API Docs#

These docs aim to cover the entire public surface of the core dagster APIs, as well as public APIs from all provided libraries.

Dagster follows SemVer. We attempt to isolate breaking changes to the public APIs to minor versions (on a roughly 12-week cadence) and will announce deprecations in Slack and in the release notes to patch versions (on a roughly weekly cadence).

Core#

APIs from the core dagster package, divided roughly by topic:

  • Jobs APIs to define jobs that execute a set of ops with specific parameters.

  • Ops APIs to define or decorate functions as ops, declare their inputs and outputs, compose ops with each other, as well as the datatypes that op execution can return or yield.

  • Graphs APIs to define a logical structure of ops.

  • Resources APIs to define resources which can provide specific implementations of certain functionality within a job.

  • Loggers APIs to define where logs go.

  • Repositories APIs to define collections of pipelines and other definitions that tools such as the Dagster CLI or Dagit can load from.

  • Config The types available to describe config schemas.

  • Types Primitive types available for the input and output values of solids, and the APIs used to define and test new Dagster types.

  • Dagster CLI Browse repositories and execute jobs from the command line

  • Schedules & Sensors APIs to define schedules and sensors that initiate job execution, as well as some built-in helpers for common cases.

  • Partitions APIs to define partitions of the config space over which job runs can be backfilled.

  • Errors Errors thrown by the Dagster framework.

  • Execution APIs to execute and test jobs and individual ops, the execution context available to ops, job configuration, and the default executors available for executing jobs.

  • Hooks APIs to define Dagster hooks, which can be triggered on specific Dagster events.

  • IO Managers APIs to define how inputs and outputs are handled and loaded.

  • Dynamic Mapping & Collect APIs that allow graph structures to be determined at run time.

  • Versioning & Memoization (Experimental) Code versioning and memoization of previous outputs based upon that versioning.

  • Software-Defined Assets (Experimental). Software-defined assets sit on top of the graph/job/op APIs and enable a novel way of constructing Dagster jobs that puts assets at the forefront.

  • Utilities Miscellaneous helpers used by Dagster that may be useful to users.

  • Internals Core internal APIs that are important if you are interested in understanding how Dagster works with an eye towards extending it: logging, executors, system storage, the Dagster instance & plugin machinery, storage, schedulers.

Legacy#

APIs from the core dagsterpackage that are still supported, but no longer recommended.

  • Solids APIs to define or decorate functions as solids, declare their inputs and outputs, compose solids with each other, as well as the datatypes that solid execution can return or yield.

  • Pipelines APIs to define pipelines, dependencies and fan-in dependencies between solids, and aliased instances of solids.

  • Modes APIs to define pipeline modes.

  • Presets APIs to define configuration presets.

Libraries#

Dagster also provides a growing set of optional add-on libraries to integrate with infrastructure and other components of the data ecosystem:

  • Airflow (dagster-airflow) Tools for compiling Dagster pipelines to Airflow DAGs, and for ingesting Airflow DAGs to represent them in Dagster.

  • AWS (dagster-aws) Dagster integrations for working with AWS resources.

  • Azure (dagster-azure) Dagster integrations for working with Microsoft Azure resources.

  • Celery (dagster-celery) Provides an executor built on top of the popular Celery task queue, and an executor with support for using Celery on Kubernetes.

  • Celery+Docker (dagster-celery-docker) Provides an executor that lets Celery workers execute in Docker containers.

  • Celery+Kubernetes (dagster-celery-k8s) Provides an executor that lets Celery workers execute on Kubernetes.

  • Dask (dagster-dask) Provides an executor built on top of dask.distributed.

  • dbt (dagster-dbt) Provides ops and resources to run dbt projects.

  • Fivetran (dagster-fivetran) Provides ops and resources to run Fivetran syncs.

  • Databricks (dagster-databricks) Provides ops and resources for integrating with Databricks.

  • Datadog (dagster-datadog) Provides an integration with Datadog, to support publishing metrics to Datadog from within Dagster solids.

  • Docker (dagster-docker) Provides components for deploying Dagster to Docker.

  • GCP (dagster-gcp) Dagster integrations for working with Google Cloud Platform resources.

  • GE (dagster-ge) Dagster integrations for working with Great Expectations data quality tests.

  • GitHub (dagster-github) Provides a resource for issuing GitHub GraphQL queries and filing GitHub issues from Dagster pipelines.

  • Kubernetes (dagster-k8s) Provides components for deploying Dagster to Kubernetes.

  • MLflow (dagster-mlflow) Provides resources and hooks for using MLflow functionalities with Dagster runs.

  • Microsoft Teams (dagster-msteams) Includes a simple integration with Microsoft Teams.

  • MLflow (dagster-mlflow) Resources and hooks for integrating with MLflow.

  • MySQL (dagster-mysql) Includes implementations of run and event log storage built on MySQL.

  • PagerDuty (dagster-pagerduty) Provides an integration for generating PagerDuty events from Dagster solids.

  • Pandas (dagster-pandas) Provides support for using pandas DataFrames in Dagster and utilities for performing data validation.

  • Papertrail (dagster-papertrail) Provides support for sending Dagster logs to Papertrail.

  • PostgreSQL (dagster-postgres) Includes implementations of run and event log storage built on Postgres.

  • Prometheus (dagster-prometheus) Provides support for sending metrics to Prometheus.

  • Pyspark (dagster-pyspark) Provides an integration with pyspark.

  • Shell (dagster-shell) Provides utilities for issuing shell commands from Dagster pipelines.

  • Slack (dagster-slack) Provides a simple integration with Slack.

  • Snowflake (dagster-snowflake) Provides a resource for querying Snowflake from Dagster.

  • Spark (dagster-spark) Provides an integration for working with Spark in Dagster.

  • SSH / SFTP (dagster-ssh) Provides an integration for running commands over SSH and retrieving / posting files via SFTP.

  • Twilio (dagster-twilio) Provides a resource for posting SMS messages from solids via Twilio.

  • GraphQL (dagster-graphql) Provides resources for interfacing with a Dagster deployment over GraphQL.