![]() Loosely, it means that a LATERAL join is like a SQL foreach loop, in which PostgreSQL will iterate over each row in a result set and evaluate a subquery using that row as a parameter. This is repeated for each row or set of rows from the column source table(s). The resulting row(s) are joined as usual with the rows they were computed from. When a FROM item contains LATERAL cross-references, evaluation proceeds as follows: for each row of the FROM item providing the cross-referenced column(s), or set of rows of multiple FROM items providing the columns, the LATERAL item is evaluated using that row or row set’s values of the columns. (Without LATERAL, each sub- SELECT is evaluated independently and so cannot cross-reference any other FROM item.) This allows the sub- SELECT to refer to columns of FROM items that appear before it in the FROM list. The LATERAL key word can precede a sub- SELECT FROM item. The best description in the documentation comes at the bottom of the list of FROM clause options : Interested in learning more about Heap Engineering? Meet our team to get a feel for what it’s like to work at Heap! What is a LATERAL join? In this post, I’ll walk through a conversion funnel analysis that wouldn’t be possible in PostgreSQL 9.2. Here, the count aggregate counts only rows with temp_lo below 45 but the max aggregate is still applied to all rows, so it still finds the reading of 46.PostgreSQL 9.3 has a new join type! Lateral joins arrived without a lot of fanfare, but they enable some powerful new queries that were previously only tractable with procedural code. ![]() SELECT city, count(*) FILTER (WHERE temp_lo < 45), max(temp_lo)įILTER is much like WHERE, except that it removes rows only from the input of the particular aggregate function that it is attached to. This is more efficient than adding the restriction to HAVING, because we avoid doing the grouping and aggregate calculations for all rows that fail the WHERE check.Īnother way to select the rows that go into an aggregate computation is to use FILTER, which is a per-aggregate option: In the previous example, we can apply the city name restriction in WHERE, since it needs no aggregate. The same condition could be used more efficiently at the WHERE stage.) (Strictly speaking, you are allowed to write a HAVING clause that doesn't use aggregates, but it's seldom useful. On the other hand, the HAVING clause always contains aggregate functions. Thus, the WHERE clause must not contain aggregate functions it makes no sense to try to use an aggregate to determine which rows will be inputs to the aggregates. The fundamental difference between WHERE and HAVING is this: WHERE selects input rows before groups and aggregates are computed (thus, it controls which rows go into the aggregate computation), whereas HAVING selects group rows after groups and aggregates are computed. It is important to understand the interaction between aggregates and SQL's WHERE and HAVING clauses. The LIKE operator does pattern matching and is explained in Section 9.7. ![]() Finally, if we only care about cities whose names begin with “ S”, we might do: Which gives us the same results for only the cities that have all temp_lo values below 40. We can filter these grouped rows using HAVING: Each aggregate result is computed over the table rows matching that city. For example, we can get the number of readings and the maximum low temperature observed in each city with: This is OK because the subquery is an independent computation that computes its own aggregate separately from what is happening in the outer query.Īggregates are also very useful in combination with GROUP BY clauses. WHERE temp_lo = (SELECT max(temp_lo) FROM weather) ![]() (This restriction exists because the WHERE clause determines which rows will be included in the aggregate calculation so obviously it has to be evaluated before aggregate functions are computed.) However, as is often the case the query can be restated to accomplish the desired result, here by using a subquery: SELECT city FROM weather WHERE temp_lo = max(temp_lo) WRONGīut this will not work since the aggregate max cannot be used in the WHERE clause. If we wanted to know what city (or cities) that reading occurred in, we might try: For example, there are aggregates to compute the count, sum, avg (average), max (maximum) and min (minimum) over a set of rows.Īs an example, we can find the highest low-temperature reading anywhere with: An aggregate function computes a single result from multiple input rows. Like most other relational database products, PostgreSQL supports aggregate functions.
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