When new releases include breaking changes or deprecations, this document describes how to migrate.
dagit python package and all references to it are now deprecated. We will continue to publish dagit and support APIs that used the term “dagit” until v2.0, but you should transition to newer dagster-webserver package. This is a drop-in replacement for dagit. Like dagit, it exposes an executable of the same name as the package itself, i.e. dagster-webserver.dagster-webserver instead, e.g.:# no (deprecated) RUN pip install dagster dagit ... ... ENTRYPOINT ["dagit", "-h", "0.0.0.0", "-p", "3000"] # yes RUN pip install dagster dagster-webserver ... ENTRYPOINT ["dagster-webserver", "-h", "0.0.0.0", "-p", "3000"]
# no (deprecated) dagit: ... # ... ingress: dagit: ... readOnlyDagit: ... # yes dagsterWebserver: ... # ... ingress: dagsterWebserver: ... readOnlyDagsterWebserver: ...
non_argument_deps parameter of @asset and @multi_asset in favor of a new deps parameter. To update your code to use deps, simply rename any instances of non_argument_deps to deps and change the type from a set to list. Additionally, you may also want to begin passing the python symbols for assets, rather than their AssetKeys to improve in-editor experience with type-aheads and linting.@asset def my_asset(): ... @asset( non_argument_deps={"my_asset"} ) def a_downstream_asset(): ... # becomes @asset def my_asset(): ... @asset( deps=["my_asset"] ) def a_downstream_asset(): ... # or @asset def my_asset(): ... @asset( deps=[my_asset] ) def a_downstream_asset(): ...
tag:DescribeResources. Without this policy, the ECS Agent will log a deprecation warning and fall back to its old behavior (listing all ECS services in the cluster and then listing each service's tags).DbtCliClientResource, dbt_cli_resource and DbtCliOutput are now being deprecated in favor of DbtCliResource. dagster-dbt Asset APIs like load_assets_from_dbt_manifest and load_assets_from_dbt_project will continue to work if given either a DbtCliClientResource or DbtCliResource.# old @op def my_dbt_op(dbt_resource: DbtCliClientResource): dbt: DbtCliClient = dbt.get_client() dbt.cli("run") dbt.cli("run", full_refresh=True) dbt.cli("test") manifest_json = dbt.get_manifest_json() # new with Path("my/dbt/manifest").open() as handle: manifest = json.loads(dbt_manifest.read()) @op def my_dbt_op(dbt: DbtCliResource): dbt.cli(["run"], manifest=manifest).stream() dbt.cli(["run", "--full-refresh"], manifest=manifest).stream() dbt_test_invocation = dbt.cli(["test"], manifest_manifest).stream() manifest_json = dbt_test_invocation.get_artifact("manifest.json") # old dbt_assets = load_assets_from_dbt_project(project_dir="my/dbt/project") defs = Definitions( assets=dbt_assets, resources={ "dbt": DbtCliClientResource(project_dir="my/dbt/project") }, ) # new dbt_assets = load_assets_from_dbt_project(project_dir="my/dbt/project") defs = Definitions( assets=dbt_assets, resources={ "dbt": DbtCliResource(project_dir="my/dbt/project") } )
load_assets_from_dbt_project and load_assets_from_dbt_manifest are now deprecated in favor of other options. Arguments will continue to work when passed into these functions, but a deprecation warning will be emitted.| Deprecated Arguments | Recommendation |
|---|---|
key_prefix | Instead, provide a custom DagsterDbtTranslator that overrides get_asset_key |
source_key_prefix | Instead, provide a custom DagsterDbtTranslator that overrides get_asset_key |
op_name | Use the @dbt_assets decorator if you need to customize your op name. |
manifest_json | Use the manifest parameter instead. |
display_raw_sql | Instead, provide a custom DagsterDbtTranslator that overrides get_description. |
selected_unique_ids | Use the select parameter instead. |
dbt_resource_key | Use the @dbt_assets decorator if you need to customize your resource key. |
use_build_command | Use the @dbt_assets decorator if you need to customize the underlying dbt commands. |
partitions_def | Use the @dbt_assets decorator to define partitioned dbt assets. |
partition_key_to_vars_fn | Use the @dbt_assets decorator to define partitioned dbt assets. |
runtime_metadata_fn | Use the @dbt_assets decorator if you need to customize runtime metadata. |
node_info_to_asset_key_fn | Instead, provide a custom DagsterDbtTranslator that overrides get_asset_key. |
node_info_to_group_fn | Instead, configure dagster groups on a dbt resource's meta field, assign dbt groups, or provide a custom DagsterDbtTranslator that overrides get_group_name. |
node_info_to_auto_materialize_policy_fn | Instead, configure Dagster auto-materialize policies on a dbt resource's meta field. |
node_info_to_freshness_policy_fn | Instead, configure Dagster freshness policies on a dbt resource's meta field. |
node_info_to_definition_metadata_fn | Instead, provide a custom DagsterDbtTranslator that overrides get_metadata. |
build_asset_reconciliation_sensor (Experimental) has been removed. It was deprecated in 1.3 in favor of AutoMaterializePolicy. Docs are here.dagster-dbt integration with dbt-rpc has been removed, as the dbt plugin is being deprecated.DbtCliResource was a class alias for DbtCliClientResource. Now, DbtCliResource is a new resource with a different API. Furthermore, it requires at least dbt-core>=1.4 to run.dagit.nameOverride value is set, you will need to either change the value or delete the existing deployment to allow helm to update values that can not be patched for the rename from dagit to dagster-webserver.load_assets_from_dbt_project and load_assets_from_dbt_manifest now default to use_build=True. To switch back to the previous behavior, use use_build=False.from dagster_dbt import group_from_dbt_resource_props_fallback_to_directory load_assets_from_dbt_project( ..., use_build=False, )
load_assets_from_dbt_project and load_assets_from_dbt_manifest has changed. Rather than assigning a group name using the model’s subdirectory, a group name will be assigned using the dbt model’s dbt group. To switch back to the previous behavior, use the following utility function, group_from_dbt_resource_props_fallback_to_directory:from dagster_dbt import group_from_dbt_resource_props_fallback_to_directory load_assets_from_dbt_project( ..., node_info_to_group_fn=group_from_dbt_resource_props_fallback_to_directory, )
node_info_to_definition_metadata_fn for load_assets_from_dbt_project and load_assets_from_dbt_manifest now overrides metadata instead of adding to it. To switch back to the previous behavior, use the following utility function:from dagster_dbt import default_metadata_from_dbt_resource_props def my_metadata_from_dbt_resource_props(dbt_resource_props): my_metadata = {...} return {**default_metadata_from_dbt_resource_props(dbt_resource_props), **my_metadata} load_assets_from_dbt_manifest( ..., node_info_to_definition_metadata_fn=my_metadata_from_dbt_resource_props )
