MIGRATION.md

Introduction

When new releases include breaking changes or deprecations, this document describes how to migrate.

Migrating to 0.13.0

Jobs, ops, and graphs have replaced pipelines, solids, modes, and presets as the stable core of the system. Here is a guide you can use to update your code using the legacy APIs into using the new Dagster core APIs. 0.13.0 is still compatible with the pipeline, solid, mode, and preset APIs, which means that you don't need to migrate your code to upgrade to 0.13.0.

Migrating to 0.12.0

The new experimental core API experience in Dagit uses some features that require a data migration. Before enabling the experimental core API flag in Dagit, you will first need to run this command:

dagster instance migrate

If you are not going to enable the experimental core API experience, this data migration is optional. However, you may still want to run the migration anyway, which will enable better performance in viewing the Asset catalog in Dagit.

Migrating to 0.11.0

Action Required: Run and event storage schema changes

Run this after migrating to 0.11.0:

dagster instance migrate

This release includes several schema changes to the Dagster storages that improve performance, allow support for MySQL, and enable new features like asset tags and reliable backfills. After upgrading to 0.11.0, run the dagster instance migrate command to migrate your instance storage to the latest schema.

Action Required: Schedule timezones

Schedules now run in UTC (instead of the system timezone) if no timezone has been set on the schedule. If you’re using a deprecated scheduler like SystemCronScheduler or K8sScheduler, we recommend that you switch to the native Dagster scheduler. The deprecated schedulers will be removed in the next Dagster release.

Action Required: Asset storage

If upgrading directly to 0.11.0 from 0.9.22 or lower, you might notice some asset keys missing from the catalog if they have not been materialized using a version 0.9.16 or greater. We removed some back-compatibility for performance reasons. If this is the case, you can either run dagster instance reindex or execute the appropriate pipelines to materialize those assets again. In either case, the full history of the asset will still be maintained.

Removals of Deprecated APIs

• The instance argument to RunLauncher.launch_run has been removed. If you have written a custom RunLauncher, you’ll need to update the signature of that method. You can still access the DagsterInstance on the RunLauncher via the _instance parameter.
• The has_config_entry, has_configurable_inputs, and has_configurable_outputs properties of solid and composite_solid have been removed.
• The deprecated optionality of the name argument to PipelineDefinition has been removed, and the argument is now required.
• The execute_run_with_structured_logs and execute_step_with_structured_logs internal CLI entry points have been removed. Use execute_run or execute_step instead.
• The python_environment key has been removed from workspace.yaml. Instead, to specify that a repository location should use a custom python environment, set the executable_path key within a python_file, python_module, or python_package key. See the docs for more information on configuring your workspace.yaml file.
• [dagster-dask] The deprecated schema for reading or materializing dataframes has been removed. Use the read or to keys accordingly.

Breaking Changes

• Names provided to alias on solids now enforce the same naming rules as solids. You may have to update provided names to meet these requirements.

• The retries method on Executor should now return a RetryMode instead of a Retries. This will only affect custom Executor classes.

• Submitting partition backfills in Dagit now requires dagster-daemon to be running. The instance setting in dagster.yaml to optionally enable daemon-based backfills has been removed, because all backfills are now daemon-based backfills.

  # removed, no longer a valid setting in dagster.yaml
  
  backfill:
    daemon_enabled: true
  

The corresponding value flag dagsterDaemon.backfill.enabled has also been removed from the Dagster helm chart.

• The sensor daemon interval settings in dagster.yaml has been removed. The sensor daemon now runs in a continuous loop so this customization is no longer useful.

  # removed, no longer a valid setting in dagster.yaml
  
  sensor_settings:
    interval_seconds: 10
  

Migrating to 0.10.0

Action Required: Run and event storage schema changes

# Run after migrating to 0.10.0

$ dagster instance migrate

This release includes several schema changes to the Dagster storages that improve performance and enable new features like sensors and run queueing. After upgrading to 0.10.0, run the dagster instance migrate command to migrate your instance storage to the latest schema. This will turn off any running schedules, so you will need to restart any previously running schedules after migrating the schema. Before turning them back on, you should follow the steps below to migrate to DagsterDaemonScheduler.

