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Testing GridGain with Spark-shell

Starting up the cluster

Here we will briefly cover the process of Spark and GridGain cluster startup. Refer to Spark documentation for more details.

For the testing you will need a Spark master process and at least one Spark worker. Usually Spark master and workers are separate machines, but for the test purposes you can start worker on the same machine where master starts.

  1. Download and unpack Spark binary distribution to the same location (let it be SPARK_HOME) on all nodes.

  2. Download and unpack GridGain binary distribution to the same location (let it be IGNITE_HOME) on all nodes.

  3. On master node cd to $SPARK_HOME and run the following command:


    The script should output the path to log file of the started process. Check the log file for the master URL which has the following format: spark://master_host:master_port Also check the log file for the Web UI url (usually it is http://master_host:8080).

  4. On each of the worker nodes cd to $SPARK_HOME and run the following command:

    bin/spark-class org.apache.spark.deploy.worker.Worker spark://master_host:master_port

    where spark://master_host:master_port is the master URL you grabbed from the master log file. After workers has started check the master Web UI interface, it should show all of your workers registered in status ALIVE.

  5. On each of the worker nodes cd to $IGNITE_HOME and start an Ignite node by running the following command:


You should see GridGain nodes discover each other with default configuration. If your network does not allow multicast traffic, you will need to change the default configuration file and configure TCP discovery.

Working with Spark-Shell

Now that you have your cluster up and running, you can run spark-shell and check the integration.

  1. Start spark shell:

    • Either by providing Maven coordinates to Ignite artifacts (you can use --repositories if you need, but it may be omitted):

          --packages org.apache.ignite:ignite-spark-ext:2.0.0
        --master spark://master_host:master_port
    • Or by providing paths to Ignite jar file paths using --jars parameter

      ./bin/spark-shell --jars path/to/ignite-core.jar,path/to/ignite-spark-ext.jar,path/to/cache-api.jar,path/to/ignite-log4j2.jar,path/to/log4j.jar --master spark://master_host:master_port

    You should see Spark shell started up.

    Note that if you are planning to use spring configuration loading, you will need to add the ignite-spring dependency as well:

        --packages org.apache.ignite:ignite-spark-ext:2.0.0,org.apache.ignite:ignite-spring:2.13.0
      --master spark://master_host:master_port
  2. Let’s create an instance of Ignite context using default configuration:

    import org.apache.ignite.spark._
    import org.apache.ignite.configuration._
    val ic = new IgniteContext(sc, () => new IgniteConfiguration())

    You should see something like

    ic: org.apache.ignite.spark.IgniteContext = org.apache.ignite.spark.IgniteContext@62be2836

    An alternative way to create an instance of IgniteContext is to use a configuration file. Note that if path to configuration is specified in a relative form, then the IGNITE_HOME environment variable should be globally set in the system as the path is resolved relative to IGNITE_HOME

    import org.apache.ignite.spark._
    import org.apache.ignite.configuration._
    val ic = new IgniteContext(sc, "examples/config/spark/example-shared-rdd.xml")
  3. Let’s now create an instance of IgniteRDD using "partitioned" cache in default configuration:

    val sharedRDD = ic.fromCache[Integer, Integer]("partitioned")

    You should see an instance of RDD created for partitioned cache:

    shareRDD: org.apache.ignite.spark.IgniteRDD[Integer,Integer] = IgniteRDD[0] at RDD at IgniteAbstractRDD.scala:27

    Note that creation of RDD is a local operation and will not create a cache in Ignite cluster.

  4. Let’s now actually ask Spark to do something with our RDD, for example, get all pairs where value is less than 10:

    sharedRDD.filter(_._2 < 10).collect()

    As our cache has not been filled yet, the result will be an empty array:

    res0: Array[(Integer, Integer)] = Array()

    Check the logs of remote spark workers and see how Ignite context will start clients on all remote workers in the cluster. You can also start command-line Visor and check that "partitioned" cache has been created.

  5. Let’s now save some values into Ignite:

    sharedRDD.savePairs(sc.parallelize(1 to 100000, 10).map(i => (i, i)))

    After running this command you can check with command-line Visor that cache size is 100000 elements.

  6. We can now check how the state we created will survive job restart. Shut down the spark shell and repeat steps 1-3. You should again have an instance of Ignite context and RDD for "partitioned" cache. We can now check how many keys there are in our RDD which value is greater than 50000:

    sharedRDD.filter(_._2 > 50000).count

    Since we filled up cache with a sequence of number from 1 to 100000 inclusive, we should see 50000 as a result:

    res0: Long = 50000