load_assets_from_dbt_project and load_assets_from_dbt_manifest now must be specified using keyword arguments.DbtCliResource with load_assets_from_dbt_project and load_assets_from_dbt_manifest, stdout logs from the dbt process will now appear in the compute logs instead of the event logs.build_asset_reconciliation_sensor, which was experimental, is now deprecated, in favor of setting AutoMaterializePolicy on assets. Refer to the docs on AutoMaterializePolicy for how this works: https://docs.dagster.io/concepts/assets/asset-auto-execution.yield job_def.run_request_for_partition(partition_key="..."). This has been deprecated, in favor of yield RunRequest(partition_key="...").Definitions are now automatically bound to jobs. This will only result in a change in behavior if you a) have a job with no "io_manager" defined in its resource_defs and b) have supplied an IOManager with key "io_manager" to the resource_defs argument of your Definitions. Prior to 1.3.0, this would result in the job using the default filesystem-based IOManager for the key "io_manager". In 1.3.0, this will result in the "io_manager" supplied to your Definitions being used instead. The BindResourcesToJobs wrapper, introduced in 1.2 to simulate this behavior, no longer has any effect.minutes_late and previous_minutes_late properties on the experimental FreshnesPolicySensorContext have been renamed to minutes_overdue and previous_minutes_overdue, respectively.metadata_entries arguments to user-constructed events (AssetObservation, AssetMaterialization, ExpectationResult, TypeCheck, Failure, Output, DynamicOutput), as well as the DagsterType object have been removed. Instead, a dictionary of metadata should be passed into the metadata argument.CeleryK8sRunLauncher is now the same namespace as the Helm chart, instead of the default namespace. To restore the previous behavior, you can set the celeryK8sRunLauncher.jobNamespace field to the string default.dagster-snowflake version 0.19.0 the Snowflake I/O manager converted all timestamp data to strings before loading the data in Snowflake, and did the opposite conversion when fetching a DataFrame from Snowflake. The I/O manager now ensures timestamp data has a timezone attached and stores the data as TIMESTAMP_NTZ(9) type. If you used the Snowflake I/O manager prior to version 0.19.0 you can set the store_timestamps_as_strings=True configuration value for the Snowflake I/O manager to continue storing time data as strings while you do table migrations.To migrate a table created prior to 0.19.0 to one with a TIMESTAMP_NTZ(9) type, you can run the follow SQL queries in Snowflake. In the example, our table is located at database.schema.table and the column we want to migrate is called time:
// Add a column of type TIMESTAMP_NTZ(9) ALTER TABLE database.schema.table ADD COLUMN time_copy TIMESTAMP_NTZ(9) // copy the data from time and convert to timestamp data UPDATE database.schema.table SET time_copy = to_timestamp_ntz(time) // drop the time column ALTER TABLE database.schema.table DROP COLUMN time // rename the time_copy column to time ALTER TABLER database.schema.table RENAME COLUMN time_copy TO time
1.2.0 adds a set of optional database schema migrations, which can be run via dagster instance migrate:
id column to the kvs, daemon_heartbeats and instance_info tables, enforcing that all tables have a primary key.grpcio version supported by Dagster has been increased to 1.44.0 so that Dagster can support both protobuf 3 and protobuf 4. Similarly, the minimum protobuf version supported by Dagster has been increased to 3.20.0. We are working closely with the gRPC team on resolving the upstream issues keeping the upper-bound grpcio pin in place in Dagster, and hope to be able to remove it very soon.LogicalVersion has been renamed to DataVersion and LogicalVersionProvenance has been renamed to DataProvenance.DynamicPartitionsDefinition to add, remove, and check for existence of partitions have been removed. Refer to documentation for updated API methods.MetadataEntry have been removed.DagsterTypeMaterializer, DagsterTypeMaterializerContext, and @dagster_type_materializer have been removed.PartitionScheduleDefinition has been removed.RunRecord.pipeline_run has been removed (use RunRecord.dagster_run).DependencyDefinition.solid has been removed (use DependencyDefinition.node).pipeline_run argument to build_resources has been removed (use dagster_run)[dagster-snowflake] The execute_queryand execute_queries methods of the SnowflakeResource now have consistent behavior based on the values of the fetch_results and use_pandas_result parameters. If fetch_results is True, the standard Snowflake result will be returned. If fetch_results and use_pandas_result are True, a pandas DataFrame will be returned. If fetch_results is False and use_pandas_result is True, an error will be raised. If both are False, no result will be returned.
[dagster-snowflake] The execute_queries command now returns a list of DataFrames when use_pandas_result is True, rather than appending the results of each query to a single DataFrame.
[dagster-shell] The default behavior of the execute and execute_shell_command functions is now to include any environment variables in the calling op. To restore the previous behavior, you can pass in env={} to these functions.
[dagster-k8s] Several Dagster features that were previously disabled by default in the Dagster Helm chart are now enabled by default. These features are:
dagster/max_retries tag set. You can configure a global number of retries in the Helm chart by setting run_retries.max_retries to a value greater than the default of 0.Each of these features can be disabled in the Helm chart to restore the previous behavior.
[dagster-k8s] The experimental [k8s_job_op](https://docs.dagster.io/_apidocs/libraries/dagster-k8s#dagster_k8s.k8s_job_op) op and [execute_k8s_job](https://docs.dagster.io/_apidocs/libraries/dagster-k8s#dagster_k8s.execute_k8s_job) functions no longer automatically include configuration from a dagster-k8s/config tag on the Dagster job in the launched Kubernetes job. To include raw Kubernetes configuration in a k8s_job_op, you can set the container_config, pod_template_spec_metadata, pod_spec_config, or job_metadata config fields on the k8s_job_op (or arguments to the execute_k8s_job function).
