Bloom Filters for Web Caching - Lightning Talk
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This document discusses using Bloom filters to cache dynamic web content for low latency. It describes how Bloom filters can be used to proactively revalidate cached data and check if it is still fresh. An end-to-end example is provided showing how Bloom filters could work from the browser cache to a CDN to check cached objects and estimate their time-to-live. Code snippets demonstrate integrating Bloom filters into querying and loading data from a backend database to leverage caching. The goal is to serve dynamic content from ubiquitous web caches for low latency with less processing.
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Bloom Filters for Web Caching - Lightning Talk
- 1. Bloom Filters for Web Caching Felix Gessert [email protected] @baqendcom
- 3. Average: 9,3s The Latency Problem Loading… -1% Revenue 100ms -9% Visitors 400ms 500ms -20% Traffic
- 4. If perceived speed is such an import factor for business ...what causes slow page load times?
- 5. The Problem Three Bottlenecks: Latency, Backend & Frontend High Latency Backend Frontend
- 6. Network Latency: Impact I. Grigorik, High performance browser networking. O’Reilly Media, 2013.
- 7. Network Latency: Impact I. Grigorik, High performance browser networking. O’Reilly Media, 2013. 2× Bandwidth = Same Load Time ½ Latency ≈ ½ Load Time
- 8. Goal: Low-Latency for Dynamic Content By Serving Data from Ubiquitous Web Caches Low Latency Less Processing
- 9. Idea: use HTTP Caching for dynamic data (e.g. queries) Our Approach Caching Dynamic Data with Bloom filters How to keep the browser cache up-to- date? How to automatically cache complex dynamic data in a CDN? When is data cacheable and for how long approximately?
- 10. In a nutshell Problem: changes cause stale data
- 11. Stale Data In a nutshell Problem: changes cause stale data
- 12. In a nutshell Solution: Proactively Revalidate Data Cache Sketch (Bloom filter) updateIs still fresh? 1 0 11 0 0 10 1 1
- 13. Innovation Solution: Proactively Revalidate Data F. Gessert, F. Bücklers, und N. Ritter, „ORESTES: a Scalable Database-as-a-Service Architecture for Low Latency“, in CloudDB 2014, 2014. F. Gessert und F. Bücklers, „ORESTES: ein System für horizontal skalierbaren Zugriff auf Cloud-Datenbanken“, in Informatiktage 2013, 2013. F. Gessert, S. Friedrich, W. Wingerath, M. Schaarschmidt, und N. Ritter, „Towards a Scalable and Unified REST API for Cloud Data Stores“, in 44. Jahrestagung der GI, Bd. 232, S. 723–734. F. Gessert, M. Schaarschmidt, W. Wingerath, S. Friedrich, und N. Ritter, „The Cache Sketch: Revisiting Expiration-based Caching in the Age of Cloud Data Management“, in BTW 2015. F. Gessert und F. Bücklers, Performanz- und Reaktivitätssteigerung von OODBMS vermittels der Web- Caching-Hierarchie. Bachelorarbeit, 2010. F. Gessert und F. Bücklers, Kohärentes Web-Caching von Datenbankobjekten im Cloud Computing. Masterarbeit 2012. W. Wingerath, S. Friedrich, und F. Gessert, „Who Watches the Watchmen? On the Lack of Validation in NoSQL Benchmarking“, in BTW 2015. M. Schaarschmidt, F. Gessert, und N. Ritter, „Towards Automated Polyglot Persistence“, in BTW 2015. S. Friedrich, W. Wingerath, F. Gessert, und N. Ritter, „NoSQL OLTP Benchmarking: A Survey“, in 44. Jahrestagung der Gesellschaft für Informatik, 2014, Bd. 232, S. 693–704. F. Gessert, „Skalierbare NoSQL- und Cloud-Datenbanken in Forschung und Praxis“, BTW 2015
- 14. Bit array of length m and k independent hash functions insert(obj): add to set contains(obj): might give a false positive What is a Bloom filter? Compact Probabilistic Sets https://github.com/Baqend/ Orestes-Bloomfilter 1 m 1 1 0 0 1 0 1 0 1 1 Insert y h1h2 h3 y Query x 1 m 1 1 0 0 1 0 1 0 1 1 h1h2 h3 =1? n y contained
- 15. 1 4 020 Browser Cache CDN Bloom filters for Caching End-to-End Example
- 16. 1 4 020 Browser Cache CDN Bloom filters for Caching End-to-End Example Gets Time-to-Live Estimation by the server
- 17. 1 4 020 Browser Cache CDN Bloom filters for Caching End-to-End Example
- 18. 1 4 020 Browser Cache CDN Bloom filters for Caching End-to-End Example
- 19. 1 4 020 purge(obj) hashB(oid)hashA(oid) 3 Browser Cache CDN 1 Bloom filters for Caching End-to-End Example
- 20. 1 4 020 31 1 110 Flat(Counting Bloomfilter) Browser Cache CDN 1 Bloom filters for Caching End-to-End Example
- 21. 1 4 020 31 1 110 hashB(oid)hashA(oid) Browser Cache CDN 1 Bloom filters for Caching End-to-End Example
- 22. 1 4 020 31 1 110 hashB(oid)hashA(oid) Browser Cache CDN 1 Bloom filters for Caching End-to-End Example
- 23. 1 4 020 hashB(oid)hashA(oid) 1 1 110 Browser Cache CDN Bloom filters for Caching End-to-End Example
- 24. 1 4 020 hashB(oid)hashA(oid) 1 1 110 Browser Cache CDN Bloom filters for Caching End-to-End Example 𝑓 ≈ 1 − 𝑒− 𝑘𝑛 𝑚 𝑘 𝑘 = ln 2 ⋅ ( 𝑛 𝑚 ) False-Positive Rate: Hash- Functions: With 20.000 distinct updates and 5% error rate: 11 Kbyte Consistency Guarantees: Δ-Atomicity, Read-Your-Writes, Monotonic Reads, Monotonic Writes, Causal Consistency
- 25. Show me some Code! End-to-End Example <script src="//baqend.global.ssl.fastly.net/js- sdk/latest/baqend.min.js"></script> Loads the JS SDK including the Bloom filter logic DB.connect('my-app'); Connects to the backend & loads the Bloom filter DB.News.load('2dsfhj3902h3c').then(myCB); DB.Todo.find() .notEqual('done', true) .resultList(myCB); Load & query data using the Bloom filter + Browser Cache
- 26. Show me some Code! End-to-End Example <script src="//baqend.global.ssl.fastly.net/js- sdk/latest/baqend.min.js"></script> Loads the JS SDK including the Bloom filter logic DB.connect('my-app'); Connects to the backend & loads the Bloom filter DB.News.load('2dsfhj3902h3c').then(myCB); DB.Todo.find() .notEqual('done', true) .resultList(myCB); Load & query data using the Bloom filter + Browser Cache Try this: www.baqend.com#tutorial
- 27. Ziel mit InnoRampUpOur Mission at Baqend: „Kick load times in the ass using Bloom filters“ www.baqend.com @Baqendcom