dagster instance migrate after upgrading to 1.2.0.dagster dev command that can be used to run both Dagit and the Dagster daemon in the same process during local development. [docs]DynamicPartitionsDefinition allows partitioning assets dynamically - you can add and remove partitions without reloading your definitions (experimental). [docs]TimeWindowPartitionMapping now accepts start_offset and end_offset arguments that allow specifying that time partitions depend on earlier or later time partitions of upstream assets [docs].dagster-wandb with Weights & Biases allows you to orchestrate your MLOps pipelines and maintain ML assets with Dagster. [docs]dagster-snowflake-pyspark allows you to store and load PySpark DataFrames as Snowflake tables using the snowflake_pyspark_io_manager. [docs]dagster-gcp-pandas and dagster-gcp-pyspark allow you to store and load Pandas and PySpark DataFrames as BigQuery tables using the bigquery_pandas_io_manager and bigquery_pyspark_io_manager. [docs]dagster-airflow integration library was bumped to 1.x.x, with that major bump the library has been refocused on enabling migration from Airflow to Dagster. Refer to the docs for an in-depth migration guide.create_databricks_run_now_op), as well as submitting one-off Databricks jobs (create_databricks_submit_run_op).create_databricks_job_op op factory is now deprecated.Stay tuned, as this is only the first part of the overhaul. We’ll be adding more chapters - including automating materializations, using resources, using I/O managers, and more - in the next few weeks.
@graph_asset and @graph_multi_asset.project_fully_featured example now uses the built in DuckDB and Snowflake I/O managers.make_persistent_airflow_db_resource this enables support for Airflow features like pools and cross-dagrun state sharing. In particular retry-from-failure now works for jobs generated from Airflow DAGs.BigQueryPandasTypeHandler now uses google.bigquery.Client methods load_table_from_dataframe and query rather than the pandas_gbq library to store and fetch DataFrames.args instead of both command and args for user code deployments, allowing to include a custom ENTRYPOINT in your the Dockerfile that loads your code.protobuf<4 pin in Dagster has been removed. Installing either protobuf 3 or protobuf 4 will both work with Dagster.@graph_asset and @graph_multi_asset now support passing metadata (thanks @askvinni)!@graph_asset and @graph_multi_asset to be ignored.TableRecord.@multi_asset and other functions would register as type errors for mypy and other static analyzers.start_date would not be converted correctly causing runs to fail.dagster instance migrate:id column to the kvs, daemon_heartbeats and instance_info tables, enforcing that all tables have a primary key.The minimum 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.
Prior to 0.9.19, asset keys were serialized in a legacy format. This release removes support for querying asset events serialized with this legacy format. Contact #dagster-support for tooling to migrate legacy events to the supported version. Users who began using assets after 0.9.19 will not be affected by this change.
[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 op and 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://.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)iteritems usage was removed and changed to the recommended items within dagster-snowflake-pandas (thanks @sethkimmel3)!@asset_graph decorator (thanks @simonvanderveldt)!DataVersions can now be returned on OutputOpExecutionContext.get_asset_provenancePicking a github issue was not correctly rendering, this has been fixed (thanks @Sedosa)!Further performance improvements for build_asset_reconciliation_sensor.
Dagster now allows you to backfill asset selections that include mapped partition definitions, such as a daily asset which rolls up into a weekly asset, as long as the root assets in your selection share a partition definition.
Dagit now includes information about the cause of an asset’s staleness.
Improved the error message for non-matching cron schedules in TimeWindowPartitionMappings with offsets. (Thanks Sean Han!)
[dagster-aws] The EcsRunLauncher now allows you to configure the runtimePlatform field for the task definitions of the runs that it launches, allowing it to launch runs using Windows Docker images.
[dagster-azure] Add support for DefaultAzureCredential for adls2_resource (Thanks Martin Picard!)
[dagster-databricks] Added op factories to create ops for running existing Databricks jobs (create_databricks_run_now_op), as well as submitting one-off Databricks jobs (create_databricks_submit_run_op). See the new Databricks guide for more details.
[dagster-duckdb-polars] Added a dagster-duckdb-polars library that includes a DuckDBPolarsTypeHandler for use with build_duckdb_io_manager, which allows loading / storing Polars DataFrames from/to DuckDB. (Thanks Pezhman Zarabadi-Poor!)