New scheduler: DagsterDaemonScheduler

This release includes a new DagsterDaemonScheduler with improved fault tolerance and full support for timezones. We highly recommend upgrading to the new scheduler during this release. The existing schedulers, SystemCronScheduler and K8sScheduler, are deprecated and will be removed in a future release.

Steps to migrate

Instead of relying on system cron or k8s cron jobs, the DaemonScheduler uses the new dagster-daemon service to run schedules. This requires running the dagster-daemon service as a part of your deployment.

Refer to our deployment documentation for a guides on how to set up and run the daemon process for local development, Docker, or Kubernetes deployments.

If you are currently using the SystemCronScheduler or K8sScheduler:

1. Stop any currently running schedules, to prevent any dangling cron jobs from being left behind. You can do this through the Dagit UI, or using the following command:

   dagster schedule stop --location {repository_location_name} {schedule_name}

   If you do not stop running schedules before changing schedulers, Dagster will throw an exception on startup due to the misconfigured running schedules.

2. In your dagster.yaml file, remove the scheduler: entry. If there is no scheduler: entry, the DagsterDaemonScheduler is automatically used as the default scheduler.

3. Start the dagster-daemon process. Guides can be found in our deployment documentations.

See our schedules troubleshooting guide for help if you experience any problems with the new scheduler.

If you are not using a legacy scheduler:

No migration steps are needed, but make sure you run dagster instance migrate as a part of upgrading to 0.10.0.

Deprecation: Intermediate Storage

We have deprecated the intermediate storage machinery in favor of the new IO manager abstraction, which offers finer-grained control over how inputs and outputs are serialized and persisted. Check out the IO Managers Overview for more information.

Steps to Migrate

• We have deprecated the top level "storage" and "intermediate_storage" fields on run_config. If you are currently executing pipelines as follows:

  @pipeline
  def my_pipeline():
      ...
  
  execute_pipeline(
      my_pipeline,
      run_config={
          "intermediate_storage": {
              "filesystem": {"base_dir": ...}
          }
      },
  )
  
  execute_pipeline(
      my_pipeline,
      run_config={
          "storage": {
              "filesystem": {"base_dir": ...}
          }
      },
  )

  You should instead use the built-in IO manager fs_io_manager, which can be attached to your pipeline as a resource:

  @pipeline(
      mode_defs=[
          ModeDefinition(
              resource_defs={"io_manager": fs_io_manager}
          )
      ],
  )
  def my_pipeline():
      ...
  
  execute_pipeline(
      my_pipeline,
      run_config={
          "resources": {
              "io_manager": {"config": {"base_dir": ...}}
          }
      },
  )

  There are corresponding IO managers for other intermediate storages, such as the S3- and ADLS2-based storages

• We have deprecated IntermediateStorageDefinition and @intermediate_storage.

  If you have written custom intermediate storage, you should migrate to custom IO managers defined using the @io_manager API. We have provided a helper method, io_manager_from_intermediate_storage, to help migrate your existing custom intermediate storages to IO managers.

  my_io_manager_def = io_manager_from_intermediate_storage(
      my_intermediate_storage_def
  )
  
  @pipeline(
      mode_defs=[
          ModeDefinition(
              resource_defs={
                  "io_manager": my_io_manager_def
              }
          ),
      ],
  )
  def my_pipeline():
      ...

• We have deprecated the intermediate_storage_defs argument to ModeDefinition, in favor of the new IO managers, which should be attached using the resource_defs argument.

Removal: input_hydration_config and output_materialization_config

Use dagster_type_loader instead of input_hydration_config and dagster_type_materializer instead of output_materialization_config.

On DagsterType and type constructors in dagster_pandas use the loader argument instead of input_hydration_config and the materializer argument instead of dagster_type_materializer argument.

Removal: repository key in workspace YAML

We have removed the ability to specify a repository in your workspace using the repository: key. Use load_from: instead when specifying how to load the repositories in your workspace.

Deprecated: python_environment key in workspace YAML

The python_environment: key is now deprecated and will be removed in a future release.

