Schema changes in traditional databases mean downtime, lost revenue, and deployment chaos across multiple systems. This piece demonstrates how Apache Ignite's flexible schema approach helps lets data model evolve at the pace of your business requirements.
Traditional databases force a choice: fast memory access or durable storage. High-velocity applications processing 10,000+ events per second hit a wall when disk I/O adds 5-15ms to every transaction.
Apache Ignite shows what really happens once your good enough multi-system setup starts cracking under high-volume load. This piece breaks down why the old stack stalls at scale and how a unified, memory-first architecture removes the latency tax entirely.
Discover how Apache Ignite 3 keeps related data together with schema-driven colocation, cutting cross-node traffic and making distributed queries fast, local and predictable.
Apache Ignite 3 is a memory-first distributed SQL database platform that consolidates transactions, analytics, and compute workloads previously requiring separate systems. Built from the ground up, it represents a complete departure from traditional caching solutions toward a unified distributed computing platform with microsecond latencies and collocated processing capabilities.
Apache Ignite 3.1 improves the three areas that matter most when running distributed systems: performance at scale, language flexibility, and operational visibility. The release also fixes hundreds of bugs related to data corruption, race conditions, and edge cases discovered since 3.0.
Apache Ignite 3.0 is the latest milestone in Apache Ignite evolution that enhances developer experience, platform resilience, and efficiency. In this article, we’ll explore the key new features and improvements in Apache Ignite 3.0.
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.
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 liary to the general availability condition meticulously. And Ignite 2.4 became a milestone that let us consider the ML Grid to be production ready.
As promised in my initial blog post on this matter, Apache Ignite community applied security patches against the notorious Meltdown Spectre vulnerabilities and completed performance testing of general operations and workloads that are typical for Ignite deployments.
The security patches were applied only for CVE-2017-5754 (Meltdown) and CVE-2017-5753 (Spectre Variant 1) vulnerabilities. The patches for CVE-2017-5715 (Spectre Variant 2) for the hardware the community used for testing are not stable yet an can cause system reboot issues or another unpredictable behavior.
The applied patches have shown that the performance implications are negligible - the performance drop is just in the 0 - 7% range as the figure shows: