0.8.8

New

• The new configured API makes it easy to create configured versions of resources.
• Deprecated the Materialization event type in favor of the new AssetMaterialization event type,
  which requires the asset_key parameter. Solids yielding Materialization events will continue
  to work as before, though the Materialization event will be removed in a future release.
• We have added an intermediate_store_defs argument to ModeDefinition, which will eventually
  replace system storage. You can only use one or the other for now. We will eventually deprecate
  system storage entirely, but continued usage for the time being is fine.
• The help panel in the dagit config editor can now be resized and toggled open or closed, to
  enable easier editing on smaller screens.

Bugfixes

• Opening new Dagit browser windows maintains your current repository selection. #2722
• Pipelines with the same name in different repositories no longer incorrectly share playground state. #2720
• Setting default_value config on a field now works as expected. #2725
• Fixed rendering bug in the dagit run reviewer where yet-to-be executed execution steps were
  rendered on left-hand side instead of the right.

0.8.7

Breaking Changes

• Loading python modules reliant on the working directory being on the PYTHONPATH is no longer
  supported. The dagster and dagit CLI commands no longer add the working directory to the
  PYTHONPATH when resolving modules, which may break some imports. Explicitly installed python
  packages can be specified in workspaces using the python_package workspace yaml config option.
  The python_module config option is deprecated and will be removed in a future release.

New

• Dagit can be hosted on a sub-path by passing --path-prefix to the dagit CLI. #2073
• The date_partition_range util function now accepts an optional inclusive boolean argument. By default, the function does not return include the partition for which the end time of the date range is greater than the current time. If inclusive=True, then the list of partitions returned will include the extra partition.
• MultiDependency or fan-in inputs will now only cause the solid step to skip if all of the
  fanned-in inputs upstream outputs were skipped

Bugfixes

• Fixed accidental breaking change with input_hydration_config arguments
• Fixed an issue with yaml merging (thanks @shasha79!)
• Invoking alias on a solid output will produce a useful error message (thanks @iKintosh!)
• Restored missing run pagination controls
• Fixed error resolving partition-based schedules created via dagster schedule decorators (e.g. daily_schedule) for certain workspace.yaml formats

0.8.6

Breaking Changes

• The dagster-celery module has been broken apart to manage dependencies more coherently. There are now three modules: dagster celery, dagster-celery-k8s, and dagster-celery-docker.
• Related to above, the dagster-celery worker start command now takes a required -A parameter which must point to the app.py file within the appropriate module. E.g if you are using the celery_k8s_job_executor then you must use the -A dagster_celery_k8s.app option when using the celery or dagster-celery cli tools. Similar for the celery_docker_executor: -A dagster_celery_docker.app must be used.
• Renamed the input_hydration_config and output_materialization_config decorators to dagster_type_ and dagster_type_materializer respectively. Renamed DagsterType's input_hydration_config and output_materialization_config arguments to loader and materializer respectively.

New

• New pipeline scoped runs tab in Dagit

• Add the following Dask Job Queue clusters: moab, sge, lsf, slurm, oar (thanks @DavidKatz-il!)

• K8s resource-requirements for run coordinator pods can be specified using the dagster-k8s/resource_requirements tag on pipeline definitions:

  @pipeline(
      tags={
          'dagster-k8s/resource_requirements': {
              'requests': {'cpu': '250m', 'memory': '64Mi'},
              'limits': {'cpu': '500m', 'memory': '2560Mi'},
          }
      },
  )
  def foo_bar_pipeline():

• Added better error messaging in dagit for partition set and schedule configuration errors

• An initial version of the CeleryDockerExecutor was added (thanks @mrdrprofuroboros!). The celery workers will launch tasks in docker containers.

• Experimental: Great Expectations integration is currently under development in the new library dagster-ge. Example usage can be found here

0.8.5

Breaking Changes

• Python 3.5 is no longer under test.
• Engine and ExecutorConfig have been deleted in favor of Executor. Instead of the @executor decorator decorating a function that returns an ExecutorConfig it should now decorate a function that returns an Executor.

New

• The python built-in dict can be used as an alias for Permissive() within a config schema declaration.
• Use StringSource in the S3ComputeLogManager configuration schema to support using environment variables in the configuration (Thanks @mrdrprofuroboros!)
• Improve Backfill CLI help text
• Add options to spark_df_output_schema (Thanks @DavidKatz-il!)
• Helm: Added support for overriding the PostgreSQL image/version used in the init container checks.
• Update celery k8s helm chart to include liveness checks for celery workers and flower
• Support step level retries to celery k8s executor

Bugfixes

• Improve error message shown when a RepositoryDefinition returns objects that are not one of the allowed definition types (Thanks @sd2k!)
• Show error message when $DAGSTER_HOME environment variable is not an absolute path (Thanks @AndersonReyes!)
• Update default value for staging_prefix in the DatabricksPySparkStepLauncher configuration to be an absolute path (Thanks @sd2k!)
• Improve error message shown when Databricks logs can't be retrieved (Thanks @sd2k!)
• Fix errors in documentation fo input_hydration_config (Thanks @joeyfreund!)

