github dagster-io/dagster 0.8.0
0.8.0 "In The Zone"

Major Changes

Please see the 080_MIGRATION.md migration guide for details on updating existing code to be
compatible with 0.8.0

  • Workspace, host and user process separation, and repository definition Dagit and other tools no
    longer load a single repository containing user definitions such as pipelines into the same
    process as the framework code. Instead, they load a "workspace" that can contain multiple
    repositories sourced from a variety of different external locations (e.g., Python modules and
    Python virtualenvs, with containers and source control repositories soon to come).

The repositories in a workspace are loaded into their own "user" processes distinct from the
"host" framework process. Dagit and other tools now communicate with user code over an IPC
mechanism. This architectural change has a couple of advantages:

  • Dagit no longer needs to be restarted when there is an update to user code.
  • Users can use repositories to organize their pipelines, but still work on all of their
    repositories using a single running Dagit.
  • The Dagit process can now run in a separate Python environment from user code so pipeline
    dependencies do not need to be installed into the Dagit environment.
  • Each repository can be sourced from a separate Python virtualenv, so teams can manage their
    dependencies (or even their own Python versions) separately.

We have introduced a new file format, workspace.yaml, in order to support this new architecture.
The workspace yaml encodes what repositories to load and their location, and supersedes the
repository.yaml file and associated machinery.

As a consequence, Dagster internals are now stricter about how pipelines are loaded. If you have
written scripts or tests in which a pipeline is defined and then passed across a process boundary
(e.g., using the multiprocess_executor or dagstermill), you may now need to wrap the pipeline
in the reconstructable utility function for it to be reconstructed across the process boundary.

In addition, rather than instantiate the RepositoryDefinition class directly, users should now
prefer the @repository decorator. As part of this change, the @scheduler and
@repository_partitions decorators have been removed, and their functionality subsumed under
@repository.

  • Dagit organization The Dagit interface has changed substantially and is now oriented around
    pipelines. Within the context of each pipeline in an environment, the previous "Pipelines" and
    "Solids" tabs have been collapsed into the "Definition" tab; a new "Overview" tab provides
    summary information about the pipeline, its schedules, its assets, and recent runs; the previous
    "Playground" tab has been moved within the context of an individual pipeline. Related runs (e.g.,
    runs created by re-executing subsets of previous runs) are now grouped together in the Playground
    for easy reference. Dagit also now includes more advanced support for display of scheduled runs
    that may not have executed ("schedule ticks"), as well as longitudinal views over scheduled runs,
    and asset-oriented views of historical pipeline runs.

  • Assets Assets are named materializations that can be generated by your pipeline solids, which
    support specialized views in Dagit. For example, if we represent a database table with an asset
    key, we can now index all of the pipelines and pipeline runs that materialize that table, and
    view them in a single place. To use the asset system, you must enable an asset-aware storage such
    as Postgres.

  • Run launchers The distinction between "starting" and "launching" a run has been effaced. All
    pipeline runs instigated through Dagit now make use of the RunLauncher configured on the
    Dagster instance, if one is configured. Additionally, run launchers can now support termination of
    previously launched runs. If you have written your own run launcher, you may want to update it to
    support termination. Note also that as of 0.7.9, the semantics of RunLauncher.launch_run have
    changed; this method now takes the run_id of an existing run and should no longer attempt to
    create the run in the instance.

  • Flexible reexecution Pipeline re-execution from Dagit is now fully flexible. You may
    re-execute arbitrary subsets of a pipeline's execution steps, and the re-execution now appears
    in the interface as a child run of the original execution.

  • Support for historical runs Snapshots of pipelines and other Dagster objects are now persisted
    along with pipeline runs, so that historial runs can be loaded for review with the correct
    execution plans even when pipeline code has changed. This prepares the system to be able to diff
    pipeline runs and other objects against each other.

  • Step launchers and expanded support for PySpark on EMR and Databricks We've introduced a new
    StepLauncher abstraction that uses the resource system to allow individual execution steps to
    be run in separate processes (and thus on separate execution substrates). This has made extensive
    improvements to our PySpark support possible, including the option to execute individual PySpark
    steps on EMR using the EmrPySparkStepLauncher and on Databricks using the
    DatabricksPySparkStepLauncher The emr_pyspark example demonstrates how to use a step launcher.