[dagster-databricks] The integration has now been refactored to support the official Databricks API.
create_databricks_job_op is now deprecated. To submit one-off runs of Databricks tasks, you must now use the create_databricks_submit_run_op.databricks_client resource must now begin with https://.Two optional database schema migrations, which can be run via dagster instance migrate:
define_dagstermill_solid, a legacy API, has been removed from dagstermill. Use define_dagstermill_op or define_dagstermill_asset instead to create an op or asset from a Jupyter notebook, respectively.ComputeLogManager API is marked as deprecated in favor of an updated interface: CapturedLogManager. It will be removed in 1.2.0. This should only affect dagster instances that have implemented a custom compute log manager.configured in Dagit. Up through this point, values provided to configured have not been sent anywhere outside the process where they were used. This change will mean that, like other places you can supply configuration, configured is not a good place to put secrets: You should not include any values in configuration that you don't want to be stored in the Dagster database and displayed inside Dagit.dagster.experimental submodule has been deleted, which previously contained dynamic output APIs, which are available from the top level of the dagster module.pip install dagster=={DAGSTER_VERSION} dagster-somelibrary=={DAGSTER_VERSION}
this should be converted to:
pip install dagster=={DAGSTER_VERSION} dagster-somelibrary
to make sure the correct library version is installed.
@solid, SolidDefinition, @pipeline, PipelineDefinition, @composite_solid, CompositeSolidDefinition, ModeDefinition, PresetDefinition, PartitionSetDefinition, InputDefinition, OutputDefinition, DynamicOutputDefinition, pipeline_failure_sensor, @hourly_schedule, @daily_schedule, @weekly_schedule, and @monthly_schedule. Here is a guide to migrating from the legacy APIs to the stable APIs.input_defs/output_defs arguments on define_dagstermill_op, which have been changed to ins/outs respectively, and input_defs argument on create_shell_script_op, which has been changed to ins.pipeline_selection argument has been removed from run_failure_sensor and related decorators / functions, and job_selection has been deprecated. Instead, use monitored_jobs.ScheduleExecutionContext and SensorExecutionContext APIs have been removed. In 0.13.0, these were renamed to ScheduleEvaluationContext and SensorEvaluationContext respectively, and marked deprecated.execute_pipeline has been removed. The functionality previously supplied by execute_pipeline has been split between JobDefinition.execute_in_process (docs) and execute_job (docs). If you were previously using execute_pipeline for in-process testing, then JobDefinition.execute_in_process should replace. If using execute_pipeline for out-of-process execution, or non-testing workflows, then execute_job is the recommended replacement.dagster pipeline CLI subgroup has been removed in favor of dagster job.dagster new-project CLI subgroup has been removed in favor of dagster project.AssetGroup and build_assets_job, which were advertised in an experimental iteration of software-defined assets, have been removed. Instead, check out the docs on grouping assets, and the docs on defining asset jobs.partition_mappings arguments on @asset and @multi_asset have been removed. Instead, user the partition_mapping argument the corresponding AssetIns.namespace arguments on @asset and AssetIn have been removed. Instead, use the key_prefix argument.input_defs and output_defs arguments on OpDefinition have been removed, and replaced with ins and outs arguments. input_defs/output_defs have been deprecated since 0.13.0.preset_defs argument on JobDefinition has been removed. When constructing a JobDefinition directly, config can be provided using the config argument instead. preset_defs has been deprecated since 0.13.0.EventMetadata and EventMetadataEntryData APIs have been removed. Instead, metadata should be specified using the MetadataValue APIs.define_dagstermill_solid, make_dagster_pipeline_from_airflow_dag, create_databricks_job_solid, the various dbt_cli_* and dbt_rpc_* solids, bq_solid_for_queries, ge_validation_solid_factory, end_mlflow_run_on_pipeline_finished, the various shell_command_solid APIs, make_slack_on_pipeline_failure_sensor, snowflake_solid_for_query, end_mlflow_run_on_pipeline_finished, and create_spark_solid.custom_path_fs_io_manager has been removed, as its functionality is entirely subsumed by the fs_io_manager, where a custom path can be specified via config.This serves as an exhaustive list of the removed APIs.
From the main Dagster module:
AssetGroupDagsterPipelineRunMetadataValueCompositeSolidDefinitionInputDefinitionMaterializationModeDefinitionOutputDefinitionPipelineDefinitionPresetDefinitionSolidDefinitionSolidInvocationDynamicOutputDefinitioncomposite_solidlambda_solidpipelinesolidpipeline_failure_sensorCompositeSolidExecutionResultPipelineExecutionResultSolidExecutionResultSolidExecutionContextbuild_solid_contextPipelineRunPipelineRunStatusdefault_executorsexecute_pipeline_iteratorexecute_pipelineexecute_solid_within_pipelinereexecute_pipeline_iteratorreexecute_pipelineexecute_solidexecute_solids_within_pipelinebuild_assets_jobschedule_from_partitionsPartitionSetDefinitionScheduleExecutionContextSensorExecutionContextPipelineFailureSensorContextdaily_schedulehourly_schedulemonthly_scheduleweekly_schedulecreate_offset_partition_selectordate_partition_rangeidentity_partition_selectorcustom_path_fs_io_managerFrom libraries (APIs removed in 0.16.0 onwards):
dagster_airflow.make_dagster_pipeline_from_airflow_dagdagster_databricks.create_databricks_job_soliddagster_dbt.dbt_cli_compiledagster_dbt.dbt_cli_rundagster_dbt.dbt_cli_run_operationdagster_dbt.dbt_cli_snapshotdagster_dbt.dbt_cli_snapshot_freshnessdagster_dbt.dbt_cli_testdagster_dbt.create_dbt_rpc_run_sql_soliddagster_dbt.dbt_rpc_rundagster_dbt.dbt_rpc_run_and_waitdagster_dbt.dbt_rpc_run_operationdagster_dbt.dbt_rpc_run_operation_and_waitdagster_dbt.dbt_rpc_snapshotdagster_dbt.dbt_rpc_snapshot_and_waitdagster_dbt.dbt_rpc_snapshot_freshnessdagster_dbt.dbt_rpc_snapshot_freshness_and_waitdagster_dbt.dbt_rpc_testdagster_dbt.dbt_rpc_test_and_waitdagster_gcp.bq_solid_for_queriesdagster_ge.ge_validation_solid_factorydagster_mlflow.end_mlflow_run_on_pipeline_finishesdagster_shell.create_shell_command_soliddagster_shell.create_shell_script_soliddagster_shell.shell_soliddagster_slack.make_slack_on_pipeline_failure_sensordagster_msteams.make_teams_on_pipeline_failure_sensordagster_snowflake.snowflake_solid_for_querydagster_spark.create_spark_solidAll items below are breaking changes unless marked with (deprecation).
