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I will give a few examples of the tables we have:

Table1 - Most queries are State Code and Year, has about 1000 rows, will grow by 1000 rows a year, used Round Robin

Table2 - Most queries are by State Code, Year, Column 1 (String), has about 1000 rows, will grow by 1000 rows a year, used Round Robin

Table3 - Most queries are by State Code and Year, has about 100,000 rows, will grow by about 25000 rows a year, used Hash

Table4 - Most queries are by Year, has about 100,000 rows, will grow by about 25000 rows a year, plan to use Hash

Lookup Table 5 - Most queries are by State Code and Look up Id, has about 10 rows, used Replication

Lookup Table 6 - Most queries are by State Code and Look pup Id, has about 500 rows, used Replication

Did we use the correct types of distribution? Can someone give a more concrete/better example/clarification of when/why you should use each type of distribution? Microsoft's documentation/guidance wasn't very helpful.

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  • Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.
    – Community Bot
    Dec 20, 2022 at 21:31
  • I'm asking for some guidance. We read the Microsoft Documentation and want to know if we applied the correct solution.
    – xmlapi
    Dec 20, 2022 at 22:15

1 Answer 1

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For tables of this size you shouldn't use Synapse Dedicated SQL pool. Per the Synapse guidance all those tiny tables should be Replicated, which is a strong indication that your data is too small for a Massively Parallel database system like Synapse SQL Pool.

Use Azure SQL Database instead, scales down to less than 1 core, and up to around 80 cores, plus additional readable replicas, and which supports columnstore tables, and partitioning.

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  • Interesting, thank you for some guidance. However, what about the tables that are expected to grow by at least 200k every year?
    – xmlapi
    Dec 20, 2022 at 23:02
  • Still tiny. Nowhere near needing an MPP database. Dec 20, 2022 at 23:29

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