- Supplying the "metadata" argument to InputDefinitions and OutputDefinitions is no longer considered experimental.
- The "context" argument can now be omitted for solids that have required resource keys.
- The S3ComputeLogManager now takes a boolean config argument skip_empty_files, which skips uploading empty log files to S3. This should enable a work around of timeout errors when using the S3ComputeLogManager to persist logs to MinIO object storage.
- The Helm subchart for user code deployments now allows for extra manifests.
- Running
dagit with flag --suppress-warnings will now ignore all warnings, such as ExperimentalWarnings. - PipelineRunStatus, which represents the run status, is now exported in the public API.
- The asset catalog now has better backwards compatibility for supporting deprecated Materialization events. Previously, these events were causing loading errors.
- Improved documentation of the
dagster-dbt library with some helpful tips and example code (thanks @makotonium!). - Fixed the example code in the
dagster-pyspark documentation for providing and accessing the pyspark resource (thanks @Andrew-Crosby!). - Helm chart serviceaccounts now allow annotations (thanks @jrouly!).
- Added section on testing resources (link).
- Revamped IO manager testing section to use
build_input_context and build_output_context APIs (link).
- Added an example that demonstrates what a complete repository that takes advantage of many Dagster features might look like. Includes usage of IO Managers, modes / resources, unit tests, several cloud service integrations, and more! Check it out at
examples/hacker_news! retry_number is now available on SolidExecutionContext, allowing you to determine within a solid function how many times the solid has been previously retried.- Errors that are surfaced during solid execution now have clearer stack traces.
- When using Postgres or MySQL storage, the database mutations that initialize Dagster tables on startup now happen in atomic transactions, rather than individual SQL queries.
- For versions >=0.11.13, when specifying the
--version flag when installing the Helm chart, the tags for Dagster-provided images in the Helm chart will now default to the current Chart version. For --version \<0.11.13, the image tags will still need to be updated properly to use old chart version. - Removed the
PIPELINE_INIT_FAILURE event type. A failure that occurs during pipeline initialization will now produce a PIPELINE_FAILURE as with all other pipeline failures.
- When viewing run logs in Dagit, in the stdout/stderr log view, switching the filtered step did not work. This has been fixed. Additionally, the filtered step is now present as a URL query parameter.
- The
get_run_status method on the Python GraphQL client now returns a PipelineRunStatus enum instead of the raw string value in order to align with the mypy type annotation. Thanks to Dylan Bienstock for surfacing this bug! - When a docstring on a solid doesn’t match the reST, Google, or Numpydoc formats, Dagster no longer raises an error.
- Fixed a bug where memoized runs would sometimes fail to execute when specifying a non-default IO manager key.
- Added the
k8s_job_executor, which executes solids in separate kubernetes jobs. With the addition of this executor, you can now choose at runtime between single pod and multi-pod isolation for solids in your run. Previously this was only configurable for the entire deployment - you could either use the K8sRunLauncher with the default executors (in_process and multiprocess) for low isolation, or you could use the CeleryK8sRunLauncher with the celery_k8s_job_executor for pod-level isolation. Now, your instance can be configured with the K8sRunLauncher and you can choose between the default executors or the k8s_job_executor. - The
DagsterGraphQLClient now allows you to specify whether to use HTTP or HTTPS when connecting to the GraphQL server. In addition, error messages during query execution or connecting to dagit are now clearer. Thanks to @emily-hawkins for raising this issue! - Added experimental hook invocation functionality. Invoking a hook will call the underlying decorated function. For example:
from dagster import build_hook_context
my_hook(build_hook_context(resources={"foo_resource": "foo"}))
- Resources can now be directly invoked as functions. Invoking a resource will call the underlying decorated initialization function.
from dagster import build_init_resource_context
@resource(config_schema=str)
def my_basic_resource(init_context):
return init_context.resource_config
context = build_init_resource_context(config="foo")
assert my_basic_resource(context) == "foo"
- Improved the error message when a pipeline definition is incorrectly invoked as a function.
ScheduleDefinition and SensorDefinition now carry over properties from functions decorated by @sensor and @schedule. Ie: docstrings.- Fixed a bug with configured on resources where the version set on a
ResourceDefinition was not being passed to the ResourceDefinition created by the call to configured. - Previously, if an error was raised in an
IOManager handle_output implementation that was a generator, it would not be wrapped DagsterExecutionHandleOutputError. Now, it is wrapped. - Dagit will now gracefully degrade if websockets are not available. Previously launching runs and viewing the event logs would block on a websocket conection.
