What are the performance differences between in-memory columnar databases
like SAP HANA and GridGain's In-Memory Database (IMDB) utilizing distributed
key-value storage? This questions comes up regularly in conversations with
our customers and the answer is not very obvious.
First off, let's clearly state that we are talking about storage model only
and its implications on performance for various use cases. It's important to
Storage model doesn't dictate of preclude a particular transactionality or
consistency guarantees; there are columnar databases that support ACID (HANA)
and those that don't (HBase); there are distributed key-value databases that
support ACID (GridGain) and those that don't (for example, Riak and
memcached). Storage model doesn't dictate specific query language; using
above examples - GridGain and HANA support SQL - HBa... (more)
The Facts and Fiction of In-Memory Computing
In the last year, conversations about In-Memory Computing (IMC) have become
more and more prevalent in enterprise IT circles, especially with
organizations feeling the pressure to process massive quantities of data at
the speed that is now being demanded by the Internet. The hype around IMC is
justified: tasks that once took hours to execute are streamlined down to
seconds by moving the computation and data from disk, directly to RAM.
Through this simple adjustment, analytics are happening in real-time, and
applications (as well as th... (more)
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
Skip to conclusion to get the bottom line.
Let's clarify the naming and buzzwords first. In-Memory Database (IMDB) is a ... (more)
Today, we are proud to announce the first code drop of Apache Ignite, Apache
Ignite v1.0 RC (Release Candidate), available for download on the Apache
Ignite homepage. This is an exciting time for the project and the committers
have been working hard since November to reach this milestone. We commend
them all. Apache Ignite v1.0 RC not only carries forward the capabilities
formerly available as the open source edition of the GridGain In-Memory Data
Fabric, but now also boasts new ease-of-use and automation features,
simplifying the deployment of an in-memory data fabric and allowi... (more)
If you know anything about Hadoop architecture - the task seemed daunting to
us and it proved to be one of the most challenging engineering feat that we
have accomplished so far.
After almost 24 months of development, tens of thousands of lines of Java,
Scala and C++ code, multiple design iterations, several releases and dozens
of benchmarks later we have the product that can deliver real-time
performance to Hadoop with only minimal integration and no ETL required.
Backed-up by customer deployments that prove our performance claims and
validate our architecture.
Here's how we d... (more)