hawkore/ignite-hk

Apache Ignite + Hawkore's Apache Ignite Extensions

Apache Ignite + Hawkore's improvements

Hawkore's improvements

• Several performance improvements (queues, serialization, messaging)
• Enhanced Spring Data 2 modules
• Supports Advanced Indexing integration
• Supports Dynamic query entities integration

Full Hawkore's Apache Ignite Extensions Documentation

Take a look at examples project.

Sign up at www.hawkore.com to access full documentation.

What is Apache Ignite?

Apache Ignite is a horizontally scalable, fault-tolerant distributed in-memory computing platform for building real-time applications that can process terabytes of data with in-memory speed.

Multi-Tier Storage

Apache Ignite is designed to work with memory, disk, and Intel Optane as active storage tiers. The memory tier allows using DRAM and Intel® Optane™ operating in the Memory Mode for data storage and processing needs. The disk tier is optional with the support of two options -- you can persist data in an external database or keep it in the Ignite native persistence. SSD, Flash, HDD, or Intel Optane operating in the AppDirect Mode can be used as a storage device.

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Ignite Native Persistence

Even though Apache Ignite is broadly used as a caching layer on top of external databases, it comes with its native persistence - a distributed, ACID, and SQL-compliant disk-based store. The native persistence integrates into the Ignite multi-tier storage as a disk tier that can be turned on to let Ignite store more data on disk than it can cache in memory and to enable fast cluster restarts.

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ACID Compliance

Data stored in Ignite is ACID-compliant both in memory and on disk, making Ignite a strongly consistent system. Ignite transactions work across the network and can span multiple servers.

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ANSI SQL Support

Apache Ignite comes with a ANSI-99 compliant, horizontally scalable, and fault-tolerant SQL engine that allows you to interact with Ignite as with a regular SQL database using JDBC, ODBC drivers, or native SQL APIs available for Java, C#, C++, Python, and other programming languages. Ignite supports all DML commands, including SELECT, UPDATE, INSERT, and DELETE queries as well as a subset of DDL commands relevant for distributed systems.

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Machine Learning and High-Performance Computing

Apache Ignite Machine Learning is a set of simple, scalable, and efficient tools that allow building predictive machine learning models without costly data transfers. The rationale for adding machine and deep learning to Apache Ignite is quite simple. Today's data scientists have to deal with two major factors that keep ML from mainstream adoption.

High-performance computing (HPC) is the ability to process data and perform complex calculations at high speeds. Using Apache Ignite as a high-performance compute cluster, you can turn a group of commodity machines or a cloud environment into a distributed supercomputer of interconnected Ignite nodes. Ignite enables speed and scale by processing records in memory and reducing network utilization with APIs for data and compute-intensive calculations. Those APIs implement the MapReduce paradigm and allow you to run arbitrary tasks across the cluster of nodes.

ignite-hk's Issues

ClassCastException with Pagealbe only method argument

Hi,
while trying your ignite-spring-data_2.0 module I encounter bug with repository query method defined with only Pageable argument. While debugging I located the problem in IgniteRepositoryQuery#prepareQuery method where Pageable/Sort object is not removed from parameters and then is converted in IgniteCacheProxyImpl#convertToBinary method into BinaryObjectImpl object which causes a problem later on.

Example:

public class TestRepository {

    @Query(...custom query...)
    Page<...custom object...> findAll(Pageable page);

}

Stacktrace:

java.lang.ClassCastException: class org.apache.ignite.internal.binary.BinaryObjectImpl cannot be cast to class org.springframework.data.domain.Pageable (org.apache.ignite.internal.binary.BinaryObjectImpl and org.springframework.data.domain.Pageable are in unnamed module of loader 'app')

	at org.apache.ignite.springdata20.repository.query.IgniteRepositoryQuery.transformQueryCursor(IgniteRepositoryQuery.java:473)
	at org.apache.ignite.springdata20.repository.query.IgniteRepositoryQuery.execute(IgniteRepositoryQuery.java:314)
	at org.springframework.data.repository.core.support.RepositoryFactorySupport$QueryExecutorMethodInterceptor.doInvoke(RepositoryFactorySupport.java:605)
	at org.springframework.data.repository.core.support.RepositoryFactorySupport$QueryExecutorMethodInterceptor.lambda$invoke$3(RepositoryFactorySupport.java:595)
	at org.springframework.data.repository.core.support.RepositoryFactorySupport$QueryExecutorMethodInterceptor.invoke(RepositoryFactorySupport.java:595)
	at org.springframework.aop.framework.ReflectiveMethodInvocation.proceed(ReflectiveMethodInvocation.java:186)
	at org.springframework.data.projection.DefaultMethodInvokingMethodInterceptor.invoke(DefaultMethodInvokingMethodInterceptor.java:59)
	at org.springframework.aop.framework.ReflectiveMethodInvocation.proceed(ReflectiveMethodInvocation.java:186)
	at org.springframework.aop.interceptor.ExposeInvocationInterceptor.invoke(ExposeInvocationInterceptor.java:93)
	at org.springframework.aop.framework.ReflectiveMethodInvocation.proceed(ReflectiveMethodInvocation.java:186)
	at org.springframework.data.repository.core.support.SurroundingTransactionDetectorMethodInterceptor.invoke(SurroundingTransactionDetectorMethodInterceptor.java:61)
	at org.springframework.aop.framework.ReflectiveMethodInvocation.proceed(ReflectiveMethodInvocation.java:186)
	at org.springframework.aop.framework.JdkDynamicAopProxy.invoke(JdkDynamicAopProxy.java:212)
	at com.sun.proxy.$Proxy69.findAll(Unknown Source)