Mad scalability: Scaling when you are not Google
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A brief history of how Cabify grew during the last couple of years without using cloud platforms like AWS or Google Cloud I gave this talk at the madScalability meet up on Madrid, March 2017.
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Mad scalability: Scaling when you are not Google
- 1. Scaling when you are not Google Abel Muiño
- 2. Abel Muino ‣ Lead Software Engineer ‣ Tweets as @amuino ‣ In another life, co-owned 1uptalent.com, played with Docker and used AWS for everything.
- 3. Disclaimer ‣ Cabify is 5 years old ‣ I joined Cabify about 1.5 years ago to work on product ‣ What you will hear today might be ‣ 70% folklore / 30% experience ‣ Only about production ‣ Not applicable to other areas (data analytics)
- 5. Cabify
- 6. 2011 2012 2013 2014 2015 2016 Completed Journeys (Axis has no legend because NDA and stuff)
- 7. Backend committers 0 5 9 14 18 2011 2012 2013 2014 2015 2016
- 8. We are hiring! (As if it wasn’t obvious from the charts)
- 11. Cabify foundations ‣ Mostly Ruby, some Go ‣ Running on VPS ‣ No sysadmins (devops?) ‣ CouchDB ‣ Redis ‣ Home-grown metrics & monitoring (limited)
- 12. Servers ‣ 3 ⨉ Host servers ‣ Horizontally scalable ‣ Most services included (sidecars) ‣ Front + Back + Queue workers ‣ 1 ⨉ Realtime server ‣ Single Point of Failure ‣ Ansible for setting them up VPS Provider LB web1 web2 web3 worker1 LBLB redis1 redis2elastic realtime osrm websock
- 13. CouchDB ‣ Used to be run in-house → Unreliable ‣ Moved to Cloudant ‣ Managed ‣ Bare metal servers ‣ Requisite for everything else: to run on the same datacenter ‣ …because the network matters Database of choice for Cabify
- 14. Pros ‣ Cheap servers ‣ Profesional DB management ‣ Still cheaper than in-house staff ‣ Scales up by either ‣ Emailing Cloudant ‣ Deploying new VPSs ‣ Datacenter lock-in ‣ Scarce visibility on load ‣ Low VPS utilization (for some services) Cons
- 15. Tl;dr: everything was fine Until it wasn’t
- 16. 2015 Road to bare metal
- 17. In 2014 we handled 7 times the load of 2013
- 18. Installed NewRelic ‣ Monitors our ruby stack ‣ Built custom adapters for API toolkit and CouchDB ‣ Golang not supported 😭 ‣ Low hanging fruit for increasing performance ‣ Hint: Always contact a Sales Rep ‣ Bye bye home-grown monitoring! 👋
- 19. VPS provider DDoSed ‣ Several times a week ‣ Cabify was unreachable ‣ VPSs where unreachable on the internal network ‣ Slow & bad support ‣ Reputation ‣ Solution: Level up!
- 20. Nobody ever got fired for choosing IBM Moved to Bare Metal @ Softlayer Same guys hosting our Cloudant cluster 👍
- 21. Mindset Control the core, minimise work for everything else
- 22. Everything must go VPS Provider web1 web2 web3 worker1realtime LBLBLB redis1 redis2elastic osrm subscriber
- 23. Load Balancer ‣ Multiple PoP (starting operations in several countries) ‣ CDN ‣ Supporting websockets ‣ … and Load Balancing ‣ Low TCO ‣ https://www.incapsula.com
- 24. Redis, ElasticSearch ‣ Same datacenter ‣ Completely managed ‣ Clustered / reliable ‣ RedisLabs ‣ Bonus: Memcached ‣ Qbox
- 25. OSRM ‣ Same datacenter ‣ Completely managed ‣ Enhanced dataset ‣ Google Maps & Places (with enterprise license) ‣ 2 / 3, good enough
- 26. Can do better? Can we manage less infra? Softlayer web1 web2 web3 worker1realtime Google subscriber Incapsula RedislabsRedislabsRedislabs qboxqboxQbox RedislabsRedislabsCloudant
- 27. Subscriber ‣ Felt like reinventing the wheel ‣ Looked for battle-tested bus / queue / broker ‣ In the same datacenter ‣ Had previous experience with RabbitMQ ‣ CloudAMQP Homebrew message bus / queue
- 28. Sidecars ‣ Every server could run Cabify ‣ All services installed ‣ Except Realtime (SPOF) ‣ Horizontal scaling ‣ Good server utilisation (bare metal servers are larger) Make each host self-sufficient
