Spark Interpreter for Apache Zeppelin


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 five interpreters.

Name Class Description
%spark SparkInterpreter Creates a SparkContext and provides a scala environment
%pyspark PySparkInterpreter Provides a python environment
%r SparkRInterpreter Provides an R environment with SparkR support
%sql SparkSQLInterpreter Provides a SQL environment
%dep DepInterpreter Dependency loader


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.

Property Default Description
args Spark commandline args
master local[*] Spark master uri.
ex) spark://masterhost:7077 Zeppelin The name of spark application.
spark.cores.max Total number of cores to use.
Empty value uses all available core.
spark.executor.memory 512m Executor memory per worker instance.
ex) 512m, 32g
zeppelin.dep.additionalRemoteRepository spark-packages,,
A list of id,remote-repository-URL,is-snapshot;
for each remote repository.
zeppelin.dep.localrepo local-repo Local repository for dependency loader
zeppelin.pyspark.python python Python command to run pyspark with
zeppelin.spark.concurrentSQL false Execute multiple SQL concurrently if set true.
zeppelin.spark.maxResult 1000 Max number of SparkSQL result to display.
zeppelin.spark.printREPLOutput true Print REPL output
zeppelin.spark.useHiveContext true Use HiveContext instead of SQLContext if it is true.
zeppelin.spark.importImplicit true Import implicits, UDF collection, and sql if set true.

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/, export SPARK_HOME environment variable with your Spark installation path.

for example

export SPARK_HOME=/usr/lib/spark

You can optionally export HADOOP_CONF_DIR and SPARK_SUBMIT_OPTIONS

export HADOOP_CONF_DIR=/usr/lib/hadoop
export SPARK_SUBMIT_OPTIONS="--packages com.databricks:spark-csv_2.10:1.2.0"

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

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. (Zeppelin 0.5.6-incubating release works up to Spark 1.6.1 )

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, ZeppelinContext

SparkContext, SQLContext, ZeppelinContext are automatically created and exposed as variable names 'sc', 'sqlContext' and 'z', respectively, both in scala and python environments.

Note that scala / python environment shares the same SparkContext, SQLContext, ZeppelinContext instance.

Dependency Management

There are two ways to load external library 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 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/ Second is reading configuration options from SPARK_HOME/conf/spark-defaults.conf. Spark properites that user can set to distribute libraries are:

spark-defaults.conf SPARK_SUBMIT_OPTIONS Applicable Interpreter Description
spark.jars --jars %spark Comma-separated list of local jars to include on the driver and executor classpaths.
spark.jars.packages --packages %spark Comma-separated list of maven coordinates of jars to include on the driver and executor classpaths. Will search the local maven repo, then maven central and any additional remote repositories given by --repositories. The format for the coordinates should be groupId:artifactId:version.
spark.files --files %pyspark Comma-separated list of files to be placed in the working directory of each executor.

Note that adding jar to pyspark is only availabe via %dep interpreter at the moment.

Here are few examples:


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

3. Dynamic Dependency Loading via %dep interpreter

Note: %dep interpreter is deprecated since v0.6.0. %dep interpreter load libraries to %spark and %pyspark but not to %spark.sql interpreter so we recommend you to use first option instead.

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

Here's usages.

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

// add maven repository

// add maven snapshot repository

// add credentials for private maven repository

// add artifact from filesystem

// add artifact from maven repository, with no dependency

// add artifact recursively

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

// exclude with pattern

// local() skips adding artifact to spark clusters (skipping sc.addJar())


Zeppelin automatically injects ZeppelinContext as variable 'z' in your scala/python environment. ZeppelinContext provides some additional functions and utility.

Object Exchange

ZeppelinContext extends map and it's shared between scala, python environment. So you can put some object from scala and read it from python, vise versa.

// Put object from scala
val myObject = ...
z.put("objName", myObject)
# Get object from python
myObject = z.get("objName")

Form Creation

ZeppelinContext provides functions for creating forms. In scala and python environments, you can create forms programmatically.

/* Create text input form */

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

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

/* Create select form with default value*/"formName", "option1", Seq(("option1", "option1DisplayName"),
                                    ("option2", "option2DisplayName")))
# Create text input form

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

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

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

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

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

To learn more about dynamic form, checkout Dynamic Form.

Interpreter setting option

Interpreter setting can choose one of 'shared', 'scoped', 'isolated' option. Spark interpreter creates separate scala compiler per each notebook but share a single SparkContext in 'scoped' mode (experimental). It creates separate 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/ 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):


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

  4. That's it. Play with Zeppelin!