The Spline Agent for Apache Spark™ is a complementary module to the Spline project that captures runtime lineage information from the Apache Spark jobs.

The agent is a Scala library that is embedded into the Spark driver, listening to Spark events, and capturing logical execution plans. The collected metadata is then handed over to the lineage dispatcher, from where it can either be sent to the Spline server (e.g. via REST API or Kafka), or used in another way, depending on selected dispatcher type (see Lineage Dispatchers).

The agent can be used with or without a Spline server, depending on your use case. See References.

• Versioning
  • Spark / Scala version compatibility matrix
• Usage
  • Selecting artifact
  • Initialization
    • Codeless
    • Programmatic
• Configuration
  • Properties
  • Lineage Dispatchers
  • Post Processing Filters
• Spark features coverage
• Developer documentation
  • Plugin API
  • Building for different Scala and Spark versions
• References and Examples

The Spline Spark Agent follows the Semantic Versioning principles. The Public API is defined as a set of entry-point classes (SparkLineageInitializer, SplineSparkSessionWrapper), extension APIs (Plugin API, filters, dispatchers), configuration properties and a set of supported Spark versions. In other words, the Spline Spark Agent Public API in terms of SemVer covers all entities and abstractions that are designed to be used or extended by client applications.

The version number does not directly reflect the relation of the Agent to the Spline Producer API (the Spline server). Both the Spline Server and the Agent are designed to be as much mutually compatible as possible, assuming long-term operation and a possibly significant gap in the server and the agent release dates. Such requirement is dictated by the nature of the Agent that could be embedded into some Spark jobs and only rarely if ever updated without posing a risk to stop working because of eventual Spline server update. Likewise, it should be possible to update the Agent anytime (e.g. to fix a bug or support a newer Spark version or a feature that earlier agent version didn't support) without requiring a Spline server upgrade.

Although not required by the above statement, for minimizing user astonishment when the compatibility between too distant Agent and Server versions is dropped, we'll increment the Major version component.

There are two main agent artifacts:

• agent-core is a Java library that you can use with any compatible Spark version. Use this one if you want to include Spline agent into your custom Spark application, and you want to manage all transitive dependencies yourself.

• spark-spline-agent-bundle is a fat jar that is designed to be embedded into the Spark driver, either by manually copying it to the Spark's /jars directory, or by using --jars or --packages argument for the spark-submit, spark-shell or pyspark commands. This artifact is self-sufficient and is aimed to be used by most users.

Because the bundle is pre-built with all necessary dependencies, it is important to select a proper version of it that matches the minor Spark and Scala versions of your target Spark installation.

spark-A.B-spline-agent-bundle_X.Y.jar

here A.B is the first two Spark version numbers and X.Y is the first two Scala version numbers. For example, if you have Spark 2.4.4 pre-built with Scala 2.12.10 then select the following agent bundle:

spark-2.4-spline-agent-bundle_2.12.jar

AWS Glue Note: dependency on org.yaml:snakeyaml:1.33 is missing in Glue flavour of Spark. Please add this dependency on the classpath.

Spline agent is basically a Spark query listener that needs to be registered in a Spark session before is can be used. Depending on if you are using it as a library in your custom Spark application, or as a standalone bundle you can choose one of the following initialization approaches.

This way is the most convenient one, can be used in majority use-cases. Simply include the Spline listener into the spark.sql.queryExecutionListeners config property (see Static SQL Configuration)

Example:

pyspark \
  --packages za.co.absa.spline.agent.spark:spark-2.4-spline-agent-bundle_2.12:<VERSION> \
  --conf "spark.sql.queryExecutionListeners=za.co.absa.spline.harvester.listener.SplineQueryExecutionListener" \
  --conf "spark.spline.lineageDispatcher.http.producer.url=http://localhost:9090/producer"

The same approach works for spark-submit and spark-shell commands.

Note: all Spline properties set via Spark conf should be prefixed with spark. prefix in order to be visible to the Spline agent.
See Configuration section for details.

Note: starting from Spline 0.6 most agent components can be configured or even replaced in a declarative manner either using Configuration or Plugin API. So normally there should be no need to use a programmatic initialization method. We recommend to use Codeless Initialization instead.