- 29. Cut own servers in 50% Served 5 times more requests Softlayer host01 host02 host03 realtime Google Incapsula RedislabsRedislabsRedislabs qboxqboxqbox RedislabsRedislabsCloudant CloudAMQPCloudAMQP
- 30. Pros ‣ Same-datacenter latencies ‣ Only care about our product ‣ Still cheaper than in-house staff ‣ Scales up by either ‣ Emailing a provider ‣ Deploying new Servers ‣ Good visibility on perf ‣ Datacenter lock-in ‣ Still no visibility on Golang perf ‣ Competing services on each server with different needs ‣ Fast & light http requests ‣ Slow & heavy queue workers ‣ Debugability Cons
- 31. Tl;dr: everything was fine Until it wasn’t
- 32. 2016, pushing to the limit
- 33. In 2015 we handled 5 times the load of 2014
- 34. In 2016 we would invade LatAm (new countries, cities, marketing…)
- 35. Bumps on the road ‣ Start seeing intermittent latency spikes on Cloudant ‣ Disable some services, get back on track ‣ Tied to peak hours ‣ We lived through these, but was stressful
- 36. Be easy on the database ‣ Removed frequent N+1 queries patterns ‣ Moved some queries to ElasticSearch ‣ Started caching more on Memcache ‣ Grew the cluster ‣ From 200ms to 100ms (average) 👏 (trying to sleep better)
- 37. RabbitMQ can’t cope ‣ We saturated the cluster CPU with moderate load ‣ Tied to us using tag-based routing ‣ Messages were delivered much later than expected ‣ Made changes to use simpler routing ‣ Is there anything simpler than RabbitMQ for simple routing? 🤔
- 38. Interlude DynDNS goes down, Cloudant uses them We lose access to our databases cluster load balancer Patched /etc/hosts with the actual ips in 30 minutes
- 39. The right tool for the job ‣ Clouchdb / Cloudant, not the best database for frequent updates ‣ Looking for alternatives to store fast-changing models ‣ RethinkDB ‣ Fast, easy to use, hosted options in same datacenter ‣ Streaming query updates Expecting growth in line with previous years
- 40. Broke RethinkDB load balancer Database stats were OK, but the LB couldn’t handle our rate Slow support, no “enterprise” option Decided to phase out RethinkDB
- 41. Wrote our first «database» Simple in-memory store, backed by Couchdb Update indexes on writes. All queries are indexed Implemented in Golang, consumed from Ruby Replaces RethinkDB, which replaced CouchDB
- 42. Cloudant latency spikes fixed! Grow the cluster for the second time in the year Load balancers hardware upgraded, problems gone Also reduced the number of connections from ruby
- 43. Relax the Sidecars ‣ Load on background workers interfering with serving http ‣ Split the servers: ‣ Front (ruby/golang http interface) ‣ Workers (ruby job queues, ruby background)
- 44. Remove RabbitMQ Replace with NSQ Nice mix of sidecar and discovery
- 45. Softlayer Multiplied own servers by 3 Served 4 times more requests Google Incapsula RedislabsRedislabsRedislabs qboxqboxqbox RedislabsRedislabsCloudant CloudAMQPCloudAMQP host01-09host01-09host01-09host01-09 rt01-02rt01-02 work01-03work01-03work01-03
- 46. Pros ‣ Despite the problems, we had top-notch support from Cloudant ‣ Easy to scale out ‣ In-process database opened doors to new features ‣ Datacenter lock-in ‣ Still no visibility on Golang perf Cons
- 47. Cabify @ 2017
- 48. In 2016 we handled 4 times the load of 2015
- 49. Hired our first full-time sysadmin!
- 50. Taking ownership Improve our infra
- 51. Own load balancers ‣ Still use Incapsula for its PoP ‣ Achieved much better load balancing ‣ 3 new dedicated servers Better control & traceability
- 52. Plans for the future
- 53. Own redis cluster ‣ Migrating from Redislabs hosted to Redislabs Enterprise ‣ hosted used virtual servers ‣ we rely heavily on redis (and memcached) ‣ 3 new dedicated servers ‣ WIP Better control & traceability
- 54. Ruby → Elixir ‣ Fun to code with ‣ Higher performance ‣ Less memory ‣ Investment, about to release first service to production
- 55. Extract from Product Dedicated teams and resources for specific components Make the core of Cabify leaner
- 56. Thanks! And sorry for the 60 slides
- 57. Questions?