NoSQL databases should offer superior performance and scalability. You’re giving up your trusted SQL for something, right?
This benchmark follows a simple use case:
Fetch 100.000 records from the database and stream that into an HTTP response. The test was done with 20 concurrent users.
We wanted to test the performance of different databases. Our options:
- Cassandra – easily scalable
- MongoDB – fast and flexible
- PostresSQL – trusted technology
- Datastore – App Engine Scalable Database
Reading 100.000 records from the database requires the result to be paged. Having concurrent users means you don’t want to fetch the full result set into memory before serving it.
Multiple clients accessing the same data can be good or bad for the database. Caching will work well since data is effectively read multiple times. Concurrency might also introduce competition for this resource depending on the architecture of the database.
The result were quite surprising, reading 100k rows from Cassandra with 20 threads took on average 61 seconds. MongoDB was roughly twice as fast and PostgreSQL almost 30 to 60 times faster than Cassandra! Our experience with importing and exporting data from Cassandra matches these numbers, mutation small amounts of data is fast, getting large amounts of data out is slow.
Times are in seconds, PostgreSQL average response time is 0.9 seconds. Cassandra 61 seconds, MongoDB 17 seconds.
Lower is better. The 90% bar means: 90 percent of the requests were delivered in the given time