IO managers are user-provided objects that store op outputs and load them as inputs to downstream ops.
@dagster.io_manager(config_schema=None, description=None, output_config_schema=None, input_config_schema=None, required_resource_keys=None, version=None)[source]¶Define an IO manager.
IOManagers are used to store op outputs and load them as inputs to downstream ops.
The decorated function should accept an InitResourceContext and return an
IOManager.
config_schema (Optional[ConfigSchema]) – The schema for the resource config. Configuration data available in init_context.resource_config. If not set, Dagster will accept any config provided.
description (Optional[str]) – A human-readable description of the resource.
output_config_schema (Optional[ConfigSchema]) – The schema for per-output config. If not set, no per-output configuration will be allowed.
input_config_schema (Optional[ConfigSchema]) – The schema for per-input config. If not set, Dagster will accept any config provided.
required_resource_keys (Optional[Set[str]]) – Keys for the resources required by the object manager.
version (Optional[str]) – (Experimental) The version of a resource function. Two wrapped resource functions should only have the same version if they produce the same resource definition when provided with the same inputs.
Examples:
class MyIOManager(IOManager):
def handle_output(self, context, obj):
write_csv("some/path")
def load_input(self, context):
return read_csv("some/path")
@io_manager
def my_io_manager(init_context):
return MyIOManager()
@op(out=Out(io_manager_key="my_io_manager_key"))
def my_op(_):
return do_stuff()
@job(resource_defs={"my_io_manager_key": my_io_manager})
def my_job():
my_op()
dagster.IOManager[source]¶Base class for user-provided IO managers.
IOManagers are used to store op outputs and load them as inputs to downstream ops.
Extend this class to handle how objects are loaded and stored. Users should implement
handle_output to store an object and load_input to retrieve an object.
get_input_asset_key(context)[source]¶User-defined method that associates inputs loaded by this IOManager with a particular AssetKey.
context (InputContext) – The input context, which describes the input that’s being loaded and the upstream output that’s being loaded from.
get_input_asset_partitions(context)[source]¶User-defined method that associates inputs loaded by this IOManager with a set of partitions of an AssetKey.
context (InputContext) – The input context, which describes the input that’s being loaded and the upstream output that’s being loaded from.
get_output_asset_key(_context)[source]¶User-defined method that associates outputs handled by this IOManager with a particular AssetKey.
context (OutputContext) – The context of the step output that produces this object.
get_output_asset_partitions(_context)[source]¶User-defined method that associates outputs handled by this IOManager with a set of partitions of an AssetKey.
context (OutputContext) – The context of the step output that produces this object.
handle_output(context, obj)[source]¶User-defined method that stores an output of an op.
context (OutputContext) – The context of the step output that produces this object.
obj (Any) – The object, returned by the op, to be stored.
load_input(context)[source]¶User-defined method that loads an input to an op.
context (InputContext) – The input context, which describes the input that’s being loaded and the upstream output that’s being loaded from.
The data object.
Any
dagster.IOManagerDefinition(resource_fn=None, config_schema=None, description=None, required_resource_keys=None, version=None, input_config_schema=None, output_config_schema=None)[source]¶Definition of an IO manager resource.
IOManagers are used to store op outputs and load them as inputs to downstream ops.
An IOManagerDefinition is a ResourceDefinition whose resource_fn returns an
IOManager.
The easiest way to create an IOManagerDefnition is with the @io_manager
decorator.
hardcoded_io_manager(value, description=None)[source]¶A helper function that creates an IOManagerDefinition with a hardcoded IOManager.
value (Any) – A hardcoded IO Manager which helps mock the definition.
description ([Optional[str]]) – The description of the IO Manager. Defaults to None.
A hardcoded resource.
input_config_schema¶The schema for per-input configuration for inputs that are managed by this input manager
output_config_schema¶The schema for per-output configuration for outputs that are managed by this manager
dagster.InputContext(name=None, pipeline_name=None, solid_def=None, config=None, metadata=None, upstream_output=None, dagster_type=None, log_manager=None, resource_config=None, resources=None, step_context=None, op_def=None)[source]¶The context object available to the load_input method of RootInputManager.
solid_def¶The definition of the solid that’s loading the input.
Optional[SolidDefinition]
config¶The config attached to the input that we’re loading.
Optional[Any]
metadata¶A dict of metadata that is assigned to the InputDefinition that we’re loading for.
Optional[Dict[str, Any]]
upstream_output¶Info about the output that produced the object we’re loading.
Optional[OutputContext]
dagster_type¶The type of this input.
Optional[DagsterType]
log¶The log manager to use for this input.
Optional[DagsterLogManager]
resource_config¶The config associated with the resource that initializes the RootInputManager.
