1. Message Queues at salesforce.com
The subtitle goes here
Vijay Devadhar
Developer
Salesforce.com

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3. Vijay Devadhar
Developer

4. Message Queues – what are they?
 Asynchronous job queue infrastructure in salesforce.com server
 Engine behind
• Dashboards
• Reports,
• Batch/Async Apex
• Bulk API
• and many more…

5. Message Queues – what are our volumes?
 Averages about 60 million messages a day
 Biggest instances account for 10 million messages a day
 95 percentile for dequeue latency is 10 minutes

6. Message Queues – session description
 Discuss scaling techniques used to
• Manage capacity
• Resource allocation
 and lessons learned..

9. Visit with Princess Aurora
 Average wait time – 90 mins
 Average time with princess – 120 seconds
 What you remember the most after a year? The long……… wait. 

11. Visit with Goofy
 Average wait time – none
 Average time with goofy – 120 seconds or until you get bored

12. Meanwhile…..

13. Characters are not Created Equal

14. If Only We Hired More Princess Aurora

15. What can we do better?
 Fire Goofy(s)
 Hire more Princesses
 Convert Goofy to Princess and vice-versa when needed

16. Swap Goofy for Princess Aurora

18. Message Queues Amusement Park
 300 + rides and characters
 Traffic which ebbs and flows with time of day, day of the week etc.,
 Plenty, but finite set of resources available

19. Goal
 Reduce wait times
 Fairly allocate resources
 Adapt to varying traffic patterns

20. Solution
 A large Shared thread pool – No ride specific silos
 Round robin the process of picking work
 If world wants Dashboards, do Dashboards

22. Message Queue Real Time Latencies
 Unlike Disneyland, each job takes variable amount of time
 Wait time prediction is not accurate at the tail of the queue
 We report real time and act on them if needed

25. Elastic Thread Pools
 Can grow from initial size
 This allows growth as traffic demands
 Wait times feedback to thread pool grow, shrink decisions

26. Let’s Do a Puzzle
 A man has
• One Sheep
• One Tiger
• One bundle of Grass
• One small boat
 and a big River to cross…

28. Similar Puzzle with Messages
 Forecasting has
• Several sales reps whose forecasts need update
• Forecast update for sales rep should also update VP of sales
• Multiple sales reps for same VP
 and a big sales projection to put out ….

29. Solution
 Browse and Cache
 Pick up work on which you can obtain mutex lock
 Jump ahead if needed

30. And in Real World..
 Cache capacity is tuned to typical traffic pattern
 At times cache can fill up
 Messages may be escorted to the back of the queue

31. Bread Lines Vs. Turkey Lines
 Same set of ovens baking both
 Bread is the basic need, Turkey when ovens are free
 If bread lines build up, stop cooking turkey
 If no one wants bread, just give all ovens to cooking turkey

32. User-facing vs Background jobs
 Same set of servers for both
 Users need fast response; Background can wait
 If user requests pile up, stop processing background
 If no user requests, just process background jobs

33. But how do you make Turkeys stop?
 Traffic lights
 Measure key resources
 When resource usage crosses threshold, slow down on background
 Sensitive to CPU, Memory, I/O, connection usage

34. Lessons in Real World
 Traffic types vary, traffic volumes vary
 Handlers misbehave, components have bugs
 Distributed systems scale very well, not if you need mutex.
 Real time alerting, trending, traffic isolation, troubleshooting are
necessary