create_pull_request method on GithubClient that enables creating a pull request.create_ref method on GithubClient that enables creating a new branch.dlt.run(...).fetch_row_count() for both partitioned and non-partitioned assets. Thanks @kristianandre!PartitionedConfig objects can now return a RunConfig without causing a crash.AssetIn.__new__ typing for the dagster_type argument.fetch_row_count() method now works for databases returning uppercase column names. Thanks @kristianandre!GCSFileManager.read operations. Thanks @ollie-bell!JobDefinition, @job, and define_asset_job now take a run_tags parameter. If run_tags are defined, they will be attached to all runs of the job, and tags will not be. If run_tags is not set, then tags are attached to all runs of the job (status quo behavior). This change enables the separation of definition-level and run-level tags on jobs.DAGSTER_COMPUTE_LOG_TAIL_WAIT_AFTER_FINISH can now be used to pause before capturing logs (thanks @HynekBlaha!)kinds parameter is now available on AssetSpec.OutputContext now exposes the AssetSpec of the asset that is being stored as an output (thanks, @marijncv!)AutomationCondition on an asset, the label of this condition will now be shown in the sidebar on the Asset Details page.dagster definitions validate have been added.AssetSpec now has a with_io_manager_key method that returns an AssetSpec with the appropriate metadata entry to dictate the key for the IO manager used to load it. The deprecation warning for SourceAsset now references this method.max_runtime_seconds configuration option to run monitoring, allowing you to specify that any run in your Dagster deployment should terminate if it exceeds a certain runtime. Prevoiusly, jobs had to be individually tagged with a dagster/max_runtime tag in order to take advantage of this feature. Jobs and runs can still be tagged in order to override this value for an individual run.tags and a custom execution_fn on a ScheduleDefinition. Schedule tags are intended to annotate the definition and can be used to search and filter in the UI. They will not be attached to run requests emitted from the schedule if a custom execution_fn is provided. If no custom execution_fn is provided, then for back-compatibility the tags will also be automatically attached to run requests emitted from the schedule.SensorDefinition and all of its variants/decorators now accept a tags parameter. The tags annotate the definition and can be used to search and filter in the UI.dagster definitions validate command to Dagster CLI. This command validates if Dagster definitions are loadable.build_op_context in a unit test would sometimes raise a TypeError: signal handler must be signal.SIG_IGN, signal.SIG_DFL, or a callable object Exception on process shutdown.DefaultScheduleStatus.STOPPED / DefaultSensorStatus.STOPPED after performing a reset.--partition-range option to dagster asset materialize CLI. This option only works for assets with single-run Backfill Policies..without() method to AutomationCondition.eager(), AutomationCondition.on_cron(), and AutomationCondition.on_missing() which allows sub-conditions to be removed, e.g. AutomationCondition.eager().without(AutomationCondition.in_latest_time_window()).AutomationCondition.on_missing(), which materializes an asset partition as soon as all of its parent partitions are filled in.pyproject.toml can now load multiple Python modules as individual Code Locations. Thanks, @bdart!ADLS2PickleIOManager now accepts lease_duration configuration. Thanks, @0xfabioo!sling.replicate(...).fetch_row_count().AutomationCondition.any_downstream_condition() with downstream AutoMaterializePolicy objects.process_config_and_initialize did not properly handle processing nested resource config.DepConditionWrapperCondition instead of the key that they were evaluated against.wait_for_pod check now waits until all pods are available, rather than erroneously returning after the first pod becomes available. Thanks @easontm!AssetSpec constructor now raises an error if an invalid group name is provided, instead of an error being raised when constructing the Definitions object.dagster/relation_identifier metadata is now automatically attached to assets which are stored using a DbIOManager.DbtCliResource, the project_dir argument will now override the DBT_PROJECT_DIR environment variable if it exists in the local environment (thanks, @marijncv!).rows_loaded metadata (thanks, @kristianandre!).asset_selection=[] on RunRequest objects yielded from sensors using asset_selection would select all assets instead of none.assets_dbt_python example.database field set (thanks, @dargmuesli!)dbt.adapters.duckdb module when loading dbt assets without that package installed.DagsterInstance.wipe_asset_partitions.allow_missing_partitions as metadata on a downstream asset.object has no attribute '_base_path' error.statuses filter argument to the sensorsOrError GraphQL field was sometimes ignored when querying GraphQL for multiple sensors at the same time.CapturedLogManager and ComputeLogManager APIs into a single base class.PipesECSClient to allow Dagster to interface with ECS tasks.dbt subprocess to wait 25 seconds for the subprocess to cleanly terminate. Previously, it would only wait 2 seconds.sdf subprocess to wait 25 seconds for the subprocess to cleanly terminate. Previously, it would only wait 2 seconds.AutomationCondition using DagsterDltTranslator.get_automation_condition() (Thanks, aksestok!)dagsterDaemon.runRetries.retryOnAssetOrOpFailure to False in the Dagster Helm chart to prevent op retries and run retries from simultaneously firing on the same failure.recursive parameter (Thanks, chrishiste!)dagster/image label when image is provided from user_defined_k8s_config. (Thanks, @HynekBlaha!)custom_user_agent, was provided by defaultPipesGlueClient.run have been deprecated and will be removed in 1.9.0. The new params argument should be used instead.allow_missing_partitions configuration option.PartitionsDefinitions, there will no longer be an implicit asset job __ASSET_JOB_... for each PartitionsDefinition; there will just be one with all the assets. This reduces the time it takes to load code locations with assets with many different PartitionsDefinitions.OpenAIResource (Thanks, @chasleslr!)MultiPartitionMapping with @dbt_assets (Thanks, @arookieds!)AzureBlobComputeLogManager without a secret_key (Thanks, @ion-elgreco and @HynekBlaha!)AutomationCondition and associated static constructors.PipesCloudWatchMessageReaderMajor 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.yamlNew
New
dagster schedule logs {schedule_name} command will show the log file for a given schedule. This
helps uncover errors like missing environment variables and import errors.dagster schedule debug command. As before, these
errors can be resolve using dagster schedule upBugfix
dagster.yamlBreaking Changes
dagster pipeline backfill command no longer takes a mode flag. Instead, it uses the mode
specified on the PartitionSetDefinition. Similarly, the runs created from the backfill also use
the solid_subset specified on the PartitionSetDefinitionBugFix
dagster schedule debug
command will display issues related to missing crob jobs, extraneous cron jobs, and duplicate cron
jobs. Running dagster schedule up will fix any issues.New
dagster
package.