本篇文章主要介绍了"Cassandra vs MongoDB vs CouchDB vs Redis vs Riak vs HBase vs Couchbase vs Orient"，主要涉及到方面的内容，对于其他数据库感兴趣的同学可以参考一下：
The most popular onesRedis (V3.0RC)Written in: CMain point: Blazing fastLicense:...
The most popular ones
Redis (V3.0RC)
- Written in: C
- Main point: Blazing fast
- License: BSD
- Protocol: Telnet-like, binary safe
- Disk-backed in-memory database,
- Dataset size limited to computer RAM (but can span multiple machines' RAM with clustering)
- Master-slave replication, automatic failover
- Simple values or data structures by keys
- but complex operations like ZREVRANGEBYSCORE.
- INCR & co (good for rate limiting or statistics)
- Bit operations (for example to implement bloom filters)
- Has sets (also union/diff/inter)
- Has lists (also a queue; blocking pop)
- Has hashes (objects of multiple fields)
- Sorted sets (high score table, good for range queries)
- Lua scripting capabilities (!)
- Has transactions (!)
- Values can be set to expire (as in a cache)
- Pub/Sub lets one implement messaging
Best used: For rapidly changing data with a foreseeable database size (should fit mostly in memory).
For example: To store real-time stock prices. Real-time analytics. Leaderboards. Real-time communication. And wherever you used memcached before.
MongoDB (2.6.7)
- Written in: C++
- Main point: Retains some friendly properties of SQL. (Query, index)
- License: AGPL (Drivers: Apache)
- Protocol: Custom, binary (BSON)
- Master/slave replication (auto failover with replica sets)
- Sharding built-in
- Queries are javascript expressions
- Run arbitrary javascript functions server-side
- Better update-in-place than CouchDB
- Uses memory mapped files for data storage
- Performance over features
- Journaling (with --journal) is best turned on
- On 32bit systems, limited to ~2.5Gb
- Text search integrated
- GridFS to store big data + metadata (not actually an FS)
- Has geospatial indexing
- Data center aware
Best used: If you need dynamic queries. If you prefer to define indexes, not map/reduce functions. If you need good performance on a big DB. If you wanted CouchDB, but your data changes too much, filling up disks.
For example: For most things that you would do with MySQL or PostgreSQL, but having predefined columns really holds you back.
Cassandra (2.0)
- Written in: Java
- Main point: Store huge datasets in "almost" SQL
- License: Apache
- Protocol: CQL3 & Thrift
- CQL3 is very similar SQL, but with some limitations that come from the scalability (most notably: no JOINs, no aggregate functions.)
- CQL3 is now the official interface. Don't look at Thrift, unless you're working on a legacy app. This way, you can live without understanding ColumnFamilies, SuperColumns, etc.
- Querying by key, or key range (secondary indices are also available)
- Tunable trade-offs for distribution and replication (N, R, W)
- Data can have expiration (set on INSERT).
- Writes can be much faster than reads (when reads are disk-bound)
- Map/reduce possible with Apache Hadoop
- All nodes are similar, as opposed to Hadoop/HBase
- Very good and reliable cross-datacenter replication
- Distributed counter datatype.
- You can write triggers in Java.
Best used: When you need to store data so huge that it doesn't fit on server, but still want a friendly familiar interface to it.
For example: Web analytics, to count hits by hour, by browser, by IP, etc. Transaction logging. Data collection from huge sensor arrays.
ElasticSearch (0.20.1)
