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Deployment and testing strategies

Blue-Green deployment

  • two identical environments, a “blue” and a “green” environment with different versions of a service
  • once the software is working in the green environment, router can be switched so that all incoming requests go to the green environment - the blue one is now idle
  • replicating a production environment can be complex and expensive

  • similar to our current approach in Packit service - staging and production environment
  • requires more resources

Canary deployment

  • running two versions of the application simultaneously - stable, canary
  • the new version of the application is gradually deployed while getting a very small amount of live traffic
  • test in production with real users and use cases and compare different service versions side by side
  • based on the following assumptions:
    1. multiple versions of application can exist together at the same time, getting live traffic
    2. some customers might hit a production server in one request and a canary server in another (if no sticky session mechanism used)
  • usually the traffic is split based on weight (e.g. 90 percent of the requests go to new version, 10 percent go to old version)
  • good for performance and error rate monitoring, when tests are unreliable or lacking

  • for our use case this could be done on different levels, e.g. do the canary deployment only for the worker, also for the service
  • to split the traffic on the route level, it is possible to set up a route with multiple services (each service different version) in Openshift with defining weight for each (docs):
apiVersion: v1
kind: Route
name: route-alternate-service
annotations: roundrobin
kind: Service
name: service-name
weight: 20
- kind: Service
name: service-name2
weight: 10
- kind: Service
name: service-name3
weight: 10
  • one service can back up more pods, grouping is done by labels
  • Celery routing - also for retrying the jobs and routing them after failure to the stable version worker - requires changes in code
  • one database - inconsistency, change between versions and then simultaneous use is risky

A/B testing

  • different versions of the same service run simultaneously as “experiments” in the same environment for a period of time
  • released to subset of users under specific condition
  • primarily focused on experimentation and exploration
  • for testing new features

  • probably not really relevant to us

Shadow deployment

  • the new version is available alongside the old version, but a copy or forked version of traffic to the older version is sent to the new version for production testing
  • doesn’t affect users
  • service tested with production traffic
  • more expensive, more complex setup

  • lot of operations need to be mocked (e.g. so we don't set the Github status 2 times) - requires extra coding work and doesn't verify the code so well



Deployment or testing patternZero downtimeReal production traffic testingReleasing to users based on conditionsRollback duration
Blue/green (Version 2 is released alongside Version 1; the traffic is switched to Version 2 after it is tested. )xxInstant
Canary (Version 2 is released to a subset of users, followed by a full rollout.)xFast
A/B (Version 2 is released, under specific conditions, to a subset of users.)Fast
Shadow (Version 2 receives real-world traffic without impacting user requests.)xDoes not apply