The first thing that we will do with RavenDB and the NuGet data is to issue the same logical query as the one used to populate the packages page.
An idempotent function gives the same result even if it is applied several times. That is exactly how a database update script should behave. It shouldn’t matter if it is run on or multiple times. The result should be the same.
The idea is: you have found and fixed a bug (so you’ve cloned the mongo repository, created a branch named SERVER-1234, and committed your change on it). You’ve had your fix code-reviewed. Now you’re ready to submit your change, this is where our tutorial picks up. Enjoy...
For the NoSQL job trends, I am continuing to focus the list on the same tools. So, the list includes Cassandra, Redis, Voldemort, SimpleDB, CouchDB, MongoDB, HBase, and Riak.
The first part of actually showing how RavenDB can handle the NuGet scenario was to actually get the data to RavenDB. Luckily, NuGet makes the data accessible using OData, so I quickly hacked up the following program:
In The Princess Bride, every night the Dread Pirate Roberts tells Westley: “Good night, Westley. Good work. Sleep well. I’ll most likely kill you in the morning.” Let’s say the Dread Pirate Roberts wants to optimize this process, so he stores prisoners in a database. This is how...
I wrote about the performance gains with libmysqld a few days ago but I had just too many things in my head to do a proper comparison with the MySQL Cluster / Server protocol.
MySQL vs. MongoDB: The showdown. One of our MVBs is on a quest to make this test work in his favor! He's documenting his every step through a series of articles and you can find them all here!
The last time I used MySQL Embedded Library to bypass the MySQL Client Server protocol to see what the overhead was, and the result was that it is big
If you've followed my KVS series, you'll know that I've tried and failed in my attempt but that hasn't stopped me yet. Here's the answer to my question: What's the client/server protocol?
For quite a while, RavenDB had geo-spatial search capabilities, but ever since it was introduced it was limited to finding documents with latitude and longitude within a radius from a given point.
For the "Lunch and Learn around Neo4j" with Andreas Kollegger we wanted to use a dataset that is easy to understand and interesting enough for attendees of the conference.
Well, it seems that we have reached this stage of maturity that RavenDB is starting to develop it own eco system and 3rd party toolsets.
Ok, in the previous post we determined how we could attach to LocalDB using SSMS 2012. Next stop, creating a database. Turns out that it's potentially harder than it seems at first.
Looking at my benchmarking code, I realized I used the CLIENT_COMPRESS flag when I connected to MySQL and it may be harmful... find out more!
My now long-running series of posts on getting max performance from a very simple MySQL Cluster setup (see details here) is continuing here.
My twitter feed spewed a very good list of distributed computing related papers (compiled by Dan Creswell). There are links to a lot of papers there.
This is another stream of conciseness post, with me trying to figure out what is the best way to resolve a given problem.
Today I decided to look into how to perform a search for data based on an array property. Search is - to me - the most critical weak point of IndexedDB now.
I think it’s time for some fun with Datomic and Datalog. In order to learn to know Datomic better, I will attempt to implement linked lists as a Datomic data structure.
August 17th has come and gone, and NoSQL Distilled is now officially available. My copy arrived a day or two ago, and it’s good to see it in paper. Amazon has it available for order for physical copy.
Social applications and Graph Databases go together like peanut butter and jelly. I’m going to walk you through the steps of building an application that connects to Facebook, pulls your friends and likes data and visualizes it.
Through this I am sharing the most simple scenario to follow in using Java keytool for the requirements of Apache Wookie projects digital signature implementation.
This post shows a few more updates to the book examples to get databases to run against this more current version of Riak.
The main idea why retrieval speeds up with the new algorithm is that typology needs to make sorting over all outedges of a node. This is rather slow especially if one only needs the top k elements. Since neo4j as a graph data base does not provide indices for this kind of data I was forced to look for another way to presort the data.