Internals¶

Please note that internal APIs are likely to be in much greater flux pre-1.0 than user-facing APIs, particularly if not exported in the top level dagster module.

If you find yourself consulting these docs because you are writing custom components and plug-ins, please get in touch with the core team on our Slack. We’re curious what you’re up to, happy to help, excited for new community contributions, and eager to make the system as easy to work with as possible – including for teams who are looking to customize it.

Logging¶

class dagster.DagsterLogManager(run_id, logging_tags, loggers)[source]¶

Centralized dispatch for logging from user code.

Handles the construction of uniform structured log messages and passes them through to the underlying loggers.

An instance of the log manager is made available to solids as context.log. Users should not initialize instances of the log manager directly. To configure custom loggers, set the logger_defs on a ModeDefinition for a pipeline.

The log manager supports standard convenience methods like those exposed by the Python standard library logging module (i.e., within the body of a solid, context.log.{debug, info, warning, warn, error, critical, fatal}).

The underlying integer API can also be called directly using, e.g. context.log.log(5, msg), and the log manager will delegate to the log method defined on each of the loggers it manages.

User-defined custom log levels are not supported, and calls to, e.g., context.log.trace or context.log.notice will result in hard exceptions at runtime.

critical(msg, **kwargs)[source]¶

Log at the logging.CRITICAL level.

See debug().

debug(msg, **kwargs)[source]¶

Log at the logging.DEBUG level.

The message will be automatically adorned with contextual information about the name of the pipeline, the name of the solid, etc., so it is generally unnecessary to include this type of information in the log message.

You can optionally additional key-value pairs to an individual log message using the kwargs to this method.

Parameters

msg (str) – The message to log.
**kwargs (Optional[Any]) – Any additional key-value pairs for only this log message.

error(msg, **kwargs)[source]¶

Log at the logging.ERROR level.

See debug().

fatal(msg, **kwargs)¶: Alias for critical()

info(msg, **kwargs)[source]¶

Log at the logging.INFO level.

See debug().

log(level, msg, **kwargs)[source]¶

Invoke the underlying loggers for a given integer log level.

Parameters

level (int) – An integer represeting a Python logging level.
orig_message (str) – The message to log.

warn(msg, **kwargs)¶: Alias for warning()

warning(msg, **kwargs)[source]¶

Log at the logging.WARNING level.

See debug().

with_tags(**new_tags)[source]¶

Add new tags in “new_tags” to the set of tags attached to this log manager instance, and return a new DagsterLogManager with the merged set of tags.

Parameters

tags (Dict[str,str]) – Dictionary of tags

Returns

a new DagsterLogManager namedtuple with updated tags for the same: run ID and loggers.

Return type

DagsterLogManager

Executors¶

@dagster.executor(name=None, config_schema=None, requirements=None)[source]¶

Define an executor.

The decorated function should accept an InitExecutorContext and return an instance of Executor.

Parameters

name (Optional[str]) – The name of the executor.
config_schema (Optional[ConfigSchema]) – The schema for the config. Configuration data available in init_context.executor_config.
requirements (Optional[List[ExecutorRequirement]]) – Any requirements that must be met in order for the executor to be usable for a particular pipeline execution.

class dagster.ExecutorDefinition(name, config_schema=None, requirements=None, executor_creation_fn=None, description=None)[source]¶

Parameters

name (str) – The name of the executor.
config_schema (Optional[ConfigSchema]) – The schema for the config. Configuration data available in init_context.executor_config.
requirements (Optional[List[ExecutorRequirement]]) – Any requirements that must be met in order for the executor to be usable for a particular pipeline execution.
executor_creation_fn (Optional[Callable]) – Should accept an InitExecutorContext and return an instance of Executor
required_resource_keys (Optional[Set[str]]) – Keys for the resources required by the executor.

configured(config_or_config_fn, name=None, config_schema=None, description=None)[source]¶

Wraps this object in an object of the same type that provides configuration to the inner object.

Parameters

config_or_config_fn (Union[Any, Callable[[Any], Any]]) – Either (1) Run configuration that fully satisfies this object’s config schema or (2) A function that accepts run configuration and returns run configuration that fully satisfies this object’s config schema. In the latter case, config_schema must be specified. When passing a function, it’s easiest to use configured().
name (Optional[str]) – Name of the new definition. If not provided, the emitted definition will inherit the name of the ExecutorDefinition upon which this function is called.
config_schema (ConfigSchema) – If config_or_config_fn is a function, the config schema that its input must satisfy.
description (Optional[str]) – Description of the new definition. If not specified, inherits the description of the definition being configured.

