Joins in single-table queries

Tables are only metadata. They don’t store data.

I’ve written something about this before, but I want to take a viewpoint of this idea around the topic of joins, especially since it’s the topic for T-SQL Tuesday this month. Hosted this time by Sebastian Meine (@sqlity), who has a whole series on joins this month. Good for him – it’s a great topic.

In that last post I discussed the fact that we write queries against tables, but that the engine turns it into a plan against indexes. My point wasn’t simply that a table is actually just a Clustered Index (or heap, which I consider just a special type of index), but that data access always happens against indexes – never tables – and we should be thinking about the indexes (specifically the non-clustered ones) when we write our queries.

I described the scenario of looking up phone numbers, and how it never really occurs to us that there is a master list of phone numbers, because we think in terms of the useful non-clustered indexes that the phone companies provide us, but anyway – that’s not the point of this post.

So a table is metadata. It stores information about the names of columns and their data types. Nullability, default values, constraints, triggers – these are all things that define the table, but the data isn’t stored in the table. The data that a table describes is stored in a heap or clustered index, but it goes further than this.

All the useful data is going to live in non-clustered indexes. Remember this. It’s important. Stop thinking about tables, and start thinking about indexes.

So let’s think about tables as indexes. This applies even in a world created by someone else, who doesn’t have the best indexes in mind for you.

I’m sure you don’t need me to explain Covering Index bit – the fact that if you don’t have sufficient columns “included” in your index, your query plan will either have to do a Lookup, or else it’ll give up using your index and use one that does have everything it needs (even if that means scanning it). If you haven’t seen that before, drop me a line and I’ll run through it with you. Or go and read a post I did a long while ago about the maths involved in that decision.

So – what I’m going to tell you is that a Lookup is a join.

When I run SELECT CustomerID FROM Sales.SalesOrderHeader WHERE SalesPersonID = 285; against the AdventureWorks2012 get the following plan:

I’m sure you can see the join. Don’t look in the query, it’s not there. But you should be able to see the join in the plan. It’s an Inner Join, implemented by a Nested Loop. It’s pulling data in from the Index Seek, and joining that to the results of a Key Lookup.

It clearly is – the QO wouldn’t call it that if it wasn’t really one. It behaves exactly like any other Nested Loop (Inner Join) operator, pulling rows from one side and putting a request in from the other. You wouldn’t have a problem accepting it as a join if the query were slightly different, such as

Amazingly similar, of course. This one is an explicit join, the first example was just as much a join, even thought you didn’t actually ask for one.

You need to consider this when you’re thinking about your queries.

But it gets more interesting.

Consider this query:

It doesn’t look like there’s a join here either, but look at the plan.

That’s not some Lookup in action – that’s a proper Merge Join. The Query Optimizer has worked out that it can get the data it needs by looking in two separate indexes and then doing a Merge Join on the data that it gets. Both indexes used are ordered by the column that’s indexed (one on SalesPersonID, one on CustomerID), and then by the CIX key SalesOrderID. Just like when you seek in the phone book to Farley, the Farleys you have are ordered by FirstName, these seek operations return the data ordered by the next field. This order is SalesOrderID, even though you didn’t explicitly put that column in the index definition. The result is two datasets that are ordered by SalesOrderID, making them very mergeable.

Another example is the simple query

This one prefers a Hash Match to a standard lookup even! This isn’t just ordinary index intersection, this is something else again! Just like before, we could imagine it better with two whole tables, but we shouldn’t try to distinguish between joining two tables and joining two indexes.

The Query Optimizer can see (using basic maths) that it’s worth doing these particular operations using these two less-than-ideal indexes (because of course, the best indexese would be on both columns – a composite such as (SalesPersonID, CustomerID – and it would have the SalesOrderID column as part of it as the CIX key still).

You need to think like this too.

Not in terms of excusing single-column indexes like the ones in AdventureWorks2012, but in terms of having a picture about how you’d like your queries to run. If you start to think about what data you need, where it’s coming from, and how it’s going to be used, then you will almost certainly write better queries.

…and yes, this would include when you’re dealing with regular joins across multiples, not just against joins within single table queries.