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).
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Path is no longer valid in config schemas.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.
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
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.
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.