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Changelog#

0.7.9#

Breaking Changes

  • If you are launching runs using DagsterInstance.launch_run, this method now takes a run id instead of an instance of PipelineRun. Additionally, DagsterInstance.create_run and DagsterInstance.create_empty_run have been replaced by DagsterInstance.get_or_create_run and DagsterInstance.create_run_for_pipeline.
  • If you have implemented your own RunLauncher, there are two required changes:
    • RunLauncher.launch_run takes a pipeline run that has already been created. You should remove any calls to instance.create_run in this method.
    • Instead of calling startPipelineExecution (defined in the dagster_graphql.client.query.START_PIPELINE_EXECUTION_MUTATION) in the run launcher, you should call startPipelineExecutionForCreatedRun (defined in dagster_graphql.client.query.START_PIPELINE_EXECUTION_FOR_CREATED_RUN_MUTATION).
    • Refer to the RemoteDagitRunLauncher for an example implementation.

New

  • Improvements to preset and solid subselection in the playground. An inline preview of the pipeline instead of a modal when doing subselection, and the correct subselection is chosen when selecting a preset.
  • Improvements to the log searching. Tokenization and autocompletion for searching messages types and for specific steps.
  • You can now view the structure of pipelines from historical runs, even if that pipeline no longer exists in the loaded repository or has changed structure.
  • Historical execution plans are now viewable, even if the pipeline has changed structure.
  • Added metadata link to raw compute logs for all StepStart events in PipelineRun view and Step view.
  • Improved error handling for the scheduler. If a scheduled run has config errors, the errors are persisted to the event log for the run and can be viewed in Dagit.

Bugfix

  • No longer manually dispose sqlalchemy engine in dagster-postgres
  • Made boto3 dependency in dagster-aws more flexible (#2418)
  • Fixed tooltip UI cleanup in partitioned schedule view

Documentation

  • Brand new documentation site, available at https://docs.dagster.io
  • The tutorial has been restructured to multiple sections, and the examples in intro_tutorial have been rearranged to separate folders to reflect this.

0.7.8#

Breaking Changes

  • The execute_pipeline_with_mode and execute_pipeline_with_preset APIs have been dropped in favor of new top level arguments to execute_pipeline, mode and preset.
  • The use of RunConfig to pass options to execute_pipeline has been deprecated, and RunConfig will be removed in 0.8.0.
  • The execute_solid_within_pipeline and execute_solids_within_pipeline APIs, intended to support tests, now take new top level arguments mode and preset.

New

  • The dagster-aws Redshift resource now supports providing an error callback to debug failed queries.
  • We now persist serialized execution plans for historical runs. They will render correctly even if the pipeline structure has changed or if it does not exist in the current loaded repository.
  • Clicking on a pipeline tag in the Runs view will apply that tag as a filter.

Bugfix

  • Fixed a bug where telemetry logger would create a log file (but not write any logs) even when telemetry was disabled.

Experimental

  • The dagster-airflow package supports ingesting Airflow dags and running them as dagster pipelines (see: make_dagster_pipeline_from_airflow_dag). This is in the early experimentation phase.
  • Improved the layout of the experimental partition runs table on the Schedules detailed view.

Documentation

  • Fixed a grammatical error (Thanks @flowersw!)

0.7.7#

Breaking Changes

  • The default sqlite and dagster-postgres implementations have been altered to extract the event step_key field as a column, to enable faster per-step queries. You will need to run dagster instance migrate to update the schema. You may optionally migrate your historical event log data to extract the step_key using the migrate_event_log_data function. This will ensure that your historical event log data will be captured in future step-key based views. 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())

  • We have made pipeline metadata serializable and persist that along with run information. While there are no user-facing features to leverage this yet, it does require an instance migration. Run dagster instance migrate. If you have already run the migration for the event_log changes above, you do not need to run it again. Any unforeseen errors related to the new snapshot_id in the runs table or the new snapshots table are related to this migration.

  • dagster-pandas ColumnTypeConstraint has been removed in favor of ColumnDTypeFnConstraint and ColumnDTypeInSetConstraint.

New

  • You can now specify that dagstermill output notebooks be yielded as an output from dagstermill solids, in addition to being materialized.
  • You may now set the extension on files created using the FileManager machinery.
  • dagster-pandas typed PandasColumn constructors now support pandas 1.0 dtypes.
  • The Dagit Playground has been restructured to make the relationship between Preset, Partition Sets, Modes, and subsets more clear. All of these buttons have be reconciled and moved to the left side of the Playground.
  • Config sections that are required but not filled out in the Dagit playground are now detected and labeled in orange.
  • dagster-celery config now support using env: to load from environment variables.

Bugfix

  • Fixed a bug where selecting a preset in dagit would not populate tags specified on the pipeline definition.
  • Fixed a bug where metadata attached to a raised Failure was not displayed in the error modal in dagit.
  • Fixed an issue where reimporting dagstermill and calling dagstermill.get_context() outside of the parameters cell of a dagstermill notebook could lead to unexpected behavior.
  • Fixed an issue with connection pooling in dagster-postgres, improving responsiveness when using the Postgres-backed storages.

