Understanding Parquet: An Efficient Columnar File Format

Introduction

Parquet has quickly become one of the most popular file formats for storing large-scale analytics data. Parquet is now a top choice due to its efficiency, compression, and seamless integration with big data frameworks. My experience contributing to Apache DataFusion, a query engine that extensively uses Parquet, has deepened my understanding and appreciation of this format.

What is Parquet?

Parquet is an open-source, columnar storage file format optimized for large-scale data processing and analysis. Unlike traditional row-oriented formats like CSV or JSON, Parquet stores data column-wise, offering significant performance improvements for analytical queries.

Why Columnar Storage Matters

In row-oriented formats, accessing a single column requires scanning entire rows, including unnecessary data. Columnar storage like Parquet solves this by:

• Efficient Querying: Columns can be read independently, dramatically speeding up analytical queries.

• Better Compression: Columnar data tends to have similar values, making compression techniques like RLE and dictionary encoding highly effective.

• Reduced I/O: Less disk access as queries often target specific columns.

While contributing to DataFusion, I realized how crucial predicate pushdown and efficient column pruning are, especially for performance-critical queries.

Parquet File Structure

A Parquet file consists of:

• Row Groups: Logical partitions of data within a file, each containing column chunks.

• Column Chunks: Segments within row groups storing individual columns.

• Page Headers and Pages: Within column chunks, data is divided into pages containing actual values.

• Metadata: Contains schema information and statistics like min/max values that help query optimization.

Understanding Parquet’s metadata handling significantly improved my contributions to DataFusion’s query optimizer, particularly in filtering and skipping irrelevant data.

Advantages of Parquet

Performance

• Faster query execution.

• Supports predicate pushdown (skips irrelevant data based on query predicates), a critical aspect I optimized while working on DataFusion.

Compression Efficiency

• Built-in compression codecs like Snappy, GZIP, LZO, Brotli, and Zstd.

Schema Evolution

• Flexible schema management allows you to add or remove columns over time without breaking compatibility.

Integration

• Seamlessly integrates with frameworks like Apache Spark, Hadoop, AWS Athena, Apache Drill, Apache Impala, and Apache DataFusion.

Use Cases

Parquet is ideal for:

• Big Data analytics

• Data warehousing

• Machine learning pipelines

• Ad-hoc querying and BI tools

Conclusion

Parquet's columnar structure, efficient compression, and strong ecosystem support make it indispensable for modern data engineering. Contributing to Apache DataFusion has shown me firsthand the value of efficient column pruning, predicate pushdown, and metadata utilization, making Parquet an exceptional format for scalable and performant data workflows.

Happy querying!