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<p>
Use Ignite as a traditional SQL database by leveraging JDBC drivers, ODBC drivers,
or the native SQL APIs that are available for Java, C#, C++, Python, and other
programming languages. Seamlessly join, group, aggregate, and order your
distributed in-memory and on-disk data:
</p>
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<pre class="language-sql">
<code>
SELECT country.name, city.name, MAX(city.population) as max_pop
FROM country JOIN city ON city.countrycode = country.code
WHERE country.code IN ('USA','BRA','ESP','JPN')
GROUP BY country.name, city.name
ORDER BY max_pop DESC LIMIT 3;
</code>
</pre>
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title="Apache Ignite Distributed SQL" aria-label="Distributed SQL"
class="btn btn-secondary">Start Using SQL</a>
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<p>
Ignite scales up and out across memory and disk. By default, Ignite operates
in a pure in-memory mode. But, by toggling a single configuration setting,
you can turn a cluster into a database that can grow beyond
the cluster's memory capacity:
</p>
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<pre class="language-xml">
<code>
&lt;bean class="org.apache.ignite.configuration.IgniteConfiguration"&gt;
&lt;property name="dataStorageConfiguration"&gt;
&lt;bean class="org.apache.ignite.configuration.DataStorageConfiguration"&gt;
&lt;property name="defaultDataRegionConfiguration"&gt;
&lt;bean class="org.apache.ignite.configuration.DataRegionConfiguration"&gt;
&lt;property name="persistenceEnabled" value="true"/&gt;
&lt;/bean&gt;
&lt;/property&gt;
&lt;/bean&gt;
&lt;/property&gt;
&lt;/bean&gt;
</code>
</pre>
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<div class="tab-pane " id="storage-java">
<pre class="language-java">
<code>
IgniteConfiguration cfg = new IgniteConfiguration();
DataStorageConfiguration storageCfg = new DataStorageConfiguration();
// Enable Ignite Persistence
storageCfg.getDefaultDataRegionConfiguration().setPersistenceEnabled(true);
// Using the new storage configuration
cfg.setDataStorageConfiguration(storageCfg);
</code>
</pre>
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<div class="tab-pane " id="storage-csharp">
<pre class="language-csharp">
<code>
var cfg = new IgniteConfiguration
{
DataStorageConfiguration = new DataStorageConfiguration
{
DefaultDataRegionConfiguration = new DataRegionConfiguration
{
Name = "Default_Region",
PersistenceEnabled = true
}
}
};
</code>
</pre>
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class="btn btn-secondary">Learn More About Storage Engine</a>
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<span class="d-block d-md-none">Co-located Compute</span>
<span class="d-none d-md-block">Co-located Compute in Java, Scala, Kotlin, C#, C++</span>
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<p>
With traditional databases, for in-place calculations, you use stored procedures
that are written in a language such as PL/SQL. With Ignite, you use modern
JVM languages, C# or C++ to develop and execute custom tasks across your
distributed database:
</p>
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<div class="tab-pane active" id="compute-java">
<pre class="language-java">
<code>
// Broadcast the task to server nodes only.
IgniteCompute compute = ignite.compute(ignite.cluster().forServers());
// Each remote server node will execute the logic of the task/lambda below.
compute.broadcast(() -> System.out.println(
"Hello Node: " + ignite.cluster().localNode().id()));
</code>
</pre>
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<div class="tab-pane" id="compute-csharp">
<pre class="language-csharp">
<code>
// Broadcast the task to server nodes only.
var compute = ignite.GetCluster().ForServers().GetCompute();
// Each remote server node will execute the custom PrintNodeIdAction task.
compute.Broadcast(new PrintNodeIdAction());
</code>
</pre>
</div>
</div>
</div>
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onclick="gtag('event', 'homepage_click', {'event_category':'apache_ignite_features', 'event_label': 'co_located_processsing'});"
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aria-label="Apache Ignite Co-located Processing"
class="btn btn-secondary">Start Using Compute APIs</a>
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<p>
Ignite can operate in a strongly consistent mode that provides full support
for distributed ACID transactions. Transact across multiple cluster nodes,
caches, tables, and partitions:
</p>
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<code>
IgniteTransactions transactions = ignite.transactions();
try (Transaction tx = transactions.txStart()) {
Integer hello = cache.get("Hello");
if (hello == 1)
cache.put("Hello", 11);
cache.put("World", 22);
tx.commit();
}
</code>
</pre>
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<div class="tab-pane" id="transactions-csharp">
<pre class="language-csharp">
<code>
var transactions = ignite.GetTransactions();
using (var tx = transactions.TxStart()) {
int hello = cache.Get("Hello");
if (hello == 1) {
cache.Put("Hello", 11);
}
cache.Put("World", 22);
tx.Commit();
}
</code>
</pre>
</div>
</div>
</div>
<a href="/features/acid-transactions.html"
onclick="gtag('event', 'homepage_click', {'event_category':'apache_ignite_features', 'event_label': 'transactions'});"
title="Apache Ignite ACID Transactions"
aria-label="Apache Ignite ACID Transactions"
class="btn btn-secondary">Learn More About Transactions</a>
</div>
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<span class="d-block d-md-none">Machine Learning</span>
<span class="d-none d-md-block">Built-In Machine Learning</span>
</a></h3>
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<p>
Ignite machine learning uses built-in algorithms and tools, as well as
TensorFlow integration, to enable the building of scalable machine learning
models and avoid costly data transfers. Train, deploy, evaluate, and update
your ML and DL models continuously and at scale:
</p>
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<pre class="language-java">
<code>
// Create the trainer
KNNClassificationTrainer trainer = new KNNClassificationTrainer()
.withK(3).withIdxType(SpatialIndexType.BALL_TREE)
.withDistanceMeasure(new EuclideanDistance())
.withWeighted(true);
// Train the model
KNNClassificationModel knnMdl = trainer.fit(ignite, dataCache, vectorizer);
// Make a prediction
double prediction = knnMdl.predict(observation);
</code>
</pre>
</div>
</div>
</div>
<a href="/docs/latest/machine-learning/machine-learning"
onclick="gtag('event', 'homepage_click', {'event_category':'apache_ignite_features',
'event_label': 'machine_learning'});"
title="Apache Ignite Machine Learning"
aria-label="Apache Ignite Machine Learning"
class="btn btn-secondary">Start Using Ignite Machine Learning</a>
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<span class="d-block">Continuous Queries</span>
</a></h3>
</div>
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<div class="card-body">
<p>
With relational databases, you use triggers to react to certain events.
