Hardware and performance

Kiwi TCMS is predominantly an I/O driven application where disk latency is more important than CPU performance and memory speed. This chapter documents our experiments and findings to establish a baseline against which administrators can plan their deployments.

Hardware requirements

In its default configuration Kiwi TCMS runs a web application and a database server as containers on the same hardware.

  • Minimum: 1 CPU, 1 GiB memory: t2.micro AWS instance works but runs at >90% memory utilization and risks unnecessary swapping and/or going out of memory! If you need to be on the low end use t2.small or t3.small instance

  • Recommended: 2 CPU, 4 GiB memory: the Kiwi TCMS team has had positive experience running on t2.medium and t3.medium AWS instances


We’ve seen satisfactory performance with the default disk volume settings for AWS instances: EBS-optimized, General Purpose SSD (gp2), 100/3000 IOPS block storage. This is without any Linux filesystem related tweaks or changes to the default storage configuration of Docker Engine!

Write APIs execution speed

The various API methods in Kiwi TCMS will have vastly different execution speeds. Telemetry and search for example query tons of information from the database while browsing pages and reporting results uses less queries. A question that we often hear is How many test execution results can Kiwi TCMS deal with?


The information below has been gathered by using the following environment:

  • Client: t2.small in us-east-1a (same availability zone as server)

  • Server: t3.medium in use-east-1a, 30GB gp2 disk with 100 IOPS

  • Kiwi TCMS v12.0 via docker compose up

  • Database is mariadb:10.10.2 with a persistent volume backed onto the host filesystem

  • Host OS - Amazon Linux, freshly provisioned, no changes from defaults

  • perf-script-ng version fd942d9 with RANGE_SIZE=100 (called R below)

  • For each invocation perf-script-ng creates new Product, Version Build and TestPlan. Test plan contains R x test cases then R x test runs, each containing the previous test cases and finally updating results for all of them. This simulates a huge test matrix against the same test plan/product/version/build, e.g. testing on multiple different platforms (browser versions + OS combinations for example)

  • The total number of test execution results is R^2

  • The total number of API calls is 10 + 3R + 2R^2

  • Single client, no other server load in parallel

For R=100 we’ve got 10000 test execution results and 20310 API calls in a single script invocation!

The average results are:

  • 43000 test execution results/hour

  • 90000 API calls/hour

  • 25 requests/second

  • 40 ms/request

t3.medium metrics


We’ve experimented with an i3.large storage optimized instance which has a Non-Volatile Memory Express (NVMe) SSD-backed storage optimized for low latency and very high random I/O performance. We’ve had to mkfs.xfs /dev/nvme0n1 && mount /dev/nvme0n1 /var/lib/docker before starting the containers.

While you can see that nvme disk latency is an order of magnitude faster (< 0.1 ms) with the occasional peak from the root filesystem the overall application performance didn’t change a lot. The times for R=30 improved but the times for R=100 worsened a bit.

i3.large metrics

This means few things:

  1. The presented metrics above are generally representative and you can use them to plan your deployment

  2. Going overboard on hardware, especially disk performance isn’t necessary

  3. Somewhere else in Kiwi TCMS there is a bottleneck which we’re still to investigate and improve! Pull requests and more profiling information are welcome.

Upon further investigation we devised two additional scripts to aid in discovering possible bottlenecks:

  • perf-script-orm which talks directly to the ORM layer simulating comparable number of DB operations

  • perf-script-static which simulates the same number of API requests without touching the database. This can be used as a rough estimate of how much time is spent during web/API handling

  • During experiments with these two scripts CPU, Network and Disk metrics remained similar to previous executions which supports the theory of bottleneck in the application instead of hardware or operating system.

Results against the same server with R=100 yielded the following:

  1. 1120 sec for perf-script-ng

  2. 234 sec for perf-script-orm

  3. 333 sec for perf-script-static

Which translates as:

  1. 20% of the time is spent in ORM/DB operations

  2. 30% of the time is spent in the web/API stack

  3. 50% of the time is spent in additional computation for each API function, e.g.

    • permissions check

    • input validation

    • fetching objects by id

    • calculating sortkey and/or test run completion status

    • serialization

Each API function has its own individuality but the biggest contenders in this case seem to be TestRun.add_case and TestExecution.update. However more profiling information for every API function is needed in order to make a final verdict.

Read APIs execution speed

To establish a baseline for read APIs we’ve chosen the TestCase.filter and TestRun.filter methods which are used in the search pages. The experiment is performed inside the following environment:

  • Client is t3.small AWS instance

  • Server is t3.medium AWS instance

  • Both client and server are located in the us-east-1a region in AWS

  • Result size for both methods is 10000 records serialized as JSON

  • Search page was loaded and then the Search button was pressed additional times for a total of 5 executions

The results are as follow:

  • TestCase.filter: min 725 ms, max 930 ms for 5.73 MB data

    TestCase.filter metrics

    TestCase.filter slowest info

  • TestRun.filter: min 560 ms, max 921 ms for 5.16 MB data

    TestRun.filter metrics

    TestRun.filter slowest info

In the case where the client is across the world reaching the server through the Internet the timings are quite different with most of the time being taken to transfer the actual information:

TestCase.filter metrics via Internet


Firefox timing metrics are explained in Mozilla’s documentation