Posts Tagged → NoSQL

MongoDB – Part 1 – Collections, CRUD, Modifiers, Commands

Recently I’ve been posting a lot of articles about database and more specifically NoSQL. I’ve given a quick overview of each of the NoSQL database types as well as an overview of common concepts that appear throughout most NoSQL databases. Now I want to focus on a specific NoSQL database and I have Chosen MongoDB.

MongoDB is the most popular NoSQL database out there right now and you don’t have to take my word for it. Looking at Google Trends, you can see that MongoDB is getting more searches than Redis, Neo4j and Cassandra combined. Each of these databases are leading the way for their own database model (key-value, document, column and graph). So if you plan on learning a new database, MongoDB is without a doubt a fantastic choice. I’ve personally really enjoyed working with it the last 3 months and can see it being a popular choice for a very long time.

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NoSQL Essentials – Part 6 – Migrations, Polyglot persistence and more

This is the final article on my NoSQL concepts series, before I start to focus on a specific database probably MongoDB. I’ve covered all of the popular NoSQL database types (key-value, document, column-family and graph) as well as given an in-depth overview of NoSQL as a whole. Just to round everything up, I’m writing this article to give some final tips and tricks.

Throughout this series I have been writing about, what I have learnt from reading the NoSQL Distilled book by Martin Fowler and Pramod J. Sadalage. Here I am going to be writing about the final few chapters: Handling schema migration, using polyglot persistence, additional storage engines and how to choose the right database.

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NoSQL Essentials – Part 5 – Graph databases

Neo4J

So far I’ve covered key-value, document and column-family databases. All that’s left now are graph databases. However surprisingly, they’re nothing like what I’ve covered so far. They’re used in completely different scenarios to other NoSQL stores and are also structured like no other database out there.

When thinking about graph databases you can almost forget about everything you know about NoSQL. You can forget about tables, columns, rows, aggregates. Instead you need to know about entities aka nodes, edges aka relationships and properties. Common graph databases include Neo4J, Infinite Graph, OrientDB, FlockDB. In this article I am going to be focusing on the most popular of the bunch, Neo4J.

Graph database are commonly used in the social scene. Their main goal is to map huge amounts of data and to find patterns in data. For example this could be to find people you may know based on who your friends know or to finds bands you might like, based on who your friends like.

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NoSQL Essentials – Part 4 – Column-family databases

Column-Family Databases

Column-family databases aren’t a million miles different from document databases. They hold most of the same pros and cons. There is however one significant difference, column-family stores use peer-to-peer replication, where document databases, use master-slave or primary-secondary replication.

Just like all NoSQL database types, you have a lot of databases choices. Common column-family data stores include HBase, Cassandra, Amazon SimpleDB and Hypertable. HBase was built by Apache and is debatably the most popular column-family store. However recently Cassandra has been getting a lot of attention, so that is what I will be covering.

Differences from relational databases.

  1. In column-family databases, each row consist of a collection of columns=>value pairs. A collection of similar rows then makes up a column family. In a relational databases, this would be equivalent to a collection of rows making up a table. The main difference is that in a column-family database, rows do not have to contain the same columns.
  2. RDBMS impose high cost of schema change for low cost of query change. Column families impose little to no cost in schema change, for slightly more cost in query change.

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