stress-testing

The Company I work for decided on moving their entire stack to Heroku. The main motivation was it's ease of use: No sysAdmin, no cry. But I still have some questions about it... I'm making some load and stress tests on both application platform and Postgres service. I'm using blitz as an addon of Heroku. I attack on the site with number of users between 1 to 250. There are some very interesting results I got and I need help on evaluating them. The Test Stack: Application specifications It hasn't anything that much special at all. Rails 4.0.4 Unicorn database.yml set up to connect to Heroku

I'm seeing an issue with some code I maintain. The code below has a private static SHA1 member (which is an IDisposable but since it's static , it should never get finalized). However, under stress this code throws an exception that suggests it has been closed: Caught exception. Safe handle has been closed" Stack trace: Call stack where exception was thrown at System.Runtime.InteropServices.SafeHandle.DangerousAddRef(Boolean& success) at System.Security.Cryptography.Utils.HashData(SafeHashHandle hHash, Byte[] data, Int32 cbData, Int32 ibStart, Int32 cbSize) at System.Security.Cryptography

I'm figuring what a timer implementation to use if you need to schedule tons of (non blocking) tasks as fast as possible inside jvm on one machine. I've studied ScheduledThreadPoolExecutor and HashedWheelTimer sources (+wheel timer general docs) and here are basic differences (N - number of all outstanding scheduled tasks so far, C - wheel size): ScheduledThreadPoolExecutor O(log N) for adding new task O(1) per each timer tick (but tick per each task, so N overall) O(log N) cancelling the task lock per each tick/task HashedWheelTimer O(1) adding new task O(m) per each timer tick (m ~ N/C where

We’ve come across this question fairly often at Load Impact, so I thought I’d add it to the Stack Overflow community to make it easier to find. How do I calculate the number of concurrent users (VUs) that I need to simulate during a load test, in order to stress my system with the same kind of traffic that it will normally see in the course of a month, week or day? Running a load test requires that you specify how many concurrent users should be simulated during testing. In other words, how many simulated users will be active, loading things or interacting with your site/app at the same time.

I have been looking around for tools that can help me to do load testing and benchmarking. I found couples like: https://github.com/wg/wrk , http://www.joedog.org/siege-home/ , https://github.com/rakyll/boom . I'm wondering if anyone has any experience with these tools and have any feedback pros vs cons of these tools. My load stress will include different test cases using DELETE, PUT, GET, POST... headers Thanks I've used wrk and siege, siege is a really easy to use tool, but I'm not sure if you can test DELETE or PUT with siege. Wrk can use provided lua script to generate requests, so DELETE

I need to stress-test a system and http://locust.io seems like the best way to go about this. However, it looks like it is set up to use the same user every time. I need each spawn to log in as a different user. How do I go about setting that up? Alternatively, is there another system that would be good to use? Locust author here. By default, each HttpLocust user instance has an HTTP client that has it's own separate session . Locust doesn't have any feature for providing a list of user credentials or similar. However, your load testing scripts are just python code, and luckily it's trivial to

Is it possible to stresstest an Android system to check what happens if available memory decreases (and therefor which applications are killed, ...). I currently have a testing application that periodically allocates 10MB in a vector. But an OutOfMemory exception is thrown reaching 500MB. But the system under test was not influenced sufficiently. I need to aquire even more memory. Are there any known libraries/programs to use or is there any other method that let an application use more memory than 500MB. Add: I'm already using android:largeHeap="true" in my manifest. Other than rooting the