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Scalable Web Site Antipatterns. Justin Leitgeb Stack Builders Inc. Overview. Based on architectures that have caused significant down-time and pain Like examples in Nygard's book, but more emphasis on essential rather than accidental properties of system.

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Scalable Web Site Antipatterns

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Scalable web site antipatterns l.jpg

Scalable Web Site Antipatterns

Justin Leitgeb

Stack Builders Inc.

Overview l.jpg


  • Based on architectures that have caused significant down-time and pain

  • Like examples in Nygard's book, but more emphasis on essential rather than accidental properties of system

Anti pattern 1 monotonically increasing data set with rapid growth l.jpg

Anti-pattern 1: Monotonically-increasing data set with rapid growth

  • Having a system that relies on querying all historical data

  • Requires joins from mega-tables (hundreds of millions of rows)

  • Often from automatically aggregated data

Detection l.jpg


  • Slow query log



  • vmstat

Vmstat l.jpg


Anti solutions l.jpg


  • Partitioning

  • Pre-caching (cron jobs)

  • Switching to MyISAM

  • NoSQL?

Nosql l.jpg


  • Out-of-the box solutions with NoSQL (e.g., Mongo) help with data modeling

  • Use CAP instead of ACID

  • May lead to better ability to distribute algorithms

  • But:

    • Haven't had as much effort yet expended on engines as MySQL (INNODB)

    • Often use the same algorithms (e.g., Btree indexes)

    • Can require more dev time (e.g., Cassandra and good implementation of distributed algorithms)

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Stop the bleeding

  • Cut off long queries

  • Turn off site sections

  • Fail whale

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  • Obvious - adding app servers, memcached, bigger DB server

  • Adding app servers puts more pressure on DB server

  • HTTP Caching (varnish)

  • MySQL tuning (look for things like FILESORT)

  • Read slaves

Solutions l.jpg


  • Hard-limit data volume - look for cases where data decreases in value with time

    • Add features related to scale

  • Distributed algorithms and data stores

  • Data warehousing

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Anti-pattern 2: Allowing "risky" writes to block HTTP responses 

  • Symptoms:

    • Slow requests

    • Servers hitting MaxClients and 500 error

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Possible Causes

  • Possible causes: database backed analytics tracking

  • Session management


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Risk increases with:

  • The number of requests invoking the write operation 

  • Traffic

  • Concurrent background operations

  • The algorithmic complexity of the write

  • Slow AWS I/O on EBS

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  • Asynchronize!

    • Write to a queue

  • Write to memcached or other non-ACID store

    • Later bring to data warehouse for advanced analytics

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More info

  • Nygard, Michael T. Release It!: Design and Deploy Production-ready Software. Raleigh, NC: Pragmatic, 2007.

  • Fowler, Martin. Patterns of Enterprise Application Architecture. Boston: Addison-Wesley, 2003.

  • Kimball, Ralph. The Data Warehouse Toolkit: Practical Techniques for Building Dimensional Data Warehouses: John Wiley & Sons 2010.

  • Schwartz, Baron. High Performance MySQL: O'Reilly, 2008

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