The Performance of Various To-Many Nesting Algorithms

It's been a while since jOOQ 3.15 has been released with its revolutionary standard SQL MULTISET emulation feature. A thing that has been long overdue and which I promised on twitter a few times is to run a few benchmarks comparing the performance of various approaches to nesting to-many relationships with jOOQ. This article will … Continue reading The Performance of Various To-Many Nesting Algorithms

Setting the JDBC Statement.setFetchSize() to 1 for Single Row Queries

An interesting hint by Vladimir Sitnikov has made me think about a new benchmark for jOOQ: https://twitter.com/lukaseder/status/1407662449331949568 The benchmark should check whether single row queries should have a JDBC Statement.setFetchSize(1) call made to them by default. The Javadoc of the method says: Gives the JDBC driver a hint as to the number of rows that … Continue reading Setting the JDBC Statement.setFetchSize() to 1 for Single Row Queries

Vendor Agnostic, Dynamic Procedural Logic with jOOQ

One of the strengths of modern RDBMS is the capability to mix the powerful SQL language with procedural code. SQL is a 4th generation programming language (4GL), and as such, extremely well suited for querying and bulk data manipulation. Its functional-declarative nature allows for it to be optimised in highly efficient ways using cost-based optimisation, … Continue reading Vendor Agnostic, Dynamic Procedural Logic with jOOQ

jOOQ 3.15’s New Multiset Operator Will Change How You Think About SQL

This is how SQL should have been used all along. They called it The Third Manifesto, ORDBMS, or other things. Regrettably, it never really took off. Because most vendors didn't adopt it. And those who did, didn't agree on syntax. But this is about to change. Thanks to the now ubiquitous SQL/JSON support (which jOOQ … Continue reading jOOQ 3.15’s New Multiset Operator Will Change How You Think About SQL

Use IN List Padding to Your JDBC Application to Avoid Cursor Cache Contention Problems

A problem few developers are aware of is the possibility of running into "cursor cache contention" or "execution plan cache contention" problems when using IN lists in SQL. The problem that is described in lengths in previous articles, can be summarised as this. All of these are distinct SQL queries and need to be parsed … Continue reading Use IN List Padding to Your JDBC Application to Avoid Cursor Cache Contention Problems

Having “constant” columns in foreign keys

I was asked a very interesting question on Twitter just now: https://twitter.com/connolly_s/status/1303957373107818497 Can we have "constant" foreign key columns in (PostgreSQL) tables? Luckily, yes, we can. Using a nice standard feature that is "computed columns" or "generated columns" Sometimes, you cannot completely normalise your schema for whatever reason. There may be a case where you … Continue reading Having “constant” columns in foreign keys

What’s Faster? COUNT(*) or COUNT(1)?

One of the biggest and undead myths in SQL is that COUNT(*) is faster than COUNT(1). Or was it that COUNT(1) is faster than COUNT(*)? Impossible to remember, because there's really no reason at all why one should be faster than the other. But is the myth justified? Let's measure! How does COUNT(...) work? But … Continue reading What’s Faster? COUNT(*) or COUNT(1)?

Calculate Percentiles to Learn About Data Set Skew in SQL

B-Tree indexes are perfect when your data is uniformly distributed. They are not really useful, when you have skewed data. I'll explain later why this is the case, but let's first learn how to detect "skew" What is skew? Skew is a term from statistics when a normal distribution is not symmetric. The example given … Continue reading Calculate Percentiles to Learn About Data Set Skew in SQL

How to Aggregate an Archive Log’s Deltas into a Snapshot with SQL

A customer of my popular SQL training (which you should book!) has recently challenged me to optimise a hierarchical query that merges an archive log's deltas in order to obtain a snapshot of some record at a given point in time. In this article, I will reproduce their problem statement in a simplified version and … Continue reading How to Aggregate an Archive Log’s Deltas into a Snapshot with SQL

JOIN Elimination: An Essential Optimiser Feature for Advanced SQL Usage

The SQL language has one great advantage over procedural, object oriented, and "ordinary" functional programming languages. The fact that it is truly declarative (i.e. a 4GL / fourth generation programming language) means that a sophisticated optimiser can easily transform one SQL expression into another, equivalent SQL expression, which might be faster to execute. How does … Continue reading JOIN Elimination: An Essential Optimiser Feature for Advanced SQL Usage