Distributed Database For
High‑Performance Applications
With In‑Memory Speed

Distributed SQL

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:

 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;

ACID Transactions

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:

 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();
}

 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();
}

Compute APIs In Java, Scala, Kotlin, C#, C++

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:

 // 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()));

 // 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());

Built-In Machine Learning

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:

 // 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);

Continuous Queries

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:

 ContinuousQuery 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);

 var cache = ignite.GetOrCreateCache("myCache");

var query = new ContinuousQuery(
    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);