MySQL vs. MongoDB: Looking At Relational and Non-Relational Databases | Neon Rain Interactive 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. For example, if you have a "post" object that represents a blog post, you can access it's comments through the property "post.comments". Thankfully, we never jumped on to the ORM bandwagon. Data Representation MySQL represents data in tables and rows. MongoDB represents data as collections of JSON documents. Querying MongoDB uses object querying.
Explaining Non-Relational Databases To My Mom | Ignored by Dinosaurs 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. The Relational model The relational model of storing data has been around for more than 40 years. The classic example I gave to my mom was that of a common blog. The relational model typically comes into play when you visit a blog that has comments. Issues with the relational model For the purposes of this simplistic example, this hopefully isn’t that hard to get your head around. Very good. The non-relational model
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 other class is databases that do not support the relational model, but rely on SQL as a primary means of manipulating the data within.
Non-Relational-Database Technologies For over thirty years, relational database technology has been the gold standard. Modern workloads and unprecedented data volumes, however, are driving businesses to look at alternatives to the traditional relational database. This “NoSQL movement” has given rise to a host of non-relational-database technologies, designed for large-capacity storage and scalability. Some businesses may find that the best solution is a combination of both relational and non-relational databases—whichever tool is best for the job. In this regard, “NoSQL” is probably better referred to as, “Not Only SQL,” rather than “No SQL at all.” NoSQL technologies vary widely, but they can be evaluated based on three key features: scalability, data and query model, and persistence design. Scalability In this context, “scalability” refers to scaling writes by automatically partitioning data across multiple machines. When choosing a distributed database, look for: 1) support for multiple datacenters and Data and Query Model
What is MapReduce What is MapReduce? About MapReduce MapReduce is the heart of Hadoop®. It is this programming paradigm that allows for massive scalability across hundreds or thousands of servers in a Hadoop cluster. Stay on top of all the changes including, Hadoop-based analytics, streaming analytics, warehousing (including BigSQL), data asset discovery, integration, and governance For people new to this topic, it can be somewhat difficult to grasp, because it’s not typically something people have been exposed to previously. The term MapReduce actually refers to two separate and distinct tasks that Hadoop programs perform. An example of MapReduce Let’s look at a simple example. Toronto, 20 Whitby, 25 New York, 22 Rome, 32 Toronto, 4 Rome, 33 New York, 18 Out of all the data we have collected, we want to find the maximum temperature for each city across all of the data files (note that each file might have the same city represented multiple times). (Toronto, 20) (Whitby, 25) (New York, 22) (Rome, 33)
Non-relational DBMSes Hierarchical DBMSes IMS - IBM’s hierarchical mainframe DBMS Model 204 - CCA’s hierarchical DBMS Database management systems have been around for a long time... a lot longer than the relational model and SQL. The links on this page highlight some of the more popular non-relational database products that are available. Let’s take a look at some of the more popular non-relational DBMS product offerings: One popular non-relational DBMS type is the hierarchical DBMS. If interested, you can find a nice primer on IMS here. Other Non-Relational DBMSes Adabas - Software AG’s inverted list DBMS Cache - Intersystem’s object DBMS IDMS - CA’s network/CODASYL DBMS U2 - Rocket Software’s multi-value DBMS Versant Object Database - its name says it all There are many other types of database systems, too. Some are more “relational”-like than others. Some are completely different and if the only thing you know is SQL, don’t attempt to use these DBMSes without some help. Dr. Inventor, Relational Model