Apache ORC

{{Short description|Column-oriented data storage format}}

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{{notability|Products|date=February 2019}}

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{{Infobox software

| name = Apache ORC

| logo = Apache Orc logo.svg

| screenshot =

| caption = Apache ORC

| developer =

| released = {{Start date and age|2013|02|20|df=yes}}

| latest release version = 2.1.2

| latest release date = {{Start date and age|2025|05|06|df=yes}}{{cite web|title=Apache ORC - Releases|url=https://orc.apache.org/news/releases/|access-date=15 May 2025}}

| latest preview version =

| latest preview date =

| operating system = Cross-platform

| repo = {{URL|https://github.com/apache/orc|ORC Repository}}

| programming language =

| genre = Database management system

| license = Apache License 2.0

| website = {{URL|https://orc.apache.org/}}

}}

Apache ORC (Optimized Row Columnar) is a free and open-source column-oriented data storage format.{{cite conference |author= Yin Huai, Siyuan Ma, Rubao Lee, Owen O'Malley, and Xiaodong Zhang| title="Understanding Insights into the Basic Structure and Essential Issues of Table Placement Methods in Clusters " |conference= VLDB' 39|pages=1750–1761 | year=2013 | doi=10.14778/2556549.2556559 |citeseerx=10.1.1.406.4342 }} It is similar to the other columnar-storage file formats available in the Hadoop ecosystem such as RCFile and Parquet. It is used by most of the data processing frameworks Apache Spark, Apache Hive, Apache Flink, and Apache Hadoop.

In February 2013, the Optimized Row Columnar (ORC) file format was announced by Hortonworks in collaboration with Facebook.{{cite news |title= The Stinger Initiative: Making Apache Hive 100 Times Faster |work= Hortonworks blog |date= February 20, 2013 |author= Alan Gates |url= https://hortonworks.com/blog/100x-faster-hive/ |archive-url= https://web.archive.org/web/20130328050712/http://hortonworks.com/blog/100x-faster-hive |archive-date= March 28, 2013 |url-status= dead }}

A month later, the Apache Parquet format was announced, developed by Cloudera and Twitter.{{cite web |title= Introducing Parquet: Efficient Columnar Storage for Apache Hadoop |date= March 13, 2013 |author= Justin Kestelyn |work= Cloudera blog |url= http://blog.cloudera.com/blog/2013/03/introducing-parquet-columnar-storage-for-apache-hadoop/ |access-date= May 4, 2017 |archive-url= https://web.archive.org/web/20160919221247/http://blog.cloudera.com/blog/2013/03/introducing-parquet-columnar-storage-for-apache-hadoop/ |archive-date= September 19, 2016 |url-status= dead }}

Apache ORC format is widely supported including Amazon Web Services' Glue{{cite web|url=https://docs.aws.amazon.com/glue/latest/dg/aws-glue-programming-etl-format-orc-home.html|title=Using the ORC format in AWS Glue|work=docs.aws.amazon.com|access-date=August 21, 2024}},Google Cloud Platform's BigQuery{{cite web|url=https://cloud.google.com/bigquery/docs/samples/bigquery-load-table-gcs-orc|title=Load an ORC file|work=cloud.google.com/bigquery/docs|access-date=May 15, 2025}}, and Pandas (software).{{cite web|url=https://pandas.pydata.org/docs/reference/api/pandas.read_orc.html|title=pandas.read_orc|work=pandas.pydata.org|access-date=May 15, 2025}}