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Is it possible to split traffic by fixed ratio among two Deployments using only Service

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guilherme last edited by

Context: Kubernetes (1.23), 2 Deployments of the same app with different version/config/etc., 1 Service

Let's say I have a web application deployed in Kubernetes using a Deployment. I want to add a new (canary, A/B testing) version of the same app as a new separate Deployment, so we're not touching the original Deployment. I want to set fixed ratio of traffic between these two Deployments.

Is it possible to achieve this using Service, or other Kube native objects? (Not by utilizing a service mesh).
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shizuka last edited by

Closest solution I found so far is creating those two deployments with such number of pod replicas, that corresponds to the required ratio (or close to it). I.e. if the required ratio is 7:3, Deployment A would have 7 pods, Deployment B would have 3. Service being simple round-robin, should ensure the requests spread according to the ratio.

It's not a very elegant solution though, this might be quite a big waste of resources in some cases.
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I'd investigate what would it take to bundle all these customizations into something like a per-customer configurable theme that can be served by a single, more generic application code. It probably won't be easy and it may take a long time to reach such capability, but it can be done gradually: start with the N*5 repos and, as you inch forward toward this ideal solution you'll see the deltas in the application code from the repos starting to shrink - the differences will gradually migrate towards these custom themes/artifacts. When there are no more app code differences left then you can replace the N*5 repos with just 5 app code repos for now common app code and N*5 theme configurations/artifacts repos. Much easier to maintain.
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As long as you have access to the FROM image registry, you are totally free to choose where to pull from and where to push to.
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first of all You need to test connection from slave to master You can do this from command line (CMD windows) mysql --host=xxx.xxx.xxx.xx --port=3306 --user=my_slave --password=my_slave_password! if it will be successful You will have normal mysql console Welcome to the MySQL monitor. Commands end with ; or \g. Your MySQL connection id is 29 Server version: 5.7.15 MySQL Community Server (GPL) Copyright (c) 2000, 2016, Oracle and/or its affiliates. All rights reserved. Oracle is a registered trademark of Oracle Corporation and/or its affiliates. Other names may be trademarks of their respective owners. Type 'help;' or '\h' for help. Type '\c' to clear the current input statement. mysql> if not, You must check: network connection my_slave user credentials, including allowed host, like my_slave%yyy.yyy.yyy.yyy where yyy.yyy.yyy.yyy - Your real ip address (in case of MAMP it could be ip of router). this is one of the frequent reasons for Error_code: 1130 if all ok, You can make dump direct from slave: mysqldump --host=xxx.xxx.xxx.xx --port=3306 --user=my_slave --password=my_slave_password! --single-transaction --master-data=1 --all-databases > c:\dumps\all_db.sql restore dump on slave: mysql --host=xxx.xxx.xxx.xx --port=3306 --user=root --password=slave_root_password < c:\dumps\all_db.sql login to mysql console and start slave: mysql> start slave; generally - that all
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As per my experience, it is not a good idea because the availability of a pod is not directly related to how fast the container is up, a lot of other things affects the time the pod is ready and the time kubernetes sends traffic to that new pod. You need to think about how your scalability strategy is trying to adapt to your actual traffic and the different health checks change that readiness time. In Kubernete you have the livenessProbe and radynessProbe used to check two different thinks on the lifecycle of the pod, because of that you need to review the lifecycle of a pod in kubernetes and if your app can adapt to it. https://kubernetes.io/docs/concepts/workloads/pods/pod-lifecycle/ In case you are worried about the number of requests you can handle per second my advice is to have a pool of pods ready to response a good amount of it and take metrics to know better how your app responds to a high demanding traffic context and understand how the loadbalancing of kubenetes work and can affect your app, with that try to scale in a more smart way.
