Java MySQL JDBC Slow/Taking turns

Question

We're currently trying to make our server software use a connection pool to greatly reduce lag however instead of reducing the time queries take to run, it is doubling the time and making it even slower than it was before the connection pool.

Are there any reasons for this? Does JDBC only allow a single query at a time or is there another issue?

Also, does anyone have any examples of multi-threaded connection pools to reduce the time hundreds of queries take as the examples we have found only made it worse.

We've tried using BoneCP and Apache DBCP with similar results...

That one is using Apache's DBCP. We also have tried using BoneCP with the same result...

Answer 1

A connection pool helps mitigating the overhead/cost of creating new connections to the database, by reusing already existing ones. This is important if your workload requires many, short to medium living connections, eg an app that processes concurrent user requests by querying the database. Unfortunately your example benchmark code does not have such a profile. You are just using 4 connections in parallel and there is no reuse involved.

What a connection pool cannot achieve is magically speeding up execution times or improving the concurrency level beyond that, which is provided by the database. If the benchmark code represents the expected workload, I would advise you to look into batching statements instead of threading. That will massively increase performance of INSERT/UPDATE operations.

update :

Using multiple connections in parallel can enhance performance. Just keep in mind, that there is not necessarily a relation between multiple threads in your Java application and in the database. JDBC is just a wrapper around the database driver, using multiple connections results in multiple queries being submitted to the database server in parallel. If those queries are suited for it, every modern RDBMS will be able to process them in parallel. But if those queries are very work intensive, or even worse include table locks or conflicting updates, the DB may not be able to do so. If you experience bad performance, check which queries are lagging and optimize them (are they efficient? proper indexes in place? denormalizing the schema may help in more extreme cases. Use prepared statements and batch mode for larger updates, etc.). If your db is overloaded with many, similar and small queries, consider caching frequently used data.