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| <li> |
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| <a href="/docs/latest/distributed-computing/executor-service" |
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| class='' |
| >Executor Service</a> |
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| <li> |
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| <a href="/docs/latest/distributed-computing/map-reduce" |
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| >MapReduce API</a> |
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| <li> |
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| <a href="/docs/latest/distributed-computing/load-balancing" |
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| class='' |
| >Load Balancing</a> |
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| <li> |
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| <a href="/docs/latest/distributed-computing/fault-tolerance" |
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| class='' |
| >Fault Tolerance</a> |
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| <li> |
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| <a href="/docs/latest/distributed-computing/job-scheduling" |
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| >Job Scheduling</a> |
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| <a href="/docs/latest/collocated-computations" class='' >Colocating Computations with Data</a> |
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| <li><a href="/docs/latest//machine-learning/clustering/introduction" class=''>Introduction</a></li> |
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| <li><a href="/docs/latest//machine-learning/clustering/gaussian-mixture" class=''>Gaussian mixture (GMM)</a></li> |
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| <li><a href="/docs/latest//machine-learning/model-selection/introduction" class=''>Introduction</a></li> |
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| <li><a href="/docs/latest//machine-learning/model-selection/split-the-dataset-on-test-and-train-datasets" class=''>Split the dataset on test and train datasets</a></li> |
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| <li><a href="/docs/latest//machine-learning/model-selection/hyper-parameter-tuning" class=''>Hyper-parameter tuning</a></li> |
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| <li><a href="/docs/latest//machine-learning/ensemble-methods/introduction" class=''></a></li> |
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| <li><a href="/docs/latest//machine-learning/ensemble-methods/baggin" class=''>Bagging</a></li> |
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| <li><a href="/docs/latest//machine-learning/ensemble-methods/gradient-boosting" class=''>Gradient Boosting</a></li> |
| |
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| <li> |
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| <a href="/docs/latest/machine-learning/recommendation-systems" |
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| >Recommendation Systems</a> |
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| <li><a href="/docs/latest//machine-learning/importing-model/introduction" class=''>Introduction</a></li> |
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| <li><a href="/docs/latest//machine-learning/importing-model/model-import-from-gxboost" class=''>Import Model from XGBoost</a></li> |
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| <li><a href="/docs/latest//machine-learning/importing-model/model-import-from-apache-spark" class=''>Import Model from Apache Spark</a></li> |
| |
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| </nav> |
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| <a href="/docs/latest/monitoring-metrics/intro" |
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| <li> |
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| <a href="/docs/latest/monitoring-metrics/cluster-id" |
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| <li><a href="/docs/latest//monitoring-metrics/configuring-metrics" class=''>Configuring Metrics</a></li> |
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| <li><a href="/docs/latest//monitoring-metrics/metrics" class=''>JMX Metrics</a></li> |
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| >Python Thin Client</a> |
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| >ODBC Driver</a> |
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| <li> |
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| <a href="/docs/latest/SQL/ODBC/connection-string-dsn" |
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| >Connection String and DSN</a> |
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| <a href="/docs/latest/SQL/ODBC/querying-modifying-data" |
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| <a href="/docs/latest/SQL/ODBC/specification" |
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| >Specification</a> |
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| <li> |
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| <a href="/docs/latest/SQL/ODBC/data-types" |
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| class='' |
| >Data Types</a> |
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| <li> |
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| <a href="/docs/latest/SQL/ODBC/error-codes" |
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| class='' |
| >Error Codes</a> |
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| |
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| <a href="/docs/latest/restapi" class='' >REST API</a> |
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| <a href="/docs/latest/control-script" class='' >Control Script</a> |
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| <a href="/docs/latest/plugins" class='' >Plugins</a> |
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| <a href="/docs/latest/sqlline" class='' >SQLLine</a> |
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| <button type='button' data-guide-url="" class='group-toggle collapsed '>Ignite for Spark<img class="state-indicator" src="/assets/images/left-nav-arrow.svg"></button> |
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| <article data-swiftype-index='true'> |
| <a class='edit-link' href="https://github.com/apache/ignite/tree/IGNITE-7595/docs/_docs/data-modeling/data-modeling.adoc" target="_blank">Edit</a> |
| |
| <h1>Data Modeling</h1> |
| |
| <div id="preamble"> |
| <div class="sectionbody"> |
| <div class="paragraph"> |
| <p>A well-designed data model can improve your application’s performance, utilize resources more efficiently, and help achieve your business goals. When designing a data model, it is important to understand how data is distributed in an Ignite cluster and the different ways you can access the data.</p> |
