- Improved the error message for situations where you try
a, b = my_op(), inside @graph or @job, but my_op only has a single Out. - [dagster-dbt] A new integration with dbt Cloud allows you to launch dbt Cloud jobs as part of your Dagster jobs. This comes complete with rich error messages, links back to the dbt Cloud UI, and automatically generated Asset Materializations to help keep track of your dbt models in Dagit. It provides a pre-built
dbt_cloud_run_op, as well as a more flexible dbt_cloud_resource for more customized use cases. Check out the api docs to learn more! - [dagster-gcp] Pinned the google-cloud-bigquery dependency to <3, because the new 3.0.0b1 version was causing some problems in tests.
- [dagit] Verbiage update to make it clear that wiping an asset means deleting the materialization events for that asset.
- Fixed a bug with the
pipeline launch / job launch CLIs that would spin up an ephemeral dagster instance for the launch, then tear it down before the run actually executed. Now, the CLI will enforce that your instance is non-ephemeral. - Fixed a bug with re-execution when upstream step skips some outputs. Previously, it mistakenly tried to load inputs from parent runs. Now, if an upstream step doesn’t yield outputs, the downstream step would skip.
- [dagit] Fixed a bug where configs for unsatisfied input wasn’t properly resolved when op selection is specified in Launchpad.
- [dagit] Restored local font files for Inter and Inconsolata instead of using the Google Fonts API. This allows correct font rendering for offline use.
- [dagit] Improved initial workspace loading screen to indicate loading state instead of showing an empty repository message.
- The
pipeline argument of the InitExecutorContext constructor has been changed to job.
- The
@asset decorator now accepts a dagster_type argument, which determines the DagsterType for the output of the asset op. build_assets_job accepts an executor_def argument, which determines the executor for the job.
- A docs section on context manager resources has been added. Check it out here.
- Removed the versions of the Hacker News example jobs that used the legacy solid & pipeline APIs.
- The Runs page in Dagit now loads much more quickly.
- Fixed an issue where Dagit would sometimes display a red "Invalid JSON" error message.
google-cloud-bigquery is temporarily pinned to be prior to version 3 due to a breaking change in that version.
- Previously, the
EcsRunLauncher tagged each ECS task with its corresponding Dagster Run ID. ECS tagging isn't supported for AWS accounts that have not yet migrated to using the long ARN format. Now, the EcsRunLauncher only adds this tag if your AWS account has the long ARN format enabled. - Fixed a bug in the
k8s_job_executor and docker_executor that could result in jobs exiting as SUCCESS before all ops have run. - Fixed a bug in the
k8s_job_executor and docker_executor that could result in jobs failing when an op is skipped.
graphene is temporarily pinned to be prior to version 3 to unbreak Dagit dependencies.
- [dagster-fivetran] A new dagster-fivetran integration allows you to launch Fivetran syncs and monitor their progress from within Dagster. It provides a pre-built
fivetran_sync_op, as well as a more flexible fivetran_resource for more customized use cases. Check out the api docs to learn more! - When inferring a graph/job/op/solid/pipeline description from the docstring of the decorated function, we now dedent the docstring even if the first line isn’t indented. This allows descriptions to be formatted nicely even when the first line is on the same line as the triple-quotes.
- The
SourceHashVersionStrategy class has been added, which versions op and resource code. It can be provided to a job like so:
from dagster import job, SourceHashVersionStrategy
@job(version_strategy=SourceHashVersionStrategy())
def my_job():
...
- [dagit] Improved performance on the initial page load of the Run page, as well as the partitions UI / launch backfill modal
- [dagit] Fixed a bug where top-level graphs in the repo could not be viewed in the
Workspace > Graph view.
- Fixed an issue where turning a partitioned schedule off and on again would sometimes result in unexpected past runs being created. (#5604)
- Fixed an issue where partition sets that didn’t return a new copy of run configuration on each function call would sometimes apply the wrong config to partitions during backfills.
- Fixed rare issue where using dynamic outputs in combination with optional outputs would cause errors when using certain executors.
- [dagster-celery-k8s] Fixed bug where CeleryK8s executor would not respect job run config
- [dagit] Fixed bug where graphs would sometimes appear off-center.
- In 0.13.0, job CLI commands executed via
dagster job selected both pipelines and jobs. This release changes the dagster job command to select only jobs and not pipelines.
- [dagster-dask] Updated DaskClusterTypes to have the correct import paths for certain cluster managers (thanks @kudryk!)
- [dagster-azure] Updated version requirements for Azure to be more recent and more permissive (thanks @roeap !)
- [dagster-shell] Ops will now copy the host environment variables at runtime, rather than copying them from the environment that their job is launched from (thanks @alexismanuel !)
- The job, op, graph migration guide was erroneously marked experimental. This has been fixed.
- [dagster-k8s] The
k8s_job_executor is no longer experimental, and is recommended for production workloads. This executor runs each op in a separate Kubernetes job. We recommend this executor for Dagster jobs that require greater isolation than the multiprocess executor can provide within a single Kubernetes pod. The celery_k8s_job_executor will still be supported, but is recommended only for use cases where Celery is required (The most common example is to offer step concurrency limits using multiple Celery queues). Otherwise, the k8s_job_executor is the best way to get Kubernetes job isolation. - [dagster-airflow] Updated dagster-airflow to better support job/op/graph changes by adding a
make_dagster_job_from_airflow_dag factory function. Deprecated pipeline_name argument in favor of job_name in all the APIs. - Removed a version pin of the
chardet library that was required due to an incompatibility with an old version of the aiohttp library, which has since been fixed. - We now raise a more informative error if the wrong type is passed to the
ins argument of the op decorator. - In the Dagit Launchpad, the button for launching a run now says “Launch Run” instead of “Launch Execution”
- Fixed an issue where job entries from Dagit search navigation were not linking to the correct job pages.
- Fixed an issue where jobs / pipelines were showing up instead of the underlying graph in the list of repository graph definitions.
- Fixed a bug with using custom loggers with default config on a job.
- [dagster-slack] The
slack_on_run_failure_sensor now says “Job” instead of “Pipeline” in its default message.
- Fixed a bug that was incorrectly causing a
DagsterTypeCheckDidNotPass error when a Dagster Type contained a List inside a Tuple (thanks @jan-eat!) - Added information for setting DAGSTER_HOME in Powershell and batch for windows users. (thanks @slamer59!)
- Changed the job explorer view in Dagit to show asset-based graphs when the experimental Asset API flag is turned on for any job that has at least one software-defined asset.
- Updated API docs and integration guides to reference job/op/graph for various libraries (
dagstermill, dagster-pandas, dagster-airflow, etc) - Improved documentation when attempting to retrieve output value from
execute_in_process, when job does not have a top-level output.