With Ignite, you deploy continuous queries that are written in a modern
programming language such as Java or C# and process streams of changes on
the database and application side:
</p>
<div class="code-tabs">
<ul class="nav nav-tabs">
<li class="nav-item">
<a class="nav-link active" data-toggle="tab" href="#conquery-java">Java</a>
</li>
<li class="nav-item">
<a class="nav-link" data-toggle="tab" href="#conquery-csharp">C#/.NET</a>
</li>
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<!-- Tab panes -->
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<div class="tab-pane active" id="conquery-java">
<pre class="language-java">
<code>
ContinuousQuery<Integer, String> qry = new ContinuousQuery<>();
// The callback that will be triggered on the application side.
qry.setLocalListener(new MyLocalListener());
// The callback that will be executed on the server side.
qry.setRemoteFilterFactory(new MyRemoteFilterFactory());
// Deploy the query in the cluster.
cache.query(query);
</code>
</pre>
</div>
<div class="tab-pane" id="conquery-csharp">
<pre class="language-csharp">
<code>
var cache = ignite.GetOrCreateCache<int, string>("myCache");
var query = new ContinuousQuery<int, string>(
new MyLocalListener(), // Will be triggered on the application side.
new MyRemoteFilter()); // Will be executed on the server side.
// Deploy the query in the cluster.
var handle = cache.QueryContinuous(query);
</code>
</pre>
</div>
</div>
</div>
<a href="/docs/latest/key-value-api/continuous-queries"
onclick="gtag('event', 'homepage_click', {'event_category':'apache_ignite_features',
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title="Apache Ignite Continuous Queries"
aria-label="Apache Ignite Continuous Queries"
class="btn btn-secondary">Start Using Continuous Queries</a>
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</div>
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</div>
</div>
</div>
</div>
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</section>
<section id="home-use-cases" class="section-block">
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<h2>How to <strong>Use</strong></h2>
<div class="row">
<div class="txt-wrapper">
<h3>Applications Acceleration &amp; Data Caching</h3>
<p>
Accelerate your existing applications by 100x using Ignite as an in-memory cache or in-memory
data grid that is deployed over one or more external databases. Think of a cache that you can
query with SQL, transact, and compute on.
</p>
<a href="/use-cases/in-memory-data-grid.html" role="button" class="btn btn-primary" title="In-Memory Data Grid"
onclick="gtag('event', 'homepage_click', {'event_category':'apache_ignite_use_cases', 'event_label': 'data_grid'});">
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width="546" height="371"
data-src="/images/svg-diagrams/home--acc-and-data-caching--alt.svg"
alt="Applications Acceleration & Data Caching Diagram"/>
</div>
</div>
<div class="row">
<div class="txt-wrapper">
<h3>Database For Mixed Workloads</h3>
<p>
Build modern applications that support transactional and analytical workloads by using Ignite as
a database that scales beyond the available memory capacity. Ignite allocates memory for your hot
data and goes to disk whenever applications query cold records.
</p>
<a href="/use-cases/in-memory-database.html" role="button" class="btn btn-primary"
aria-label="Learn More About In-Memory Database" title="In-Memory Database"
onclick="gtag('event', 'homepage_click', {'event_category':'apache_ignite_use_cases', 'event_label': 'database'});">
Learn More
</a>
</div>
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width="522" height="395"
alt="Distributed Database Diagram"/>
</div>
</div>
<div class="row">
<div class="txt-wrapper">
<h3>High-Performance Computing</h3>
<p>
Execute kilobyte-size custom code over petabytes of data. Turn your Ignite database into a
distributed supercomputer for low-latency calculations, complex analytics, and machine learning.
</p>
<a href="/use-cases/high-performance-computing.html" role="button" class="btn btn-primary"
aria-label="Learn More About High-Performance Computing" title="High-Performance Computin"
onclick="gtag('event', 'homepage_click', {'event_category':'apache_ignite_use_cases', 'event_label': 'high_performance_compute_cluster'});">
Learn More
</a>
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<img class="lazyload"
width="546" height="388"
data-src="/images/svg-diagrams/home--high-perf-compute-cluster.svg?20201228"
alt="High-Performance Compute Cluster Diagram"/>
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