![]() So we need something that will group some of the column values together in a distinctly addressable group. There’s nothing to unify some collection of name/value pairs, and no way to repeat the same column names. It doesn’t give us much if we want to store multiple entities with the same structure, which is certainly what we want to do. This is a somewhat richer structure to work with.īut the structure we’ve built so far works only if we have one instance of a given entity, such as a single person, user, hotel, or tweet. So if we decided that our map would hold User information, we could have column names like first_name, last_name, phone, email, and so on. This is an improvement because we can know the names of our values. Along the way we’ll get more familiar with CQL and learn how it implements this data model. So in this chapter, we start from relational database terminology and introduce Cassandra’s view of the world. ![]() For those familiar with NoSQL technologies such as Dynamo or Bigtable, it can also be confusing, because although Cassandra may be based on those technologies, its own data model is significantly different. The syntax of CQL is similar in many ways to SQL, but with some important differences. Some terms, such as “keyspace,” are completely new, and some, such as “column,” exist in both worlds but have slightly different meanings. ![]() We’ll show how CQL supports Cassandra’s design goals and look at some general behavior characteristics.įor developers and administrators coming from the relational world, the Cassandra data model can be difficult to understand initially. In this chapter, you’ll gain an understanding of Cassandra’s data model and how that data model is implemented by the Cassandra Query Language (CQL). ![]()
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