[Case Simulation Workshop] Michael Evans - High performance system building
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Michael is a software architecture leader who has experience building high performance systems. He discusses several techniques for optimizing system performance including: 1) Caching frequently accessed data like query results and API responses to improve response times. 2) Using a message queue to run long-running tasks like sending notifications asynchronously in the background. 3) Batching database operations like multiple inserts into a single query to reduce the number of roundtrips to the database.
[Case Simulation Workshop] Michael Evans - High performance system building
- 1. Michael Basic: High performance system building contact: michael@cinnamon.is
- 2. Introduction ❖ Graduated: Ha Noi University of Science and Technology - Software Engineer - 2010 ❖ Got No.3, Viet Name Talent in IT field - 2011. ❖ from 2007 - 2013: work at Med-Aid as Team Leader, a partner of Prowess. ❖ From 2013 - Now: working at Cinnamon as Software Architecture Leader.
- 3. Main points researching ❖ High performance system building ❖ Data mining ❖ Artificial Intelligent
- 4. How to build high performance system?
- 5. Normal system architecture database System logic Server side Third party system Client side Problem come from here!!!
- 6. Real use case ❖ Social network ❖ one user have: followers, followings, posts. ❖ …
- 7. Server side - Database
- 8. Database issues? Queries So slow - Multiple queries in one request - Each query take time to run System run slow
- 9. Database solution? Queries So slow ❖ Set Index for all important fields ❖ Optimise number of queries on each request / task ❖ Anything else?
- 10. Caching solution ❖ always check if needed data exist on cache or not ❖ if exist —> get from cache. ❖ if not —> query from DB and add to cache too. Logic CacheExist? Query if not exist in cache Update cache for each query
- 11. Caching solution ❖ Redis cache: https://redis.io ❖ Memcache: https://memcached.org ❖ …
- 12. Example ❖ If User 1111 create new post which has ID = cccc —> system need update that post id for all his following users. ❖ How will you do? UserId PostId 2222 aaaa 2222 bbbb 3333 aaaa Feeds table UserId FollowingID 1111 2222 1111 3333 1111 4444 Following table
- 13. Normal way ❖ Step 1: Get all following user’s ids from 1111. (0.2s) ❖ Step 2: For each following users: ❖ Step 2.1: Insert new record on Feeds table for that user. (0.2s) ❖ If 1111 have 50 following —> system will take: 0.2 + 50*0.2 = 10.2s !!!! —> Too slow!!!
- 14. Improved way ❖ Step 1: Get all following user’s ids from 1111. (0.2s) ❖ Step 2: For each following users: ❖ Step 2.1: Create feed’s record for each user. (so small time) ❖ Step 3: Insert all records in one command. (0.3s) ❖ If 1111 have 50 following —> system will take: 0.2 + 0.3 = 0.5s !!!! —>Much faster!!!
- 15. Apply cache? ❖ Step 1: Get all following user’s ids from cache. (0.002s) ❖ Step 2: For each following users: ❖ Step 2.1: Create feed’s record for each user. (so small time) ❖ Step 3: Insert all records in one command. (0.3s) ❖ Step 4: Insert all records to cache. (0.003s) ❖ If 1111 have 50 following —> system will take: 0.002 + 0.3 + 0.003 = 0.305s !!!! —>Much faster!!!
- 16. Server side - Logic
- 17. Server side - Logic ❖ POST / PUT/ DELETE ❖ GET Third party Data database Logic SLOW Third party Data Logic SLOW database Third party
- 18. POST/PUT/DELETE issues? Tasks need to do Database Third Party Request Response time > 2s Too much tasks need to do
- 19. POST/PUT/DELETE solution? Database Third Party Request Response time < 500 ms Realtime Background
- 20. How background work? ❖ It should be use message queue system. ❖ some message queue system: RabbitMQ, ZeroMQ, Kafka, Redis PubSub… Main server Message queue Background server 1 Background server 2
- 21. Messaging queue technology ❖ PubSub pattern (AMQP, MQTT…)
- 22. Can we make Realtime task run faster?
- 23. Caching for realtime task Task 1 Access DB Task 2 Update DB …. OLD way Task 1 Access Cache Task 2 Update Cache …. NEW way Task n Task n
- 24. Example ❖ when new post created by user 1111 —> push notification for all following users about that. ❖ How will you do?
- 25. Normal way ❖ User create new post ❖ Get all following user ids. ❖ for each following user: ❖ Insert new record to Feed table ❖ call push notification method to send push notification. ❖ Return result. So take time here
- 26. Improved way ❖ User create new post ❖ Get all following user ids. ❖ Insert all records in one command into Feed table ❖ For each following users: ❖ Add one task to message queue to run in background. ❖ Return result. So Fast
- 27. GET issues? Data queries Database Third Party Request Response time > 2s Too much thing need to format Format response data Too slow
- 28. Solution Data queries Database Third Party Request Response time <500ms Format response data Cache
- 29. How to make cache data optimise for GET apis? API get list of following users Get following Ids Format following objects Following cache Following formatted cache Always have cache object for any data we need for our apis.
- 30. How to apply them to your project / company?