# Parquet行组大小不匹配导致读取时内存错误

- **ID:** `data/parquet-row-group-size-mismatch`
- **领域:** data
- **类别:** resource_error
- **错误码:** `OutOfMemoryError`
- **验证级别:** ai_generated
- **修复率:** 86%

## 根因

写入时行组过大（例如>1GB）的Parquet文件，在被需要将整个行组加载到内存的系统读取时（尤其是在Spark执行器或pandas等内存受限的环境中），会导致内存耗尽。

## 版本兼容性

| 版本 | 状态 | 引入 | 弃用 |
|------|------|------|------|
| Apache Spark 3.4 | active | — | — |
| Apache Spark 3.5 | active | — | — |
| pyarrow 13.0.0 | active | — | — |
| pandas 2.1.4 | active | — | — |

## 解决方案

1. ```
   Rewrite the Parquet file with smaller row group size: `import pyarrow.parquet as pq; table = pq.read_table('large.parquet'); pq.write_table(table, 'small.parquet', row_group_size=100000)`
   ```
2. ```
   Use Spark with parquet.row-group-size-bytes set: `spark.conf.set('spark.sql.parquet.rowGroupSize', 256 * 1024 * 1024)` and repartition the data before writing
   ```
3. ```
   Read the Parquet file in chunks using pyarrow's read_row_group method: `for i in range(parquet_file.metadata.num_row_groups): table = parquet_file.read_row_group(i)`
   ```

## 无效尝试

- **** — This only postpones the problem; row groups can grow unboundedly and may still exceed the increased heap, causing OOM again. (65% 失败率)
- **** — This setting adjusts shuffle partitions, not Parquet row group sizes; it does not affect how row groups are read. (80% 失败率)
- **** — Compression reduces file size on disk but row groups are decompressed in memory; the memory footprint remains the same. (75% 失败率)