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| <span class="sr-only">Apache ORC</span> |
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| <option value="">Navigate the docs…</option> |
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| <optgroup label="Overview"> |
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| <option value="/docs/index.html">Background</option> |
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| <option value="/docs/adopters.html">ORC Adopters</option> |
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| <option value="/docs/types.html">Types</option> |
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| <option value="/docs/indexes.html">Indexes</option> |
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| <option value="/docs/acid.html">ACID support</option> |
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| <optgroup label="Installing"> |
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| <option value="/docs/building.html">Building ORC</option> |
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| <optgroup label="Using in Spark"> |
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| <option value="/docs/spark-ddl.html">Spark DDL</option> |
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| <option value="/docs/spark-config.html">Spark Configuration</option> |
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| <optgroup label="Using in Python"> |
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| <option value="/docs/pyarrow.html">PyArrow</option> |
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| <option value="/docs/dask.html">Dask</option> |
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| <optgroup label="Using in Hive"> |
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| <option value="/docs/hive-ddl.html">Hive DDL</option> |
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| <option value="/docs/hive-config.html">Hive Configuration</option> |
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| </optgroup> |
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| <optgroup label="Using in MapReduce"> |
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| <option value="/docs/mapred.html">Using in MapRed</option> |
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| <option value="/docs/mapreduce.html">Using in MapReduce</option> |
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| </optgroup> |
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| <optgroup label="Using ORC Core"> |
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| <option value="/docs/core-java.html">Using Core Java</option> |
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| <option value="/docs/core-cpp.html">Using Core C++</option> |
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| <option value="/docs/core-java-config.html">ORC Java configuration</option> |
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| </optgroup> |
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| <optgroup label="Tools"> |
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| <option value="/docs/cpp-tools.html">C++ Tools</option> |
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| <option value="/docs/java-tools.html">Java Tools</option> |
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| </optgroup> |
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| </select> |
| </div> |
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| <div class="unit four-fifths"> |
| <article> |
| <h1>ORC Adopters</h1> |
| <p>If your company or tool uses ORC, please let us know so that we can update |
| this page.</p> |
| |
| <h3 id="apache-hadoop"><a href="https://hadoop.apache.org/">Apache Hadoop</a></h3> |
| |
| <p>ORC files have always supporting reading and writing from Hadoop’s MapReduce, |
| but with the ORC 1.1.0 release it is now easier than ever without pulling in |
| Hive’s exec jar and all of its dependencies. OrcStruct now also implements |
| WritableComparable and can be serialized through the MapReduce shuffle.</p> |
| |
| <h3 id="apache-spark"><a href="https://spark.apache.org/">Apache Spark</a></h3> |
| |
| <p>Apache Spark has <a href="https://databricks.com/blog/2015/07/16/joint-blog-post-bringing-orc-support-into-apache-spark.html">added |
| support</a> |
| for reading and writing ORC files with support for column project and |
| predicate push down.</p> |
| |
| <h3 id="apache-arrow"><a href="https://arrow.apache.org/">Apache Arrow</a></h3> |
| |
| <p>Apache Arrow supports reading and writing <a href="https://arrow.apache.org/docs/index.html?highlight=orc#apache-arrow">ORC file format</a>.</p> |
| |
| <h3 id="apache-flink"><a href="https://flink.apache.org/">Apache Flink</a></h3> |
| |
| <p>Apache Flink supports |
| <a href="https://nightlies.apache.org/flink/flink-docs-release-1.14/docs/connectors/table/formats/orc/">ORC format in Table API</a> |
| for reading and writing ORC files.</p> |
| |
| <h3 id="apache-iceberg"><a href="https://iceberg.apache.org/">Apache Iceberg</a></h3> |
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| <p>Apache Iceberg supports <a href="https://iceberg.apache.org/#spec/#orc">ORC spec</a> to use ORC tables.</p> |
| |
| <h3 id="apache-druid"><a href="https://druid.apache.org/">Apache Druid</a></h3> |
| |
| <p>Apache Druid supports |
| <a href="https://druid.apache.org/docs/0.22.1/development/extensions-core/orc.html#orc-extension">ORC extension</a> |
| to ingest and understand the Apache ORC data format.</p> |
| |
| <h3 id="apache-hive"><a href="https://hive.apache.org/">Apache Hive</a></h3> |
| |
| <p>Apache Hive was the original use case and home for ORC. ORC’s strong |
| type system, advanced compression, column projection, predicate push |
| down, and vectorization support make Hive <a href="https://hortonworks.com/blog/orcfile-in-hdp-2-better-compression-better-performance/">perform |
| better</a> |
| than any other format for your data.</p> |
| |
| <h3 id="apache-impala"><a href="https://impala.apache.org/">Apache Impala</a></h3> |
| |
| <p>Apache Impala supports <a href="https://impala.apache.org/docs/build/html/topics/impala_orc.html">reading from ORC format Hive tables</a> |
| by leveraging the ORC C++ library.</p> |
| |
| <h3 id="apache-gobblin"><a href="https://gobblin.apache.org/">Apache Gobblin</a></h3> |
| |
| <p>Apache Gobblin supports |
| <a href="https://gobblin.apache.org/docs/case-studies/Writing-ORC-Data/">writing data to ORC files</a> |
| by leveraging Apache Hive’s SerDe library.</p> |
| |
| <h3 id="apache-nifi"><a href="https://nifi.apache.org/">Apache Nifi</a></h3> |
| |
| <p>Apache Nifi is <a href="https://issues.apache.org/jira/browse/NIFI-1663">adding |
| support</a> for writing |
| ORC files.</p> |
| |
| <h3 id="apache-pig"><a href="https://pig.apache.org/">Apache Pig</a></h3> |
| |
| <p>Apache Pig added support for reading and writing ORC files in <a href="https://hortonworks.com/blog/announcing-apache-pig-0-14-0/">Pig |