But if for some reason, Codeless Initialization doesn't fit your needs, or you want to do more customization on Spark agent, you can use programmatic initialization method.

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

// ... enable data lineage tracking with Spline
import za.co.absa.spline.harvester.SparkLineageInitializer._
sparkSession.enableLineageTracking()

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

or in Java syntax:

import za.co.absa.spline.harvester.SparkLineageInitializer;
// ...
SparkLineageInitializer.enableLineageTracking(session);

The method enableLineageTracking() accepts optional AgentConfig object that can be used to customize Spline behavior. This is an alternative way to configure Spline. The other one if via the property based configuration.

The instance of AgentConfig can be created by using a builder or one of the factory methods.

// from a sequence of key-value pairs 
val config = AgentConfig.from(???: Iterable[(String, Any)])

// from a Common Configuration
val config = AgentConfig.from(???: org.apache.commons.configuration.Configuration)

// using a builder
val config = AgentConfig.builder()
  // call some builder methods here...
  .build()

sparkSession.enableLineageTracking(config)

Note: AgentConfig object doesn't override the standard configuration stack. Instead, it serves as an additional configuration mean with the precedence set between the spline.yaml and spline.default.yaml files (see below).

The agent looks for configuration in the following sources (listed in order of precedence):

• Hadoop configuration (core-site.xml)
• Spark configuration
• JVM system properties
• spline.properties file on classpath
• spline.yaml file on classpath
• AgentConfig object
• spline.default.yaml file on classpath

The file spline.default.yaml contains default values for all Spline properties along with additional documentation. It's a good idea to look in the file to see what properties are available.

The order of precedence might look counter-intuitive, as one would expect that explicitly provided config (AgentConfig instance) should override ones defined in the outer scope. However, prioritizing global config to local one makes it easier to manage Spline settings centrally on clusters, while still allowing room for customization by job developers.

For example, a company could require lineage metadata from jobs executed on a particular cluster to be sanitized, enhanced with some metrics and credentials and stored in a certain metadata store (a database, file, Spline server etc). The Spline configuration needs to be set globally and applied to all Spark jobs automatically. However, some jobs might contain hardcoded properties that the developers used locally or on a testing environment, and forgot to remove them before submitting jobs into a production. In such situation we want cluster settings to have precedence over the job settings. Assuming that hardcoded settings would most likely be defined in the AgentConfig object, a property file or a JVM properties, on the cluster we could define them in the Spark config or Hadoop config.

In case of multiple definitions of property the first occurrence wins, but spline.lineageDispatcher and spline.postProcessingFilter properties are composed instead. E.g. if a LineageDispatcher is set to be Kafka in one config source and 'Http' in another, they would be implicitly wrapped by a composite dispatcher, so both would be called in the order corresponding the config source precedence. See CompositeLineageDispatcher and CompositePostProcessingFilter.

Every config property is resolved independently. So, for instance, if a DataSourcePasswordReplacingFilter is used some of its properties might be taken from one config source and the other ones form another, according to the conflict resolution rules described above. This allows administrators to tweak settings of individual Spline components (filters, dispatchers or plugins) without having to redefine and override the whole piece of configuration for a given component.

• ENABLED [default]

  Spline will try to initialize itself, but if it fails it switches to DISABLED mode allowing the Spark application to proceed normally without Lineage tracking.

• DISABLED

  Lineage tracking is completely disabled and Spline is unhooked from Spark.

The logical name of the root lineage dispatcher. See Lineage Dispatchers chapter.

The logical name of the root post-processing filter. See Post Processing Filters chapter.

The LineageDispatcher trait is responsible for sending out the captured lineage information. By default, the HttpLineageDispatcher is used, that sends the lineage data to the Spline REST endpoint (see Spline Producer API).

Available dispatchers:

• HttpLineageDispatcher - sends lineage to an HTTP endpoint
• KafkaLineageDispatcher - sends lineage to a Kafka topic
• ConsoleLineageDispatcher - writes lineage to the console
• LoggingLineageDispatcher - logs lineage using the Spark logger
• FallbackLineageDispatcher - sends lineage to a fallback dispatcher if the primary one fails
• CompositeLineageDispatcher - allows to combine multiple dispatchers to send lineage to multiple endpoints

Each dispatcher can have different configuration parameters. To make the configs clearly separated each dispatcher has its own namespace in which all it's parameters are defined. I will explain it on a Kafka example.

