clusterloader2

ClusterLoader

ClusterLoader2 (CL2) is a "bring your own yaml" Kubernetes load testing tool being an official K8s scalability and performance testing framework.

The CL2 tests are written in yaml using the semi-declarative paradigm. A test defines a set of states in which a cluster should be (e.g. I want to run 10k pods, 2k cluster-ip services, 5 daemon-sets, etc.) and specifies how fast (e.g. pod throughput) a given state should be reached. In addition, it defines which performance characteristics should be measured Measurements list. Last but not least, CL2 provides an extra observability of the cluster during the test with Prometheus.

The CL2 test API is described here.

Getting started

See Getting started guide if you are new user of ClusterLoader.

Flags

Required

These flags are required for any test to be run.

• kubeconfig - path to the kubeconfig file.
• testconfig - path to the test config file. This flag can be used multiple times if more than one test should be run.
• provider - Cluster provider, options are: gce, gke, kind, kubemark, aws, local, vsphere, skeleton

Optional

• nodes - number of nodes in the cluster. If not provided, test will assign the number of schedulable cluster nodes.
• report-dir - path to directory, where summaries files should be stored. If not specified, summaries are printed to standard log.
• mastername - Name of the master node
• masterip - DNS Name / IP of the master node
• testoverrides - path to file with overrides.
• kubelet-port - Port of the kubelet to use (default: 10250)

Tests

Test definition

Test definition is an instantiation of this api (in json or yaml). The motivation and description of the API can be found in design doc. Definitions of test as well as definitions of individual objects support templating. Templates for test definition come with one predefined value - {{.Nodes}}, which represents the number of schedulable nodes in the cluster.
Example of a test definition can be found here: load test.

Modules

ClusterLoader2 supports modularization of the test configs via the Module API. With the Module API, you can divide a single test config file into multiple module files. A module can be parameterized and used multiple times by the test or other module. This provides a convenient way to avoid copy-pasting and maintaining super-long, unreadable test configs¹.

Object template

Object template is similar to standard kubernetes object definition with the only difference being templating mechanism. Parameters can be passed from the test definition to the object template using the templateFillMap map. Two always available parameters are {{.Name}} and {{.Index}} which specifies object name and object replica index respectively.
Example of a template can be found here: load deployment template.

Overrides

Overrides allow to inject new variables values to the template.
Many tests define input parameters. Input parameter is a variable that potentially will be provided by the test framework. Cause input parameters are optional, each reference has to be opaqued with DefaultParam function that will handle case if given variable doesn't exist.
Example of overrides can be found here: overrides

Passing environment variables

Instead of using overrides in file, it is possible to depend on environment variables. Only variables that start with CL2_ prefix will be parsed and available in script.

Environment variables can be used with DefaultParam function to provide sane default values.

export CL2_ACCESS_TOKENS_QPS=5

{{$qpsPerToken := DefaultParam .CL2_ACCESS_TOKENS_QPS 0.1}}

Measurement

Currently available measurements are:

• APIAvailabilityMeasurement
  This measurement collects information about the availability of cluster's control plane.
  There are two slightly different ways this is measured:
  • cluster-level availability, where we periodically issue an API call to /readyz,
  • host-level availability, where we periodically poll each of the control plane's host /readyz endpoint.
    • this requires the exec service to be enabled.
• APIResponsivenessPrometheusSimple
  This measurement creates percentiles of latency and number for server api calls based on the data collected by the prometheus server. Api calls are divided by resource, subresource, verb and scope.
  This measurement verifies if API call latencies SLO is satisfied. If prometheus server is not available, the measurement will be skipped.
• APIResponsivenessPrometheus
  This measurement creates summary for latency and number for server api calls based on the data collected by the prometheus server. Api calls are divided by resource, subresource, verb and scope.
  This measurement verifies if API call latencies SLO is satisfied. If prometheus server is not available, the measurement will be skipped.
• CPUProfile
  This measurement gathers the cpu usage profile provided by pprof for a given component.
• EtcdMetrics
  This measurement gathers a set of etcd metrics and its database size.
• MemoryProfile
  This measurement gathers the memory profile provided by pprof for a given component.
• MetricsForE2E
  The measurement gathers metrics from kube-apiserver, controller manager, scheduler and optionally all kubelets.
• PodPeriodicCommand
  This measurement continually runs commands on an interval in pods targeted with a label selector. The output from each command is collected, allowing for information to be polled throughout the duration of the measurement, such as CPU and memory profiles.
• PodStartupLatency
  This measurement verifies if pod startup SLO is satisfied.
• ResourceUsageSummary
  This measurement collects the resource usage per component. During gather execution, the collected data will be converted into summary presenting 90th, 99th and 100th usage percentile for each observed component.
  Optionally resource constraints file can be provided to the measurement. Resource constraints file specifies cpu and/or memory constraint for a given component. If any of the constraint is violated, an error will be returned, causing test to fail.
• SchedulingMetrics
  This measurement gathers a set of scheduler metrics.
• SchedulingThroughput
  This measurement gathers scheduling throughput.
• Timer
  Timer allows for measuring latencies of certain parts of the test (single timer allows for independent measurements of different actions).
• WaitForControlledPodsRunning
  This measurement works as a barrier that waits until specified controlling objects (ReplicationController, ReplicaSet, Deployment, DaemonSet and Job) have all pods running. Controlling objects can be specified by label selector, field selector and namespace. In case of timeout test continues to run, with error (causing marking test as failed) being logged.
• WaitForRunningPods
  This is a barrier that waits until required number of pods are running. Pods can be specified by label selector, field selector and namespace. In case of timeout test continues to run, with error (causing marking test as failed) being logged.
• Sleep
  This is a barrier that waits until requested amount of the time passes.
• WaitForGenericK8sObjects
  This is a barrier that waits until required number of k8s object fulfill given condition requirements. Those conditions can be specified as a list of requirements of Type=Status format, e.g.: NodeReady=True. In case of timeout test continues to run, with error (causing marking test as failed) being logged.

Prometheus metrics

There are two ways of scraping metrics from pods within cluster:

• ServiceMonitor
  Allows to scrape metrics from all pods in service. Here you can find example Service monitor
• PodMonitor
  Allows to scrape metrics from all pods with specific label. Here you can find example Pod monitor

Vendor

Vendor is created using Go modules.

^{1. As an example and anti-pattern see the 900 line load test config.yaml we ended up maintaining at some point. ↩}