Streaming Parquet to Solve OOM Issues | Generated by AI
Question: What is this transcript describing, and how does it solve the out-of-memory (OOM) problem when processing large parquet files?
Answer:
Overview
This is a transcript of someone working through a memory management problem while trying to extract text from large Hugging Face dataset parquet files on a resource-constrained server (2 vCPU, 3.8GB RAM).
The Core Problem
They’re trying to download and extract text from a massive dataset stored in parquet format (each shard ~2.1GB compressed). The server only has 3.8GB RAM, which is not enough to load a full parquet file into memory at once.
The Progression of Failed Attempts
Each approach ran out of memory (OOM = Out Of Memory, process gets “killed” by the Linux OOM killer):
| Attempt | Method | Why It Failed |
|---|---|---|
| 1 | HuggingFace datasets streaming |
Parquet decompression still buffers heavily in memory |
| 2 | wget download + pd.read_parquet() |
pandas loads the entire 2.1GB file into RAM at once (~6–8GB needed) |
| 3 | urllib.request.urlretrieve + streaming |
Still buffered; pd.read_parquet too large |
| 4 | PyArrow reader + .to_pandas() |
.to_pandas() converts whole row groups into DataFrames in memory |
The Working Solution: iter_batches()
The final fix was using PyArrow’s iter_batches(), which is a true streaming reader:
import pyarrow.parquet as pq
parquet_file = pq.ParquetFile("shard.parquet")
for batch in parquet_file.iter_batches(batch_size=1000):
# Process only 1000 rows at a time
for text in batch.column("text").to_pylist():
outfile.write(text + "\n")
Why iter_batches() Works
- Instead of loading the whole file, it reads one small batch at a time (e.g., 1,000 rows)
- Memory usage stays constant regardless of file size
- The rest of the file stays on disk until needed
- Successfully processed 100K rows from a 2GB parquet file without OOM
Key Concepts
Parquet Row Groups — Parquet files are divided into “row groups” internally. Earlier PyArrow methods read one entire row group at a time, which can still be hundreds of MB. iter_batches() subdivides even further.
OOM Killer — Linux automatically kills processes that exceed available RAM. The log shows the process getting “killed” mid-run — this is the kernel protecting system stability.
Shard-based datasets — Large HuggingFace datasets are split into multiple parquet “shards” (shard 0, shard 1, shard 2…). The plan is to process each shard sequentially, extracting text to a flat .txt file, which is far more memory-efficient for downstream tasks.
Result
- Shard 0 → extracted 19M lines / 3.1GB of text before the process died (but the output was saved)
- Shard 1 → tested successfully with
iter_batches - Shards 2+ → now running with the memory-safe pipeline