What is Apache Parquet?
Apache Parquet is a columnar storage file format optimized for analytics workloads in big data processing. Developed as part of the Apache Hadoop ecosystem, Parquet provides efficient data compression and encoding schemes that significantly improve query performance and reduce storage costs for analytical applications.
Unlike row-based formats that store data record by record, Parquet organizes data by column, enabling better compression ratios, column pruning, and predicate pushdown optimizations. It's widely adopted by modern data processing engines like Apache Spark, Presto, and Amazon Athena.
Parquet Performance Calculator
I/O Reduction: 50%
Predicate Pushdown: 85% effective
Columns Scanned: 25/50
Parquet File Structure
File Header
Contains magic number and file metadata.
• Version information
• Schema definition
• Column statistics
Row Groups
Horizontal partitions containing column chunks.
• Column chunks per column
• Statistics and metadata
• Parallel processing unit
Column Chunks
Data for a specific column within a row group.
• Dictionary pages
• Column metadata
• Encoding information
Footer
Contains file metadata and schema information.
• Schema definition
• Column statistics
• Key-value metadata
Real-World Parquet Implementations
Netflix
Uses Parquet for storing and analyzing petabytes of viewing data and content metadata.
- • 100+ TB of new Parquet data daily
- • 70% storage cost reduction vs JSON
- • 10x faster analytical queries
- • Integration with Spark and Presto
Uber
Leverages Parquet in their data lake for trip data, driver analytics, and ML features.
- • Exabytes of Parquet data
- • Real-time and batch analytics
- • ML feature store integration
- • Cost-optimized storage on S3
Airbnb
Powers their data warehouse and analytics platform with Parquet for all structured data.
- • Complete migration from Avro
- • 60% reduction in storage costs
- • Faster query performance on Presto
- • Schema evolution support
Uses Parquet for tweet analytics, user behavior analysis, and ad targeting data.
- • Billions of tweets in Parquet format
- • Real-time streaming to Parquet
- • Integration with Hadoop ecosystem
- • Efficient analytical workloads
Compression & Encoding Strategies
Dictionary Encoding
Replaces repeated values with integer references to a dictionary, ideal for categorical data.
Original: ["US", "CA", "US", "UK", "US", "CA"]
Dictionary: {0: "US", 1: "CA", 2: "UK"}
Encoded: [0, 1, 0, 2, 0, 1]
# Compression: 50%+ for categorical dataRun Length Encoding
Compresses sequences of identical values by storing the value and count.
Original: [1, 1, 1, 1, 2, 2, 3, 3, 3, 3, 3]
RLE: [(1, 4), (2, 2), (3, 5)]
# Ideal for sorted or grouped dataDelta Encoding
Stores differences between consecutive values, effective for timestamps and numeric sequences.
Original: [1000, 1001, 1005, 1008, 1010]
Delta: [1000, 1, 4, 3, 2]
# Reduces value ranges for better compressionParquet Best Practices
✅ Do
- • Use Snappy compression for balanced performance
- • Partition data by frequently filtered columns
- • Optimize row group size (128MB-1GB)
- • Sort data by commonly filtered columns
- • Use appropriate data types (avoid strings for IDs)
- • Enable predicate pushdown in query engines
❌ Don't
- • Create too many small files (< 1MB)
- • Use Parquet for frequent updates/deletes
- • Ignore schema evolution considerations
- • Over-partition your data
- • Use overly complex nested schemas
- • Forget to tune row group size for your workload