`w.pipelines`: Pipelines¶

class databricks.sdk.service.pipelines.PipelinesAPI¶

The Lakeflow Spark Declarative Pipelines API allows you to create, edit, delete, start, and view details about pipelines.

Spark Declarative Pipelines is a framework for building reliable, maintainable, and testable data processing pipelines. You define the transformations to perform on your data, and Spark Declarative Pipelines manages task orchestration, cluster management, monitoring, data quality, and error handling.

Instead of defining your data pipelines using a series of separate Apache Spark tasks, Spark Declarative Pipelines manages how your data is transformed based on a target schema you define for each processing step. You can also enforce data quality with Spark Declarative Pipelines expectations. Expectations allow you to define expected data quality and specify how to handle records that fail those expectations.

apply_environment(pipeline_id: str) → ApplyEnvironmentRequestResponse¶

Applies the current pipeline environment onto the pipeline compute. The environment applied can be

used by subsequent dev-mode updates.

Parameters:: pipeline_id – str
Returns:: ApplyEnvironmentRequestResponse

clone(pipeline_id: str [, allow_duplicate_names: Optional[bool], budget_policy_id: Optional[str], catalog: Optional[str], channel: Optional[str], clone_mode: Optional[CloneMode], clusters: Optional[List[PipelineCluster]], configuration: Optional[Dict[str, str]], continuous: Optional[bool], deployment: Optional[PipelineDeployment], development: Optional[bool], edition: Optional[str], environment: Optional[PipelinesEnvironment], event_log: Optional[EventLogSpec], expected_last_modified: Optional[int], filters: Optional[Filters], gateway_definition: Optional[IngestionGatewayPipelineDefinition], id: Optional[str], ingestion_definition: Optional[IngestionPipelineDefinition], libraries: Optional[List[PipelineLibrary]], name: Optional[str], notifications: Optional[List[Notifications]], photon: Optional[bool], restart_window: Optional[RestartWindow], root_path: Optional[str], schema: Optional[str], serverless: Optional[bool], storage: Optional[str], tags: Optional[Dict[str, str]], target: Optional[str], trigger: Optional[PipelineTrigger], usage_policy_id: Optional[str]]) → ClonePipelineResponse¶

Creates a new pipeline using Unity Catalog from a pipeline using Hive Metastore. This method returns the ID of the newly created clone. Additionally, this method starts an update for the newly created pipeline.

Parameters:

pipeline_id – str Source pipeline to clone from
allow_duplicate_names – bool (optional) If false, deployment will fail if name conflicts with that of another pipeline.
budget_policy_id – str (optional) Budget policy of this pipeline.
catalog – str (optional) A catalog in Unity Catalog to publish data from this pipeline to. If target is specified, tables in this pipeline are published to a target schema inside catalog (for example, catalog.`target`.`table`). If target is not specified, no data is published to Unity Catalog.
channel – str (optional) SDP Release Channel that specifies which version to use.
clone_mode – CloneMode (optional) The type of clone to perform. Currently, only deep copies are supported
clusters – List[PipelineCluster] (optional) Cluster settings for this pipeline deployment.
configuration – Dict[str,str] (optional) String-String configuration for this pipeline execution.
continuous – bool (optional) Whether the pipeline is continuous or triggered. This replaces trigger.
deployment – PipelineDeployment (optional) Deployment type of this pipeline.
development – bool (optional) Whether the pipeline is in Development mode. Defaults to false.
edition – str (optional) Pipeline product edition.
environment – PipelinesEnvironment (optional) Environment specification for this pipeline used to install dependencies.
event_log – EventLogSpec (optional) Event log configuration for this pipeline
expected_last_modified – int (optional) If present, the last-modified time of the pipeline settings before the clone. If the settings were modified after that time, then the request will fail with a conflict.
filters – Filters (optional) Filters on which Pipeline packages to include in the deployed graph.
gateway_definition – IngestionGatewayPipelineDefinition (optional) The definition of a gateway pipeline to support change data capture.
id – str (optional) Unique identifier for this pipeline.
ingestion_definition – IngestionPipelineDefinition (optional) The configuration for a managed ingestion pipeline. These settings cannot be used with the ‘libraries’, ‘schema’, ‘target’, or ‘catalog’ settings.
libraries – List[PipelineLibrary] (optional) Libraries or code needed by this deployment.
name – str (optional) Friendly identifier for this pipeline.
notifications – List[Notifications] (optional) List of notification settings for this pipeline.
photon – bool (optional) Whether Photon is enabled for this pipeline.
restart_window – RestartWindow (optional) Restart window of this pipeline.
root_path – str (optional) Root path for this pipeline. This is used as the root directory when editing the pipeline in the Databricks user interface and it is added to sys.path when executing Python sources during pipeline execution.
schema – str (optional) The default schema (database) where tables are read from or published to.
serverless – bool (optional) Whether serverless compute is enabled for this pipeline.
storage – str (optional) DBFS root directory for storing checkpoints and tables.
tags – Dict[str,str] (optional) A map of tags associated with the pipeline. These are forwarded to the cluster as cluster tags, and are therefore subject to the same limitations. A maximum of 25 tags can be added to the pipeline.
target – str (optional) Target schema (database) to add tables in this pipeline to. Exactly one of schema or target must be specified. To publish to Unity Catalog, also specify catalog. This legacy field is deprecated for pipeline creation in favor of the schema field.
trigger – PipelineTrigger (optional) Which pipeline trigger to use. Deprecated: Use continuous instead.
usage_policy_id – str (optional) Usage policy of this pipeline.

