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fix(pipeline): limit streaming aggregation memory without spill - #67501

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JackyWoo:limit_streaming_preagg_memory
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fix(pipeline): limit streaming aggregation memory without spill#67501
JackyWoo wants to merge 1 commit into
apache:masterfrom
JackyWoo:limit_streaming_preagg_memory

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@JackyWoo

@JackyWoo JackyWoo commented Sep 3, 2026

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What problem does this PR solve?

Issue Number: close #xxx

Related PR: #xxx

Problem Summary:
spill_streaming_agg_mem_limit (default 256MB) was only applied when query-level
spill was enabled (enable_spill = true). When spill is disabled — which is still
a common deployment — the streaming aggregation operator now only limit the hastable by
row count but not data size, which may causing OOM on query with large agg_state queries.

Release note

None

Check List (For Author)

  • Test

    • Regression test
    • Unit Test
    • Manual test (add detailed scripts or steps below)
    • No need to test or manual test. Explain why:
      • This is a refactor/code format and no logic has been changed.
      • Previous test can cover this change.
      • No code files have been changed.
      • Other reason
  • Behavior changed:

    • No.
    • Yes.

Limit streaming aggregation memory without spill.

The value of spill_streaming_agg_mem_limit is a tradeoff between query time and memory.
The following is a query in my prodoction env.

Metric Before After Change
Total runtime 67 s 76 s +13.4%
Streaming agg memory / instance 992.99 MB 363.00 MB -63.4%
  • Does this need documentation?
    • No.
    • Yes.

Check List (For Reviewer who merge this PR)

  • Confirm the release note
  • Confirm test cases
  • Confirm document
  • Add branch pick label

@hello-stephen

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Thank you for your contribution to Apache Doris.
Don't know what should be done next? See How to process your PR.

Please clearly describe your PR:

  1. What problem was fixed (it's best to include specific error reporting information). How it was fixed.
  2. Which behaviors were modified. What was the previous behavior, what is it now, why was it modified, and what possible impacts might there be.
  3. What features were added. Why was this function added?
  4. Which code was refactored and why was this part of the code refactored?
  5. Which functions were optimized and what is the difference before and after the optimization?

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2 participants