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.
Cassandra users looking to make their lives easier might benefit from using Cassandra on Apache Mesos. This recent post provides a tutorial on how to get started, arguing that the two technologies are a great fit for each other because of Cassandra's peer-to-peer architecture.
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 debugging a failing unit-test which interacts with RavenDB, part two of a tutorial on building a recommendation engine in Neo4j, why Cassandra's plainness makes it better than MongoDB, and more!
In this series of blog posts, the author describe the most interesting changes in TokuMX 1.4.0 and how they’ll affect users. Part 3 covers performance improvements that were achieved by making two big changes to how updates are implemented.
Finding relationships that should not be there is a great use case for Neo4j, and today the author wants to highlight an example of why: One of the hardest things for SQL based systems to do is cross-check the incoming payment information against existing data looking for relationships that shouldn’t be there.
In MongoDB, the replication oplog is a capped collection, with a fixed size on disk, and therefore the amount of history (measured in days) varies as the application makes changes faster or slower. In TokuMX, capped collections are handled differently.
When your MongoDB becomes unresponsive, it’s imperative that you can quickly identify the cause. Although there can be many reasons for unresponsiveness, we sometimes find that particularly long-running and/or blocking operations (either initiated by a human or an application) are the culprit.
The author got tired of doing arbitrary performance testing, so he decided to work with the FreeDB dataset, a dataset used to look up CD information. It contains a lot of data, and it is production data. That means that it is dirty, which makes it perfect to run all sort of interesting scenarios.
Tokutek just released version 1.4.0 of TokuMX, our high-performance distribution of MongoDB. There are a lot of improvements in this version (release notes), the most of any release yet. In this series of blog posts, the authors describe the most interesting changes and how they’ll affect users.
This recent article presents a comparison of Cassandra and MongoDB, although it's pretty clearly weighted in one direction. Yes, saying bad things about MongoDB is nothing new, but this article is interesting in that the anti-MongoDB framing is really not the core of his point. Ultimately, this is all about Cassandra.