Returns (ConfigurableDefinition): A configured version of this object.

class dagster.InitExecutorContext(pipeline, executor_def, executor_config, instance)[source]¶

Executor-specific initialization context.

pipeline¶

The pipeline to be executed.

Type: IPipeline

executor_def¶

The definition of the executor currently being constructed.

Type: ExecutorDefinition

executor_config¶

The parsed config passed to the executor.

Type: dict

instance¶

The current instance.

Type: DagsterInstance

class dagster.Executor[source]¶

abstract execute(pipeline_context, execution_plan)[source]¶

For the given context and execution plan, orchestrate a series of sub plan executions in a way that satisfies the whole plan being executed.

Parameters

pipeline_context (SystemPipelineExecutionContext) – The pipeline execution context.
execution_plan (ExecutionPlan) – The plan to execute.

Returns

A stream of dagster events.

abstract property retries¶

Whether retries are enabled or disabled for this instance of the executor.

Executors should allow this to be controlled via configuration if possible.

Returns: RetryMode

File Manager¶

class dagster.core.storage.file_manager.FileManager[source]¶

Base class for all file managers in dagster.

The file manager is an interface that can be implemented by resources to provide abstract access to a file system such as local disk, S3, or other cloud storage.

For examples of usage, see the documentation of the concrete file manager implementations.

abstract copy_handle_to_local_temp(file_handle)[source]¶

Copy a file represented by a file handle to a temp file.

In an implementation built around an object store such as S3, this method would be expected to download the file from S3 to local filesystem in a location assigned by the standard library’s tempfile module.

Temp files returned by this method are not guaranteed to be reusable across solid boundaries. For files that must be available across solid boundaries, use the read(), read_data(), write(), and write_data() methods.

Parameters: file_handle (FileHandle) – The handle to the file to make available as a local temp file.
Returns: Path to the local temp file.
Return type: str

abstract delete_local_temp()[source]¶

Delete all local temporary files created by previous calls to copy_handle_to_local_temp().

Should typically only be called by framework implementors.

abstract read(file_handle, mode='rb')[source]¶

Return a file-like stream for the file handle.

This may incur an expensive network call for file managers backed by object stores such as S3.

Parameters

file_handle (FileHandle) – The file handle to make available as a stream.
mode (str) – The mode in which to open the file. Default: "rb".

Returns

A file-like stream.

Return type

Union[TextIO, BinaryIO]

abstract read_data(file_handle)[source]¶

Return the bytes for a given file handle. This may incur an expensive network call for file managers backed by object stores such as s3.

Parameters: file_handle (FileHandle) – The file handle for which to return bytes.
Returns: Bytes for a given file handle.
Return type: bytes

abstract write(file_obj, mode='wb', ext=None)[source]¶

Write the bytes contained within the given file object into the file manager.

Parameters

file_obj (Union[TextIO, StringIO]) – A file-like object.
mode (Optional[str]) – The mode in which to write the file into the file manager. Default: "wb".
ext (Optional[str]) – For file managers that support file extensions, the extension with which to write the file. Default: None.

Returns

A handle to the newly created file.

Return type

FileHandle

abstract write_data(data, ext=None)[source]¶

Write raw bytes into the file manager.

Parameters

data (bytes) – The bytes to write into the file manager.
ext (Optional[str]) – For file managers that support file extensions, the extension with which to write the file. Default: None.

Returns

A handle to the newly created file.

Return type

FileHandle

dagster.local_file_manager ResourceDefinition[source]¶

FileManager that provides abstract access to a local filesystem.

Implements the FileManager API.

Examples:

import tempfile

from dagster import ModeDefinition, local_file_manager, pipeline, solid


@solid(required_resource_keys={"file_manager"})
def write_files(context):
    fh_1 = context.resources.file_manager.write_data(b"foo")

    with tempfile.NamedTemporaryFile("w+") as fd:
        fd.write("bar")
        fd.seek(0)
        fh_2 = context.resources.file_manager.write(fd, mode="w", ext=".txt")

    return (fh_1, fh_2)


@solid(required_resource_keys={"file_manager"})
def read_files(context, file_handles):
    fh_1, fh_2 = file_handles
    assert context.resources.file_manager.read_data(fh_2) == b"bar"
    fd = context.resources.file_manager.read(fh_2, mode="r")
    assert fd.read() == "foo"
    fd.close()


@pipeline(mode_defs=[ModeDefinition(resource_defs={"file_manager": local_file_manager})])
def files_pipeline():
    read_files(write_files())

Intermediate Storage¶

@dagster.intermediate_storage(required_resource_keys=None, name=None, is_persistent=True, config_schema=None)[source]¶

Creates an intermediate storage definition

The decorated function will be passed as the intermediate_storage_creation_fn to a IntermediateStorageDefinition.

