AssetSelection.dagster-snowflake now requires snowflake-connector-python>=3.4.0@sling_assets accepts an optional name parameter for the underlying opdagster-openai library is now available.DagsterDbtTranslatorSettings called enable_duplicate_source_asset_keys that allows users to set duplicate asset keys for their dbt sources. Thanks @hello-world-bfree!ConfigurableResource.SlingConnectionResource where a blank keyword argument would be emitted as an environment variabledbt source freshness would cause an error.DbtArtifacts has been added for managing the behavior of rebuilding the manifest during development but expecting a pre-built one in production.send sample alert button now exists on both the alert policies page and in the alert policies editor to make it easier to debug and configure alerts without having to wait for an event to kick them off.SlingConnectionResource that raised an error when connecting to a database.graph_multi_assets with check_specs now support subsetting.run_retries.retry_on_op_or_asset_failures setting that can be set to false to make run retries only occur when there is an unexpected failure that crashes the run, allowing run-level retries to co-exist more naturally with op or asset retries. See the docs for more information.dagster dev now sets the environment variable DAGSTER_IS_DEV_CLI allowing subprocesses to know that they were launched in a development context.@multi_observable_source_asset decorator enables defining a set of assets that can be observed together with the same function.@sling_assets and Resource SlingConnectionResource have been added for the [dagster-embedded-elt.sling](http://dagster-embedded-elt.sling) package. Deprecated build_sling_asset, SlingSourceConnection and SlingTargetConnection.QueuedRunCoordinator.dagster-polars has been added as an integration. Thanks @danielgafni!@dbt_assets now supports loading projects with semantic models.@dbt_assets now supports loading projects with model versions.get_asset_key_for_model now supports retrieving asset keys for seeds and snapshots. Thanks @aksestok!@asset decorator causing a false positive in Pyright strict mode. Thanks @tylershunt!security_context field on the k8s_job_executor didn't correctly set the security context on the launched step pods. Thanks @krgn!ObserveResults with no data_version.FreshnessPolicys on observable source assets. These assets will be considered “Overdue” when the latest value for the “dagster/data_time” metadata value is older than what’s allowed by the freshness policy.DbtCliResource now include metadata from the dbt adapter response. This includes fields like rows_affected, query_id from the Snowflake adapter, or bytes_processed from the BigQuery adapter.k8s_job_executor.context.log.exception in a sensor or schedule did not properly capture exception information.@observable_source_asset-decorated functions can now return an ObserveResult. This allows including metadata on the observation, in addition to a data version. This is currently only supported for non-partitioned assets.AutoMaterializeRule.skip_on_not_all_parents_updated_since_cron class allows you to construct AutoMaterializePolicys which wait for all parents to be updated after the latest tick of a given cron schedule.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
reexecute_pipeline to reexecute an existing pipeline run.project field to the gcs_resource in dagster_gcp.dagster asset wipe to remove all existing asset keys.Bugfix
executeRunInProcess.dagster schedule up output to be repository location scopedBugfix
dagster instance migrate.launch_scheduled_execution that would mask configuration errors.dagster-k8s when specifying per-step resources.New
label optional parameter for materializations with asset_key specified.Assets page to have a typeahead selector and hierarchical views based on asset_key path.SSHResource, replacing sftp_solid.Docs
Bugfix
OSError: [Errno 24] Too many open files when enough
temporary files were created.New
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:
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.
The deprecated runtime_type property on InputDefinition and OutputDefinition has been
removed. Use dagster_type instead.
The deprecated has_runtime_type, runtime_type_named, and all_runtime_types methods on
PipelineDefinition have been removed. Use has_dagster_type, dagster_type_named, and
all_dagster_types instead.
The deprecated all_runtime_types method on SolidDefinition and CompositeSolidDefinition
has been removed. Use all_dagster_types instead.
The deprecated metadata argument to SolidDefinition and @solid has been removed. Use
tags instead.
The graphviz-based DAG visualization in Dagster core has been removed. Please use Dagit!
dagit
dagit-cli has been removed, and dagit is now the only console entrypoint.dagster-aws
dagster_aws.EmrRunJobFlowSolidDefinition has been removed.dagster-bash
bash_command_solid and bash_script_solid
solid factory functions have been renamed to create_shell_command_solid and
create_shell_script_solid.dagster-celery
--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 }'dagster-dask
dagster_dask.dask_executor has changed. The previous config should
now be nested under the key local.dagster-gcp
BigQueryClient has been removed. Use bigquery_resource instead.dagster-dbt
dagster-spark
dagster_spark.SparkSolidDefinition has been removed - use create_spark_solid instead.SparkRDD Dagster type, which only worked with an in-memory engine, has been removed.dagster-twilio
TwilioClient has been removed. Use twilio_resource instead.New
dagster
asset_key on any Materialization to use the new asset system. You will also
need to configure an asset-aware storage, such as Postgres. The longitudinal_pipeline example
demonstrates this system.end_time.dagit
/graphiql as well as at /graphql.dagster-aws
dagster_aws.S3ComputeLogManager may now be configured to override the S3 endpoint and
associated SSL settings.dagster-azure
adls2_system_storage or, for direct access, the adls2_resource resource. (Thanks
@sd2k!)dagster-dask
dagster_dask.dask_executor. For full support, you will need
to install extras with pip install dagster-dask[yarn, pbs, kube]. (Thanks @DavidKatz-il!)dagster-databricks
databricks_pyspark_step_launcher. (Thanks @sd2k!)dagster-gcp
dagster-k8s
CeleryK8sRunLauncher to submit execution plan steps to Celery task queues for
execution as k8s Jobs.dagster-pandas
dagster-papertrail
papertrail_logger may now be set using either
environment variables or literals.dagster-pyspark
emr_pyspark_step_launcher, or on Databricks using
the new dagster-databricks package. The emr_pyspark example demonstrates how to use a step
launcher.dagster-snowflake
snowflake_resource may now be set using either
environment variables or literals.dagster-spark
dagster_spark.create_spark_solid now accepts a required_resource_keys argument, which
enables setting up a step launcher for Spark solids, like the emr_pyspark_step_launcher.Bugfix
dagster pipeline execute now sets a non-zero exit code when pipeline execution fails.Bugfix
NoOpComputeLogManager to be configured as the compute_logs implementation in
dagster.yaml