Entries tagged [machine]
Apache Ignite 2.4 Brings Advanced Machine Learning and Spark DataFrames Capabilities
Usually, Ignite community rolls out a new version once in 3 months, but we had to make an exception for Apache Ignite 2.4 that consumed five months in total. We could easily blame Thanksgiving, Christmas and New Year holidays for the delay and would be forgiven, but, in fact, we were forging the release you can't simply pass by.
Let's dive in and search for a big fish.
Machine Learning General Availability
Eight months ago, at the time of Apache Ignite 2.0, we put out the first APIs that formed the foundation of the Ignite's machine learning component of today. Since that time, Ignite machine learning experts and enthusiasts have been moving the library to the general availability condition meticulously. And Ignite 2.4 became a milestone that let us consider the ML Grid to be production ready.
The component gained a variety of algorithms that can solve a myriad of regression and classification tasks, gave an ability to train models avoiding ETL from Ignite to other systems, paved a way to deep learning usage scenarios. All that now empowers Ignite users with the tools for dealing with fraud detection, predictive analytics, and for building recommendation systems...if you want. Note, ETL is optional, and the whole memory-centric cluster is at your service!
Moreover, Machine Learning Grid welcomed a software donation by NetMillennium, Inc. in the form of genetic algorithms that solve optimization problems by simulating the process of biological evolution. The algorithms haven't got to Ignite 2.4 and waiting for their time for a release in the master branch. Once you get them, you can apply the biological evolution simulation for real-world applications including automotive design, computer gaming, robotics, investments, traffic/shipment routing and more.
It's not a joke or misprint. Spark users, the DataFrames are now officially supported for you! Many of you have been anticipating them for years and, thanks to Nikolay Izhikov, who was "promoted" to an Ignite committer for the contribution, now you can leverage from them.
No need to be wordy here. Just go ahead and start with DataFrames in Ignite.
Expanding Ignite ecosystem
It was unfair that only Java, C#, and C++ developers could utilize the breadth and depth of Ignite APIs in their applications. Ignite 2.4 solved the injustice with its new low-level binary client protocol. The protocol communicates with an existing Ignite cluster without starting a full-fledged Ignite node. An application can connect to the cluster through a raw TCP socket from any programming language you like.
The beauty of the protocol is that you can develop a so-called Ignite thin client that is a lightweight client connected to the cluster and interacts with it using key-value, SQL, and other APIs. .NET thin client is already at your service and Node.JS, Python, PHP, Java thin clients are in a forge and being developed for the next releases.
RPM repository and much more
So, now Apache Ignite can also be installed from the official RPM repository. Debian users, the packages for your operating systems to be assembled soon.
Overall, if to list all the features and benefits Ignite 2.4 brings, only 2 people will read the article till the end - me and my dear mom Thus, I'll let you discover the rest from the release notes.
Apache Ignite 2.0: Redesigned Off-heap Memory, DDL and Machine Learning
We released the long-awaited Apache Ignite version 2.0 on May 5. The community spent almost a year incorporating tremendous changes to the legacy Apache Ignite 1.x architecture. And all of that effort paid off. Our collective blood, sweat (and perhaps even a few tears) opened up new and exciting opportunities for the Apache Ignite project.
Have I piqued your interest about this new release yet? Let's walk through some of the main new features that have appeared under the hood of Apache Ignite 2.0.
Reengineered Off-Heap Memory Architecture.
The platform’s entire memory architecture was reengineered from scratch. In a nutshell, all of the data and indexes are now stored in a completely new manageable off-heap memory that has no issues with memory fragmentation, accelerates SQL Grid significantly and helps your application easily tolerate Java GC pauses.
Take a peek at the illustration below and try to guess what’s changed. Afterward, please read this documentation to see if your eye caught everything that’s new.
Here’s something extremely noteworthy: the architecture now integrates seamlessly with disk drives. Why do we care about this? Stay tuned!
Data Definition Language.
This release introduces support for Data Definition Language (DDL) as a part of its SQL Grid functionality. Now you can define -- and, what’s more important, alter -- indexes in runtime without the need to restart your cluster. Apache Ignite users have long awaited this feature! Even more exciting news: users can leverage this with standard SQL commands like CREATE or DROP index. This is only the beginning! Go to this page to learn more about current DDL support.
Machine Learning Grid Beta - Distributed Algebra.
Apache Ignite is about more than in-memory storage. And it’s not just one more product for distributed computations or real-time streaming. It's much, much more than that. It's a hot blend of well-integrated distributed and highly concurrent modules that turned Apache Ignite into what is today: A robust data-fabric and framework with the goal of making your application thrive and outperform even the best of expectations.
But there was one thing missing until now. Drumroll, please: machine-learning support!
With Apache Ignite 2.0 you can check project’s own distributed algebra implementation. The distributed algebra is the foundation of the entire component. And soon you can expect to get distributed versions of widely used regression algorithms, decision trees and more.
Spring Data Integration.
Spring Data integration allows the interaction of an Apache Ignite cluster using the well-known and highly adopted Spring Data Framework. You can connect to the cluster by means of Spring Data repositories and start executing distributed SQL queries as well as simple CRUD operations.
Are you using Rocket MQ in your project and need to push data from the Rocket to Ignite? Here is an easy solution.
Hibernate L2 cache users have been anticipating support of Hibernate 5 on Apache Ignite for quite a long time. Apache Ignite 2.0 grants this desire. The integration now supports Hibernate 5 and contains a number of bug fixes and improvements.
Ignite.NET has been enhanced with an addition of a plugin system that allows the writing and embedding 3rd party .NET components into Ignite.NET.
The Ignite.C++ part of the community finally came up with a way to execute arbitrary C++ code on remote cluster machines.
This approach was initially tested for continuous queries. You can now register continuous queries' remote filters on any cluster node you like. Going forward you can expect support for the Ignite.C++ compute grid and more.
Want to learn more? Please join me June 7 for a webinar titled, “Apache® Ignite™: What’s New in Version 2.0.” I hope to see you there!
P.S. Just in case you can’t wait until June… here's a full list of the changes inside Apache Ignite 2.0.