Bugfixes
New
minimum_interval_seconds argument, which determines the minimum amount of time between sensor evaluations.Bugfixes
-n/--max_workers default value for the dagster api grpc command to be None. When set to None, the gRPC server will use the default number of workers which is based on the CPU count. If you were previously setting this value to 1, we recommend removing the argument or increasing the number.Community Contributions
New
Bugfixes
Fixed an issue where run start times and end times were displayed in the wrong timezone in Dagit when using Postgres storage.
Schedules with partitions that weren’t able to execute due to not being able to find a partition will now display the name of the partition they were unable to find on the “Last tick” entry for that schedule.
Improved timing information display for queued and canceled runs within the Runs table view and on individual Run pages in Dagit.
Improvements to the tick history view for schedules and sensors.
Fixed formatting issues on the Dagit instance configuration page.
Miscellaneous Dagit bugfixes and improvements.
The dagster pipeline launch command will now respect run concurrency limits if they are applied on your instance.
Fixed an issue where re-executing a run created by a sensor would cause the daemon to stop executing any additional runs from that sensor.
Sensor runs with invalid run configuration will no longer create a failed run - instead, an error will appear on the page for the sensor, allowing you to fix the configuration issue.
General dagstermill housekeeping: test refactoring & type annotations, as well as repinning ipykernel to solve #3401
Documentation
Community Contributions
k8s-example by 25% (104 MB) (thanks @alex-treebeard and @mrdavidlaing!)snowflake_resource can now be configured to use the SQLAlchemy connector (thanks @basilvetas!)New
userDeployments.deployments in the Helm chart, replicaCount now defaults to 1 if not specified.Bugfixes
env, envConfigMaps, and envSecrets.Documentation
QueuedRunCoordinator to limit run concurrency.SystemCronScheduler or K8sScheduler to the new scheduler.IOManager abstraction provides a new, streamlined primitive for granular control over where
and how solid outputs are stored and loaded. This is intended to replace the (deprecated)
intermediate/system storage abstractions, See the
IO Manager Overview for more
information.required_resource_keys parameter on @resource.DynamicOutputDefinition API.
Dagster can now map the downstream dependencies over a dynamic output at runtime.Dropping Python 2 support
Removal of deprecated APIs
These APIs were marked for deprecation with warnings in the 0.9.0 release, and have been removed in the 0.10.0 release.
input_hydration_config has been removed. Use the dagster_type_loader decorator
instead.output_materialization_config has been removed. Use dagster_type_materializer
instead.SystemStorageDefinition,
@system_storage, and default_system_storage_defs . Use the new IOManagers API instead. See
the IO Manager Overview for more
information.config_field argument on decorators and definitions classes has been removed and replaced
with config_schema. This is a drop-in rename.step_keys_to_execute to the functions reexecute_pipeline and
reexecute_pipeline_iterator has been removed. Use the step_selection argument to select
subsets for execution instead.repository key in your workspace.yaml;
use load_from instead. See the
Workspaces Overview for
documentation about how to define a workspace.Breaking API Changes
SolidExecutionResult.compute_output_event_dict has been renamed to
SolidExecutionResult.compute_output_events_dict. A solid execution result is returned from
methods such as result_for_solid. Any call sites will need to be updated..compute suffix is no longer applied to step keys. Step keys that were previously named
my_solid.compute will now be named my_solid. If you are using any API method that takes a
step_selection argument, you will need to update the step keys accordingly.pipeline_def property has been removed from the InitResourceContext passed to functions
decorated with @resource.Dagstermill
If you are using define_dagstermill_solid with the output_notebook parameter set to True,
you will now need to provide a file manager resource (subclass of
dagster.core.storage.FileManager) on your pipeline mode under the resource key "file_manager",
e.g.:
from dagster import ModeDefinition, local_file_manager, pipeline from dagstermill import define_dagstermill_solid my_dagstermill_solid = define_dagstermill_solid("my_dagstermill_solid", output_notebook=True, ...) @pipeline(mode_defs=[ModeDefinition(resource_defs={"file_manager": local_file_manager})]) def my_dagstermill_pipeline(): my_dagstermill_solid(...)
Helm Chart
scheduler values in the helm chart has changed. Instead of a simple toggle
on/off, we now require an explicit scheduler.type to specify usage of the
DagsterDaemonScheduler, K8sScheduler, or otherwise. If your specified scheduler.type has
required config, these fields must be specified under scheduler.config.snake_case fields have been changed to camelCase. Please update your values.yaml as follows:pipeline_run → pipelineRundagster_home → dagsterHomeenv_secrets → envSecretsenv_config_maps → envConfigMapscelery and k8sRunLauncher have now been consolidated under the Helm value
runLauncher for simplicity. Use the field runLauncher.type to specify usage of the
K8sRunLauncher, CeleryK8sRunLauncher, or otherwise. By default, the K8sRunLauncher is
enabled.CeleryK8sRunLauncher, you should explicitly enable your message broker of choice.userDeployments are now enabled by default.Event log messages streamed to stdout and stderr have been streamlined to be a single line
per event.
Experimental support for memoization and versioning lets you execute pipelines incrementally, selecting which solids need to be rerun based on runtime criteria and versioning their outputs with configurable identifiers that capture their upstream dependencies.
To set up memoized step selection, users can provide a MemoizableIOManager, whose has_output
function decides whether a given solid output needs to be computed or already exists. To execute
a pipeline with memoized step selection, users can supply the dagster/is_memoized_run run tag
to execute_pipeline.
To set the version on a solid or resource, users can supply the version field on the definition.
To access the derived version for a step output, users can access the version field on the
OutputContext passed to the handle_output and load_input methods of IOManager and the
has_output method of MemoizableIOManager.
Schedules that are executed using the new DagsterDaemonScheduler can now execute in any
timezone by adding an execution_timezone parameter to the schedule. Daylight Savings Time
transitions are also supported. See the
Schedules Overview for
more information and examples.
Helm
We've added schema validation to our Helm chart. You can now check that your values YAML file is correct by running:
helm lint helm/dagster -f helm/dagster/values.yaml
Added support for resource annotations throughout our Helm chart.
Added Helm deployment of the dagster daemon & daemon scheduler.
Added Helm support for configuring a compute log manager in your dagster instance.
User code deployments now include a user ConfigMap by default.
Changed the default liveness probe for Dagit to use httpGet "/dagit_info" instead of
tcpSocket:80
Dagster-K8s [Kubernetes]
Dagster-Celery-K8s
dagster-docker library with a DockerRunLauncher that launches each run in its own
Docker container. (See Deploying with Docker docs
for an example.)create_databricks_job_solid for creating solids that launch Databricks jobs.