awehflow 0.0.0

Configuration based Apache Airflow

Awehflow

Configuration based Airflow pipelines with metric logging and alerting out the box.

Prerequisites

Development environment

In order to develop awehflow for a given version of Airflow follow these steps

1. Install and configure miniconda
2. On Mac, if running ARM create an x86 version of conda using the snippet below

  ### add this to ~/.zshrc (or ~/.bashrc if you're using Bash)
  create_x86_conda_environment () {
    # create a conda environment using x86 architecture
    # first argument is environment name, all subsequent arguments will be passed to `conda create`
    # example usage: create_x86_conda_environment myenv_x86 python=3.9
    
    CONDA_SUBDIR=osx-64 conda create $@
    conda activate $2
    conda config --env --set subdir osx-64
  }

1. Define the version that you'd like to install

export AIRFLOW_VERSION="2.1.4"

1. Create a conda environment for your version of Airflow, the bash below

create_x86_conda_environment -n "airflow_$AIRFLOW_VERSION" "python=3.8.12"

1. Configure the AIRFLOW_HOME directory

conda deactivate
conda activate "airflow_$AIRFLOW_VERSION"
conda env config vars set AIRFLOW_HOME="$HOME/airflow/airflow_$AIRFLOW_VERSION"
conda deactivate
conda activate airflow_"$AIRFLOW_VERSION"
echo "$AIRFLOW_HOME"

1. Install airflow using pip

conda activate airflow_$AIRFLOW_VERSION
pip install --no-cache-dir "apache-airflow==$AIRFLOW_VERSION"

1. Install required providers

conda activate airflow_$AIRFLOW_VERSION
pip install --no-cache-dir "apache-airflow[google]==$AIRFLOW_VERSION"
pip install --no-cache-dir "apache-airflow-providers-ssh==3.7.0"
pip install --no-cache-dir "apache-airflow[postgres]==$AIRFLOW_VERSION"

1. On MacOS ARM install the psycop binary bash pip install --no-cache-dir "psycopg2-binary==`pip list | grep -i 'psycopg2 ' | tr -s ' ' | cut -d' ' -f 2`"
2. Customisation per version
3. For 2.2.3 1. force the MarkupSafe package version

   pip install --no-cache-dir markupsafe==2.0.1
   

4. For 2.5.3 1. force pendulum package version

   pip install --no-cache-dir "pendulum==2.0.0"
   

1. force Flask-Session package version
  ```bash
  pip install --no-cache-dir "Flask-Session==0.5.0"
  ```

1. Install the awehflow requirements

pip install --no-cache-dir -r requirements.txt

1. Init the airflow db

airflow db init

You will need the following to run this code:

• Python 3

Installation

pip install awehflow[default]

If you are installing on Google Cloud Composer with Airflow 1.10.2:

pip install awehflow[composer]

Event & metric tables

Create a postgresql database that can be referenced via Airflow connection. In the DB create the following tables

• Jobs data table

  CREATE TABLE public.jobs (
    id serial4 NOT NULL,
    run_id varchar NOT NULL,
    dag_id varchar NULL,
    "name" varchar NULL,
    project varchar NULL,
    status varchar NULL,
    engineers json NULL,
    error json NULL,
    start_time timestamptz NULL,
    end_time timestamptz NULL,
    reference_time timestamptz NULL,
    CONSTRAINT job_id_pkey PRIMARY KEY (id),
    CONSTRAINT run_id_dag_id_unique UNIQUE (run_id, dag_id)
  );
  

• Task metrics table

  CREATE TABLE public.task_metrics (
    id serial4 NOT NULL,
    run_id varchar NULL,
    dag_id varchar NULL,
    task_id varchar NULL,
    job_name varchar NULL,
    value json NULL,
    created_time timestamptz NULL,
    reference_time timestamptz NULL,
    CONSTRAINT task_metrics_id_pkey PRIMARY KEY (id)
  );
  

• Data metrics table

  CREATE TABLE public.data_metrics (
    id serial4 NOT NULL,
    platform varchar NULL,
    "source" varchar NULL,
    "key" varchar NULL,
    value json NULL,
    reference_time timestamptz NULL,
    CONSTRAINT data_metrics_pkey PRIMARY KEY (id),
    CONSTRAINT unique_metric UNIQUE (platform, source, key, reference_time)
  );
  

Usage

Usage of awehflow can be broken up into two parts: bootstrapping and configuration of pipelines

Bootstrap

In order to expose the generated pipelines (airflow DAGs) for airflow to pick up when scanning for DAGs, one has to create a DagLoader that points to a folder where the pipeline configuration files will be located:

import os

from awehflow.alerts.slack import SlackAlerter
from awehflow.core import DagLoader
from awehflow.events.postgres import PostgresMetricsEventHandler

"""airflow doesn't pick up DAGs in files unless 
the words 'airflow' and 'DAG' features"""

configs_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'configs')

metrics_handler = PostgresMetricsEventHandler(jobs_table='jobs', task_metrics_table='task_metrics')

slack_alerter = SlackAlerter(channel='#airflow')

loader = DagLoader(
    project="awehflow-demo",
    configs_path=configs_path,
    event_handlers=[metrics_handler],
    alerters=[slack_alerter]
)

dags = loader.load(global_symbol_table=globals())

As seen in the code snippet, one can also pass in "event handlers" and "alerters" to perform actions on certain pipeline events and potentially alert the user of certain events on a given channel. See the sections below for more detail. The global symbol table needs to be passed to the loader since airflow scans it for objects of type DAG, and then synchronises the state with its own internal state store.

*caveat: airflow ignores python files that don't contain the words "airflow" and "DAG". It is thus advised to put those words in a comment to ensure the generated DAGs get picked up when the DagBag is getting filled.

