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.

0.7.2

Docs

• New docs site at docs.dagster.io.
• dagster.readthedocs.io is currently stale due to availability issues.

New

• Improvements to S3 Resource. (Thanks @dwallace0723!)
• Better error messages in Dagit.
• Better font/styling support in Dagit.
• Changed OutputDefinition to take is_required rather than is_optional argument. This is to
  remain consistent with changes to Field in 0.7.1 and to avoid confusion
  with python's typing and dagster's definition of Optional, which indicates None-ability,
  rather than existence. is_optional is deprecated and will be removed in a future version.
• Added support for Flower in dagster-k8s.
• Added support for environment variable config in dagster-snowflake.

Bugfixes

• Improved performance in Dagit waterfall view.
• Fixed bug when executing solids downstream of a skipped solid.
• Improved navigation experience for pipelines in Dagit.
• Fixed for the dagster-aws CLI tool.
• Fixed issue starting Dagit without DAGSTER_HOME set on windows.
• Fixed pipeline subset execution in partition-based schedules.

0.7.1

Dagit

• Dagit now looks up an available port on which to run when the default port is
  not available. (Thanks @rparrapy!)

dagster_pandas

• Hydration and materialization are now configurable on dagster_pandas dataframes.

dagster_aws

• The s3_resource no longer uses an unsigned session by default.

Bugfixes

• Type check messages are now displayed in Dagit.
• Failure metadata is now surfaced in Dagit.
• Dagit now correctly displays the execution time of steps that error.
• Error messages now appear correctly in console logging.
• GCS storage is now more robust to transient failures.
• Fixed an issue where some event logs could be duplicated in Dagit.
• Fixed an issue when reading config from an environment variable that wasn't set.
• Fixed an issue when loading a repository or pipeline from a file target on Windows.
• Fixed an issue where deleted runs could cause the scheduler page to crash in Dagit.

Documentation

• Expanded and improved docs and error messages.

Waiting To Exhale

🎆 🚢 🎆 Dagster 0.7.0: Waiting To Exhale 😤 😌 🍵

We are pleased to announce version 0.7.0 of Dagster, codenamed “Waiting To Exhale”. We set out to make Dagster a solution for production-grade pipelines on modern cloud infrastructure. In service of that goal, we needed to fill missing gaps and incorporate feedback from the community at large.

Our last release, 0.6.0, expanded Dagster from local developer experience to a hostable product, allowing for scheduling, execution, and monitoring of pipelines in the cloud.

This release goes further, supporting pipelines with 100s and 1000s of nodes, deployable to modern, scalable cloud infrastructure, with dramatically improved monitoring tools, as well as other features.

Given this, 0.7.0 introduces the following:

• Revamped, Scalable Dagit A completely redesigned Dagit with a more intuitive navigation structure, beautiful look-and-feel, and massive performance improvements to handle pipelines with hundreds or even thousands of nodes.
• Execution Viewer Executing and historical runs within Dagit uses a new live-updating, queryable waterfall viewer. See below for a preview of the new UI:

https://media.giphy.com/media/Rhx6ujovXlvuKaLCGY/giphy.gif

• A Dagster-K8s library which provides the ability to launch runs in ephemeral Kubernetes Pods, as well as an early helm chart for executing pipelines.
• A Dagster-Celery library designed to work with K8s that provides global resource management using dedicated queues, and distributed execution of dagster pipelines across a cluster.
• Streamlined scheduler configuration and new backfill APIs and tools to help manage your scheduled workflows in production.
• A Dagster-Pandas integration that provides useful APIs for dataframe validation, summary statistics emission, and auto-documentation in dagit so that you can better understand and control how data flows through your pipelines.
• Redesigned documentation, examples, and guides to help flesh out the core ideas behind the system.

Warning

There are a substantial number of breaking changes in the 0.7.0 release. These changes effect the scheduler system, config system, required resources, and the type system. We apologize for the thrash, and thank you for bearing with us!

For more info on changes check out the following resources:

Changelog: https://github.com/dagster-io/dagster/blob/master/CHANGES.md

0.7.0 migration guide: https://github.com/dagster-io/dagster/blob/master/070_MIGRATION.md

0.4.0

API Changes

• There is now a new top-level configuration section storage which controls whether or not
  execution should store intermediate values and the history of pipeline runs on the filesystem,
  on S3, or in memory. The dagster CLI now includes options to list and wipe pipeline run
  history. Facilities are provided for user-defined types to override the default serialization
  used for storage.
• Similarily, there is a new configuration for RunConfig where the user can specify
  intermediate value storage via an API.
• OutputDefinition now contains an explicit is_optional parameter and defaults to being
  not optional.
• New functionality in dagster.check: is_list
• New functionality in dagster.seven: py23-compatible FileNotFoundError, json.dump,
  json.dumps.
• Dagster default logging is now multiline for readability.
• The Nothing type now allows dependencies to be constructed between solids that do not have
  data dependencies.
• Many error messages have been improved.
• throw_on_user_error has been renamed to raise_on_error in all APIs, public and private

GraphQL

• The GraphQL layer has been extracted out of Dagit into a separate dagster-graphql package.
• startSubplanExecution has been replaced by executePlan.
• startPipelineExecution now supports reexecution of pipeline subsets.

Dagit

• It is now possible to reexecute subsets of a pipeline run from Dagit.
• Dagit's Execute tab now opens runs in separate browser tabs and a new Runs tab allows you to
  browse and view historical runs.
• Dagit no longer scaffolds configuration when creating new Execute tabs. This functionality will
  be refined and revisited in the future.
• Dagit's Explore tab is more performant on large DAGs.
• The dagit -q command line flag has been deprecated in favor of a separate command-line
  dagster-graphql utility.
• The execute button is now greyed out when Dagit is offline.
• The Dagit UI now includes more contextual cues to make the solid in focus and its connections
  more salient.
• Dagit no longer offers to open materializations on your machine. Clicking an on-disk
  materialization now copies the path to your clipboard.
• Pressing Ctrl-Enter now starts execution in Dagit's Execute tab.
• Dagit properly shows List and Nullable types in the DAG view.

Dagster-Airflow

• Dagster-Airflow includes functions to dynamically generate containerized (DockerOperator-based)
  and uncontainerized (PythonOperator-based) Airflow DAGs from Dagster pipelines and config.

Libraries

• Dagster integration code with AWS, Great Expectations, Pandas, Pyspark, Snowflake, and Spark
  has been reorganized into a new top-level libraries directory. These modules are now
  importable as dagster_aws, dagster_ge, dagster_pandas, dagster_pyspark,
  dagster_snowflake, and dagster_spark.
• Removed dagster-sqlalchemy and dagma

Examples

• Added the event-pipeline-demo, a realistic web event data pipeline using Spark and Scala.
• Added the Pyspark pagerank example, which demonstrates how to incrementally introduce dagster
  into existing data processing workflows.

Documentation

• Docs have been expanded, reorganized, and reformatted.

0.2.8.post3

Hotfix to not put config values in error messages. Had to re-release because of packaging errors uploaded pypi (.pyc files or similar were included)