Defining dispatcher

spline.lineageDispatcher=kafka

Once you defined the dispatcher all other parameters will have a namespace spline.lineageDispatcher.{{dipatcher-name}}. as a prefix. In this case it is spline.lineageDispatcher.kafka..

To find out which parameters you can use look into spline.default.yaml. For kafka I would have to define at least these two properties:

spline.lineageDispatcher.kafka.topic=foo
spline.lineageDispatcher.kafka.producer.bootstrap.servers=localhost:9092

This dispatcher is used by default. The only mandatory configuration is url of the producer API rest endpoint (spline.lineageDispatcher.http.producer.url). Additionally, timeouts, apiVersion and multiple custom headers can be set.

spline.lineageDispatcher.http.producer.url=
spline.lineageDispatcher.http.timeout.connection=2000
spline.lineageDispatcher.http.timeout.read=120000
spline.lineageDispatcher.http.apiVersion=LATEST
spline.lineageDispatcher.http.header.X-CUSTOM-HEADER=custom-header-value

If the producer requires token based authentication for requests, below mentioned details must be included in configuration.

spline.lineageDispatcher.http.authentication.type=OAUTH
spline.lineageDispatcher.http.authentication.grantType=client_credentials
spline.lineageDispatcher.http.authentication.clientId=<client_id>
spline.lineageDispatcher.http.authentication.clientSecret=<secret>
spline.lineageDispatcher.http.authentication.scope=<scope>
spline.lineageDispatcher.http.authentication.tokenUrl=<token_url>

Example: Azure HTTP trigger template API key header can be set like this:

spline.lineageDispatcher.http.header.X-FUNCTIONS-KEY=USER_API_KEY

Example: AWS Rest API key header can be set like this:

spline.lineageDispatcher.http.header.X-API-Key=USER_API_KEY

The FallbackDispatcher is a proxy dispatcher that sends lineage to the primary dispatcher first, and then if there is an error it calls the fallback one.

In the following example the HttpLineageDispatcher will be used as a primary, and the ConsoleLineageDispatcher as fallback.

spline.lineageDispatcher=fallback
spline.lineageDispatcher.fallback.primaryDispatcher=http
spline.lineageDispatcher.fallback.fallbackDispatcher=console

The CompositeDispatcher is a proxy dispatcher that forwards lineage data to multiple dispatchers.

For example, if you want the lineage data to be sent to an HTTP endpoint and to be logged to the console at the same time you can do the following:

spline.lineageDispatcher=composite
spline.lineageDispatcher.composite.dispatchers=http,console

By default, if some dispatchers in the list fail, the others are still attempted. If you want the error in any dispatcher to be treated as fatal, and be propagated to the main process, you set the failOnErrors property to true:

spline.lineageDispatcher.composite.failOnErrors=true

There is also a possibility to create your own dispatcher. It must implement LineageDispatcher trait and have a constructor with a single parameter of type org.apache.commons.configuration.Configuration. To use it you must define name and class and also all other parameters you need. For example:

spline.lineageDispatcher=my-dispatcher
spline.lineageDispatcher.my-dispatcher.className=org.example.spline.MyDispatcherImpl
spline.lineageDispatcher.my-dispatcher.prop1=value1
spline.lineageDispatcher.my-dispatcher.prop2=value2

If you need, you can combine multiple dispatchers into a single one using CompositeLineageDispatcher and FallbackLineageDispatcher in any combination as you wish.

In the following example the lineage will be first sent to the HTTP endpoint "http://10.20.111.222/lineage-primary", if that fails it's redirected to the "http://10.20.111.222/lineage-secondary" endpoint, and if that one fails as well, lineage is logged to the ERROR logs and the console at the same time.