Event Handlers

As a pipeline generated using awehflow is running, certain events get emitted. An event handler gives the user the option of running code when these events occur.

The following events are (potentially) potentially emitted as a pipeline runs:

• start
• success
• failure
• task_metric

Existing event handlers include:

• PostgresMetricsEventHandler: persists pipeline metrics to a Postgres database
• PublishToGooglePubSubEventHandler: events get passed straight to a Google Pub/Sub topic

An AlertsEventHandler gets automatically added to a pipeline. Events get passed along to registered alerters.

Alerters

An Alerter is merely a class that implements an alert method. By default a SlackAlerter is configured in the dags/PROJECT/bootstrap.py file of an awehflow project. awehflow supports the addition of multiple alerters, which allows success or failure events to be sent to mutliple channels

YAML configuration

In order to add alerts to an awehflow DAG add the following to the root space of the configuration

alert_on:
  - 'failure' # Send out a formatted message if a task in the DAG fails. This is optional
  - 'success' # Send out a formatted message once the DAG completes successfully. This is optional

Available alerters

SlackAlerter - awehflow.alerts.slack.SlackAlerter

Sends an alert to a specified slack channel via the Slack webhook functionality

• Parameters
  • channel - The name of the channel that the alerts should be sent to
  • slack_conn_id - The name of the airflow connection that contains the token information, default: slack_default
• Connection requirements - Create a HTTP connection with the name specified for slack_conn_id, the required HTTP fields are:
  • password - The slack token issued by your admin team, which allows for the sending of messages via the slack python API

GoogleChatAlerter - awehflow.alerts.googlechat.GoogleChatAlerter

Sends an alert to the configured Google Chat space

• Parameters
  • gchat_conn_id - The name of the airflow connection that contains the GChat space information, default: gchat_default
• Connection requirements - Create a HTTP connection with the name specified for the gchat_conn_id, the requried HTTP fields are:
  • host - The GChat spaces URL https://chat.googleapis.com/v1/spaces
  • password - The GChat spaces key configuration information, ex https://chat.googleapis.com/v1/spaces/SPACES_ID?key=SPACES_KEY
    • SPACES_ID - Should be supplied by your GChat admin team
    • SPACES_KEY - Should be supplied by your GChat admin team

Configuration

Awehflow configuration files can be written as .yml OR .hocon files either formats are supported

Shown below is sample hocon configuration file

{
  name: my_first_dag,
  version: 1,
  description: "This is my first dag",
  owner: The name of the owner of the DAG
  schedule: "10 0 * * *",
  start_date: 2022-01-01,
  end_date: 2022-01-01,
  catchup: true,
  concurrency: 1 // Defaults to airflow configuration
  max_active_tasks: 1 // Defaults to airflow configuration
  max_active_runs: 1 // Defaults to airflow configuration
  dagrun_timeout: None
  doc_md: The DAG documentation markdown
  access_control: None // A dict of roles that have specific permissions
  is_paused_upon_creation: None // Defaults to airflow configuration
  tags: [
    'tag one',
    'tag two'
  ],
  dag_params: { 
    /* This dict will define DAG parameters and defaulted when triggering a DAG manually with CONF,
    Values are accessible as template values {{ dag_run.conf["config_value_1"] }}
    */
    'config_value_1': 'SOME TEXT',
    'config_value_2': 1234
  },
  alert_on:[ // Whether the events alert should send a message on success OR failure
    success,
    failure
  ],
  params: { // parameter values that will be passed in to each task for rendering
    default: {
      source_folder: /tmp
    },
    production: {
      source_folder: /data
    }
  },
  default_dag_args: { //The default DAG arguments whichis also passed to each task in the dag
    retries: 1
  },
  pre_hooks: [ // Pre hook sensors are executed BEFORE the start task
    {
      id: 'pre_hook_ping_sensor'
      operator: 'airflow.sensors.bash.BashSensor'
      params: {
        bash_command: 'echo ping'
        mode: 'reschedule'
      }
    }
  ],
  dependencies: [ // Dependencies sensors are executed AFTER the start task to the DAG start time being logged
    {
      id: 'dependenciy_ping_sensor'
      operator: 'airflow.sensors.bash.BashSensor'
      params: {
        bash_command: 'echo ping'
        mode: 'reschedule'
      }
    }
  ],
  tasks: [ // The array of Tasks that defines the DAG
      {
        id: first_dummy_task, // The task ID that will be shown in the task bubble or tree
        operator: airflow.operators.dummy.DummyOperator, // The fully qualified path to the Class of the Operator
      },
      {
        id: first_bash_task, // The task ID that will be shown in the task bubble or tree
        operator: airflow.operators.bash.BashOperator, // The fully qualified path to the Class of the Operator
        params: { 
          /* 
          The dictionary of parameters that will be passed to the Operator, the "default_dag_args" dict will be merged with this.
          Any parameter of the Operator Class can be added to this dict, template rending of values depends on the specific Operator
          */
          bash_command: 'echo "Hello World"'
        },
        upstream: [ // The list of tasks that must be executed prior to this task
          first_dummy_task
        ]
      }
    ]
}

This configuration does the following:

• Creates a DAG called my_first_dag
  • Scheduled to run daily 10min past midnight
  • Catchup has been enabled to ensure all runs of the DAG since 2022-01-01 are executed
• Pre hooks
  • Check if the command echo ping succeeds
• Dependencies
  • Check if the command echo ping succeeds
• Tasks
  • First run a dummy task that does nothing
  • If the dummy task succeeds, execute the bash command

Running the tests

Tests may be run with

python -m unittest discover tests

or to run code coverage too:

coverage run -m unittest discover tests && coverage html