Optional[Dict[str, Any]]
resources¶The resources required by the resource that initializes the
input manager. If using the @root_input_manager() decorator, these resources
correspond to those requested with the required_resource_keys parameter.
Optional[Resources]
op_def¶The definition of the op that’s loading the input.
Optional[OpDefinition]
add_input_metadata(metadata, description=None)[source]¶Accepts a dictionary of metadata. Metadata entries will appear on the LOADED_INPUT event. If the input is an asset, metadata will be attached to an asset observation.
The asset observation will be yielded from the run and appear in the event log. Only valid if the context has an asset key.
asset_partition_key¶The partition key for input asset.
Raises an error if the input asset has no partitioning, or if the run covers a partition range for the input asset.
asset_partition_key_range¶The partition key range for input asset.
Raises an error if the input asset has no partitioning.
asset_partitions_time_window¶The time window for the partitions of the input asset.
Raises an error if either of the following are true: - The input asset has no partitioning. - The input asset is not partitioned with a TimeWindowPartitionsDefinition.
consume_events()[source]¶Pops and yields all user-generated events that have been recorded from this context.
If consume_events has not yet been called, this will yield all logged events since the call to handle_input. If consume_events has been called, it will yield all events since the last time consume_events was called. Designed for internal use. Users should never need to invoke this method.
get_observations()[source]¶Retrieve the list of user-generated asset observations that were observed via the context.
User-generated events that were yielded will not appear in this list.
Examples:
from dagster import IOManager, build_input_context, AssetObservation
class MyIOManager(IOManager):
def load_input(self, context, obj):
...
def test_load_input():
mgr = MyIOManager()
context = build_input_context()
mgr.load_input(context)
observations = context.get_observations()
...
has_input_name¶If we’re the InputContext is being used to load the result of a run from outside the run, then it won’t have an input name.
has_partition_key¶Whether the current run is a partitioned run
partition_key¶The partition key for the current run.
Raises an error if the current run is not a partitioned run.
dagster.OutputContext(step_key=None, name=None, pipeline_name=None, run_id=None, metadata=None, mapping_key=None, config=None, solid_def=None, dagster_type=None, log_manager=None, version=None, resource_config=None, resources=None, step_context=None, op_def=None, asset_info=None)[source]¶The context object that is available to the handle_output method of an IOManager.
metadata¶A dict of the metadata that is assigned to the OutputDefinition that produced the output.
Optional[Dict[str, Any]]
mapping_key¶The key that identifies a unique mapped output. None for regular outputs.
Optional[str]
config¶The configuration for the output.
Optional[Any]
solid_def¶The definition of the solid that produced the output.
Optional[SolidDefinition]
dagster_type¶The type of this output.
Optional[DagsterType]
log¶The log manager to use for this output.
Optional[DagsterLogManager]
resource_config¶The config associated with the resource that initializes the RootInputManager.
Optional[Dict[str, Any]]
resources¶The resources required by the output manager, specified by the required_resource_keys parameter.
Optional[Resources]
op_def¶The definition of the op that produced the output.
Optional[OpDefinition]
asset_info¶Optional[AssetOutputInfo]: (Experimental) Asset info corresponding to the output.
add_output_metadata(metadata)[source]¶Add a dictionary of metadata to the handled output.
Metadata entries added will show up in the HANDLED_OUTPUT and ASSET_MATERIALIZATION events for the run.
metadata (Dict[str, Any]) – A metadata dictionary to log
Examples:
from dagster import IOManager
class MyIOManager(IOManager):
def handle_output(self, context, obj):
context.add_output_metadata({"foo": "bar"})
asset_partition_key¶The partition key for output asset.
Raises an error if the output asset has no partitioning, or if the run covers a partition range for the output asset.
asset_partition_key_range¶The partition key range for output asset.
Raises an error if the output asset has no partitioning.
asset_partitions_time_window¶The time window for the partitions of the output asset.
Raises an error if either of the following are true: - The output asset has no partitioning. - The output asset is not partitioned with a TimeWindowPartitionsDefinition.
consume_events()[source]¶Pops and yields all user-generated events that have been recorded from this context.
If consume_events has not yet been called, this will yield all logged events since the call to handle_output. If consume_events has been called, it will yield all events since the last time consume_events was called. Designed for internal use. Users should never need to invoke this method.
consume_logged_metadata_entries()[source]¶Pops and yields all user-generated metadata entries that have been recorded from this context.
If consume_logged_metadata_entries has not yet been called, this will yield all logged events since the call to handle_output. If consume_logged_metadata_entries has been called, it will yield all events since the last time consume_logged_metadata_entries was called. Designed for internal use. Users should never need to invoke this method.
get_logged_events()[source]¶Retrieve the list of user-generated events that were logged via the context.
User-generated events that were yielded will not appear in this list.
Examples:
from dagster import IOManager, build_output_context, AssetMaterialization
class MyIOManager(IOManager):
def handle_output(self, context, obj):
...
def test_handle_output():
mgr = MyIOManager()
context = build_output_context()
mgr.handle_output(context)
all_user_events = context.get_logged_events()
materializations = [event for event in all_user_events if isinstance(event, AssetMaterialization)]
...
get_logged_metadata_entries()[source]¶Get the list of metadata entries that have been logged for use with this output.
get_output_identifier()[source]¶Utility method to get a collection of identifiers that as a whole represent a unique step output.
If not using memoization, the unique identifier collection consists of
run_id: the id of the run which generates the output.Note: This method also handles the re-execution memoization logic. If the step that
generates the output is skipped in the re-execution, the run_id will be the id
of its parent run.
step_key: the key for a compute step.
name: the name of the output. (default: ‘result’).
If using memoization, the version corresponding to the step output is used in place of
the run_id.
A list of identifiers, i.e. (run_id or version), step_key, and output_name
List[str, ..]
get_run_scoped_output_identifier()[source]¶Utility method to get a collection of identifiers that as a whole represent a unique step output.
The unique identifier collection consists of
run_id: the id of the run which generates the output.Note: This method also handles the re-execution memoization logic. If the step that
generates the output is skipped in the re-execution, the run_id will be the id
of its parent run.
step_key: the key for a compute step.
name: the name of the output. (default: ‘result’).
A list of identifiers, i.e. run id, step key, and output name
List[str, ..]
has_partition_key¶Whether the current run is a partitioned run
log_event(event)[source]¶Log an AssetMaterialization or AssetObservation from within the body of an io manager’s handle_output method.
Events logged with this method will appear in the event log.
event (Union[AssetMaterialization, Materialization, AssetObservation]) – The event to log.
Examples:
from dagster import IOManager, AssetMaterialization
class MyIOManager(IOManager):
def handle_output(self, context, obj):
context.log_event(AssetMaterialization("foo"))
partition_key¶The partition key for the current run.
Raises an error if the current run is not a partitioned run.
dagster.build_input_context(name=None, config=None, metadata=None, upstream_output=None, dagster_type=None, resource_config=None, resources=None, op_def=None, step_context=None)[source]¶Builds input context from provided parameters.
build_input_context can be used as either a function, or a context manager. If resources
that are also context managers are provided, then build_input_context must be used as a
context manager.
name (Optional[str]) – The name of the input that we’re loading.
config (Optional[Any]) – The config attached to the input that we’re loading.
metadata (Optional[Dict[str, Any]]) – A dict of metadata that is assigned to the InputDefinition that we’re loading for.
upstream_output (Optional[OutputContext]) – Info about the output that produced the object we’re loading.
dagster_type (Optional[DagsterType]) – The type of this input.
resource_config (Optional[Dict[str, Any]]) – The resource config to make available from the input context. This usually corresponds to the config provided to the resource that loads the input manager.
resources (Optional[Dict[str, Any]]) – The resources to make available from the context. For a given key, you can provide either an actual instance of an object, or a resource definition.
asset_key (Optional[AssetKey]) – The asset key attached to the InputDefinition.
op_def (Optional[OpDefinition]) – The definition of the op that’s loading the input.
step_context (Optional[StepExecutionContext]) – For internal use.
Examples
build_input_context()
with build_input_context(resources={"foo": context_manager_resource}) as context:
do_something
dagster.build_output_context(step_key=None, name=None, metadata=None, run_id=None, mapping_key=None, config=None, dagster_type=None, version=None, resource_config=None, resources=None, solid_def=None, op_def=None, asset_key=None)[source]¶Builds output context from provided parameters.
build_output_context can be used as either a function, or a context manager. If resources
that are also context managers are provided, then build_output_context must be used as a
context manager.
step_key (Optional[str]) – The step_key for the compute step that produced the output.
name (Optional[str]) – The name of the output that produced the output.
metadata (Optional[Dict[str, Any]]) – A dict of the metadata that is assigned to the OutputDefinition that produced the output.
mapping_key (Optional[str]) – The key that identifies a unique mapped output. None for regular outputs.
config (Optional[Any]) – The configuration for the output.
dagster_type (Optional[DagsterType]) – The type of this output.
version (Optional[str]) – (Experimental) The version of the output.
resource_config (Optional[Dict[str, Any]]) – The resource config to make available from the input context. This usually corresponds to the config provided to the resource that loads the output manager.
resources (Optional[Resources]) – The resources to make available from the context. For a given key, you can provide either an actual instance of an object, or a resource definition.
solid_def (Optional[SolidDefinition]) – The definition of the solid that produced the output.
op_def (Optional[OpDefinition]) – The definition of the op that produced the output.
asset_key – Optional[Union[AssetKey, Sequence[str], str]]: The asset key corresponding to the output.