Previously, when you wanted to load a repository location in your workspace using a different Python environment from Dagit’s Python environment, you needed to use a python_environment: key under load_from: instead of the python_file: or python_package: keys. Now, you can simply customize the executable_path in your workspace entries without needing to change to the python_environment: key.

For example, the following workspace entry:

- python_environment:
    executable_path: '/path/to/venvs/dagster-dev-3.7.6/bin/python'
    target:
      python_package:
        package_name: dagster_examples
        location_name: dagster_examples

should now be expressed as:

- python_package:
    executable_path: '/path/to/venvs/dagster-dev-3.7.6/bin/python'
    package_name: dagster_examples
    location_name: dagster_examples

See our Workspaces Overview for more information and examples.

Removal: config_field property on definition classes

We have removed the property config_field on definition classes. Use config_schema instead.

Removal: System Storage

We have removed the system storage abstractions, i.e. SystemStorageDefinition and @system_storage (deprecated in 0.9.0).

Please note that the intermediate storage abstraction is also deprecated and will be removed in 0.11.0. Use IO managers instead.

• We have removed the system_storage_defs argument (deprecated in 0.9.0) to ModeDefinition, in favor of intermediate_storage_defs.
• We have removed the built-in system storages, e.g. default_system_storage_defs (deprecated in 0.9.0).

Removal: step_keys_to_execute

We have removed the step_keys_to_execute argument to reexecute_pipeline and reexecute_pipeline_iterator, in favor of step_selection. This argument accepts the Dagster selection syntax, so, for example, *solid_a+ represents solid_a, all of its upstream steps, and its immediate downstream steps.

Breaking Change: date_partition_range

Starting in 0.10.0, Dagster uses the pendulum library to ensure that schedules and partitions behave correctly with respect to timezones. As part of this change, the delta parameter to date_partition_range (which determined the time different between partitions and was a datetime.timedelta) has been replaced by a delta_range parameter (which must be a string that's a valid argument to the pendulum.period function, such as "days", "hours", or "months").

For example, the following partition range for a monthly partition set:

date_partition_range(
    start=datetime.datetime(2018, 1, 1),
    end=datetime.datetime(2019, 1, 1),
    delta=datetime.timedelta(months=1)
)

should now be expressed as:

date_partition_range(
    start=datetime.datetime(2018, 1, 1),
    end=datetime.datetime(2019, 1, 1),
    delta_range="months"
)

Breaking Change: PartitionSetDefinition.create_schedule_definition

When you create a schedule from a partition set using PartitionSetDefinition.create_schedule_definition, you now must supply a partition_selector argument that tells the scheduler which partition to use for a given schedule time.

We have added two helper functions, create_offset_partition_selector and identity_partition_selector, that capture two common partition selectors (schedules that execute at a fixed offset from the partition times, e.g. a schedule that creates the previous day's partition each morning, and schedules that execute at the same time as the partition times).

The previous default partition selector was last_partition, which didn't always work as expected when using the default scheduler and has been removed in favor of the two helper partition selectors above.

For example, a schedule created from a daily partition set that fills in each partition the next day at 10AM would be created as follows:

partition_set = PartitionSetDefinition(
    name='hello_world_partition_set',
    pipeline_name='hello_world_pipeline',
    partition_fn= date_partition_range(
        start=datetime.datetime(2021, 1, 1),
        delta_range="days",
        timezone="US/Central",
    )
    run_config_fn_for_partition=my_run_config_fn,
)

schedule_definition = partition_set.create_schedule_definition(
    "daily_10am_schedule",
    "0 10 * * *",
    partition_selector=create_offset_partition_selector(lambda d: d.subtract(hours=10, days=1))
    execution_timezone="US/Central",
)

Renamed: Helm values

Following convention in the Helm docs, we now camel case all of our Helm values. To migrate to 0.10.0, you'll need to update your values.yaml with the following renames:

• pipeline_run → pipelineRun
• dagster_home → dagsterHome
• env_secrets → envSecrets
• env_config_maps → envConfigMaps

Restructured: scheduler in Helm values

When specifying the Dagster instance scheduler, rather than using a boolean field to switch between the current options of K8sScheduler and DagsterDaemonScheduler, we now require the scheduler type to be explicitly defined under scheduler.type. If the user specified scheduler.type has required config, additional fields will need to be specified under scheduler.config.

scheduler.type and corresponding scheduler.config values are enforced via JSON Schema.

