![]() ![]() Hardware can not keep up with the timing, Playr will give up its It is not designed toīe a benchmark application in the traditional sense. Where the log files were originally taken from. Playr is not designed to work with lesser hardware than the machine By using the binary capture files, Playr is able to recreate the production workload exactly all concurrency, timing, and commit ordering remain identical to production. Once the workload capture and processing has been completed, replay can be performed using one or more driver systems. ![]() This sounds good, but how does it work? Playr works by capturing your production database workload, analyzing it, and processing it into a set of binary capture files. pgTsung: app-specific testing methodologyĭesigned to identify potential issues resulting from software or hardware upgrades on 's high-volume databases, Playr is able test anĮntire application and provide the administrator with a report detailing the performance and error-related divergence of a Postgres configuration from the production workload.Load-testing a Postgresql server with Tsung.Tsung tutorial on load-testing Postgresql servers: Depending on your real workload, this may be an insurmountable obstacle. It's designed for artificial workload generation, so it's very limited in the number of sessions it can replay. Note that Tsung is not really designed to replay logs. Then in the configuration you get to choose how many of each sessions you want to mix. You can connect it to your server, connect your client to it, and let it record a session at a time. Tsung also comes with a recorder which is a PostgreSQL proxy. Tsung implements a multi-threaded model that tracks which transactions each query belonged to and runs them with the same concurrency as the original. ![]()
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