What are the performance differences between Parquet and ORC formats?

What are the performance differences between Parquet and ORC formats?

Parquet and ORC are both columnar storage formats, but they have different performance characteristics. Parquet is optimized for read-heavy workloads, while ORC is often better for write-heavy operations. Would you like to know more about specific use cases?

Yes, can you explain when to use each format?

Sure! Use Parquet when you need efficient data compression and fast read performance, especially for analytics. ORC is preferable for scenarios where you have heavy write operations and need to optimize for storage efficiency.

Are there any benchmarks comparing their performance?

Yes, benchmarks typically show that Parquet performs better in read-heavy scenarios, while ORC can outperform in write-heavy situations. The actual performance can vary based on the specific data and query patterns.

What about compatibility with data processing frameworks?

Both formats are widely supported by data processing frameworks like Apache Spark and Hive. However, Parquet has broader compatibility with various tools in the Hadoop ecosystem.