78b8af9fed
The previous solution had some shortcomings: - It was based on the assumption that the goroutine gets woken up at regular intervals. This is not actually guaranteed. Now the code keeps track of the actual start and end of an interval and verifies that assumption. - If no pod was scheduled (unlikely, but could happen), then "0 pods/s" got recorded. In such a case, the metric was always either zero or >= 1. A better solution is to extend the interval until some pod gets scheduled. With the larger time interval it is then possible to also track, for example, 0.5 pods/s.
Scheduler Performance Test
Motivation
We already have a performance testing system -- Kubemark. However, Kubemark requires setting up and bootstrapping a whole cluster, which takes a lot of time.
We want to have a standard way to reproduce scheduling latency metrics result and benchmark scheduler as simple and fast as possible. We have the following goals:
- Save time on testing
- The test and benchmark can be run in a single box. We only set up components necessary to scheduling without booting up a cluster.
- Profiling runtime metrics to find out bottleneck
- Write scheduler integration test but focus on performance measurement. Take advantage of go profiling tools and collect fine-grained metrics, like cpu-profiling, memory-profiling and block-profiling.
- Reproduce test result easily
- We want to have a known place to do the performance related test for scheduler. Developers should just run one script to collect all the information they need.
Currently the test suite has the following:
- benchmark
- make use of
go test -benchand report nanosecond/op. - schedule b.N pods when the cluster has N nodes and P scheduled pods. Since it takes relatively long time to finish one round, b.N is small: 10 - 100.
- make use of
How To Run
Benchmark tests
# In Kubernetes root path
make test-integration WHAT=./test/integration/scheduler_perf ETCD_LOGLEVEL=warn KUBE_TEST_VMODULE="''" KUBE_TEST_ARGS="-run=^$$ -benchtime=1ns -bench=BenchmarkPerfScheduling"
The benchmark suite runs all the tests specified under config/performance-config.yaml.
Once the benchmark is finished, JSON file with metrics is available in the current directory (test/integration/scheduler_perf). Look for BenchmarkPerfScheduling_YYYY-MM-DDTHH:MM:SSZ.json.
You can use -data-items-dir to generate the metrics file elsewhere.
In case you want to run a specific test in the suite, you can specify the test through -bench flag:
Also, bench time is explicitly set to 1ns (-benchtime=1ns flag) so each test is run only once.
Otherwise, the golang benchmark framework will try to run a test more than once in case it ran for less than 1s.
# In Kubernetes root path
make test-integration WHAT=./test/integration/scheduler_perf ETCD_LOGLEVEL=warn KUBE_TEST_VMODULE="''" KUBE_TEST_ARGS="-run=^$$ -benchtime=1ns -bench=BenchmarkPerfScheduling/SchedulingBasic/5000Nodes/5000InitPods/1000PodsToSchedule"
To produce a cpu profile:
# In Kubernetes root path
make test-integration WHAT=./test/integration/scheduler_perf KUBE_TIMEOUT="-timeout=3600s" ETCD_LOGLEVEL=warn KUBE_TEST_VMODULE="''" KUBE_TEST_ARGS="-run=^$$ -benchtime=1ns -bench=BenchmarkPerfScheduling -cpuprofile ~/cpu-profile.out"
How to configure benchmark tests
Configuration file located under config/performance-config.yaml contains a list of templates.
Each template allows to set:
- node manifest
- manifests for initial and testing pod
- number of nodes, number of initial and testing pods
- templates for PVs and PVCs
- feature gates
See op data type implementation in scheduler_perf_test.go
for available operations to build WorkloadTemplate.
Initial pods create a state of a cluster before the scheduler performance measurement can begin. Testing pods are then subject to performance measurement.
The configuration file under config/performance-config.yaml contains a default list of templates to cover
various scenarios. In case you want to add your own, you can extend the list with new templates.
It's also possible to extend op data type, respectively its underlying data types
to extend configuration of possible test cases.
Logging
The default verbosity is 2 (the recommended value for production). -v can be used to change this. The log format can be changed with -logging-format=text|json. The default is to write into a log file (when using the text format) or stderr (when using JSON). Together these options allow simulating different real production configurations and to compare their performance.
During interactive debugging sessions it is possible to enable per-test output via -use-testing-log.