0.8.4

Bugfix

• Reverted changed in 0.8.3 that caused error during run launch in certain circumstances
• Updated partition graphs on schedule page to select most recent run
• Forced reload of partitions for partition sets to ensure not serving stale data

New

• Added reload button to dagit to reload current repository
• Added option to wipe a single asset key by using dagster asset wipe <asset_key>
• Simplified schedule page, removing ticks table, adding tags for last tick attempt
• Better debugging tools for launch errors

0.8.3

Breaking Changes

• Previously, the gcs_resource returned a GCSResource wrapper which had a single client property that returned a google.cloud.storage.client.Client. Now, the gcs_resource returns the client directly.

  To update solids that use the gcp_resource, change:

  context.resources.gcs.client
  

  To:

  context.resources.gcs
  

New

• Introduced a new Python API reexecute_pipeline to reexecute an existing pipeline run.
• Performance improvements in Pipeline Overview and other pages.
• Long metadata entries in the asset details view are now scrollable.
• Added a project field to the gcs_resource in dagster_gcp.
• Added new CLI command dagster asset wipe to remove all existing asset keys.

Bugfix

• Several Dagit bugfixes and performance improvements
• Fixes pipeline execution issue with custom run launchers that call executeRunInProcess.
• Updates dagster schedule up output to be repository location scoped

0.8.2

Bugfix

• Fixes issues with dagster instance migrate.
• Fixes bug in launch_scheduled_execution that would mask configuration errors.
• Fixes bug in dagit where schedule related errors were not shown.
• Fixes JSON-serialization error in dagster-k8s when specifying per-step resources.

New

• Makes label optional parameter for materializations with asset_key specified.
• Changes Assets page to have a typeahead selector and hierarchical views based on asset_key path.
• dagster-ssh
  • adds SFTP get and put functions to SSHResource, replacing sftp_solid.

Docs

• Various docs corrections

0.8.1

Bugfix

• Fixed a file descriptor leak that caused OSError: [Errno 24] Too many open files when enough
  temporary files were created.
• Fixed an issue where an empty config in the Playground would unexpectedly be marked as invalid
  YAML.
• Removed "config" deprecation warnings for dask and celery executors.

New

• Improved performance of the Assets page.

0.8.0 "In The Zone"

Major Changes

Please see the 080_MIGRATION.md migration guide for details on updating existing code to be
compatible with 0.8.0

• Workspace, host and user process separation, and repository definition Dagit and other tools no
  longer load a single repository containing user definitions such as pipelines into the same
  process as the framework code. Instead, they load a "workspace" that can contain multiple
  repositories sourced from a variety of different external locations (e.g., Python modules and
  Python virtualenvs, with containers and source control repositories soon to come).

  The repositories in a workspace are loaded into their own "user" processes distinct from the
  "host" framework process. Dagit and other tools now communicate with user code over an IPC
  mechanism. This architectural change has a couple of advantages:

  • Dagit no longer needs to be restarted when there is an update to user code.
  • Users can use repositories to organize their pipelines, but still work on all of their
    repositories using a single running Dagit.
  • The Dagit process can now run in a separate Python environment from user code so pipeline
    dependencies do not need to be installed into the Dagit environment.
  • Each repository can be sourced from a separate Python virtualenv, so teams can manage their
    dependencies (or even their own Python versions) separately.

  We have introduced a new file format, workspace.yaml, in order to support this new architecture.
  The workspace yaml encodes what repositories to load and their location, and supersedes the
  repository.yaml file and associated machinery.

  As a consequence, Dagster internals are now stricter about how pipelines are loaded. If you have
  written scripts or tests in which a pipeline is defined and then passed across a process boundary
  (e.g., using the multiprocess_executor or dagstermill), you may now need to wrap the pipeline
  in the reconstructable utility function for it to be reconstructed across the process boundary.

  In addition, rather than instantiate the RepositoryDefinition class directly, users should now
  prefer the @repository decorator. As part of this change, the @scheduler and
  @repository_partitions decorators have been removed, and their functionality subsumed under
  @repository.

• Dagit organization The Dagit interface has changed substantially and is now oriented around
  pipelines. Within the context of each pipeline in an environment, the previous "Pipelines" and
  "Solids" tabs have been collapsed into the "Definition" tab; a new "Overview" tab provides
  summary information about the pipeline, its schedules, its assets, and recent runs; the previous
  "Playground" tab has been moved within the context of an individual pipeline. Related runs (e.g.,
  runs created by re-executing subsets of previous runs) are now grouped together in the Playground
  for easy reference. Dagit also now includes more advanced support for display of scheduled runs
  that may not have executed ("schedule ticks"), as well as longitudinal views over scheduled runs,
  and asset-oriented views of historical pipeline runs.

• Assets Assets are named materializations that can be generated by your pipeline solids, which
  support specialized views in Dagit. For example, if we represent a database table with an asset
  key, we can now index all of the pipelines and pipeline runs that materialize that table, and
  view them in a single place. To use the asset system, you must enable an asset-aware storage such
  as Postgres.

