Apache Parquet, What It Is and Why to Use It
Apache Parquet is a columnar storage file format designed for efficient data processing and analytics. It organizes data by column rather than row, enabling better compression and faster queries for analytical workloads. Parquet is particularly well-suited for time-series data and large-scale data processing systems.
How Parquet works
Parquet stores data in a column-oriented format, which means values from the same column are stored together physically. This differs from row-oriented formats like CSV where all fields of a record are stored together.
The columnar layout provides several key benefits:
- Better compression since similar data is stored together
- Efficient column pruning for queries that only need specific fields
- Optimized I/O when reading subset of columns
- Rich metadata and statistics for query optimization
Next generation time-series database
QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.
Key features and capabilities
Schema evolution
Parquet supports schema evolution, allowing you to:
- Add new columns
- Remove existing columns
- Modify nullable constraints
- Change field names
This makes it well-suited for time-series databases where schemas may evolve over time.
Predicate pushdown
The format includes statistics and metadata that enable:
- Min/max values per column chunk
- Null counts and distinct counts
- Bloom filters for membership testing
These statistics allow query engines to skip reading irrelevant data blocks.
Next generation time-series database
QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.
Performance benefits
Compression
Column-oriented storage allows Parquet to achieve high compression ratios:
- Similar values are stored together
- Dictionary encoding for repeated values
- Run-length encoding for sequences
- Support for various compression codecs (Snappy, Gzip, etc.)
Query performance
Parquet's design accelerates analytical queries through:
- Columnar storage for efficient column access
- Statistics for predicate pushdown
- Block-level parallelism
- Optimized I/O patterns
Integration with time-series workloads
Parquet works particularly well with time-series data:
- Efficient storage of repeated timestamps
- Good compression for sensor/metric values
- Support for nested event data
- Statistics helpful for time-range queries
The format enables high-performance batch ingestion and analytics on historical time-series data.