Synapse Spark command execution vs job execution

@UKjonnyb

In your Azure Synapse Spark environment, you are observing a significant difference between Command Execution Time (4 minutes 41 seconds) and Job Execution Time (14 seconds) for your DataFrame operations. This difference is due to how Spark and Synapse execute and manage commands.

Why the Delay Occurs

Command Execution Time includes everything from submitting your code, initializing the Spark session (if idle), planning the query, and finally executing the job.
Job Execution Time only measures the actual computation time on the Spark cluster.

Possible Causes

Session Initialization - If the session was idle, it might take time to reconnect.
Lazy Evaluation - Spark only starts processing when an action (like .show() or .count()) is triggered.
Inefficient Caching - If your DataFrame is not cached properly, it may be recomputed every time you use it.

How to Optimize Performance

Cache Your DataFrame - This prevents re-computation.

df_with_cumsum.cache()
df_with_cumsum.count()  # Trigger caching

Warm Up the Session - Avoid session idle time by keeping the session active.

Optimize Cluster Configuration - Ensure your Spark pool has enough executors, memory, and cores for your workload.

Minimize UI Rendering - Use .show(10) instead of larger values.

Enable Speculative Execution - This can help with slow tasks.

spark.conf.set("spark.speculation", "true")

Monitor Query Performance - Use the Synapse Spark UI (Job details, SQL graph, and timeline view) to identify performance bottlenecks. Pay attention to any stages that take disproportionately long.

Leverage Parallelism - If you have multiple queries, distribute them across parallel Spark applications to maximize cluster utilization.

I hope this information helps. Please do let us know if you have any further queries.

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Thank you.

@UKjonnyb

We haven’t heard from you on the last response and was just checking back to see if you have a resolution yet. In case if you have any resolution, please do share that same with the community as it can be helpful to others. Otherwise, will respond with more details and we will try to help.