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Spark

To integrate Spark with DataHub, we provide a lightweight Java agent that listens for Spark application and job events and pushes metadata out to DataHub in real-time. The agent listens to events such application start/end, and SQLExecution start/end to create pipelines (i.e. DataJob) and tasks (i.e. DataFlow) in Datahub along with lineage to datasets that are being read from and written to. Read on to learn how to configure this for different Spark scenarios.

Configuring Spark agent​

The Spark agent can be configured using a config file or while creating a Spark Session. If you are using Spark on Databricks, refer Configuration Instructions for Databricks.

Before you begin: Versions and Release Notes​

Versioning of the jar artifact will follow the semantic versioning of the main DataHub repo and release notes will be available here. Always check the Maven central repository for the latest released version.

Configuration Instructions: spark-submit​

When running jobs using spark-submit, the agent needs to be configured in the config file.

#Configuring DataHub spark agent jar
spark.jars.packages                          io.acryl:acryl-spark-lineage:0.2.16
spark.extraListeners                         datahub.spark.DatahubSparkListener
spark.datahub.rest.server                    http://localhost:8080

spark-submit command line​

spark-submit --packages io.acryl:acryl-spark-lineage:0.2.16 --conf "spark.extraListeners=datahub.spark.DatahubSparkListener" my_spark_job_to_run.py

Configuration Instructions: Amazon EMR​

Set the following spark-defaults configuration properties as it stated here

spark.jars.packages                          io.acryl:acryl-spark-lineage:0.2.16
spark.extraListeners                         datahub.spark.DatahubSparkListener
spark.datahub.rest.server                    https://your_datahub_host/gms
#If you have authentication set up then you also need to specify the Datahub access token
spark.datahub.rest.token                     yourtoken

Configuration Instructions: Notebooks​

When running interactive jobs from a notebook, the listener can be configured while building the Spark Session.

spark = SparkSession.builder
.master("spark://spark-master:7077")
.appName("test-application")
.config("spark.jars.packages", "io.acryl:acryl-spark-lineage:0.2.16")
.config("spark.extraListeners", "datahub.spark.DatahubSparkListener")
.config("spark.datahub.rest.server", "http://localhost:8080")
.enableHiveSupport()
.getOrCreate()

Configuration Instructions: Standalone Java Applications​

The configuration for standalone Java apps is very similar.

spark =SparkSession.

builder()
        .

appName("test-application")
        .

config("spark.master","spark://spark-master:7077")
        .

config("spark.jars.packages","io.acryl:acryl-spark-lineage:0.2.16")
        .

config("spark.extraListeners","datahub.spark.DatahubSparkListener")
        .

config("spark.datahub.rest.server","http://localhost:8080")
        .

enableHiveSupport()
        .

getOrCreate();

Configuration Instructions: Databricks​

The Spark agent can be configured using Databricks Cluster Spark configuration and Init script.

Databricks Secrets can be leveraged to store sensitive information like tokens.

  • Download datahub-spark-lineage jar from the Maven central repository.

  • Create init.sh with below content

    #!/bin/bash
    cp /dbfs/datahub/datahub-spark-lineage*.jar /databricks/jars

  • Install and configure Databricks CLI.

  • Copy jar and init script to Databricks File System(DBFS) using Databricks CLI.

    databricks fs mkdirs dbfs:/datahub
    databricks fs cp --overwrite datahub-spark-lineage*.jar dbfs:/datahub
    databricks fs cp --overwrite init.sh dbfs:/datahub

  • Open Databricks Cluster configuration page. Click the Advanced Options toggle. Click the Spark tab. Add below configurations under Spark Config.

    spark.extraListeners                    datahub.spark.DatahubSparkListener
    spark.datahub.rest.server               http://localhost:8080
    spark.datahub.stage_metadata_coalescing true
    spark.datahub.databricks.cluster        cluster-name<any preferred cluster identifier>

  • Click the Init Scripts tab. Set cluster init script as dbfs:/datahub/init.sh.

  • Configuring DataHub authentication token

    • Add below config in cluster spark config.

      spark.datahub.rest.token <token>

    • Alternatively, Databricks secrets can be used to secure token.