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As per Martin Flowers' definition of Blue-Green deployment The blue-green deployment approach does this by ensuring you have two production environments, as identical as possible. At any time one of them, let's say blue for the example, is live. As you prepare a new release of your software you do your final stage of testing in the green environment. Once the software is working in the green environment, you switch the router so that all incoming requests go to the green environment - the blue one is now idle. Steps You've said that DBs are synced using master-slave topology. So, to switch traffic from one instance to another, we have to make steps: Switch web-traffic from APP v1 to APP v2 Shutdown deprecated DB Blue Promote DB Green to master mode Start new DB Blue v2 in slave mode (you may ask - why shutdown DB Blue instead of using master-master replication or other ways to sync DB Blue v1 with DB Green in master mode. See explanation in DB replication) Graceful switch web-traffic To switch web-traffic from APP v1 APP v2 - just use nginx reload - it will graceful end all working connection and move traffic from App v1 to App v2 There are many scripting solutions for switch blue/green using shell/python scripts E.g.: meappy/docker-nginx-blue-green: Docker blue green upstreams for nginx with ngx_http_perl_module Simple 0-Downtime Blue Green Deployments: Python simple 0-Downtime Blue Green Deployments example The logic is simple: add green upstream in nginx config, use it as default and send reload to nginx: Sample nginx.conf for reverse proxy/load balancer http { upstream appv1 { zone appv1 64K; server 10.10.0.1:80; } upstream appv2 { zone appv2 64K; server 10.10.0.3:80; } server { ... location / { proxy_pass http://$appv1; } } } Replace proxy_pass http://$appv1; with proxy_pass http://$appvv; And then nginx -s reload Stop DB Blue systemctl stop postgresql Promote DB Green to master mode Trigger the promotion using your defined trigger in $PGDATA/recovery.conf touch $PGDATA/failover Remove failover trigger from Green DB cd $PRIMARY_DATA rm -f recovery.* failover Make sure that hot stanby mode is on on Green DB in master mode cat postgresql.conf | grep '#hot_standby = on' DB replication Make a backup of master Green DB psql -c "SELECT pg_start_backup('Streaming Replication', true)" postgresql://postgres@GreenDB/postgres Send a backup to Blue DB rsync -a $PG_DATA_Green_DB/ $BlueDB_IP:$PG_DATA_Blue_DB/ --exclude postmaster.pid --exclude postmaster.opts See also Evolution of Fault Tolerance in PostgreSQL DB Migration (More complex but more robust database migration scheme) You may use more complex scheme for migration using Ansible and `Pglupgrade tool: See for details: Near-Zero Downtime Automated Upgrades of PostgreSQL Clusters in Cloud (Part I) Near-Zero Downtime Automated Upgrades of PostgreSQL Clusters in Cloud (Part II) P.s some definitions. Just to... ... sort the buyers from the spyers, the needy from the greedy... And blue/green deployment vs canary deployment vs A/B test Definitions, kinds of deployments and tests (see details in these answers ) Blue-Green Deployment - When deploying a new version of an application, a second environment is created. Once the new environment is tested, it takes over from the old version. The old environment can then be turned off. A/B Testing - Two versions of an application are running at the same time. A portion of requests go to each. Developers can then compare the versions. Canary Release - A new version of a microservice is started along with the old versions. That new version can then take a portion of the requests and the team can test how this new version interacts with the overall system. Feature flagging - The action of "configuring" (cold, or even hot) which functionality is (not)available for which (group) of users. 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It is more about the predictable release with zero downtime deployment. 2. Easy rollbacks in case of failure. 3. Completely automated deployment process 4. In cloud environment where it is easier to script & recreate infrastructure, blue/green deployment is preferred as it allows the infrastructure to be in sync with the automation Canary Release A new version of a microservice is started along with the old versions. That new version can then take a portion of the requests and the team can test how this new version interacts with the overall system. 1. The canary release is a technique to reduce the risk of introducing a new software version in production by slowly rolling out the change to a small subset of users before rolling it out to the entire infrastructure. 2. It is about to get an idea of how new version will perform (integrate with other apps, CPU, memory, disk usage, etc). See also illustration (source Blue/Green: Canary:
