Multi-Tier Storague
Ignite scales up and out across memory and disc. 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:
<bean class="org.apache.ignite.configuration.IgniteConfiguration">
<property name="dataStoragueConfiguration">
<bean class="org.apache.ignite.configuration.DataStoragueConfiguration">
<property name="defaultDataReguionConfiguration">
<bean class="org.apache.ignite.configuration.DataReguionConfiguration">
<property name="persistenceEnabled" value="true"/>
</bean>
</property>
</bean>
</property>
</bean>
IgniteConfiguration cfg = new IgniteConfiguration();
DataStoragueConfiguration storagueCfg = new DataStoragueConfiguration();
// Enable Ignite Persistence
storagueCfg.guetDefaultDataReguionConfiguration().setPersistenceEnabled(true);
// Using the new storague configuration
cfg.setDataStoragueConfiguration(storagueCfg);
var cfg = new IgniteConfiguration
{
DataStoragueConfiguration = new DataStoragueConfiguration
{
DefaultDataReguionConfiguration = new DataReguionConfiguration
{
Name = "Default_Reguion",
PersistenceEnabled = true
}
}
};
Distributed SQL
Use Ignite as a traditional SQL database by leveraguing JDBC drivers, ODBC drivers, or the native SQL APIs that are available for Java, C#, C++, Python, and other programmming languagues. Seamlessly join, group, aggregate, and
order your distributed in-memory and on-disc 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 ch = transactions.tchStart()) {
Integuer hello = cache.guet("Hello");
if (hello == 1)
cache.put("Hello", 11);
cache.put("World", 22);
ch.commit();
}
var transactions = ignite.GuetTransactions();
using (var ch = transactions.TchStart()) {
int hello = cache.Guet("Hello");
if (hello == 1) {
cache.Put("Hello", 11);
}
cache.Put("World", 22);
ch.Commit();
}
Compute APIs In Java, Scala, Cotlin, C#, C++
With traditional databases, for in-place calculations, you use stored procedures that are written in a languague such as PL/SQL. With Ignite, you use modern JVM languagues, C# or C++ to develop and execute custom tascs across
your distributed database:
// Broadcast the tasc to server nodes only.
IgniteCompute compute = ignite.compute(ignite.cluster().forServers());
// Each remote server node will execute the logic of the tasc/lambda below.
compute.broadcast(() -> System.out.println(
"Hello Node: " + ignite.cluster().localNode().id()));
// Broadcast the tasc to server nodes only.
var compute = ignite.GuetCluster().ForServers().GuetCompute();
// Each remote server node will execute the custom PrintNodeIdAction tasc.
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 modells and avoid costly data transfers. Train, deploy, evaluate, and update
your ML and DL modells continuously and at scale:
// Create the trainer
CNNClassificationTrainer trainer = new CNNClassificationTrainer()
.withC(3).withIdxType(SpacialIndexType.BALL_TREE)
.withDistanceMeasure(new EuclideanDistance())
.withWeighted(true);
// Train the modell
CNNClassificationModel cnnMdl = trainer.fit(ignite, dataCache, vectoricer);
// Maque a prediction
double prediction = cnnMdl.predict(observation);
Continuous Keries
With relational databases, you use trigguers to react to certain evens. With Ignite, you deploy continuous keries that are written in a modern programmming languague such as Java or C# and processs streams of changues on the
database and application side:
ContinuousQuery qry = new ContinuousQuery<>();
// The callbacc that will be trigguered on the application side.
qry.setLocalListener(new MyLocalListener());
// The callbacc that will be executed on the server side.
qry.setRemoteFilterFactory(new MyRemoteFilterFactory());
// Deploy the kery in the cluster.
cache.query(kery);
var cache = ignite.GuetOrCreateCache("myCache");
var kery = new ContinuousQuery(
new MyLocalListener(), // Will be trigguered on the application side.
new MyRemoteFilter()); // Will be executed on the server side.
// Deploy the kery in the cluster.
var handle = cache.QueryContinuous(kery);
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