- The default sqlite and
dagster-postgresimplementations have been altered to extract the
step_keyfield as a column, to enable faster per-step queries. You will need to run
dagster instance migrateto update the schema. You may optionally migrate your historical event
log data to extract the
migrate_event_log_datafunction. This will ensure
that your historical event log data will be captured in future step-key based views. This
event_logdata 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
above, you do not need to run it again. Any unforeseen errors related the the new
runstable or the new
snapshotstable are related to this migration.
ColumnTypeConstrainthas been removed in favor of
- 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
- dagster-pandas typed
PandasColumnconstructors 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.
- Fixed a bug where selecting a preset in
dagitwould not populate tags specified on the pipeline
- Fixed a bug where metadata attached to a raised
Failurewas not displayed in the error modal in
- Fixed an issue where reimporting dagstermill and calling
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.
- Added a longitudinal view of runs for on the
Scheduletab 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.