See DATA STORES section in this article for JSON samples that show how to use a data store as a source and/or sink. See DATA TRANSFORMATION ACTIVITIES section in this article for JSON samples that define transformation activities in a pipeline.Ĭopy activity has two subsections in the typeProperties section: source and sink. Transformation activities have just the type properties. The typeProperties section is different for each activity. Typically, higher values imply other systemic issues. Word of caution: do not set high values for longRetry or longRetryInterval. In such cases, doing retries one after another may not help and doing so after an interval of time results in the desired output. longRetry may be used in situations where dependent data arrives at non-deterministic times or the overall environment is flaky under which data processing occurs. If any execution succeeds, the slice status would be Ready and no more retries are attempted. After that, the slice status would be Failed and no more retries would be attempted. After an hour (that is, longRetryInteval’s value), there would be another set of 3 consecutive execution attempts. After first 3 attempts are over, the slice status would be LongRetry. After each attempt, the slice status would be Retry. Initially there would be 3 consecutive execution attempts. For example, if we have the following settings in the activity policy: Retry: 3 longRetry: 2 longRetryInterval: 01:00:00 Assume there is only one slice to execute (status is Waiting) and the activity execution fails every time. If both Retry and longRetry are specified, each longRetry attempt includes Retry attempts and the max number of attempts is Retry * longRetry. So if you need to specify a time between retry attempts, use longRetry. longRetry attempts are spaced by longRetryInterval. The number of long retry attempts before the slice execution is failed. Example: 00:10:00 (implies delay of 10 mins) The execution of activity for a data slice is started after the Delay is past the expected execution time. Specify the delay before data processing of the slice starts. When timeout occurs, the status is set to TimedOut. The number of retries depends on the retry property. If the data processing time on a slice exceeds the timeout value, it is canceled, and the system attempts to retry the processing. Example: 00:10:00 (implies timeout 10 mins) If a value is not specified or is 0, the timeout is infinite. The retry is done as soon as possible after the failure. Activity execution for a data slice is retried up to the specified retry count. Number of retries before the data processing for the slice is marked as Failure. Similarly if you set the executionPriorityORder to be OldestFIrst, then the slice at 4 PM is processed. If you set the executionPriorityOrder to be NewestFirst, the slice at 5 PM is processed first. For example, if you have 2 slices (one happening at 4pm, and another one at 5pm), and both are pending execution. For example, if an activity needs to go through a large set of available data, having a larger concurrency value speeds up the data processing.ĭetermines the ordering of data slices that are being processed. It determines the number of parallel activity executions that can happen on different slices. Number of concurrent executions of the activity. The following table provides the details. Policies affect the run-time behavior of an activity, specifically when the slice of a table is processed. Its subproperties are the same as the ones in the availability property in a dataset. “scheduler” property is used to define desired scheduling for the activity. If it is not specified, default policies are used. Policies that affect the run-time behavior of the activity. Properties in the typeProperties section depend on type of the activity. Yes for HDInsight activities, ML Studio (classic) activities, and Stored Procedure Activity. An activity may require that you specify the linked service that links to the required compute environment. Name of the linked service used by the activity. The high-level structure for a pipeline definition is as follows: ], To learn how to migrate to the Az PowerShell module, see Migrate Azure PowerShell from AzureRM to Az. See Install Azure PowerShell to get started. We recommend that you use the Azure Az PowerShell module to interact with Azure.
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