Ask AI

You are viewing an unreleased or outdated version of the documentation

View Latest Documentation

Changelog#

0.7.11#

Bugfix

  • Fixed an issue with strict snapshot ID matching when loading historical snapshots, which caused errors on the Runs page when viewing historical runs.
  • Fixed an issue where dagster_celery had introduced a spurious dependency on dagster_k8s (#2435)
  • Fixed an issue where our Airflow, Celery, and Dask integrations required S3 or GCS storage and prevented use of filesystem storage. Filesystem storage is now also permitted, to enable use of these integrations with distributed filesystems like NFS (#2436).

0.7.10#

New

  • RepositoryDefinition now takes schedule_defs and partition_set_defs directly. The loading scheme for these definitions via repository.yaml under the scheduler: and partitions: keys is deprecated and expected to be removed in 0.8.0.
  • Mark published modules as python 3.8 compatible.
  • The dagster-airflow package supports loading all Airflow DAGs within a directory path, file path, or Airflow DagBag.
  • The dagster-airflow package supports loading all 23 DAGs in Airflow example_dags folder and execution of 17 of them (see: make_dagster_repo_from_airflow_example_dags).
  • The dagster-celery CLI tools now allow you to pass additional arguments through to the underlying celery CLI, e.g., running dagster-celery worker start -n my-worker -- --uid=42 will pass the --uid flag to celery.
  • It is now possible to create a PresetDefinition that has no environment defined.
  • Added dagster schedule debug command to help debug scheduler state.
  • The SystemCronScheduler now verifies that a cron job has been successfully been added to the crontab when turning a schedule on, and shows an error message if unsuccessful.

Breaking Changes

  • A dagster instance migrate is required for this release to support the new experimental assets view.
  • Runs created prior to 0.7.8 will no longer render their execution plans as DAGs. We are only rendering execution plans that have been persisted. Logs are still available.
  • Path is no longer valid in config schemas. Use str or dagster.String instead.
  • Removed the @pyspark_solid decorator - its functionality, which was experimental, is subsumed by requiring a StepLauncher resource (e.g. emr_pyspark_step_launcher) on the solid.

Dagit

  • Merged "re-execute", "single-step re-execute", "resume/retry" buttons into one "re-execute" button with three dropdown selections on the Run page.

Experimental

  • Added new asset_key string parameter to Materializations and created a new “Assets” tab in Dagit to view pipelines and runs associated with these keys. The API and UI of these asset-based are likely to change, but feedback is welcome and will be used to inform these changes.
  • Added an emr_pyspark_step_launcher that enables launching PySpark solids in EMR. The "simple_pyspark" example demonstrates how it’s used.

Bugfix

  • Fixed an issue when running Jupyter notebooks in a Python 2 kernel through dagstermill with Dagster running in Python 3.
  • Improved error messages produced when dagstermill spins up an in-notebook context.
  • Fixed an issue with retrieving step events from CompositeSolidResult objects.

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.