spline.lineageDispatcher.http1.className=za.co.absa.spline.harvester.dispatcher.HttpLineageDispatcher
spline.lineageDispatcher.http1.producer.url=http://10.20.111.222/lineage-primary

spline.lineageDispatcher.http2.className=za.co.absa.spline.harvester.dispatcher.HttpLineageDispatcher
spline.lineageDispatcher.http2.producer.url=http://10.20.111.222/lineage-secondary

spline.lineageDispatcher.errorLogs.className=za.co.absa.spline.harvester.dispatcher.LoggingLineageDispatcher
spline.lineageDispatcher.errorLogs.level=ERROR

spline.lineageDispatcher.disp1.className=za.co.absa.spline.harvester.dispatcher.FallbackLineageDispatcher
spline.lineageDispatcher.disp1.primaryDispatcher=http1
spline.lineageDispatcher.disp1.fallbackDispatcher=disp2

spline.lineageDispatcher.disp2.className=za.co.absa.spline.harvester.dispatcher.FallbackLineageDispatcher
spline.lineageDispatcher.disp2.primaryDispatcher=http2
spline.lineageDispatcher.disp2.fallbackDispatcher=disp3

spline.lineageDispatcher.disp3.className=za.co.absa.spline.harvester.dispatcher.CompositeLineageDispatcher
spline.lineageDispatcher.composite.dispatchers=errorLogs,console

spline.lineageDispatcher=disp1

Filters can be used to enrich the lineage with your own custom data or to remove unwanted data like passwords. All filters are applied after the Spark plan is converted to Spline DTOs, but before the dispatcher is called.

The procedure how filters are registered and configured is similar to the LineageDispatcher registration and configuration procedure. A custom filter class must implement za.co.absa.spline.harvester.postprocessing.PostProcessingFilter trait and declare a constructor with a single parameter of type org.apache.commons.configuration.Configuration. Then register and configure it like this:

spline.postProcessingFilter=my-filter
spline.postProcessingFilter.my-filter.className=my.awesome.CustomFilter
spline.postProcessingFilter.my-filter.prop1=value1
spline.postProcessingFilter.my-filter.prop2=value2

Use pre-registered CompositePostProcessingFilter to chain up multiple filters:

spline.postProcessingFilter=composite
spline.postProcessingFilter.composite.filters=myFilter1,myFilter2

(see spline.default.yaml for details and examples)

MetadataCollectingFilter provides a way to add additional data to lineage produced by Spline Agent.

Data can be added to the following lineage entities: executionPlan, executionEvent, operation, read and write.

Inside each entity is dedicated map named extra that can store any additional user data.

executionPlan and executionEvent have additional map labels. Labels are intended for identification and filtering on the server.

Example usage:

spline.postProcessingFilter=userExtraMeta
spline.postProcessingFilter.userExtraMeta.rules=file:///path/to/json-with-rules.json

json-with-rules.json could look like this:

{
    "executionPlan": {
        "extra": {
            "my-extra-1": 42,
            "my-extra-2": [ "aaa", "bbb", "ccc" ]
        },
        "labels": {
            "my-label": "my-value"
        }
    },
    "write": {
        "extra": {
            "foo": "extra-value"
        }
    }
}

The spline.postProcessingFilter.userExtraMeta.rules can be either url pointing to json file or a json string. The rules definition can be quite long and when providing string directly a lot of escaping may be necessary so using a file is recommended.

Example of escaping the rules string in Scala String:

.config("spline.postProcessingFilter.userExtraMeta.rules", "{\"executionPlan\":{\"extra\":{\"qux\":42\\,\"tags\":[\"aaa\"\\,\"bbb\"\\,\"ccc\"]}}}")

• " needs to be escaped because it would end the string
• , needs to be escaped because when passing configuration via Java properties the comma is used as a separator under the hood and must be explicitly escaped.

Example of escaping the rules string as VM option:

-Dspline.postProcessingFilter.userExtraMeta.rules={\"executionPlan\":{\"extra\":{\"qux\":42\,\"tags\":[\"aaa\"\,\"bbb\"\,\"ccc\"]}}}

A convenient way how to provide rules json without need for escaping may be to specify the property in yaml config file. An example of this can be seen in spline examples yaml config.

There is also option to get environment variables using $env, jvm properties using $jvm and execute javascript using $js. See the following example:

{
    "executionPlan": {
        "extra": {
            "my-extra-1": 42,
            "my-extra-2": [ "aaa", "bbb", "ccc" ],
            "bar": { "$env": "BAR_HOME" },
            "baz": { "$jvm": "some.jvm.prop" },
            "daz": { "$js": "session.conf().get('k')" },
            "appName": { "$js":"session.sparkContext().appName()" }
       }
    }
}

For the javascript evaluation following variables are available by default:

Using those objects it should be possible to extract almost any relevant information from Spark.

