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[Task] Establish systematic performance baselines and optimization tracking

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评估

难度
5/5
预计耗时
一周以上
新手友好度
12/100
Issue 类型
重构
描述清晰度
需要澄清
活跃度
活跃
技术栈
java
领域
databases, performance

调研方向

This is a long-running tracking task, not a single change. Start with the linked Server PR #3342, which fixes lost Kout results, and its measurement review, to see which benchmark setup it used. The first concrete step would be a reproducible, correctness-checked Kout baseline with recorded commands, data generation, and environment details. Done means that baseline exists and candidate optimizations are ranked against it, not that any optimization is merged.

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描述

help wanted improvement rust

Goal

Track long-term, evidence-driven performance work across HugeGraph traversal and its shared execution/collection paths. Start with Kout as a reproducible case, then extend to other representative operations when measurements reveal shared bottlenecks.

Optimize end-to-end efficiency under realistic workloads, rather than greedily improving one method, microbenchmark, or configuration at the expense of correctness, memory, latency, or concurrent throughput.

Starting evidence

🔗 Server #3342 fixes lost Kout results by serializing shared result-set updates and distinct-limit admission. Preserve this correctness guarantee. (Other cases like)

Bounded measurements and limitations show workload-dependent trade-offs between internal parallelism and concurrent requests. They do not establish a production regression caused by the fix, isolate monitor cost, or establish production-wide performance acceptance. No narrowed-lock optimization has been benchmarked.

This is a long-term task, not an additional 1.8 release gate. Candidate optimizations below are hypotheses to evaluate, not commitments to implement.

TODO

  • Establish a reusable, correctness-checked baseline and retain reproducible commands, data generation, raw results, environment details, and exact tested revisions.
  • Profile representative end-to-end workloads before prioritizing changes.
  • Rank opportunities by measured user impact, resource cost, implementation risk, and maintenance burden.
  • Implement justified optimizations separately, with before/after comparisons against a correct baseline.
  • Add stable regression coverage and document deployment/concurrency trade-offs.
  • Expand coverage to related traversals and shared infrastructure as evidence warrants.
Candidate investigations
  • Internal worker counts, executor reuse/admission, and resource budgets across concurrent requests.
  • Bounded/lazy edge-query and iterator preparation to avoid unnecessary work after limits are satisfied.
  • Primitive IdSet bulk union to reduce numeric-ID wrapping during layer merges.
  • Empty-frontier termination and avoidable allocation/cancellation overhead.
  • Critical-section scope or aggregation strategies only where profiling justifies them; preserve exact distinct limits, deduplication, visibility, resource cleanup, and off-heap ID safety.

Measurement and acceptance

  • Cover single and concurrent requests; several worker settings including the actual default; numeric/string IDs; unique/duplicate/convergent graphs; finite/unlimited limits; nearest modes; and representative graph sizes.
  • Include relevant backends and full API/service paths, not only isolated consumers. Distinguish warmed caches from genuinely cold storage.
  • Record throughput, latency distributions, CPU, allocation/GC, memory, and lock/scheduling observations. Use sufficient independent runs, warm-up, sustained durations, and variability reporting; short repeated samples are not independent experiments or production tail-latency proof.
  • Verify complete results where deterministic, exact counts and legal results for bounded queries, plus cancellation, capacity and resource-lifecycle behavior.
  • Agree workload-specific acceptance budgets before claiming success. Check for displaced costs and regressions in other supported workloads.
  • Change one causal factor at a time where practical. Report inconclusive results honestly; do not use unsafe old concurrent behavior as a performance target.

Keep findings and follow-up PRs linked here so improvements accumulate into a coherent performance baseline rather than isolated tuning.

主要语言
Java
星标
3.2k
派生
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平均合并
1 天 9 小时
30 天内合并 PR
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环境准备

从这里开始

  1. 先读完整个 Issue，再读项目的贡献指南。
  2. 在 Issue 下留言说明你要接手 —— 这能避免两个人做同样的事。
  3. Fork 仓库，在一个分支上完成修改。
  4. 提交 Pull Request，并在描述里引用这个 Issue 编号。

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