Guide to Apache Arrow & Feather
What is Apache Arrow IPC?
Apache Arrow IPC (Inter-Process Communication) is a binary format designed for efficient data exchange between different programs and programming languages. Key characteristics:
- Columnar: Data is organized by column, not row
- Zero-copy: Can be read without deserialization
- Language-agnostic: Works across Python, R, Java, C++, etc.
- Rich types: Supports nested structures, timestamps, decimals, and more
What is Feather?
Feather is a fast, lightweight file format for storing DataFrames. There are two versions:
- Feather v1: Original format, now legacy
- Feather v2: Based on Apache Arrow IPC (current standard)
Feather v2 is essentially Arrow IPC with a .feather extension. It's the default output format for Polars and widely supported.
When to Use Arrow/Feather?
✅ Use Arrow/Feather for:
- Fast intermediate storage: Faster read/write than CSV or JSON
- Data exchange: Between Python and R, or between microservices
- Type preservation: Maintains exact data types (unlike CSV)
- In-memory processing: Polars, DuckDB, DataFusion
- Quick iterations: Fast saves during data exploration
❌ Don't use for:
- Long-term storage: Use Parquet instead (compression + partitioning)
- Human-readable data: Use CSV or JSON
- Streaming large datasets: Parquet handles this better
- Cross-platform archives: CSV is more universally readable
How to Create Arrow/Feather Files
Using Polars (Python)
import polars as pl
# Create a DataFrame
df = pl.DataFrame({
"id": [1, 2, 3],
"name": ["Alice", "Bob", "Charlie"],
"score": [95.5, 87.2, 91.8]
})
# Save as Feather (default format)
df.write_ipc("data.feather")
# Or explicitly as Arrow IPC
df.write_ipc("data.arrow")
Using Pandas (Python)
import pandas as pd
df = pd.DataFrame({
"id": [1, 2, 3],
"name": ["Alice", "Bob", "Charlie"],
"score": [95.5, 87.2, 91.8]
})
# Save as Feather
df.to_feather("data.feather")
Using R (arrow package)
library(arrow)
df <- data.frame(
id = c(1, 2, 3),
name = c("Alice", "Bob", "Charlie"),
score = c(95.5, 87.2, 91.8)
)
# Save as Feather
write_feather(df, "data.feather")
# Or Arrow IPC
write_ipc_file(df, "data.arrow")
Using DuckDB (SQL)
-- Export query results to Arrow COPY (SELECT * FROM my_table) TO 'data.arrow' (FORMAT 'arrow');
Comparison: CSV vs Feather vs Parquet
| Feature | CSV | Feather | Parquet |
|---|---|---|---|
| Speed | Slow | ⚡ Very Fast | Fast |
| File Size | Large | Medium | Small (compressed) |
| Type Preservation | ❌ No | ✅ Yes | ✅ Yes |
| Human Readable | ✅ Yes | ❌ No | ❌ No |
| Compression | External only | Optional (LZ4) | Built-in |
| Columnar | ❌ No | ✅ Yes | ✅ Yes |
| Best For | Interchange, debugging | Speed, temp storage | Long-term, analytics |
Frequently Asked Questions
Q: Can I open Arrow/Feather files in Excel?
A: No, they're binary formats. Use ArrowScope to preview, then export to CSV if needed.
Q: Are .arrow and .feather files the same?
A: Feather v2 and Arrow IPC are the same format. Feather v1 is older and different.
Q: Why is Feather faster than CSV?
A: Feather is binary, columnar, and doesn't require parsing text. It can be memory-mapped for zero-copy reads.
Q: Should I use Feather or Parquet for my project?
A: Use Feather for speed and temporary storage during development. Use Parquet for production, long-term storage, and big data.
Q: How big can Arrow/Feather files be?
A: There's no hard limit, but they're designed for in-memory processing. For multi-GB datasets, consider Parquet with partitioning.
Q: Can I append data to an existing Arrow/Feather file?
A: No, they're immutable. You need to read, modify, and rewrite the entire file.
Q: What tools support Arrow/Feather?
A: Polars, Pandas, DuckDB, R arrow package, Apache Spark, DataFusion, and many more.
Resources
- Apache Arrow Official Site
- Polars (uses Feather by default)
- DuckDB (supports Arrow)
- Arrow Feather Documentation
Need Help?
Questions about Arrow, Feather, or ArrowScope? Contact us at nullkit.dev@outlook.com