| </div> |
| <div class="paragraph"> |
| <p>In this chapter, we discuss important components of the Ignite data distribution model, including partitioning and affinity colocation, as well as the two distinct interfaces that you can use to access your data (key-value API and SQL).</p> |
| </div> |
| </div> |
| </div> |
| <div class="sect1"> |
| <h2 id="overview">Overview</h2> |
| <div class="sectionbody"> |
| <div class="paragraph"> |
| <p>To understand how data is stored and used in Ignite, it is useful to draw a distinction between the physical organization of data in a cluster and the logical representation of data, i.e. how users are going to view their data in their applications.</p> |
| </div> |
| <div class="paragraph"> |
| <p>On the physical level, each data entry (either cache entry or table row) is stored in the form of a <a href="#binary-object-format">binary object</a>, and the entire data set is divided into smaller sets called <em>partitions</em>. The partitions are evenly distributed between all the nodes. The way data is divided into partitions and partitions into nodes is controlled by the <a href="/docs/latest/data-modeling/affinity-collocation">affinity function</a>.</p> |
| </div> |
| <div class="paragraph"> |
| <p>On the logical level, data should be represented in a way that is easy to work with and convenient for end users to use in their applications. |
| Ignite provides two distinct logical representations of data: <em>key-value cache</em> and <em>SQL tables (schema)</em>. |
| Although these two representations may seem different, in reality they are equivalent and can represent the same set of data.</p> |
| </div> |
| <div class="admonitionblock important"> |
| <table> |
| <tr> |
| <td class="icon"> |
| <div class="title">Important</div> |
| </td> |
| <td class="content"> |
| Keep in mind that, in Ignite, the concepts of a SQL table and a key-value cache are two equivalent representations of the same (internal) data structure. You can access your data using either the key-value API or SQL statements, or both. |
| </td> |
| </tr> |
| </table> |
| </div> |
| </div> |
| </div> |
| <div class="sect1"> |
| <h2 id="key-value-cache-vs-sql-table">Key-Value Cache vs. SQL Table</h2> |
| <div class="sectionbody"> |
| <div class="paragraph"> |
| <p>A cache is a collection of key-value pairs that can be accessed through the key-value API. A SQL table in Ignite corresponds to the notion of tables in traditional RDBMSs with some additional constraints; for example, each SQL table must have a primary key.</p> |
| </div> |
| <div class="paragraph"> |
| <p>A table with a primary key can be presented as a key-value cache, in which the primary key column serves as the key, and the rest of the table columns represent the fields of the object (the value).</p> |
| </div> |
| <div class="paragraph"> |
| <p><span class="image"><img src="/docs/2.9.0/images/cache_table.png" alt="Key-value cache vs SQL table"></span></p> |
| </div> |
| <div class="paragraph"> |
| <p>The difference between these two representations is in the way you access the data. The key-value cache allows you to work with objects via supported programming languages. SQL tables support traditional SQL syntax and can help you, for example, migrate from an existing database. You can combine the two approaches and use either — or both — depending on your use case.</p> |
| </div> |
| <div class="paragraph"> |
| <p>Cache API supports the following features:</p> |
| </div> |
| <div class="ulist"> |
| <ul> |
| <li> |
| <p>Support for JCache (JSR 107) specification</p> |
| </li> |
| <li> |
| <p>ACID Transactions</p> |
| </li> |
| <li> |
| <p>Continuous Queries</p> |
| </li> |
| <li> |
| <p>Events</p> |
| </li> |
| </ul> |
| </div> |
| <div class="admonitionblock note"> |
| <table> |
| <tr> |
| <td class="icon"> |
| <div class="title">Note</div> |
| </td> |
| <td class="content"> |
| Even after you get your cluster up and running, you can create both key-value caches and SQL tables <a href="/docs/latest/key-value-api/basic-cache-operations#creating-caches-dynamically">dynamically</a>. |
| </td> |
| </tr> |
| </table> |
| </div> |
| </div> |
| </div> |
| <div class="sect1"> |
| <h2 id="binary-object-format">Binary Object Format</h2> |
| <div class="sectionbody"> |
| <div class="paragraph"> |
| <p>Ignite stores data entries in a specific format called <em>binary objects</em>. This serialization format provides several advantages:</p> |
| </div> |
| <div class="ulist"> |
| <ul> |
| <li> |
| <p>You can read an arbitrary field from a serialized object without full object deserialization. This completely removes the requirement to have the key and value classes deployed on the server node’s classpath.</p> |
| </li> |
| <li> |
| <p>You can add or remove fields from objects of the same type. Given that server nodes do not have model classes' definitions, this ability allows dynamic change to the object’s structure, and even allows multiple clients with different versions of class definitions to co-exist.</p> |
| </li> |
| <li> |
| <p>It enables you to construct new objects based on a type name without having class definitions at all, hence allowing dynamic type creation.</p> |
| </li> |
| <li> |
| <p>It allows seamless interoperability between the Java, .NET, and C++ platforms.</p> |
| </li> |
| </ul> |
| </div> |
| <div class="paragraph"> |
| <p>Binary objects can be used only when the default binary marshaller is used (i.e., no other marshaller is set in the configuration).</p> |
| </div> |
| <div class="paragraph"> |
| <p>For more information on how to configure and use binary objects, refer to the <a href="/docs/latest/key-value-api/binary-objects">Working with Binary Objects</a> page.</p> |
| </div> |
| </div> |
| </div> |
| <div class="sect1"> |
| <h2 id="data-partitioning">Data Partitioning</h2> |
| <div class="sectionbody"> |
| <div class="paragraph"> |
| <p>Data partitioning is a method of subdividing large sets of data into smaller chunks and distributing them between all server nodes in a balanced manner. Data partitioning is discussed at length in the <a href="/docs/latest/data-modeling/data-partitioning">Data Partitioning</a> section.</p> |
| </div> |
| </div> |
| </div> |
| <div class="copyright"> |
| © 2020 The Apache Software Foundation.<br/> |
| Apache, Apache Ignite, the Apache feather and the Apache Ignite logo are either registered trademarks or trademarks of The Apache Software Foundation. |
| |
| </div> |
| |
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| <li><a href="#overview">Overview</a></li> |
| <li><a href="#key-value-cache-vs-sql-table">Key-Value Cache vs. SQL Table</a></li> |
| <li><a href="#binary-object-format">Binary Object Format</a></li> |
| <li><a href="#data-partitioning">Data Partitioning</a></li> |
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