dbloy 0.3.0

Continuous Delivery tool for PySpark Notebooks based jobs on Databricks.

DBloy

A Databricks deployment CLI tool to enable Continuous Delivery of PySpark Notebooks based jobs.

Installation

$ pip install dbloy

Usage

Authenticate with Databricks using authentication token:

$ dbloy configure 

Update Databricks Job

$ dbloy apply --deploy-yml deploy.yml --configmap-yml configmap.yml --version <my_version_number>

where deploy.yml and configmap.yml contain the Job specification. The Job version is specified in <my_version_number>

Workflow

source: https://databricks.com/blog/2017/10/30/continuous-integration-continuous-delivery-databricks.html

Example Usage

See example/gitlab_my-etl-job for a example ETL repository using Gitlab's CI/CD.

A Deployment requires the following:

• Deployment manifest
• Configuration manifest
• A main Databricks Notebook source file available locally.
• (Optional) Attached python library containing the core logic. This allows easier unit testing of

Creating a Deployment

deploy.yml

kind: Deployment
metadata:
  name: my-etl-job
  workspace: Shared
template:
  job:
    name: My ETL Job
  notifications:
    email:
      no_alert_for_skipped_runs: true
      on_failure :
        - my_email@my_org.com
  base_notebook: main
  notebooks:
    - EPHEMERAL_NOTEBOOK_1: notebook_name1
    - EPHEMERAL_NOTEBOOK_2: notebook_name2
  libraries:
    - egg_main: dbfs:/python35/my_python_lib/my_python_lib-VERSION-py3.5.egg
    - egg: dbfs:/python35/static_python_lib.egg
    - pypi:
        package: scikit-learn==0.20.3
    - pypi:
        package: statsmodels==0.10.1
    - pypi:
        package: prometheus-client==0.7.1
    - jar: dbfs:/FileStore/jars/e9b87e4c_c754_4707_a62a_44ef47535b39-azure_cosmosdb_spark_2_4_0_2_11_1_3_4_uber-38021.jar
  run:
    max_concurrent_runs: 1
    max_retries: 1
    min_retry_interval_millis: 600000
    retry_on_timeout: true
    timeout_seconds: 10800

configmap.yml

kind: ConfigMap
metadata:
  namespace: production
params:
  DB_URL: production_db_url_1
  DB_PASSWORD: production_password123
job:
  id: 289
  schedule:
    quartz_cron_expression: "0 0 0 * * ?"
    timezone_id: "Europe/Berlin"
  max_retries: "1"
cluster:
  spark_version: "5.3.x-scala2.11"
  node_type_id: "Standard_DS3_v2"
  driver_node_type_id: "Standard_DS3_v2"
  autoscale:
    min_workers: 1
    max_workers: 2
  spark_env_vars:
    PYSPARK_PYTHON: "/databricks/python3/bin/python3"

In this example:

• Job id 289 on Databricks, indicated by the .job.id field in configmap.yml, will be updated with the name My ETL Job, indicated by the .template.job.name field in deploy.yml.
• A cluster will be created on demand which is specified by the field .cluster in configmap.yml. See https://docs.databricks.com/api/latest/clusters.html#request-structure for a complete list of cluster settings. Note: Setting .cluster.existing_cluster_id will use an existing cluster.
• Libraries specified by the field .template.libraries in .deploy.yml will be installed on the cluster. See https://docs.databricks.com/api/latest/libraries.html#library. Note: The field .template.libraries.egg_main is reserved for python .egg file that is versioned with the ETL job. For example when the main logic of the ETL job is put into a library. The .egg version number is expected to be the same as the ETL version number.
• The main task notebook that will be executed by the job is defined by the field .template.base_notebook in deploy.yml. Task parameters are specified by the field .params in configmap.yml which will be accessible in the Notebooks via dbutils.
• The notebook main, indicated by the field .template.base_notebook is the Task notebook. This notebook should be found in the workspace /Shared/my-etl-job/<my_version_number>/main specified by the fields .metadata and .template.base_notebook in deploy.yml. The version number <my_version_number> will be specified in the CLI command.
• Two ephemeral notebooks are available under /Shared/my-etl-job/<my_version_number>/notebook_name1 and /Shared/my-etl-job/<my_version_number>/notebook_name2. This allows the main task to execute nested Notebooks, e.g.

notebook_path_1 = dbutils.widgets.get("EPHEMERAL_NOTEBOOK_1")
dbutils.notebook.run(notebook_path_1)

Create the Deployment by running the following command:

$ dbloy apply --deploy-yml deploy.yml --configmap-yml configmap.yml --version <my_version_number>