Entries tagged [sql]

Apache Ignite 2.5: Scaling to 1000s Nodes Clusters

May 31, 2018 by Denis Magda. Share in Facebook, Twitter

Apache Ignite was always appreciated by its users for two primary things it delivers - scalability and performance. Throughout the lifetime many distributed systems tend to do performance optimizations from a release to release while making scalability related improvements just a couple of times. It's not because the scalability is of no interest. Usually, scalability requirements are set and solved once by a distributed system and don't require significant additional interventions by engineers.

However, Apache Ignite grew to the point when the community decided to revisit its discovery subsystem that influences how well and far Ignite scales out. The goal was pretty clear - Ignite has to scale to 1000s of nodes as good as it scales to 100s now.

It took many months to get the task implemented. So, please join me in welcoming Apache Ignite 2.5 that now can be scaled easily to 1000s of nodes and goes with other exciting capabilities. Let's check out the most prominent ones.

Apache Ignite 2.3 - More SQL and Persistence Capabilities

November 1, 2017 by Denis Magda. Share in Facebook, Twitter

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.


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.

Apache Ignite 2.1 - A Leap from In-Memory to Memory-Centric Architecture

July 27, 2017 by Denis Magda. Share in Facebook, Twitter

The power and beauty of in-memory computing projects are that they truly do what they state -- deliver outstanding performance improvements by moving data closer to the CPU, using RAM as a storage and spreading the data sets out across a cluster of machines relying on horizontal scalability.

However, there is an unspoken side of the story. No matter how fast a platform is, we do not want to lose the data and encounter cluster restarts or other outages. To guarantee this we need to somehow make data persistent on the disk.

Most in-memory computing projects address the persistence dilemma by giving a way to sync data back to a relational database (RDBMS). That sounds reasonable and undoubtedly works pretty well in practice, but if we dig deeper, you’ll likely encounter the following limitations:

Apache Ignite 2.0: Redesigned Off-heap Memory, DDL and Machine Learning

May 5, 2017 by Denis Magda. Share in Facebook, Twitter

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.