marquez-airflow

A library that integrates Airflow DAGs with Marquez for automatic metadata collection.

Features

Metadata

• Task lifecycle
• Task parameters
• Task runs linked to versioned code
• Task inputs / outputs

Lineage

• Track inter-DAG dependencies

Built-in

• SQL parser
• Link to code builder (ex: GitHub)
• Metadata extractors

Requirements

• Python 3.5.0+
• Airflow 1.10.4+

Installation

$ pip3 install marquez-airflow

Note: You can also add marquez-airflow to your requirements.txt for Airflow.

To install from source, run:

$ python3 setup.py install

Configuration

The library depends on a backend. A Backend is configurable and lets the library know where to write dataset and job metadata.

Backends

• HTTP: Write metadata to Marquez
• FILE: Write metadata to a file (as json) under /tmp/marquez
• LOG: Simply just logs the metadata to the console

By default, the HTTP backend will be used (see next section). To override the default backend and write metadata to a file, use MARQUEZ_BACKEND:

MARQUEZ_BACKEND=FILE

Note: Metadata will be written to /tmp/marquez/client.requests.log, but can be overridden with MARQUEZ_FILE.

HTTP Backend Authentication

The HTTP backend supports using API keys to authenticate requests via Bearer auth. To include a key when making an API request, use MARQUEZ_API_KEY:

MARQUEZ_BACKEND=HTTP
MARQUEZ_API_KEY=[API_KEY]

Pointing to your Marquez service

marquez-airflow needs to know where to talk to the Marquez server API. You can set these using environment variables to be read by your Airflow service.

You will also need to set the namespace if you are using something other than the default namespace.

MARQUEZ_BACKEND=HTTP
MARQUEZ_URL=http://my_hosted_marquez.example.com:5000
MARQUEZ_NAMESPACE=my_special_ns

Extractors : Sending the correct data from your DAGs

If you do nothing, Marquez will receive the Job and the Run from your DAGs, but sources and datasets will not be sent.

marquez-airflow allows you to do more than that by building "Extractors". Extractors are in the process of changing right now, but they basically take a task and extract:

1. Name : The name of the task
2. Location : Location of the code for the task
3. Inputs : List of input datasets
4. Outputs : List of output datasets
5. Context : The Airflow context for the task

It's important to understand the inputs and outputs are lists and relate directly to the Dataset object in Marquez. Datasets also include a source which relates directly to the Source object in Marquez.

A PostgresExtractor is currently in progress. When that's merged, it will represent a good example of how to write custom extractors

Usage

To begin collecting Airflow DAG metadata with Marquez, use:

- from airflow import DAG
+ from marquez_airflow import DAG

When enabled, the library will:

1. On DAG start, collect metadata for each task using an Extractor (the library defines a default extractor to use otherwise)
2. Collect task input / output metadata (source, schema, etc)
3. Collect task run-level metadata (execution time, state, parameters, etc)
4. On DAG complete, also mark the task as complete in Marquez

To enable logging, set the environment variable MARQUEZ_LOG_LEVEL to DEBUG, INFO, or ERROR:

$ export MARQUEZ_LOG_LEVEL=INFO

Example

from datetime import datetime
from marquez_airflow import DAG
from airflow.operators.postgres_operator import PostgresOperator
from airflow.utils.dates import days_ago

default_args = {
    'owner': 'datascience',
    'depends_on_past': False,
    'start_date': days_ago(1),
    'email_on_failure': False,
    'email_on_retry': False,
    'email': ['[email protected]']
}

dag = DAG(
    'orders_popular_day_of_week',
    schedule_interval='@weekly',
    default_args=default_args,
    description='Determines the popular day of week orders are placed.'
)

t1 = PostgresOperator(
    task_id='if_not_exists',
    postgres_conn_id='food_delivery_db',
    sql='''
    CREATE TABLE IF NOT EXISTS popular_orders_day_of_week (
      order_day_of_week VARCHAR(64) NOT NULL,
      order_placed_on   TIMESTAMP NOT NULL,
      orders_placed     INTEGER NOT NULL
    );''',
    dag=dag
)

t2 = PostgresOperator(
    task_id='insert',
    postgres_conn_id='food_delivery_db',
    sql='''
    INSERT INTO popular_orders_day_of_week (order_day_of_week, order_placed_on, orders_placed)
      SELECT EXTRACT(ISODOW FROM order_placed_on) AS order_day_of_week,
             order_placed_on,
             COUNT(*) AS orders_placed
        FROM top_delivery_times
       GROUP BY order_placed_on;
    ''',
    dag=dag
)

t1 >> t2