This release marks the official transition of software-defined assets from experimental to stable. We made some final changes to incorporate feedback and make the APIs as consistent as possible:
If migrating from below 0.13.17, you can run
dagster instance migrate
This optional migration makes performance improvements to the runs page in Dagit.
The Dagster Daemon now uses the same workspace.yaml file as Dagit to locate your Dagster code. You should ensure that if you make any changes to your workspace.yaml file, they are included in both Dagit’s copy and the Dagster Daemon’s copy. When you make changes to the workspace.yaml file, you don’t need to restart either Dagit or the Dagster Daemon - in Dagit, you can reload the workspace from the Workspace tab, and the Dagster Daemon will periodically check the workspace.yaml file for changes every 60 seconds. If you are using the Dagster Helm chart, no changes are required to include the workspace in the Dagster Daemon.
In previous releases, it was possible to supply either an AssetKey, or a function that produced an AssetKey from an OutputContext as the asset_key argument to an Out/OutputDefinition. The latter behavior makes it impossible to gain information about these relationships without running a job, and has been deprecated. However, we still support supplying a static AssetKey as an argument.
We have renamed many of the core APIs that interact with ScheduleStorage, which keeps track of sensor/schedule state and ticks. The old term for the generic schedule/sensor “job” has been replaced by the term “instigator” in order to avoid confusion with the execution API introduced in 0.12.0. If you have implemented your own schedule storage, you may need to change your method signatures appropriately.
Dagit is now powered by Starlette instead of Flask. If you have implemented a custom run coordinator, you may need to make the following change:
from flask import has_request_context, request def submit_run(self, context: SubmitRunContext) -> PipelineRun: jwt_claims_header = ( request.headers.get("X-Amzn-Oidc-Data", None) if has_request_context() else None )
Should be replaced by:
def submit_run(self, context: SubmitRunContext) -> PipelineRun: jwt_claims_header = context.get_request_header("X-Amzn-Oidc-Data")
The Dagster Daemon now requires a workspace.yaml file, much like Dagit.
Ellipsis (“...”) is now an invalid substring of a partition key. This is because Dagit accepts an ellipsis to specify partition ranges.
[Helm] The Dagster Helm chart now only supported Kubernetes clusters above version 1.18.
Dagster’s metadata API has undergone a signficant overhaul. Changes include:
EventMetadata > MetadataValueEventMetadataEntry > MetadataEntryXMetadataEntryData > XMetadataValue (e.g. TextMetadataEntryData > TextMetadataValue)metadata_entries keyword argument to events and Dagster types is deprecated. Instead, users should use the metadata keyword argument, which takes a dictionary mapping string labels to MetadataValues.MetadataValue (i.e. function like metadata everywhere else in Dagster).EventMetadataEntry is deprecated.EventMetadataEntry (e.g. EventMetadataEntry.text) is deprecated. In 0.15.0, users should avoid constructing EventMetadataEntry objects directly, instead utilizing the metadata dictionary keyword argument, which maps string labels to MetadataValues.Jobs, ops, and graphs have replaced pipelines, solids, modes, and presets as the stable core of the system. Here is a guide you can use to update your code using the legacy APIs into using the new Dagster core APIs. 0.13.0 is still compatible with the pipeline, solid, mode, and preset APIs, which means that you don't need to migrate your code to upgrade to 0.13.0.
The new experimental core API experience in Dagit uses some features that require a data migration. Before enabling the experimental core API flag in Dagit, you will first need to run this command:
dagster instance migrate
If you are not going to enable the experimental core API experience, this data migration is optional. However, you may still want to run the migration anyway, which will enable better performance in viewing the Asset catalog in Dagit.
Run this after migrating to 0.11.0:
dagster instance migrate
This release includes several schema changes to the Dagster storages that improve performance, allow support for MySQL, and enable new features like asset tags and reliable backfills. After upgrading to 0.11.0, run the dagster instance migrate command to migrate your instance storage to the latest schema.
Schedules now run in UTC (instead of the system timezone) if no timezone has been set on the schedule. If you’re using a deprecated scheduler like SystemCronScheduler or K8sScheduler, we recommend that you switch to the native Dagster scheduler. The deprecated schedulers will be removed in the next Dagster release.
If upgrading directly to 0.11.0 from 0.9.22 or lower, you might notice some asset keys missing from the catalog if they have not been materialized using a version 0.9.16 or greater. We removed some back-compatibility for performance reasons. If this is the case, you can either run dagster instance reindex or execute the appropriate pipelines to materialize those assets again. In either case, the full history of the asset will still be maintained.
instance argument to RunLauncher.launch_run has been removed. If you have written a custom RunLauncher, you’ll need to update the signature of that method. You can still access the DagsterInstance on the RunLauncher via the _instance parameter.has_config_entry, has_configurable_inputs, and has_configurable_outputs properties of solid and composite_solid have been removed.name argument to PipelineDefinition has been removed, and the argument is now required.execute_run_with_structured_logs and execute_step_with_structured_logs internal CLI entry points have been removed. Use execute_run or execute_step instead.python_environment key has been removed from workspace.yaml. Instead, to specify that a repository location should use a custom python environment, set the executable_path key within a python_file or python_module key. See the docs for more information on configuring your workspace.yaml file.read or to keys accordingly.Names provided to alias on solids now enforce the same naming rules as solids. You may have to update provided names to meet these requirements.
The retries method on Executor should now return a RetryMode instead of a Retries. This will only affect custom Executor classes.
Submitting partition backfills in Dagit now requires dagster-daemon to be running. The instance setting in dagster.yaml to optionally enable daemon-based backfills has been removed, because all backfills are now daemon-based backfills.
