Exploring the Spline Data Tracker and Visualization tool for Apache Spark (Part 2)

In part 1 we have learned how to test data lineage info collection with
Spline from a Spark shell. The same can be done in any Scala or Java Spark application. The same dependencies for the Spark shell need to be registered in your build tool of choice (Maven, Gradle or sbt):

groupId: za.co.absa.spline
artifactId: spline-core
version: 0.3.5
groupId: za.co.absa.spline
artifactId: spline-persistence-mongo
groupId: za.co.absa.spline

With reference to Scala and Spark 2.3.x, a Spark job like this:

// Create the Spark session
val sparkSession = SparkSession
.appName("Spline Tester")
// Init Spline
System.setProperty("spline.persistence.factory", "za.co.absa.spline.persistence.mongo.MongoPersistenceFactory")
System.setProperty("spline.mongodb.url", args(0))
System.setProperty("spline.mongodb.name", args(1))
import za.co.absa.spline.core.SparkLineageInitializer._
//Do something with DataFrames
import sparkSession.sqlContext.implicits._
val df1 = sparkSession.sparkContext.parallelize(1 to 10000, 42).toDF("FirstValue")
val df2 = sparkSession.sparkContext.parallelize(1.to(100000, 17), 42).toDF("SecondValue")
val output = df1.crossJoin(df2).where('FirstValue % 42 === 'SecondValue % 42)
// Write results to file system
// Stop the Spark Session

can be submitted to a Spark cluster this way:

$SPARK_HOME/bin/spark-submit --class org.googlielmo.splinetest.SplineExample --master <url> --packages "za.co.absa.spline:spline-core:0.3.5,za.co.absa.spline:spline-persistence-mongo:0.3.5,za.co.absa.spline:spline-core-spark-adapter-2.3:0.3.5" splinetest-1.0.jar mongodb://<username>:<password>@<hostname>:<port> <dbname>

The Spline configuration properties can be also stored into a properties file in the application classpath. Here’s the full list of the available Spline properties:

The first time Spline is enabled from a Spark job, it creates 6 collections into the destination MongoDB database:

The documents in those 6 collections are used by the Spline web application to generate the visual representation of the lineages in the UI.

In the third and last part of this series, I am going to share the outcome after the first weeks of adoption of this project in Spark pre-production environments.

Published on Java Code Geeks with permission by Guglielmo Iozzia, partner at our JCG program. See the original article here: Exploring the Spline Data Tracker and Visualization tool for Apache Spark (Part 2)

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