- Added an example of run attribution via a custom run coordinator, which reads a user’s email from HTTP headers on the Dagster GraphQL server and attaches the email as a run tag. Custom run coordinator are also now specifiable in the Helm chart, under
queuedRunCoordinator. See the docs for more information on setup. RetryPolicy now supports backoff and jitter settings, to allow for modulating the delay as a function of attempt number and randomness.
- [Helm] Added
dagit.enableReadOnly . When enabled, a separate Dagit instance is deployed in —read-only mode. You can use this feature to serve Dagit to users who you do not want to able to kick off new runs or make other changes to application state. - [dagstermill] Dagstermill is now compatible with current versions of papermill (2.x). Previously we required papermill to be pinned to 1.x.
- Added a new metadata type that links to the asset catalog, which can be invoked using
EventMetadata.asset. - Added a new log event type
LOGS_CAPTURED, which explicitly links to the captured stdout/stderr logs for a given step, as determined by the configured ComputeLogManager on the Dagster instance. Previously, these links were available on the STEP_START event. - The
network key on DockerRunLauncher config can now be sourced from an environment variable. - The Workspace section of the Status page in Dagit now shows more metadata about your workspace, including the python file, python package, and Docker image of each of your repository locations.
- In Dagit, settings for how executions are viewed now persist across sessions.
- The
get_execution_data method of SensorDefinition and ScheduleDefinition has been renamed to evaluate_tick. We expect few to no users of the previous name, and are renaming to prepare for improved testing support for schedules and sensors.
- README has been updated to remove typos (thanks @gogi2811).
- Configured API doc examples have been fixed (thanks @jrouly).
- Documentation on testing sensors using experimental
build_sensor_context API. See Testing sensors.
- Some mypy errors encountered when using the built-in Dagster types (e.g.,
dagster.Int ) as type annotations on functions decorated with @solid have been resolved. - Fixed an issue where the
K8sRunLauncher sometimes hanged while launching a run due to holding a stale Kubernetes client. - Fixed an issue with direct solid invocation where default config values would not be applied.
- Fixed a bug where resource dependencies to io managers were not being initialized during memoization.
- Dagit can once again override pipeline tags that were set on the definition, and UI clarity around the override behavior has been improved.
- Markdown event metadata rendering in dagit has been repaired.
- Sensors can now set a string cursor using
context.update_cursor(str_value) that is persisted across evaluations to save unnecessary computation. This persisted string value is made available on the context as context.cursor. Previously, we encouraged cursor-like behavior by exposing last_run_key on the sensor context, to keep track of the last time the sensor successfully requested a run. This, however, was not useful for avoiding unnecessary computation when the sensor evaluation did not result in a run request. - Dagit may now be run in
--read-only mode, which will disable mutations in the user interface and on the server. You can use this feature to run instances of Dagit that are visible to users who you do not want to able to kick off new runs or make other changes to application state. - In
dagster-pandas, the event_metadata_fn parameter to the function create_dagster_pandas_dataframe_type may now return a dictionary of EventMetadata values, keyed by their string labels. This should now be consistent with the parameters accepted by Dagster events, including the TypeCheck event.
MyDataFrame = create_dagster_pandas_dataframe_type(
"MyDataFrame",
event_metadata_fn=lambda df: [
EventMetadataEntry.int(len(df), "number of rows"),
EventMetadataEntry.int(len(df.columns), "number of columns"),
]
)
MyDataFrame = create_dagster_pandas_dataframe_type(
"MyDataFrame",
event_metadata_fn=lambda df: {
"number of rows": len(df),
"number of columns": len(dataframe.columns),
},
)
- dagster-pandas’
PandasColumn.datetime_column() now has a new tz parameter, allowing you to constrain the column to a specific timezone (thanks @mrdavidlaing!) - The
DagsterGraphQLClient now takes in an optional transport argument, which may be useful in cases where you need to authenticate your GQL requests:
authed_client = DagsterGraphQLClient(
"my_dagit_url.com",
transport=RequestsHTTPTransport(..., auth=<some auth>),
)
- Added an
ecr_public_resource to get login credentials for the AWS ECR Public Gallery. This is useful if any of your pipelines need to push images. - Failed backfills may now be resumed in Dagit, by putting them back into a “requested” state. These backfill jobs should then be picked up by the backfill daemon, which will then attempt to create and submit runs for any of the outstanding requested partitions . This should help backfill jobs recover from any deployment or framework issues that occurred during the backfill prior to all the runs being launched. This will not, however, attempt to re-execute any of the individual pipeline runs that were successfully launched but resulted in a pipeline failure.