Returns:

ClonePipelineResponse

create([, allow_duplicate_names: Optional[bool], budget_policy_id: Optional[str], catalog: Optional[str], channel: Optional[str], clusters: Optional[List[PipelineCluster]], configuration: Optional[Dict[str, str]], continuous: Optional[bool], deployment: Optional[PipelineDeployment], development: Optional[bool], dry_run: Optional[bool], edition: Optional[str], environment: Optional[PipelinesEnvironment], event_log: Optional[EventLogSpec], filters: Optional[Filters], gateway_definition: Optional[IngestionGatewayPipelineDefinition], id: Optional[str], ingestion_definition: Optional[IngestionPipelineDefinition], libraries: Optional[List[PipelineLibrary]], name: Optional[str], notifications: Optional[List[Notifications]], parameters: Optional[Dict[str, str]], photon: Optional[bool], restart_window: Optional[RestartWindow], root_path: Optional[str], run_as: Optional[RunAs], schema: Optional[str], serverless: Optional[bool], storage: Optional[str], tags: Optional[Dict[str, str]], target: Optional[str], trigger: Optional[PipelineTrigger], usage_policy_id: Optional[str]]) → CreatePipelineResponse¶

Usage:

import os
import time

from databricks.sdk import WorkspaceClient
from databricks.sdk.service import pipelines

w = WorkspaceClient()

notebook_path = f"/Users/{w.current_user.me().user_name}/sdk-{time.time_ns()}"

created = w.pipelines.create(
    continuous=False,
    name=f"sdk-{time.time_ns()}",
    libraries=[pipelines.PipelineLibrary(notebook=pipelines.NotebookLibrary(path=notebook_path))],
    clusters=[
        pipelines.PipelineCluster(
            instance_pool_id=os.environ["TEST_INSTANCE_POOL_ID"],
            label="default",
            num_workers=1,
            custom_tags={
                "cluster_type": "default",
            },
        )
    ],
)

# cleanup
w.pipelines.delete(pipeline_id=created.pipeline_id)

Creates a new data processing pipeline based on the requested configuration. If successful, this method returns the ID of the new pipeline.

Parameters:

