cassandra mongodb选择——cassandra：分布式扩展好，写性能强，以及可以预料的查询；mongodb：非事务，支持复杂查...
Of course, like any technology MongoDB has its strengths and weaknesses. MongoDB is designed for OLTP workloads. It can do complex queries, but it’s not necessarily the best fit for reporting-style workloads. Or if you need complex transactions, it’s not going to be a good choice. However, MongoDB’s simplicity makes it a great place to start. mongodb——非事务，支持复杂查询，但是不适合报表 This ease of scaling, coupled with exceptional write performance (“All you’re doing is appending to the end of a log file”) and predictable query performance, add up to a high-performance workhorse in Cassandra. cassandra——分布式扩展好，写性能强，以及可以预料的查询 Cassandra does not support Range based row-scans which may be limiting in certain use-cases. Cassandra is well suited for supporting single-row queries, or selecting multiple rows based on a Column-Value index.Cassandra supports secondary indexes on column families where the column name is known.Aggregations in Cassandra are not supported by the Cassandra nodes - client must provide aggregations. When the aggregation requirement spans multiple rows,Random Partitioning makes aggregations very difficult for the client. Recommendation is to use Storm or Hadoop for aggregations. 摘自：http://www.infoworld.com/article/2848722/nosql/mongodb-cassandra-hbase-three-nosql-databases-to-watch.html Comparison Of NoSQL Databases HBase, Cassandra & MongoDB: HBase: Key characteristics: · Distributed and scalable big data store · Strong consistency · Built on top of Hadoop HDFS · CP on CAP Good for: · Optimized for read · Well suited for range based scan · Strict consistency · Fast read and write with scalability Not good for: · Classic transactional applications or even relational analytics · Applications need full table scan · Data to be aggregated, rolled up, analyzed cross rows Usage Case: Facebook message Cassandra: Key characteristics: · High availability · Incremental scalability · Eventually consistent · Trade-offs between consistency and latency · Minimal administration · No SPF (Single point of failure) – all nodes are the same in Cassandra · AP on CAP Good for: · Simple setup, maintenance code · Fast random read/write · Flexible parsing/wide column requirement · No multiple secondary index needed Not good for: · Secondary index · Relational data · Transactional operations (Rollback, Commit) · Primary & Financial record · Stringent and authorization needed on data · Dynamic queries/searching on column data · Low latency Usage Case: Twitter, Travel portal MongoDB: Key characteristics: · Schemas to change as applications evolve (Schema-free) · Full index support for high performance · Replication and failover for high availability · Auto Sharding for easy Scalability · Rich document based queries for easy readability · Master-slave model · CP on CAP Good for: · RDBMS replacement for web applications · Semi-structured content management · Real-time analytics and high-speed logging, caching and high scalability · Web 2.0, Media, SAAS, Gaming Not good for: · Highly transactional system · Applications with traditional database requirements such as foreign key constraints Usage Case: Craigslist, Foursquare 摘自：https://www.linkedin.com/pulse/real-comparison-nosql-databases-hbase-cassandra-mongodb-sahu 针对分析任务： For analytics, MongoDB provides a custom map/reduce implementation; Cassandra provides native Hadoop support, including forHive(a SQL data warehouse built on Hadoop map/reduce) andPig(a Hadoop-specific analysis language that many think is a better fit for map/reduce workloads than SQL). http://stackoverflow.com/questions/2892729/mongodb-vs-cassandra 本文转自张昺华-sky博客园博客，原文链接：http://www.cnblogs.com/bonelee/p/6305992.html，如需转载请自行联系原作者
)
)