# removed, no longer a valid setting in dagster.yaml backfill: daemon_enabled: true
The corresponding value flag dagsterDaemon.backfill.enabled has also been removed from the Dagster helm chart.
The sensor daemon interval settings in dagster.yaml has been removed. The sensor daemon now runs in a continuous loop so this customization is no longer useful.
# removed, no longer a valid setting in dagster.yaml sensor_settings: interval_seconds: 10
# Run after migrating to 0.10.0 $ dagster instance migrate
This release includes several schema changes to the Dagster storages that improve performance and
enable new features like sensors and run queueing. After upgrading to 0.10.0, run the
dagster instance migrate command to migrate your instance storage to the latest schema. This will
turn off any running schedules, so you will need to restart any previously running schedules after
migrating the schema. Before turning them back on, you should follow the steps below to migrate
to DagsterDaemonScheduler.
This release includes a new DagsterDaemonScheduler with improved fault tolerance and full support
for timezones. We highly recommend upgrading to the new scheduler during this release. The existing
schedulers, SystemCronScheduler and K8sScheduler, are deprecated and will be removed in a
future release.
Instead of relying on system cron or k8s cron jobs, the DaemonScheduler uses the new
dagster-daemon service to run schedules. This requires running the dagster-daemon service as a
part of your deployment.
Refer to our deployment documentation for a guides on how to set up and run the daemon process for local development, Docker, or Kubernetes deployments.
If you are currently using the SystemCronScheduler or K8sScheduler:
Stop any currently running schedules, to prevent any dangling cron jobs from being left behind. You can do this through the Dagit UI, or using the following command:
dagster schedule stop --location {repository_location_name} {schedule_name}
If you do not stop running schedules before changing schedulers, Dagster will throw an exception on startup due to the misconfigured running schedules.
In your dagster.yaml file, remove the scheduler: entry. If there is no scheduler: entry,
the DagsterDaemonScheduler is automatically used as the default scheduler.
Start the dagster-daemon process. Guides can be found in our
deployment documentations.
See our schedules troubleshooting guide for help if you experience any problems with the new scheduler.
If you are not using a legacy scheduler:
No migration steps are needed, but make sure you run dagster instance migrate as a part of
upgrading to 0.10.0.
We have deprecated the intermediate storage machinery in favor of the new IO manager abstraction, which offers finer-grained control over how inputs and outputs are serialized and persisted. Check out the IO Managers Overview for more information.
We have deprecated the top level "storage" and "intermediate_storage" fields on run_config.
If you are currently executing pipelines as follows:
@pipeline def my_pipeline(): ... execute_pipeline( my_pipeline, run_config={ "intermediate_storage": { "filesystem": {"base_dir": ...} } }, ) execute_pipeline( my_pipeline, run_config={ "storage": { "filesystem": {"base_dir": ...} } }, )
You should instead use the built-in IO manager fs_io_manager, which can be attached to your
pipeline as a resource:
@pipeline( mode_defs=[ ModeDefinition( resource_defs={"io_manager": fs_io_manager} ) ], ) def my_pipeline(): ... execute_pipeline( my_pipeline, run_config={ "resources": { "io_manager": {"config": {"base_dir": ...}} } }, )
There are corresponding IO managers for other intermediate storages, such as the S3- and ADLS2-based storages
We have deprecated IntermediateStorageDefinition and @intermediate_storage.
If you have written custom intermediate storage, you should migrate to custom IO managers
defined using the @io_manager API. We have provided a helper method,
io_manager_from_intermediate_storage, to help migrate your existing custom intermediate
storages to IO managers.
my_io_manager_def = io_manager_from_intermediate_storage( my_intermediate_storage_def ) @pipeline( mode_defs=[ ModeDefinition( resource_defs={ "io_manager": my_io_manager_def } ), ], ) def my_pipeline(): ...
We have deprecated the intermediate_storage_defs argument to ModeDefinition, in favor of the
new IO managers, which should be attached using the resource_defs argument.
Use dagster_type_loader instead of input_hydration_config and dagster_type_materializer
instead of output_materialization_config.
On DagsterType and type constructors in dagster_pandas use the loader argument instead of
input_hydration_config and the materializer argument instead of dagster_type_materializer
argument.
We have removed the ability to specify a repository in your workspace using the repository: key.
Use load_from: instead when specifying how to load the repositories in your workspace.
The python_environment: key is now deprecated and will be removed in a future release.
Previously, when you wanted to load a repository location in your workspace using a different
Python environment from Dagit’s Python environment, you needed to use a python_environment: key
under load_from: instead of the python_file: or python_package: keys. Now, you can simply
customize the executable_path in your workspace entries without needing to change to the
python_environment: key.
For example, the following workspace entry:
- python_environment: executable_path: "/path/to/venvs/dagster-dev-3.7.6/bin/python" target: python_package: package_name: dagster_examples location_name: dagster_examples
should now be expressed as:
- python_package: executable_path: "/path/to/venvs/dagster-dev-3.7.6/bin/python" package_name: dagster_examples location_name: dagster_examples
See our Workspaces Overview for more information and examples.
We have removed the property config_field on definition classes. Use config_schema instead.
We have removed the system storage abstractions, i.e. SystemStorageDefinition and
@system_storage (deprecated in 0.9.0).
Please note that the intermediate storage abstraction is also deprecated and will be removed in 0.11.0. Use IO managers instead.
system_storage_defs argument (deprecated in 0.9.0) to ModeDefinition, in
favor of intermediate_storage_defs.default_system_storage_defs
(deprecated in 0.9.0).We have removed the step_keys_to_execute argument to reexecute_pipeline and
reexecute_pipeline_iterator, in favor of step_selection. This argument accepts the Dagster
selection syntax, so, for example, *solid_a+ represents solid_a, all of its upstream steps,
and its immediate downstream steps.
Starting in 0.10.0, Dagster uses the pendulum library to
ensure that schedules and partitions behave correctly with respect to timezones. As part of this
change, the delta parameter to date_partition_range (which determined the time different between
partitions and was a datetime.timedelta) has been replaced by a delta_range parameter
(which must be a string that's a valid argument to the pendulum.period function, such as
"days", "hours", or "months").