  • Clearer names What was previously known as the environment dictionary is now called the
    run_config, and the previous environment_dict argument to APIs such as execute_pipeline is
    now deprecated. We renamed this argument to focus attention on the configuration of the run
    being launched or executed, rather than on an ambiguous "environment". We've also renamed the
    config argument to all use definitions to be config_schema, which should reduce ambiguity
    between the configuration schema and the value being passed in some particular case. We've also
    consolidated and improved documentation of the valid types for a config schema.

  • Lakehouse We're pleased to introduce Lakehouse, an experimental, alternative programming model
    for data applications, built on top of Dagster core. Lakehouse allows developers to define data
    applications in terms of data assets, such as database tables or ML models, rather than in terms
    of the computations that produce those assets. The simple_lakehouse example gives a taste of
    what it's like to program in Lakehouse. We'd love feedback on whether this model is helpful!

  • Airflow ingest We've expanded the tooling available to teams with existing Airflow installations
    that are interested in incrementally adopting Dagster. Previously, we provided only injection
    tools that allowed developers to write Dagster pipelines and then compile them into Airflow DAGs
    for execution. We've now added ingestion tools that allow teams to move to Dagster for execution
    without having to rewrite all of their legacy pipelines in Dagster. In this approach, Airflow
    DAGs are kept in their own container/environment, compiled into Dagster pipelines, and run via
    the Dagster orchestrator. See the airflow_ingest example for details!

Breaking Changes

  • dagster

    • The @scheduler and @repository_partitions decorators have been removed. Instances of
      ScheduleDefinition and PartitionSetDefinition belonging to a repository should be specified
      using the @repository decorator instead.
    • Support for the Dagster solid selection DSL, previously introduced in Dagit, is now uniform
      throughout the Python codebase, with the previous solid_subset arguments (--solid-subset in
      the CLI) being replaced by solid_selection (--solid-selection). In addition to the names of
      individual solids, this argument now supports selection queries like *solid_name++ (i.e.,
      solid_name, all of its ancestors, its immediate descendants, and their immediate descendants).
    • The built-in Dagster type Path has been removed.
    • PartitionSetDefinition names, including those defined by a PartitionScheduleDefinition,
      must now be unique within a single repository.
    • Asset keys are now sanitized for non-alphanumeric characters. All characters besides
      alphanumerics and _ are treated as path delimiters. Asset keys can also be specified using
      AssetKey, which accepts a list of strings as an explicit path. If you are running 0.7.10 or
      later and using assets, you may need to migrate your historical event log data for asset keys
      from previous runs to be attributed correctly. This event_log data migration can be invoked
      as follows:
    from dagster.core.storage.event_log.migration import migrate_event_log_data
    from dagster import DagsterInstance
    
    
    migrate_event_log_data(instance=DagsterInstance.get())
    
