让我们来读一些真实的案例:

http://planetcassandra.org/apache-cassandra-use-cases/

本文地址:http://planetcassandra.org/blog/post/agentis-energy-stores-over-15-billion-records-of-time-series-usage-data-in-apache-cassandra

他们详细阐述了不选择MySql的原因，因为数据库同步太慢。

(也是由于2- phase commit, FK, PK)

Cassandra基于Amazon Dynamo纸

特点:

稳定

高可用性

备份性能良好

读写比HBase好，(java中的BigTable克隆)。

wiki http://en.wikipedia.org/wiki/Apache_Cassandra

他们的结论是:

We looked at HBase, Dynamo, Mongo and Cassandra. 

Cassandra was simply the best storage solution for the majority of our data.

截至2018年，

如果你需要支援，我建议你用ScyllaDB代替经典的cassandra。

Postgres kv插件也比cassandra快。无论如何不会有多实例可伸缩性。

Apache cassandra是一个分布式数据库，用于跨许多商用服务器管理大量结构化数据，同时提供高可用性服务，没有单点故障。

该架构完全基于上限定理，即可用性和分区容忍，有趣的是最终一致。

不要使用它，如果你不存储数据卷的机架集群， 如果您不存储时间序列数据，请不要使用， 不要使用如果你不分区你的服务器， 如果你要求强烈的一致性，请不要使用。

没有什么是银弹，任何东西都是为了解决特定的问题而构建的，有自己的优点和缺点。这取决于你，你有什么问题陈述，什么是该问题的最佳解决方案。

我会按照你问的顺序一个一个地回答你的问题。因为Cassandra是基于NoSQL数据库家族的，所以在我回答你的问题之前，理解为什么使用NoSQL数据库是很重要的。

为什么使用NoSQL

In the case of RDBMS, making a choice is quite easy because all the databases like MySQL, Oracle, MS SQL, PostgreSQL in this category offer almost the same kind of solutions oriented toward ACID properties. When it comes to NoSQL, the decision becomes difficult because every NoSQL database offers different solutions and you have to understand which one is best suited for your app/system requirements. For example, MongoDB is fit for use cases where your system demands a schema-less document store. HBase might be fit for search engines, analyzing log data, or any place where scanning huge, two-dimensional join-less tables is a requirement. Redis is built to provide In-Memory search for varieties of data structures like trees, queues, linked lists, etc and can be a good fit for making real-time leaderboards, pub-sub kind of system. Similarly there are other databases in this category (Including Cassandra) which are fit for different problem statements. Now lets move to the original questions, and answer them one by one.

何时使用卡桑德拉

Being a part of the NoSQL family, Cassandra offers a solution for problems where one of your requirements is to have a very heavy write system and you want to have a quite responsive reporting system on top of that stored data. Consider the use case of Web analytics where log data is stored for each request and you want to built an analytical platform around it to count hits per hour, by browser, by IP, etc in a real time manner. You can refer to this blog post to understand more about the use cases where Cassandra fits in.

什么时候使用RDMS而不是Cassandra

Cassandra基于NoSQL数据库，不提供ACID和关系数据属性。如果您对ACID属性有强烈的需求(例如财务数据)，Cassandra将不适合这种情况。显然，您可以为此制定一个变通方案，但是您最终将编写大量的应用程序代码来模拟ACID属性，并将严重延误上市时间。同时，使用Cassandra管理这种系统对您来说也是复杂而乏味的。

什么时候不用卡桑德拉

我认为上面的解释是否有意义不需要回答。

在评估分布式数据系统时，您必须考虑CAP定理——您可以选择以下两个:一致性、可用性和分区容差。

Cassandra是一个可用的、支持最终一致性的分区容忍系统。要了解更多信息，请参阅我写的这篇博客文章:NoSQL系统的可视化指南。

你应该问自己以下问题:

(Volume, Velocity) Will you be writing and reading TONS of information , so much information that no one computer could handle the writes. (Global) Will you need this writing and reading capability around the world so that the writes in one part of the world are accessible in another part of the world? (Reliability) Do you need this database to be up and running all the time and never go down regardless of which Cloud, which country, whether it's VM , Container, or Bare metal? (Scale-ability) Do you need this database to be able to continue to grow easily and scale linearly (Consistency) Do you need TUNABLE consistency where some writes can happen asynchronously where as others need to be certified? (Skill) Are you willing to do what it takes to learn this technology and the data modeling that goes with creating a globally distributed database that can be fast for everyone, everywhere?

如果在这些问题中，你认为“可能”或“不”，你应该用别的词。如果你对所有问题的答案都是“当然”，那么你应该用卡桑德拉。

当你可以在一个盒子上做所有事情时，使用RDBMS。它可能比大多数方法都简单，任何人都可以使用它。

Cassandra是个不错的选择，如果:

您不需要DB中的ACID属性。 DB上会有大量的写操作。 需要与大数据、Hadoop、Hive和Spark集成。 需要实时数据分析和生成报告。 有一个强大的容错机制的要求。 有一个齐次系统的要求。 调优需要大量的自定义。