Performance Optimization¶

Advanced techniques for optimizing ParquetFrame performance.

Performance Optimization Strategies¶

Learn how to squeeze maximum performance out of ParquetFrame for your specific use cases.

Memory Management¶

Optimize memory usage for large datasets: - Lazy Loading: Load data on-demand - Chunked Processing: Process data in manageable chunks - Memory Profiling: Monitor and optimize memory usage - Garbage Collection: Manage memory lifecycle effectively

I/O Optimization¶

Maximize read/write performance: - Column Selection: Read only necessary columns - Predicate Pushdown: Filter data during read - Compression: Choose optimal compression algorithms - Parallel I/O: Utilize multiple threads for I/O operations

Backend Selection¶

Choose the right backend for your workload: - File Size Analysis: Automatic backend selection based on data size - Memory Constraints: Consider available system memory - Processing Type: Match backend to operation characteristics

Advanced Techniques¶

Professional optimization strategies: - Data Partitioning: Organize data for optimal access patterns - Caching: Implement intelligent caching strategies - Parallel Processing: Scale across multiple cores - Pipeline Optimization: Optimize entire data processing workflows

Summary¶

Performance optimization requires understanding your data characteristics, system resources, and processing requirements.

Examples¶

import parquetframe as pf

# Lazy loading for large files
df = pf.read("huge_dataset.parquet", lazy=True)

# Column selection optimization
df = pf.read("data.parquet", columns=["id", "value", "timestamp"])

# Predicate pushdown
df = pf.read("data.parquet", filters=[("date", ">=", "2023-01-01")])

# Memory-efficient processing
for chunk in df.iter_chunks(chunk_size=10000):
    processed = chunk.process()
    processed.save(f"output_{chunk.index}.parquet")

# Backend selection
small_df = pf.read("small.parquet", backend="pandas")
large_df = pf.read("large.parquet", backend="dask")

# Performance monitoring
with pf.profiler():
    result = df.groupby("category").agg({"sales": "sum"})

Performance Optimization¶