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Metadata Ingestion

This module hosts an extensible Python-based metadata ingestion system for DataHub. This supports sending data to DataHub using Kafka or through the REST api. It can be used through our CLI tool or as a library e.g. with an orchestrator like Airflow.

Architecture

metadata ingestion framework layout

The architecture of this metadata ingestion framework is heavily inspired by Apache Gobblin (also originally a LinkedIn project!). We have a standardized format - the MetadataChangeEvent - and sources and sinks which respectively produce and consume these objects. The sources pull metadata from a variety of data systems, while the sinks are primarily for moving this metadata into DataHub.

Getting Started

Prerequisites

Before running any metadata ingestion job, you should make sure that DataHub backend services are all running. If you are trying this out locally, the easiest way to do that is through quickstart Docker images.

Install

Requirements

  1. Python 3.6+ must be installed in your host environment.
  2. You also need to build the mxe-schemas module as below. 
    (cd .. && ./gradlew :metadata-events:mxe-schemas:build)
    This is needed to generate MetadataChangeEvent.avsc which is the schema for the MetadataChangeEvent_v4 Kafka topic.
  3. On MacOS: brew install librdkafka
  4. On Debian/Ubuntu: sudo apt install librdkafka-dev python3-dev python3-venv

Set up your Python environment

python3 -m venv venv
source venv/bin/activate
pip install --upgrade pip wheel setuptools
pip install -e .
./scripts/codegen.sh

Common issues (click to expand):

Wheel issues e.g. "Failed building wheel for avro-python3" or "error: invalid command 'bdist_wheel'"

This means Python's wheel is not installed. Try running the following commands and then retry.

pip install --upgrade pip wheel setuptools
pip cache purge
Failure to install confluent_kafka: "error: command 'x86_64-linux-gnu-gcc' failed with exit status 1"

This sometimes happens if there's a version mismatch between the Kafka's C library and the Python wrapper library. Try running pip install confluent_kafka==1.5.0 and then retrying.

Failure to install avro-python3: "distutils.errors.DistutilsOptionError: Version loaded from file: avro/VERSION.txt does not comply with PEP 440"

The underlying avro-python3 package is buggy. In particular, it often only installs correctly when installed from a pre-built "wheel" but not when from source. Try running the following commands and then retry.

pip uninstall avro-python3  # sanity check, ok if this fails
pip install --upgrade pip wheel setuptools
pip cache purge
pip install avro-python3

Installing Plugins

We use a plugin architecture so that you can install only the dependencies you actually need.

Plugin Name Install Command Provides
file included by default File source and sink
console included by default Console sink
athena pip install -e '.[athena]' AWS Athena source
bigquery pip install -e '.[bigquery]' BigQuery source
hive pip install -e '.[hive]' Hive source
mssql pip install -e '.[mssql]' SQL Server source
mysql pip install -e '.[mysql]' MySQL source
postgres pip install -e '.[postgres]' Postgres source
snowflake pip install -e '.[snowflake]' Snowflake source
ldap pip install -e '.[ldap]' (extra requirements) LDAP source
kakfa pip install -e '.[kafka]' Kafka source
druid pip install -e '.[druid]' Druid Source
datahub-rest pip install -e '.[datahub-rest]' DataHub sink over REST API
datahub-kafka pip install -e '.[datahub-kafka]' DataHub sink over Kafka

These plugins can be mixed and matched as desired. For example:

pip install -e '.[bigquery,datahub-rest]

You can check the active plugins:

datahub ingest-list-plugins

Basic Usage

pip install -e '.[datahub-rest]'  # install the required plugin
datahub ingest -c ./examples/recipes/example_to_datahub_rest.yml

We have also included a couple sample DAGs that can be used with Airflow.

  • generic_recipe_sample_dag.py - a simple Airflow DAG that picks up a DataHub ingestion recipe configuration and runs it.
  • mysql_sample_dag.py - an Airflow DAG that runs a MySQL metadata ingestion pipeline using an inlined configuration.

Recipes

A recipe is a configuration file that tells our ingestion scripts where to pull data from (source) and where to put it (sink). Here's a simple example that pulls metadata from MSSQL and puts it into datahub.

