When a read request for a row comes in to a node, the row must be combined from all SSTables on that node that contain columns from the row in question, as well as from any unflushed memtables, to produce the requested data. To optimize this piecing-together process, Cassandra uses an in-memory structure called a Bloom filter. Each SSTable has a Bloom filter associated with it that checks if any data for the requested row exists in the SSTable before doing any disk I/O. As a result, Cassandra is very performant on reads when compared to other storage systems, even for read-heavy workloads.
As with any database, reads are fastest when the most in-demand data (or hot working set) fits into memory. Although all modern storage systems rely on some form of caching to allow for fast access to hot data, not all of them degrade gracefully when the cache capacity is exceeded and disk I/O is required. Cassandra's read performance benefits from built-in caching, but it also does not dip dramatically when random disk seeks are required. When I/O activity starts to increase in Cassandra due to increased read load, it is easy to remedy by adding more nodes to the cluster.
For rows that are accessed frequently, Cassandra has a built-in key cache (and an optional row cache). For more information about optimizing read performance using the built-in caching feature, see Tuning Data Caches.
For a detailed explanation of how client read and write requests are handled in Cassandra, also see About Client Requests in Cassandra.