allow_duplicate_names – bool (optional) If false, deployment will fail if name conflicts with that of another pipeline.
budget_policy_id – str (optional) Budget policy of this pipeline.
catalog – str (optional) A catalog in Unity Catalog to publish data from this pipeline to. If target is specified, tables in this pipeline are published to a target schema inside catalog (for example, catalog.`target`.`table`). If target is not specified, no data is published to Unity Catalog.
channel – str (optional) SDP Release Channel that specifies which version to use.
clusters – List[PipelineCluster] (optional) Cluster settings for this pipeline deployment.
configuration – Dict[str,str] (optional) String-String configuration for this pipeline execution.
continuous – bool (optional) Whether the pipeline is continuous or triggered. This replaces trigger.
deployment – PipelineDeployment (optional) Deployment type of this pipeline.
development – bool (optional) Whether the pipeline is in Development mode. Defaults to false.
dry_run – bool (optional)
edition – str (optional) Pipeline product edition.
environment – PipelinesEnvironment (optional) Environment specification for this pipeline used to install dependencies.
event_log – EventLogSpec (optional) Event log configuration for this pipeline
filters – Filters (optional) Filters on which Pipeline packages to include in the deployed graph.
gateway_definition – IngestionGatewayPipelineDefinition (optional) The definition of a gateway pipeline to support change data capture.
id – str (optional) Unique identifier for this pipeline.
ingestion_definition – IngestionPipelineDefinition (optional) The configuration for a managed ingestion pipeline. These settings cannot be used with the ‘libraries’, ‘schema’, ‘target’, or ‘catalog’ settings.
libraries – List[PipelineLibrary] (optional) Libraries or code needed by this deployment.
name – str (optional) Friendly identifier for this pipeline.
notifications – List[Notifications] (optional) List of notification settings for this pipeline.
parameters – Dict[str,str] (optional) Key/value map of default parameters to use for pipeline execution. Maximum total size: 10k characters (JSON format)
photon – bool (optional) Whether Photon is enabled for this pipeline.
restart_window – RestartWindow (optional) Restart window of this pipeline.
root_path – str (optional) Root path for this pipeline. This is used as the root directory when editing the pipeline in the Databricks user interface and it is added to sys.path when executing Python sources during pipeline execution.
run_as – RunAs (optional)
schema – str (optional) The default schema (database) where tables are read from or published to.
serverless – bool (optional) Whether serverless compute is enabled for this pipeline.
storage – str (optional) DBFS root directory for storing checkpoints and tables.
tags – Dict[str,str] (optional) A map of tags associated with the pipeline. These are forwarded to the cluster as cluster tags, and are therefore subject to the same limitations. A maximum of 25 tags can be added to the pipeline.
target – str (optional) Target schema (database) to add tables in this pipeline to. Exactly one of schema or target must be specified. To publish to Unity Catalog, also specify catalog. This legacy field is deprecated for pipeline creation in favor of the schema field.
trigger – PipelineTrigger (optional) Which pipeline trigger to use. Deprecated: Use continuous instead.
usage_policy_id – str (optional) Usage policy of this pipeline.

Returns:

CreatePipelineResponse

delete(pipeline_id: str [, cascade: Optional[bool], force: Optional[bool]])¶

Deletes a pipeline. If the pipeline publishes to Unity Catalog, pipeline deletion will cascade to all pipeline tables. Please reach out to Databricks support for assistance to undo this action.

Parameters:

pipeline_id – str
cascade – bool (optional) If false, pipeline deletion will not cascade to its datasets (MVs, STs, Views). By default, this parameter will be true and all tables will be deleted with the pipeline.
force – bool (optional) If true, deletion will proceed even if resource cleanup fails. By default, deletion will fail if resources cleanup is required but fails.

get(pipeline_id: str) → GetPipelineResponse¶

Usage:

import os
import time

from databricks.sdk import WorkspaceClient
from databricks.sdk.service import pipelines

w = WorkspaceClient()

notebook_path = f"/Users/{w.current_user.me().user_name}/sdk-{time.time_ns()}"

created = w.pipelines.create(
    continuous=False,
    name=f"sdk-{time.time_ns()}",
    libraries=[pipelines.PipelineLibrary(notebook=pipelines.NotebookLibrary(path=notebook_path))],
    clusters=[
        pipelines.PipelineCluster(
            instance_pool_id=os.environ["TEST_INSTANCE_POOL_ID"],
            label="default",
            num_workers=1,
            custom_tags={
                "cluster_type": "default",
            },
        )
    ],
)

by_id = w.pipelines.get(pipeline_id=created.pipeline_id)

# cleanup
w.pipelines.delete(pipeline_id=created.pipeline_id)

Get a pipeline.

Parameters:: pipeline_id – str
Returns:: GetPipelineResponse

get_permission_levels(pipeline_id: str) → GetPipelinePermissionLevelsResponse¶

Gets the permission levels that a user can have on an object.

Parameters:: pipeline_id – str The pipeline for which to get or manage permissions.
Returns:: GetPipelinePermissionLevelsResponse

get_permissions(pipeline_id: str) → PipelinePermissions¶

Gets the permissions of a pipeline. Pipelines can inherit permissions from their root object.

Parameters:: pipeline_id – str The pipeline for which to get or manage permissions.
Returns:: PipelinePermissions

get_update(pipeline_id: str, update_id: str) → GetUpdateResponse¶

Gets an update from an active pipeline.

Parameters:

pipeline_id – str The ID of the pipeline.
update_id – str The ID of the update.

