exasol / spark-exasol-connector

A connector for Apache Spark to access Exasol


Spark Exasol Connector

spark-exasol-connector logo

Build Status Codecov Maven Central

🛈 Please note that this is an open source project which is officially supported by Exasol. For any question, you can contact our support team or open a Github issue.


This is a connector library that supports an integration between Exasol and Apache Spark. Using this connector, users can create Spark dataframes from Exasol queries and save Spark dataframes as Exasol tables.

The implementation is based on Spark DataSources API and Exasol Sub Connections.


  • Deployed and running Spark cluster
  • Deployed and running Exasol cluster
  • Make sure Spark cluster has enough resources to start executors that are more or equal to the number of Exasol data nodes
  • Make sure that Spark cluster can connect to Exasol nodes using private ip addresses, e.g,
  • Make sure that Exasol nodes are reachable from Spark cluster on port 8563 and on port range 20000-21000

Quick Start

Here is short code snippets on how to use the connector in your Spark / Scala applications.

Reading data from Exasol as Spark dataframe:

// An Exasol sql syntax query string
val exasolQueryString =
    WHERE MARKET_ID IN (661, 534, 667)

val df = sparkSession
     .option("host", "")
     .option("port", "8563")
     .option("username", "sys")
     .option("password", "exaPass")
     .option("query", exasolQueryString)

Saving a Spark dataframe as an Exasol table:

     .option("host", "")
     .option("port", "8563")
     .option("username", "sys")
     .option("password", "exaPass")
     .option("table", "RETAIL.ADJUSTED_SALES")

Additionally, you can set the parameter on SparkConf:

// Configure spark session
val sparkConf = new SparkConf()
  .set("spark.exasol.host", "localhost")
  .set("spark.exasol.port", "8563")
  .set("spark.exasol.username", "sys")
  .set("spark.exasol.password", "exasol")
  .set("spark.exasol.max_nodes", "200")

val sparkSession = SparkSession


val df = sparkSession
     .option("query", queryStr)

Please note that parameter values set on Spark configuration will have higher priority.

For an example walkthrough please check doc/example-walkthrough.

Additionally, you can read about the latest changes in the Changelog file.


The latest release version (Maven Central) is compiled against Scala 2.11 and Spark 2.1+.

In order to use the connector in your Java or Scala applications, you can include it as a dependency to your projects by adding artifact information into build.sbt or pom.xml files.


resolvers ++= Seq("Exasol Releases" at "https://maven.exasol.com/artifactory/exasol-releases")

libraryDependencies += "com.exasol" % "spark-connector_2.11" % "$LATEST_VERSION"




Alternative Option

As an alternative, you can provide --repositories and --packages artifact coordinates to the spark-submit, spark-shell or pyspark commands.

For example:

spark-shell \
    --repositories https://maven.exasol.com/artifactory/exasol-releases \
    --packages com.exasol:spark-connector_2.11:$LATEST_VERSION


Similarly, you can submit packaged application into the Spark cluster.

Using spark-submit:

spark-submit \
    --master spark://spark-master-url:7077
    --repositories https://maven.exasol.com/artifactory/exasol-releases \
    --packages com.exasol:spark-connector_2.11:$LATEST_VERSION \
    --class com.myorg.SparkExasolConnectorApp \
    --conf spark.exasol.password=exaTru3P@ss \

This deployment example also shows that you can configure the Exasol parameters at startup using --conf spark.exasol.keyName=value syntax.

Please update the $LATEST_VERION accordingly with the latest artifact version number.


The following configuration parameters can be provided mainly to facilitate a connection to Exasol cluster.

Spark Configuration Configuration Default Description
query A query string to send to Exasol
table A table name (with schema, e.g. my_schema.my_table) to save dataframe
spark.exasol.host host localhost A host ip address to the first Exasol node (e.g.
spark.exasol.port port 8888 A port number to connect to Exasol nodes (e.g. 8563)
spark.exasol.username username sys An Exasol username for logging in
spark.exasol.password password exasol An Exasol password for logging in
spark.exasol.max_nodes max_nodes 200 The number of data nodes in Exasol cluster
spark.exasol.batch_size batch_size 1000 The number of records batched before running execute statement when saving dataframe
spark.exasol.create_table create_table false A permission to create a table if it does not exist in Exasol when saving dataframe
spark.exasol.drop_table drop_table false A permission to drop the table if it exists in Exasol when saving dataframe

Building and Testing

Clone the repository,

git clone https://github.com/exasol/spark-exasol-connector

cd spark-exasol-connector/


./sbtx compile

Run unit tests,

./sbtx test

To run integration tests, a separate docker network should be created first,

docker network create -d bridge --subnet --gateway dockernet

then run,

./sbtx it:test

The integration tests requires docker, exasol/docker-db, testcontainers and spark-testing-base.

