vegeta.md

Vegeta

Vegeta is a versatile HTTP load testing tool.

Running Vegeta

Given that you followed instructions to deploy operator, you can modify Vegeta's cr.yaml to make it fit with your requirements.

apiVersion: ripsaw.cloudbulldozer.io/v1alpha1
kind: Benchmark
metadata:
  name: vegeta-benchmark
  namespace: my-ripsaw
spec:
  workload:
    name: vegeta
    args:
      clients: 2
      image: quay.io/cloud-bulldozer/vegeta:latest
      hostnetwork: false
      targets:
        - name: 100w-ka
          urls:
            - GET https://mydomain.com/test.png
            - GET http://myunsecuredomain.com
          samples: 2
          workers: 100
          duration: 10
          keepalive: true
        - name: 20w
          urls:
            - GET https://domain.com/api/endpoint
          workers: 20
          duration: 10

It accepts the following parameters:

Parameter	Description	Optional	Default
clients	Number of vegeta clients to launch	yes	1
image	Overwrites vegeta image	yes	quay.io/cloud-bulldozer/vegeta:latest
hostnetwork	Will launch vegeta clients in the same host network namespace	yes	false
nodeselector	Allow to specify a k8s nodeSelector for the clients	yes	-

The targets parameter contains the list of tests the vegeta pods will perform. Each of the test elements may have the following parameters:

Option	Description	Optional	Default
name	Test name	no	-
urls	List of URLs. Specified using the vegeta http format	no	-
samples	Number of iterations to execute	yes	1
workers	Number of vegeta workers	yes	1
duration	Test duration in seconds	no	-
keepalive	Specifies whether to reuse TCP connections between HTTP requests	yes	false

Vegeta benchmark behaviour

Tests described under the targets section are executed in series. In case of running multiple clients, each vegeta pod run their tests in parallel with the others. This test synchronization is achieved using the redis message bus running in the benchmark operator pod. Vegeta jobs make usage of pod anti-affinity rules to to deploy vegeta clients on different nodes:

    affinity:                      
      podAntiAffinity:                                                  
        preferredDuringSchedulingIgnoredDuringExecution:                          
          - weight: 100                              
            podAffinityTerm:                                        
              labelSelector:                                        
                matchExpressions:                                        
                - key: app                                 
                  operator: In                              
                  values:                                        
                  - vegeta-benchmark-{{ trunc_uuid }}

Storing results into Elasticsearch

It's possible to index reults in Elasticseach specifing using a configuration like the below.

apiVersion: ripsaw.cloudbulldozer.io/v1alpha1
kind: Benchmark
metadata:
  name: vegeta-benchmark
  namespace: my-ripsaw
spec:
  elasticsearch:
    server: esinstance.com
    port: 9200
    index_name: ripsaw-vegeta
  workload:
    name: vegeta
    args:
      hostnetwork: false
      image: quay.io/cloud-bulldozer/vegeta:latest
      targets:
        - name: 100w-ka
          urls:
            - GET https://mydomain.com/test.png
            - GET http://myunsecuredomain.com
          duration: 10

Where:

• elasticsearch.server this is the elasticsearch cluster ip you want to send the result data to for long term storage.
• elasticsearch.port port which elasticsearch is listening, typically 9200.
• elasticsearch.index_name Elasticseach to index the logs in. This parameter is optional, and defaults to ripsaw-vegeta.

In addition, the following parameters can be also specified at the spec level to improve ES indexing.

• test_user user is a key that points to user triggering ripsaw, useful to search results in ES. Defaults to ripsaw.
• clustername an arbitrary name for your system under test (SUT) that can aid indexing.

The following metrics are indexed at one second intervals:

• rps: Rate of sent requests per second..
• throughput: Rate of successful requests per second.
• status_codes: Breakdown of the number status codes observed over the interval.
• requests: Total number of requests executed until that moment.
• p99_latency: 99th percentile of the request latency observed over the interval in µs.
• req_latency: Average latency of all requests observed over the interval in µs.
• bytes_in: Incoming byte metrics over the interval.
• bytes_out: Outgoing byte metrics over the interval.
• targets: Targets file used.
• iteration: Iteration number.
• workers: Number of workers.
• hostname: Hostname from the host where the test is launched.
• keepalive: Whether keepalive is used or not.

Dashboard example

Using the Elasticsearch storage described above, we can build dashboards like the below.

The previous dashboard is available at arsenal