Indexes And Data Access Patterns

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When I talk about “data access patterns” in relation to databases, it’s often met with blank stares.

What is a data access pattern? What does it have to do with indexes?

Well, my dear friend, today you’re gonna learn it good and hard.

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Data access patterns refer to the most common ways that queries filter, join, order, and display data.

The earliest point of a data access pattern is your where clause. Different queries may have different patterns of data to look for.

Giving some generic OLTP-ish examples, you might have queries that look for:

  • Customer orders
    • Within a date range
    • Ordered by most recent
  • Items in a customer order
    • With the total price
    • Plus shipping
    • Plus tax
  • Items in stock
    • Total quantity

Depending on how normalized your data is, getting some of this stuff will likely require 2-3 tables getting joins together in some manner.

But all of these different scenarios define your data access patterns, and this is how you need to gear your indexes.

A lot of people get caught up on the minutiae of indexes without taking care of any of the basics, worrying about foreign keys, GUIDs, fragmentation, and other ridiculous memes.

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Your data access patterns should define your indexes, because that — along with well-written queries to use those indexes most efficiently — is what’s going to make your application fast.

I’ve blogged about some of the fundamental concepts behind this in the past:

If you still need help with your index design after reading those, drop me a line! That’s the kind of thing I love helping people out with.

Thanks for reading!



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