# A sample recipe that pulls metadata from MSSQL and puts it into DataHub
# using the Rest API.
source:
  type: mssql
  config:
    username: sa
    password: test!Password
    database: DemoData

sink:
  type: "datahub-rest"
  config:
    server: "http://localhost:8080"

Running a recipe is quite easy.

datahub ingest -c ./examples/recipes/mssql_to_datahub.yml

A number of recipes are included in the examples/recipes directory.

Sources

Kafka Metadata kafka

Extracts:

  • List of topics - from the Kafka broker
  • Schemas associated with each topic - from the schema registry
source:
  type: "kafka"
  config:
    connection:
      bootstrap: "broker:9092"
      schema_registry_url: http://localhost:8081
      consumer_config: {} # passed to https://docs.confluent.io/platform/current/clients/confluent-kafka-python/index.html#deserializingconsumer

MySQL Metadata mysql

Extracts:

  • List of databases and tables
  • Column types and schema associated with each table
source:
  type: mysql
  config:
    username: root
    password: example
    database: dbname
    host_port: localhost:3306
    table_pattern:
      allow:
        - "schema1.table2"
      deny:
        - "performance_schema"
      # Although the 'table_pattern' enables you to skip everything from certain schemas,
      # having another option to allow/deny on schema level is an optimization for the case when there is a large number
      # of schemas that one wants to skip and you want to avoid the time to needlessly fetch those tables only to filter
      # them out afterwards via the table_pattern.
    schema_pattern:
      allow:
        - "schema1"
      deny:
        - "garbage_schema"

Microsoft SQL Server Metadata mssql

Extracts:

  • List of databases, schema, and tables
  • Column types associated with each table
source:
  type: mssql
  config:
    username: user
    password: pass
    host_port: localhost:1433
    database: DemoDatabase
    table_pattern:
      allow:
        - "schema1.table1"
        - "schema1.table2"
      deny:
        - "^.*\\.sys_.*" # deny all tables that start with sys_
    options:
      # Any options specified here will be passed to SQLAlchemy's create_engine as kwargs.
      # See https://docs.sqlalchemy.org/en/14/core/engines.html for details.
      charset: "utf8"

Hive hive

Extracts:

  • List of databases, schema, and tables
  • Column types associated with each table
source:
  type: hive
  config:
    username: user
    password: pass
    host_port: localhost:10000
    database: DemoDatabase
    # table_pattern/schema_pattern is same as above
    # options is same as above

PostgreSQL postgres

Extracts:

  • List of databases, schema, and tables
  • Column types associated with each table
  • Also supports PostGIS extensions
source:
  type: postgres
  config:
    username: user
    password: pass
    host_port: localhost:5432
    database: DemoDatabase
    # table_pattern/schema_pattern is same as above
    # options is same as above

Snowflake snowflake

Extracts:

  • List of databases, schema, and tables
  • Column types associated with each table
source:
  type: snowflake
  config:
    username: user
    password: pass
    host_port: account_name
    # table_pattern/schema_pattern is same as above
    # options is same as above

Google BigQuery bigquery

Extracts:

  • List of databases, schema, and tables
  • Column types associated with each table
source:
  type: bigquery
  config:
    project_id: project # optional - can autodetect from environment
    dataset: dataset_name
    options: # options is same as above
      # See https://github.com/mxmzdlv/pybigquery#authentication for details.
      credentials_path: "/path/to/keyfile.json" # optional
    # table_pattern/schema_pattern is same as above

AWS Athena athena

Extracts:

  • List of databases and tables
  • Column types associated with each table
source:
  type: athena
  config:
    username: aws_access_key_id # Optional. If not specified, credentials are picked up according to boto3 rules.
    # See https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
    password: aws_secret_access_key # Optional.
    database: database # Optional, defaults to "default"
    aws_region: aws_region_name # i.e. "eu-west-1"
    s3_staging_dir: s3_location # "s3://<bucket-name>/prefix/"
    # The s3_staging_dir parameter is needed because Athena always writes query results to S3.
    # See https://docs.aws.amazon.com/athena/latest/ug/querying.html
    # However, the athena driver will transparently fetch these results as you would expect from any other sql client.
    work_group: athena_workgroup # "primary"
    # table_pattern/schema_pattern is same as above

