I've been using GROUP BY
for all types of aggregate queries over the years. Recently, I've been reverse-engineering some code that uses PARTITION BY
to perform aggregations. In reading through all the documentation I can find about PARTITION BY
, it sounds a lot like GROUP BY
, maybe with a little extra functionality added in? Are they two versions of the same general functionality, or are they something different entirely?
However, it's still slower than the GROUP BY. The IO for the PARTITION BY is now much less than for the GROUP BY, but the CPU for the PARTITION BY is still much higher. Even when there is lots of memory, PARTITION BY – and many analytical functions – are very CPU intensive.
Therefore, in conclusion, the PARTITION BY retrieves all the records in the table, while the GROUP BY only returns a limited number. One more thing is that GROUP BY does not allow to add columns which are not parts of GROUP BY clause in select statement. However, with PARTITION BY clause, we can add required columns.
PARTITION BYThe window function is applied to each partition separately and computation restarts for each partition. If PARTITION BY is not specified, the function treats all rows of the query result set as a single partition. Function will be applied on all rows in the partition if you don't specify ORDER BY clause.
The GROUP BY clause specifies how to group rows from a data table when aggregating information, while the HAVING clause filters out rows that do not belong in specified groups. Aggregate functions perform a variety of actions such as counting all the rows in a table, averaging a column's data, and summing numeric data.
They're used in different places. group by
modifies the entire query, like:
select customerId, count(*) as orderCount from Orders group by customerId
But partition by
just works on a window function, like row_number
:
select row_number() over (partition by customerId order by orderId) as OrderNumberForThisCustomer from Orders
A group by
normally reduces the number of rows returned by rolling them up and calculating averages or sums for each row. partition by
does not affect the number of rows returned, but it changes how a window function's result is calculated.
We can take a simple example.
Consider a table named TableA
with the following values:
id firstname lastname Mark ------------------------------------------------------------------- 1 arun prasanth 40 2 ann antony 45 3 sruthy abc 41 6 new abc 47 1 arun prasanth 45 1 arun prasanth 49 2 ann antony 49
GROUP BY
The SQL GROUP BY clause can be used in a SELECT statement to collect data across multiple records and group the results by one or more columns.
In more simple words GROUP BY statement is used in conjunction with the aggregate functions to group the result-set by one or more columns.
Syntax:
SELECT expression1, expression2, ... expression_n, aggregate_function (aggregate_expression) FROM tables WHERE conditions GROUP BY expression1, expression2, ... expression_n;
We can apply GROUP BY
in our table:
select SUM(Mark)marksum,firstname from TableA group by id,firstName
Results:
marksum firstname ---------------- 94 ann 134 arun 47 new 41 sruthy
In our real table we have 7 rows and when we apply GROUP BY id
, the server group the results based on id
:
In simple words:
here
GROUP BY
normally reduces the number of rows returned by rolling them up and calculatingSum()
for each row.
PARTITION BY
Before going to PARTITION BY, let us look at the OVER
clause:
According to the MSDN definition:
OVER clause defines a window or user-specified set of rows within a query result set. A window function then computes a value for each row in the window. You can use the OVER clause with functions to compute aggregated values such as moving averages, cumulative aggregates, running totals, or a top N per group results.
PARTITION BY will not reduce the number of rows returned.
We can apply PARTITION BY in our example table:
SELECT SUM(Mark) OVER (PARTITION BY id) AS marksum, firstname FROM TableA
Result:
marksum firstname ------------------- 134 arun 134 arun 134 arun 94 ann 94 ann 41 sruthy 47 new
Look at the results - it will partition the rows and returns all rows, unlike GROUP BY.
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