MySQL - Best method for selecting data for this Hierarchical Model? - mysql

Question

Suppose I had these 4 tables, consisting of various foreign key relationships (eg a area must belong to a location, a shop must belong to area, an item price must belong to a shop ect..)
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|Location Name | Location ID |
| | |
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|Area Name | Area ID | Location ID |
| | | |
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| Shop Name | Shop ID | Area ID |
| | | |
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| Item Price | Shop ID |
| | |
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And I wanted the sum of 'Item Price' that belonged to a specific location id. So all the areas and shops item price total for location id 'x'.
One way I found to do this is to join all the tables for one location and get the amount eg:
SELECT SUM(Item Price) FROM
items
left join shops ON (items.shop id = shops.shop id)
left join areas ON (shops.area id = areas.area id)
left join locations ON (areas.location id = location.location id)
WHERE Location Id = 4;
However is this the best way to do this since it involves retrieving the full tree of the data and filtering it out? Would there be a better way if there are a million rows or is this the best way?

Answer 1

You can try sub query --
SELECT SUM(Item Price) FROM
items
left join shops ON (items.shop id = shops.shop id)
left join (select area id from areas where Location Id = 4) as Ar ON (shops.area id = areas.area id)

Answer 2

If you define the right indexes, then the query does not read all the millions of rows for each table.
Think about a telephone book and how you look up a name. Do you read the whole book cover to cover looking for the name? No, you take advantage of the fact that the book is sorted by lastname, firstname and you go directly to the name. It takes only a few tries to find the right page. In fact, on average it takes about log2N tries for a book with N names in it.
The same kind of search happens for each join. If you have indexes, each comparison expression uses a similar lookup to find matching rows in the joined table. It's pretty fast.
But if that's not fast enough, you can also use denormalization, which in this case would be storing all the data in one table, with many columns wide.
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|Location Name | Area Name | Shop Name | Item Name | Item Price |
| | | | | |
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The advantage of denormalization is that it avoids certain joins. It stores the row just like one of the rows you'd get from the result set of your example joined SQL query. You just read one row from the table and you have all the information you need.
The disadvantage of denormalization is the redundant storage of data. Presumably each shop has many items. But each item is stored on a row of its own, which means that row has to repeat the names of the shop, area, and location.
By storing those data repeatedly, you create an opportunity for "anomalies" like if you change the name of a given shop, but you mistakenly change it only on a few rows instead of everywhere the shop name appears. Now you have two names for the same shop, and someone else looking at the database has no way of knowing which one is correct.
In general, maintaining multiple normalized tables in preferable, because each "fact" is stored exactly once, so there can be no anomalies.
Creating indexes to help your queries is sufficient for most applications.
You might like my presentation, How to Design Indexes, Really, and the video: https://www.youtube.com/watch?v=ELR7-RdU9XU