Merge pull request #188421 from kromerm/reuse

Reuse

Author: PRMerger5

articles/data-factory/concepts-integration-runtime-performance.md

@@ -51,8 +51,6 @@ A best practice is to start small and scale up to meet your performance needs.
 
 By default, every data flow activity spins up a new Spark cluster based upon the Azure IR configuration. Cold cluster start-up time takes a few minutes and data processing can't start until it is complete. If your pipelines contain multiple **sequential** data flows, you can enable a time to live (TTL) value. Specifying a time to live value keeps a cluster alive for a certain period of time after its execution completes. If a new job starts using the IR during the TTL time, it will reuse the existing cluster and start up time will greatly reduced. After the second job completes, the cluster will again stay alive for the TTL time.
 
-You can additionally minimize the startup time of warm clusters by setting the "Quick re-use" option in the Azure Integration runtime under Data Flow Properties. Setting this to true will tell the service to not teardown the existing cluster after each job and instead re-use the existing cluster, essentially keeping the compute environment you've set in your Azure IR alive for up to the period of time specified in your TTL. This option makes for the shortest start-up time of your data flow activities when executing from a pipeline.
-
 However, if most of your data flows execute in parallel, it is not recommended that you enable TTL for the IR that you use for those activities. Only one job can run on a single cluster at a time. If there is an available cluster, but two data flows start, only one will use the live cluster. The second job will spin up its own isolated cluster.
 
 > [!NOTE]