      • Create secret using Databricks CLI.

        databricks secrets create-scope --scope datahub --initial-manage-principal users
        databricks secrets put --scope datahub --key rest-token
        databricks secrets list --scope datahub &lt;&lt;Edit prompted file with token value&gt;&gt;

      • Add in spark config

        spark.datahub.rest.token {{secrets/datahub/rest-token}}

Configuration Options​

FieldRequiredDefaultDescription
spark.jars.packages✅Set with latest/required version io.acryl:acryl-spark-lineage:0.2.15
spark.extraListeners✅datahub.spark.DatahubSparkListener
spark.datahub.emitterrestSpecify the ways to emit metadata. By default it sends to DataHub using REST emitter. Valid options are rest, kafka or file
spark.datahub.rest.serverhttp://localhost:8080Datahub server url eg:http://localhost:8080
spark.datahub.rest.tokenAuthentication token.
spark.datahub.rest.disable_ssl_verificationfalseDisable SSL certificate validation. Caution: Only use this if you know what you are doing!
spark.datahub.rest.disable_chunked_encodingfalseDisable Chunked Transfer Encoding. In some environment chunked encoding causes issues. With this config option it can be disabled.
spark.datahub.rest.max_retries0Number of times a request retried if failed
spark.datahub.rest.retry_interval10Number of seconds to wait between retries
spark.datahub.file.filenameThe file where metadata will be written if file emitter is set
spark.datahub.kafka.bootstrapThe Kafka bootstrap server url to use if the Kafka emitter is set
spark.datahub.kafka.schema_registry_urlThe Schema registry url to use if the Kafka emitter is set
spark.datahub.kafka.schema_registry_config.Additional config to pass in to the Schema Registry Client
spark.datahub.kafka.producer_config.Additional config to pass in to the Kafka producer. For example: --conf "spark.datahub.kafka.producer_config.client.id=my_client_id"
spark.datahub.metadata.pipeline.platformInstancePipeline level platform instance
spark.datahub.metadata.dataset.platformInstancedataset level platform instance (it is usefult to set if you have it in your glue ingestion)
spark.datahub.metadata.dataset.envPRODSupported values. In all other cases, will fallback to PROD
spark.datahub.metadata.dataset.hivePlatformAliashiveBy default, datahub assigns Hive-like tables to the Hive platform. If you are using Glue as your Hive metastore, set this config flag to glue
spark.datahub.metadata.include_schemetrueInclude scheme from the path URI (e.g. hdfs://, s3://) in the dataset URN. We recommend setting this value to false, it is set to true for backwards compatibility with previous versions
spark.datahub.metadata.remove_partition_patternRemove partition pattern. (e.g. /partition=\d+) It change database/table/partition=123 to database/table
spark.datahub.coalesce_jobstrueOnly one datajob(task) will be emitted containing all input and output datasets for the spark application
spark.datahub.parent.datajob_urnSpecified dataset will be set as upstream dataset for datajob created. Effective only when spark.datahub.coalesce_jobs is set to true
spark.datahub.metadata.dataset.materializefalseMaterialize Datasets in DataHub
spark.datahub.platform.s3.path_spec_listList of pathspec per platform
spark.datahub.metadata.dataset.include_schema_metadatafalseEmit dataset schema metadata based on the spark execution. It is recommended to get schema information from platform specific DataHub sources as this is less reliable
spark.datahub.flow_nameIf it is set it will be used as the DataFlow name otherwise it uses spark app name as flow_name
spark.datahub.file_partition_regexpStrip partition part from the path if path end matches with the specified regexp. Example year=.*/month=.*/day=.*
spark.datahub.tagsComma separated list of tags to attach to the DataFlow
spark.datahub.domainsComma separated list of domain urns to attach to the DataFlow
spark.datahub.stage_metadata_coalescingNormally it coalesces and sends metadata at the onApplicationEnd event which is never called on Databricks or on Glue. You should enable this on Databricks if you want coalesced run.
spark.datahub.patch.enabledfalseSet this to true to send lineage as a patch, which appends rather than overwrites existing Dataset lineage edges. By default, it is disabled.
spark.datahub.metadata.dataset.lowerCaseUrnsfalseSet this to true to lowercase dataset urns. By default, it is disabled.
spark.datahub.disableSymlinkResolutionfalseSet this to true if you prefer using the s3 location instead of the Hive table. By default, it is disabled.
spark.datahub.s3.bucketThe name of the bucket where metadata will be written if s3 emitter is set
spark.datahub.s3.prefixThe prefix for the file where metadata will be written on s3 if s3 emitter is set
spark.datahub.s3.filenameThe name of the file where metadata will be written if it is not set random filename will be used on s3 if s3 emitter is set

What to Expect: The Metadata Model​

As of current writing, the Spark agent produces metadata related to the Spark job, tasks and lineage edges to datasets.

