However, both forms of count(1) and count(*) are fundamentally slow.
Running the above benchmark with count(1) results in: # average 98.896 ms (Historically, the expression ought to have been defined as count().) On the other hand, count(1) takes an argument and PostgreSQL has to check at every row to see that its argument, 1, is indeed still not NULL. PostgreSQL parses the expression count(*) as a special case taking no arguments. The star symbol is meaningless here, unlike its use in SELECT *. count(*): One might think that count(1) would be faster because count(*) appears to consult the data for a whole row. # Tests in this article were run against PostgreSQL 9.5.4Įcho "SELECT count(*) FROM items " | pgbench -d count -t 50 -P 1 -f -Ī note about count(1) vs. Pgbench provides a convenient way to run a query repeatedly and collect statistics about performance. Measuring the time to run this command provides a basis for evaluating the speed of other types of counting. Exact counts allow duplication over some or all of a table - good old count(*). Let's look at exact counts and estimated counts. inform planner of big table size change create a million random numbers and strings The sections below use the following table for benchmarks.
#Count filter postgresql how to#
After learning about techniques for a single database, we’ll use Citus to demonstrate how to parallelize counts in a distributed database. We’ll analyze the techniques available for each situation and compare their speed and resource consumption. Next, are you counting duplicates or just distinct values? Finally, do you want a lump count of an entire table or will you want to count only those rows matching extra criteria? First, think about whether you need an exact count or if an estimate suffices. The problem is actually under-described - there are several variations of counting, each with their own methods. If you know the tricks, there are ways to count rows orders of magnitude faster than you do already. This article takes a close look into how PostgreSQL optimizes counting. Everybody counts - but not always quickly.
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