Changelog#

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.

0.7.0 "Waiting to Exhale"#

Breaking Changes

There are a substantial number of breaking changes in the 0.7.0 release. Please see 070_MIGRATION.md for instructions regarding migrating old code.

Scheduler

The scheduler configuration has been moved from the @schedules decorator to DagsterInstance. Existing schedules that have been running are no longer compatible with current storage. To migrate, remove the scheduler argument on all @schedules decorators:
instead of:
```
@schedules(scheduler=SystemCronScheduler)
def define_schedules():
  ...
```
Remove the scheduler argument:
```
@schedules
def define_schedules():
  ...
```
Next, configure the scheduler on your instance by adding the following to $DAGSTER_HOME/dagster.yaml:
```
scheduler:
  module: dagster_cron.cron_scheduler
  class: SystemCronScheduler
```
Finally, if you had any existing schedules running, delete the existing $DAGSTER_HOME/schedules directory and run dagster schedule wipe && dagster schedule up to re-instatiate schedules in a valid state.
The should_execute and environment_dict_fn argument to ScheduleDefinition now have a required first argument context, representing the ScheduleExecutionContext

Config System Changes

In the config system, Dict has been renamed to Shape; List to Array; Optional to Noneable; and PermissiveDict to Permissive. The motivation here is to clearly delineate config use cases versus cases where you are using types as the inputs and outputs of solids as well as python typing types (for mypy and friends). We believe this will be clearer to users in addition to simplifying our own implementation and internal abstractions.
Our recommended fix is not to use Shape and Array, but instead to use our new condensed config specification API. This allow one to use bare dictionaries instead of Shape, lists with one member instead of Array, bare types instead of Field with a single argument, and python primitive types (int, bool etc) instead of the dagster equivalents. These result in dramatically less verbose config specs in most cases.
So instead of
```
from dagster import Shape, Field, Int, Array, String
# ... code
config=Shape({ # Dict prior to change
      'some_int' : Field(Int),
      'some_list: Field(Array[String]) # List prior to change
  })
```
one can instead write:
```
config={'some_int': int, 'some_list': [str]}
```
No imports and much simpler, cleaner syntax.
config_field is no longer a valid argument on solid, SolidDefinition, ExecutorDefintion, executor, LoggerDefinition, logger, ResourceDefinition, resource, system_storage, and SystemStorageDefinition. Use config instead.

For composite solids, the config_fn no longer takes a ConfigMappingContext, and the context has been deleted. To upgrade, remove the first argument to config_fn.

So instead of


@composite_solid(config={}, config_fn=lambda context, config: {})

one must instead write:


@composite_solid(config={}, config_fn=lambda config: {})

Field takes a is_required rather than a is_optional argument. This is 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.

Required Resources

All solids, types, and config functions that use a resource must explicitly list that resource using the argument required_resource_keys. This is to enable efficient resource management during pipeline execution, especially in a multiprocessing or remote execution environment.
The @system_storage decorator now requires argument required_resource_keys, which was previously optional.

Dagster Type System Changes

dagster.Set and dagster.Tuple can no longer be used within the config system.
Dagster types are now instances of DagsterType, rather than a class than inherits from RuntimeType. Instead of dynamically generating a class to create a custom runtime type, just create an instance of a DagsterType. The type checking function is now an argument to the DagsterType, rather than an abstract method that has to be implemented in a subclass.
RuntimeType has been renamed to DagsterType is now an encouraged API for type creation.
Core type check function of DagsterType can now return a naked bool in addition to a TypeCheck object.
type_check_fn on DagsterType (formerly type_check and RuntimeType, respectively) now takes a first argument context of type TypeCheckContext in addition to the second argument of value.
define_python_dagster_type has been eliminated in favor of PythonObjectDagsterType .
dagster_type has been renamed to usable_as_dagster_type.
as_dagster_type has been removed and similar capabilities added as make_python_type_usable_as_dagster_type.
PythonObjectDagsterType and usable_as_dagster_type no longer take a type_check argument. If a custom type_check is needed, use DagsterType.
As a consequence of these changes, if you were previously using dagster_pyspark or dagster_pandas and expecting Pyspark or Pandas types to work as Dagster types, e.g., in type annotations to functions decorated with @solid to indicate that they are input or output types for a solid, you will need to call make_python_type_usable_as_dagster_type from your code in order to map the Python types to the Dagster types, or just use the Dagster types (dagster_pandas.DataFrame instead of pandas.DataFrame) directly.

