Caching - A Primer

Transcript

1. Caching and Django
   A Primer
   @ashchristopher
   

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2. What is a Cache?
   

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3. Temporary storage
   of non-complex data
   

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4. Provides very
   fast lookups
   

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5. Django supports simple
   caching
   

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6. Django
   includes support for a number of
   cache
   backends out-of-the-box
   

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7. Memcache
   cluster of Memcache servers
   Database cache
   uses database table for cache
   Filesystem cache
   file on the local filesystem
   Local memory cache
   only good for local development
   

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8. Easy to make your own
   cache backend
   

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9. Extend
   core.cache.backends.base.BaseCache
   

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10. Simple caching follows the
    same pattern
    

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11. 1.
    Check the cache for data and if found,
    return it
    2. If data is not in cache -
    get data from the
    database,
    put data in cache then return the
    data
    3.
    Invalidate cache on data change.
    

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12. Your system
    works
    even if your cache is
    offline
    

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13. Some cool projects/apps to
    help you with it
    

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14. Django Middleware
    (per-site and per-view)
    

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15. Cache Machine
    

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16. Johnny-Cache
    

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18. Low-level Caching
    (doing it by hand)
    

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19. Caching is simple...
    

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20. ... except when it isn’t!
    

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21. Cache invalidation is hard
    

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22. Cache invalidation is especially
    hard at scale
    

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23. What happens when an
    expensive
    operation falls out of cache?
    

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24. Thundering Herd
    

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25. Fundamental
    flaw?
    

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26. I think so!
    

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27. Caching doesn’t mean what it
    used to mean
    

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28. What you want is
    probably not caching
    

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29. Denormalized Data
    

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30. Actively
    set new data instead of letting things fall
    out of cache
    

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31. Nothing falls out of cache
    

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32. Set your data via
    asynchronous processes
    

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34. No Silver Bullet
    

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35. Optimizing read
    speed by adding
    redundant data
    

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36. Persistent storage
    

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37. NoSQL
    

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38. There is a
    problem with denormalized data at
    scale
    

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39. Denormalized data becomes a
    requirement
    

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40. What problem?
    

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41. Embrace denormalized data
    

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42. @ashchristopher
    ash.christopher@gmail.com
    

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