Spark Interpreter for Apache Zeppelin

Overview

Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs. Apache Spark is supported in Zeppelin with Spark interpreter group which consists of below five interpreters.

Configuration

The Spark interpreter can be configured with properties provided by Zeppelin. You can also set other Spark properties which are not listed in the table. For a list of additional properties, refer to Spark Available Properties.

Without any configuration, Spark interpreter works out of box in local mode. But if you want to connect to your Spark cluster, you'll need to follow below two simple steps.

1. Export SPARK_HOME

In conf/zeppelin-env.sh, export SPARK_HOME environment variable with your Spark installation path.

For example,

export SPARK_HOME=/usr/lib/spark

You can optionally set more environment variables

# set hadoop conf dir
export HADOOP_CONF_DIR=/usr/lib/hadoop

# set options to pass spark-submit command
export SPARK_SUBMIT_OPTIONS="--packages com.databricks:spark-csv_2.10:1.2.0"

# extra classpath. e.g. set classpath for hive-site.xml
export ZEPPELIN_INTP_CLASSPATH_OVERRIDES=/etc/hive/conf

For Windows, ensure you have winutils.exe in %HADOOP_HOME%\bin. Please see Problems running Hadoop on Windows for the details.

2. Set master in Interpreter menu

After start Zeppelin, go to Interpreter menu and edit master property in your Spark interpreter setting. The value may vary depending on your Spark cluster deployment type.

For example,

• local[*] in local mode
• spark://master:7077 in standalone cluster
• yarn-client in Yarn client mode
• mesos://host:5050 in Mesos cluster

That's it. Zeppelin will work with any version of Spark and any deployment type without rebuilding Zeppelin in this way. For the further information about Spark & Zeppelin version compatibility, please refer to "Available Interpreters" section in Zeppelin download page.

Note that without exporting SPARK_HOME, it's running in local mode with included version of Spark. The included version may vary depending on the build profile.

SparkContext, SQLContext, SparkSession, ZeppelinContext

SparkContext, SQLContext and ZeppelinContext are automatically created and exposed as variable names sc, sqlContext and z, respectively, in Scala, Python and R environments. Staring from 0.6.1 SparkSession is available as variable spark when you are using Spark 2.x.

Note that Scala/Python/R environment shares the same SparkContext, SQLContext and ZeppelinContext instance.

How to pass property to SparkConf

There're 2 kinds of properties that would be passed to SparkConf

• Standard spark property (prefix with spark.). e.g. spark.executor.memory will be passed to SparkConf
• Non-standard spark property (prefix with zeppelin.spark.). e.g. zeppelin.spark.property_1, property_1 will be passed to SparkConf

Dependency Management

There are two ways to load external libraries in Spark interpreter. First is using interpreter setting menu and second is loading Spark properties.

1. Setting Dependencies via Interpreter Setting

Please see Dependency Management for the details.

2. Loading Spark Properties

Once SPARK_HOME is set in conf/zeppelin-env.sh, Zeppelin uses spark-submit as spark interpreter runner. spark-submit supports two ways to load configurations. The first is command line options such as --master and Zeppelin can pass these options to spark-submit by exporting SPARK_SUBMIT_OPTIONS in conf/zeppelin-env.sh. Second is reading configuration options from SPARK_HOME/conf/spark-defaults.conf. Spark properties that user can set to distribute libraries are:

Here are few examples:

• SPARK_SUBMIT_OPTIONS in conf/zeppelin-env.sh

  export SPARK_SUBMIT_OPTIONS="--packages com.databricks:spark-csv_2.10:1.2.0 --jars /path/mylib1.jar,/path/mylib2.jar --files /path/mylib1.py,/path/mylib2.zip,/path/mylib3.egg"
  

• SPARK_HOME/conf/spark-defaults.conf

  spark.jars        /path/mylib1.jar,/path/mylib2.jar
  spark.jars.packages   com.databricks:spark-csv_2.10:1.2.0
  spark.files       /path/mylib1.py,/path/mylib2.egg,/path/mylib3.zip
  

3. Dynamic Dependency Loading via %spark.dep interpreter

Note: %spark.dep interpreter loads libraries to %spark and %spark.pyspark but not to %spark.sql interpreter. So we recommend you to use the first option instead.

When your code requires external library, instead of doing download/copy/restart Zeppelin, you can easily do following jobs using %spark.dep interpreter.

• Load libraries recursively from maven repository
• Load libraries from local filesystem
• Add additional maven repository
• Automatically add libraries to SparkCluster (You can turn off)

Dep interpreter leverages Scala environment. So you can write any Scala code here. Note that %spark.dep interpreter should be used before %spark, %spark.pyspark, %spark.sql.

