By J. Chris Anderson, Jan Lehnardt, Noah Slater
Three of CouchDB's creators make it easier to use this document-oriented database as a standalone software framework or with high-volume, disbursed functions. With its uncomplicated version for storing, processing, and having access to information, CouchDB is perfect for net functions that deal with large quantities of loosely based facts. That on my own may stretch the bounds of a relational database, but CouchDB bargains an open resource resolution that's trustworthy, scales simply, and responds fast.
CouchDB works with self-contained information that has unfastened or ad-hoc connections. It's a version that matches many real-world goods, reminiscent of contacts, invoices, and receipts, yet you'll become aware of that this database can simply deal with info of any style. With this ebook, you'll find out how to paintings with CouchDB via its RESTful internet interface, and familiarize yourself with key good points akin to basic rfile CRUD (create, learn, replace, delete), complicated MapReduce, deployment tuning, and extra.
* comprehend the fundamentals of document-oriented garage and manipulation
* have interaction with CouchDB completely although HTTP utilizing its RESTful interface
* version facts as self-contained JSON records
* deal with evolving info schemas evidently
* question and combination facts in CouchDB utilizing MapReduce perspectives
* mirror info among nodes
* track CouchDB for elevated functionality and reliability
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Additional info for CouchDB: The Definitive Guide: Time to Relax
CouchDB is very flexible and gives you enough building blocks to create a system shaped to suit your exact problem. That’s not saying that CouchDB can be bent to solve any problem—CouchDB is no silver bullet—but in the area of data storage, it can get you a long way. CouchDB Replication CouchDB replication is one of these building blocks. Its fundamental function is to synchronize two or more CouchDB databases. This may sound simple, but the simplicity is key to allowing replication to solve a number of problems: reliably synchronize databases between multiple machines for redundant data storage; distribute data to a cluster of CouchDB instances that share a subset of the total number of requests that hit the cluster (load balancing); and distribute data between physically distant locations, such as one office in New York and another in Tokyo.
However, there are situations where availability trumps consistency: Each node in a system should be able to make decisions purely based on local state. If you need to do something under high load with failures occurring and you need to reach agreement, you’re lost. If you’re concerned about scalability, any algorithm that forces you to run agreement will eventually become your bottleneck. Take that as a given. --Werner Vogels, Amazon CTO and Vice President If availability is a priority, we can let clients write data to one node of the database without waiting for other nodes to come into agreement.