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Home Time series basics Postgres basics Postgres guides Postgres best practices Postgres extensions Postgres and vector data

Postgres overview

Understanding PostgreSQL Where Did PostgreSQL Come From?

Postgres clauses

Understanding FROM in PostgreSQL (With Examples)Understanding FILTER in PostgreSQL (With Examples)Understanding HAVING in PostgreSQL (With Examples)Understanding GROUP BY in PostgreSQL (With Examples)Understanding LIMIT in PostgreSQL (With Examples)Understanding ORDER BY in PostgreSQL (With Examples)Understanding WINDOW in PostgreSQL (With Examples)Understanding PostgreSQL WITHIN GROUP Understanding DISTINCT in PostgreSQL (With Examples)Understanding WHERE in PostgreSQL (With Examples)Understanding OFFSET in PostgreSQL (With Examples)

Postgres errors

How to Address ‘Error: Could Not Resize Shared Memory Segment’ 5 Common Connection Errors in PostgreSQL and How to Solve Them How to Fix No Partition of Relation Found for Row in Postgres Databases How to Fix Transaction ID Wraparound Exhaustion

PostgreSQL Joins : A Summary What Is a PostgreSQL Full Outer Join?What Is a PostgreSQL Cross Join?What Is a PostgreSQL Inner Join?What Is a PostgreSQL Left Join? And a Right Join?PostgreSQL Join Type Theory Strategies for Improving Postgres JOIN Performance

Postgres statements

Understanding PostgreSQL SELECT

Postgres operations

A Guide to PostgreSQL Views Building a Scalable Database Data Partitioning: What It Is and Why It Matters What Is Data Compression and How Does It Work?Self-Hosted or Cloud Database? A Countryside Reflection on Infrastructure Choices

Understanding ACID Compliance Structured vs. Semi-Structured vs. Unstructured Data in PostgreSQL Understanding Foreign Keys in PostgreSQL

Postgres functions

Understanding PostgreSQL Functions PostgreSQL Mathematical Functions: Enhancing Coding Efficiency Using PostgreSQL String Functions for Improved Data Analysis Data Processing With PostgreSQL Window Functions Understanding PostgreSQL Date and Time Functions Understanding the Postgres string_agg Function Understanding SQL Aggregate Functions Understanding the Postgres extract() Function Understanding percentile_cont() and percentile_disc() in PostgreSQL Understanding the rank() and dense_rank() Functions in PostgreSQL

Understanding PostgreSQL Functions

A purple circle over a black background.

PostgreSQL offers a myriad of functions that are essential for data analysis, manipulation, and processing. This guide will provide you with a comprehensive list of PostgreSQL built-in functions, complete with brief descriptions, code snippets, and Timescale custom SQL queries that will take your data analysis to another level.

More on specific PostgreSQL functions:

Use Timescale Functions for Hyper Speed and Ease

Now that you’ve learned the basics of PostgreSQL functions, it’s time for a better alternative. Hyperfunctions are a series of SQL functions within TimescaleDB that make it easier to manipulate and analyze time-series data in PostgreSQL with fewer lines of code.

You can use hyperfunctions to calculate percentile approximations of data, compute time-weighted averages, downsample and smooth data, and perform faster COUNT DISTINCT queries using approximations. Moreover, hyperfunctions are simple to use: you call a hyperfunction using the same SQL syntax you know and love.

Learn more about hyperfunctions on our Docs page.