  • A pipeline is created per Spark <master, appName>.
  • A task is created per unique Spark query execution within an app.

For Spark on Databricks,

  • A pipeline is created per
    • cluster_identifier: specified with spark.datahub.databricks.cluster
    • applicationID: on every restart of the cluster new spark applicationID will be created.
  • A task is created per unique Spark query execution.

Custom properties & relating to Spark UI​

The following custom properties in pipelines and tasks relate to the Spark UI:

  • appName and appId in a pipeline can be used to determine the Spark application
  • Other custom properties of pipelines and tasks capture the start and end times of execution etc.

For Spark on Databricks, pipeline start time is the cluster start time.

Spark versions supported​

Supports Spark 3.x series.

Environments tested with​

This initial release has been tested with the following environments:

  • spark-submit of Python/Java applications to local and remote servers
  • Standalone Java applications
  • Databricks Standalone Cluster
  • EMR

Testing with Databricks Standard and High-concurrency Cluster is not done yet.

Configuring Hdfs based dataset URNs​

Spark emits lineage between datasets. It has its own logic for generating urns. Python sources emit metadata of datasets. To link these 2 things, urns generated by both have to match. This section will help you to match urns to that of other ingestion sources. By default, URNs are created using template urn:li:dataset:(urn:li:dataPlatform:<$platform>,<platformInstance>.<name>,<env>). We can configure these 4 things to generate the desired urn.

Platform: Hdfs-based platforms supported explicitly:

  • AWS S3 (s3)
  • Google Cloud Storage (gcs)
  • local ( local file system) (local) All other platforms will have "hdfs" as a platform.

Name: By default, the name is the complete path. For Hdfs base datasets, tables can be at different levels in the path than that of the actual file read due to various reasons like partitioning, and sharding. 'path_spec' is used to alter the name. {table} marker is used to specify the table level. Below are a few examples. One can specify multiple path_specs for different paths specified in the path_spec_list. Each actual path is matched against all path_spes present in the list. First, one to match will be used to generate urn.

path_spec Examples

spark.datahub.platform.s3.path_spec_list=s3://my-bucket/foo/{table}/year=*/month=*/day=*/*,s3://my-other-bucket/foo/{table}/year=*/month=*/day=*/*"
Absolute pathpath_specUrn
s3://my-bucket/foo/tests/bar.avroNot providedurn:li:dataset:(urn:li:dataPlatform:s3,my-bucket/foo/tests/bar.avro,PROD)
s3://my-bucket/foo/tests/bar.avros3://my-bucket/foo/{table}/*urn:li:dataset:(urn:li:dataPlatform:s3,my-bucket/foo/tests,PROD)
s3://my-bucket/foo/tests/bar.avros3://my-bucket/foo/tests/{table}urn:li:dataset:(urn:li:dataPlatform:s3,my-bucket/foo/tests/bar.avro,PROD)
gs://my-bucket/foo/tests/bar.avrogs://my-bucket/{table}//urn:li:dataset:(urn:li:dataPlatform:gcs,my-bucket/foo,PROD)
gs://my-bucket/foo/tests/bar.avrogs://my-bucket/{table}urn:li:dataset:(urn:li:dataPlatform:gcs,my-bucket/foo,PROD)
file:///my-bucket/foo/tests/bar.avrofile:///my-bucket///{table}urn:li:dataset:(urn:li:dataPlatform:local,my-bucket/foo/tests/bar.avro,PROD)

platform instance and env:

The default value for env is 'PROD' and the platform instance is None. env and platform instances can be set for all datasets using configurations 'spark.datahub.metadata.dataset.env' and 'spark.datahub.metadata.dataset.platformInstace'. If spark is processing data that belongs to a different env or platform instance, then 'path_alias' can be used to specify path_spec specific values of these. 'path_alias' groups the 'path_spec_list', its env, and platform instance together.