In order to create a bundled jar,

./sbtx assembly

This creates a jar file under target/ folder. The jar file can be used with spark-submit, spark-shell or pyspark commands. For example,

spark-shell --jars /path/to/spark-exasol-connector-assembly-*.jar


  • Getting an com.exasol.jdbc.ConnectFailed: Connection refused exception

    This usually occurs when the Spark connector cannot reach Exasol data nodes. Please make sure that the Exasol data nodes are reachable on port 8563 and on port ranges 20000-21000.

    Additionally, please make sure that the host parameter value is set to the first Exasol datanode address, for example,

  • Getting an Connection was lost and could not be reestablished error

    For example:

    [error] Caused by: com.exasol.jdbc.ConnectFailed: Connection was lost and could not be reestablished.  (SessionID: 1615669509094853970)
    [error]         at com.exasol.jdbc.AbstractEXAConnection.reconnect(AbstractEXAConnection.java:3505)
    [error]         at com.exasol.jdbc.ServerCommunication.handle(ServerCommunication.java:98)
    [error]         at com.exasol.jdbc.AbstractEXAConnection.communication(AbstractEXAConnection.java:2537)
    [error]         at com.exasol.jdbc.AbstractEXAConnection.communication_resultset(AbstractEXAConnection.java:2257)
    [error]         at com.exasol.jdbc.AbstractEXAStatement.execute(AbstractEXAStatement.java:456)
    [error]         at com.exasol.jdbc.EXAStatement.execute(EXAStatement.java:278)
    [error]         at com.exasol.jdbc.AbstractEXAStatement.executeQuery(AbstractEXAStatement.java:601)
    [error]         at com.exasol.spark.rdd.ExasolRDD.compute(ExasolRDD.scala:125)
    [error]         at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    [error]         at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)

    This is one of the known issues. This happens when Spark scheduled parallel tasks are less than the number of sub connections. This can be mitigated by submitting Spark application with enough resources so that it can start parallel tasks that are more or equal to number of parallel Exasol connections.

    Additionally, you can limit the Exasol parallel connections using max_nodes parameter. However, it is not advised to limit this value in production environment.


In this section, we define all the dependencies together with their licenses that are required for building, testing and running the connector.

The Java 8 is required for compiling and building the project. In the future versions of Spark, we are planning to change to the newer JVM versions.

Runtime Dependencies

Dependency Purpose License
Exasol JDBC Accessing Exasol using JDBC and sub-connections MIT License
Spark Core Apache Spark core libraries for optimized computation Apache License 2.0
Spark SQL Apache Spark higher-level SQL and Dataframe interface libraries Apache License 2.0

Test Dependencies

Dependency Purpose License
Scalatest Testing tool for Scala and Java developers Apache License 2.0
Scalatest Plus Integration support between Scalatest and Mockito Apache License 2.0
Mockito Core Mocking framework for unit tests MIT License
Testcontainers JDBC Testcontainers JDBC to help create JDBC based Docker containers MIT License
Testcontainers Scala Scala wrapper for testcontainers-java MIT License
Spark Testing Base Library that helps to create tests for Spark applications Apache License 2.0

Compiler Plugin Dependencies

These plugins help with project development.

Plugin Name Purpose License
SBT Coursier Pure Scala artifact fetching Apache License 2.0
SBT Wartremover Flexible Scala code linting tool Apache License 2.0
SBT Wartremover Contrib Community managed additional warts for wartremover Apache License 2.0
SBT Assembly Create fat jars with all project dependencies MIT License
SBT API Mappings Plugin that fetches API mappings for common Scala libraries Apache License 2.0
SBT Scoverage Integrates the scoverage code coverage library Apache License 2.0
SBT Updates Checks Maven and Ivy repositories for dependency updates BSD 3-Clause License
SBT Scalafmt Plugin for https://scalameta.org/scalafmt/ formatting Apache License 2.0
SBT Scalastyle Plugin for http://www.scalastyle.org/ Scala style checker Apache License 2.0
SBT Dependency Graph Plugin for visualizing dependency graph of your project Apache License 2.0
SBT Sonatype Sbt plugin for publishing Scala projects to the Maven central Apache License 2.0
SBT PGP PGP plugin for sbt BSD 3-Clause License
SBT Git Plugin for Git integration, used to version the release jars BSD 2-Clause License