CouchDB: The Definitive Guide

Chapter 23. High Performance

This chapter will teach you the fastest ways to insert and query data with CouchDB. It will also explain why there is a wide range of performance across various techniques.

The take-home message: bulk operations result in lower overhead, higher throughput, and more space efficiency. If you can’t work in bulk in your application, we’ll also describe other options to get throughput and space benefits. Finally, we describe interfacing directly with CouchDB from Erlang, which can be a useful technique if you want to integrate CouchDB storage with a server for non-HTTP protocols, like SMTP (email) or XMPP (chat).

Good Benchmarks Are Non-Trivial

Each application is different. Performance requirements are not always obvious. Different use cases need to tune different parameters. A classic trade-off is latency versus throughput. Concurrency is another factor. Many database platforms behave very differently with 100 clients than they do with 1,000 or more concurrent clients. Some data profiles require serialized operations, which increase total time (latency) for the client, and load on the server. We think simpler data and access patterns can make a big difference in the cacheability and scalability of your app, but we’ll get to that later.

The upshot: real benchmarks require real-world load. Simulating load is hard. Erlang tends to perform better under load (especially on multiple cores), so we’ve often seen test rigs that can’t drive CouchDB hard enough to see ...