Parquet (Columnar Storage Format) /vs/ ORC (Optimized Row Columnar)

ORC vs Parquet Comparison

1. Overview

Feature	ORC	Parquet
Developed By	Apache Hive (part of the Hadoop ecosystem)	Apache Parquet (part of the Apache ecosystem)
Storage Format	Columnar	Columnar
Compression	Highly compressed (e.g., ZLIB, Snappy)	Highly compressed (e.g., Snappy, GZIP)
Schema Evolution	Limited support	Limited support
ACID Transactions	Supported (via Hive)	Not supported
Use Case	Hadoop ecosystem, Hive	General-purpose, cross-platform

2. Key Differences

a. Compression

• ORC: Uses advanced compression techniques (e.g., ZLIB, Snappy). Typically achieves higher compression ratios compared to Parquet.
• Parquet: Also uses advanced compression techniques (e.g., Snappy, GZIP). Compression ratios are slightly lower than ORC but still highly efficient.

b. Performance

• ORC: Optimized for Hive and the Hadoop ecosystem. Faster for Hive queries due to native integration.
• Parquet: Optimized for general-purpose big data processing. Faster for Spark and other non-Hive tools.

c. Schema Evolution

• ORC: Limited support for schema evolution. Adding or renaming columns requires rewriting the entire dataset.
• Parquet: Also has limited support for schema evolution. Adding or renaming columns requires rewriting the entire dataset.

d. ACID Transactions

• ORC: Supports ACID transactions when used with Hive. Enables features like updates, deletes, and merges.
• Parquet: Does not support ACID transactions. Primarily used for append-only workloads.

e. Ecosystem Integration

• ORC: Tightly integrated with Hive and the Hadoop ecosystem. Less support in non-Hadoop tools.
• Parquet: Widely supported across multiple platforms (e.g., Spark, Presto, Hive). More versatile for cross-platform use cases.

3. Use Cases

ORC

• Hadoop Ecosystem: Ideal for Hive-based data warehouses.
• ACID Transactions: Use cases requiring updates, deletes, and merges (via Hive).
• High Compression: Scenarios where storage efficiency is critical.

Parquet

• General-Purpose Analytics: Ideal for big data processing with tools like Spark, Presto, and Hive.
• Cross-Platform Use: Use cases requiring compatibility across multiple platforms.
• Append-Only Workloads: Scenarios where data is primarily appended (e.g., log data).

4. Pros and Cons

ORC

Pros	Cons
High compression ratios	Limited support outside the Hadoop ecosystem
ACID transactions with Hive	Limited schema evolution capabilities
Optimized for Hive queries	Less versatile for cross-platform use

Parquet

Pros	Cons
Widely supported across platforms	No ACID transaction support
Efficient for general-purpose analytics	Limited schema evolution capabilities
Open-source and free	Slightly lower compression ratios than ORC

5. Summary

Feature	ORC	Parquet
Compression	Higher compression ratios	Slightly lower compression ratios
Performance	Optimized for Hive	Optimized for Spark and general-purpose
ACID Transactions	Supported (via Hive)	Not supported
Ecosystem	Hadoop ecosystem	Cross-platform
Use Case	Hive-based data warehouses	General-purpose analytics

6. Conclusion

• Use ORC if you're working in the Hadoop ecosystem, especially with Hive, and need ACID transactions.
• Use Parquet if you need a general-purpose, cross-platform columnar storage format for big data analytics.