Here's usages.

%spark.dep
z.reset() // clean up previously added artifact and repository

// add maven repository
z.addRepo("RepoName").url("RepoURL")

// add maven snapshot repository
z.addRepo("RepoName").url("RepoURL").snapshot()

// add credentials for private maven repository
z.addRepo("RepoName").url("RepoURL").username("username").password("password")

// add artifact from filesystem
z.load("/path/to.jar")

// add artifact from maven repository, with no dependency
z.load("groupId:artifactId:version").excludeAll()

// add artifact recursively
z.load("groupId:artifactId:version")

// add artifact recursively except comma separated GroupID:ArtifactId list
z.load("groupId:artifactId:version").exclude("groupId:artifactId,groupId:artifactId, ...")

// exclude with pattern
z.load("groupId:artifactId:version").exclude(*)
z.load("groupId:artifactId:version").exclude("groupId:artifactId:*")
z.load("groupId:artifactId:version").exclude("groupId:*")

// local() skips adding artifact to spark clusters (skipping sc.addJar())
z.load("groupId:artifactId:version").local()

ZeppelinContext

Zeppelin automatically injects ZeppelinContext as variable z in your Scala/Python environment. ZeppelinContext provides some additional functions and utilities.

Object Exchange

ZeppelinContext extends map and it's shared between Scala and Python environment. So you can put some objects from Scala and read it from Python, vice versa.

// Put object from scala
%spark
val myObject = ...
z.put("objName", myObject)

// Exchanging data frames
myScalaDataFrame = ...
z.put("myScalaDataFrame", myScalaDataFrame)

val myPythonDataFrame = z.get("myPythonDataFrame").asInstanceOf[DataFrame]

# Get object from python
%spark.pyspark
myObject = z.get("objName")

# Exchanging data frames
myPythonDataFrame = ...
z.put("myPythonDataFrame", postsDf._jdf)

myScalaDataFrame = DataFrame(z.get("myScalaDataFrame"), sqlContext)

Form Creation

ZeppelinContext provides functions for creating forms. In Scala and Python environments, you can create forms programmatically.

%spark
/* Create text input form */
z.input("formName")

/* Create text input form with default value */
z.input("formName", "defaultValue")

/* Create select form */
z.select("formName", Seq(("option1", "option1DisplayName"),
                         ("option2", "option2DisplayName")))

/* Create select form with default value*/
z.select("formName", "option1", Seq(("option1", "option1DisplayName"),
                                    ("option2", "option2DisplayName")))

%spark.pyspark
# Create text input form
z.input("formName")

# Create text input form with default value
z.input("formName", "defaultValue")

# Create select form
z.select("formName", [("option1", "option1DisplayName"),
                      ("option2", "option2DisplayName")])

# Create select form with default value
z.select("formName", [("option1", "option1DisplayName"),
                      ("option2", "option2DisplayName")], "option1")

In sql environment, you can create form in simple template.

%spark.sql
select * from ${table=defaultTableName} where text like '%${search}%'

To learn more about dynamic form, checkout Dynamic Form.

Matplotlib Integration (pyspark)

Both the python and pyspark interpreters have built-in support for inline visualization using matplotlib, a popular plotting library for python. More details can be found in the python interpreter documentation, since matplotlib support is identical. More advanced interactive plotting can be done with pyspark through utilizing Zeppelin's built-in Angular Display System, as shown below:

Interpreter setting option

You can choose one of shared, scoped and isolated options wheh you configure Spark interpreter. Spark interpreter creates separated Scala compiler per each notebook but share a single SparkContext in scoped mode (experimental). It creates separated SparkContext per each notebook in isolated mode.

Setting up Zeppelin with Kerberos

Logical setup with Zeppelin, Kerberos Key Distribution Center (KDC), and Spark on YARN:

Configuration Setup

1. On the server that Zeppelin is installed, install Kerberos client modules and configuration, krb5.conf. This is to make the server communicate with KDC.

2. Set SPARK_HOME in [ZEPPELIN_HOME]/conf/zeppelin-env.sh to use spark-submit (Additionally, you might have to set export HADOOP_CONF_DIR=/etc/hadoop/conf)

3. Add the two properties below to Spark configuration ([SPARK_HOME]/conf/spark-defaults.conf):

   spark.yarn.principal
   spark.yarn.keytab
   

   NOTE: If you do not have permission to access for the above spark-defaults.conf file, optionally, you can add the above lines to the Spark Interpreter setting through the Interpreter tab in the Zeppelin UI.

4. That's it. Play with Zeppelin!