For large-scale data benchmarks, start with Parquet for compressed analytical files, test ORC when selective scans or a Hadoop-oriented stack matter, and include Arrow IPC/Feather when data stays in Arrow’s in-memory representation. Keep CSV as a baseline if easy inspection, broad interoperability, or incremental text streaming is important. There is no universal winner: the format that performs best depends on the engine, data, and queries being benchmarked.
Which format should you benchmark?
| Format | Best fit | Main trade-off |
|---|---|---|
| Parquet | Compressed, columnar files for analytical storage and scans | Typically smaller files than Arrow IPC, but data must be decoded when read |
| ORC | Hadoop-oriented workloads and selective scans that can use indexes or predicate pushdown | Support and results depend on the execution stack and data layout |
| Arrow IPC / Feather V2 | On-disk interchange or processing where Arrow-aware consumers benefit from memory mapping and reduced deserialization | Files can be larger than Parquet, making storage and network transfer more expensive |
| CSV | Human inspection, interoperability, and sequential text streaming | Text must be scanned and types inferred, adding parsing work and potential ambiguity |
Apache Arrow’s documentation distinguishes Parquet’s storage-oriented role from Arrow IPC’s closer match to Arrow’s in-memory layout. As the Arrow FAQ puts it, “Therefore, Arrow and Parquet complement each other and are commonly used together in applications.” Apache Arrow FAQ.
Parquet: a practical on-disk starting point
Parquet is a columnar format designed for compressed storage. It is a sensible first candidate when the benchmark concerns analytical data stored on disk, especially when reducing bytes stored or read matters. Its compactness is not free: a reader must decode the stored representation. The balance between storage savings and read cost should be measured in the engine that will use the data.
ORC: test it for selective scans
ORC is a self-describing, type-aware columnar format designed for Hadoop workloads. It supports indexes and predicate pushdown, which can let readers skip stripes or narrow a search to row ranges. The cited ORC documentation describes a default stripe size of roughly 64 MB; actual configurations and behavior can vary. ORC merits a benchmark when the engine can exploit those features and the workload filters or selects a subset of data.
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Arrow IPC and Feather: favor Arrow-aware pipelines
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Arrow streams: incremental batches
Arrow streams are distinct from Arrow IPC files: the schema comes before record batches, so a receiver can process batches as they arrive. That can suit incremental transfer and processing. CSV is also naturally consumed sequentially, but it is text and typically requires parsing and type inference.
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CSV: retain it when its simplicity is part of the requirement
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What published benchmark results do—and do not—show
Storage totals vary by data and encoding
A 2024 Microsoft Research paper, A Deep Dive into Common Open Formats for Analytical DBMSs, reports totals for selected real-world column data: 489.7 GB raw CSV, 64.7 GB Parquet, 133.9 GB ORC, 522.5 GB Arrow with default settings, and 237.4 GB Arrow with dictionary encoding. In those selected data, Parquet totaled about 13% of raw CSV size and ORC about 27%; Arrow’s default total exceeded CSV, while dictionary encoding reduced it. These are dataset-specific totals, not general compression ratios. The paper separates integer, float, and string columns and finds that results vary by dataset and encoding; even integer compression outcomes between ORC and Parquet vary with distinct-value distributions. Microsoft Research paper.
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Query results depend on the experiment
A broader study by Chunwei Liu, Anna Pavlenko, Matteo Interlandi, and Brandon Haynes, published in The VLDB Journal in November 2024, evaluates Arrow, Parquet, and ORC using TPC-DS scale 10, the Join Order Benchmark, the Public BI Benchmark, and real-world GIS, machine-learning, financial, RAG, and embedding datasets. Tested versions included Arrow 5.0.0, ORC 1.7.2, Parquet Java API 1.9.0, and PyArrow 17.0.0. The authors report that format trade-offs differ and that none is optimal for certain popular machine-learning tasks. In one query comparison, ORC outperformed both Parquet and Arrow Feather; compressed Arrow Feather was 3–4 times worse than Parquet, and uncompressed Feather was more than 7 times worse. That result belongs to that experiment, not to every ORC, Parquet, or Arrow workload. The VLDB Journal study.
Together, these studies argue for reproducing the intended workload rather than adopting a format based on one published ranking. Dataset composition, encoding, engine versions, query patterns, and cache state can change the outcome.
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How to design a fair format benchmark
- Define the real workload. Use the queries and ingest path people actually run. Include writes as well as reads if data must be generated or updated, and avoid using only a full-file scan to represent every use case.
- Test projection and filtering. Measure queries that select only some columns and filter rows. Columnar storage and predicate pushdown may avoid reading irrelevant data, but the engine, file layout, and implementation determine whether they do. Apache Arrow Dataset’s C++ API documents predicate pushdown, projection, and optional parallel reading. Arrow Dataset documentation.
- Record bytes and time together. Capture file size and bytes read alongside elapsed time. Results depend on column types, repeated values, encoding, and compression codec; a faster result that reads substantially more data may have different operational costs.
- Separate cold-cache and warm-cache runs. The 2024 comparative study reports cold-cache results by default and warmed results for selected experiments. State cache conditions so readers can interpret whether a result reflects storage access or reuse from memory.
- Measure conversion and memory costs. Include time and memory for turning file data into the engine’s working representation. Arrow IPC can reduce decode and copy work when the pipeline remains Arrow-based, while Parquet may save storage; timing only the file read can hide downstream conversion costs.
- Measure startup latency and streaming behavior. CSV and Arrow streams can be consumed incrementally. Parquet and ORC require footer metadata before normal processing can begin. For short-lived tasks or live streams, time to first usable rows may matter as much as total throughput.
- Control file and partition layout. Too many tiny files or partitions add listing, filesystem, and metadata overhead; larger files can affect parallelism and pruning. For its Dataset workflows, Arrow’s documentation gives general guidance to avoid files below 20 MB or above 2 GB and layouts with more than 10,000 distinct partitions. Treat those as Arrow documentation guidance, not universal limits for every storage system.
- Publish enough detail to reproduce the result. Report engine and library versions, schema and data types, compression settings, row-group or stripe sizes, partition and file layout, query mix, cache state, and hardware.
Check support in the exact library and engine
Support is not identical across APIs. Apache Arrow Dataset documentation lists Parquet, Feather/Arrow IPC, CSV, and ORC among formats supported by its C++ Dataset API, but says ORC can currently be read and not written through that API. Do not generalize this limitation to every Arrow binding or to other libraries. Confirm that the specific reader and writer in your benchmark support the formats and features you intend to test.
A workload-based decision
- Choose Parquet as the first on-disk candidate for compressed analytical storage, then compare it with the alternatives using the actual query mix.
- Add ORC when the execution stack supports it, particularly if selective scans and its indexing or predicate-pushdown features could help.
- Add Arrow IPC/Feather when the benchmark concerns in-memory processing or interchange among Arrow-aware systems, and include conversion, memory, and file-size effects.
- Keep CSV when inspection, interoperability, or incremental text processing is a real requirement, rather than treating it only as an outdated baseline.
Without a specified engine, workload, and hardware, the evidence does not support declaring one format universally fastest or recommending particular hardware.
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