The GridGain Systems In-Memory Computing Blog
Today the GridGain team has announced the release of enterprise-grade GridGain In-Memory Data Fabric v. 7.5, based on Apache® Ignite™ v. 1.5. For those not familiar with GridGain or Apache Ignite, it provides the ability to distribute, cache, and compute on data in memory, including such features as in-memory data grid, compute grid, ANSI-99 in-memory SQL, real-time streaming, in-memory file…
I would like to give my ideas on why I believe we are on the cusp of a new chapter in storage technology — namely an ascendance of an open smart storage standard. This standard should define an open vendor-agnostic interface for integration between compute and data processing and traditional storage systems. Using these open APIs, developers should be able run SQL, MapReduce, K/V access, file…
In this blog I will describe how a large bank was able to scale a multi-geographical deployment on top of Apache Ignite™ (incubating) In-Memory Data Grid.
Problem Definition
Imagine a bank offering variety of services to its customers. The customers of the bank are located in different geo-zones (regions), and most of the operations performed by a customer are zone-local, like ATM withdrawals or…
In my previous post I have demonstrated benchmarks for atomic JCache (JSR 107) operations and optimistic transactions between Apache Ignite™ data grid and Hazelcast. In this blog I will focus on benchmarking the pessimistic transactions.
The difference between optimistic and pessimistic modes is in the lock acquisition. In pessimistic mode locks are acquired on first access, while in optimistic…
Recently I have been doing many benchmarks comparing the incubating Apache Ignite™ (incubating) project to other products. In this blog I will describe my experience in comparing Apache Ignite ™ (incubating) Data Grid vs Hazelcast Data Grid.
Yardstick Framework
I will be using Yardstick Framework for the benchmarks, specifically Yardstick-Docker extension. Yardstick is an open…
Much of what human beings experience as commonplace today — social networking, on-line gaming, mobile and wearable computing — was impossible a decade ago. One thing is certain: we're going to see even more impressive advances in the next few years. However, this will be the result of a fundamental change in computing, as current methods have reached their limit in terms of speed and volume.…
In this blog we will cover a case when an in-memory cache serves as a layer on top of a persistent database. In this case the database serves as a primary system of records, and distributed in-memory cache is added for performance and scalability reasons to accelerate reads and (sometimes) writes to the data.
If you prefer a video demo with coding examples, visit the original blog post at gridgain.blogspot.com.
Distributed In-Memory Caching generally allows you to replicate or partition your data in memory across your cluster. Memory provides a much faster access to the data, and by utilizing multiple cluster nodes the performance and scalability of the application increases significantly.
Majority…
We are pleased to announce that GridGain 6.1.0 has been released today. This is the first main upgrade since GridGain 6.0.0 was released in February and contains some cool new functionality and performance improvements:
Support for JDK8
With GridGain 6.1.0 you can execute JDK8 closures and functions in distributed fashion on the grid:
[java]
try (Grid grid = GridGain.start()) {
grid.compute…
World's fastest, most scalable In-Memory Computing Platform now available under Apache 2.0 license
FOSTER CITY, Calif., March 3, 2014 /PRNewswire/ -- Today GridGain (www.gridgain.com) officially released its industry leading In-Memory Computing Platform through an Apache 2.0 open source license, offering the world access to its technology for a broad range of real-time data processing…