVersion=version```, where ```version``` is spark version -for which to generate docker file. Latest corresponding sparkling water release to be used within docker image is determined -automatically based on spark version. - -The gradle task can be called without the parameter as ```./gradlew createDockerFile```, which creates docker file for spark version -defined in gradle.properties file. - -## Container requirements -To run Sparkling Water in the container, the host has to provide a machine with at least 5G of total memory. -If this is not met, Sparkling Water scripts print warning but still attempt to run. - -## Building a container - -``` -$ cd docker && ./build.sh -``` - -## Run bash inside container - -``` -$ cd docker && docker run -i -t sparkling-water-base /bin/bash -``` - -## Run Sparkling Shell inside container - -``` -$ cd docker && docker run -i -t --rm sparkling-water-base bin/sparkling-shell -``` - -## Running examples in container - -``` -$ cd docker && docker run -i -t --rm sparkling-water-base bin/run-example.sh -``` diff --git a/docker/README.rst b/docker/README.rst new file mode 100644 index 000000000..b051b3996 --- /dev/null +++ b/docker/README.rst @@ -0,0 +1,50 @@ +Docker Support +============== + +Create DockerFile +----------------- + +Docker file can be created by calling +``./gradlew createDockerFile -PsparkVersion=version``, where ``version`` +is spark version for which to generate docker file. Latest corresponding +sparkling water release to be used within docker image is determined +automatically based on spark version. + +The gradle task can be called without the parameter as +``./gradlew createDockerFile``, which creates docker file for spark +version defined in gradle.properties file. + +Container requirements +---------------------- + +To run Sparkling Water in the container, the host has to provide a +machine with at least 5G of total memory. If this is not met, Sparkling +Water scripts print warning but still attempt to run. + +Building a container +-------------------- + +.. code:: bash + + $ cd docker && ./build.sh + +Run bash inside container +------------------------- + +.. code:: bash + + $ cd docker && docker run -i -t sparkling-water-base /bin/bash + +Run Sparkling Shell inside container +------------------------------------ + +.. code:: bash + + $ cd docker && docker run -i -t --rm sparkling-water-base bin/sparkling-shell + +Running examples in container +----------------------------- + +.. code:: bash + + $ cd docker && docker run -i -t --rm sparkling-water-base bin/run-example.sh From 32cfb1970dd630c710cfb8f95aa9d31abac7f24c Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Fri, 23 Jun 2017 11:59:15 +0200 Subject: [PATCH 68/72] fix link --- examples/README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/README.rst b/examples/README.rst index c2ec7ffdf..0cd538565 100644 --- a/examples/README.rst +++ b/examples/README.rst @@ -67,7 +67,7 @@ and run examples. Configuring Sparkling Water Variables ------------------------------------- -Please see `Available Sparkling Water Configuration Properties <../doc/configuration/configuration_properties>`__ for +Please see `Available Sparkling Water Configuration Properties <../doc/configuration/configuration_properties.rst>`__ for more information about possible Sparkling Water configurations. Step-by-Step Weather Data Example From a7817b8cbde46c44de6b275181cf6995457408d3 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Fri, 23 Jun 2017 12:06:44 +0200 Subject: [PATCH 69/72] fix links to examples --- examples/README.rst | 44 ++++++++++++++++++++++---------------------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/examples/README.rst b/examples/README.rst index 0cd538565..79c45e30e 100644 --- a/examples/README.rst +++ b/examples/README.rst @@ -36,23 +36,23 @@ Available Demos And Applications Available Demos for Sparkling Shell ----------------------------------- -+---------------------------------------+--------------------------------------------------------------------------+ -| Example | Description | -+=======================================+==========================================================================+ -| |chicagoCrimeSmallShellScriptScala| | Demo showing full source code of predicting arrest probability for a | -| | given crime. It covers whole machine learning process from loading and | -| | transforming data, building models, scoring incoming events. | -+---------------------------------------+--------------------------------------------------------------------------+ -| |chicagoCrimeSmallScriptScala| | Example of using |ChicagoCrimeApp|. Creating application and using it | -| | for scoring individual crime events. | -+---------------------------------------+--------------------------------------------------------------------------+ -| |mlconf2015HamSpamScriptScala| | HamOrSpam application which detects Spam messages. Presented at | -| | MLConf 2015 NYC. | -+---------------------------------------+--------------------------------------------------------------------------+ -| |strata2015DemoScala| | NYC CitiBike demo presented at Strata 2015 in San Jose. | -+---------------------------------------+--------------------------------------------------------------------------+ -| |StrataAirlinesScala| | Example of using flights and weather data to predict delay of a flight. | -+---------------------------------------+--------------------------------------------------------------------------+ ++-----------------------------------+--------------------------------------------------------------------------+ +| Example | Description | ++===================================+==========================================================================+ +| |chicagoCrimeSmallShellScript| | Demo showing full source code of predicting arrest probability for a | +| | given crime. It covers whole machine learning process from loading and | +| | transforming data, building models, scoring incoming events. | ++-----------------------------------+--------------------------------------------------------------------------+ +| |chicagoCrimeSmallScript| | Example of using |ChicagoCrimeApp|. Creating application and using it | +| | for scoring individual crime events. | ++-----------------------------------+--------------------------------------------------------------------------+ +| |hamOrSpamScript| | HamOrSpam application which detects Spam messages. Presented at | +| | MLConf 2015 NYC. | ++-----------------------------------+--------------------------------------------------------------------------+ +| |strata2015Script| | NYC CitiBike demo presented at Strata 2015 in San Jose. | ++-----------------------------------+--------------------------------------------------------------------------+ +| |StrataAirlinesScript| | Example of using flights and weather data to predict delay of a flight. | ++-----------------------------------+--------------------------------------------------------------------------+ Run examples by typing ``bin/sparkling-shell -i `` @@ -228,8 +228,8 @@ Step-by-Step Weather Data Example .. |DeepLearningDemo| replace:: `DeepLearningDemo `__ .. |AirlinesWithWeatherDemo| replace:: `AirlinesWithWeatherDemo `__ .. |AirlinesWithWeatherDemo2| replace:: `AirlinesWithWeatherDemo2 `__ -.. |chicagoCrimeSmallShellScriptScala| replace:: `chicagoCrimeSmallShell.script.scala `__ -.. |chicagoCrimeSmallScriptScala| replace:: `chicagoCrimeSmall.script.scala `__ -.. |mlconf2015HamSpamScriptScala| replace:: `mlconf_2015_hamSpam.script.scala `__ -.. |strata2015DemoScala| replace:: `strata2015_demo.scala `__ -.. |StrataAirlinesScala| replace:: `StrataAirlines.scala `__ +.. |chicagoCrimeSmallShellScript| replace:: `chicagoCrimeSmallShell.script.scala `__ +.. |chicagoCrimeSmallScript| replace:: `chicagoCrimeSmall.script.scala `__ +.. |hamOrSpamScript| replace:: `hamOrSpam.script.scala `__ +.. |strata2015Script| replace:: `strata2015_demo.script.scala `__ +.. |StrataAirlinesScript| replace:: `StrataAirlines.script.scala `__ From 945580ce96158e6c381acbfaf1815efc2885a726 Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Fri, 23 Jun 2017 12:08:58 +0200 Subject: [PATCH 70/72] better name --- examples/README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/README.rst b/examples/README.rst index 79c45e30e..2020b51c0 100644 --- a/examples/README.rst +++ b/examples/README.rst @@ -231,5 +231,5 @@ Step-by-Step Weather Data Example .. |chicagoCrimeSmallShellScript| replace:: `chicagoCrimeSmallShell.script.scala `__ .. |chicagoCrimeSmallScript| replace:: `chicagoCrimeSmall.script.scala `__ .. |hamOrSpamScript| replace:: `hamOrSpam.script.scala `__ -.. |strata2015Script| replace:: `strata2015_demo.script.scala `__ +.. |strata2015Script| replace:: `strata2015.script.scala `__ .. |StrataAirlinesScript| replace:: `StrataAirlines.script.scala `__ From b629f79dee11f9694ae72a59c3da62903b8c5b0a Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Fri, 23 Jun 2017 12:22:24 +0200 Subject: [PATCH 71/72] Convert last readme to rst --- apps/streaming/README.md | 58 -------------------------------------- apps/streaming/README.rst | 72 +++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 72 insertions(+), 58 deletions(-) delete mode 100644 apps/streaming/README.md create mode 100644 apps/streaming/README.rst diff --git a/apps/streaming/README.md b/apps/streaming/README.md deleted file mode 100644 index 9e03b9017..000000000 --- a/apps/streaming/README.md +++ /dev/null @@ -1,58 +0,0 @@ -# Real Time Pipeline To H2OFrames Using Sparkling Water - -This demo will show you how to take streaming data and create a "live" dataframe over some rolling time window. The potential use cases could be using H2O data munging capabilities on a real time distributed dataframe or mini batch training for online ML using H2O [checkpointing models] (http://docs.h2o.ai/h2o/latest-stable/h2o-docs/index.html?#%E2%80%A6%20Building%20Models-Viewing%20Models-Checkpointing%20Models). - -## Requirements - -sbt -Spark 2.0 -Sparkling Water 2.0.x -Python - -## Directions - 1. Build the project `./gradlew clean jar` - - 2. Run the demo class `ai.h2o.PipelineDemo` - ```bash - $SPARK_HOME/bin/spark-submit \ - --class ai.h2o.PipelineDemo \ - --master 'local[*]' \ - --driver-memory 2G \ - --packages ai.h2o:sparkling-water-core_2.11:2.0.0 \ - --conf spark.scheduler.minRegisteredResourcesRatio=1 \ - --conf spark.ext.h2o.repl.enabled=False \ - ./build/libs/*.jar \ - 9999 - ``` - - 3. You should see some errors `Error connecting to localhost:9999` as Spark Streaming starts to run but this can be fixed by connecting the stream source with: - ```bash - python socket_send_spark.py - ``` - - 4. Then you should be able to login in to Flow/R/Python and see the live H2OFrame - -## Caveats - -When Sparkling Water converts the RDD to an H2OFrame, you will not be able to access it during -this time. Its good to test and find the best periodicity in generating your H2OFrame. also, it -is preferable that all the steps for your use case is done in the events.window loop: - -```scala - events.window(Seconds(300), Seconds(10)).foreachRDD(rdd => - { - if (!rdd.isEmpty ) { - try { - hf.delete() - } catch { case e: Exception => println("Initialized frame") } - hf = hc.asH2OFrame(rdd.toDF(), "demoFrame10s") //the name of the frame - // perform your munging, score your events with a pretrained model or - // mini-batch training on checkpointed models, etc - // make sure your execution finishes within the batch cycle (the - // second arg in the window) - } - } - ) -``` - - diff --git a/apps/streaming/README.rst b/apps/streaming/README.rst new file mode 100644 index 000000000..6f484b937 --- /dev/null +++ b/apps/streaming/README.rst @@ -0,0 +1,72 @@ +Real Time Pipeline To H2OFrames Using Sparkling Water +===================================================== + +This demo shows you how to take streaming data and create a "live" +dataframe over some rolling time window. The potential use cases could +be using H2O data munging capabilities on a real time distributed +dataframe or mini batch training for online ML using H2O. +Requirements +------------ + +- sbt +- Spark 2.+ +- Sparkling Water 2.+ +- Python 2.6+ + +Directions +---------- + +1. Build the project: + +.. code:: bash + + ./gradlew build -x check + +2. Run the demo class ``ai.h2o.PipelineDemo``: + +.. code:: bash + + $SPARK_HOME/bin/spark-submit \ + --class ai.h2o.PipelineDemo \ + --master 'local[*]' \ + --driver-memory 2G \ + --packages ai.h2o:sparkling-water-core_2.11:2.1.0 \ + --conf spark.scheduler.minRegisteredResourcesRatio=1 \ + --conf spark.ext.h2o.repl.enabled=False \ + ./build/libs/*.jar 9999 + +3. You should see some errors ``Error connecting to localhost:9999`` as + Spark Streaming starts to run but this can be fixed by connecting the + stream source with: + +.. code:: bash + + python socket_send_spark.py`` + +4. Then you should be able to login in to Flow/R/Python and see the live + ``H2OFrame`` + +Caveats +------- + +When Sparkling Water converts the ``RDD`` to an ``H2OFrame``, you will not be +able to access it during this time. Its good to test and find the best +periodicity in generating your H2OFrame. Also, it is preferable that all +the steps for your use case are done in the events.window loop: + +.. code:: scala + + events.window(Seconds(300), Seconds(10)).foreachRDD(rdd => + { + if (!rdd.isEmpty ) { + try { + hf.delete() + } catch { case e: Exception => println("Initialized frame") } + hf = hc.asH2OFrame(rdd.toDF(), "demoFrame10s") //the name of the frame + // perform your munging, score your events with a pretrained model or + // mini-batch training on checkpointed models, etc + // make sure your execution finishes within the batch cycle (the + // second arg in the window) + } + } + ) From 6c3d6f578e62769596218512603cd2e6f524fade Mon Sep 17 00:00:00 2001 From: Jakub Hava Date: Fri, 23 Jun 2017 12:24:07 +0200 Subject: [PATCH 72/72] fixes --- apps/streaming/README.rst | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/apps/streaming/README.rst b/apps/streaming/README.rst index 6f484b937..e98f8692f 100644 --- a/apps/streaming/README.rst +++ b/apps/streaming/README.rst @@ -5,6 +5,7 @@ This demo shows you how to take streaming data and create a "live" dataframe over some rolling time window. The potential use cases could be using H2O data munging capabilities on a real time distributed dataframe or mini batch training for online ML using H2O. + Requirements ------------ @@ -41,10 +42,10 @@ Directions .. code:: bash - python socket_send_spark.py`` + python socket_send_spark.py 4. Then you should be able to login in to Flow/R/Python and see the live - ``H2OFrame`` + ``H2OFrame``. Caveats ------- @@ -61,7 +62,9 @@ the steps for your use case are done in the events.window loop: if (!rdd.isEmpty ) { try { hf.delete() - } catch { case e: Exception => println("Initialized frame") } + } catch { + case e: Exception => println("Initialized frame") + } hf = hc.asH2OFrame(rdd.toDF(), "demoFrame10s") //the name of the frame // perform your munging, score your events with a pretrained model or // mini-batch training on checkpointed models, etc

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