For example, the following partition range for a monthly partition set:
date_partition_range( start=datetime.datetime(2018, 1, 1), end=datetime.datetime(2019, 1, 1), delta=datetime.timedelta(months=1) )
should now be expressed as:
date_partition_range( start=datetime.datetime(2018, 1, 1), end=datetime.datetime(2019, 1, 1), delta_range="months" )
When you create a schedule from a partition set using
PartitionSetDefinition.create_schedule_definition, you now must supply a partition_selector
argument that tells the scheduler which partition to use for a given schedule time.
We have added two helper functions, create_offset_partition_selector and
identity_partition_selector, that capture two common partition selectors (schedules that execute
at a fixed offset from the partition times, e.g. a schedule that creates the previous day's
partition each morning, and schedules that execute at the same time as the partition times).
The previous default partition selector was last_partition, which didn't always work as expected
when using the default scheduler and has been removed in favor of the two helper partition selectors
above.
For example, a schedule created from a daily partition set that fills in each partition the next day at 10AM would be created as follows:
partition_set = PartitionSetDefinition( name='hello_world_partition_set', pipeline_name='hello_world_pipeline', partition_fn= date_partition_range( start=datetime.datetime(2021, 1, 1), delta_range="days", timezone="US/Central", ) run_config_fn_for_partition=my_run_config_fn, ) schedule_definition = partition_set.create_schedule_definition( "daily_10am_schedule", "0 10 * * *", partition_selector=create_offset_partition_selector(lambda d: d.subtract(hours=10, days=1)) execution_timezone="US/Central", )
Following convention in the Helm docs,
we now camel case all of our Helm values. To migrate to 0.10.0, you'll need to update your
values.yaml with the following renames:
pipeline_run → pipelineRundagster_home → dagsterHomeenv_secrets → envSecretsenv_config_maps → envConfigMapsWhen specifying the Dagster instance scheduler, rather than using a boolean field to switch between
the current options of K8sScheduler and DagsterDaemonScheduler, we now require the scheduler
type to be explicitly defined under scheduler.type. If the user specified scheduler.type has
required config, additional fields will need to be specified under scheduler.config.
scheduler.type and corresponding scheduler.config values are enforced via
JSON Schema.
For example, if your Helm values previously were set like this to enable the
DagsterDaemonScheduler:
scheduler: k8sEnabled: false
You should instead have:
scheduler: type: DagsterDaemonScheduler
celery and k8sRunLauncher now live under runLauncher.config.celeryK8sRunLauncher and
runLauncher.config.k8sRunLauncher respectively. Now, to enable celery, runLauncher.type must
equal CeleryK8sRunLauncher. To enable the vanilla K8s run launcher, runLauncher.type must
equal K8sRunLauncher.
runLauncher.type and corresponding runLauncher.config values are enforced via
JSON Schema.
For example, if your Helm values previously were set like this to enable the K8sRunLauncher:
celery: enabled: false k8sRunLauncher: enabled: true jobNamespace: ~ loadInclusterConfig: true kubeconfigFile: ~ envConfigMaps: [] envSecrets: []
You should instead have:
runLauncher: type: K8sRunLauncher config: k8sRunLauncher: jobNamespace: ~ loadInclusterConfig: true kubeconfigFile: ~ envConfigMaps: [] envSecrets: []
By default, userDeployments is enabled and the runLauncher is set to the K8sRunLauncher.
Along with the latter change, all message brokers (e.g. rabbitmq and redis) are now disabled
by default.
If you were using the CeleryK8sRunLauncher, one of rabbitmq or redis must now be explicitly
enabled in your Helm values.
We have removed the config argument to the ConfigMapping, @composite_solid, @solid,
SolidDefinition, @executor, ExecutorDefinition, @logger, LoggerDefinition, @resource,
and ResourceDefinition APIs, which we deprecated in 0.8.0, in favor of config_schema, as
described here.
We 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 are starting to deprecate "system storages" - instead of pipelines having a system storage definition which creates an intermediate storage, pipelines now directly have an intermediate storage definition.
intermediate_storage_defs argument to ModeDefinition, which accepts a
list of IntermediateStorageDefinitions, e.g. s3_plus_default_intermediate_storage_defs.
As before, the default includes an in-memory intermediate and a local filesystem intermediate
storage.system_storage_defs argument to ModeDefinition in favor of
intermediate_storage_defs. system_storage_defs will be removed in 0.10.0 at the earliest.@intermediate_storage decorator, which makes it easy to define intermediate
storages.s3_file_manager and local_file_manager resources to replace the file managers
that previously lived inside system storages. The airline demo has been updated to include
an example of how to do this:
https://github.com/dagster-io/dagster/blob/0.8.8/examples/airline_demo/airline_demo/solids.py#L171.For example, if your ModeDefinition looks like this:
from dagster_aws.s3 import s3_plus_default_storage_defs ModeDefinition(system_storage_defs=s3_plus_default_storage_defs)
it is recommended to make it look like this:
from dagster_aws.s3 import s3_plus_default_intermediate_storage_defs ModeDefinition(intermediate_storage_defs=s3_plus_default_intermediate_storage_defs)
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.
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.
We renamed the input_hydration_config and output_materialization_config decorators to
dagster_type_ and dagster_type_materializer respectively. We also renamed DagsterType's
input_hydration_config and output_materialization_config arguments to loader and materializer
respectively.
For example, if your dagster type definition looks like this:
from dagster import DagsterType, input_hydration_config, output_materialization_config @input_hydration_config(config_schema=my_config_schema) def my_loader(_context, config): '''some implementation''' @output_materialization_config(config_schema=my_config_schema) def my_materializer(_context, config): '''some implementation''' MyType = DagsterType( input_hydration_config=my_loader, output_materialization_config=my_materializer, type_check_fn=my_type_check, )
it is recommended to make it look like this:
from dagster import DagsterType, dagster_type_loader, dagster_type_materializer @dagster_type_loader(config_schema=my_config_schema) def my_loader(_context, config): '''some implementation''' @dagster_type_materializer(config_schema=my_config_schema) def my_materializer(_context, config): '''some implementation''' MyType = DagsterType( loader=my_loader, materializer=my_materializer, type_check_fn=my_type_check, )
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.
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
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.
As a consequence, the former repository.yaml and the associated -y/--repository-yaml CLI
arguments are deprecated in favor of a new workspace.yaml file format and associated
-w/--workspace-yaml arguments.
