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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchThere is no format that wins every large-scale data benchmark. Start with Parquet for compressed on-disk analytics, test ORC when selective scans or a Hadoop-oriented stack matter, and include Arrow IPC/Feather when the workload benefits from processing data in Arrow’s in-memory layout. Keep CSV as a baseline for interoperability, inspection, or sequential streaming. The useful winner is the format that performs best on the queries, software, and hardware your benchmark actually uses.
Which format should you test first?
For a general analytical benchmark, use Parquet as the on-disk starting point, then add ORC and Arrow IPC/Feather if their strengths match the workload. Retain CSV when readers need a straightforward text representation or a sequential stream. A benchmark that compares only whole-file read time can miss the reasons these formats differ: projection, filtering, compression, conversion, and startup behavior all affect results.
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| Format | Best fit to evaluate | Trade-off to account for |
|---|---|---|
| Parquet | Compressed columnar files for analytical storage and scans. | Often smaller than Arrow IPC, but data must be decoded for reading. |
| ORC | Hadoop-oriented workloads and selective scans that can use indexes or predicate pushdown. | Benefits depend on reader support, data layout, and query; do not assume it beats Parquet universally. |
| Arrow IPC / Feather V2 | Arrow-aware in-memory processing or interchange where avoiding deserialization and extra copies matters. | Files may be larger than Parquet, so storage and network costs can offset faster access. |
| CSV | Interoperability, inspection, and sequential streaming. | Text must be scanned and types inferred, adding parsing work and potential ambiguity. |
Apache Arrow describes Parquet and Arrow as complementary: “Therefore, Arrow and Parquet complement each other and are commonly used together in applications.” Parquet can be the durable, compact file format while Arrow is used for in-memory processing.
How the formats behave in a benchmark
Parquet: a practical storage baseline
Parquet is columnar and compressed, making it a strong first candidate when the benchmark stores analytical data on disk and reads selected columns. Its compact files do not make reads free: the reader decodes the stored representation, and the cost depends on the data, codecs, and query.
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ORC: test it for selective scans
ORC is self-describing and type-aware, and supports indexes and predicate pushdown that can help readers skip stripes or narrow work to row ranges. The ORC documentation describes a default stripe size of roughly 64 MB. This is a format capability, not a guarantee of faster queries: the engine, predicates, and file organization determine whether the reader can exploit it.
Arrow IPC and Feather V2: reduce conversion work
Arrow IPC stores Arrow’s columnar in-memory layout on disk and can be memory-mapped. If the consuming system works natively with Arrow, this can avoid deserialization and extra copies. The trade-off is file size: Arrow IPC may take more space than Parquet. Feather V2 is the Arrow IPC file format under a retained name and API.
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Arrow streams and CSV: incremental processing
An Arrow stream places the schema before record batches, so a receiver can process batches as they arrive. CSV can also be consumed sequentially, and remains convenient to inspect and exchange. Unlike typed, self-describing formats, CSV requires parsing text and inferring or separately specifying types.
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Microsoft Research’s 2024 paper, A Deep Dive into Common Open Formats for Analytical DBMSs, reports selected real-world column data totals of 489.7 GB in raw CSV, 64.7 GB in Parquet, and 133.9 GB in ORC. In those selected data, Parquet occupied about 13% of the raw CSV total and ORC about 27%. The paper also reports 522.5 GB for Arrow with default settings and 237.4 GB for Arrow with dictionary encoding. These are totals for the paper’s selected data, not general compression ratios; results varied by dataset, column type, and encoding, including integer columns with different distinct-value distributions.
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A broader study by Chunwei Liu, Anna Pavlenko, Matteo Interlandi, and Brandon Haynes in The VLDB Journal (November 2024) evaluated 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. It tested Arrow 5.0.0, ORC 1.7.2, Parquet Java API 1.9.0, and PyArrow 17.0.0. The authors find different trade-offs among formats and report that none is optimal for certain popular machine-learning tasks.
One query comparison in that study found ORC outperforming Parquet and Arrow Feather; in that experiment, compressed Arrow Feather was 3–4 times slower than Parquet, and uncompressed Feather was more than 7 times slower. That result belongs to its specific query and test setup, not to every ORC workload. A useful benchmark should reproduce the workload of interest rather than promote a single published result into a universal ranking.
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Build a benchmark that reflects real use
- Fix the schema and workload. Use the same data, column types, query mix, and output requirements for every format. Include ingest and writes if they matter; do not test only full-file reads when users run filtered queries.
- Measure both full reads and selective work. Include queries that read a subset of columns and filter rows. Columnar storage and predicate pushdown can avoid irrelevant data, but whether they do depends on reader implementation and layout.
- Record size and bytes read alongside time. Report file size, bytes scanned, elapsed time, and throughput. Compression and read performance change with data types, repetition, encodings, and codecs.
- Separate cold-cache and warm-cache runs. State cache conditions for every result. The 2024 comparative study used cold-cache results by default and warmed-cache results for selected experiments, illustrating why the condition matters.
- Include conversion and memory costs. Measure the time and memory to get data into the engine’s working representation, not just the format reader’s file-read time. Arrow IPC may reduce conversion when Arrow is already the target representation; Parquet may reduce storage.
- Test startup and streaming behavior. Record time to first useful batch as well as total completion time if consumers process incrementally. CSV and Arrow streams can be consumed as data arrives; Parquet and ORC need footer metadata before normal processing can begin.
- Hold layout choices accountable. Document row-group or stripe sizes, compression settings, partitioning, and file counts. In Arrow Dataset workflows, the documentation gives general guidance to avoid files below 20 MB or above 2 GB and layouts with more than 10,000 distinct partitions; these are not universal rules for every engine or filesystem.
- Publish the execution context. Include hardware, engine and library versions, schema, data types, query mix, cache state, compression, and layout so readers can judge whether the comparison applies to their own stack.
Check reader and library support before choosing
Apache Arrow’s C++ Dataset API lists Parquet, Feather/Arrow IPC, CSV, and ORC among its supported formats. In that API, ORC can currently be read but not written; that restriction is specific to the documented C++ API, not a claim about every Arrow binding or library. The API also supports projection, predicate pushdown, and optional parallel reading. Confirm the features available in the exact engine and version used for the benchmark instead of assuming a format’s capabilities are exposed everywhere.
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- To get set up, connect the portable hard drive to a computer for automatic recognition software required
- This USB drive provides plug and play simplicity with the included 18 inch USB 3.0 cable
- The available storage capacity may vary.
A straightforward shortlist
- Storage-conscious analytical scans: begin with Parquet and compare its read and write costs with the actual query mix.
- Selective scans or Hadoop-centric execution: include ORC and verify that your reader uses its pruning features.
- Arrow-native in-memory work or interchange: include Arrow IPC/Feather and measure memory, conversion, and file-size effects.
- Text interoperability or sequential consumption: keep CSV as a meaningful baseline, especially where inspection or incremental text processing is part of the use case.
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