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,支持复杂查询,但是不适合报表-阿里云开发者社区

开发者社区> 数据库> 正文
登录阅读全文

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,支持复杂查询,但是不适合报表

简介:

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 for Hive (a SQL data warehouse built on Hadoop map/reduce) and Pig (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


版权声明:本文内容由阿里云实名注册用户自发贡献,版权归原作者所有,阿里云开发者社区不拥有其著作权,亦不承担相应法律责任。具体规则请查看《阿里云开发者社区用户服务协议》和《阿里云开发者社区知识产权保护指引》。如果您发现本社区中有涉嫌抄袭的内容,填写侵权投诉表单进行举报,一经查实,本社区将立刻删除涉嫌侵权内容。

分享:
数据库
使用钉钉扫一扫加入圈子
+ 订阅

分享数据库前沿,解构实战干货,推动数据库技术变革

其他文章
最新文章
相关文章