Fire up big data processing with Apache Ignite

Apache Ignite brings RDBMS, NoSQL, and Hadoop data sets into memory to deliver huge performance gains

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While very powerful in certain cases, Cassandra lacks an in-memory option that can severely limit performance. Cassandra can be useful for OLAP applications but lacks support for transactions, ACID or otherwise, so is not employed for OLTP. Predefined queries can be efficient with Cassandra, but Cassandra lacks SQL support and does not support joins, aggregations, groupings, or usable indexes. These limitations mean Cassandra cannot support ad hoc queries.

Apache Ignite offers native support for Cassandra. With Ignite, Cassandra users gain very powerful capabilities such as the ability to leverage in-memory computing to reduce query times by 1,000x. They can also leverage ANSI-compliant SQL support to run ad hoc and structured queries against in-memory data using joins, aggregations, groupings, and usable indexes.

Installing Ignite

Despite the breadth of its feature set, Apache Ignite is very easy to use and deploy. There are no custom installers. The code base comes as a single Zip file with only one mandatory dependency: ignite-core.jar. All other dependencies, such as integration with Spring for configuration, can be added to the process à la carte. The project is fully Mavenized; it is composed of more than a dozen Maven artifacts that can be imported and used in any combination. Apache Ignite is based on standard Java APIs. For distributed caches and data grid functionality, Apache Ignite implements the JCache (JSR107) standard.

Apache Ignite is a high-performance, distributed in-memory computing platform for large-scale data sets. It offers performance gains to transactional and analytical applications on the order of 1,000 to 1 million times faster throughput, as well as lower latencies than are possible with traditional disk-based or flash technologies. Ignite sits between the application and data layers and does not require the rip-and-replacement of existing RDBMS, NoSQL, or Hadoop data stores.

Apache Ignite is composed of, in one well-integrated framework, a set of in-memory computing capabilities, including an in-memory data grid, an in-memory compute grid, an in-memory service grid, in-memory stream processing, and in-memory acceleration for Hadoop, Spark, and Cassandra. In combination with traditional or distributed data stores, Apache Ignite holds the key to high-volume transactions, real-time analytics, and the emerging class of hybrid transaction/analytical processing (HTAP) workloads.

Apache Ignite resources and documentation, including white papers, recorded webinars, and code samples, are available on the GridGain website.

Nikita Ivanov is founder and CTO of GridGain Systems, where he has led the development of advanced and distributed in-memory data processing technologies. He has more than 20 years of experience in software application development, building HPC and middleware platforms, and contributing to the efforts of companies including Adaptec, Visa, and BEA Systems.

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