    • The interface of the Scheduler base class has changed substantially. If you've written a
      custom scheduler, please get in touch!
    • The partitioned schedule decorators now generate PartitionSetDefinition names using
      the schedule name, suffixed with _partitions.
    • The repository property on ScheduleExecutionContext is no longer available. If you were
      using this property to pass to Scheduler instance methods, this interface has changed
      significantly. Please see the Scheduler class documentation for details.
    • The CLI option --celery-base-priority is no longer available for the command:
      dagster pipeline backfill. Use the tags option to specify the celery priority, (e.g.
      dagster pipeline backfill my_pipeline --tags '{ "dagster-celery/run_priority": 3 }'
    • The execute_partition_set API has been removed.
    • The deprecated is_optional parameter to Field and OutputDefinition has been removed.
      Use is_required instead.
    • The deprecated runtime_type property on InputDefinition and OutputDefinition has been
      removed. Use dagster_type instead.
    • The deprecated has_runtime_type, runtime_type_named, and all_runtime_types methods on
      PipelineDefinition have been removed. Use has_dagster_type, dagster_type_named, and
      all_dagster_types instead.
    • The deprecated all_runtime_types method on SolidDefinition and CompositeSolidDefinition
      has been removed. Use all_dagster_types instead.
    • The deprecated metadata argument to SolidDefinition and @solid has been removed. Use
      tags instead.
    • The graphviz-based DAG visualization in Dagster core has been removed. Please use Dagit!
    • dagit
    • dagit-cli has been removed, and dagit is now the only console entrypoint.
    • dagster-aws
    • The AWS CLI has been removed.
    • dagster_aws.EmrRunJobFlowSolidDefinition has been removed.
    • dagster-bash
    • This package has been renamed to dagster-shell. Thebash_command_solid and bash_script_solid
      solid factory functions have been renamed to create_shell_command_solid and
      create_shell_script_solid.
    • dagster-celery
    • The CLI option --celery-base-priority is no longer available for the command:
      dagster pipeline backfill. Use the tags option to specify the celery priority, (e.g.
      dagster pipeline backfill my_pipeline --tags '{ "dagster-celery/run_priority": 3 }'
    • dagster-dask
    • The config schema for the dagster_dask.dask_executor has changed. The previous config should
      now be nested under the key local.
    • dagster-gcp
    • The BigQueryClient has been removed. Use bigquery_resource instead.
    • dagster-dbt
    • The dagster-dbt package has been removed. This was inadequate as a reference integration, and
      will be replaced in 0.8.x.
    • dagster-spark
    • dagster_spark.SparkSolidDefinition has been removed - use create_spark_solid instead.
    • The SparkRDD Dagster type, which only worked with an in-memory engine, has been removed.
    • dagster-twilio
    • The TwilioClient has been removed. Use twilio_resource instead.
      New
    • dagster
    • You may now set asset_key on any Materialization to use the new asset system. You will also
      need to configure an asset-aware storage, such as Postgres. The longitudinal_pipeline example
      demonstrates this system.
    • The partitioned schedule decorators now support an optional end_time.
    • Opt-in telemetry now reports the Python version being used.
    • dagit
    • Dagit's GraphQL playground is now available at /graphiql as well as at /graphql.
    • dagster-aws
    • The dagster_aws.S3ComputeLogManager may now be configured to override the S3 endpoint and
      associated SSL settings.
    • Config string and integer values in the S3 tooling may now be set using either environment
      variables or literals.
    • dagster-azure
    • We've added the dagster-azure package, with support for Azure Data Lake Storage Gen2; you can
      use the adls2_system_storage or, for direct access, the adls2_resource resource. (Thanks
      @sd2k!)
    • dagster-dask
    • Dask clusters are now supported by dagster_dask.dask_executor. For full support, you will need
      to install extras with pip install dagster-dask[yarn, pbs, kube]. (Thanks @DavidKatz-il!)
    • dagster-databricks
    • We've added the dagster-databricks package, with support for running PySpark steps on Databricks
      clusters through the databricks_pyspark_step_launcher. (Thanks @sd2k!)
    • dagster-gcp
    • Config string and integer values in the BigQuery, Dataproc, and GCS tooling may now be set
      using either environment variables or literals.
    • dagster-k8s
    • Added the CeleryK8sRunLauncher to submit execution plan steps to Celery task queues for
      execution as k8s Jobs.
    • Added the ability to specify resource limits on a per-pipeline and per-step basis for k8s Jobs.
    • Many improvements and bug fixes to the dagster-k8s Helm chart.
    • dagster-pandas
    • Config string and integer values in the dagster-pandas input and output schemas may now be set
      using either environment variables or literals.
    • dagster-papertrail
    • Config string and integer values in the papertrail_logger may now be set using either
      environment variables or literals.
    • dagster-pyspark
    • PySpark solids can now run on EMR, using the emr_pyspark_step_launcher, or on Databricks using
      the new dagster-databricks package. The emr_pyspark example demonstrates how to use a step
      launcher.
    • dagster-snowflake
    • Config string and integer values in the snowflake_resource may now be set using either
      environment variables or literals.
    • dagster-spark
    • dagster_spark.create_spark_solid now accepts a required_resource_keys argument, which
      enables setting up a step launcher for Spark solids, like the emr_pyspark_step_launcher.
      Bugfix
    • dagster pipeline execute now sets a non-zero exit code when pipeline execution fails.
latest releases: 0.10.1.pre0, 0.10.1, 0.10.0...
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