You should replace your repository.yaml files with workspace.yaml files, which can define a
number of possible sources from which to load repositories.
load_from: - python_module: module_name: dagster_examples attribute: define_internal_dagit_repository - python_module: dagster_examples.intro_tutorial.repos - python_file: repos.py - python_environment: executable_path: "/path/to/venvs/dagster-dev-3.7.6/bin/python" target: python_module: module_name: dagster_examples location_name: dagster_examples attribute: define_internal_dagit_repository
The @scheduler and @repository_partitions decorators have been removed. In addition, users
should prefer the new @repository decorator to instantiating RepositoryDefinition directly.
One consequence of this change is that PartitionSetDefinition names, including those defined by
a PartitionScheduleDefinition, must now be unique within a single repository.
Previously you might have defined your pipelines, schedules, partition sets, and repositories in a python file such as the following:
@pipeline def test(): ... @daily_schedule( pipeline_name='test', start_date=datetime.datetime(2020, 1, 1), ) def daily_test_schedule(_): return {} test_partition_set = PartitionSetDefinition( name="test", pipeline_name="test", partition_fn=lambda: ["test"], environment_dict_fn_for_partition=lambda _: {}, ) @schedules def define_schedules(): return [daily_test_schedule] @repository_partitions def define_partitions(): return [test_partition_set] def define_repository(): return RepositoryDefinition('test', pipeline_defs=[test])
With a repository.yaml such as:
repository: file: repo.py fn: define_repository scheduler: file: repo.py fn: define_schedules partitions: file: repo.py fn: define_partitions
In 0.8.0, you'll write Python like:
@pipeline def test_pipeline(): ... @daily_schedule( pipeline_name='test', start_date=datetime.datetime(2020, 1, 1), ) def daily_test_schedule(_): return {} test_partition_set = PartitionSetDefinition( name="test", pipeline_name="test", partition_fn=lambda: ["test"], run_config_fn_for_partition=lambda _: {}, ) @repository def test_repository(): return [test_pipeline, daily_test_schedule, test_partition_set]
Your workspace.yaml will look like:
load_from: - python_file: repo.py
If you have more than one repository defined in a single Python file, you'll want to instead load
the repository using workspace.yaml like:
load_from: - python_file: relative_path: repo.py attribute: test_repository - python_file: relative_path: repo.py attribute: other_repository
Of course, the workspace.yaml also supports loading from a python_module, or with a specific
Python interpreter from a python_environment.
Note that the @repository decorator also supports more sophisticated, lazily-loaded repositories.
Consult the documentation for the decorator for more details.
In 0.7.x, dagster attempted to elide the difference between a pipeline that was defined in memory
and one that was loaded through machinery that used the ExecutionTargetHandle machinery. This
resulted in opaque and hard-to-predict errors and unpleasant workarounds, for instance:
execute_pipeline would suddenly fail when a multiprocess
executor was used.handle = handle_for_pipeline_cli_args( {'module_name': 'some_module.repository', 'fn_name': 'some_pipeline'} ) pipeline = handle.build_pipeline_definition() result = execute_pipeline(pipeline, ...)
In 0.8.0, we've added the reconstructable helper to explicitly convert in-memory pipelines into
reconstructable pipelines that can be passed between processes.
@pipeline(...) def some_pipeline(): ... execute_pipeline(reconstructable(some_pipeline), {'execution': {'multiprocess': {}})
Pipelines must be defined in module scope in order for reconstructable to be used. Note that
pipelines defined interactively, e.g., in the Python REPL, cannot be passed between processes.
In 0.8.0, we've renamed the common environment_dict parameter to many user-facing APIs to
run_config, and we've dropped the previous run_config parameter. This change affects the
execute_pipeline_iterator and execute_pipeline APIs, the PresetDefinition and
ScheduleDefinition, and the execute_solid test API. Similarly, the environment_dict_fn, user_defined_environment_dict_fn_for_partition, and environment_dict_fn_for_partition parameters
to ScheduleDefinition, PartitionSetDefinition, and PartitionScheduleDefinition have been
renamed to run_config_fn, user_defined_run_config_fn_for_partition, and
run_config_fn_for_partition respectively.
The previous run_config parameter has been removed, as has the backing RunConfig class. This
change affects the execute_pipeline_iterator and execute_pipeline APIs, and the
execute_solids_within_pipeline and execute_solid_within_pipeline test APIs. Instead, you should
set the mode, preset, tags, solid_selection, and, in test, `raise_on_error parameters
directly.
This change is intended to reduce ambiguity around the notion of a pipeline execution's
"environment", since the config value passed as run_config is scoped to a single execution.
In 0.8.0, we've renamed the common config parameter to the user-facing definition APIs to
config_schema. This is intended to reduce ambiguity between config values (provided at
execution time) and their user-specified schemas (provided at definition time). This change affects
the ConfigMapping, @composite_solid, @solid, SolidDefinition, @executor,
ExecutorDefinition, @logger, LoggerDefinition, @resource, and ResourceDefinition APIs.
In the CLI, dagster pipeline execute and dagster pipeline launch now take -c/--config instead
of -e/--env.
In 0.8.0, we've renamed the solid_subset/--solid-subset argument to
solid_selection/--solid-selection throughout the Python API and CLI. This affects the
dagster pipeline execute, dagster pipeline launch, and dagster pipeline backfill CLI commands,
and the @schedule, @monthly_schedule, @weekly_schedule, @daily_schedule, @hourly_schedule,
ScheduleDefinition, PresetDefinition, PartitionSetDefinition, PartitionScheduleDefinition,
execute_pipeline, execute_pipeline_iterator, DagsterInstance.create_run_for_pipeline,
DagsterInstance.create_run APIs.
In addition to the names of individual solids, the new solid_selection argument supports selection
queries like *solid_name++ (i.e., solid_name, all of its ancestors, its immediate descendants,
and their immediate descendants), previously supported only in Dagit views.
runtime_type property on InputDefinition and OutputDefinition has been
removed. Use dagster_type instead.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.all_runtime_types method on SolidDefinition and CompositeSolidDefinition
has been removed. Use all_dagster_types instead.metadata argument to SolidDefinition and @solid has been removed. Use
tags instead.is_optional throughout the codebase was deprecated in 0.7.x and has been removed. Use
is_required instead.The built-in config type Path has been removed. Use String.