For example, if your Helm values previously were set like this to enable the DagsterDaemonScheduler: ​

scheduler:
  k8sEnabled: false

​ You should instead have: ​

scheduler:
  type: DagsterDaemonScheduler

Restructured: celery and k8sRunLauncher in Helm values

celery and k8sRunLauncher now live under runLauncher.config.celeryK8sRunLauncher and runLauncher.config.k8sRunLauncher respectively. Now, to enable celery, runLauncher.type must equal CeleryK8sRunLauncher. To enable the vanilla K8s run launcher, runLauncher.type must equal K8sRunLauncher.

runLauncher.type and corresponding runLauncher.config values are enforced via JSON Schema.

For example, if your Helm values previously were set like this to enable the K8sRunLauncher: ​

celery:
  enabled: false
​
k8sRunLauncher:
  enabled: true
  jobNamespace: ~
  loadInclusterConfig: true
  kubeconfigFile: ~
  envConfigMaps: []
  envSecrets: []

​ You should instead have: ​

runLauncher:
  type: K8sRunLauncher
  config:
    k8sRunLauncher:
      jobNamespace: ~
      loadInclusterConfig: true
      kubeconfigFile: ~
      envConfigMaps: []
      envSecrets: []

New Helm defaults

By default, userDeployments is enabled and the runLauncher is set to the K8sRunLauncher. Along with the latter change, all message brokers (e.g. rabbitmq and redis) are now disabled by default.

If you were using the CeleryK8sRunLauncher, one of rabbitmq or redis must now be explicitly enabled in your Helm values.

Migrating to 0.9.0

Removal: config argument

We have removed the config argument to the ConfigMapping, @composite_solid, @solid, SolidDefinition, @executor, ExecutorDefinition, @logger, LoggerDefinition, @resource, and ResourceDefinition APIs, which we deprecated in 0.8.0, in favor of config_schema, as described here.

Migrating to 0.8.8

Deprecation: Materialization

We deprecated the Materialization event type in favor of the new AssetMaterialization event type, which requires the asset_key parameter. Solids yielding Materialization events will continue to work as before, though the Materialization event will be removed in a future release.

Deprecation: system_storage_defs

We are starting to deprecate "system storages" - instead of pipelines having a system storage definition which creates an intermediate storage, pipelines now directly have an intermediate storage definition.

• We have added an intermediate_storage_defs argument to ModeDefinition, which accepts a list of IntermediateStorageDefinitions, e.g. s3_plus_default_intermediate_storage_defs. As before, the default includes an in-memory intermediate and a local filesystem intermediate storage.
• We have deprecated system_storage_defs argument to ModeDefinition in favor of intermediate_storage_defs. system_storage_defs will be removed in 0.10.0 at the earliest.
• We have added an @intermediate_storage decorator, which makes it easy to define intermediate storages.
• We have added s3_file_manager and local_file_manager resources to replace the file managers that previously lived inside system storages. The airline demo has been updated to include an example of how to do this: https://github.com/dagster-io/dagster/blob/0.8.8/examples/airline_demo/airline_demo/solids.py#L171.

For example, if your ModeDefinition looks like this:

from dagster_aws.s3 import s3_plus_default_storage_defs

ModeDefinition(system_storage_defs=s3_plus_default_storage_defs)

it is recommended to make it look like this:

from dagster_aws.s3 import s3_plus_default_intermediate_storage_defs

ModeDefinition(intermediate_storage_defs=s3_plus_default_intermediate_storage_defs)

Migrating to 0.8.7

Loading python modules from the working directory

Loading python modules reliant on the working directory being on the PYTHONPATH is no longer supported. The dagster and dagit CLI commands no longer add the working directory to the PYTHONPATH when resolving modules, which may break some imports. Explicitly installed python packages can be specified in workspaces using the python_package workspace yaml config option. The python_module config option is deprecated and will be removed in a future release.