Examples
build_output_context()
with build_output_context(resources={"foo": context_manager_resource}) as context:
do_something
dagster.mem_io_manager IOManagerDefinition[source]¶Built-in IO manager that stores and retrieves values in memory.
dagster.fs_io_manager IOManagerDefinition[source]¶Built-in filesystem IO manager that stores and retrieves values using pickling.
Allows users to specify a base directory where all the step outputs will be stored. By default, step outputs will be stored in the directory specified by local_artifact_storage in your dagster.yaml file (which will be a temporary directory if not explicitly set).
Serializes and deserializes output values using pickling and automatically constructs the filepaths for the assets.
Example usage:
1. Specify a job-level IO manager using the reserved resource key "io_manager",
which will set the given IO manager on all ops in a job.
from dagster import fs_io_manager, job, op
@op
def op_a():
# create df ...
return df
@op
def op_b(df):
return df[:5]
@job(
resource_defs={
"io_manager": fs_io_manager.configured({"base_dir": "/my/base/path"})
}
)
def job():
op_b(op_a())
2. Specify IO manager on Out, which allows the user to set different IO managers on
different step outputs.
from dagster import fs_io_manager, job, op, Out
@op(out=Out(io_manager_key="my_io_manager"))
def op_a():
# create df ...
return df
@op
def op_b(df):
return df[:5]
@job(resource_defs={"my_io_manager": fs_io_manager})
def job():
op_b(op_a())
dagster.custom_path_fs_io_manager IOManagerDefinition[source]¶Built-in IO manager that allows users to custom output file path per output definition.
It requires users to specify a base directory where all the step output will be stored in. It serializes and deserializes output values (assets) using pickling and stores the pickled object in the user-provided file paths.
Example usage:
from dagster import custom_path_fs_io_manager, job, op
@op(out=Out(metadata={"path": "path/to/sample_output"}))
def sample_data(df):
return df[:5]
my_custom_path_fs_io_manager = custom_path_fs_io_manager.configured(
{"base_dir": "path/to/basedir"}
)
@job(resource_defs={"io_manager": my_custom_path_fs_io_manager})
def my_job():
sample_data()
Root input managers are user-provided objects that specify how to load inputs that aren’t connected to upstream outputs.
@dagster.root_input_manager(config_schema=None, description=None, input_config_schema=None, required_resource_keys=None, version=None)[source]¶Define a root input manager.
Root input managers load op inputs that aren’t connected to upstream outputs.
The decorated function should accept a InputContext and resource config, and return
a loaded object that will be passed into one of the inputs of an op.
The decorator produces an RootInputManagerDefinition.
config_schema (Optional[ConfigSchema]) – The schema for the resource-level config. If not set, Dagster will accept any config provided.
description (Optional[str]) – A human-readable description of the resource.
input_config_schema (Optional[ConfigSchema]) – A schema for the input-level config. Each input that uses this input manager can be configured separately using this config. If not set, Dagster will accept any config provided.
required_resource_keys (Optional[Set[str]]) – Keys for the resources required by the input manager.
version (Optional[str]) – (Experimental) the version of the input manager definition.
Examples:
from dagster import root_input_manager, op, job, In
@root_input_manager
def csv_loader(_):
return read_csv("some/path")
@op(ins={"input1": In(root_manager_key="csv_loader_key")})
def my_op(_, input1):
do_stuff(input1)
@job(resource_defs={"csv_loader_key": csv_loader})
def my_job():
my_op()
@root_input_manager(config_schema={"base_dir": str})
def csv_loader(context):
return read_csv(context.resource_config["base_dir"] + "/some/path")
@root_input_manager(input_config_schema={"path": str})
def csv_loader(context):
return read_csv(context.config["path"])
dagster.RootInputManager[source]¶RootInputManagers are used to load inputs to ops at the root of a job.
The easiest way to define an RootInputManager is with the
@root_input_manager decorator.
load_input(context)[source]¶The user-defined read method that loads data given its metadata.
context (InputContext) – The context of the step output that produces this asset.
The data object.
Any
dagster.RootInputManagerDefinition(resource_fn=None, config_schema=None, description=None, input_config_schema=None, required_resource_keys=None, version=None)[source]¶Definition of a root input manager resource.
Root input managers load op inputs that aren’t connected to upstream outputs.
An RootInputManagerDefinition is a ResourceDefinition whose resource_fn returns an
RootInputManager.
The easiest way to create an RootInputManagerDefinition is with the
@root_input_manager decorator.
input_config_schema¶The schema for per-input configuration for inputs that are managed by this input manager