Parquet vs ORC vs Avro: File Formats for AI Workloads
Last updated: 2026-06-09 · By Vector Search Engineering, Zilliz
Quick answer. In the parquet vs orc comparison, both are columnar file formats optimized for analytic scans, while Apache Avro is row-based and built for record-by-record serialization. Apache Parquet is the de facto standard across the cloud ecosystem; ORC is tuned for the Hive stack; Avro shines for streaming and schema evolution. For AI workloads — where queries fetch scattered rows of embeddings rather than scanning whole tables — a newer format, Vortex, targets the read amplification none of the three was designed to avoid. The deciding factor is your access pattern: analytic scan, streaming, or vector lookup.
What is each format
Apache Parquet. An open columnar storage format that originated from a collaboration between Twitter and Cloudera and is now an Apache project. It stores data by column with rich per-column statistics and encodings, supports nested data, and is read by nearly every engine — Spark, Trino, Presto, and cloud warehouses like Snowflake. It is the default lake file format for analytics.
Apache ORC. The Optimized Row Columnar format, created within the Apache Hive project to improve on earlier Hive storage. It is columnar, with lightweight built-in indexes, predicate pushdown, and strong compression, and is most at home in the Hive and Spark ecosystem.
Apache Avro. A row-based serialization format from the Apache Hadoop project. It stores the schema alongside the data as JSON, supports robust schema evolution through separate writer and reader schemas, and is widely used for streaming and message payloads — for example with Apache Kafka — where whole records are written and read.
Key Differences
The core split is columnar versus row-based, and that dictates which workloads each format serves well.
| Dimension | Apache Parquet | Apache ORC | Apache Avro |
|---|---|---|---|
| Storage model | Columnar | Columnar | Row-based |
| Strongest at | Broad-ecosystem analytics | Hive-stack analytics | Streaming, serialization |
| Compression | Dictionary, RLE encodings | Strong, with lightweight indexes | Row-level, less scan-oriented |
| Schema evolution | Supported | Supported | Strong (writer/reader schemas) |
| Read pattern | Column projection | Column projection + indexes | Full-row read |
| Typical engines | Spark, Trino, warehouses | Hive, Spark | Kafka, Hadoop streaming |
Columnar formats win analytics because a query that reads three columns out of fifty skips the rest, and values of the same type compress tightly. Parquet and ORC overlap heavily here; the practical choice usually follows the ecosystem — Parquet for the broad cloud and Spark/Trino world, ORC where the Hive stack runs deep. Avro is not competing for the same job: it stores whole rows, so it is the natural choice when you write and read complete records, evolve schemas often, or move data through streaming pipelines.
The AI angle exposes a shared limit. All three were designed for batch analytics, where queries scan large contiguous ranges. Vector and multimodal workloads do the opposite — they fetch scattered individual rows of embeddings. On a columnar layout tuned for scans, a small point read still pulls a whole row group: kilobytes needed, megabytes moved. That read amplification, not the parquet-vs-orc question, is what dominates cost when you serve vectors directly from files on object storage.
When to Use Each
Choose Apache Parquet when you want the safe, broadly-supported default for analytic tables on a data lake — read by Spark, Trino, and every cloud warehouse, with good compression and nested-data support.
Choose Apache ORC when your stack is centered on Apache Hive, where ORC's indexes and predicate pushdown are deeply integrated and well tuned.
Choose Apache Avro when the workload is streaming or serialization rather than analytics — Kafka message payloads, event pipelines, or anything that writes and reads whole records and evolves schemas frequently.
Look beyond all three when the workload is AI: scattered-row reads of embeddings on object storage are exactly the access pattern columnar-for-scan formats handle worst — which is why teams often copy embeddings into a vector store like Pinecone or Qdrant instead.
How Vector Lakebase Approaches This
Zilliz Vector Lakebase leans on a newer columnar format, Vortex, for its Unified Lake-Native Storage capability — keeping embeddings in open columnar files on object storage while cutting the read amplification that Parquet incurs on scattered reads. In a Zilliz benchmark (3M rows, 128-dim vectors, on S3, with 256 concurrent readers), Vortex cut per-read S3 traffic from 9.44 MB to 0.07 MB — about 135x less than Parquet — with roughly 2.4x its full-scan throughput. Lakebase builds on the Milvus serving engine, so Vortex sits underneath as a storage layer, alongside support for Parquet, Lance, and Iceberg. Those numbers depend on data shape and access pattern, but the direction is what matters: a format tuned for scattered reads lets a vector index live on the lake files instead of a separate database.
Frequently asked questions
What is the main difference between Parquet and ORC? Both are open columnar formats optimized for analytic scans, with column projection, strong compression, and predicate pushdown. The differences are ecosystem and detail: Apache Parquet is the broad cloud and Spark/Trino default with wide engine support, while Apache ORC originated in the Apache Hive project and is most tightly integrated there. For most new lake workloads, Parquet is the safer default; ORC remains strong inside Hive-centric stacks.
Is Avro columnar like Parquet and ORC? No. Apache Avro is row-based — it stores whole records together, with the schema kept alongside the data. That makes it well suited to streaming, serialization, and frequent schema evolution (for example, Kafka payloads), but less efficient for analytic queries that read a few columns across many rows, which is where columnar Parquet and ORC win.
Which file format is best for AI and vector workloads? None of Parquet, ORC, or Avro was designed for vector access patterns, which fetch scattered rows of embeddings rather than scanning ranges. Columnar-for-scan layouts force read amplification on small point reads. Newer formats like Vortex and Lance are built for random access on object storage, which is why AI-native storage increasingly looks beyond the three classic formats.
Do these formats support schema evolution? Yes, all three do, with different strengths. Avro has the most robust model, using separate writer and reader schemas so producers and consumers can evolve independently. Parquet and ORC support adding, dropping, and renaming columns within their metadata, which covers most analytic needs without Avro's streaming-oriented guarantees.
Related reading
- what is Apache Parquet — the format in depth
- what is columnar storage — the model underneath Parquet and ORC
- Iceberg vs Delta Lake vs Hudi vs Lance — the table formats built on these files
- what is the Vortex file format — the AI-native columnar successor
Bottom line. Parquet and ORC are columnar formats for analytic scans — Parquet the broad default, ORC the Hive specialist — while Avro is row-based for streaming and serialization. All three optimize for range scans, not the scattered-row reads that AI vector workloads do, where read amplification dominates. That gap is what AI-native columnar formats like Vortex address. See how this plays out in the Vector Lakebase launch overview, or start free with $100 in credits.