Guide
Clean large files without running out of memory
Large files fail in ordinary tools not because the data is complex, but because the process loads too much at once. This guide shows how to clean large data files step by step without exceeding memory limits.
The problem
A common situation: a CSV file that looks manageable on disk becomes impossible to open or clean once processing starts. Memory usage grows, the system slows down, and the process fails before results appear.
Why common approaches fail
- Loading the entire file into memory at once
- Using default settings that assume small data
- Cleaning after loading instead of during reading
- Keeping unnecessary columns and types
Large data cleaning works best when reading, cleaning, and writing happen in controlled pieces rather than all at once.
A working approach
Step 1 — Inspect without loading
Check file size, column count, and data types using metadata or a small sample.
Step 2 — Read in chunks
Process the file in fixed-size blocks so memory use stays stable.
Step 3 — Clean during reading
Drop columns, fix types, and remove invalid rows before storing anything.
Step 4 — Write incrementally
Save cleaned chunks immediately instead of keeping them in memory.
Trade-offs
- Chunked processing is slower than full-memory operations
- Errors may appear later in the pipeline
- Intermediate files increase disk usage
Failure modes to watch
- Chunk size too large for available memory
- Type conversion errors spreading across chunks
- Disk becoming the new bottleneck
If memory usage grows with time, something is being retained instead of released between chunks.
Checklist
- File size and column count checked first
- Chunk size chosen conservatively
- Cleaning applied during reading
- Output written incrementally
- Memory usage monitored during run
Next steps
Related pages that help apply this guide.