In this post I will present how to connect to MongoDB from a stateless Java EE application, to take advantage of the built-in pool of connections to the database offered by the MongoDB Java Driver. This might be the case if you develop a REST API, that executes operations against a MongoDB.
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 part 1 of a series on MongoDB sharding pitfalls, the release of Redis Cluster as a minimum viable product, a new source for MEAN stack resources, and more.
Redis is blazing fast and can easily handle hundreds of thousands to millions of operations per second (of course, YMMV depending on your setup), but there are cases in which you may feel that it is underperforming.
An approach to modeling that I often see while working with Neo4j users is creating very generic relationships (e.g. HAS, CONTAINS, IS) and filtering on a relationship property or on a property/label at the end node.
The MongoDB Aggregation pipeline is a framework for data aggregation. Documents enter a multi-stage pipeline that transforms the documents into an aggregated results. It was introduced in MongoDB 2.2 to do aggregation operations without needing to use map-reduce.
One of the important roles operations has is going to an existing server and checking if everything is fine. This is routine maintenance stuff. It can be things like checking if we have enough disk space for our expected growth, or if we don’t have too many indexes.
Recently, I had the pleasure of doing a talk at the Brussels Data Science meetup. Some really cool people there, with interesting things to say. My talk was about how graph databases like Neo4j can contribute to HR Analytics. Here are the slides of the talk.
Unit testing requires isolating individual components from their dependencies. Dependencies are replaced with mocks, which simulate certain use cases.
This week, DZone released its latest Refcard. If you're interested in learning more about MongoDB or sharpening your skills, we decided to dig into the DZone archives and find some of the most popular posts we've had on the topic.
Many clients don’t quite realize how much powerful ad-hoc query capability they’re losing by leaving SQL. But how can we possibly have the best of both worlds? Well, luckily for us, Postgres is working on a very handy solution.
After looking at all the pretty pictures, let us take a look at what we have available for us for behind the cover for ops. The first such change is abandoning performance counters.
Sharding is a popular feature in MongoDB, primarily used for distributing data across clusters for horizontal scaling. But as you add complexity to a distributed system, you increase the chances of hitting a problem.
One of my favourite functions in Neo4j’s cypher query language is COLLECT, which allows us to group items into an array for later consumption. However, I’ve noticed that people sometimes have trouble working out how to collect multiple items with COLLECT and struggle to find a way to do so.
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 the rise (and fall?) of NoSQL, a look at using MongoDB with Go and mgo, the dissection of Fall 2014's NoSQL benchmark, and more.
It all comes down to preferences. While there are Redis users who are familiar with the Redis command line interface (CLI) and rely on it, there are those who prefer using a GUI. There are several Redis GUIs available, for different platforms, and in this article I'll try to review a few of them.
That is one scary headline, isn’t it? A customer called me in a state of panic: their database was not loading, and nothing they tried worked. Here is the story as I got it from the customer in question, only embellished to give the proper context for the story.
If you've been following the development of Redis for a while, you may have heard about Redis Cluster in the past - it's been around, to some degree, since 2011. Well, now Redis Cluster actually exists!
In this blog post you will get an overview of two related performance optimizations that you can do for Couchbase 2.5.1 and below. This is not for 3.x for reasons you can read at the bottom of this post.
One of my recent posts generated some good discussion. One particular question, asking me about GemFire as a NoSQL database, caused me to write a long reply. After reading said reply, I realized it would serve better as a post, so here we are. The good, bad, and the ugly of data grids.
I’ve been demonstrating how easy it is to create modern web apps using AngularJS, Java and MongoDB. I also use Groovy during this demo to do the sorts of things Groovy is really good at - writing descriptive tests, and creating scripts.
I say the word “NoSQL” a lot. When I say NoSQL, I tend to talk about denormalized and hierarchical document/row-based data stores like Cassandra, Mongo, Couch, or HBase. But its a terrible way to use that term.
One of the most challenging things to do in production is to know what is going on? In order to facilitate that, we have dedicate some time to exposing the internal guts of RavenDB to the outside world (assuming that the outside world has the appropriate permissions).
In MongoDB there are multiple guarantee levels available for reporting the success of a write operation, called Write Concerns. The strength of the write concerns determine the level of guarantee.
Today I released version 0.3.3 of Motor, the asynchronous MongoDB driver for Python and Tornado. This release is compatible with MongoDB 2.2, 2.4, and 2.6. It requires PyMongo 2.7.1.