an aggregator for blogs about MongoDB

Meltdown 1.1.0-beta1 is released


Meltdown is a Clojure interface to Reactor, an asynchronous programming, event passing and stream processing toolkit for the JVM.

1.1.0-beta1 is a development mileston that updates Reactor to the most recent point release.

Changes between 1.0.0 and 1.1.0

Reactor Update

Reactor is updated to 1.1.x.

Change log

Meltodwn change log is available on GitHub.

Meltdown is a ClojureWerkz Project

Meltdown is part of the group of libraries known as ClojureWerkz, together with

  • Langohr, a Clojure client for RabbitMQ that embraces the AMQP 0.9.1 model
  • Elastisch, a Clojure client for ElasticSearch
  • Monger, a Clojure MongoDB client for a more civilized age
  • Cassaforte, a Clojure Cassandra clie...

Social Status Feed in MongoDB


At MongoDBWorld, my colleague Darren Wood and I gave three back-to-back presentations about an open source project called Socialite which is a reference architecture implementation of a social status feed service. Darren was the one who wrote the bulk of the source code and I installed and ran extensive performance tests in different configurations to determine how the different schema and indexing options scale and to get an understanding of the resources required for various sizes and distributions of workloads.

The recordings and slides are now available on MongoDB website, if you want to jump in and watch, but since we had to race through the material, I'm going to blog abo...

Natural Language Sorting with MongoDB

Natural Language Sorting with MongoDB

Arranging English words in order is simple—well, most of the time. You simply arrange them in alphabetical order. However sorting a set of German words, or French words with all their accents, or Chinese with their different characters is a lot harder than it looks. Sorting rules are specified through "locales", which determine how accents are sorted, in which order the letters are in and how to do case-insensitive sorts. There is a good set of those sorting rules available through CLDR, and there is a neat example to play with all kinds o...

How to Perform Fuzzy-Matching with Mongo Connector and Elastic Search

By Luke Lovett, Python Engineer at MongoDB


Suppose you’re running MongoDB. Great! Now you can find exact matches to all the queries you can throw at the database. Now, imagine that you’re also building a text-search feature into your application. It has to draw words out of misspelled noise, and results may match on synonyms, too! For this daunting task you’ve chosen to use one of the Lucene-based projects, Elasticsearch or Solr. But now you have a problem— How will this tool search through your documents stored in MongoDB? And how will you keep the contents of the search engine up-to-date?

Mongo Connector fills a gap between MongoDB and some of the best search to...

How we do it: Backups

We’ve talked about how you can produce backups on-demand both manually and using the API, but what happens behind the curtain at Compose to make it so easy and how do we take a consistent backup without stopping your database? For our MongoDB elastic deployments, the secret is hidden – a hidden replica set member.…