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| content="The Apache Ignite® in-memory data grid accelerates and scales out databases, services, and APIs and supports key-value, ANSI SQL, and machine learning APIs"/> |
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| <title>In-Memory Data Grid - Apache Ignite</title> |
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| <article> |
| <header> |
| <div class="container"> |
| <h1><strong>In-Memory Data Grid</strong> with Apache Ignite</h1> |
| </div> |
| </header> |
| <div class="container"> |
| |
| <p> |
| The Apache Ignite® in-memory data grid accelerates and scales your databases, services, |
| and APIs. It supports key-value and ANSI SQL APIs, ACID transactions, co-located processing, |
| and machine learning libraries. As an in-memory data grid, Ignite is frequently used to increase the performance and |
| scalability of existing real-time applications, for <a href="/use-cases/high-performance-computing.html"> |
| high-performance computing</a>, or for caching of data scattered across databases. |
| </p> |
| <img class="img-fluid diagram-right" src="/images/svg-diagrams/data_grid.svg" alt="In-Memory Data Grid with Apache Ignite"/> |
| |
| <h2>What is an In-Memory Data Grid?</h2> |
| |
| <p>An in-memory data grid is a read-through/write-through caching strategy in which the application |
| layer treats the data grid as the primary data store. Applications write to and read |
| from the in-memory cluster that propagates all the changes to any underlying databases automatically keeping them |
| updated and consistent with the in-memory data.</p> |
| |
| <p>Ignite as an in-memory data grid provides all essential APIs needed to simplify its adoption. The |
| APIs include <a href="/use-cases/key-value-store.html">distributed key-value</a> and <a href="/features/sql.html">ANSI SQL</a> queries, <a href="/features/acid-transactions.html">ACID transactions</a>, <a href="/features/collocated-processing.html">co-located processing</a>, |
| and <a href="/features/machinelearning.html">machine learning</a> models. While key-value and SQL calls let you request, join, and group |
| distributed data sets, the compute and machine learning components help to eliminate data |
| shuffling over the network, thus, boosting compute and data-intensive calculations.</p> |
| |
| <p>Ignite can store data both in memory and on disk with two options for data persistence. You |
| can persist changes in an external database or have Ignite keep data in its <a href="/arch/native-persistence.html">native persistence</a>. </p> |
| |
| <h2>IGNITE as an in-memory data grid AND EXTERNAL DATABASES</h2> |
| |
| <p>Ignite can improve the performance and scalability of any external |
| database such as RDBMS, NoSQL or Hadoop, by sliding in as an in-memory data grid between the |
| application and the database layer. When an application writes data to the cache, Ignite |
| automatically writes-through or writes-behind all data modifications to the underlying |
| external store. Ignite also performs ACID transactions where it coordinates and commits |
| a transaction across the cluster as well as the database.</p> |
| |
| <p>Additionally, Ignite can be deployed as a shared and unified in-memory layer that stores |
| data sets originating from disjointed databases. Your applications can consume all the |
| data from Ignite as a single store while Ignite can keep the original databases in sync |
| whenever in-memory data gets updated.</p> |
| |
| <p>However, there are some limitations if an external database is used as a persistence |
| layer for Ignite in-memory data grid deployments. For instance, if you run Ignite SQL |
| or scan queries, you need to ensure that all data is preloaded to the in-memory cluster. |
| Note that Ignite SQL or scan queries can read data from disk only if the data is stored |
| in the Ignite native persistence.</p> |
| |
| <h2>IGNITE NATIVE PERSISTENCE</h2> |
| |
| <p>Ignite native persistence is a distributed ACID and SQL-compliant disk store that |
| transparently integrates with the Ignite in-memory layer. With native persistence |
| enabled, Ignite stores both data and indexes on disk and eliminates the time-consuming |
| cache warm-up step. The native persistence always keeps a full copy of data on disk so |
| you are free to cache a subset of records in memory. If a required data record is missing |
| in memory, Ignite reads it from the disk automatically regardless of the API, whether |
| you use SQL, key-value, or scan queries.</p> |
| |
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| <div class="display-6 title">Learn More</div> |
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| <a href="/features/sql.html"> |
| Distributed SQL <i class="fas fa-angle-double-right"></i> |
| </a> |
| </li> |
| <li> |
| <a href="/features/collocated-processing.html"> |
| Co-located Processing <i class="fas fa-angle-double-right"></i> |
| </a> |
| </li> |
| <li><a href="/features/acid-transactions.html"> |
| ACID Transactions <i class="fas fa-angle-double-right"></i> |
| </a></li> |
| <li><a href="/features/machinelearning.html"> |
| Machine and Deep Learning <i class="fas fa-angle-double-right"></i> |
| </a></li> |
| </ul> |
| </div> |
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| <ul> |
| <li> |
| <a href="/arch/native-persistence.html"> |
| Native Persistence <i class="fas fa-angle-double-right"></i> |
| </a> |
| </li> |
| <li><a href="/use-cases/in-memory-database.html"> |
| Ignite as an In-Memory Database <i class="fas fa-angle-double-right"></i> |
| </a></li> |
| <li><a href="/use-cases/digital-integration-hub.html"> |
| Ignite as a Digital Integration Hub <i class="fas fa-angle-double-right"></i> |
| </a></li> |
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