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 The following table presents the various SQL statements related to table-level actions and the corresponding MongoDB statements. For more information, see db.collection.insert(), db.createCollection(), db.collection.update(), $set, $unset, db.collection.ensureIndex(), indexes, db.collection.drop(), and Data Modeling Concepts. Insert The following table presents the various SQL statements related to inserting records into tables and the corresponding MongoDB statements. For more information, see db.collection.insert(). Select The following table presents the various SQL statements related to reading records from tables and the corresponding MongoDB statements.
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.
MapReduce-MPI Library 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... ... through this route ... ... you will end up with a String containing this CSV message Sending the Map below through this route will result in a CSV message that looks like foo,bar Unmarshalling a CSV message into a Java List Unmarshalling will transform a CSV messsage into a Java List with CSV file lines (containing another List with all the field values). An example: we have a CSV file with names of persons, their IQ and their current activity. We can now use the CSV component to unmarshal this file: The resulting message will contain a List<List<String>> like... Marshalling a List<Map> to CSV Available as of Camel 2.1 File Poller of CSV, then unmarshaling
nosql - Non-Relational Database Design 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: In this map-reduce operation, MongoDB applies the map phase to each input document (i.e. the documents in the collection that match the query condition). All map-reduce functions in MongoDB are JavaScript and run within the mongod process. Note For most aggregation operations, the Aggregation Pipeline provides better performance and more coherent interface. Map-Reduce JavaScript Functions In MongoDB, map-reduce operations use custom JavaScript functions to map, or associate, values to a key. The use of custom JavaScript functions provide flexibility to map-reduce operations. Map-Reduce Behavior In MongoDB, the map-reduce operation can write results to a collection or return the results inline. MongoDB supports map-reduce operations on sharded collections.
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