Entries tagged [database]

Thursday May 31, 2018

Apache Ignite 2.5: Scaling to 1000s Nodes Clusters

Apache Ignite was always appreciated by its users for two primary things it delivers - scalability and performance. And now it grew to the point when the community decided to revisit its discovery subsystem that influences how well and far the database scales out. The goal was pretty clear - Ignite has to scale to 1000s of nodes as good as it scales to 100s now. Check what we did to solve the challenge.

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Thursday March 15, 2018

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.

Spark DataFrames

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.

Wednesday November 01, 2017

Apache Ignite 2.3 - More SQL and Persistence Capabilities

Putting aside the regular bug fixes and performance optimizations, the Apache Ignite 2.3 release brings new SQL capabilities and Ignite persistence improvements that are worth mentioning.

SQL

Let's start with SQL first.

Apache Ignite users have consistently told us that despite all of Ignite’s SQL capabilities, it’s been at times challenging trying to figure out how to start using Ignite as an SQL database.

This was mostly caused by scattered documentation pages, lack of “getting started” guides and tutorials. We’ve remedied this oversight! All related SQL knowledge has been curated in a single documentation domain.

Are you curious about the SQL scope? Go to the new SQL Reference Overview section!

Cannot wait to learn how the Ignite SQL engine runs internally? We’ve prepared an Architectural Overview section for you.

Simply need to know how to connect to an Ignite cluster from an SQL tool? Here is a tooling section for you.

Let’s take a look at some specific SQL features released in Ignite 2.3.

First, we’re proud to deliver support of ALTER TABLE command. Presently, the command allows adding new columns to an SQL schema in runtime -- avoiding any cluster restarts. Once a new column is added, it can be turned into an index. Again, in runtime. No restarts!

Another significant addition seen in Ignite 2.3 is the integration with SQLLine tool that is bundled with every Apache Ignite release and can be used as a default command line tool for SQL based interactions.

To prove that it's fairly simple to work with Ignite as with an SQL database using the tool, we recorded a short screencast for you:

screencast.png

Ignite Persistence

Ignite native persistence keeps getting more attention and installs -- which is why the community released a feature requested by at least a dozen users. The feature allows enabling the persistence for specific data sets. Before Ignite version 2.3, the persistence could be enabled globally only.

Now, it's up to you to decide which data to persist and which to store in RAM only. The persistence can be configured via data regions as shown below:

persistence_cfg.png

This data region will consume up to 500 MB of RAM and will store a superset of data on disk ensuring that no data loss happens in case of a crash or even if there is no more space left in RAM.

Anything else?

Flip through our release notes to see all the changes and improvements available in Apache Ignite 2.3 -- and, for sure, download and use this version in production.

Questions, comments? Let us know!

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