Other

We no longer publish base Docker images. Please see the updated deployment docs for an example Dockerfile off of which you can work.
step_metadata_fn has been removed from SolidDefinition & @solid.
SolidDefinition & @solid now takes tags and enforces that values are strings or are safely encoded as JSON. metadata is deprecated and will be removed in a future version.
resource_mapper_fn has been removed from SolidInvocation.

New

Dagit now includes a much richer execution view, with a Gantt-style visualization of step execution and a live timeline.
Early support for Python 3.8 is now available, and Dagster/Dagit along with many of our libraries are now tested against 3.8. Note that several of our upstream dependencies have yet to publish wheels for 3.8 on all platforms, so running on Python 3.8 likely still involves building some dependencies from source.
dagster/priority tags can now be used to prioritize the order of execution for the built-in in-process and multiprocess engines.

dagster-postgres storages can now be configured with separate arguments and environment variables, such as:


run_storage:
  module: dagster_postgres.run_storage
  class: PostgresRunStorage
  config:
    postgres_db:
      username: test
      password:
        env: ENV_VAR_FOR_PG_PASSWORD
      hostname: localhost
      db_name: test

Support for RunLaunchers on DagsterInstance allows for execution to be "launched" outside of the Dagit/Dagster process. As one example, this is used by dagster-k8s to submit pipeline execution as a Kubernetes Job.
Added support for adding tags to runs initiated from the Playground view in Dagit.
Added @monthly_schedule decorator.
Added Enum.from_python_enum helper to wrap Python enums for config. (Thanks @kdungs!)
[dagster-bash] The Dagster bash solid factory now passes along kwargs to the underlying solid construction, and now has a single Nothing input by default to make it easier to create a sequencing dependency. Also, logs are now buffered by default to make execution less noisy.
[dagster-aws] We've improved our EMR support substantially in this release. The dagster_aws.emr library now provides an EmrJobRunner with various utilities for creating EMR clusters, submitting jobs, and waiting for jobs/logs. We also now provide a emr_pyspark_resource, which together with the new @pyspark_solid decorator makes moving pyspark execution from your laptop to EMR as simple as changing modes. [dagster-pandas] Added create_dagster_pandas_dataframe_type, PandasColumn, and Constraint API's in order for users to create custom types which perform column validation, dataframe validation, summary statistics emission, and dataframe serialization/deserialization.
[dagster-gcp] GCS is now supported for system storage, as well as being supported with the Dask executor. (Thanks @habibutsu!) Bigquery solids have also been updated to support the new API.

Bugfix

Ensured that all implementations of RunStorage clean up pipeline run tags when a run is deleted. Requires a storage migration, using dagster instance migrate.
The multiprocess and Celery engines now handle solid subsets correctly.
The multiprocess and Celery engines will now correctly emit skip events for steps downstream of failures and other skips.
The @solid and @lambda_solid decorators now correctly wrap their decorated functions, in the sense of functools.wraps.
Performance improvements in Dagit when working with runs with large configurations.
The Helm chart in dagster_k8s has been hardened against various failure modes and is now compatible with Helm 2.
SQLite run and event log storages are more robust to concurrent use.
Improvements to error messages and to handling of user code errors in input hydration and output materialization logic.
Fixed an issue where the Airflow scheduler could hang when attempting to load dagster-airflow pipelines.
We now handle our SQLAlchemy connections in a more canonical way (thanks @zzztimbo!).
Fixed an issue using S3 system storage with certain custom serialization strategies.
Fixed an issue leaking orphan processes from compute logging.
Fixed an issue leaking semaphores from Dagit.
Setting the raise_error flag in execute_pipeline now actually raises user exceptions instead of a wrapper type.

Documentation

Our docs have been reorganized and expanded (thanks @habibutsu, @vatervonacht, @zzztimbo). We'd love feedback and contributions!

Thank you Thank you to all of the community contributors to this release!! In alphabetical order: @habibutsu, @kdungs, @vatervonacht, @zzztimbo.