Migrating to 0.8.6

dagster-celery

The dagster-celery module has been broken apart to manage dependencies more coherently. There are now three modules: dagster-celery, dagster-celery-k8s, and dagster-celery-docker.

Related to above, the dagster-celery worker start command now takes a required -A parameter which must point to the app.py file within the appropriate module. E.g if you are using the celery_k8s_job_executor then you must use the -A dagster_celery_k8s.app option when using the celery or dagster-celery cli tools. Similar for the celery_docker_executor: -A dagster_celery_docker.app must be used.

Deprecation: input_hydration_config and output_materialization_config

We renamed the input_hydration_config and output_materialization_config decorators to dagster_type_ and dagster_type_materializer respectively. We also renamed DagsterType's input_hydration_config and output_materialization_config arguments to loader and materializer respectively.

For example, if your dagster type definition looks like this:

from dagster import DagsterType, input_hydration_config, output_materialization_config


@input_hydration_config(config_schema=my_config_schema)
def my_loader(_context, config):
    '''some implementation'''


@output_materialization_config(config_schema=my_config_schema)
def my_materializer(_context, config):
    '''some implementation'''


MyType = DagsterType(
    input_hydration_config=my_loader,
    output_materialization_config=my_materializer,
    type_check_fn=my_type_check,
)

it is recommended to make it look like this:

from dagster import DagsterType, dagster_type_loader, dagster_type_materializer


@dagster_type_loader(config_schema=my_config_schema)
def my_loader(_context, config):
    '''some implementation'''


@dagster_type_materializer(config_schema=my_config_schema)
def my_materializer(_context, config):
    '''some implementation'''


MyType = DagsterType(
    loader=my_loader,
    materializer=my_materializer,
    type_check_fn=my_type_check,
)

Migrating to 0.8.5

Python 3.5

Python 3.5 is no longer under test.

Engine and ExecutorConfig -> Executor

Engine and ExecutorConfig have been deleted in favor of Executor. Instead of the @executor decorator decorating a function that returns an ExecutorConfig it should now decorate a function that returns an Executor.

Migrating to 0.8.3

Change: gcs_resource

Previously, the gcs_resource returned a GCSResource wrapper which had a single client property that returned a google.cloud.storage.client.Client. Now, the gcs_resource returns the client directly.

To update solids that use the gcp_resource, change:

context.resources.gcs.client

To:

context.resources.gcs

Migrating to 0.8.0

Repository loading

Dagit and other tools no longer load a single repository containing user definitions such as pipelines into the same process as the framework code. Instead, they load a "workspace" that can contain multiple repositories sourced from a variety of different external locations (e.g., Python modules and Python virtualenvs, with containers and source control repositories soon to come).

The repositories in a workspace are loaded into their own "user" processes distinct from the "host" framework process. Dagit and other tools now communicate with user code over an IPC mechanism.

As a consequence, the former repository.yaml and the associated -y/--repository-yaml CLI arguments are deprecated in favor of a new workspace.yaml file format and associated -w/--workspace-yaml arguments.

Steps to migrate

You should replace your repository.yaml files with workspace.yaml files, which can define a number of possible sources from which to load repositories.

load_from:
  - python_module:
      module_name: dagster_examples
      attribute: define_internal_dagit_repository
  - python_module: dagster_examples.intro_tutorial.repos
  - python_file: repos.py
  - python_environment:
      executable_path: '/path/to/venvs/dagster-dev-3.7.6/bin/python'
      target:
        python_module:
          module_name: dagster_examples
          location_name: dagster_examples
          attribute: define_internal_dagit_repository

Repository definition

The @scheduler and @repository_partitions decorators have been removed. In addition, users should prefer the new @repository decorator to instantiating RepositoryDefinition directly.

One consequence of this change is that PartitionSetDefinition names, including those defined by a PartitionScheduleDefinition, must now be unique within a single repository.

Steps to migrate

Previously you might have defined your pipelines, schedules, partition sets, and repositories in a python file such as the following:

@pipeline
def test():
    ...

