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In this case I get True, which means that the table does exist (and I have access to it).ĭepending on your configuration, you may get t/ f instead of True/ False. We can use it to check if a given table exists in the current database: SELECT EXISTS ( The pg_tables view contains information about each table in the database. Setting the JDBC Statement.Below are five ways to check if a table exists in a PostgreSQL database.The Many Different Ways to Fetch Data in jOOQ May 19, 2022.The Performance of Various To-Many Nesting Algorithms June 9, 2022.
#Postgresql count how to
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#Postgresql count code
Using Testcontainers to Generate jOOQ Code.Create Dynamic Views with jOOQ 3.17's new Virtual Client Side Computed Columns.How to Filter a SQL Nested Collection by a Value.In any case, there shouldn’t be any difference, and I’m sure that a future PostgreSQL version will optimise the constant expression in the COUNT() aggregate function directly in the parser to avoid the extra work.įor other interesting optimisations that do not depend on the cost model, see this article here. when using COUNT() in HAVING or ORDER BY or with window functions, etc.
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Luckily (and this is rare in SQL), all the other dialects don’t care and thus, consistently using COUNT(*), rather than COUNT(1) is a slightly better choice for ALL measured database products from this article.ĭo note that the benchmark only tried a very simple query! The results might be different when using joins, unions, or any other SQL constructs, or in other edge cases, e.g. No relevant difference, nor a clear winner Run 1, Statement 1: 1.00442Īs it is now in 2019, given the database versions mentioned above, unfortunately, there is a significant difference between COUNT(*) and COUNT(1) in PostgreSQL. Curious to hear about your own results in the comments, or further ideas why this is so significant in PostgreSQL. No relevant difference, nor a clear winner Run 1, Statement 1 : 1.06874Ī significant, consistent difference of almost 10%: RUN 1, Statement 1: 1.00134Īgain, I’m surprised by the order of magnitude of this difference. No relevant difference, nor a clear winner: RUN STMT RELATIVE_TIME This removes any distraction that may be caused by interpreting actual execution times as we do not want to compare database products against each other. The times displayed are relative to the fastest run per database product.
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Each benchmark run repeated SELECT COUNT(*) FROM t or SELECT COUNT(1) FROM t 100 times on a 1M row table, and then the benchmark was repeated 5 times to mitigate any warmup penalties and be fair with respect to caching. The benchmark code can be found in the following gists: COUNT(*) was consistently faster by around 10% on 1M rows, that’s much more than I had expected Sometimes COUNT(1) was faster, sometimes COUNT(*) was faster, so all differences were only benchmark artifacts I ran a benchmark on the 4 most popular RDBMS, with these results: Okay, are you ready? The difference between count(*) and count(1) is a spin through this:īut for each row, so it's probably like tens of microseconds on a large grouping!- Vik Fearing September 15, 2019 Actors with their first_name containing A.Actors with their first_name ending with A.Actors with their first_name starting with A.We’ve already blogged about this technique in our previous post about aggregating several expressions in one single query. Now, we’re getting the correct result: actor_id|first_name |last_name |c |Īn even more powerful application of counting only non-null evaluations of an expression is counting only subsets of a group. Notice, we could count other things than the primary key, but with the primary key, we’re quite certain we don’t get any other “accidental” nulls in our groups, which we did not want to exclude from the count value. LEFT JOIN film_actor AS fa USING (actor_id) SELECT actor_id, a.first_name, a.last_name, count(film_id) AS c