Scale down Flink parallelism by 4

[piyush-zlai]

Summary

Grant and I were chatting about the high number of hosts needed for the beacon top Flink jobs (24). This is because the topic parallelism is 96 and we squeeze 4 slots per TM (so 96 / 4 = 24 hosts). Given that folks often over provision Kafka topics in terms of partitions, going with a default of scaling down by 1/4th. Will look into wiring up Flink autoscaling as a follow up to not have this hardcoded.

Checklist

• Added Unit Tests
• Covered by existing CI
• Integration tested
• Documentation update

Summary by CodeRabbit

• Refactor
  • Optimized stream processing by refining the parallelism calculation. The system now applies a scaling factor to better adjust the number of active processing units, which may result in improved efficiency under certain conditions.

[coderabbitai]

Walkthrough

The pull request updates the KafkaFlinkSource class by introducing a constant scaleFactor set to 0.25 and modifying the calculation of the implicit parallelism. Instead of directly using the partition count from TopicChecker.getPartitions, it now multiplies the result by scaleFactor and rounds up using math.ceil before converting to an integer.

Changes

File(s)	Change Summary
flink/.../KafkaFlinkSource.scala	Added val scaleFactor: Double = 0.25 and updated the definition of implicit val parallelism to use math.ceil(TopicChecker.getPartitions(...) * scaleFactor).toInt

Sequence Diagram(s)

sequenceDiagram
    participant KFS as KafkaFlinkSource
    participant TC as TopicChecker
    KFS->>TC: getPartitions(topicName, bootstrap, params)
    TC-->>KFS: partitionsCount
    KFS->>KFS: Multiply partitionsCount by 0.25
    KFS->>KFS: Apply math.ceil and convert to Int
    Note right of KFS: Set as implicit parallelism

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Possibly related PRs

• Coalesce the scanned input dataframe to limit the number of tasks for repartitioning #238: Modifications in TableUtils that similarly adjust how parallelism is calculated.

Suggested reviewers

• nikhil-zlai
• tchow-zlai

Poem

In the realm where code takes flight,
A scale factor shines its light.
Partitions adjusted with a math embrace,
Parallelism refined with steady grace.
Cheers to clear code in every trace!
🚀💻

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[coderabbitai]

Actionable comments posted: 0

🧹 Nitpick comments (1)

flink/src/main/scala/ai/chronon/flink/KafkaFlinkSource.scala (1)

32-33: Make scaleFactor configurable via topicInfo params.

The comment suggests configurability, but the value is hardcoded.

-  val scaleFactor = 0.25
+  val scaleFactor = topicInfo.params.get("scaleFactor").map(_.toDouble).getOrElse(0.25)

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Configuration used: CodeRabbit UI
Review profile: CHILL
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📥 Commits

Reviewing files that changed from the base of the PR and between 868feed and 2985845.

📒 Files selected for processing (1)

• flink/src/main/scala/ai/chronon/flink/KafkaFlinkSource.scala (1 hunks)

⏰ Context from checks skipped due to timeout of 90000ms (3)

• GitHub Check: scala_compile_fmt_fix
• GitHub Check: non_spark_tests
• GitHub Check: enforce_triggered_workflows

🔇 Additional comments (1)

flink/src/main/scala/ai/chronon/flink/KafkaFlinkSource.scala (1)

35-37: Add minimum parallelism validation.

Ensure scaled parallelism doesn't go below a safe minimum.

   implicit val parallelism: Int = {
-    math.ceil(TopicChecker.getPartitions(topicInfo.name, bootstrap, topicInfo.params) * scaleFactor).toInt
+    val minParallelism = 1
+    val calculated = math.ceil(TopicChecker.getPartitions(topicInfo.name, bootstrap, topicInfo.params) * scaleFactor).toInt
+    math.max(calculated, minParallelism)
   }