Druid druid

Extracts:

  • List of databases, schema, and tables
  • Column types associated with each table

Note It is important to define a explicitly define deny schema pattern for internal druid databases (lookup & sys) if adding a schema pattern otherwise the crawler may crash before processing relevant databases. This deny pattern is defined by default but is overriden by user-submitted configurations

source:
  type: druid
  config:
    # Point to broker address
    host_port: localhost:8082
    schema_pattern:
      deny:
        - "^(lookup|sys).*"
    # options is same as above

LDAP ldap

Extracts:

  • List of people
  • Names, emails, titles, and manager information for each person
source:
  type: "ldap"
  config:
    ldap_server: ldap://localhost
    ldap_user: "cn=admin,dc=example,dc=org"
    ldap_password: "admin"
    base_dn: "dc=example,dc=org"
    filter: "(objectClass=*)" # optional field

File file

Pulls metadata from a previously generated file. Note that the file sink can produce such files, and a number of samples are included in the examples/mce_files directory.

source:
  type: file
  filename: ./path/to/mce/file.json

Sinks

DataHub Rest datahub-rest

Pushes metadata to DataHub using the GMA rest API. The advantage of the rest-based interface is that any errors can immediately be reported.

sink:
  type: "datahub-rest"
  config:
    server: "http://localhost:8080"

DataHub Kafka datahub-kafka

Pushes metadata to DataHub by publishing messages to Kafka. The advantage of the Kafka-based interface is that it's asynchronous and can handle higher throughput. This requires the Datahub mce-consumer container to be running.

sink:
  type: "datahub-kafka"
  config:
    connection:
      bootstrap: "localhost:9092"
      producer_config: {} # passed to https://docs.confluent.io/platform/current/clients/confluent-kafka-python/index.html#serializingproducer

Console console

Simply prints each metadata event to stdout. Useful for experimentation and debugging purposes.

sink:
  type: "console"

File file

Outputs metadata to a file. This can be used to decouple metadata sourcing from the process of pushing it into DataHub, and is particularly useful for debugging purposes. Note that the file source can read files generated by this sink.

sink:
  type: file
  filename: ./path/to/mce/file.json

Using as a library

In some cases, you might want to construct the MetadataChangeEvents yourself but still use this framework to emit that metadata to DataHub. In this case, take a look at the emitter interfaces, which can easily be imported and called from your own code.

Migrating from the old scripts

If you were previously using the mce_cli.py tool to push metadata into DataHub: the new way for doing this is by creating a recipe with a file source pointing at your JSON file and a DataHub sink to push that metadata into DataHub. This example recipe demonstrates how to ingest the sample data (previously called bootstrap_mce.dat) into DataHub over the REST API. Note that we no longer use the .dat format, but instead use JSON. The main differences are that the JSON uses null instead of None and uses objects/dictionaries instead of tuples when representing unions.

If you were previously using one of the sql-etl scripts: the new way for doing this is by using the associated source. See above for configuration details. Note that the source needs to be paired with a sink - likely datahub-kafka or datahub-rest, depending on your needs.

Contributing

Contributions welcome!

Code layout

  • The CLI interface is defined in entrypoints.py.
  • The high level interfaces are defined in the API directory.
  • The actual sources and sinks have their own directories. The registry files in those directories import the implementations.
  • The metadata models are created using code generation, and eventually live in the ./src/datahub/metadata directory. However, these files are not checked in and instead are generated at build time. See the codegen script for details.

Testing

# Follow standard install procedure - see above.

# Install, including all dev requirements.
pip install -e '.[dev]'

# Run unit tests.
pytest tests/unit

# Run integration tests. Note that the integration tests require docker.
pytest tests/integration

Sanity check code before committing

# Assumes: pip install -e '.[dev]'
black --exclude 'datahub/metadata' -S -t py36 src tests
isort src tests
flake8 src tests
mypy -p datahub
pytest