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The documentation shows that you can have 40+ branches with different configurations. You express concern that the configuration becomes large. With a good editor that isn't really a problem. Yet that feels wrong, right? With git, in general, a repository per independent deployable works better than a single repository holding many independent deployables. So an alternative layout is not 1 repo with 40 branches, but 1+40 repos. That would be 1 repo that builds the latest code and creates releases. Then 40 repos that represent the configuration to deploy a particular release of the code configured for one website. The 40 repos that deploy the code can hold the 40 per-website configurations and a standardised build script that simply downloads the right release of the code to deploy along with the website configuration into the environment. We use 1+N+M git repos. We have 1 repo that holds the deploy scripts. They are versioned and released. We have N repos that build the different apps into releases. Currently, two php apps and two react apps that work together but are deployed and upgraded independently. Then M repos that represent the environments/sites we deploy into which is currently only staging and live. We are using k8s (OKD) and the deployment scripts are using helm. That just means that our deployment scripts and environment setup are simpler and rollback/downgrade is fully automated. We don't actually download the app to deploy during our build. Instead, we simply push out the docker registry tag of the version of the code we want to deploy into a given environment. I started a project to opensource our approach to deploying apps from git which is called OCD. It is still a work in progress but it demonstrates some of the above ideas.
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Your container is not set up to use systemd.docker.io/centos/systemd pull /usr/sbin/init I think it is good to use it to start.(Reference) https://hub.docker.com/_/centos/
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Indeed, if data are stored in the container ' s file system, the security is not guaranteed. kubernetes can at any time stop (destruct) the container in order, for example, to move it to another load-sharing facility. In this case, all data in the file system of such a container will be lost.The right approach is to use it. https://kubernetes.io/docs/concepts/storage/persistent-volumes/ which in turn are based on volume of docker♪ It is a long-term repository, essentially as a separate disc that can be attached to the container but not tied to its life cycle. I mean, even with the stops and redesigns of the coniner/substance, it will store data.
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I stumbled across the answer. From within the profile directory, run this command: inspec exec . Don't forget to set the depends key in inspec.yml.
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One option is to inherit your environment variable from another job. You can create two different jobs, one to create the environment variable, and one to trigger the local CI pipeline. Once all that is done, you should have something like this: stages: - build - deploy nightlies_env: stage: build variables: BUILD_NAME: "nightly" script: - BUILD_NAME="${BUILD_NAME}-$(date +"%Y%m%d")" - echo "BUILD_NAME=$BUILD_NAME" >> build.env artifacts: reports: dotenv: build.env nightlies: stage: deploy variables: BUILD_NAME: $BUILD_NAME trigger: include: build.yml strategy: depend
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Yes.. we can control the packages using nuspec file which will be added to your solution and you will be specifying the list of folders/files you dont wanna deploy. And you will use that nuspec to create the nuget package for deployments
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Yes, this is pretty easy with Jenkinsfiles with no need for any third-party plugins or anything along those lines: use the built-in Pipeline build step. I use this to trigger builds of projects in a dependency chain, so that after one project builds successfully, other projects that depend on it will pull in the updated dependency and build against it. Here is what this looks like in a Jenkinsfile: build repoName where repoName is a variable containing the name of the repository you wish to build. Things can get a little more complicated, such as if you're using folders or Multibranch Pipelines: build "${projectName}/${repoName}/${branchName}" This example builds a job within a Multibranch project where the variables are: projectName - the name of my Multibranch project repoName - the name of the repository branchName - the name of the branch to build, usually master Or this example builds a job with parameters and also triggers the build asynchronously (parent job won't wait for child job to complete before moving on to the next step): build( jobName, wait: false, parameters: [ [ $class: 'BooleanParameterValue', name: 'someBooleanParameter', value: true, ], [ $class: 'StringParameterValue', name: 'someStringParameter', value: 'some string value', ], ], ) As you can see, the invocation of build can get relatively complex. See the documentation linked above for more complete information.
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kubectl cp by default does recursive copies when given a directory, although it seems to be picky about trailing slashes. If foo/bar is the directory you'd like to copy, simply run kubectl cp /path/to/foo/bar <pod-id>:/path/in/container/foo/

Is it possible to split traffic by fixed ratio among two Deployments using only Service

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