The GridGain In-Memory Computing Performance Blog

Information and Insights on In-Memory Computing

Dane Christensen
Thursday, September 25, 2014
Hi, this is Max Herrmann from GridGain Systems, and today is a big day as in-memory computing as you know it is about to be redefined. Sure, in-memory computing technologies have been around for many years in one form or another. First there was caching, which graduated to distributed caching over time by affording itself a scale-out architecture. Then came in-memory databases which, it turns out, often don’t scale so well, and/or they don’t support ACID-compliant transactions.
Dmitriy Setrakyan
Tuesday, September 16, 2014
In this blog we cover a very important optimization that can be utilized for in-memory caches, specifically for cases where data is partitioned across the network.
Dmitriy Setrakyan
Tuesday, September 9, 2014
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.
Dmitriy Setrakyan
Tuesday, September 2, 2014
2-Phase-Commit is probably one of the oldest consensus protocols and is known for its deficiencies when it comes to handling failures, as it may indefinitely block the servers waiting in prepare state. To mitigate this, a 3-Phase-Commit protocol was introduced which adds better fault tolerance at the expense of extra network round-trip message and higher latencies.
Dmitriy Setrakyan
Wednesday, August 27, 2014
We are pleased to announce the release of GridGain Open Source In-Memory Computing Platform 6.2.0. The main components of the platform are: compute grid, data grid (or in-memory distributed cache), and CEP streaming. This release revolves primarily around Portable Object functionality as well as Distributed (or Guaranteed) Services.
Dmitriy Setrakyan
Thursday, July 3, 2014
With release 6.1.9, GridGain significantly simplified its installation and deployment. GridGain now allows for: One Click Installation: The product simply has to be downloaded and unzipped. After that it is ready to be used. One Jar Dependency: GridGain now has only one mandatory dependency - gridgain-6.1.9.jar. All other jars are optional.
Dmitriy Setrakyan
Monday, June 16, 2014
At GridGain, having worked on a distributed caching (data grid) product for many years, we constantly benchmark with various Garbage Collectors to find the optimal configuration for larger heap sizes. From conducting numerous tests, we have concluded that unless you are utilizing some off-heap technology (e.g. GridGain OffHeap), no Garbage Collector provided with JDK will render any kind of stable GC performance with heap sizes larger that 16GB.
Dmitriy Setrakyan
Tuesday, June 10, 2014
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.
Nikita Ivanov
Monday, June 9, 2014
A few months ago, I spoke at the conference where I explained the difference between caching and an in-memory data grid. Today, having realized that many people are also looking to better understand the difference between two major categories in in-memory computing: In-Memory Database and In-Memory Data Grid, I am sharing the succinct version of my thinking on this topic - thanks to a recent analyst call that helped to put everything in place :) TL;DR Skip to conclusion to get the bottom line. Nomenclature
Dane Christensen
Wednesday, May 14, 2014
Today GridGain™ Systems ( GridGain.com ), provider of the leading open source In-Memory Computing (IMC) Platform, announced that Konstantin Boudnik has joined its Advisory Board. Boudnik brings 20 years of expertise in enterprise IT infrastructure management and development, and is a recognized thought leader in the open source community through his role as Vice President at the Apache Software Foundation .
Dane Christensen
Wednesday, May 7, 2014
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().broadcast((GridRunnable)() ->
Dmitriy Setrakyan
Thursday, May 1, 2014
What do Clustering frameworks really do? More often than not clustering frameworks will provide capability to auto-discover servers on the network, share resources, and schedule tasks. Some will also add distributed messaging and distributed event notification capabilities.
Dane Christensen
Wednesday, April 30, 2014
Vendors selected for the “Cool Vendor” report are innovative, impactful and intriguing FOSTER CITY, California – April 30, 2014 – GridGain™ Systems (Gridgain.com), provider of the leading open source In-Memory Computing (IMC) Platform , today announced that Gartner has recognized it in its “Cool Vendors in In-Memory Computing Technologies, 2014” report .
Dane Christensen
Monday, April 28, 2014
GridGain Systems (GridGain.com), provider of the leading open source In-Memory Computing Platform, today announced the appointment of Max Herrmann as Executive Vice President of Marketing. Herrmann comes to GridGain from Microsoft’s cloud and enterprise marketing team and will lend his expertise to growing awareness and adoption of in-memory computing across the enterprise, cloud computing providers and the developer community.
Dmitriy Setrakyan
Wednesday, April 16, 2014
Having spoken with many customers evaluating our product I am noticing that a majority of folks evaluating in-memory computing, whether it be data grid, map reduce, or streaming, do not know how to appropriately perform benchmarking. The right approach to distributed in-memory benchmarking is very different than benchmarking disk-based products, like databases, and generally requires experience and understanding of the delicate details of how network and garbage collections behave under load.