Returns:

GetUpdateResponse

list_pipeline_events(pipeline_id: str [, filter: Optional[str], max_results: Optional[int], order_by: Optional[List[str]], page_token: Optional[str]]) → Iterator[PipelineEvent]¶

Usage:

import os
import time

from databricks.sdk import WorkspaceClient
from databricks.sdk.service import pipelines

w = WorkspaceClient()

notebook_path = f"/Users/{w.current_user.me().user_name}/sdk-{time.time_ns()}"

created = w.pipelines.create(
    continuous=False,
    name=f"sdk-{time.time_ns()}",
    libraries=[pipelines.PipelineLibrary(notebook=pipelines.NotebookLibrary(path=notebook_path))],
    clusters=[
        pipelines.PipelineCluster(
            instance_pool_id=os.environ["TEST_INSTANCE_POOL_ID"],
            label="default",
            num_workers=1,
            custom_tags={
                "cluster_type": "default",
            },
        )
    ],
)

events = w.pipelines.list_pipeline_events(pipeline_id=created.pipeline_id)

# cleanup
w.pipelines.delete(pipeline_id=created.pipeline_id)

Retrieves events for a pipeline.

Parameters:

pipeline_id – str The pipeline to return events for.
filter –
str (optional) Criteria to select a subset of results, expressed using a SQL-like syntax. The supported filters are: 1. level=’INFO’ (or WARN or ERROR) 2. level in (‘INFO’, ‘WARN’) 3. id=’[event-id]’ 4. timestamp > ‘TIMESTAMP’ (or >=,<,<=,=)

Composite expressions are supported, for example: level in (‘ERROR’, ‘WARN’) AND timestamp> ‘2021-07-22T06:37:33.083Z’
max_results – int (optional) Max number of entries to return in a single page. The system may return fewer than max_results events in a response, even if there are more events available.
order_by – List[str] (optional) A string indicating a sort order by timestamp for the results, for example, [“timestamp asc”]. The sort order can be ascending or descending. By default, events are returned in descending order by timestamp.
page_token – str (optional) Page token returned by previous call. This field is mutually exclusive with all fields in this request except max_results. An error is returned if any fields other than max_results are set when this field is set.

Returns:

Iterator over PipelineEvent

list_pipelines([, filter: Optional[str], max_results: Optional[int], order_by: Optional[List[str]], page_token: Optional[str]]) → Iterator[PipelineStateInfo]¶

Usage:

from databricks.sdk import WorkspaceClient
from databricks.sdk.service import pipelines

w = WorkspaceClient()

all = w.pipelines.list_pipelines(pipelines.ListPipelinesRequest())

Lists pipelines defined in the Spark Declarative Pipelines system.

Parameters:

filter –
str (optional) Select a subset of results based on the specified criteria. The supported filters are:
- notebook=’<path>’ to select pipelines that reference the provided notebook path. * `name LIKE
’[pattern]’` to select pipelines with a name that matches pattern. Wildcards are supported, for example: name LIKE ‘%shopping%’

Composite filters are not supported. This field is optional.
max_results – int (optional) The maximum number of entries to return in a single page. The system may return fewer than max_results events in a response, even if there are more events available. This field is optional. The default value is 25. The maximum value is 100. An error is returned if the value of max_results is greater than 100.
order_by – List[str] (optional) A list of strings specifying the order of results. Supported order_by fields are id and name. The default is id asc. This field is optional.
page_token – str (optional) Page token returned by previous call

Returns:

Iterator over PipelineStateInfo

list_updates(pipeline_id: str [, max_results: Optional[int], page_token: Optional[str], until_update_id: Optional[str]]) → ListUpdatesResponse¶

List updates for an active pipeline.

Parameters:

pipeline_id – str The pipeline to return updates for.
max_results – int (optional) Max number of entries to return in a single page.
page_token – str (optional) Page token returned by previous call
until_update_id – str (optional) If present, returns updates until and including this update_id.

Returns:

ListUpdatesResponse

set_permissions(pipeline_id: str [, access_control_list: Optional[List[PipelineAccessControlRequest]]]) → PipelinePermissions¶

Sets permissions on an object, replacing existing permissions if they exist. Deletes all direct permissions if none are specified. Objects can inherit permissions from their root object.

