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344 lines
19 KiB
Markdown
344 lines
19 KiB
Markdown
# Spark
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To integrate Spark with DataHub, we provide a lightweight Java agent that listens for Spark application and job events
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and pushes metadata out to DataHub in real-time. The agent listens to events such application start/end, and
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SQLExecution start/end to create pipelines (i.e. DataJob) and tasks (i.e. DataFlow) in Datahub along with lineage to
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datasets that are being read from and written to. Read on to learn how to configure this for different Spark scenarios.
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## Configuring Spark agent
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The Spark agent can be configured using a config file or while creating a Spark Session. If you are using Spark on
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Databricks, refer [Configuration Instructions for Databricks](#configuration-instructions--databricks).
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### Before you begin: Versions and Release Notes
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Versioning of the jar artifact will follow the semantic versioning of the
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main [DataHub repo](https://github.com/datahub-project/datahub) and release notes will be
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available [here](https://github.com/datahub-project/datahub/releases).
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Always check [the Maven central repository](https://search.maven.org/search?q=a:acryl-spark-lineage) for the latest
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released version.
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### Configuration Instructions: spark-submit
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When running jobs using spark-submit, the agent needs to be configured in the config file.
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```text
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#Configuring DataHub spark agent jar
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spark.jars.packages io.acryl:acryl-spark-lineage:0.2.1
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spark.extraListeners datahub.spark.DatahubSparkListener
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spark.datahub.rest.server http://localhost:8080
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```
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## spark-submit command line
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```sh
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spark-submit --packages io.acryl:acryl-spark-lineage:0.2.1 --conf "spark.extraListeners=datahub.spark.DatahubSparkListener" my_spark_job_to_run.py
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```
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### Configuration Instructions: Amazon EMR
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Set the following spark-defaults configuration properties as it
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stated [here](https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-spark-configure.html)
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```text
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spark.jars.packages io.acryl:acryl-spark-lineage:0.2.1
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spark.extraListeners datahub.spark.DatahubSparkListener
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spark.datahub.rest.server https://your_datahub_host/gms
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#If you have authentication set up then you also need to specify the Datahub access token
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spark.datahub.rest.token yourtoken
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```
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### Configuration Instructions: Notebooks
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When running interactive jobs from a notebook, the listener can be configured while building the Spark Session.
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```python
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spark = SparkSession.builder
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.master("spark://spark-master:7077")
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.appName("test-application")
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.config("spark.jars.packages", "io.acryl:acryl-spark-lineage:0.1.0")
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.config("spark.extraListeners", "datahub.spark.DatahubSparkListener")
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.config("spark.datahub.rest.server", "http://localhost:8080")
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.enableHiveSupport()
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.getOrCreate()
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```
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### Configuration Instructions: Standalone Java Applications
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The configuration for standalone Java apps is very similar.
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```java
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spark =SparkSession.
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builder()
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.
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appName("test-application")
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.
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config("spark.master","spark://spark-master:7077")
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.
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config("spark.jars.packages","io.acryl:acryl-spark-lineage:0.2.1")
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.
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config("spark.extraListeners","datahub.spark.DatahubSparkListener")
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.
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config("spark.datahub.rest.server","http://localhost:8080")
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.
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enableHiveSupport()
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.
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getOrCreate();
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```
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### Configuration Instructions: Databricks
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The Spark agent can be configured using Databricks
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Cluster [Spark configuration](https://docs.databricks.com/clusters/configure.html#spark-configuration)
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and [Init script](https://docs.databricks.com/clusters/configure.html#init-scripts).
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[Databricks Secrets](https://docs.databricks.com/security/secrets/secrets.html) can be leveraged to store sensitive
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information like tokens.
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- Download `datahub-spark-lineage` jar
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from [the Maven central repository](https://s01.oss.sonatype.org/content/groups/public/io/acryl/acryl-spark-lineage/).
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- Create `init.sh` with below content
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```sh
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#!/bin/bash
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cp /dbfs/datahub/datahub-spark-lineage*.jar /databricks/jars
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```
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- Install and configure [Databricks CLI](https://docs.databricks.com/dev-tools/cli/index.html).
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- Copy jar and init script to Databricks File System(DBFS) using Databricks CLI.
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```sh
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databricks fs mkdirs dbfs:/datahub
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databricks fs cp --overwrite datahub-spark-lineage*.jar dbfs:/datahub
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databricks fs cp --overwrite init.sh dbfs:/datahub
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```
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- Open Databricks Cluster configuration page. Click the **Advanced Options** toggle. Click the **Spark** tab. Add below
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configurations under `Spark Config`.
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```text
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spark.extraListeners datahub.spark.DatahubSparkListener
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spark.datahub.rest.server http://localhost:8080
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spark.datahub.databricks.cluster cluster-name<any preferred cluster identifier>
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```
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- Click the **Init Scripts** tab. Set cluster init script as `dbfs:/datahub/init.sh`.
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- Configuring DataHub authentication token
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- Add below config in cluster spark config.
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```text
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spark.datahub.rest.token <token>
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```
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- Alternatively, Databricks secrets can be used to secure token.
