dagster-io/dagster 0.10.0 on GitHub

0.10.0 Edge of Glory

Major Changes

A native scheduler with support for exactly-once, fault tolerant, timezone-aware scheduling.
A new Dagster daemon process has been added to manage your schedules and sensors with a
reconciliation loop, ensuring that all runs are executed exactly once, even if the Dagster daemon
experiences occasional failure. See the Migration Guide for
instructions on moving from SystemCronScheduler or K8sScheduler to the new scheduler.
First-class sensors, built on the new Dagster daemon, allow you to instigate runs based on
changes in external state - for example, files on S3 or assets materialized by other Dagster
pipelines. See the Sensors Overview
for more information.
Dagster now supports pipeline run queueing. You can apply instance-level run concurrency
limits and prioritization rules by adding the QueuedRunCoordinator to your Dagster instance. See
the Run Concurrency Overview
for more information.
The 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.
A new Partitions page in Dagit lets you view your your pipeline runs organized by partition.
You can also launch backfills from Dagit and monitor them from this page.
A new Instance Status page in Dagit lets you monitor the health of your Dagster instance,
with repository location information, daemon statuses, instance-level schedule and sensor
information, and linkable instance configuration.
Resources can now declare their dependencies on other resources via the
required_resource_keys parameter on @resource.
Our support for deploying on Kubernetes is now mature and battle-tested Our Helm chart is
now easier to configure and deploy, and we’ve made big investments in observability and
reliability. You can view Kubernetes interactions in the structured event log and use Dagit to
help you understand what’s happening in your deployment. The defaults in the Helm chart will
give you graceful degradation and failure recovery right out of the box.
Experimental support for dynamic orchestration with the new DynamicOutputDefinition API.
Dagster can now map the downstream dependencies over a dynamic output at runtime.

Breaking Changes

Dropping Python 2 support

We’ve dropped support for Python 2.7, based on community usage and enthusiasm for Python 3-native
public APIs.

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.

The decorator input_hydration_config has been removed. Use the dagster_type_loader decorator
instead.
The decorator output_materialization_config has been removed. Use dagster_type_materializer
instead.
The system storage subsystem has been removed. This includes SystemStorageDefinition,
@system_storage, and default_system_storage_defs . Use the new IOManagers API instead. See
the IO Manager Overview for more
information.
The config_field argument on decorators and definitions classes has been removed and replaced
with config_schema. This is a drop-in rename.
The argument 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.
Repositories can no longer be loaded using the legacy 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.
The .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.
The pipeline_def property has been removed from the InitResourceContext passed to functions
decorated with @resource.

Helm Chart

The schema for the 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 → pipelineRun
- dagster_home → dagsterHome
- env_secrets → envSecrets
- env_config_maps → envConfigMaps
The Helm values celery 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.
All Celery message brokers (i.e. RabbitMQ and Redis) are disabled by default. If you are using
the CeleryK8sRunLauncher, you should explicitly enable your message broker of choice.
userDeployments are now enabled by default.

Core

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.

Dagit

Countdown and refresh buttons have been added for pages with regular polling queries (e.g. Runs,
Schedules).
Confirmation and progress dialogs are now presented when performing run terminations and
deletions. Additionally, hanging/orphaned runs can now be forced to terminate, by selecting
"Force termination immediately" in the run termination dialog.
The Runs page now shows counts for "Queued" and "In progress" tabs, and individual run pages
show timing, tags, and configuration metadata.
The backfill experience has been improved with means to view progress and terminate the entire
backfill via the partition set page. Additionally, errors related to backfills are now surfaced
more clearly.
Shortcut hints are no longer displayed when attempting to use the screen capture command.
The asset page has been revamped to include a table of events and enable organizing events by
partition. Asset key escaping issues in other views have been fixed as well.
Miscellaneous bug fixes, frontend performance tweaks, and other improvements are also included.

Kubernetes/Helm

The Dagster Kubernetes documentation has been refreshed.

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]
- Added support for user code deployments on Kubernetes.
- Added support for tagging pipeline executions.
- Fixes to support version 12.0.0 of the Python Kubernetes client.
- Improved implementation of Kubernetes+Dagster retries.
- Many logging improvements to surface debugging information and failures in the structured event
  log.
  Dagster-Celery-K8s
- Improved interrupt/termination handling in Celery workers.
  Integrations & Libraries
- Added a new dagster-docker library with a DockerRunLauncher that launches each run in its own
  Docker container. (See Deploying with Docker docs
  for an example.)
- Added support for AWS Athena. (Thanks @jmsanders!)
- Added mocks for AWS S3, Athena, and Cloudwatch in tests. (Thanks @jmsanders!)
- Allow setting of S3 endpoint through env variables. (Thanks @marksteve!)
- Various bug fixes and new features for the Azure, Databricks, and Dask integrations.
- Added a create_databricks_job_solid for creating solids that launch Databricks jobs.

dagster-io/dagster 0.10.0 0.10.0 Edge of Glory on GitHub