Compare
Arrow IPC vs Parquet
Both Arrow IPC and Parquet handle columnar data, but Arrow IPC is mainly for fast exchange between programs, while Parquet is designed for storing and retrieving analytical data.
Which format should you choose?
Choose Arrow IPC to move data between compatible languages and analytics engines. Consider Parquet for data lakes and workloads that read selected columns or row groups efficiently.
Good fit for Apache Arrow IPC
Exchanging columnar table data between compatible programs and languages · Passing analytical data sets between Arrow-aware tools
- Moving typed data between Arrow libraries and analytical engines
- Random access to record batches in the IPC file format
Good fit for Parquet
Store structured tables for analytics
- Large datasets read by selected columns
Basic information
| Field | Apache Arrow IPC | Parquet |
|---|---|---|
| Extension | .arrow, .feather, .arrows | .parquet |
| MIME type | application/vnd.apache.arrow.file, application/vnd.apache.arrow.stream | application/vnd.apache.parquet |
| Common uses | Exchanging columnar table data between compatible programs and languages · Passing analytical data sets between Arrow-aware tools | Store structured tables for analytics |
| How to open | Read it with an Apache Arrow library or an analytics tool that supports Arrow IPC. The .arrow extension commonly indicates the file format and .arrows the stream format, but verify what the receiving tool expects and which compression codecs it supports. For Feather files, check compatibility with the older Feather V1 format. | Use a Parquet-aware tool to inspect schema, column types, row counts, and values. |
| Export or convert | Parquet may suit analytical storage and delivery; CSV may suit simple table exchange. Conversion can change or discard Arrow-specific types, nested structures, dictionary encoding, or compression settings. Check required types and precision, and retain the source data. | From CSV or other formats, check types, nulls, timestamps, encoding, partitions, and row counts. |
Choose by use case
Moving typed data between Arrow libraries and analytical engines
Exchanging columnar table data between compatible programs and languages · Passing analytical data sets between Arrow-aware tools
Apache Arrow IPCRandom access to record batches in the IPC file format
Exchanging columnar table data between compatible programs and languages · Passing analytical data sets between Arrow-aware tools
Apache Arrow IPCLarge datasets read by selected columns
Store structured tables for analytics
ParquetKey point 1
Arrow IPC serializes Arrow types and record batches as a file or stream; its file format supports random access through footer metadata.
More on the differencesKey point 2
Parquet organizes stored data into row groups, column chunks, and pages, with encoding and compression for storage and retrieval efficiency.
More on the differencesMore on the differences
Use Arrow IPC when an application-to-application transfer benefits from Arrow's type system and interchange libraries. Use Parquet for durable analytical datasets where readers can select columns and row groups. Test the actual readers and writers for required types and precision before standardizing a workflow.
- Arrow IPC is often an in-memory interchange companion serialized for transport; Parquet is a persistent columnar file format.
- The Arrow IPC file footer indexes record batches, while Parquet metadata indexes row groups and column chunks.
- Both formats can compress data, but they offer different options and readers may support different features. Validate interoperability with the tools in your pipeline.
Frequently asked questions
Can Parquet replace Arrow IPC?
Sometimes, but they solve different tasks. Parquet can be read into Arrow tables by compatible libraries, while Arrow IPC is useful for direct exchange of Arrow data. Choose according to storage, transfer, and reader requirements.