![{
"container": {
"type": "DOCKER",
"docker": {
"image": "redis:2.6.17",
"network": "BRIDGE",
"portMappings": [
{
"containerPort": 6379,
"hostPort": 0,
"protocol": "tcp"
}
]
}
},
"id": "redis",
"instances": 1,
"cpus": 0.5,
"mem": 1024,
"healthChecks": [
{
"protocol": "TCP",
"portIndex": 0
}
]
}
MARATHON
wants
JSON](https://image.slidesharecdn.com/yuzu-vixnsmeetup-docker-151217123940/85/How-we-dockerized-a-startup-meetup-docker-21-320.jpg)
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How we dockerized a startup? #meetup #docker
- 1. How we dockerized a startup? #docker-marseille
- 3. #architecture #hosting #opsdev #libre #r&d #techtrends #IoT #artprojects
- 4. #startup #newyork #marseille #r&d #frenchtech #ecommerce #bpi #shake #network #bigdata #dockerlover #opensourcelover #hashtaglover
- 5. The first global cross-selling network between eMerchants “ “
- 8. months to do a MVP 4
- 9. No time to reinvent the wheel
- 10. Symfony - Java - MongoDB
- 11. PRODUCTION Web BigData - 2 physical servers - Multiple VMs - Chef & Chef server - 2 physical servers - Stack Cloudera (Hadoop/Spark/...)
- 13. A lot of technos joined Yuzu during the MVP Welcome to nodejs, scala, elasticsearch, hbase, redis, kafka, couchbase,...
- 14. They lived happily ever after
- 15. It doesn’t work in production… … that’s works fine in my local env “ “
- 16. This Lib isn’t in the right version... It’s slow… why?? Where are my logs... “ “
- 17. Time to look around...
- 18. Be kind, rewind Spring cleaning Microservices / 12 factors Resources isolation Improved security Continuous deployment Start-up compliant workflow Orchestration & supervision
- 20. Docker + Mesos + Marathon + Consul
- 21. { "container": { "type": "DOCKER", "docker": { "image": "redis:2.6.17", "network": "BRIDGE", "portMappings": [ { "containerPort": 6379, "hostPort": 0, "protocol": "tcp" } ] } }, "id": "redis", "instances": 1, "cpus": 0.5, "mem": 1024, "healthChecks": [ { "protocol": "TCP", "portIndex": 0 } ] } MARATHON wants JSON
- 25. Collect everything collectd / fluentd / rsyslog Convert to async messages kafka Filter and keep valuable data riemann / elasticsearch / influxdb Generate realtime alerts riemann Addictive dashboards kibana / grafana / riemann-dash
- 28. Our workflow
- 29. Git flow Jenkins DinD Private Registry Docker Doploy Marathon & Chronos Boot2Docker + Parallels
- 30. Don’t / Do
- 31. use tag "latest" on production use explicit version tags for your docker images DON’T
- 32. use data containers for valuable data storage layer may crash / change easy to forget when cleaning unused containers / images DON’T
- 33. use shared storage (glusterfs, nfs, ...) for your codebase, working dirs, config files or sessions sloooooooooow generate scary lock errors and timeouts fscache crash full systems easily DON’T
- 34. tag your custom images with the VCS commit hash ● makes your workflow better (same version tag in VCS and images) ● prevent useless image rebuilds ( speedup deploys ) ● easy way to know exactly which code version is running, even if the tag was changed / deleted in the VCS DO
- 35. avoid shared filesystems ● put all versioned data in containers ● use external object storage for user files (S3, ceph, swift ,...) ● use database / memcached / couchbase for sessions ● use templates to generate local config files (consul-template , confd) DO
- 36. Use docker independant storage for critical data ● lvm is your friend (unless you use and saturate thin volume metadata, use thin volumes with care) ● use storage plugins with docker >= 1.9.0 (convoy ?) ● redundant backups saves lives DO
- 38. - we tried to put the chicken in the egg - generate config files to shared storage - dns for discovery - use mongo replicaset without sharding - ...
- 39. Switch from registrator to mesos-consul Remove the lasts SPOFs Move user files to S3 Use the docker storage/network plugins XDCC Improve logs & metrics filtering and alerting
- 40. PHP/Symfony ReactJS Scala Go Clojure NodeJS Spark Langages/technos MongoDB Redis CouchBase Hdfs/HBase ElasticSearch Databases InfluxDB Sentry Statsd Grafana Riemann Collectd Kibana Fluentd Monitoring Gearman Kafka Queue Jenkins Doploy Continuous Integration & Delivery Route53 S3 CloudFront AWS Debian Docker Mesos Registrator Zookeeper Consul Marathon Chronos SYSTEM
- 41. BigData
- 43. Mesos and the yellow elephant - yarn, mesos, or both ? (myriad) - hdfs/hbase on mesos, not so simple (still testing) - kafka is awesome (we run it with docker) - spark on mesos - elasticsearch, born to cluster, also in docker
- 44. 2015 Other mesos/docker stories IPROFS A large scale php/drupal worldwide social app for “Institut français” ARTE.TV We are migrating all their vod and svod services to mesos/docker (java apps) VIXNS A mesos/docker cluster is collecting all logs and metrics from hundreds of servers
- 45. Conclusion