Changelog#

1.6.6 (core) / 0.22.6 (libraries)#

New#

• Dagster officially supports Python 3.12.
• dagster-polars has been added as an integration. Thanks @danielgafni!
• [dagster-dbt] @dbt_assets now supports loading projects with semantic models.
• [dagster-dbt] @dbt_assets now supports loading projects with model versions.
• [dagster-dbt] get_asset_key_for_model now supports retrieving asset keys for seeds and snapshots. Thanks @aksestok!
• [dagster-duckdb] The Dagster DuckDB integration supports DuckDB version 0.10.0.
• [UPath I/O manager] If a non-partitioned asset is updated to have partitions, the file containing the non-partitioned asset data will be deleted when the partitioned asset is materialized, rather than raising an error.

Bugfixes#

• Fixed an issue where creating a backfill of assets with dynamic partitions and a backfill policy would sometimes fail with an exception.
• Fixed an issue with the type annotations on the @asset decorator causing a false positive in Pyright strict mode. Thanks @tylershunt!
• [ui] On the asset graph, nodes are slightly wider allowing more text to be displayed, and group names are no longer truncated.
• [ui] Fixed an issue where the groups in the asset graph would not update after an asset was switched between groups.
• [dagster-k8s] Fixed an issue where setting the security_context field on the k8s_job_executor didn't correctly set the security context on the launched step pods. Thanks @krgn!

Experimental#

• Observable source assets can now yield ObserveResults with no data_version.
• You can now include FreshnessPolicys on observable source assets. These assets will be considered “Overdue” when the latest value for the “dagster/data_time” metadata value is older than what’s allowed by the freshness policy.
• [ui] In Dagster Cloud, a new feature flag allows you to enable an overhauled asset overview page with a high-level stakeholder view of the asset’s health, properties, and column schema.

Documentation#

• Updated docs to reflect newly-added support for Python 3.12.

Dagster Cloud#

• [kubernetes] Fixed an issue where the Kubernetes agent would sometimes leave dangling kubernetes services if the agent was interrupted during the middle of being terminated.

1.6.5 (core) / 0.22.5 (libraries)#

New#

• Within a backfill or within auto-materialize, when submitting runs for partitions of the same assets, runs are now submitted in lexicographical order of partition key, instead of in an unpredictable order.
• [dagster-k8s] Include k8s pod debug info in run worker failure messages.
• [dagster-dbt] Events emitted by DbtCliResource now include metadata from the dbt adapter response. This includes fields like rows_affected, query_id from the Snowflake adapter, or bytes_processed from the BigQuery adapter.

Bugfixes#

• A previous change prevented asset backfills from grouping multiple assets into the same run when using BackfillPolicies under certain conditions. While the backfills would still execute in the proper order, this could lead to more individual runs than necessary. This has been fixed.
• [dagster-k8s] Fixed an issue introduced in the 1.6.4 release where upgrading the Helm chart without upgrading the Dagster version used by user code caused failures in jobs using the k8s_job_executor.
• [instigator-tick-logs] Fixed an issue where invoking context.log.exception in a sensor or schedule did not properly capture exception information.
• [asset-checks] Fixed an issue where additional dependencies for dbt tests modeled as Dagster asset checks were not properly being deduplicated.
• [dagster-dbt] Fixed an issue where dbt model, seed, or snapshot names with periods were not supported.

Experimental#

• @observable_source_asset-decorated functions can now return an ObserveResult. This allows including metadata on the observation, in addition to a data version. This is currently only supported for non-partitioned assets.
• [auto-materialize] A new AutoMaterializeRule.skip_on_not_all_parents_updated_since_cron class allows you to construct AutoMaterializePolicys which wait for all parents to be updated after the latest tick of a given cron schedule.
• [Global op/asset concurrency] Ops and assets now take run priority into account when claiming global op/asset concurrency slots.

Documentation#

• Fixed an error in our asset checks docs. Thanks @vaharoni!
• Fixed an error in our Dagster Pipes Kubernetes docs. Thanks @cameronmartin!
• Fixed an issue on the Hello Dagster! guide that prevented it from loading.
• Add specific capabilities of the Airflow integration to the Airflow integration page.
• Re-arranged sections in the I/O manager concept page to make info about using I/O versus resources more prominent.

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.

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!!)