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| |
| <h1 id="use-cases">Use Cases</h1> |
| |
| <p>Here are some example applications of the Apache Arrow format and libraries. |
| For more, see our <a href="/blog/">blog</a> and the list of projects |
| <a href="/powered_by/">powered by Arrow</a>.</p> |
| |
| <h2 id="readingwriting-columnar-storage-formats">Reading/writing columnar storage formats</h2> |
| |
| <p>Many Arrow libraries provide convenient methods for reading and writing |
| columnar file formats, including the Arrow IPC file format (“Feather”) |
| and the <a href="https://parquet.apache.org/">Apache Parquet</a> format.</p> |
| |
| <!-- Link to implementation matrix? --> |
| |
| <ul> |
| <li>Feather: C++, <a href="/docs/python/feather.html">Python</a>, |
| <a href="/docs/r/reference/read_feather.html">R</a></li> |
| <li>Parquet: <a href="/docs/cpp/parquet.html">C++</a>, |
| <a href="/docs/python/parquet.html">Python</a>, |
| <a href="/docs/r/reference/read_parquet.html">R</a></li> |
| </ul> |
| |
| <p>In addition to single-file readers, some libraries (C++, |
| <a href="/docs/python/dataset.html">Python</a>, |
| <a href="/docs/r/articles/dataset.html">R</a>) support reading |
| entire directories of files and treating them as a single dataset. These |
| datasets may be on the local file system or on a remote storage system, such |
| as HDFS, S3, etc.</p> |
| |
| <h2 id="sharing-memory-locally">Sharing memory locally</h2> |
| |
| <p>Arrow IPC files can be memory-mapped locally, which allow you to work with |
| data bigger than memory and to share data across languages and processes. |
| <!-- example? --></p> |
| |
| <p>The Arrow project includes <a href="/blog/2017/08/08/plasma-in-memory-object-store/">Plasma</a>, |
| a shared-memory object store written in C++ and exposed in Python. Plasma |
| holds immutable objects in shared memory so that they can be accessed |
| efficiently by many clients across process boundaries.</p> |
| |
| <p>The Arrow format also defines a <a href="/blog/2020/05/03/introducing-arrow-c-data-interface/">C data interface</a>, |
| which allows zero-copy data sharing inside a single process without any |
| build-time or link-time dependency requirements. This allows, for example, |
| <a href="/docs/r/articles/python.html">R users to access <code class="language-plaintext highlighter-rouge">pyarrow</code>-based projects</a> |
| using the <code class="language-plaintext highlighter-rouge">reticulate</code> package.</p> |
| |
| <h2 id="moving-data-over-the-network">Moving data over the network</h2> |
| |
| <p>The Arrow format allows serializing and shipping columnar data |
| over the network - or any kind of streaming transport. |
| <a href="https://spark.apache.org/">Apache Spark</a> uses Arrow as a |
| data interchange format, and both <a href="/blog/2017/07/26/spark-arrow/">PySpark</a> |
| and <a href="/blog/2019/01/25/r-spark-improvements/">sparklyr</a> can take |
| advantage of Arrow for significant performance gains when transferring data. |
| <a href="https://cloud.google.com/bigquery/docs/reference/storage">Google BigQuery</a>, |
| <a href="https://www.tensorflow.org/tfx">TensorFlow</a>, |
| <a href="https://docs.aws.amazon.com/athena/latest/ug/connect-to-a-data-source.html">AWS Athena</a>, |
| and <a href="/powered_by/">others</a> also use Arrow similarly.</p> |
| |
| <p>The Arrow project also defines <a href="/blog/2019/10/13/introducing-arrow-flight/">Flight</a>, |
| a client-server RPC framework to build rich services exchanging data according |
| to application-defined semantics.</p> |
| |
| <!-- turbodbc --> |
| |
| <h2 id="in-memory-data-structure-for-analytics">In-memory data structure for analytics</h2> |
| |
| <p>The Arrow format is designed to enable fast computation. Some projects have |
| begun to take advantage of that design. Within the Apache Arrow project, |
| <a href="/blog/2019/02/04/datafusion-donation/">DataFusion</a> is a query engine |
| using Arrow data built in Rust.</p> |
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