Spark requests for more core than asked when calling POST livy batch api in azure synapse

Question

I have an azure synapse spark cluster with 3 nodes of 4 vCores and 32 GB memory each. I am trying to submit a spark job using azure synapse Livy batch APIs. The request looks like this,

curl --location --request POST 'https://<synapse-workspace>.dev.azuresynapse.net/livyApi/versions/2019-11-01-preview/sparkPools/<pool-name>/batches?detailed=true' `
--header 'cache-control: no-cache' `
--header 'Authorization: Bearer <Token>' `
--header 'Content-Type: application/json' `
--data-raw '{
    "name": "T1",
    "file": "folder/file.py",
    "driverMemory": "1g",
    "driverCores": 1,
    "executorMemory": "1g",
    "executorCores":1,
    "numExecutors": 3
}'

The response I get is this,

{
    "TraceId": "<some-guid>",
    "Message": "Your Spark job requested 16 vcores. However, the pool has a 12 core limit. Try reducing the numbers of vcores requested or increasing your pool size."
}

I cannot figure out why is it asking for 16 cores. Shouldn't it ask for 4 (3 * 1 + 1) cores?

Update: I tried changing the node pool size to 3 nodes each of 8 vCores and 64 GB memory. And, with this configuration,

{
    "name": "T1",
    "file": "folder/file.py",
    "driverMemory": "1g",
    "driverCores": 1,
    "executorMemory": "1g",
    "executorCores": 1,
    "numExecutors": 6
}

It requests for 28 cores (even for executorCores 2,3,4). And if I change executorCores to 5,6,7 or 8, it will request for 56 cores.

Answer 1

From the portal there is no way to do what you are trying to do.

But you can still submit spark job by specifying driver (core and memory) and executor (core and memory). For example with something like this: Submit Spark job in Azure Synapse from Java

Using the above code, I am able to submit 9 concurrent jobs (with 1 driver and 1 executor, both consuming a single core) in 3 node Medium instances (8 cores each, though only 7 are available for use as 1 is reserved for hadoop daemon).