GridGain 6.0 includes a lot of news and enhancements, including significantly reworked and simplified APIs, bi-directional WAN Data Center Replication across different geographies, massive improvements in data grid, and more.
MongoDB has optimized count queries to be very fast. As a result, MongoDB developers design applications with this in mind. Prior to 1.4, TokuMX count queries were not optimized, and were as fast as other non-counting queries, without returning the results over the network. In 1.4, Tokutek addressed this.
The author has been thinking about this quite a lot in the past few days. He is trying to see if there is a common solution to replication in general that we can utilize across a number of solutions. If we can do that, we can provide much better feature set for a wide variety of scenarios.
Recently the author attended a Meetup at MongoDB’s new Palo Alto office to hear the CTO, Eliot Horowitz, speak about the product roadmap. With a new production release right around the corner and MongoDB World in the not-so-distant future, the buzz and excitement around all things MongoDB is high.
The author was recently reminded of a Neo4j Cypher query that he wrote a couple of years ago to find the colleagues that he hadn’t worked with in the ThoughtWorks London office. In this article, you'll find a model to help explain how to write such a query.
Moving from MySQL to Cassandra can be beneficial for a number of reasons, particularly when it comes to spreading out failure scenarios. However, there are still challenges to be faced. According to this recent blog post on the transition, the Rackspace team encountered a number of hiccups in the process.
If you're looking for a practical application to help you get started with MongoDB (or Node.js, or Express.js, for that matter), you might be interested in this presentation from Karan Goel on getting started with Node.js, Express.js, and MongoDB. You can find the video below, and Goel's slides here.
Sharding in MongoDB and TokuMX does a great job of scaling an application beyond what a single machine can do, but it also brings new challenges to the table. One of those challenges is how to deal with the impact of migrations on the running system.
Couchbase has released the first developer preview of the 1.4.0 Java SDK. Aside from the usual bugfixes and enhancements, this new release provides support for optimized connection management, which was recently introduced in Couchbase Server 2.5.0. This article provides more information on what's new here.
NoSQL has been a hot buzz in the air for a pretty long time (well, it's not only a buzz anymore), and MongoDB has been a major player. However, when should we really use it?
If you're interested in using Cassandra for real-time analytics, you might find something useful in this talk from Stephane Legay, CTO at LoopLogic, on LoopLogic's use case.
Make sure you didn't miss anything with this list of the Best of the Week in the NoSQL Zone. This week's best include 30 years of NBA data crunched with MongoDB, a response using PostgreSQL, thoughts on when to use GridFS on MongoDB, and more!
In the timeless words of a great man: "It's a bughunt." Last week, the MongoDB team released MongoDB 2.6.0-rc0, and they're running a contest to find bugs. Bug "quality" is judged on severity, impact, and prevalence, and as long as you get your bug reports in by March 4th, you'll be up for some prizes.
Last week Alistair and the author were porting some Neo4j cypher queries from 1.8 to 2.0, and one of the queries they had to change was an interesting one that created a bunch of relationships from a list/array of maps.
Anybody working with ArangoDB might be interested in Stefan Edlich's work-in-progress Clojure driver, Clarango. The current version is 0.3.0, and 1.0 is expected in late 2014, so obviously there is still a lot to be done, but according to the GitHub, the features list is already pretty interesting.
Earlier this month, Gartner released survey results that suggest that there aren't too many DBAs in the NoSQL space. But why would that be? Quite a few people have weighed in, blaming everything from stick-in-the-mud DBAs to the "cool guys" of DevOps.
GridFS is a simple file system abstraction on top of MongoDB. If you are familiar with Amazon S3, GridFS is a very similar abstraction. Now why does a document oriented database like MongoDB provide a file layer abstraction? Turns out there are some very good reasons
The authors had made the decision to go forward with Cassandra, but didn't see any bridge between Storm and Cassandra -- so they built one. By December 2011, they had made enough progress on Storm-Cassandra that it made it into the Cassandra Summit, and they started building out their first topologies.
There is a long-ish tradition of comparing things to MongoDB. You know, MongoDB vs. Oracle, and MongoDB vs. Cassandra, and MongoDB vs. Redis and CouchDB. Now, Dmitri Fontaine at tapoueh.org has provided a new comparison: MongoDB vs. PostgreSQL.
In the first part of this series, the author introduced a new feature, the ability to define the primary key for a collection. Today, you’ll see how we use it to reduce the disk footprint of sharded clusters.
We have to categorize everything, so we categorized NoSQL implementations. There are several categories, but I will focus on three: Distributed Caches, Key / Value Stores, and Document Databases. What if all three requirements must be met? Keep it simple, stupid.
So far, we have just put the data in and out. And we have had a pretty good track record doing so. However, what do we do with the data now that we have it? As you can expect, we need to read it out. Usually by specific date ranges.
On the data import side, Neo4j now supports CSV import directly in the Cypher query language. For large, densely-connected graphs, Neo4j has changed the way relationships are stored to make navigating densely-connected nodes much quicker for common cases.
If you've been waiting for the day when MongoDB and basketball would finally intersect, here is some good news: This recent post has crunched 30 years worth of NBA data with MongoDB aggregation.
The author has recently spent a bit of time working with people on their graph commons, and a common pattern he's come across is that although the models have lots of relationships, there are often missing nodes.