Parameters

name (str) – The name of the intermediate storage.
is_persistent (bool) – Whether the storage is persistent in a way that can cross process/node boundaries. Re-execution with, for example, the multiprocess executor, or with dagster-airflow, requires a persistent storage mode.
required_resource_keys (Optional[Set[str]]) – The resources that this storage needs at runtime to function.
config_schema (Optional[ConfigSchema]) – The schema for the config. Configuration data available in init_context.intermediate_storage_config.

class dagster.IntermediateStorageDefinition(name, is_persistent, required_resource_keys, config_schema=None, intermediate_storage_creation_fn=None, description=None)[source]¶

Defines intermediate data storage behaviors.

Parameters

name (str) – Name of the storage mode.
is_persistent (bool) – Whether the storage is persistent in a way that can cross process/node boundaries. Re-execution with, for example, the multiprocess executor, or with dagster-airflow, requires a persistent storage mode.
required_resource_keys (Optional[Set[str]]) – The resources that this storage needs at runtime to function.
config_schema (Optional[ConfigSchema]) – The schema for the storage’s configuration schema. Configuration data passed in this schema will be made available to the intermediate_storage_creation_fn under init_context.intermediate_storage_config.
intermediate_storage_creation_fn – (Callable[[InitIntermediateStorageContext], IntermediateStorage]) Called to construct the storage. This function should consume the init context and emit a IntermediateStorage.

class dagster.InitIntermediateStorageContext(pipeline_def, mode_def, intermediate_storage_def, pipeline_run, instance, environment_config, type_storage_plugin_registry, resources, intermediate_storage_config)[source]¶

Intermediate storage-specific initialization context.

pipeline_def¶

The definition of the pipeline in context.

Type: PipelineDefinition

mode_def¶

The definition of the mode in context.

Type: ModeDefinition

intermediate_storage_def¶

The definition of the intermediate storage to be constructed.

Type: IntermediateStorageDefinition

pipeline_run¶

The pipeline run in context.

Type: PipelineRun

instance¶

The instance.

Type: DagsterInstance

environment_config¶

The environment config.

Type: EnvironmentConfig

type_storage_plugin_registry¶

Registry containing custom type storage plugins.

Type: TypeStoragePluginRegistry

resources¶

Resources available in context.

Type: Any

intermediate_storage_config¶

The intermediate storage-specific configuration data provided by the environment config. The schema for this data is defined by the config_schema argument to IntermediateStorageDefinition.

Type: Dict[str, Any]

Instance¶

class dagster.DagsterInstance(instance_type, local_artifact_storage, run_storage, event_storage, compute_log_manager, schedule_storage=None, scheduler=None, run_coordinator=None, run_launcher=None, settings=None, ref=None)[source]¶

Core abstraction for managing Dagster’s access to storage and other resources.

Use DagsterInstance.get() to grab the current DagsterInstance which will load based on the values in the dagster.yaml file in $DAGSTER_HOME if set, otherwise fallback to using an ephemeral in-memory set of components.

Configuration of this class should be done by setting values in $DAGSTER_HOME/dagster.yaml. For example, to use Postgres for run and event log storage, you can write a dagster.yaml such as the following:

Parameters

instance_type (InstanceType) – Indicates whether the instance is ephemeral or persistent. Users should not attempt to set this value directly or in their dagster.yaml files.
local_artifact_storage (LocalArtifactStorage) – The local artifact storage is used to configure storage for any artifacts that require a local disk, such as schedules, or when using the filesystem system storage to manage files and intermediates. By default, this will be a dagster.core.storage.root.LocalArtifactStorage. Configurable in dagster.yaml using the ConfigurableClass machinery.
run_storage (RunStorage) – The run storage is used to store metadata about ongoing and past pipeline runs. By default, this will be a dagster.core.storage.runs.SqliteRunStorage. Configurable in dagster.yaml using the ConfigurableClass machinery.
event_storage (EventLogStorage) – Used to store the structured event logs generated by pipeline runs. By default, this will be a dagster.core.storage.event_log.SqliteEventLogStorage. Configurable in dagster.yaml using the ConfigurableClass machinery.
compute_log_manager (ComputeLogManager) – The compute log manager handles stdout and stderr logging for solid compute functions. By default, this will be a dagster.core.storage.local_compute_log_manager.LocalComputeLogManager. Configurable in dagster.yaml using the ConfigurableClass machinery.
run_coordinator (RunCoordinator) – A runs coordinator may be used to manage the execution of pipeline runs.
run_launcher (Optional[RunLauncher]) – Optionally, a run launcher may be used to enable a Dagster instance to launch pipeline runs, e.g. on a remote Kubernetes cluster, in addition to running them locally.
settings (Optional[Dict]) – Specifies certain per-instance settings, such as feature flags. These are set in the dagster.yaml under a set of whitelisted keys.
ref (Optional[InstanceRef]) – Used by internal machinery to pass instances across process boundaries.

add_daemon_heartbeat(daemon_heartbeat)[source]¶: Called on a regular interval by the daemon

get_addresses_for_step_output_versions(step_output_versions)[source]¶

For each given step output, finds whether an output exists with the given version, and returns its address if it does.

Parameters

step_output_versions (Dict[(str, StepOutputHandle), str]) – (pipeline name, step output handle) -> version.

Returns

(pipeline name, step output handle) -> address.: For each step output, an address if there is one and None otherwise.

Return type

Dict[(str, StepOutputHandle), str]

get_daemon_heartbeats()[source]¶: Latest heartbeats of all daemon types

launch_run(run_id, external_pipeline)[source]¶

Launch a pipeline run.

This method is typically called using instance.submit_run rather than being invoked directly. This method delegates to the RunLauncher, if any, configured on the instance, and will call its implementation of RunLauncher.launch_run() to begin the execution of the specified run. Runs should be created in the instance (e.g., by calling DagsterInstance.create_run()) before this method is called, and should be in the PipelineRunStatus.NOT_STARTED state.

Parameters: run_id (str) – The id of the run the launch.

report_engine_event(message, pipeline_run, engine_event_data=None, cls=None, step_key=None)[source]¶: Report a EngineEvent that occurred outside of a pipeline execution context.

submit_run(run_id, external_pipeline)[source]¶

Submit a pipeline run to the coordinator.

This method delegates to the RunCoordinator, configured on the instance, and will call its implementation of RunCoordinator.submit_run() to send the run to the coordinator for execution. Runs should be created in the instance (e.g., by calling DagsterInstance.create_run()) before this method is called, and should be in the PipelineRunStatus.NOT_STARTED state. They also must have a non-null ExternalPipelineOrigin.

Parameters: run_id (str) – The id of the run.

class dagster.core.instance.InstanceRef(local_artifact_storage_data, run_storage_data, event_storage_data, compute_logs_data, schedule_storage_data, scheduler_data, run_coordinator_data, run_launcher_data, settings, custom_instance_class_data=None)[source]¶

Serializable representation of a DagsterInstance.

Users should not instantiate this class directly.

class dagster.serdes.ConfigurableClass[source]¶

Abstract mixin for classes that can be loaded from config.

This supports a powerful plugin pattern which avoids both a) a lengthy, hard-to-synchronize list of conditional imports / optional extras_requires in dagster core and b) a magic directory or file in which third parties can place plugin packages. Instead, the intention is to make, e.g., run storage, pluggable with a config chunk like:

run_storage:
    module: very_cool_package.run_storage
    class: SplendidRunStorage
    config:
        magic_word: "quux"

This same pattern should eventually be viable for other system components, e.g. engines.

The ConfigurableClass mixin provides the necessary hooks for classes to be instantiated from an instance of ConfigurableClassData.

Pieces of the Dagster system which we wish to make pluggable in this way should consume a config type such as:

{'module': str, 'class': str, 'config': Field(Permissive())}

abstract classmethod config_type()[source]¶: dagster.ConfigType: The config type against which to validate a config yaml fragment serialized in an instance of ConfigurableClassData.

abstract static from_config_value(inst_data, config_value)[source]¶

New up an instance of the ConfigurableClass from a validated config value.

Called by ConfigurableClassData.rehydrate.

Parameters: config_value (dict) – The validated config value to use. Typically this should be the value attribute of a EvaluateValueResult.

