In Pandas, if your dataset is bigger than your RAM (e.g., a 30GB file on a 16GB laptop), you get a MemoryError. The Polars Streaming Engine offers an alternative approach for handling large datasets efficiently.
Polars solves this with its Streaming Engine. It processes data in chunks, never loading the whole file at once.
The Concept: Out-of-Core Processing
Instead of:
- Read ALL data.
- Process.
- Write result.
Polars does:
- Read a chunk.
- Process the chunk.
- Write the chunk to disk.
- Repeat.
Step 1: scan_ instead of read_
We must use the Lazy API.
import polars as pl
# Scan the massive CSV (uses 0 RAM)
lazy_df = pl.scan_csv("massive_dataset.csv")Step 2: Define the Transformations
# Filter and GroupBy
# Polars figures out how to do this without loading everything
query = (
lazy_df
.filter(pl.col("sales") > 100)
.group_by("region")
.agg(pl.col("sales").sum())
)Step 3: sink_parquet (The Magic Method)
Do not use .collect()! That tries to pull the entire result into RAM. Instead, use .sink_parquet(). This tells Polars to stream the results directly to a file on your hard drive.
# This runs the query in chunks and writes immediately to disk
query.sink_parquet("processed_results.parquet")You just processed a file larger than your computer’s memory without crashing!
Key Takeaways
- Polars Streaming Engine efficiently handles large datasets that exceed RAM by processing data in chunks.
- Instead of loading all data at once, Polars reads, processes, and writes chunks sequentially, saving memory.
- Use the Lazy API with `scan_` instead of `read_` to optimise your data processing.
- Define transformations before applying them in Polars for better efficiency.
- Utilise `sink_parquet()` to stream results directly to disk, avoiding MemoryErrors and crashes.
