Question

I have been hearing alot about nosql key/value databases online now. Can you give an example of what one is used for. What kind of real world data is best for these kind of databases?

Answer 1

I think ‘What the heck are you actually using NoSQL for‘ is an excellent read for real life usage cases for NoSQL databases. Let me quote some of them here:

• Managing large streams of non-transactional data: Apache logs, application logs, MySQL logs, clickstreams, etc.

• Syncing online and offline data. This is a niche CouchDB has targeted.
  Fast response times under all loads.

• Avoiding heavy joins for when the query load for complex joins become too large for a RDBMS.

• Soft real-time systems where low latency is critical. Games are one example.

• Applications where a wide variety of different write, read, query, and consistency patterns need to be supported. There are systems optimized for 50% reads 50% writes, 95% writes, or 95% reads.

• Read-only applications needing extreme speed and resiliency, simple
  queries, and can tolerate slightly stale data.

• Applications requiring moderate performance, read/write access, simple queries, completely
  authoritative data.

• Read-only application which complex query requirements.

• Load balance to accommodate data and usage concentrations and to help keep microprocessors busy.

• Real-time inserts, updates, and queries.

• Hierarchical data like threaded discussions and parts explosion.

• Dynamic table creation.

• Two tier applications where low latency data is made available through a fast NoSQL interface, but the data itself can be calculated and updated by high latency Hadoop apps or other low priority apps.

• Sequential data reading. The right underlying data storage model needs to be selected. A B-tree may not be the best model for sequential reads.

• Slicing off part of service that may need better performance/scalability onto it’s own system. For example, user logins may need to be high performance and this feature could use a dedicated service to meet those goals.

• Caching. A high performance caching tier for web sites and other applications. Example is a cache for the Data Aggregation System used by the Large Hadron Collider.

• Voting.

• Real-time page view counters.

• User registration, profile, and session data.

• Document, catalog management and content management systems. These are facilitated by the ability to store complex documents has a whole rather than organized as relational tables. Similar logic applies to inventory, shopping carts, and other structured data types.

• Archiving. Storing a large continual stream of data that is still accessible on-line.

• Document-oriented databases with a flexible schema that can handle schema changes over time.

• Analytics. Use MapReduce, Hive, or Pig to perform analytical queries and scale-out systems that support high write loads.