Experimental

  • Added a longitudinal view of runs for on the Schedule tab for scheduled, partitioned pipelines. Includes views of run status, execution time, and materializations across partitions. The UI is in flux and is currently optimized for daily schedules, but feedback is welcome.

0.7.6#

Breaking Changes

  • default_value in Field no longer accepts native instances of python enums. Instead the underlying string representation in the config system must be used.
  • default_value in Field no longer accepts callables.
  • The dagster_aws imports have been reorganized; you should now import resources from dagster_aws.<AWS service name>. dagster_aws provides s3, emr, redshift, and cloudwatch modules.
  • The dagster_aws S3 resource no longer attempts to model the underlying boto3 API, and you can now just use any boto3 S3 API directly on a S3 resource, e.g. context.resources.s3.list_objects_v2. (#2292)

New

  • New Playground view in dagit showing an interactive config map
  • Improved storage and UI for showing schedule attempts
  • Added the ability to set default values in InputDefinition
  • Added CLI command dagster pipeline launch to launch runs using a configured RunLauncher
  • Added ability to specify pipeline run tags using the CLI
  • Added a pdb utility to SolidExecutionContext to help with debugging, available within a solid as context.pdb
  • Added PresetDefinition.with_additional_config to allow for config overrides
  • Added resource name to log messages generated during resource initialization
  • Added grouping tags for runs that have been retried / reexecuted.

Bugfix

  • Fixed a bug where date range partitions with a specified end date was clipping the last day
  • Fixed an issue where some schedule attempts that failed to start would be marked running forever.
  • Fixed the @weekly partitioned schedule decorator
  • Fixed timezone inconsistencies between the runs view and the schedules view
  • Integers are now accepted as valid values for Float config fields
  • Fixed an issue when executing dagstermill solids with config that contained quote characters.

dagstermill

  • The Jupyter kernel to use may now be specified when creating dagster notebooks with the --kernel flag.

dagster-dbt

  • dbt_solid now has a Nothing input to allow for sequencing

dagster-k8s

  • Added get_celery_engine_config to select celery engine, leveraging Celery infrastructure

Documentation

  • Improvements to the airline and bay bikes demos
  • Improvements to our dask deployment docs (Thanks jswaney!!)

0.7.5#

New

  • Added the IntSource type, which lets integers be set from environment variables in config.

  • You may now set tags on pipeline definitions. These will resolve in the following cases:

    1. Loading in the playground view in Dagit will pre-populate the tag container.
    2. Loading partition sets from the preset/config picker will pre-populate the tag container with the union of pipeline tags and partition tags, with partition tags taking precedence.
    3. Executing from the CLI will generate runs with the pipeline tags.
    4. Executing programmatically using the execute_pipeline api will create a run with the union of pipeline tags and RunConfig tags, with RunConfig tags taking precedence.
    5. Scheduled runs (both launched and executed) will have the union of pipeline tags and the schedule tags function, with the schedule tags taking precedence.

  • Output materialization configs may now yield multiple Materializations, and the tutorial has been updated to reflect this.

  • We now export the SolidExecutionContext in the public API so that users can correctly type hint solid compute functions.

Dagit

  • Pipeline run tags are now preserved when resuming/retrying from Dagit.
  • Scheduled run stats are now grouped by partition.
  • A "preparing" section has been added to the execution viewer. This shows steps that are in progress of starting execution.
  • Markers emitted by the underlying execution engines are now visualized in the Dagit execution timeline.

Bugfix

  • Resume/retry now works as expected in the presence of solids that yield optional outputs.
  • Fixed an issue where dagster-celery workers were failing to start in the presence of config values that were None.
  • Fixed an issue with attempting to set threads_per_worker on Dask distributed clusters.

dagster-postgres

  • All postgres config may now be set using environment variables in config.

dagster-aws

  • The s3_resource now exposes a list_objects_v2 method corresponding to the underlying boto3 API. (Thanks, @basilvetas!)
  • Added the redshift_resource to access Redshift databases.

dagster-k8s

  • The K8sRunLauncher config now includes the load_kubeconfig and kubeconfig_file options.

Documentation

  • Fixes and improvements.

Dependencies

  • dagster-airflow no longer pins its werkzeug dependency.

Community

  • We've added opt-in telemetry to Dagster so we can collect usage statistics in order to inform development priorities. Telemetry data will motivate projects such as adding features in frequently-used parts of the CLI and adding more examples in the docs in areas where users encounter more errors.

    We will not see or store solid definitions (including generated context) or pipeline definitions (including modes and resources). We will not see or store any data that is processed within solids and pipelines.

    If you'd like to opt in to telemetry, please add the following to $DAGSTER_HOME/dagster.yaml:

    telemetry:
  enabled: true

  • Thanks to @basilvetas and @hspak for their contributions!