Columnar storage: Cassandra vs Redshift

Question

How is columnar storage in the context of a NoSQL database like Cassandra different from that in Redshift. If Cassandra is also a columnar storage then why isn't it used for OLAP applications like Redshift?

Answer 1

The storage engines of Cassandra and Redshift are very different, and are created for different cases. Cassandra's storage not really "columnar" in wide known meaning of this type of databases, like Redshift, Vertica etc, it is much more closer to key-value family in NoSQL world. The SQL syntax used in Cassandra is not any ANSI SQL, and it has very limited set of queries that can be ran there. Cassandra's engine built for fast writing and reading of records, based on key, while Redshift's engine is built for fast aggregations (MPP), and has wide support for analytical queries, and stores,encodes and compresses data on column level.

It can be easily understood with following example:

Suppose we have a table with user id and many metrics (for example weight, height, blood pressure etc...). I we will run aggregate the query in Redshift, like average weight, it will do the following (in best scenario):

1. Master will send query to nodes.

2. Only the data for this specific column will be fetched from storage.

3. The query will be executed in parallel on all nodes.

4. Final result will be fetched to master.

Running same query in Cassandra, will result in scan of all "rows", and each "row" can have several versions, and only the latest should be used in aggregation. If you familiar with any key-value store (Redis, Riak, DynamoDB etc..) it is less effective than scanning all keys there.

Cassandra many times used for analytical workflows with Spark, acting as a storage layer, while Spark acting as actual query engine, and basically shouldn't be used for analytical queries by its own. With each version released more and more aggregation capabilities are added, but it is very far from being real analytical database.