The rules can be conditional, meaning the specified params will be added only when some condition is met. See the following example:

{
    "executionEvent[@.timestamp > 65]": {
        "extra": { "tux": 1 }
    },
    "executionEvent[@.extra['foo'] == 'a' && @.extra['bar'] == 'x']": {
        "extra": { "bux": 2 }
    },
    "executionEvent[@.extra['foo'] == 'a' && [email protected]['bar']]": {
        "extra": { "dux": 3 }
    },
    "executionEvent[@.extra['baz'][2] >= 3]": {
        "extra": { "mux": 4 }
    },
    "executionEvent[@.extra['baz'][2] < 3]": {
        "extra": { "fux": 5 }
    },
    "executionEvent[session.sparkContext.conf['spark.ui.enabled'] == 'false']": {
      "extra": { "tux": 1 }
    }
}

The condition is enclosed by [] after entity name. Here the @ serves as a reference to currently processed entity, in this case executionEvent. The [] inside the condition statement can also serve as a way to access maps and sequences. Logical and comparison operators are available.

session and other variables available for js are available here as well.

For more examples of usage please se MetadataCollectingFilterSpec test class.

Dataset operations are fully supported

RDD transformations aren't supported due to Spark internal architecture specifics, but they might be supported semi-automatically in the future Spline versions (see #33)

SQL dialect is mostly supported.

DDL operations are not supported, excepts for CREATE TABLE ... AS SELECT ... which is supported.

Note: By default, the lineage is only captured on persistent (write) actions. To capture in-memory actions like collect(), show() etc the corresponding plugin needs to be activated by setting up the following configuration property:

spline.plugins.za.co.absa.spline.harvester.plugin.embedded.NonPersistentActionsCapturePlugin.enabled=true

(See spline.default.yaml for more information)

The following data formats and providers are supported out of the box:

• Avro
• Cassandra
• COBOL
• Delta
• ElasticSearch
• Excel
• HDFS
• Hive
• JDBC
• Kafka
• MongoDB
• XML

Although Spark being an extensible piece of software can support much more, it doesn't provide any universal API that Spline can utilize to capture reads and write from/to everything that Spark supports. Support for most of different data sources and formats has to be added to Spline one by one. Fortunately starting with Spline 0.5.4 the auto discoverable Plugin API has been introduced to make this process easier.

Below is the break-down of the read/write command list that we have come through.
Some commands are implemented, others have yet to be implemented, and finally there are such that bear no lineage information and hence are ignored.

All commands inherit from org.apache.spark.sql.catalyst.plans.logical.Command.

You can see how to produce unimplemented commands in za.co.absa.spline.harvester.SparkUnimplementedCommandsSpec.

• CreateDataSourceTableAsSelectCommand (org.apache.spark.sql.execution.command)
• CreateHiveTableAsSelectCommand (org.apache.spark.sql.hive.execution)
• CreateTableCommand (org.apache.spark.sql.execution.command)
• DropTableCommand (org.apache.spark.sql.execution.command)
• InsertIntoDataSourceDirCommand (org.apache.spark.sql.execution.command)
• InsertIntoHadoopFsRelationCommand (org.apache.spark.sql.execution.datasources)
• InsertIntoHiveDirCommand (org.apache.spark.sql.hive.execution)
• InsertIntoHiveTable (org.apache.spark.sql.hive.execution)
• SaveIntoDataSourceCommand (org.apache.spark.sql.execution.datasources)

• AlterTableAddColumnsCommand (org.apache.spark.sql.execution.command)
• AlterTableChangeColumnCommand (org.apache.spark.sql.execution.command)
• AlterTableRenameCommand (org.apache.spark.sql.execution.command)
• AlterTableSetLocationCommand (org.apache.spark.sql.execution.command)
• CreateDataSourceTableCommand (org.apache.spark.sql.execution.command)
• CreateDatabaseCommand (org.apache.spark.sql.execution.command)
• CreateTableLikeCommand (org.apache.spark.sql.execution.command)
• DropDatabaseCommand (org.apache.spark.sql.execution.command)
• LoadDataCommand (org.apache.spark.sql.execution.command)
• TruncateTableCommand (org.apache.spark.sql.execution.command)

When one of these commands occurs spline will let you know by logging a warning.

