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| <section id="memory-centric" class="page-section"> |
| <h1 class="first">What is Memory-Centric?</h1> |
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| <p> |
| Apache Ignite is based on <i>memory-centric architecture</i> that combines the performance and scale of |
| in-memory computing together with the disk durability and strong consistency in one system. |
| </p> |
| <p> |
| The main difference between the memory-centric approach and the traditional disk-centric approach is that |
| the memory is treated as a fully functional storage, not just as a caching layer, like most databases do. |
| For example, Apache Ignite can function in a pure in-memory mode, in which case it can be treated as an |
| In-Memory Database (IMDB) and In-Memory Data Grid (IMDG) in one. |
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| On the other hand, when persistence is turned on, Ignite begins to function as a memory-centric system |
| where most of the processing happens in memory, but the data and indexes get persisted to disk. The main |
| difference here from the traditional disk-centric RDBMS or NoSQL system is that Ignite is strongly consistent, |
| horizontally scalable, and supports both SQL and key-value processing APIs. |
| </p> |
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| <br/> |
| <h2>Collocated vs Client-Server Processing</h2> |
| <p> |
| The disk-centric systems, like RDBMS or NoSQL, generally utilize the classic client-server approach, where |
| the data is brought from the server to the client side where it gets processed and then is usually discarded. |
| This approach does not scale well as moving the data over the network is the most expensive operation in a distributed system. |
| </p> |
| <p> |
| A much more scalable approach is <b>collocated processing</b> that reverses the flow by bringing the computations to the |
| servers where the data actually resides. This approach allows you to execute advanced logic or distributed SQL with JOINs |
| exactly where the data is stored avoiding expensive serialization and network trips. |
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