| 14.0</a>.</p> |
| |
| <h3 id="eel"><a href="https://github.com/51zero/eel-sdk">EEL</a></h3> |
| |
| <p>EEL is a Scala BigData API that supports reading and writing data for |
| various file formats and storage systems including to and from ORC. It |
| is designed as a in-process low level API for manipulating data. Data |
| is lazily streamed from source to sink and using standard Scala |
| operations such as map, flatMap and filter, it is especially suited |
| for ETL style applications. EEL supports ORC predicate and projection |
| pushdowns and correct handles conversions from other formats including |
| complex types such as maps, lists or nested structs. A typical use |
| case would be to extract data from JDBC to ORC files housed in HDFS, |
| or directly into Hive tables backed by an ORC file format.</p> |
| |
| <h3 id="facebook"><a href="https://facebook.com">Facebook</a></h3> |
| |
| <p>With more than 300 PB of data, Facebook was an <a href="https://code.facebook.com/posts/229861827208629/scaling-the-facebook-data-warehouse-to-300-pb/">early adopter of |
| ORC</a> and quickly put it into production.</p> |
| |
| <h3 id="linkedin"><a href="https://linkedin.com">LinkedIn</a></h3> |
| |
| <p>LinkedIn uses |
| <a href="https://engineering.linkedin.com/blog/2021/fastingest-low-latency-gobblin">the ORC file format</a> |
| with Apache Iceberg metadata catalog and Apache Gobblin to provide our data customers with high-query performance.</p> |
| |
| <p>https://engineering.linkedin.com/blog/2021/fastingest-low-latency-gobblin</p> |
| |
| <h3 id="trino-formerly-presto-sql"><a href="https://trino.io/">Trino (formerly Presto SQL)</a></h3> |
| |
| <p>The Trino team has done a lot of work <a href="https://code.facebook.com/posts/370832626374903/even-faster-data-at-the-speed-of-presto-orc/">integrating |
| ORC</a> into their SQL engine.</p> |
| |
| <h3 id="timber"><a href="https://timber.io/">Timber</a></h3> |
| |
| <p>Timber adopted ORC for it’s S3 based logging platform that stores |
| petabytes of log data. ORC has been key in ensuring a fast, |
| cost-effective strategy for persisting and querying that data.</p> |
| |
| <h3 id="vertica"><a href="http://www8.hp.com/us/en/software-solutions/advanced-sql-big-data-analytics/">Vertica</a></h3> |
| |
| <p>HPE Vertica has contributed significantly to the ORC C++ library. ORC |
| is a significant part of Vertica SQL-on-Hadoop (VSQLoH) which brings |
| the performance, reliability and standards compliance of the Vertica |
| Analytic Database to the Hadoop ecosystem.</p> |
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| <div class="section-nav"> |
| <div class="left align-right"> |
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| <a href="/docs/index.html" class="prev">Back</a> |
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| <div class="right align-left"> |
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| <a href="/docs/types.html" class="next">Next</a> |
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| </div> |
| <div class="clear"></div> |
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| </article> |
| </div> |
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| <div class="unit one-fifth hide-on-mobiles"> |
| <aside> |
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| <h4>Overview</h4> |
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| <li class=""><a href="/docs/index.html">Background</a></li> |
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| <li class="current"><a href="/docs/adopters.html">ORC Adopters</a></li> |
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| <li class=""><a href="/docs/types.html">Types</a></li> |
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| <li class=""><a href="/docs/indexes.html">Indexes</a></li> |
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| <li class=""><a href="/docs/acid.html">ACID support</a></li> |
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| <h4>Installing</h4> |
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| <li class=""><a href="/docs/building.html">Building ORC</a></li> |
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| <h4>Using in Spark</h4> |
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| <ul> |
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| <li class=""><a href="/docs/spark-ddl.html">Spark DDL</a></li> |
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| <li class=""><a href="/docs/spark-config.html">Spark Configuration</a></li> |
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| </ul> |
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| <h4>Using in Python</h4> |
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| <ul> |
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| <li class=""><a href="/docs/pyarrow.html">PyArrow</a></li> |
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| <li class=""><a href="/docs/dask.html">Dask</a></li> |
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| </ul> |
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| <h4>Using in Hive</h4> |
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| <ul> |
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| <li class=""><a href="/docs/hive-ddl.html">Hive DDL</a></li> |
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| <li class=""><a href="/docs/hive-config.html">Hive Configuration</a></li> |
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| </ul> |
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| <h4>Using in MapReduce</h4> |
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| <ul> |
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| <li class=""><a href="/docs/mapred.html">Using in MapRed</a></li> |
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| <li class=""><a href="/docs/mapreduce.html">Using in MapReduce</a></li> |
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| </ul> |
| |
| |
| <h4>Using ORC Core</h4> |
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| <ul> |
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| <li class=""><a href="/docs/core-java.html">Using Core Java</a></li> |
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| <li class=""><a href="/docs/core-cpp.html">Using Core C++</a></li> |
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| <li class=""><a href="/docs/core-java-config.html">ORC Java configuration</a></li> |
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| </ul> |
| |
| |
| <h4>Tools</h4> |
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| <ul> |
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| <li class=""><a href="/docs/cpp-tools.html">C++ Tools</a></li> |
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| <li class=""><a href="/docs/java-tools.html">Java Tools</a></li> |
| |
| |
| |
| </ul> |
| |
| |
| </aside> |
| </div> |
| |
| |
| <div class="clear"></div> |
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