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Moving Beyond the Row: The OVER Keyword
4:36The OVER keyword lets you transform an aggregate function into a window function. This lets an aggregate function see, and access, all of the results of a query.
When we use aggregate functions like sum,
count, or
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average in a query,
they calculate a result using
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the records that are associated with
their row by the GROUP BY statement.
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They can't access the values of any
records outside of their grouping.
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So the values that they return are placed
in the context of the overall results
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with the over keyword
we can open a Window so
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that these aggregate functions can see and
access all the results of the query.
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This transforms a regular aggregate
function into a Window function.
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Using just the over keyword,
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we can add information about the entire
tournament to our existing report.
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To answer our first question,
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how does each player's ranking
compare to the overall field.
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We calculate the average ranking of
all the players in the tournament.
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To do this,
we'll add a new field to the report.
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Let's start out by adding
the AVG(PLAYER_RANKING) AS AVG_RANKING.
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This gives us a different number for
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each player because the report
is grouped by player.
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The average function returns
the player's own ranking on each row.
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That's not what we want,
we want the average across all the rows.
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Let's have the over keyword to give
the function access to all the rows
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then we'll add empty parentheses.
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The parentheses create a space.
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To give the window function
more instructions for
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how to aggregate which we'll cover later.
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For now, we'll leave those options blank.
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So that we're querying
over the whole result set.
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Now, when we run the report, it's easy
to see whether each player is above or
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below the average ranking
which is 1,738.7.
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We can see this directly by subtracting
that average from the player's ranking.
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Let's make a new field to do that.
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We'll start out with
the player's own ranking.
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Player ranking.
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Then we'll subtract the window
function that we just created.
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Average of player ranking
over the entire result set.
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This tells us how far above or
below average each player's ranking is.
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It's important to know that unlike regular
aggregating functions, window functions do
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not have access to all of the records in
the tables that you're selecting from.
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Instead, they have access to
the results of the grouping
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that you've specified in
the queries group by clause.
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This means that if your
query has a group by clause.
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You can only use the grouping fields
directly while any other fields
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have to be aggregated inside the window
function aggregation for example, for
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our second question how many total
matches were played in the tournament.
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We want to add up the check me
field over the entire result set
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If we try to do that directly using some
of Checkmate over empty parentheses,
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it will not run because Checkmate
is not a grouping field.
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It's helpful to think of
the window function as querying
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over the results of the existing query.
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In other words the window only looks
on to the rows returned by the query
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when the function looks at each row of
the result set it has to get one value for
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that row.
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To use and its calculation.
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This example doesn't work because
the query results don't include
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the check mate field.
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Each row represents
a collection of records with
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multiple values for check mate.
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If we want to add up all of those values,
we have to first sum them up for
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the row using sum of Checkmate.
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And then we can pass that
aggregation to the window function.
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We can do this by adding a sum call
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inside of the sum that belongs
to the window function.
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Overall, we get sum of sum of
checkmate over empty parentheses.
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By summing up the sums of
all the players matches one,
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we find that there were 90 total
matches in the tournament.
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You can make some match inner and
outer aggregations.
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For example If we pass (MAX(CHECKMATE)
to the window function instead of sum,
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we'll get the number PLAYERS_WITH_WINS
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If we reverse that and
take the MAX(SUM(CHECKMATE),
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we'll get the highest number
of wins by any player.
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Combining different aggregations and
window functions multiplies the power of
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the original aggregation, so we can answer
more complex questions about the dataset.
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