- In the run log viewer in Dagit, links to asset materializations now include the timestamp for that materialization. This will bring you directly to the state of that asset at that specific time.
- The Databricks step launcher now includes a
max_completion_wait_time_seconds configuration option, which controls how long it will wait for a Databricks job to complete before exiting.
- Solids can now be invoked outside of composition. If your solid has a context argument, the
build_solid_context function can be used to provide a context to the invocation.
from dagster import build_solid_context
@solid
def basic_solid():
return "foo"
assert basic_solid() == 5
@solid
def add_one(x):
return x + 1
assert add_one(5) == 6
@solid(required_resource_keys={"foo_resource"})
def solid_reqs_resources(context):
return context.resources.foo_resource + "bar"
context = build_solid_context(resources={"foo_resource": "foo"})
assert solid_reqs_resources(context) == "foobar"
build_schedule_context allows you to build a ScheduleExecutionContext using a DagsterInstance. This can be used to test schedules.
from dagster import build_schedule_context
with DagsterInstance.get() as instance:
context = build_schedule_context(instance)
my_schedule.get_execution_data(context)
build_sensor_context allows you to build a SensorExecutionContext using a DagsterInstance. This can be used to test sensors.
from dagster import build_sensor_context
with DagsterInstance.get() as instance:
context = build_sensor_context(instance)
my_sensor.get_execution_data(context)
build_input_context and build_output_context allow you to construct InputContext and OutputContext respectively. This can be used to test IO managers.
from dagster import build_input_context, build_output_context
io_manager = MyIoManager()
io_manager.load_input(build_input_context())
io_manager.handle_output(build_output_context(), val)
- Resources can be provided to either of these functions. If you are using context manager resources, then
build_input_context/build_output_context must be used as a context manager.
with build_input_context(resources={"cm_resource": my_cm_resource}) as context:
io_manager.load_input(context)
validate_run_config can be used to validate a run config blob against a pipeline definition & mode. If the run config is invalid for the pipeline and mode, this function will throw an error, and if correct, this function will return a dictionary representing the validated run config that Dagster uses during execution.
validate_run_config(
{"solids": {"a": {"config": {"foo": "bar"}}}},
pipeline_contains_a
)
validate_run_config(
pipeline_no_required_config
)
- The ability to set a
RetryPolicy has been added. This allows you to declare automatic retry behavior when exceptions occur during solid execution. You can set retry_policy on a solid invocation, @solid definition, or @pipeline definition.
@solid(retry_policy=RetryPolicy(max_retries=3, delay=5))
def fickle_solid():
@pipeline(
solid_retry_policy=RetryPolicy()
)
def my_pipeline():
some_solid()
fickle_solid()
fickle_solid.with_retry_policy(RetryPolicy(max_retries=2))
- Previously, asset materializations were not working in dagster-dbt for dbt >= 0.19.0. This has been fixed.
- Previously, using the
dagster/priority tag directly on pipeline definitions would cause an error. This has been fixed. - In dagster-pandas, the
create_dagster_pandas_dataframe_type() function would, in some scenarios, not use the specified materializer argument when provided. This has been fixed (thanks @drewsonne!) dagster-graphql --remote now sends the query and variables as post body data, avoiding uri length limit issues.- In the Dagit pipeline definition view, we no longer render config nubs for solids that do not need them.
- In the run log viewer in Dagit, truncated row contents (including errors with long stack traces) now have a larger and clearer button to expand the full content in a dialog.
- [dagster-mysql] Fixed a bug where database connections accumulated by
sqlalchemy.Engine objects would be invalidated after 8 hours of idle time due to MySQL’s default configuration, resulting in an sqlalchemy.exc.OperationalError when attempting to view pages in Dagit in long-running deployments.
- In 0.11.9, context was made an optional argument on the function decorated by @solid. The solids throughout tutorials and snippets that do not need a context argument have been altered to omit that argument, and better reflect this change.
- In a previous docs revision, a tutorial section on accessing resources within solids was removed. This has been re-added to the site.