Azure synapse analytics – Spark
FAQ 1-
Answer: Yes
I’m having workspace capacity of 80vCores
Taking an example, if you have created a spark pool of Node size:
Small (4 vCore – 32 GB size) with 8 nodes.
Total pool size = 32vCores
You can set the number of nodes to be used for each notebook
Notebook 1: Total 3 nodes = (1 driver node and 2
executor nodes)
4vCores x 3 nodes = 12 vCores used
Notebook 2:
Total 4 nodes (1 driver node and 3 executor nodes)
4vCores x 4 = 16 vCores used
12 vCores + 16 vCores = 28 vCores
Total of pool size with 32vCores, 28vCores were utilized which
is 87.5% utilization.
You can run 2 notebooks having a single Spark pool
FAQ 2: Can these share the variables or Temporary views
created in their notebooks as they are attached to same pool?
Answer: No
Explanation:
Apache Spark for Synapse is designed as a job service and
not a cluster model. It creates a separate Apache Spark application to run each
notebook.
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ReplyDeleteRunning multiple Spark notebooks in parallel on a shared Spark pool is an important consideration when designing efficient data engineering workflows. As this example demonstrates, understanding available vCores, driver and executor allocation, and overall resource utilization helps teams plan concurrent workloads more effectively. Proper resource management can improve throughput while reducing unnecessary infrastructure costs in cloud-based analytics environments.
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