This package has been renamed to dagster-shell. Thebash_command_solid and bash_script_solid
solid factory functions have been renamed to create_shell_command_solid and
create_shell_script_solid.
The config schema for the dagster_dask.dask_executor has changed. The previous config should
now be nested under the key local.
dagster_spark.SparkSolidDefinition has been removed - use create_spark_solid instead.
The 0.7.0 release contains a number of breaking API changes. While listed in the changelog, this document goes into more detail about how to resolve the change easily. Most of the eliminated or changed APIs can be adjusted to with relatively straightforward changes.
The easiest way to use this guide is to search for associated error text.
There have been substantial changes to the core dagster type APIs.
Error:
ImportError: cannot import name 'dagster_type' from 'dagster'
Fix:
Use usable_as_dagster_type instead. If dynamically generating
types, construct using DagsterType instead.
Error:
ImportError: cannot import name 'as_dagster_type' from 'dagster'
Fix:
Use make_python_type_usable_as_dagster_type instead.
Error:
dagster.core.errors.DagsterInvalidDefinitionError: type_check_fn argument type "BadType" must take 2 arguments, received 1
Fix:
Add a context argument (named _, _context, context, or context_) as the first argument
of the type_check_fn. The second argument is the value being type-checked.
Further Information:
We have eliminated the @dagster_type and as_dagster_type
APIs, which previously were promoted as our primary type
creation API. This API automatically created a mapping
between a Python type and a Dagster Type. While convenient,
this ended up causing unpredictable behavior based on import
order, as well as being wholly incompatible with dynamically
created Dagster types.
Our core type creation API is now the DagsterType class. It creates a
Dagster type (which is just an instance of DagsterType) that can be passed
to InputDefinition and OutputDefinition.
The functionality of @dagster_type is preserved, but under a different name:
usable_as_dagster_type. This decorator signifies that the author wants
a bare Python type to be usable in contexts that expect dagster types, such as
an InputDefinition or OutputDefinition.
Any user that had been programmatically creating dagster types and was forced
to decorate classes in local scope using @dagster_type and return that class
should instead just create a DagsterType directly.
as_dagster_type has replaced by make_python_type_usable_as_dagster_type.
The semantics of as_dagster_type did not indicate what is was actually doing
very well. This function is meant to take an existing type -- often from
a library that one doesn't control -- and make that type usable as a dagster
type, the second argument.
The type_check_fn argument has been renamed from type_check and now takes
two arguments instead of one. The first argument is a instance of TypeCheckContext;
the second argument is the value being checked. This allows the type check
to have access to resources.
The config APIs have been renamed to have no collisions with names in neither python's
typing API nor the dagster type system. Here are some example errors:
Error:
dagster.core.errors.DagsterInvariantViolationError: Cannot resolve Dagster Type Optional.Int to a config type. Repr of type: <dagster.core.types.dagster_type.OptionalType object at 0x102bb2a50>
Fix:
Use Noneable of Optional.
Error:
TypeError: 'DagsterDictApi' object is not callable
Fix:
Pass a raw python dictionary instead of Dict.
config=Dict({'foo': str}) becomes config={'foo': str}
Error:
ImportError: cannot import name 'PermissiveDict' from 'dagster'
Fix:
Use Permissive instead.
Error:
dagster.core.errors.DagsterInvariantViolationError: Cannot use List in the context of config. Please use a python list (e.g. [int]) or dagster.Array (e.g. Array(int)) instead.
Fix:
This happens when a properly constructed List is used within config. Use Array instead.
Error:
dagster.core.errors.DagsterInvalidDefinitionError: Invalid type: dagster_type must be DagsterType, a python scalar, or a python type that has been marked usable as a dagster type via @usable_dagster_type or make_python_type_usable_as_dagster_type: got <dagster.config.config_type.Noneable object at 0x1029c8a10>.
Fix:
This happens when a List takes an invalid argument and is never constructed.
The error could be much better. This is what happens a config type (in this
case Noneable) is passed to a List. The fix is to use either Array or
to use a bare list with a single element, which is a config type.
Any solid, type, or configuration function that accesses a resource off of a context
object must declare that resource key with a required_resource_key argument.
Error:
DagsterUnknownResourceError: Unknown resource <resource_name>. Specify <resource_name> as a required resource on the compute / config function that accessed it.
Fix:
Find any references to context.resources.<resource_name>, and ensure that the enclosing
solid definition, type definition, or config function has the resource key specified
in its required_resource_key argument.
Further information:
When only a subset of solids are being executed in a given process, we only need to initialize resources that will be used by that subset of solids. In order to improve the performance of pipeline execution, we need each solid and type to explicitly declare its required resources.
As a result, we should see improved performance for pipeline subset execution, multiprocess execution, and retry execution.
Error:
AttributeError: 'ComputeExecutionContext' object has no attribute 'run_config'
Fix:
Replace all references to context.run_config with context.pipeline_run. The run_config field
on the pipeline execution context has been removed and replaced with pipeline_run, a PipelineRun
instance. Along with the fields previously on RunConfig, this also includes the pipeline run
status.
Scheduler configuration has been moved to the dagster.yaml. After upgrading, the previous schedule
history is no longer compatible with the new storage.
Make sure you delete your existing $DAGSTER_HOME/schedules directory, then run:
dagster schedule wipe && dagster schedule up
Error:
TypeError: schedules() got an unexpected keyword argument 'scheduler'
Fix:
The @schedules decorator no longer takes a scheduler argument. Remove the argument and instead
configure the scheduler on the instance.
Instead of:
@schedules(scheduler=SystemCronScheduler) def define_schedules(): ...
Remove the scheduler argument:
@schedules def define_schedules(): ...
Configure the scheduler on your instance by adding the following to $DAGSTER_HOME/dagster.yaml:
scheduler: module: dagster_cron.cron_scheduler class: SystemCronScheduler
Error:
TypeError: <lambda>() takes 0 positional arguments but 1 was given"
Stack Trace:
File ".../dagster/python_modules/dagster/dagster/core/definitions/schedule.py", line 171, in should_execute return self._should_execute(context)
Fix:
The should_execute and environment_dict_fn argument to ScheduleDefinition now has a required
first argument context, representing the ScheduleExecutionContext.