Compare
Parquet vs ORC
Parquet and ORC are columnar formats for large-scale analytical data. Choose according to the query engine, existing platform, and measured workload.
Which format should you choose?
Prefer the format best supported by the engines and tools already used, then test representative data and queries.
Good fit for Parquet
Store structured tables for analytics
- Large datasets read by selected columns
Good fit for ORC
Store large structured tables for analytics
- Queries reading selected columns and using indexes
Basic information
| Field | Parquet | ORC |
|---|---|---|
| Extension | .parquet | .orc |
| MIME type | application/vnd.apache.parquet | application/vnd.apache.orc |
| Common uses | Store structured tables for analytics | Store large structured tables for analytics |
| How to open | Use a Parquet-aware tool to inspect schema, column types, row counts, and values. | Use an ORC-aware tool to inspect schema, columns, row counts, compression, and sample values. |
| Export or convert | From CSV or other formats, check types, nulls, timestamps, encoding, partitions, and row counts. | When converting to Parquet or CSV, verify types, nulls, timestamps, decimals, partitions, and row counts. |
Choose by use case
Large datasets read by selected columns
Store structured tables for analytics
ParquetQueries reading selected columns and using indexes
Store large structured tables for analytics
ORCKey point 1
Both store values by column for analytical reads.
More on the differencesKey point 2
ORC includes type information and file-level indexes.
More on the differencesMore on the differences
Compare read performance, compression, schema handling, and interoperability with actual datasets. Keep the format that fits the production pipeline rather than choosing by extension alone.
- Parquet is widely used across analytical engines and data lake workflows.
- ORC provides columnar storage with indexes that can help selective reads.
- Engine support, table format, and query patterns affect results.
Frequently asked questions
Is one format always faster?
No. Performance depends on the engine, data shape, compression settings, and queries. Benchmark the intended workload.