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          content="Apache Ignite as an In-Memory Cache accelerates and scales your databases, services, and APIs
          with support of ANSI SQL, ACID transactions, co-located compute, and machine learning."/>

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        <h1><strong>In-Memory Cache</strong> With Apache Ignite</h1>
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        <p>
            Apache Ignite® is a distributed in-memory cache that supports ANSI SQL, ACID transactions, co-located
            computations and machine learning libraries. Ignite provides all essential components required to speed up
            applications including APIs and sessions caching and acceleration for databases and microservices.
        </p>

        <img class="img-fluid diagram-right" src="/images/svg-diagrams/apps_acceleration.svg" alt="In-Memory Cache With Apache Ignite" />

        <p>
            An Apache Ignite cluster can span several interconnected physical or virtual machines, allowing it to utilize
            all the available memory and CPU resources, like a classic distributed cache. The difference between Ignite
            and a classic distributed cache lies in the way you can use the cluster. With Ignite, in addition to standard
            key-value APIs, you can run distributed SQL queries joining and grouping various data sets. If strong consistency is required,
            you can execute multi-records and cross-cache ACID transactions in both pessimistic and optimistic
            modes. Additionally, if an application runs compute or data-intensive logic, you can minimize data
            shuffling and network utilization by running co-located computations and distributed machine learning
            APIs right on the cluster nodes that store your data.

        </p>

        <p>
            There are two primary deployment strategies for Ignite as an in-memory cache -- the cache-aside
            deployment and read-through/write-through caching. Let's review both of them.
        </p>


        <h2>Cache-Aside Deployment</h2>
        <p>
            With the cache-aside deployment strategy, a cache is deployed separately from the primary data store
            and might not even know that the latter exists. An application or change-data-capture process (CDC)
            becomes responsible for data synchronization between these two storage locations. For instance, if any
            record gets updated in the primary data store, then its new value needs to be replicated to the cache.
        </p>
        <p>
            This strategy works well when the cached data is rather static and not updated frequently, or temporary
            data lag/inconsistency is allowed between the two storage locations. It's usually assumed that the
            cache and the primary store will become consistent eventually when changes are replicated in full.
        </p>
        <p>
            If Apache Ignite is deployed in a cache-aside configuration, then its native persistence can be used as
            a disk store for Ignite data sets. The native persistence allows eliminating the time-consuming cache
            warm-up step. Furthermore, since the native persistence always keeps a full copy of data on disk,
            you are free to cache a subset of records in memory. If a required data record is missing in memory,
            then Ignite reads it from the disk automatically regardless of the API you use -- be it SQL, key-value,
            or scan queries.
        </p>

        <h2>Read-Through/Write-Through Caching</h2>
        <p>
            The read-through/write-through caching strategy can also be classified as an in-memory data grid type
            of deployment. When Apache Ignite is deployed as a data grid, the application layer starts treating
            Ignite as the primary store. While the applications write to and read from Ignite, the latter ensures
            that any underlying external databases stay updated and consistent with the in-memory data.
        </p>

        <p>
            This strategy is favorable for architectures that need to accelerate existing disk-based databases or
            create a shared caching layer across many disconnected data sources. Ignite integrates with many
            databases out-of-the-box and can write-through or write-behind all the changes to them. This also
            includes ACID transactions - Ignite will coordinate and commit a transaction across its in-memory
            cluster as well as to a relational database.
        </p>
        <p>
            The read-through capability implies that a cache can read data from an external database if a record is
            missing in memory. Ignite fully supports this capability for key-value APIs. However, when using Ignite
            SQL, you have to preload the entire data set in memory first (Ignite SQL can query data on
            disk only if it is located in its native persistence).
        </p>


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                                <p><a href="/features/sql.html">
                                    Distributed SQL <i class="fa fa-angle-double-right"></i>
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                                    Co-located Processing <i class="fa fa-angle-double-right"></i>
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                                ACID Transactions <i class="fa fa-angle-double-right"></i>
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                                Native Persistence <i class="fa fa-angle-double-right"></i>
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                                    Machine and Deep Learning <i class="fa fa-angle-double-right"></i>
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                                    Ignite as an In-Memory Data Grid <i class="fa fa-angle-double-right"></i>
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                            <li><p><a href="/use-cases/in-memory-database.html">
                                Ignite as an In-Memory Database <i class="fa fa-angle-double-right"></i>
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                            <li><p><a href="/use-cases/digital-integration-hub.html">
                                Ignite as a Digital Integration Hub <i class="fa fa-angle-double-right"></i>
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