Throughput and latency

Throughput and latency are key factors affecting Cassandra performance in managing data on disk.

When database operations are serial, throughput and latency are interchangeable. Cassandra operations are performed in parallel, so throughput and latency are independent. Unlike most databases, Cassandra achieves excellent throughput and latency.

Writes are very efficient in Cassandra and very inefficient in storage engines that scatter random writes around while making in-place updates. When you're doing many random writes of small amounts of data, Cassandra reads in the SSD sector. No random seeking occurs as it does in relational databases. Cassandra's log-structured design obviates the need for disk seeks. As database updates are received, Cassandra does not overwrite rows in place. In-place updates would require doing random I/O. Cassandra updates the bytes and rewrites the entire sector back out instead of modifying the data on disk. Eliminating on-disk data modification and erase-block cycles prolongs the life of the SSD and saves time: one or two milliseconds.

Cassandra does not lock the fast write request path that would negatively affect throughput. Because there is no modification of data on disk, locking for concurrency control of data on disk is unnecessary. The operational design integrates nicely with the operating system page cache. Because Cassandra does not modify the data, dirty pages that would have to be flushed are not even generated.

Using SSDs instead of rotational disks is necessary for achieving low latency. Cassandra runs the same code on every node and has no master node and no single point of failure, which also helps achieve high throughput.