@daily_schedule(
    pipeline_name='test',
    start_date=datetime.datetime(2020, 1, 1),
)
def daily_test_schedule(_):
    return {}

test_partition_set = PartitionSetDefinition(
    name="test",
    pipeline_name="test",
    partition_fn=lambda: ["test"],
    environment_dict_fn_for_partition=lambda _: {},
)

@schedules
def define_schedules():
    return [daily_test_schedule]

@repository_partitions
def define_partitions():
    return [test_partition_set]

def define_repository():
    return RepositoryDefinition('test', pipeline_defs=[test])

With a repository.yaml such as:

repository:
  file: repo.py
  fn: define_repository

scheduler:
  file: repo.py
  fn: define_schedules

partitions:
  file: repo.py
  fn: define_partitions

In 0.8.0, you'll write Python like:

@pipeline
def test_pipeline():
    ...

@daily_schedule(
    pipeline_name='test',
    start_date=datetime.datetime(2020, 1, 1),
)
def daily_test_schedule(_):
    return {}

test_partition_set = PartitionSetDefinition(
    name="test",
    pipeline_name="test",
    partition_fn=lambda: ["test"],
    run_config_fn_for_partition=lambda _: {},
)

@repository
def test_repository():
    return [test_pipeline, daily_test_schedule, test_partition_set]

Your workspace.yaml will look like:

load_from:
  - python_file: repo.py

If you have more than one repository defined in a single Python file, you'll want to instead load the repository using workspace.yaml like:

load_from:
  - python_file:
      relative_path: repo.py
      attribute: test_repository
  - python_file:
      relative_path: repo.py
      attribute: other_repository

Of course, the workspace.yaml also supports loading from a python_module, or with a specific Python interpreter from a python_environment.

Note that the @repository decorator also supports more sophisticated, lazily-loaded repositories. Consult the documentation for the decorator for more details.

Reloadable repositories

In 0.7.x, dagster attempted to elide the difference between a pipeline that was defined in memory and one that was loaded through machinery that used the ExecutionTargetHandle machinery. This resulted in opaque and hard-to-predict errors and unpleasant workarounds, for instance:

• Pipeline execution in test using execute_pipeline would suddenly fail when a multiprocess executor was used.
• Tests of pipelines with dagstermill solids had to resort to workarounds such as

    handle = handle_for_pipeline_cli_args(
        {'module_name': 'some_module.repository', 'fn_name': 'some_pipeline'}
    )
    pipeline = handle.build_pipeline_definition()
    result = execute_pipeline(pipeline, ...)

In 0.8.0, we've added the reconstructable helper to explicitly convert in-memory pipelines into reconstructable pipelines that can be passed between processes.

@pipeline(...)
def some_pipeline():
    ...

execute_pipeline(reconstructable(some_pipeline), {'execution': {'multiprocess': {}})

Pipelines must be defined in module scope in order for reconstructable to be used. Note that pipelines defined interactively, e.g., in the Python REPL, cannot be passed between processes.

Renaming environment_dict and removing RunConfig

In 0.8.0, we've renamed the common environment_dict parameter to many user-facing APIs to run_config, and we've dropped the previous run_config parameter. This change affects the execute_pipeline_iterator and execute_pipeline APIs, the PresetDefinition and ScheduleDefinition, and the execute_solid test API. Similarly, the environment_dict_fn, user_defined_environment_dict_fn_for_partition, and environment_dict_fn_for_partition parameters to ScheduleDefinition, PartitionSetDefinition, and PartitionScheduleDefinition have been renamed to run_config_fn, user_defined_run_config_fn_for_partition, and run_config_fn_for_partition respectively.

The previous run_config parameter has been removed, as has the backing RunConfig class. This change affects the execute_pipeline_iterator and execute_pipeline APIs, and the execute_solids_within_pipeline and execute_solid_within_pipeline test APIs. Instead, you should set the mode, preset, tags, solid_selection, and, in test, `raise_on_error parameters directly.

This change is intended to reduce ambiguity around the notion of a pipeline execution's "environment", since the config value passed as run_config is scoped to a single execution.

