Leveling the Field July 1, 2011 For most Riak users, Bitcask is the obvious right storage engine to use. It provides low latency, solid predictability, is robust in the face of crashes, and is friendly from a filesystem backup point of view. However, it has one notable limitation: total RAM use depends linearly (though via a small constant) on the total number of objects stored. For this reason, Riak users that need to store billions of entries per machine sometimes use Innostore, (our wrapper around embedded InnoDB) as their storage engine instead. InnoDB is a robust and well-known storage engine, and uses a more traditional design than Bitcask which allows it to tolerate a higher maximum number of items stored on a given host.
SQL to Mongo Mapping Chart In addition to the charts that follow, you might want to consider the Frequently Asked Questions section for a selection of common questions about MongoDB. The following table presents the various SQL statements and the corresponding MongoDB statements. The examples in the table assume the following conditions: Create and Alter MySQL vs. MongoDB: Looking At Relational and Non-Relational Databases When building a custom web application you need to consider the type of database that best suits the data. Here's a quick guide on the differences between MySQL (Relational) and MongoDB (Non-Relational / NoSQL). It was back in 2004 that Ruby on Rails first came out and popularized web application frameworks. What you might not know, is that it also popularized ORM (Object-Relational Mapping) layers with its ActiveRecord object. An ORM layer basically provides an object oriented interface to a relational database. That means that instead of writing a query to insert or update a record, you assign some properties to an object and call a save method.
Map-Reduce — MongoDB Manual 2.6.4 Map-reduce is a data processing paradigm for condensing large volumes of data into useful aggregated results. For map-reduce operations, MongoDB provides the mapReduce database command. Consider the following map-reduce operation: Cassandra vs MongoDB vs CouchDB vs Redis vs Riak vs HBase comparison (Yes it's a long title, since people kept asking me to write about this and that too :) I do when it has a point.) While SQL databases are insanely useful tools, their monopoly in the last decades is coming to an end. And it's just time: I can't even count the things that were forced into relational databases, but never really fitted them.
Camel: CSV The CSV Data Format uses Apache Commons CSV to handle CSV payloads (Comma Separated Values) such as those exported/imported by Excel. Options Marshalling a Map to CSV The component allows you to marshal a Java Map (or any other message type that can be converted in a Map) into a CSV payload. An example: if you send a message with this map...
Explaining Non-Relational Databases To My Mom I was on the phone with Mom yesterday, and we got to talking about technology - a thing that actually happens fairly frequently. Being an only kid, she’s genuinely interested in everything that I do and it’s been helpful to have someone who’s mostly non-technical to bounce explanations off of when I’m getting my head around a new piece of gear. The piece of gear that I was explaining the other day was something called Mongo DB. Mongo’s parent company is called 10gen, and they landed on the startup scene about 5 years ago or so with their flagship product, Mongo DB.
Nonrelational Databases in a Big Data Environment Nonrelational databases do not rely on the table/key model endemic to RDBMSs (relational database management systems). In short, specialty data in the big data world requires specialty persistence and data manipulation techniques. Although these new styles of databases offer some answers to your big data challenges, they are not an express ticket to the finish line. One emerging, popular class of nonrelational database is called not only SQL (NoSQL). Originally the originators envisioned databases that did not require the relational model and SQL. As these products were introduced into the market, the definition softened a bit and now they are thought of as “not only SQL,” again bowing to the ubiquity of SQL.
The Coming SQL Collapse I looked at neo4j briefly the other day, and quite predictably thought ‘wow, this looks like a serious tinkertoy: it‘s basically a bunch of nodes where you just blob your attributes.‘ Worse than that, to wrap objects around it, you have to have them explicitly incorporate their node class, which is ugly, smelly, violates every law of separation of concerns and logical vs. physical models. On the plus side, as I started to look at it more, I realized that it was the perfect way to implement a backend for a bayesian inference engine (more on that later). Why? Because inference doesn‘t care particularly about all the droll requirements that are settled for you by SQL, and there are no real set operations to speak of. One of the things the status quo defenders of SQL almost always forget is that they have to write themselves a pass to violate the most basic laws of their preferred screed.