A common pattern is for the implementation to align the config_value with the signature of the ConfigurableClass’s constructor:

@staticmethod
def from_config_value(inst_data, config_value):
    return MyConfigurableClass(inst_data=inst_data, **config_value)

abstract property inst_data¶: Subclass must be able to return the inst_data as a property if it has been constructed through the from_config_value code path.

class dagster.serdes.ConfigurableClassData(module_name, class_name, config_yaml)[source]¶

Serializable tuple describing where to find a class and the config fragment that should be used to instantiate it.

Users should not instantiate this class directly.

Classes intended to be serialized in this way should implement the dagster.serdes.ConfigurableClass mixin.

class dagster.core.storage.root.LocalArtifactStorage(base_dir, inst_data=None)[source]¶

classmethod config_type()[source]¶: dagster.ConfigType: The config type against which to validate a config yaml fragment serialized in an instance of ConfigurableClassData.

static from_config_value(inst_data, config_value)[source]¶

New up an instance of the ConfigurableClass from a validated config value.

Called by ConfigurableClassData.rehydrate.

Parameters: config_value (dict) – The validated config value to use. Typically this should be the value attribute of a EvaluateValueResult.

A common pattern is for the implementation to align the config_value with the signature of the ConfigurableClass’s constructor:

@staticmethod
def from_config_value(inst_data, config_value):
    return MyConfigurableClass(inst_data=inst_data, **config_value)

property inst_data¶: Subclass must be able to return the inst_data as a property if it has been constructed through the from_config_value code path.

Run storage¶

class dagster.PipelineRun(pipeline_name=None, run_id=None, run_config=None, mode=None, solid_selection=None, solids_to_execute=None, step_keys_to_execute=None, status=None, tags=None, root_run_id=None, parent_run_id=None, pipeline_snapshot_id=None, execution_plan_snapshot_id=None, external_pipeline_origin=None)[source]¶: Serializable internal representation of a pipeline run, as stored in a RunStorage.

class dagster.core.storage.runs.RunStorage[source]¶

Abstract base class for storing pipeline run history.

Note that run storages using SQL databases as backing stores should implement SqlRunStorage.

Users should not directly instantiate concrete subclasses of this class; they are instantiated by internal machinery when dagit and dagster-graphql load, based on the values in the dagster.yaml file in $DAGSTER_HOME. Configuration of concrete subclasses of this class should be done by setting values in that file.

class dagster.core.storage.runs.SqlRunStorage[source]¶: Base class for SQL based run storages

class dagster.core.storage.runs.SqliteRunStorage(conn_string, inst_data=None)[source]¶

SQLite-backed run storage.

Users should not directly instantiate this class; it is instantiated by internal machinery when dagit and dagster-graphql load, based on the values in the dagster.yaml file in $DAGSTER_HOME. Configuration of this class should be done by setting values in that file.

This is the default run storage when none is specified in the dagster.yaml.

To explicitly specify SQLite for run storage, you can add a block such as the following to your dagster.yaml:

run_storage:
  module: dagster.core.storage.runs
  class: SqliteRunStorage
  config:
    base_dir: /path/to/dir

The base_dir param tells the run storage where on disk to store the database.

Event log storage¶

class dagster.core.storage.event_log.EventLogStorage[source]¶

Abstract base class for storing structured event logs from pipeline runs.

Note that event log storages using SQL databases as backing stores should implement SqlEventLogStorage.

class dagster.core.storage.event_log.SqlEventLogStorage[source]¶

Base class for SQL backed event log storages.

Distinguishes between run-based connections and index connections in order to support run-level sharding, while maintaining the ability to do cross-run queries

class dagster.core.storage.event_log.SqliteEventLogStorage(base_dir, inst_data=None)[source]¶

SQLite-backed event log storage.

This is the default event log storage when none is specified in the dagster.yaml.

To explicitly specify SQLite for event log storage, you can add a block such as the following to your dagster.yaml:

event_log_storage:
  module: dagster.core.storage.event_log
  class: SqliteEventLogStorage
  config:
    base_dir: /path/to/dir

The base_dir param tells the event log storage where on disk to store the databases. To improve concurrent performance, event logs are stored in a separate SQLite database for each run.

class dagster.core.storage.event_log.ConsolidatedSqliteEventLogStorage(base_dir, inst_data=None)[source]¶

SQLite-backed consolidated event log storage intended for test cases only.

To explicitly specify the consolidated SQLite for event log storage, you can add a block such as the following to your dagster.yaml:

run_storage:
  module: dagster.core.storage.event_log
  class: ConsolidatedSqliteEventLogStorage
  config:
    base_dir: /path/to/dir

The base_dir param tells the event log storage where on disk to store the database.

Compute log manager¶

class dagster.core.storage.compute_log_manager.ComputeLogManager[source]¶: Abstract base class for storing unstructured compute logs (stdout/stderr) from the compute steps of pipeline solids.

class dagster.core.storage.local_compute_log_manager.LocalComputeLogManager(base_dir, inst_data=None)[source]¶: Stores copies of stdout & stderr for each compute step locally on disk.

See also: dagster_aws.S3ComputeLogManager.

Memoizable IO Manager (Experimental)¶

class dagster.core.storage.memoizable_io_manager.MemoizableIOManager[source]¶: Base class for IO manager enabled to work with memoized execution. Users should implement the load_input and handle_output methods described in the IOManager API, and the has_output method, which returns a boolean representing whether a data object can be found.

dagster.core.storage.memoizable_io_manager.versioned_filesystem_io_manager()[source]¶

Filesystem IO manager that utilizes versioning of stored objects.

It requires users to specify a base directory where all the step outputs will be stored in. It serializes and deserializes output values (assets) using pickling and automatically constructs the filepaths for the assets using the provided directory, and the version for a provided step output.

Run launcher¶

class dagster.core.launcher.RunLauncher[source]¶

class dagster.core.launcher.DefaultRunLauncher(inst_data=None, wait_for_processes=False)[source]¶: Launches runs against running GRPC servers.

Run coordinator¶

class dagster.core.run_coordinator.DefaultRunCoordinator(inst_data=None)[source]¶: Immediately send runs to the run launcher.

class dagster.core.run_coordinator.QueuedRunCoordinator(max_concurrent_runs=None, tag_concurrency_limits=None, dequeue_interval_seconds=None, inst_data=None)[source]¶: Sends runs to the dequeuer process via the run storage. Requires the external process to be alive for runs to be launched.

Scheduling¶

class dagster.core.scheduler.Scheduler[source]¶: Abstract base class for a scheduler. This component is responsible for interfacing with an external system such as cron to ensure scheduled repeated execution according.

class dagster.core.scheduler.DagsterDaemonScheduler(max_catchup_runs=5, inst_data=None)[source]¶

Default scheduler implementation that submits runs from the dagster-daemon long-lived process. Periodically checks each running schedule for execution times that don’t have runs yet and launches them.

Parameters

max_catchup_runs (int) –

For partitioned schedules, controls the maximum number of past partitions for each schedule that will be considered when looking for missing runs (defaults to 5). Generally this parameter will only come into play if the scheduler falls behind or launches after experiencing downtime. This parameter will not be checked for schedules without partition sets (for example, schedules created using the @schedule decorator) - only the most recent execution time will be considered for those schedules.

Note that no matter what this value is, the scheduler will never launch a run from a time before the schedule was turned on (even if the start_date on the schedule is earlier) - if you want to launch runs for earlier partitions, launch a backfill.

class dagster_cron.cron_scheduler.SystemCronScheduler(inst_data=None)[source]¶

Scheduler implementation that uses the local systems cron. Only works on unix systems that have cron.

Enable this scheduler by adding it to your dagster.yaml in $DAGSTER_HOME.

class dagster.core.storage.schedules.ScheduleStorage[source]¶: Abstract class for managing persistance of scheduler artifacts

class dagster.core.storage.schedules.SqlScheduleStorage[source]¶: Base class for SQL backed schedule storage

class dagster.core.storage.schedules.SqliteScheduleStorage(conn_string, inst_data=None)[source]¶: Local SQLite backed schedule storage

Exception handling¶

dagster.core.errors.user_code_error_boundary(error_cls, msg_fn, control_flow_exceptions=None, **kwargs)[source]¶

Wraps the execution of user-space code in an error boundary. This places a uniform policy around an user code invoked by the framework. This ensures that all user errors are wrapped in an exception derived from DagsterUserCodeExecutionError, and that the original stack trace of the user error is preserved, so that it can be reported without confusing framework code in the stack trace, if a tool author wishes to do so.

Examples:

with user_code_error_boundary(
    # Pass a class that inherits from DagsterUserCodeExecutionError
    DagsterExecutionStepExecutionError,
    # Pass a function that produces a message
    "Error occurred during step execution"
):
    call_user_provided_function()