Deprecation: config argument

In 0.8.0, we've renamed the common config parameter to the user-facing definition APIs to config_schema. This is intended to reduce ambiguity between config values (provided at execution time) and their user-specified schemas (provided at definition time). This change affects the ConfigMapping, @composite_solid, @solid, SolidDefinition, @executor, ExecutorDefinition, @logger, LoggerDefinition, @resource, and ResourceDefinition APIs. In the CLI, dagster pipeline execute and dagster pipeline launch now take -c/--config instead of -e/--env.

Renaming solid_subset and enabling support for solid selection DSL in Python API

In 0.8.0, we've renamed the solid_subset/--solid-subset argument to solid_selection/--solid-selection throughout the Python API and CLI. This affects the dagster pipeline execute, dagster pipeline launch, and dagster pipeline backfill CLI commands, and the @schedule, @monthly_schedule, @weekly_schedule, @daily_schedule, @hourly_schedule, ScheduleDefinition, PresetDefinition, PartitionSetDefinition, PartitionScheduleDefinition, execute_pipeline, execute_pipeline_iterator, DagsterInstance.create_run_for_pipeline, DagsterInstance.create_run APIs.

In addition to the names of individual solids, the new solid_selection argument supports selection queries like *solid_name++ (i.e., solid_name, all of its ancestors, its immediate descendants, and their immediate descendants), previously supported only in Dagit views.

Removal of deprectated properties, methods, and arguments

• The deprecated runtime_type property on InputDefinition and OutputDefinition has been removed. Use dagster_type instead.
• The deprecated has_runtime_type, runtime_type_named, and all_runtime_types methods on PipelineDefinition have been removed. Use has_dagster_type, dagster_type_named, and all_dagster_types instead.
• The deprecated all_runtime_types method on SolidDefinition and CompositeSolidDefinition has been removed. Use all_dagster_types instead.
• The deprecated metadata argument to SolidDefinition and @solid has been removed. Use tags instead.
• The use of is_optional throughout the codebase was deprecated in 0.7.x and has been removed. Use is_required instead.

Removal of Path config type

The built-in config type Path has been removed. Use String.

dagster-bash

This package has been renamed to dagster-shell. Thebash_command_solid and bash_script_solid solid factory functions have been renamed to create_shell_command_solid and create_shell_script_solid.

Dask config

The config schema for the dagster_dask.dask_executor has changed. The previous config should now be nested under the key local.

Spark solids

dagster_spark.SparkSolidDefinition has been removed - use create_spark_solid instead.

Migrating to 0.7.0

The 0.7.0 release contains a number of breaking API changes. While listed in the changelog, this document goes into more detail about how to resolve the change easily. Most of the eliminated or changed APIs can be adjusted to with relatively straightforward changes.

The easiest way to use this guide is to search for associated error text.

Dagster Types

There have been substantial changes to the core dagster type APIs.

Error:

ImportError: cannot import name 'dagster_type' from 'dagster'

Fix:

Use usable_as_dagster_type instead. If dynamically generating types, construct using DagsterType instead.

Error:

ImportError: cannot import name 'as_dagster_type' from 'dagster'

Fix:

Use make_python_type_usable_as_dagster_type instead.

Error:

dagster.core.errors.DagsterInvalidDefinitionError: type_check_fn argument type "BadType" must take 2 arguments, received 1

Fix:

Add a context argument (named _, _context, context, or context_) as the first argument of the type_check_fn. The second argument is the value being type-checked.

Further Information:

We have eliminated the @dagster_type and as_dagster_type APIs, which previously were promoted as our primary type creation API. This API automatically created a mapping between a Python type and a Dagster Type. While convenient, this ended up causing unpredictable behavior based on import order, as well as being wholly incompatible with dynamically created Dagster types.

Our core type creation API is now the DagsterType class. It creates a Dagster type (which is just an instance of DagsterType) that can be passed to InputDefinition and OutputDefinition.

The functionality of @dagster_type is preserved, but under a different name: usable_as_dagster_type. This decorator signifies that the author wants a bare Python type to be usable in contexts that expect dagster types, such as an InputDefinition or OutputDefinition.

Any user that had been programatically creating dagster types and was forced to decorate classes in local scope using @dagster_type and return that class should instead just create a DagsterType directly.

as_dagster_type has replaced by make_python_type_usable_as_dagster_type. The semantics of as_dagster_type did not indicate what is was actually doing very well. This function is meant to take an existing type -- often from a library that one doesn't control -- and make that type usable as a dagster type, the second argument.