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- Create secret using Databricks CLI.
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```sh
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databricks secrets create-scope --scope datahub --initial-manage-principal users
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databricks secrets put --scope datahub --key rest-token
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databricks secrets list --scope datahub <<Edit prompted file with token value>>
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```
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- Add in spark config
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```text
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spark.datahub.rest.token {{secrets/datahub/rest-token}}
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```
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## Configuration Options
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| Field | Required | Default | Description |
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|---------------------------------------------------------------------|----------|---------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| spark.jars.packages | ✅ | | Set with latest/required version io.acryl:datahub-spark-lineage:0.8.23 |
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| spark.extraListeners | ✅ | | datahub.spark.DatahubSparkListener |
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| spark.datahub.rest.server | ✅ | | Datahub server url eg:<http://localhost:8080> |
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| spark.datahub.rest.token | | | Authentication token. |
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| spark.datahub.rest.disable_ssl_verification | | false | Disable SSL certificate validation. Caution: Only use this if you know what you are doing! |
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| spark.datahub.metadata.pipeline.platformInstance | | | Pipeline level platform instance |
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| spark.datahub.metadata.dataset.platformInstance | | | dataset level platform instance |
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| spark.datahub.metadata.dataset.env | | PROD | [Supported values](https://datahubproject.io/docs/graphql/enums#fabrictype). In all other cases, will fallback to PROD |
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| spark.datahub.metadata.table.hive_platform_alias | | hive | By 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` |
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| spark.datahub.metadata.include_scheme | | true | Include 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 |
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| spark.datahub.metadata.remove_partition_pattern | | | Remove partition pattern. (e.g. /partition=\d+) It change database/table/partition=123 to database/table |
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| spark.datahub.coalesce_jobs | | true | Only one datajob(task) will be emitted containing all input and output datasets for the spark application |
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| spark.datahub.parent.datajob_urn | | | Specified dataset will be set as upstream dataset for datajob created. Effective only when spark.datahub.coalesce_jobs is set to true |
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| spark.datahub.metadata.dataset.materialize | | false | Materialize Datasets in DataHub |
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| spark.datahub.platform.s3.path_spec_list | | | List of pathspec per platform |
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| spark.datahub.metadata.dataset.experimental_include_schema_metadata | false | | Emit dataset schema metadata based on the spark |
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| spark.datahub.flow_name | | | If it is set it will be used as the DataFlow name otherwise it uses spark app name as flow_name |
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| spark.datahub.partition_regexp_pattern | | | Strip partition part from the path if path end matches with the specified regexp. Example `year=.*/month=.*/day=.*` |
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| spark.datahub.tags | | | Comma separated list of tags to attach to the DataFlow |
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| spark.datahub.domains | | | Comma separated list of domain urns to attach to the DataFlow |
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| spark.datahub.stage_metadata_coalescing | | | Normally it coalesce and send metadata at the onApplicationEnd event which is never called on Databricsk. You should enable this on Databricks if you want coalesced run . |
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| spark.datahub.patch.enabled | | | Set this to true to send lineage as a patch, which appends rather than overwrites existing Dataset lineage edges. By default it is enabled.
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## What to Expect: The Metadata Model
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As of current writing, the Spark agent produces metadata related to the Spark job, tasks and lineage edges to datasets.
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- A pipeline is created per Spark <master, appName>.
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- A task is created per unique Spark query execution within an app.
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For Spark on Databricks,
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- A pipeline is created per
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- cluster_identifier: specified with spark.datahub.databricks.cluster
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- applicationID: on every restart of the cluster new spark applicationID will be created.
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- A task is created per unique Spark query execution.
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### Custom properties & relating to Spark UI
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The following custom properties in pipelines and tasks relate to the Spark UI:
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- appName and appId in a pipeline can be used to determine the Spark application
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- description and SQLQueryId in a task can be used to determine the Query Execution within the application on the SQL
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tab of Spark UI
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- Other custom properties of pipelines and tasks capture the start and end times of execution etc.
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For Spark on Databricks, pipeline start time is the cluster start time.
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### Spark versions supported
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Supports Spark 3.x series.
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### Environments tested with
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This initial release has been tested with the following environments:
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- spark-submit of Python/Java applications to local and remote servers
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- Standalone Java applications
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- Databricks Standalone Cluster
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Testing with Databricks Standard and High-concurrency Cluster is not done yet.
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### Configuring Hdfs based dataset URNs
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Spark emits lineage between datasets. It has its own logic for generating urns. Python sources emit metadata of
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datasets. To link these 2 things, urns generated by both have to match.
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This section will help you to match urns to that of other ingestion sources.
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By default, URNs are created using
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template `urn:li:dataset:(urn:li:dataPlatform:<$platform>,<platformInstance>.<name>,<env>)`. We can configure these 4
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things to generate the desired urn.
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**Platform**:
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Hdfs-based platforms supported explicitly:
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- AWS S3 (s3)
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- Google Cloud Storage (gcs)
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- local ( local file system) (local)
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All other platforms will have "hdfs" as a platform.