Parameters:

pipeline_id – str The pipeline for which to get or manage permissions.
access_control_list – List[PipelineAccessControlRequest] (optional)

Returns:

PipelinePermissions

start_update(pipeline_id: str [, cause: Optional[StartUpdateCause], full_refresh: Optional[bool], full_refresh_selection: Optional[List[str]], parameters: Optional[Dict[str, str]], refresh_selection: Optional[List[str]], replace_where_overrides: Optional[List[ReplaceWhereOverride]], reset_checkpoint_selection: Optional[List[str]], rewind_spec: Optional[RewindSpec], validate_only: Optional[bool]]) → StartUpdateResponse¶

Starts a new update for the pipeline. If there is already an active update for the pipeline, the request will fail and the active update will remain running.

Parameters:

pipeline_id – str
cause – StartUpdateCause (optional)
full_refresh – bool (optional) If true, this update will reset all tables before running.
full_refresh_selection – List[str] (optional) A list of tables to update with fullRefresh. If both refresh_selection and full_refresh_selection are empty, this is a full graph update. Full Refresh on a table means that the states of the table will be reset before the refresh.
parameters – Dict[str,str] (optional) Key/value map of parameters to pass to the pipeline execution
refresh_selection – List[str] (optional) A list of tables to update without fullRefresh. If both refresh_selection and full_refresh_selection are empty, this is a full graph update. Full Refresh on a table means that the states of the table will be reset before the refresh.
replace_where_overrides – List[ReplaceWhereOverride] (optional) A list of predicate overrides for replace_where flows in this update. Only replace_where flows may be specified. Flows not listed use their original predicate.
reset_checkpoint_selection – List[str] (optional) A list of flows for which this update should reset the streaming checkpoint. This selection will not clear the data in the flow’s target table. Flows in this list may also appear in refresh_selection and full_refresh_selection.
rewind_spec – RewindSpec (optional) The information about the requested rewind operation. If specified this is a rewind mode update.
validate_only – bool (optional) If true, this update only validates the correctness of pipeline source code but does not materialize or publish any datasets.

Returns:

StartUpdateResponse

stop(pipeline_id: str) → Wait[GetPipelineResponse]¶

Stops the pipeline by canceling the active update. If there is no active update for the pipeline, this request is a no-op.

Parameters:: pipeline_id – str
Returns:: Long-running operation waiter for GetPipelineResponse. See :method:wait_get_pipeline_idle for more details.

stop_and_wait(pipeline_id: str, timeout: datetime.timedelta = 0:20:00) → GetPipelineResponse¶

update(pipeline_id: str [, allow_duplicate_names: Optional[bool], budget_policy_id: Optional[str], catalog: Optional[str], channel: Optional[str], clusters: Optional[List[PipelineCluster]], configuration: Optional[Dict[str, str]], continuous: Optional[bool], deployment: Optional[PipelineDeployment], development: Optional[bool], edition: Optional[str], environment: Optional[PipelinesEnvironment], event_log: Optional[EventLogSpec], expected_last_modified: Optional[int], filters: Optional[Filters], gateway_definition: Optional[IngestionGatewayPipelineDefinition], id: Optional[str], ingestion_definition: Optional[IngestionPipelineDefinition], libraries: Optional[List[PipelineLibrary]], name: Optional[str], notifications: Optional[List[Notifications]], parameters: Optional[Dict[str, str]], photon: Optional[bool], restart_window: Optional[RestartWindow], root_path: Optional[str], run_as: Optional[RunAs], schema: Optional[str], serverless: Optional[bool], storage: Optional[str], tags: Optional[Dict[str, str]], target: Optional[str], trigger: Optional[PipelineTrigger], usage_policy_id: Optional[str]])¶

Usage:

import os
import time

from databricks.sdk import WorkspaceClient
from databricks.sdk.service import pipelines

w = WorkspaceClient()

notebook_path = f"/Users/{w.current_user.me().user_name}/sdk-{time.time_ns()}"

created = w.pipelines.create(
    continuous=False,
    name=f"sdk-{time.time_ns()}",
    libraries=[pipelines.PipelineLibrary(notebook=pipelines.NotebookLibrary(path=notebook_path))],
    clusters=[
        pipelines.PipelineCluster(
            instance_pool_id=os.environ["TEST_INSTANCE_POOL_ID"],
            label="default",
            num_workers=1,
            custom_tags={
                "cluster_type": "default",
            },
        )
    ],
)

w.pipelines.update(
    pipeline_id=created.pipeline_id,
    name=f"sdk-{time.time_ns()}",
    libraries=[pipelines.PipelineLibrary(notebook=pipelines.NotebookLibrary(path=notebook_path))],
    clusters=[
        pipelines.PipelineCluster(
            instance_pool_id=os.environ["TEST_INSTANCE_POOL_ID"],
            label="default",
            num_workers=1,
            custom_tags={
                "cluster_type": "default",
            },
        )
    ],
)

# cleanup
w.pipelines.delete(pipeline_id=created.pipeline_id)

Updates a pipeline with the supplied configuration.