[dagster-gcp-pyspark] New PySpark TypeHandler for the BigQuery I/O manager. Store and load your PySpark DataFrames in BigQuery using bigquery_pyspark_io_manager.
[dagster-snowflake][dagster-duckdb] The Snowflake and DuckDB IO managers can now load multiple partitions in a single step - e.g. when a non-partitioned asset depends on a partitioned asset or a single partition of an asset depends on multiple partitions of an upstream asset. Loading occurs using a single SQL query and returns a single DataFrame.
[dagster-k8s] The Helm chart now supports the full kubernetes env var spec for user code deployments. Example:
dagster-user-deployments: deployments: - name: my-code env: - name: FOO valueFrom: fieldFre: fieldPath: metadata.uid
If includeConfigInLaunchedRuns is enabled, these env vars will also be applied to the containers for launched runs.
AssetSelection which matched no assets was passed into define_asset_job, the resulting job would target all assets in the repository. This has been fixed.build_schedule_from_partitioned_job.fs_io_manager to store multipartitioned materializations in directory levels by dimension. This resolves a bug on windows where multipartitioned materializations could not be stored with the fs_io_manager.context.partition_key would raise an error when executing on a partition range within a single run via Dagit.Definitions reloaded messages on fresh page loads.location information.ProgrammingError was imported from the wrong library, this has been fixed. Thanks @herbert-allium!Output objects rather than using Dagster’s auto-generated versions.get_asset_provenance method on OpExecutionContext allows fetching logical version provenance for an arbitrary asset key.examples/assets_dynamic_partitions folderThe new @graph_asset and @graph_multi_asset decorators make it more ergonomic to define graph-backed assets.
Dagster will auto-infer dependency relationships between single-dimensionally partitioned assets and multipartitioned assets, when the single-dimensional partitions definition is a dimension of the MultiPartitionsDefinition.
A new Test sensor / Test schedule button that allows you to perform a dry-run of your sensor / schedule. Check out the docs on this functionality here for sensors and here for schedules.
[dagit] Added (back) tag autocompletion in the runs filter, now with improved query performance.
[dagit] The Dagster libraries and their versions that were used when loading definitions can now be viewed in the actions menu for each code location.
New bigquery_pandas_io_manager can store and load Pandas dataframes in BigQuery.
[dagster-snowflake, dagster-duckdb] SnowflakeIOManagers and DuckDBIOManagers can now default to loading inputs as a specified type if a type annotation does not exist for the input.
[dagster-dbt] Added the ability to use the “state:” selector
[dagster-k8s] The Helm chart now supports the full kubernetes env var spec for Dagit and the Daemon. E.g.
dagit: env: - name: “FOO” valueFrom: fieldRef: fieldPath: metadata.uid
ruff, pyright).FreshnessPolicy object now supports a cron_schedule_timezone argument.AssetsDefinition.from_graph now supports a freshness_policies_by_output_name parameter.@asset_sensor will now display an informative SkipReason when no new materializations have been created since the last sensor tick.AssetsDefinition now has a to_source_asset method, which returns a representation of this asset as a SourceAsset.from dagster import asset, job, op @asset def emails_to_send(): ... @op def send_emails(emails) -> None: ... @job def send_emails_job(): send_emails(emails_to_send.to_source_asset())
--dagit-host/-h argument to the dagster dev command to allow customization of the host where Dagit runs.input_manager_key to an asset’s input, incorrect resource config could be used when loading that input. This has been fixed.runK8sConfig in the Dagster Helm chart would not pass configuration through to pods launched using the k8s_job_executor.execute_k8s_job op downstream of a dynamic output would result in k8s jobs with duplicate names being created. This has been fixed.asset_key, asset_partitions, and asset_partitions_defs arguments on Out.@multi_asset no longer accepts Out values in the dictionary passed to its outs argument. This was experimental and deprecated. Instead, use AssetOut.top_level_resources argument to the repository decorator has been renamed to _top_level_resources to emphasize that it should not be set manually.load_asset_values now accepts resource configuration (thanks @Nintorac!)UPathIOManager, paths with the "." character in them would be incorrectly truncated, which could result in multiple distinct objects being written to the same path. This has been fixed. (Thanks @spenczar!)