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**Name**:
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By default, the name is the complete path. For Hdfs base datasets, tables can be at different levels in the path than
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that of the actual file read due to various reasons like partitioning, and sharding. 'path_spec' is used to alter the
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name.
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{table} marker is used to specify the table level. Below are a few examples. One can specify multiple path_specs for
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different paths specified in the `path_spec_list`. Each actual path is matched against all path_spes present in the
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list. First, one to match will be used to generate urn.
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**path_spec Examples**
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```
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spark.datahub.platform.s3.path_spec_list=s3://my-bucket/foo/{table}/year=*/month=*/day=*/*,s3://my-other-bucket/foo/{table}/year=*/month=*/day=*/*"
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```
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| Absolute path | path_spec | Urn |
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|--------------------------------------|----------------------------------|------------------------------------------------------------------------------|
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| s3://my-bucket/foo/tests/bar.avro | Not provided | urn:li:dataset:(urn:li:dataPlatform:s3,my-bucket/foo/tests/bar.avro,PROD) |
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| s3://my-bucket/foo/tests/bar.avro | s3://my-bucket/foo/{table}/* | urn:li:dataset:(urn:li:dataPlatform:s3,my-bucket/foo/tests,PROD) |
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| s3://my-bucket/foo/tests/bar.avro | s3://my-bucket/foo/tests/{table} | urn:li:dataset:(urn:li:dataPlatform:s3,my-bucket/foo/tests/bar.avro,PROD) |
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| gs://my-bucket/foo/tests/bar.avro | gs://my-bucket/{table}/*/* | urn:li:dataset:(urn:li:dataPlatform:gcs,my-bucket/foo,PROD) |
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| gs://my-bucket/foo/tests/bar.avro | gs://my-bucket/{table} | urn:li:dataset:(urn:li:dataPlatform:gcs,my-bucket/foo,PROD) |
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| file:///my-bucket/foo/tests/bar.avro | file:///my-bucket/*/*/{table} | urn:li:dataset:(urn:li:dataPlatform:local,my-bucket/foo/tests/bar.avro,PROD) |
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**platform instance and env:**
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The default value for env is 'PROD' and the platform instance is None. env and platform instances can be set for all
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datasets using configurations 'spark.datahub.metadata.dataset.env' and 'spark.datahub.metadata.dataset.platformInstace'.
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If spark is processing data that belongs to a different env or platform instance, then 'path_alias' can be used to
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specify `path_spec` specific values of these. 'path_alias' groups the 'path_spec_list', its env, and platform instance
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together.
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path_alias_list Example:
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The below example explains the configuration of the case, where files from 2 buckets are being processed in a single
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spark application and files from my-bucket are supposed to have "instance1" as platform instance and "PROD" as env, and
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files from bucket2 should have env "DEV" in their dataset URNs.
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```
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spark.datahub.platform.s3.path_alias_list : path1,path2
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spark.datahub.platform.s3.path1.env : PROD
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spark.datahub.platform.s3.path1.path_spec_list: s3://my-bucket/*/*/{table}
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spark.datahub.platform.s3.path1.platform_instance : instance-1
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spark.datahub.platform.s3.path2.env: DEV
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spark.datahub.platform.s3.path2.path_spec_list: s3://bucket2/*/{table}
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```
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### Important notes on usage
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- It is advisable to ensure appName is used appropriately to ensure you can trace lineage from a pipeline back to your
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source code.
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- If multiple apps with the same appName run concurrently, dataset-lineage will be captured correctly but the
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custom-properties e.g. app-id, SQLQueryId would be unreliable. We expect this to be quite rare.
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- If spark execution fails, then an empty pipeline would still get created, but it may not have any tasks.
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- For HDFS sources, the folder (name) is regarded as the dataset (name) to align with typical storage of parquet/csv
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formats.
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### Debugging
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- Following info logs are generated
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On Spark context startup
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```text
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YY/MM/DD HH:mm:ss INFO DatahubSparkListener: DatahubSparkListener initialised.
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YY/MM/DD HH:mm:ss INFO SparkContext: Registered listener datahub.spark.DatahubSparkListener
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```
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On application start
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```text
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YY/MM/DD HH:mm:ss INFO DatahubSparkListener: Application started: SparkListenerApplicationStart(AppName,Some(local-1644489736794),1644489735772,user,None,None)
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YY/MM/DD HH:mm:ss INFO McpEmitter: REST Emitter Configuration: GMS url <rest.server>
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YY/MM/DD HH:mm:ss INFO McpEmitter: REST Emitter Configuration: Token XXXXX
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```
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On pushing data to server
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```text
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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])
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```
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On application end
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```text
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YY/MM/DD HH:mm:ss INFO DatahubSparkListener: Application ended : AppName AppID
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```
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- To enable debugging logs, add below configuration in log4j.properties file
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```properties
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log4j.logger.datahub.spark=DEBUG
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log4j.logger.datahub.client.rest=DEBUG
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```
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## How to build
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Use Java 8 to build the project. The project uses Gradle as the build tool. To build the project, run the following command:
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```shell
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./gradlew -PjavaClassVersionDefault=8 :metadata-integration:java:spark-lineage-beta:shadowJar
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```
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## Known limitations
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