• AddFileCommand (org.apache.spark.sql.execution.command)
• AddJarCommand (org.apache.spark.sql.execution.command)
• AlterDatabasePropertiesCommand (org.apache.spark.sql.execution.command)
• AlterTableAddPartitionCommand (org.apache.spark.sql.execution.command)
• AlterTableDropPartitionCommand (org.apache.spark.sql.execution.command)
• AlterTableRecoverPartitionsCommand (org.apache.spark.sql.execution.command)
• AlterTableRenamePartitionCommand (org.apache.spark.sql.execution.command)
• AlterTableSerDePropertiesCommand (org.apache.spark.sql.execution.command)
• AlterTableSetPropertiesCommand (org.apache.spark.sql.execution.command)
• AlterTableUnsetPropertiesCommand (org.apache.spark.sql.execution.command)
• AlterViewAsCommand (org.apache.spark.sql.execution.command)
• AnalyzeColumnCommand (org.apache.spark.sql.execution.command)
• AnalyzePartitionCommand (org.apache.spark.sql.execution.command)
• AnalyzeTableCommand (org.apache.spark.sql.execution.command)
• CacheTableCommand (org.apache.spark.sql.execution.command)
• ClearCacheCommand (org.apache.spark.sql.execution.command)
• CreateFunctionCommand (org.apache.spark.sql.execution.command)
• CreateTempViewUsing (org.apache.spark.sql.execution.datasources)
• CreateViewCommand (org.apache.spark.sql.execution.command)
• DescribeColumnCommand (org.apache.spark.sql.execution.command)
• DescribeDatabaseCommand (org.apache.spark.sql.execution.command)
• DescribeFunctionCommand (org.apache.spark.sql.execution.command)
• DescribeTableCommand (org.apache.spark.sql.execution.command)
• DropFunctionCommand (org.apache.spark.sql.execution.command)
• ExplainCommand (org.apache.spark.sql.execution.command)
• InsertIntoDataSourceCommand (org.apache.spark.sql.execution.datasources) *
• ListFilesCommand (org.apache.spark.sql.execution.command)
• ListJarsCommand (org.apache.spark.sql.execution.command)
• RefreshResource (org.apache.spark.sql.execution.datasources)
• RefreshTable (org.apache.spark.sql.execution.datasources)
• ResetCommand$ (org.apache.spark.sql.execution.command)
• SetCommand (org.apache.spark.sql.execution.command)
• SetDatabaseCommand (org.apache.spark.sql.execution.command)
• ShowColumnsCommand (org.apache.spark.sql.execution.command)
• ShowCreateTableCommand (org.apache.spark.sql.execution.command)
• ShowDatabasesCommand (org.apache.spark.sql.execution.command)
• ShowFunctionsCommand (org.apache.spark.sql.execution.command)
• ShowPartitionsCommand (org.apache.spark.sql.execution.command)
• ShowTablePropertiesCommand (org.apache.spark.sql.execution.command)
• ShowTablesCommand (org.apache.spark.sql.execution.command)
• StreamingExplainCommand (org.apache.spark.sql.execution.command)
• UncacheTableCommand (org.apache.spark.sql.execution.command)

Using a plugin API you can capture lineage from a 3rd party data source provider. Spline discover plugins automatically by scanning a classpath, so no special steps required to register and configure a plugin. All you need is to create a class extending the za.co.absa.spline.harvester.plugin.Plugin marker trait mixed with one or more *Processing traits, depending on your intention.

There are three general processing traits:

• DataSourceFormatNameResolving - returns a name of a data provider/format in use.
• ReadNodeProcessing - detects a read-command and gather meta information.
• WriteNodeProcessing - detects a write-command and gather meta information.