Parameters:

pipeline_id – str Unique identifier for this pipeline.
allow_duplicate_names – bool (optional) If false, deployment will fail if name has changed and conflicts the name of another pipeline.
budget_policy_id – str (optional) Budget policy of this pipeline.
catalog – str (optional) A catalog in Unity Catalog to publish data from this pipeline to. If target is specified, tables in this pipeline are published to a target schema inside catalog (for example, catalog.`target`.`table`). If target is not specified, no data is published to Unity Catalog.
channel – str (optional) SDP Release Channel that specifies which version to use.
clusters – List[PipelineCluster] (optional) Cluster settings for this pipeline deployment.
configuration – Dict[str,str] (optional) String-String configuration for this pipeline execution.
continuous – bool (optional) Whether the pipeline is continuous or triggered. This replaces trigger.
deployment – PipelineDeployment (optional) Deployment type of this pipeline.
development – bool (optional) Whether the pipeline is in Development mode. Defaults to false.
edition – str (optional) Pipeline product edition.
environment – PipelinesEnvironment (optional) Environment specification for this pipeline used to install dependencies.
event_log – EventLogSpec (optional) Event log configuration for this pipeline
expected_last_modified – int (optional) If present, the last-modified time of the pipeline settings before the edit. If the settings were modified after that time, then the request will fail with a conflict.
filters – Filters (optional) Filters on which Pipeline packages to include in the deployed graph.
gateway_definition – IngestionGatewayPipelineDefinition (optional) The definition of a gateway pipeline to support change data capture.
id – str (optional) Unique identifier for this pipeline.
ingestion_definition – IngestionPipelineDefinition (optional) The configuration for a managed ingestion pipeline. These settings cannot be used with the ‘libraries’, ‘schema’, ‘target’, or ‘catalog’ settings.
libraries – List[PipelineLibrary] (optional) Libraries or code needed by this deployment.
name – str (optional) Friendly identifier for this pipeline.
notifications – List[Notifications] (optional) List of notification settings for this pipeline.
parameters – Dict[str,str] (optional) Key/value map of default parameters to use for pipeline execution. Maximum total size: 10k characters (JSON format)
photon – bool (optional) Whether Photon is enabled for this pipeline.
restart_window – RestartWindow (optional) Restart window of this pipeline.
root_path – str (optional) Root path for this pipeline. This is used as the root directory when editing the pipeline in the Databricks user interface and it is added to sys.path when executing Python sources during pipeline execution.
run_as – RunAs (optional)
schema – str (optional) The default schema (database) where tables are read from or published to.
serverless – bool (optional) Whether serverless compute is enabled for this pipeline.
storage – str (optional) DBFS root directory for storing checkpoints and tables.
tags – Dict[str,str] (optional) A map of tags associated with the pipeline. These are forwarded to the cluster as cluster tags, and are therefore subject to the same limitations. A maximum of 25 tags can be added to the pipeline.
target – str (optional) Target schema (database) to add tables in this pipeline to. Exactly one of schema or target must be specified. To publish to Unity Catalog, also specify catalog. This legacy field is deprecated for pipeline creation in favor of the schema field.
trigger – PipelineTrigger (optional) Which pipeline trigger to use. Deprecated: Use continuous instead.
usage_policy_id – str (optional) Usage policy of this pipeline.

update_permissions(pipeline_id: str [, access_control_list: Optional[List[PipelineAccessControlRequest]]]) → PipelinePermissions¶

Updates the permissions on a pipeline. Pipelines can inherit permissions from their root object.

Parameters:

pipeline_id – str The pipeline for which to get or manage permissions.
access_control_list – List[PipelineAccessControlRequest] (optional)

Returns:

PipelinePermissions

wait_get_pipeline_idle(pipeline_id: str, timeout: datetime.timedelta = 0:20:00, callback: Optional[Callable[[GetPipelineResponse], None]]) → GetPipelineResponse¶

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Related Topics

`w.pipelines`: Pipelines¶

w.pipelines: Pipelines¶

`w.pipelines`: Pipelines¶