Manage Microsoft Fabric item schedules

List, create, update, or delete recurring schedules for a supported Fabric item. Use fabric_job_schedule_config() to construct the four schedule types in the current REST contract.

Usage

fabric_job_schedules(
  item,
  workspace = NULL,
  job_type = NULL,
  item_type = NULL,
  tenant_id = Sys.getenv("FABRICQUERYR_TENANT_ID"),
  client_id = Sys.getenv("FABRICQUERYR_CLIENT_ID", unset =
    "04b07795-8ddb-461a-bbee-02f9e1bf7b46"),
  token = NULL,
  auth_args = list(),
  api_base = .fabric_api_base
)

fabric_job_schedule_create(
  item,
  configuration,
  workspace = NULL,
  job_type = NULL,
  item_type = NULL,
  enabled = TRUE,
  execution_data = NULL,
  tenant_id = Sys.getenv("FABRICQUERYR_TENANT_ID"),
  client_id = Sys.getenv("FABRICQUERYR_CLIENT_ID", unset =
    "04b07795-8ddb-461a-bbee-02f9e1bf7b46"),
  token = NULL,
  auth_args = list(),
  api_base = .fabric_api_base
)

fabric_job_schedule_update(
  item,
  schedule_id,
  configuration = NULL,
  workspace = NULL,
  job_type = NULL,
  item_type = NULL,
  enabled = NULL,
  execution_data = NULL,
  tenant_id = Sys.getenv("FABRICQUERYR_TENANT_ID"),
  client_id = Sys.getenv("FABRICQUERYR_CLIENT_ID", unset =
    "04b07795-8ddb-461a-bbee-02f9e1bf7b46"),
  token = NULL,
  auth_args = list(),
  api_base = .fabric_api_base
)

fabric_job_schedule_delete(
  item,
  schedule_id,
  workspace = NULL,
  job_type = NULL,
  item_type = NULL,
  confirm = FALSE,
  tenant_id = Sys.getenv("FABRICQUERYR_TENANT_ID"),
  client_id = Sys.getenv("FABRICQUERYR_CLIENT_ID", unset =
    "04b07795-8ddb-461a-bbee-02f9e1bf7b46"),
  token = NULL,
  auth_args = list(),
  api_base = .fabric_api_base
)

Arguments

item

Item GUID, exact display name, or an item object returned by a discovery function. A discovered object is recommended because it includes the item type and workspace ID.

workspace

Workspace GUID, exact display name, or a workspace object. Omit it when item is a discovered object containing workspaceId.

job_type

Schedule job type. Notebooks default to "RunNotebook", Spark job definitions to "SparkJob", and data pipelines, Dataflows, and Data Build Tool Jobs to "Execute". For a Dataflow publish schedule, set job_type = "ApplyChanges" explicitly. Lakehouses require an explicit value because they do not have a safe generic default; use "RefreshMaterializedLakeViews" for the materialized Lake View refresh route and supply its documented execution_data. Microsoft labels the Lakehouse materialized Lake View schedule API as Preview, for evaluation and development only, and does not recommend it for production use. Unknown item types retain the Core Scheduler's "DefaultJob" fallback. Supply an explicit value for another workload-specific schedule job type. When passing one of these item types as a GUID instead of a discovered item, also supply item_type or set the documented job_type explicitly.

item_type

Optional Fabric item type when item is a GUID. A discovered item supplies this automatically.

tenant_id

Entra tenant ID. Defaults to FABRICQUERYR_TENANT_ID

client_id

Entra application ID. Defaults to FABRICQUERYR_CLIENT_ID, then the Azure CLI application ID

token

Optional access token or token-provider function. Leave NULL to let 'fabricQueryR' use its normal sign-in flow A fabric_job handle reuses its stored credential unless tenant_id, client_id, token, or non-empty auth_args is supplied explicitly

auth_args

Additional sign-in options passed to AzureAuth::get_azure_token() when no token source is supplied

api_base

Fabric REST API base URL. Most users should keep the default A discovered workspace-specific endpoint is used unless this argument is supplied explicitly

configuration

A value returned by fabric_job_schedule_config(). Advanced callers may pass a named list in the documented Fabric ScheduleConfig shape. Known types are validated; an unknown future type is passed through and remains inspectable.

enabled

Whether the schedule is enabled. Fabric can automatically disable schedules after repeated failures; updating one with enabled = TRUE explicitly restarts it.

execution_data

Optional named list of static, workload-specific execution data. Its schema is defined by the item's job type. The package preserves it without assuming that all workloads share one schema.

schedule_id

Schedule GUID, or a fabric_job_schedule record returned by a schedule function.

confirm

Must be explicitly set to TRUE before a schedule is deleted.

Value

fabric_job_schedules() returns a list of fabric_job_schedule records. Create and update return one such record. Delete invisibly returns TRUE. Records expose normalized common fields and retain the complete service response in raw.

Details

Schedule deletion is not replayed after an ambiguous transport failure. An already absent schedule is treated as deleted.

List operations need an item read permission. Create and update require item execute and read-write permissions; delete requires item read-write permission. The current service limit is 20 schedules per item.

fabric_job_schedule_update() accepts partial R input for convenience, but the Fabric PATCH contract requires enabled and a complete configuration. When either is omitted, the function first reads the current schedule and preserves the omitted value. An omitted or NULL execution_data is also preserved from the original response JSON, retaining numeric precision and empty objects or arrays; supply a named list to replace it. Decoded record fields use ordinary R JSON types and cannot represent arbitrary decimals.

The published REST response currently exposes enabled but no standard auto-disable reason. auto_disabled is therefore NA unless Fabric returns an explicit marker. The complete response stays available in raw.

Semantic-model refresh schedules use the Power BI dataset schedule API, not the Fabric Core Job Scheduler. These functions reject a discovered semantic model unless job_type is supplied explicitly for a future or custom route.

References

Fabric Job Scheduler REST API

Update a semantic-model refresh schedule

Schedule a Data Pipeline

Schedule Dataflow Execute

Schedule Dataflow Apply Changes

Schedule a Lakehouse materialized Lake View refresh

Schedule a Data Build Tool Job

Fabric Data Pipeline REST API capabilities

Fabric job scheduler behavior

Examples

if (FALSE) { # \dontrun{
# Discover the Notebook instead of copying workspace and item IDs
workspace <- fabric_workspaces()[[1L]]
notebook <- fabric_notebooks(workspace)[[1L]]

# Inspect existing schedules before creating another one
existing <- fabric_job_schedules(notebook)

# Build a weekly configuration using Fabric's Windows time-zone name
configuration <- fabric_job_schedule_config(
  "Weekly",
  start_time = "2026-10-01T00:00:00Z",
  end_time = "2027-10-01T00:00:00Z",
  time_zone = "W. Europe Standard Time",
  times = "07:30",
  weekdays = c("Monday", "Thursday")
)

# Create, disable, and finally delete the schedule returned by Fabric
schedule <- fabric_job_schedule_create(notebook, configuration)
fabric_job_schedule_update(notebook, schedule, enabled = FALSE)
fabric_job_schedule_delete(notebook, schedule, confirm = TRUE)
} # }