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Is Your Data Time Series? Data Types Supported by PostgreSQL and TimescaleUnderstanding Database Workloads: Variable, Bursty, and Uniform PatternsTime-Series Analysis and Forecasting With Python What Is Temporal Data?What Are Open-Source Time-Series Databases—Understanding Your OptionsAWS Time-Series Database: Understanding Your OptionsStationary Time-Series AnalysisThe Best Time-Series Databases ComparedAlternatives to TimescaleWhy Consider Using PostgreSQL for Time-Series Data?Time-Series Analysis in RWhat Is a Time Series and How Is It Used?How to Work With Time Series in Python?Tools for Working With Time-Series Analysis in PythonGuide to Time-Series Analysis in PythonUnderstanding Autoregressive Time-Series ModelingCreating a Fast Time-Series Graph With Postgres Materialized Views
Optimizing Your Database: A Deep Dive into PostgreSQL Data TypesHow to Install PostgreSQL on LinuxUnderstanding FROM in PostgreSQL (With Examples)How to Address ‘Error: Could Not Resize Shared Memory Segment’ Understanding FILTER in PostgreSQL (With Examples)How to Install PostgreSQL on MacOS5 Common Connection Errors in PostgreSQL and How to Solve ThemUnderstanding HAVING in PostgreSQL (With Examples)How to Fix No Partition of Relation Found for Row in Postgres DatabasesHow to Fix Transaction ID Wraparound ExhaustionUnderstanding LIMIT in PostgreSQL (With Examples)Understanding PostgreSQL FunctionsUnderstanding ORDER BY in PostgreSQL (With Examples)PostgreSQL Mathematical Functions: Enhancing Coding EfficiencyUnderstanding PostgreSQL WITHIN GROUPUnderstanding WINDOW in PostgreSQL (With Examples)Using PostgreSQL String Functions for Improved Data AnalysisPostgreSQL Joins : A SummaryUnderstanding PostgreSQL Conditional FunctionsStructured vs. Semi-Structured vs. Unstructured Data in PostgreSQLUnderstanding DISTINCT in PostgreSQL (With Examples)What Is a PostgreSQL Cross Join?Understanding percentile_cont() and percentile_disc() in PostgreSQLUnderstanding GROUP BY in PostgreSQL (With Examples)Data Processing With PostgreSQL Window FunctionsUnderstanding WHERE in PostgreSQL (With Examples)Data Partitioning: What It Is and Why It MattersUnderstanding PostgreSQL Array FunctionsUnderstanding ACID Compliance Understanding PostgreSQL's COALESCE FunctionUnderstanding the rank() and dense_rank() Functions in PostgreSQLUnderstanding PostgreSQLUnderstanding OFFSET in PostgreSQL (With Examples)Understanding PostgreSQL Date and Time FunctionsUnderstanding the Postgres string_agg FunctionWhat Is a PostgreSQL Full Outer Join?What Is a PostgreSQL Inner Join?What Is a PostgreSQL Left Join? And a Right Join?Strategies for Improving Postgres JOIN PerformancePostgreSQL Join Type TheoryA Guide to PostgreSQL ViewsUnderstanding Foreign Keys in PostgreSQLUnderstanding PostgreSQL User-Defined FunctionsUnderstanding SQL Aggregate FunctionsUsing PostgreSQL UPDATE With JOINWhat Is Data Compression and How Does It Work?What Is Data Transformation, and Why Is It Important?What Characters Are Allowed in PostgreSQL Strings?Understanding PostgreSQL SELECTSelf-Hosted or Cloud Database? A Countryside Reflection on Infrastructure ChoicesUnderstanding the Postgres extract() Function
How to Choose a Database: A Decision Framework for Modern ApplicationsHandling Large Objects in PostgresDetermining the Optimal Postgres Partition SizeNavigating Growing PostgreSQL Tables With Partitioning (and More)SQL/JSON Data Model and JSON in SQL: A PostgreSQL PerspectiveHow to Use PostgreSQL for Data TransformationPostgreSQL Performance Tuning: Designing and Implementing Your Database SchemaGuide to PostgreSQL PerformancePostgreSQL Performance Tuning: Key ParametersPostgreSQL Performance Tuning: Optimizing Database IndexesHow to Reduce Bloat in Large PostgreSQL TablesAn Intro to Data Modeling on PostgreSQLDesigning Your Database Schema: Wide vs. Narrow Postgres TablesGuide to PostgreSQL Database OperationsBest Practices for Time-Series Data Modeling: Single or Multiple Partitioned Table(s) a.k.a. Hypertables Explaining PostgreSQL EXPLAINWhat Is a PostgreSQL Temporary View?PostgreSQL Performance Tuning: How to Size Your DatabaseBest Practices for (Time-)Series Metadata Tables A PostgreSQL Database Replication GuideHow to Compute Standard Deviation With PostgreSQLA Guide to Data Analysis on PostgreSQLA Guide to Scaling PostgreSQLRecursive Query in SQL: What It Is, and How to Write OneHow to Query JSON Metadata in PostgreSQLHow to Query JSONB in PostgreSQLGuide to PostgreSQL SecurityOptimizing Array Queries With GIN Indexes in PostgreSQLPg_partman vs. Hypertables for Postgres PartitioningTop PostgreSQL Drivers for PythonUnderstanding PostgreSQL TablespacesWhat Is Audit Logging and How to Enable It in PostgreSQLWhen to Consider Postgres PartitioningGuide to Postgres Data ManagementHow to Index JSONB Columns in PostgreSQLHow to Monitor and Optimize PostgreSQL Index PerformanceA Guide to pg_restore (and pg_restore Example)How PostgreSQL Data Aggregation WorksHow to Use Psycopg2: The PostgreSQL Adapter for PythonBuilding a Scalable DatabaseGuide to PostgreSQL Database Design
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Moving Past Legacy Systems: Data Historian vs. Time-Series DatabaseHow Hopthru Powers Real-Time Transit Analytics From a 1 TB TableUnderstanding IoT (Internet of Things)A Beginner’s Guide to IIoT and Industry 4.0Storing IoT Data: 8 Reasons Why You Should Use PostgreSQLHow to Choose an IoT DatabaseHow to Simulate a Basic IoT Sensor Dataset on PostgreSQLFrom Ingest to Insights in Milliseconds: Everactive's Tech Transformation With TimescaleHow Ndustrial Is Providing Fast Real-Time Queries and Safely Storing Client Data With 97 % CompressionWhy You Should Use PostgreSQL for Industrial IoT Data Migrating a Low-Code IoT Platform Storing 20M Records/DayHow United Manufacturing Hub Is Introducing Open Source to ManufacturingBuilding IoT Pipelines for Faster Analytics With IoT CoreVisualizing IoT Data at Scale With Hopara and TimescaleDB
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Published at Mar 6, 2024

