High scalability | Memcached - Distributed object caching
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This document discusses strategies for improving scalability when handling large user bases and data loads. It describes using Memcached, which is a distributed caching system that stores objects in memory to improve performance. Memcached allows caching objects across servers so requests can be handled faster by retrieving cached data rather than querying databases. The document compares Memcached to other alternatives like database replication, sharding, and pre-generating static pages. It also provides references for further information.
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High scalability | Memcached - Distributed object caching
- 2. Large Data Largest Living creation: 1 California Redwood tree named the General Sherman PROBLEM
- 3. Highly Interactive 2 Starlings forming fascinating formations over Tøndermarsken, south-west Jutland, Denmark PROBLEM
- 4. Huge user base 5 World’s largest gathering: Kumbh Mela 2007 PROBLEM 3 4
- 5. 6 Brad Fitzpatrick, LiveJournal SOUNDS FAMILIAR! WHO ELSE HAS FACED THIS BEFORE?
- 6. Secondary Storage: Low access speed, high capacity Processor: Small size, high speed Network: Blazing fast Primary Memory: Fast, cheap Machine: Low reliability COMPUTING HARDWARE
- 7. Pre-Generate Static Pages • Fast Read • Dynamic context aware content • Popularity prediction in advance Web Server Farm • More concurrent web requests served • Dynamic Content delivery DB Replication • Data Replicas for Load Balancing • Fast Read • Slow Writes for ACID conformance ALTERNATIVES DB Sharding • Fast Read due to Horizontal partitioning • Limited scalability • Reliance on file system performance
- 9. Global distributed two layered object caching MEMCACHED Distributed object caching Right granularity at right Layer hashtableon RAM Cross-Platform Application namespace isolation Network independence Caches Objects, not raw data Dynamic server cluster reconfiguration Compression No authentication Highly volatile
- 10. Servers 1 2 4 3 6 5 10 11 7 8 Clients on application Servers Data Stores 12 9 Cross-Platform Application namespace isolation Network independence Caches Objects, not raw data Dynamic server cluster reconfiguration Compression No authentication Highly volatile MEMCACHED Distributed object caching
- 11. Associative Array: Meta information Runtime Cache: Single node applications Database Cache: No large data, rarely updated, similar requests, object creation not costly Memcached: Large scale distributed applications with frequently updated data and involving complex objects File Cache: Long term, Large Objects DB Read: Small scale needs 8 Cheetah, fastest land animal: 120km/h 7,9 Slug, slowest animal: 0.03mph 8 9 P E R F O R M A N C E MEMCACHED Distributed object caching
- 12. REFERENCES 1. http://www.mnn.com/sites/default/files/imagecache/node-gallery-display/general%20sherman.jpg 2. http://en.wikipedia.org/wiki/File:Sort_sol_pdfnet.jpg 3. http://odeworld.wordpress.com/2007/05/04/size-matters/ 4. http://www.kumbhamela.net/ 5. http://en.wikipedia.org/wiki/List_of_largest_peaceful_gatherings_in_history 6. http://picasaweb.google.com/dolboeb/BradFitzInSF#5176116242455706722 7. http://lilomag.com/2010/07/29/the-7-worlds-slowest-animal/ 8. http://en.wikipedia.org/wiki/Cheetah 9. http://en.wikipedia.org/wiki/File:Slugs_1896.png Memcached: http://benrobb.com/wp-content/uploads/2009/01/memcached.pdf By: Jeremy Leishman, Ben Robison, Josh Taylor
Editor's Notes
- DB Replication Image Reference : http://technet.microsoft.com/en-us/library/ms152567.aspx
- Storage keys evenly spread across servers, application uses Hash Table to determine which server to go to Two layers: 1: Server where key is stored 2: Actual Serialized object Since Caching: Collisions result in loss or wrong data being read : Application responsibility to avoid collision Blocking vs Non-Blocking Thread invoking an I/O function, like read value, does not have to wait on any previous operation before executing Objects have multiple versions and are reference counted
- Server Instance Listens on a specified IP address and Port Multiple instances on same machine, where server total memory greater than the amount that the kernel makes available to a single process Client Instance Read: Hash(Object Key) if Memcached has return else fetch from DB and Put Write: No transactions, Pull from DB -> Update Object -> Save to DB -> Save to Cache Interdependence: Data lost but rest of the machines in farm function normally, subsequent requests can be routed accordingly Expiration: LRU