• Run launchers The distinction between "starting" and "launching" a run has been effaced. All
  pipeline runs instigated through Dagit now make use of the RunLauncher configured on the
  Dagster instance, if one is configured. Additionally, run launchers can now support termination of
  previously launched runs. If you have written your own run launcher, you may want to update it to
  support termination. Note also that as of 0.7.9, the semantics of RunLauncher.launch_run have
  changed; this method now takes the run_id of an existing run and should no longer attempt to
  create the run in the instance.

• Flexible reexecution Pipeline re-execution from Dagit is now fully flexible. You may
  re-execute arbitrary subsets of a pipeline's execution steps, and the re-execution now appears
  in the interface as a child run of the original execution.

• Support for historical runs Snapshots of pipelines and other Dagster objects are now persisted
  along with pipeline runs, so that historial runs can be loaded for review with the correct
  execution plans even when pipeline code has changed. This prepares the system to be able to diff
  pipeline runs and other objects against each other.

• Step launchers and expanded support for PySpark on EMR and Databricks We've introduced a new
  StepLauncher abstraction that uses the resource system to allow individual execution steps to
  be run in separate processes (and thus on separate execution substrates). This has made extensive
  improvements to our PySpark support possible, including the option to execute individual PySpark
  steps on EMR using the EmrPySparkStepLauncher and on Databricks using the
  DatabricksPySparkStepLauncher The emr_pyspark example demonstrates how to use a step launcher.

• Clearer names What was previously known as the environment dictionary is now called the
  run_config, and the previous environment_dict argument to APIs such as execute_pipeline is
  now deprecated. We renamed this argument to focus attention on the configuration of the run
  being launched or executed, rather than on an ambiguous "environment". We've also renamed the
  config argument to all use definitions to be config_schema, which should reduce ambiguity
  between the configuration schema and the value being passed in some particular case. We've also
  consolidated and improved documentation of the valid types for a config schema.

• Lakehouse We're pleased to introduce Lakehouse, an experimental, alternative programming model
  for data applications, built on top of Dagster core. Lakehouse allows developers to define data
  applications in terms of data assets, such as database tables or ML models, rather than in terms
  of the computations that produce those assets. The simple_lakehouse example gives a taste of
  what it's like to program in Lakehouse. We'd love feedback on whether this model is helpful!

• Airflow ingest We've expanded the tooling available to teams with existing Airflow installations
  that are interested in incrementally adopting Dagster. Previously, we provided only injection
  tools that allowed developers to write Dagster pipelines and then compile them into Airflow DAGs
  for execution. We've now added ingestion tools that allow teams to move to Dagster for execution
  without having to rewrite all of their legacy pipelines in Dagster. In this approach, Airflow
  DAGs are kept in their own container/environment, compiled into Dagster pipelines, and run via
  the Dagster orchestrator. See the airflow_ingest example for details!

Breaking Changes

• dagster

  • The @scheduler and @repository_partitions decorators have been removed. Instances of
    ScheduleDefinition and PartitionSetDefinition belonging to a repository should be specified
    using the @repository decorator instead.

  • Support for the Dagster solid selection DSL, previously introduced in Dagit, is now uniform
    throughout the Python codebase, with the previous solid_subset arguments (--solid-subset in
    the CLI) being replaced by solid_selection (--solid-selection). In addition to the names of
    individual solids, this argument now supports selection queries like *solid_name++ (i.e.,
    solid_name, all of its ancestors, its immediate descendants, and their immediate descendants).

  • The built-in Dagster type Path has been removed.

  • PartitionSetDefinition names, including those defined by a PartitionScheduleDefinition,
    must now be unique within a single repository.

  • Asset keys are now sanitized for non-alphanumeric characters. All characters besides
    alphanumerics and _ are treated as path delimiters. Asset keys can also be specified using
    AssetKey, which accepts a list of strings as an explicit path. If you are running 0.7.10 or
    later and using assets, you may need to migrate your historical event log data for asset keys
    from previous runs to be attributed correctly. 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())

  • The interface of the Scheduler base class has changed substantially. If you've written a
    custom scheduler, please get in touch!

  • The partitioned schedule decorators now generate PartitionSetDefinition names using
    the schedule name, suffixed with _partitions.

  • The repository property on ScheduleExecutionContext is no longer available. If you were
    using this property to pass to Scheduler instance methods, this interface has changed
    significantly. Please see the Scheduler class documentation for details.

  • The CLI option --celery-base-priority is no longer available for the command:
    dagster pipeline backfill. Use the tags option to specify the celery priority, (e.g.
    dagster pipeline backfill my_pipeline --tags '{ "dagster-celery/run_priority": 3 }'

  • The execute_partition_set API has been removed.

  • The deprecated is_optional parameter to Field and OutputDefinition has been removed.
    Use is_required instead.
    ...

0.7.16

Bugfix

• Enabled NoOpComputeLogManager to be configured as the compute_logs implementation in dagster.yaml
• Suppressed noisy error messages in logs from skipped step