Jul 19, 2019

Design Differences Between RDBMS and Cassandra

Design Differences Between RDBMS and Cassandra

You cannot perform joins in Cassandra. If you have designed a data model and find that you need something like a join, you’ll have to either do the work on the client side, or create a denormalized second table that represents the join results for you. This latter option is preferred in Cassandra data modeling. Performing joins on the client should be a very rare case; you really want to duplicate (denormalize) the data instead.

Although Cassandra supports features such as lightweight transactions and batches, Cassandra itself has no concept of referential integrity across tables. In a relational database, you could specify foreign keys in a table to reference the primary key of a record in another table. But Cassandra does not enforce this. It is still a common design requirement to store IDs related to other entities in your tables, but operations such as cascading deletes are not available.

In relational database design, we are often taught the importance of normalization. This is not an advantage when working with Cassandra because it performs best when the data model is denormalized. It is often the case that companies end up denormalizing data in relational databases as well. There are two common reasons for this. One is performance. Companies simply can’t get the performance they need when they have to do so many joins on years’ worth of data, so they denormalize along the lines of known queries. This ends up working, but goes against the grain of how relational databases are intended to be designed, and ultimately makes one question whether using a relational database is the best approach in these circumstances.

A second reason that relational databases get denormalized on purpose is a business document structure that requires retention. That is, you have an enclosing table that refers to a lot of external tables whose data could change over time, but you need to preserve the enclosing document as a snapshot in history. The common example here is with invoices. You already have customer and product tables, and you’d think that you could just make an invoice that refers to those tables. But this should never be done in practice. Customer or price information could change, and then you would lose the integrity of the invoice document as it was on the invoice date, which could violate audits, reports, or laws, and cause other problems.

In the relational world, denormalization violates Codd’s normal forms, and we try to avoid it. But in Cassandra, denormalization is, well, perfectly normal. It’s not required if your data model is simple.

Source: Cassandra: The Definitive Guide

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