There are also two additional trait that handle common cases of reading and writing:

• BaseRelationProcessing - similar to ReadNodeProcessing, but instead of capturing all logical plan nodes it only reacts on LogicalRelation (see LogicalRelationPlugin)
• RelationProviderProcessing - similar to WriteNodeProcessing, but it only captures SaveIntoDataSourceCommand (see SaveIntoDataSourceCommandPlugin)

The best way to illustrate how plugins work is to look at the real working example, e.g. za.co.absa.spline.harvester.plugin.embedded.JDBCPlugin

The most common simplified pattern looks like this:

package my.spline.plugin

import javax.annotation.Priority
import za.co.absa.spline.harvester.builder._
import za.co.absa.spline.harvester.plugin.Plugin._
import za.co.absa.spline.harvester.plugin._

@Priority(Precedence.User) // not required, but can be used to control your plugin precedence in the plugin chain. Default value is `User`.  
class FooBarPlugin
  extends Plugin
    with BaseRelationProcessing
    with RelationProviderProcessing {

  override def baseRelationProcessor: PartialFunction[(BaseRelation, LogicalRelation), ReadNodeInfo] = {
    case (FooBarRelation(a, b, c, d), lr) if /*more conditions*/ =>
      val dataFormat: Option[AnyRef] = ??? // data format being read (will be resolved by the `DataSourceFormatResolver` later)
      val dataSourceURI: String = ??? // a unique URI for the data source
      val params: Map[String, Any] = ??? // additional parameters characterizing the read-command. E.g. (connection protocol, access mode, driver options etc)

      (SourceIdentifier(dataFormat, dataSourceURI), params)
  }

  override def relationProviderProcessor: PartialFunction[(AnyRef, SaveIntoDataSourceCommand), WriteNodeInfo] = {
    case (provider, cmd) if provider == "foobar" || provider.isInstanceOf[FooBarProvider] =>
      val dataFormat: Option[AnyRef] = ??? // data format being written (will be resolved by the `DataSourceFormatResolver` later)
      val dataSourceURI: String = ??? // a unique URI for the data source
      val writeMode: SaveMode = ??? // was it Append or Overwrite?
      val query: LogicalPlan = ??? // the logical plan to get the rest of the lineage from
      val params: Map[String, Any] = ??? // additional parameters characterizing the write-command

      (SourceIdentifier(dataFormat, dataSourceURI), writeMode, query, params)
  }
}

Note: to avoid unwanted possible shadowing the other plugins (including the future ones), make sure that the pattern-matching criteria are as much selective as possible for your plugin needs.

A plugin class is expected to only have a single constructor. The constructor can have no arguments, or one or more of the following types (the values will be autowired):

• SparkSession
• PathQualifier
• PluginRegistry

Compile you plugin and drop it into the Spline/Spark classpath. Spline will pick it up automatically.

Note: The project requires Java version 1.8 (strictly) and Apache Maven for building.

Check the build environment:

mvn --version

Verify that Maven is configured to run on Java 1.8. For example:

Apache Maven 3.6.3 (Red Hat 3.6.3-8)
Maven home: /usr/share/maven
Java version: 1.8.0_302, vendor: Red Hat, Inc., runtime: /usr/lib/jvm/java-1.8.0-openjdk-1.8.0.302.b08-2.fc34.x86_64/jre

There are several maven profiles that makes it easy to build the project with different versions of Spark and Scala.

• Scala profiles: scala-2.11, scala-2.12 (default)
• Spark profiles: spark-2.2, spark-2.3, spark-2.4 (default), spark-3.0, spark-3.1, spark-3.2, spark-3.3

For example, to build an agent for Spark 2.4 and Scala 2.11:

# Change Scala version in pom.xml.
mvn scala-cross-build:change-version -Pscala-2.11

# now you can build for Scala 2.11
mvn clean install -Pscala-2.11,spark-2.4

The agent docker image is mainly used to run example jobs and pre-fill the database with the sample lineage data.

(Spline docker images are available on the DockerHub repo - https://hub.docker.com/u/absaoss)

mvn install -Ddocker -Ddockerfile.repositoryUrl=my

See How to build Spline Docker images for details.

./mvn verify -Dcode-coverage

If module contains measurable data the code coverage report will be generated on path:

{local-path}\spline-spark-agent\{module}\target\site\jacoco

Although the primary goal of Spline agent is to be used in combination with the Spline server, it is flexible enough to be used in isolation or integration with other data lineage tracking solutions including custom ones.

Below is a couple of examples of such integration:

• Databricks Lineage In Azure Purview
• Spark Compute Lineage to Datahub

Copyright 2019 ABSA Group Limited

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
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    http://www.apache.org/licenses/LICENSE-2.0

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