Lakeflow Declarative Pipelines properties reference

This article provides a reference for Lakeflow Declarative Pipelines JSON setting specification and table properties in Databricks. For more details on using these various properties and configurations, see the following articles:

• Configure Lakeflow Declarative Pipelines
• Pipelines REST API

Lakeflow Declarative Pipelines configurations

Fields
id Type: string A globally unique identifier for this pipeline. The identifier is assigned by the system and cannot be changed.
name Type: string A user-friendly name for this pipeline. The name can be used to identify pipeline jobs in the UI.
configuration Type: object An optional list of settings to add to the Spark configuration of the cluster that will run the pipeline. These settings are read by the Lakeflow Declarative Pipelines runtime and available to pipeline queries through the Spark configuration. Elements must be formatted as key:value pairs.
libraries Type: array of objects An array of notebooks containing the pipeline code and required artifacts.
clusters Type: array of objects An array of specifications for the clusters to run the pipeline. If this is not specified, pipelines will automatically select a default cluster configuration for the pipeline.
development Type: boolean A flag indicating whether to run the pipeline in development or production mode. The default value is true
notifications Type: array of objects An optional array of specifications for email notifications when a pipeline update completes, fails with a retryable error, fails with a non-retryable error, or a flow fails.
continuous Type: boolean A flag indicating whether to run the pipeline continuously. The default value is false.
catalog Type: string The name of the default catalog for the pipeline, where all datasets and metadata for pipeline are published. Setting this value enables Unity Catalog for the pipeline. If left unset, the pipeline publishes to the legacy Hive metastore using the location specified in storage. In legacy publishing mode, specifies the catalog containing the target schema where all datasets from the current pipeline are published. See LIVE schema (legacy).
schema Type: string The name of the default schema for the pipeline, where all datasets and metadata for the pipeline are published by default. See Set the target catalog and schema.
target (legacy) Type: string The name of the target schema where all datasets defined in the current pipeline are published. Setting target instead of schema configures the pipeline to use legacy publishing mode. See LIVE schema (legacy).
storage (legacy) Type: string A location on DBFS or cloud storage where output data and metadata required for pipeline execution are stored. Tables and metadata are stored in subdirectories of this location. When the storage setting is not specified, the system will default to a location in dbfs:/pipelines/. The storage setting cannot be changed after a pipeline is created.
channel Type: string The version of the Lakeflow Declarative Pipelines runtime to use. The supported values are: preview to test your pipeline with upcoming changes to the runtime version. current to use the current runtime version. The channel field is optional. The default value is current. Databricks recommends using the current runtime version for production workloads.
edition Type string The Lakeflow Declarative Pipelines product edition to run the pipeline. This setting allows you to choose the best product edition based on the requirements of your pipeline: CORE to run streaming ingest workloads. PRO to run streaming ingest and change data capture (CDC) workloads. ADVANCED to run streaming ingest workloads, CDC workloads, and workloads that require Lakeflow Declarative Pipelines expectations to enforce data quality constraints. The edition field is optional. The default value is ADVANCED.
photon Type: boolean A flag indicating whether to use What is Photon? to run the pipeline. Photon is the Databricks high performance Spark engine. Photon-enabled pipelines are billed at a different rate than non-Photon pipelines. The photon field is optional. The default value is false.
pipelines.maxFlowRetryAttempts Type: int If a retryable failure occurs during a pipeline update, this is the maximum number of times to retry a flow before failing the pipeline update Default: Two retry attempts. When a retryable failure occurs, the Lakeflow Declarative Pipelines runtime attempts to run the flow three times, including the original attempt.
pipelines.numUpdateRetryAttempts Type: int If a retryable failure occurs during an update, this is the maximum number of times to retry the update before permanently failing the update. The retry is run as a full update. This parameter applies only to pipelines running in production mode. Retries are not attempted if your pipeline runs in development mode or when you run a Validate update. Default: Five for triggered pipelines. Unlimited for continuous pipelines.

Lakeflow Declarative Pipelines table properties

In addition to the table properties supported by Delta Lake, you can set the following table properties.

Table properties
pipelines.autoOptimize.zOrderCols Default: None An optional string containing a comma-separated list of column names to z-order this table by. For example, pipelines.autoOptimize.zOrderCols = "year,month"
pipelines.reset.allowed Default: true Controls whether a full refresh is allowed for this table.
pipelines.autoOptimize.managed Default: true Enables or disables automatically scheduled optimization of this table. For pipelines managed by predictive optimization, this property is not used.

Pipelines trigger interval

You can specify a pipeline trigger interval for the entire pipeline or as part of a dataset declaration. See Set trigger interval for continuous pipelines.

pipelines.trigger.interval

The default is based on flow type: Five seconds for streaming queries. One minute for complete queries when all input data is from Delta sources. Ten minutes for complete queries when some data sources may be non-Delta. The value is a number plus the time unit. The following are the valid time units: second, seconds minute, minutes hour, hours day, days You can use the singular or plural unit when defining the value, for example: {"pipelines.trigger.interval" : "1 hour"} {"pipelines.trigger.interval" : "10 seconds"} {"pipelines.trigger.interval" : "30 second"} {"pipelines.trigger.interval" : "1 minute"} {"pipelines.trigger.interval" : "10 minutes"} {"pipelines.trigger.interval" : "10 minute"}

Cluster attributes that are not user settable

Because Lakeflow Declarative Pipelines manages cluster lifecycles, many cluster settings are set by Lakeflow Declarative Pipelines and cannot be manually configured by users, either in a pipeline configuration or in a cluster policy used by a pipeline. The following table lists these settings and why they cannot be manually set.

Fields
cluster_name Lakeflow Declarative Pipelines sets the names of the clusters used to run pipeline updates. These names cannot be overridden.
data_security_mode access_mode These values are automatically set by the system.
spark_version Lakeflow Declarative Pipelines clusters run on a custom version of Databricks Runtime that is continually updated to include the latest features. The version of Spark is bundled with the Databricks Runtime version and cannot be overridden.
autotermination_minutes Because Lakeflow Declarative Pipelines manages cluster auto-termination and reuse logic, the cluster auto-termination time cannot be overridden.
runtime_engine Although you can control this field by enabling Photon for your pipeline, you cannot set this value directly.
effective_spark_version This value is automatically set by the system.
cluster_source This field is set by the system and is read-only.
docker_image Because Lakeflow Declarative Pipelines manages the cluster lifecycle, you cannot use a custom container with pipeline clusters.
workload_type This value is set by the system and cannot be overridden.