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Perf: limit pushdown — avoid materializing full partitions for LIMIT queries

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难度
4/5
预计耗时
3-5 天
新手友好度
48/100
Issue 类型
功能
描述清晰度
基本清楚
活跃度
冷清
技术栈
python
领域
data, performance

调研方向

Start by reading PrunableStreamingTable::scan() and the path that carries the limit through PyArrowStreamPartition into the partition factory; the issue names no file or test. Check how projection is passed as a model, then determine how the factory can stop producing rows at the requested limit. Done means a query such as SELECT * FROM ds LIMIT 10 avoids materializing the full first matching partition.

由索引模型根据 Issue 内容生成。

描述

enhancement

Problem

The partition factory always materializes the full partition regardless of query:

df = pivot(ds.isel(block))  # always loads all 38.4M rows

For queries like SELECT * FROM ds LIMIT 100, DataFusion applies the limit after the factory produces its batch. The factory receives no signal to stop early.

Currently limit is passed from PrunableStreamingTable::scan() down to DataFusion's StreamingTable, which applies it at the execution-plan level — but only after the factory has already materialized the full partition.

Proposed fix

Pass an optional row limit into the factory (similar to projection pushdown). The scan() method can thread the limit through PyArrowStreamPartition so the factory can stop generating rows once the limit is reached.

def make_stream(projection, limit=None) -> pa.RecordBatchReader:
    rows_needed = limit or total_rows
    # only materialize up to rows_needed rows

Impact

For ARCO-ERA5, LIMIT 10 currently reads 38.4M rows from the first matching partition. With this fix it would read at most batch_size rows.

Parent: #126

主要语言
Python
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派生
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