Data Engineering

ORC vs RC vs Parquet vs Avro: A Comprehensive Comparison

By Admin

When it comes to big data processing, selecting the right file format is crucial. The file format chosen affects the performance, storage, and processing of data. Here we compare ORC, RC, Parquet, and Avro.

ORC (Optimized Row Columnar)

ORC is a columnar file format used in Hive. It has excellent compression capabilities and is ideal for storing large data sets. It supports predicate pushdown and lightweight indexing.

Advantages

  • Good compression rates, smaller file sizes
  • Support for predicate pushdown and lightweight indexing
  • Suitable for large data sets

Disadvantages

  • Limited support for non-Hadoop environments
  • Not as widely supported as Parquet

Use cases: Storing large amounts of data with high compression rates and frequent querying.

RC (Record Columnar)

RC is a row-based file format commonly used in Hadoop. It has a simple structure supporting fast read and write operations.

Advantages

  • Fast read and write operations
  • Good compression capabilities
  • Support for large data sets

Disadvantages

  • Not as efficient for analytical processing as columnar formats
  • Limited support for nested data structures

Use cases: Fast read/write operations and large data sets (e.g., data lakes).

Parquet

Parquet is a columnar file format designed for efficient analytical processing and supports nested data structures. It has excellent compression capabilities.

Advantages

  • Highly efficient for analytical processing
  • Supports nested data structures
  • Good compression capabilities

Disadvantages

  • Slower read/write operations compared to row-based formats
  • Limited support for non-Hadoop environments

Use cases: Efficient analytical processing and nested data structures (e.g., machine learning).

Avro

Avro is a row-based file format designed to support schema evolution. Data can be added or removed without breaking compatibility.

Advantages

  • Supports schema evolution
  • Supports a variety of data types
  • Easy to read and write

Disadvantages

  • Slower read/write operations compared to columnar formats
  • Limited support for nested data structures

Use cases: Schema evolution and data integration.

Conclusion

ORC and Parquet provide optimal performance and compression for distributed systems like Hadoop and Spark. RC and Avro are more versatile. Choose ORC/Parquet for large volume processing, and Avro for versatility and schema evolution.