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Semelhante a High scalability | Memcached - Distributed object caching
Semelhante a High scalability | Memcached - Distributed object caching (20)
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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
Notas do Editor
- 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