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NoSQL showdown: MongoDB vs. Couchbase

Which NoSQL database has richer querying, indexing, and ease of use?

By Rick Grehan, InfoWorld, 03/21/13

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Page 3 of 7

Couchbase Server vs. MongoDB at a glance
 
Document handling Couchbase's document database characteristics were added to its existing key/value storage architecture with the version 2.0 release. MongoDB was designed as a document database from the get-go. MongoDB's handling of documents is better developed.
Indexing With Couchbase, you use mapreduce to create additional indexes in the database; these accelerate queries. With MongoDB, you can specify that individual document fields be indexed. Mapreduce in MongoDB is used for general processing.
Memcached Couchbase includes a Memcached component that can operate independently (if you wish) from the document storage components. MongoDB has no counterpart.
Sharding Couchbase supports only hashed sharding. MongoDB supports hashed sharding and range sharding.
Geospatial MongoDB has geospatial capabilities. Couchbase does too, but they were added in version 2.0 and are considered experimental.

Couchbase Server

Couchbase promotes Couchbase Server as a solution for real-time access, not data warehousing. Nor is Couchbase Server suitable for batch-oriented analytic processing -- it is designed to be an operational data store.

Though Couchbase Server is based on Apache CouchDB, it is more than CouchDB with incremental modifications. For starters, Couchbase is an amalgam of CouchDB and Memcached, the distributed, in-memory, key/value storage system. In fact, Couchbase can be used as a direct replacement for Memcached. The system provides a separate port that unmodified, legacy Memcached clients can use, as well as "smart SDK" and proxy tools that improve its performance as a Memcached server.

For example, you can use a "thick client" deployment model, which will place the continuously updated knowledge of Memcached node topology on the client tier. This speeds response, as any request for a particular Memcached object will be sent from the client directly to the caching node for that object. This thick-client approach also plays an important role in the Couchbase system's resilience to node crashes (described later).

Couchbase includes its own object-level caching system based on Memcached, though with enhancements. For example, Couchbase tracks working sets (the documents most frequently accessed on a given node) in its object cache using NRU (not recently used) algorithms. All I/O operations act on this in-memory cache. Updates to documents in the cache are eventually persisted to disk. In addition, for updates, locking is employed at the document level -- not at the node, database, or partition level (which would hobble throughput with numerous I/O waits), nor at the field level (which would snarl the system with memory and CPU cycles required to track the locks).

Couchbase accelerates access by using "append only" persistence. This is used not only with the data, but with indexes as well. Updated information is never overwritten; instead, it is appended to the end of whatever data structure is being modified. Further, deleted space is reclaimed by compaction, an operation that can be scheduled to take place during times of low activity. Append-only storage speeds updates and allows read operations to occur while writes are taking place.


For more enterprise computing news, visit InfoWorld. Story copyright InfoWorld Media Group, Inc.

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