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Perf: add in-process timing instrumentation to replace py-spy dependency

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評估

難度
4/5
預估耗時
3-5 天
新手友好度
46/100
Issue 類型
功能
描述清晰度
基本清楚
活躍度
停滯
技術堆疊
python
領域
performance

研究方向

Start by reading read_xarray_table and _iteration_callback in the reader, then inspect perf_tests/profile.sh and perf_tests/query_large_synthetic.py to understand the current profiling workflow. Choose and implement a timing approach that reports per-partition costs across the relevant execution stages. Confirm the measurements distinguish I/O, conversion, and DataFusion work without requiring py-spy or root access.

由索引模型根據 Issue 內容生成。

描述

Problem

The current profiling workflow in perf_tests/ requires py-spy, which needs sudo on macOS:

sudo ./profile.sh groupby_air_full.py

This makes profiling unavailable in many CI environments and for users without root access. Additionally, cProfile only sees the Python side of query execution — the entire DataFusion execution appears as a single opaque to_arrow_table() call (0.66s for 320 MB of data in our benchmarks), making it impossible to distinguish:

  • Time spent in xarray I/O (ds.isel(block), .values)
  • Time spent in numpy → Arrow conversion (iter_record_batches)
  • Time spent in DataFusion (aggregation, hash-grouping, row filtering)
  • Time spent waiting for GIL between partitions

Proposed solution

Add a lightweight per-partition timing mode to the _iteration_callback mechanism already in read_xarray_table:

# reader.py — extend make_stream to record timing
def make_stream(projected_names=None) -> pa.RecordBatchReader:
    if _timing_callback is not None:
        t0 = time.perf_counter()
    result = pa.RecordBatchReader.from_batches(...)
    if _timing_callback is not None:
        _timing_callback({"elapsed_s": time.perf_counter() - t0, "block": block})
    return result

Or alternatively: add a verbose=True flag to read_xarray_table that prints per-partition timings to stderr, giving insight into whether execution is I/O-bound or compute-bound without requiring external tools.

A lower-overhead option is to expose a PartitionTimingHook via LazyArrowStreamTable that fires before/after each factory call.

Current profiling workaround

For the query_large_synthetic.py benchmark (320 MB, 8 partitions):

Full scan AVG:    0.662s  (341 MB peak)   ← all in Rust's to_arrow_table()
Filter pushdown:  0.005s  (0.7 MB peak)   ← 7/8 partitions pruned
GROUP BY lat/lon: 0.839s  (341 MB peak)   ← +27% vs. AVG for hash aggregation

cProfile shows 246 Python calls for the full scan, but 0.660s of those are attributed to {method 'to_arrow_table' of 'datafusion.DataFrame' objects} — a Rust builtin that cProfile cannot introspect.

Related

  • _iteration_callback in XarrayRecordBatchReader and read_xarray_table — already used for testing
  • perf_tests/profile.sh — current py-spy based approach
  • perf_tests/query_large_synthetic.py — new benchmark script added alongside this issue
主要語言
Python
星號
142
分支
19
平均合併
1 天 9 小時
30 天內合併 PR
9

環境準備

  • 沒有 Dockerfile 或 Docker Compose 檔案
  • 沒有 Pull Request 範本
  • 閱讀貢獻指南

從這裡開始

  1. 先讀完整個 Issue，再讀專案的貢獻指南。
  2. 在 Issue 下留言說明你要接手 —— 這能避免兩個人做同樣的事。
  3. Fork 儲存庫，在一個分支上完成修改。
  4. 送出 Pull Request，並在描述裡引用這個 Issue 編號。

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