Make sure you didn't miss anything with this list of the Best of the Week in the NoSQL Zone (August 1 to 7). This week's best include how to develop robust and scalable transactions across docs in MongoDB, how to use MongoDB with Go and mgo, the top NoSQL databases according to GitHub stars, and more.
This article presents some of the basic concepts and commands which could prove useful for rookies starting with MongoDB.
We’ve just released a new version of our Node.js SDK, now in Beta. This reflects a big change from our previous SDK releases, including a new API which should be far easier to get started with and use, better documentation, and numerous performance enhancements through our related project, libcouchbase.
“I have lots of experience with SQL, but I’m just a beginner with MongoDB. How do I model a one-to-N relationship?” This is one of the more common questions I get from users attending MongoDB office hours. In this first part, I’ll talk about the three basic ways to model One-to-N relationships.
We got tired of sending over “give me the output of the following endpoints” deal. We wanted a better story, something that would be easier and more convenient all around. So we sat down and thought about this, and came up with the idea of the Debug Info Package.
MongoDB supports ACID at a single document level. This technique actually solves a number of transactional issues for one-to-one and some one-to-many relationships. But for other cases where data must be split, how can you deal with it?
This series of installments will highlight some of the most irritating issues that come up when using Redis, along with tips on how to solve them. They are based on our real-life experience of running thousands of Redis database instances.
In this post, I assume the reader is familiar with the first two posts and discuss why data that has been successfully acknowledged with majority write concern may be lost in a failover.
This presentation will give developers an introduction and practical experience of using MongoDB with the Go language. MongoDB Chief Developer Advocate & Gopher Steve Francia presents plainly what you need to know about using MongoDB with Go.
If you're curious about who comes out on top when it comes to NoSQL databases, there are a lot of differing opinions and a lot of places to look. You can check out DB-Engines or Kristof Kovacs' list, or you can just look at GitHub. That's what Memect's Awesome GitHub does.
One thing (or maybe two) that you keep hearing from the MongoDB community (and probably also applies to Cassandra and HBase) is the lack of transactions support. For the record, MongoDB does provide some support for transactions, but to have real distributed transaction support in Mongo is not an easy task.
In this article, we will use the brand new Datastax Cassandra/Spark connector to be able to load data from a Cassandra table and run RDD operations on this data using Spark.
It's a familiar story at this point - trying out NoSQL, then moving back to relational databases - and the response is generally consistent as well: NoSQL will only be useful if you understand your problems and choose the appropriate solution. But with so many solutions cluttering the market, how can you choose?
I was recently asked how to calculate the position of a node in a linked list and realized that as the list increases in size, this is one of the occasions when we should write an unmanaged extension, rather than using Cypher.
In this article, we will see how we can use Cassandra as a resilient distributed dataset (RDD) source for Spark, in order to perform RDD operations.
In this post, I want to zero in on elections and describe how they currently work in detail. Kristina Chodorow has a really good explanation on elections here that really helped me while we were developing TokuMX. My explanation will focus on the threading model.
If you're looking for alternative high-performance NoSQL solutions, you might be interested in this new Redis-esque entry based on LevelDB and written in Go: LedisDB.
While preparing my talk on building Neo4j backed applications with Clojure, I realized that some of the queries I’d written were incredibly complicated and went against anything I’d learnt about separating different concerns.
Ever since Meteor 0.7.0 first introduced oplog tailing, we’ve had a lot of users asking us about using the MongoDB oplog with their Meteor applications. As a result, we thought a step-by-step tutorial would help folks get started.
If you missed anything on DZone this week, now's your chance to catch up! This week's best include a Spring MVC 3 view controller example, a look at the new mobile database, Realm, the Java origins of AngularJS, 5 quick points about threads in Java EE, and more.
Over the next few blog posts, I will go over Ark in layman’s terms. In this first post, I only want to set the scene, and describe what the various important replication components related to elections and failover are. Those familiar with MongoDB may already know this.
For no particular reason at all, Redis Labs' Itamar Haber took a D3.js swing at the bulk of Redis' 160-ish commands, creating an interactive visualization of the lot of them - in Redis colors, of course. The end result is useful, but more importantly, it looks pretty cool.
I was recently asked how to process an ‘array’ of values inside a column in a CSV file using Neo4j’s LOAD CSV tool and although I initially thought this wouldn’t be possible as every cell is treated as a String, Michael showed me a way of working around this which I thought was pretty neat.
Indexes in Couchbase are a flexible means of performing back end processing. When utilizing indexes it's possible to create decision tree type aggregations and selectivity functionality, such as those found in an inventory control system.
Most of the time, our blog posts explain what’s great about the MongoDB improvements we’ve already shipped in TokuMX. Sometimes, though, it’s fun to talk about what’s coming soon, especially when user feedback would really help get the feature right.