Cachematic – Automatic Invalidation in Application-Level Caching Systems

Presentation at ICPE 2019

Transcript

1. Viktor Holmqvist Jonathan Nilsfors Philipp Leitner Cachematic – Automatic Invalidation in Application-Level Caching Systems Industry Track Presentation
2. Chalmers !2 Company Partner - Bison https://bisonanalytics.com
3. Chalmers !3 Application-Level Caching https://medium.com/@MatthewFTech/spring-boot-cache-with-redis-56026f7da83a
4. Chalmers !4 Cache Invalidation When to purge / update cached data?
5. Chalmers !5 Cache Invalidation When to purge / update cached data? • Time-Based • Explicit
6. Chalmers !6 Cache Invalidation When to purge / update cached data? • Time-Based • Explicit (company requirement)
7. Chalmers !7 Current State Manual Cache Management
8. Chalmers !8 Current State Manual Cache Management Consistent with state of industry, see for example Mertz and Nunes (TSE 2017) https://ieeexplore.ieee.org/stamp/stamp.jsp? tp=&arnumber=7762909
9. Chalmers !9 Challenges of Manual Cache Management • Easy to miss cacheable queries • Development / maintenance complexity • Potential for bugs
10. Chalmers !10 Goal Develop a library for application-level caching • Automated • Reusable • Minimal developer input “Cachematic”
11. Chalmers !11 Cachematic
12. Chalmers !12 Cachematic
13. Chalmers !13 Application Code …. End Users Statement Parser Dependency Tree Hash Function Read Request Processor Write Request Processor SQL Database Key-Value Store Cachematic Code Annotations Serialization Function Cachematic Architecture
14. Chalmers !14 Cache Management Algorithm parse DB statement process based on statement type (query or write)
15. Chalmers !15 If Query: if result in cache then: return cached val else: query DB save to val to cache Cache Management Algorithm parse DB statement process based on statement type (query or write)
16. Chalmers !16 If Query: if result in cache then: return cached val else: query DB save to val to cache Cache Management Algorithm If Write: decide which tables / columns / rows are touched foreach cached result: check for invalidation (hierarchically) write update to DB parse DB statement process based on statement type (query or write)
17. Chalmers !17 Implementation Application Code …. End Users Statement Parser Dependency Tree Hash Function Read Request Processor Write Request Processor SQL Database Key-Value Store Cachematic Code Annotations Serialization Function pyparsing Open Source release planned
18. Chalmers !18 Experimental Evaluation
19. Chalmers !19 Experiment Goal • Evaluate Cachematic based on Bison’s production application
20. Chalmers !20 Experiment Goal • Evaluate Cachematic based on Bison’s production application Main RQ: • Can we improve cache hit rate / performance for read requests?
21. Chalmers !21 Experiment Setup Load Testing Runs 6 user profiles AWS using Locust (https://locust.io) 30-minute test runs
22. Chalmers !22 Read Case (median)
23. Chalmers !23 Read Case Cache Hit Rate 69% (manual: 42%)
24. Chalmers !24 Read Case (90th percentile)
25. Chalmers !25 Lessons Learned • Can we improve cache hit rate / performance for read requests? Yes (by about a factor of 1.6 in our test setup)
26. Chalmers !26 Lessons Learned • Can we improve cache hit rate / performance for read requests? Yes (by about a factor of 1.6 in our test setup) • But: significant overhead in write requests To some extent engineering limitation Needs to be improved for OSS release
27. Chalmers !27 Conclusions Cachematic: Automatic application cache management for Python Improves cache hit rate and read performance (w.r.t. manual cache management solution) However: negative impact on write requests Although acceptable for Bison, Cachematic is not (yet) a general solution for all applications
28. Chalmers !28 I Am Hiring! Talk to me about opportunities in Sweden! (PhD / postdoc)