0.7.12

Bugfix

• We now only render the subset of an execution plan that has actually executed, and persist that subset information along with the snapshot.
• @pipeline and @composite_solid now correctly capture doc from the function they decorate.
• Fixed a bug with using solid subsets in the Dagit playground

0.7.11

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• Path is no longer valid in config schemas.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.
  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 the 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!

0.7.4

New

• It is now possible to use Postgres to back schedule storage by configuring
  dagster_postgres.PostgresScheduleStorage on the instance.
• Added the execute_pipeline_with_mode API to allow executing a pipeline in test with a specific
  mode without having to specify RunConfig.
• Experimental support for retries in the Celery executor.
• It is now possible to set run-level priorities for backfills run using the Celery executor by
  passing --celery-base-priority to dagster pipeline backfill.
• Added the @weekly schedule decorator.

Deprecations

• The dagster-ge library has been removed from this release due to drift from the underlying
  Great Expectations implementation.

dagster-pandas

• PandasColumn now includes an is_optional flag, replacing the previous
  ColumnExistsConstraint.
• You can now pass the ignore_missing_values flag to PandasColumn in order to apply column
  constraints only to the non-missing rows in a column.

dagster-k8s

• The Helm chart now includes provision for an Ingress and for multiple Celery queues.

Documentation

• Improvements and fixes.

0.7.3

New

• It is now possible to configure a dagit instance to disable executing pipeline runs in a local
  subprocess.
• Resource initialization, teardown, and associated failure states now emit structured events
  visible in Dagit. Structured events for pipeline errors and multiprocess execution have been
  consolidated and rationalized.
• Support Redis queue provider in dagster-k8s Helm chart.
• Support external postgresql in dagster-k8s Helm chart.

Bugfix

• Fixed an issue with inaccurate timings on some resource initializations.
• Fixed an issue that could cause the multiprocess engine to spin forever.
• Fixed an issue with default value resolution when a config value was set using SourceString.
• Fixed an issue when loading logs from a pipeline belonging to a different repository in Dagit.
• Fixed an issue with where the CLI command dagster schedule up would fail in certain scenarios
  with the SystemCronScheduler.

Pandas

• Column constraints can now be configured to permit NaN values.

Dagstermill

• Removed a spurious dependency on sklearn.

Docs

• Improvements and fixes to docs.
• Restored dagster.readthedocs.io.

Experimental

• An initial implementation of solid retries, throwing a RetryRequested exception, was added.
  This API is experimental and likely to change.

Other

• Renamed property runtime_type to dagster_type in definitions. The following are deprecated
  and will be removed in a future version.
  • InputDefinition.runtime_type is deprecated. Use InputDefinition.dagster_type instead.
  • OutputDefinition.runtime_type is deprecated. Use OutputDefinition.dagster_type instead.
  • CompositeSolidDefinition.all_runtime_types is deprecated. Use CompositeSolidDefinition.all_dagster_types instead.
  • SolidDefinition.all_runtime_types is deprecated. Use SolidDefinition.all_dagster_types instead.
  • PipelineDefinition.has_runtime_type is deprecated. Use PipelineDefinition.has_dagster_type instead.
  • PipelineDefinition.runtime_type_named is deprecated. Use PipelineDefinition.dagster_type_named instead.
  • PipelineDefinition.all_runtime_types is deprecated. Use PipelineDefinition.all_dagster_types instead.