PostgreSQL Mathematical Functions: Enhancing Coding Efficiency

Mathematical functions are an essential part of any programming language or database system. They provide developers with ready-to-use solutions for performing mathematical calculations, thereby increasing coding efficiency and accuracy. PostgreSQL offers a comprehensive set of mathematical functions that can significantly enhance your data-handling capabilities.

Learn how to create, list, call, and edit Postgres functions.

PostgreSQL's Mathematical Functions

PostgreSQL boasts a rich set of mathematical functions that handle everything from basic arithmetic to complex trigonometric calculations. These include but are not limited to ABS(), CEIL(), FLOOR(), MOD(), ROUND(), TRUNC(), and more. These functions exhibit high performance and precision, making them an invaluable tool for developers. 

The Benefits of Using PostgreSQL Mathematical Functions

Increased accuracy

PostgreSQL's mathematical functions are designed to deliver highly accurate results, reducing the likelihood of errors due to manual calculations or less efficient functions provided by other systems.

Enhanced coding efficiency

These functions allow developers to perform complex calculations directly within the database, reducing the need for external computations and speeding up execution time.

Real-World Use Cases of PostgreSQL's Mathematical Functions

Let's take a look at some examples of how these functions can be applied to real-world programming projects:

  • E-commerce applications: The ROUND() function can be used to calculate the total cost of items in a shopping cart, including taxes and discounts.

  • Financial systems: The CEIL() and FLOOR() functions can be used for rounding up or down monetary values to the nearest whole number.

  • Data analysis tools: The MOD() function can be used to categorize data into different buckets based on certain criteria.

List of PostgreSQL Mathematical Functions

ABS(number)

The ABS() function returns the absolute value of a number.

Example:

SELECT ABS(-10); -- Returns 10

CEIL(number)

The CEIL() or CEILING() function rounds a number up to the nearest integer.

Example:

SELECT CEIL(7.4); -- Returns 8

MOD(n, m)

The MOD() function returns the remainder of n divided by m.

Example:

SELECT MOD(10, 3); -- Returns 1

FLOOR(number)

The FLOOR() function rounds a number down to the nearest integer.

Example:

SELECT FLOOR(7.8); -- Returns 7

ROUND(number, [decimals])

The ROUND() function rounds a number to a certain number of decimal places. If the decimals parameter is omitted, it rounds to the nearest whole number.

Example:

SELECT ROUND(7.456, 2); -- Returns 7.46

TRUNC(number, [decimals])

The TRUNC() function truncates a number to a certain number of decimal places without rounding. If the decimals parameter is omitted, it truncates to the nearest whole number.

Example:

SELECT TRUNC(7.456, 2); -- Returns 7.45

A Summary on PostgreSQL Mathematical Functions

Postgres’ mathematical functions offer benefits such as increased accuracy and enhanced coding efficiency, and their wide range of applications makes them a must-know for anyone working with PostgreSQL.

For further reading and a deeper understanding of these functions, you can visit the official PostgreSQL documentation or read our next article on aggregate functions.

Remember, the key to mastering these functions lies in practice. So, dive into your PostgreSQL database and start crunching those numbers!

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, or keep reading for more information on window functions.

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