The type_check_fn argument has been renamed from type_check and now takes two arguments instead of one. The first argument is a instance of TypeCheckContext; the second argument is the value being checked. This allows the type check to have access to resources.

Config System

The config APIs have been renamed to have no collisions with names in neither python's typing API nor the dagster type system. Here are some example errors:

Error:

dagster.core.errors.DagsterInvariantViolationError: Cannot resolve Dagster Type Optional.Int to a config type. Repr of type: <dagster.core.types.dagster_type.OptionalType object at 0x102bb2a50>

Fix:

Use Noneable of Optional.

Error:

TypeError: 'DagsterDictApi' object is not callable

Fix:

Pass a raw python dictionary instead of Dict.

config=Dict({'foo': str}) becomes config={'foo': str}

Error:

ImportError: cannot import name 'PermissiveDict' from 'dagster'

Fix:

Use Permissive instead.

Error:

dagster.core.errors.DagsterInvariantViolationError: Cannot use List in the context of config. Please use a python list (e.g. [int]) or dagster.Array (e.g. Array(int)) instead.

Fix:

This happens when a properly constructed List is used within config. Use Array instead.

Error:

dagster.core.errors.DagsterInvalidDefinitionError: Invalid type: dagster_type must be DagsterType, a python scalar, or a python type that has been marked usable as a dagster type via @usable_dagster_type or make_python_type_usable_as_dagster_type: got <dagster.config.config_type.Noneable object at 0x1029c8a10>.

Fix:

This happens when a List takes an invalid argument and is never constructed. The error could be much better. This is what happens a config type (in this case Noneable) is passed to a List. The fix is to use either Array or to use a bare list with a single element, which is a config type.

Required Resources

Any solid, type, or configuration function that accesses a resource off of a context object must declare that resource key with a required_resource_key argument.

Error:

DagsterUnknownResourceError: Unknown resource <resource_name>. Specify <resource_name> as a required resource on the compute / config function that accessed it.

Fix:

Find any references to context.resources.<resource_name>, and ensure that the enclosing solid definition, type definition, or config function has the resource key specified in its required_resource_key argument.

Further information:

When only a subset of solids are being executed in a given process, we only need to initialize resources that will be used by that subset of solids. In order to improve the performance of pipeline execution, we need each solid and type to explicitly declare its required resources.

As a result, we should see improved performance for pipeline subset execution, multiprocess execution, and retry execution.

RunConfig Removed

Error:

AttributeError: 'ComputeExecutionContext' object has no attribute 'run_config'

Fix:

Replace all references to context.run_config with context.pipeline_run. The run_config field on the pipeline execution context has been removed and replaced with pipeline_run, a PipelineRun instance. Along with the fields previously on RunConfig, this also includes the pipeline run status.

Scheduler

Scheduler configuration has been moved to the dagster.yaml. After upgrading, the previous schedule history is no longer compatible with the new storage.

Make sure you delete your existing $DAGSTER_HOME/schedules directory, then run:

dagster schedule wipe && dagster schedule up

Error:

TypeError: schedules() got an unexpected keyword argument 'scheduler'

Fix:

The @schedules decorator no longer takes a scheduler argument. Remove the argument and instead configure the scheduler on the instance.

Instead of:

@schedules(scheduler=SystemCronScheduler)
def define_schedules():
    ...

Remove the scheduler argument:

@schedules
def define_schedules():
    ...

Configure the scheduler on your instance by adding the following to $DAGSTER_HOME/dagster.yaml:

scheduler:
    module: dagster_cron.cron_scheduler
    class: SystemCronScheduler

Error:

TypeError: <lambda>() takes 0 positional arguments but 1 was given"

Stack Trace:

    File ".../dagster/python_modules/dagster/dagster/core/definitions/schedule.py", line 171, in should_execute
        return self._should_execute(context)

Fix:

The should_execute and environment_dict_fn argument to ScheduleDefinition now has a required first argument context, representing the ScheduleExecutionContext.