path_alias_list Example:

The below example explains the configuration of the case, where files from 2 buckets are being processed in a single spark application and files from my-bucket are supposed to have "instance1" as platform instance and "PROD" as env, and files from bucket2 should have env "DEV" in their dataset URNs.

spark.datahub.platform.s3.path_alias_list :  path1,path2
spark.datahub.platform.s3.path1.env : PROD
spark.datahub.platform.s3.path1.path_spec_list: s3://my-bucket/*/*/{table}
spark.datahub.platform.s3.path1.platform_instance : instance-1
spark.datahub.platform.s3.path2.env: DEV
spark.datahub.platform.s3.path2.path_spec_list: s3://bucket2/*/{table}

Important notes on usage​

  • It is advisable to ensure appName is used appropriately to ensure you can trace lineage from a pipeline back to your source code.
  • If multiple apps with the same appName run concurrently, dataset-lineage will be captured correctly but the custom-properties e.g. app-id, SQLQueryId would be unreliable. We expect this to be quite rare.
  • If spark execution fails, then an empty pipeline would still get created, but it may not have any tasks.
  • For HDFS sources, the folder (name) is regarded as the dataset (name) to align with typical storage of parquet/csv formats.

Debugging​

  • Following info logs are generated

On Spark context startup

YY/MM/DD HH:mm:ss INFO DatahubSparkListener: DatahubSparkListener initialised.
YY/MM/DD HH:mm:ss INFO SparkContext: Registered listener datahub.spark.DatahubSparkListener

On application start

YY/MM/DD HH:mm:ss INFO DatahubSparkListener: Application started: SparkListenerApplicationStart(AppName,Some(local-1644489736794),1644489735772,user,None,None)
YY/MM/DD HH:mm:ss INFO McpEmitter: REST Emitter Configuration: GMS url <rest.server>
YY/MM/DD HH:mm:ss INFO McpEmitter: REST Emitter Configuration: Token XXXXX

On pushing data to server

YY/MM/DD HH:mm:ss INFO McpEmitter: MetadataWriteResponse(success=true, responseContent={"value":"<URN>"}, underlyingResponse=HTTP/1.1 200 OK [Date: day, DD month year HH:mm:ss GMT, Content-Type: application/json, X-RestLi-Protocol-Version: 2.0.0, Content-Length: 97, Server: Jetty(9.4.46.v20220331)] [Content-Length: 97,Chunked: false])

On application end

YY/MM/DD HH:mm:ss INFO DatahubSparkListener: Application ended : AppName AppID
  • To enable debugging logs, add below configuration in log4j.properties file
log4j.logger.datahub.spark=DEBUG
log4j.logger.datahub.client.rest=DEBUG

How to build​

Use Java 8 to build the project. The project uses Gradle as the build tool. To build the project, run the following command:

./gradlew -PjavaClassVersionDefault=8 :metadata-integration:java:acryl-spark-lineage:shadowJar

Known limitations​

Changelog​

Version 0.2.16​

  • Remove logging DataHub config into logs

Version 0.2.15​

  • Add Kafka emitter to emit lineage to kafka
  • Add File emitter to emit lineage to file
  • Add S3 emitter to save mcps to s3
  • Upgrading OpenLineage to 1.19.0
  • Renaming project to acryl-datahub-spark-lineage
  • Supporting OpenLineage 1.17+ glue identifier changes
  • Fix handling OpenLineage input/output where wasn't any facet attached

Version 0.2.14​

  • Fix warning about MeterFilter warning from Micrometer

Version 0.2.13​

  • Add kafka emitter to emit lineage to kafka

Version 0.2.12​

  • Silencing some chatty warnings in RddPathUtils

Version 0.2.11​

  • Add option to lowercase dataset URNs
  • Add option to set platform instance and/or env per platform with spark.datahub.platform.<platform_name>.env and spark.datahub.platform.<platform_name>.platform_instance config parameter
  • Fixing platform instance setting for datasets when spark.datahub.metadata.dataset.platformInstance is set
  • Fixing column level lineage support when patch is enabled