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Data Lineage Tracking And Visualization Solution

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Spline (from Spark lineage) project helps people get insight into data processing performed by Apache Spark ™

The project consists of three main parts:

Spline diagram

There are several other tools. Check the examples to get a better idea how to use Spline.

Other docs/readme files can be found at:


Spline aims to fill a big gap within the Apache Hadoop ecosystem. Spark jobs shouldn’t be treated only as magic black boxes; people should be able to understand what happens with their data. Our main focus is to solve the following particular problems:

Getting started


Spin up a Spline server in a Docker



docker-compose up

Run you Spark-shell or PySpark as below to enable lineage tracking:
(NOTE: we use Spline Agent bundle compiled for Spark 2.4 and Scala 2.12. For other Spark or Scala versions use corresponding bundles)

pyspark \
  --packages \
  --conf "" \
  --conf "spark.spline.producer.url=http://localhost:9090/producer"

Execute any of your Spark Job that writes to a persistent storage (like file, Hive table or a database). The lineage should be captured automatically.

Open http://localhost:8080 in your browser to see the captured lineage.

See also -

Step-by-step instructions

Get Spline components

First, you need to get a minimal set of Spline’s moving parts - a server, an admin tool and a client Web UI to see the captured lineage.

Download prebuild Spline artifacts from the Maven repo

(REST Server and Web Client modules are also available as Docker containers)

Build Spline from the source code

Note: Skip this section unless you want to hack with Spline

  1. Make sure you have JDK 8+ and Maven 3.5+

  2. Get and unzip the Spline source code:
  3. Change the directory:
    cd spline-release-0.5.6
  4. Run the Maven build:
    mvn install -DskipTests

Create Spline Database

Spline server requires ArangoDB to run.

Please install ArangoDB 3.7.3 or later according to the instructions in ArangoDB documentation.

Or if you prefer the Docker way there is a ArangoDB docker image as well.

docker run -p 8529:8529 -e ARANGO_NO_AUTH=1 arangodb/arangodb:3.7.3

Once your database is running you should be able to see ArangoDB web interface at http://localhost:8529.

Next you need to create a Spline database using Spline Admin tool.

java -jar admin-0.5.6.jar db-init arangodb://localhost/spline

Start Spline Server

Use one of the following options depending on your deployment preferences:

Docker container

docker container run \
  -e spline.database.connectionUrl=arangodb://host.docker.internal/spline \
  -p 8080:8080 \

Note for Linux: If host.docker.internal does not resolve replace it with (see Docker for-linux bug report)

Java compatible Web-Container (e.g. Tomcat)

Download a WAR-file using the link below, and deploy it into any J2EE-compatible Web Container, e.g. Tomcat, Jetty, JBoss etc.

Spline server requires configuring an ArangoDB connection string.

The WAR-file provides several alternative ways how to set configuration parameters:

Verify Spline Server

Open the server root URL in you browser: http://localhost:8080/

You should see a dashboard with the updating server status information, server version, exposed API and some other useful info.

The server exposes the following REST API:

… and other useful URLs:

Start Spline UI

Just like the other Spline component you can choose if you want to use a Docker container or a WAR-file.


docker container run \
      -e SPLINE_CONSUMER_URL=http://localhost:8080/consumer \
      -p 9090:8080 \

Java compatible Web-Container (e.g. Tomcat)

You can find the WAR-file of the Web Client in the repo here:

Add the argument for the Consumer URL (the same 3 configuration options are available as for the REST Gateway above - JNDI, JVM properties and environment variable)


Open Spline Web UI in the browser


Capture some lineage

At this point you have your Spline server with the UI up and running and ready for lineage tracking. The way to do it depends on the tool or framework you are using for processing your data. Spline has started as a Apache Spark lineage tracking tool, and although it’s gradually evolving into a generic lineage tracking solution that can be used with other data processing frameworks and tools, it’s still works the best with Apache Spark.

The simplest way to track Apache Spark lineage is to enable it in you spark-submit or pyspark command line as shown in the tl;dr section.

If you want to have a fine control on Spline, customize or extend some of its components you can embed Spline as a component into your own Spark application.

Embed Spline in your Spark application

Add a dependency on the Spline Spark agent core.


(for Scala 2.12 use agent-core_2.12 respectively)

In your Spark job enable lineage tracking.

// given a Spark session ...
val sparkSession: SparkSession = ???

// ... enable data lineage tracking with Spline

// ... then run some Dataset computations as usual.
// The lineage will be captured and sent to the configured Spline Producer endpoint.


You also need to set some configuration properties. Spline combines these properties from several sources:

  1. Hadoop config (core-site.xml)

  2. JVM system properties

  3. file in the classpath




Upgrade Spline database

Upgrade from any Spline 0.4+ to the latest version

java -jar admin-0.5.6.jar db-upgrade arangodb://localhost/spline

Upgrade from Spline 0.3 to 0.4

Spline 0.3 stored data in the MongoDB. Spline 0.4 uses ArangoDB instead. You need to use another tool for migrating data between those two databases - Spline Migrator

You also need to have a new Spline server running. The migrator tool will fetch the data directly from a MongoDB, convert it and send to a new Spline Producer REST endpoint.

java -jar migrator-tool-0.4.2.jar \
  --source=mongodb://localhost:27017/splinedb \

Also run java -jar migrator-tool-0.4.2.jar --help to read about usage and available options.

For more information you may take a look at the Migrator tool source code.

Copyright 2019 ABSA Group Limited

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