惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Application and Cybersecurity Blog
Application and Cybersecurity Blog
S
Securelist
K
Kaspersky official blog
Scott Helme
Scott Helme
C
CXSECURITY Database RSS Feed - CXSecurity.com
GbyAI
GbyAI
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
C
Cisco Blogs
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
博客园 - Franky
Security Latest
Security Latest
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
Y
Y Combinator Blog
T
Threat Research - Cisco Blogs
L
LINUX DO - 热门话题
C
Cyber Attacks, Cyber Crime and Cyber Security
Project Zero
Project Zero
Cisco Talos Blog
Cisco Talos Blog
月光博客
月光博客
I
Intezer
cs.CL updates on arXiv.org
cs.CL updates on arXiv.org
人人都是产品经理
人人都是产品经理
L
Lohrmann on Cybersecurity
Recorded Future
Recorded Future
Latest news
Latest news
V2EX - 技术
V2EX - 技术
T
The Exploit Database - CXSecurity.com
H
Heimdal Security Blog
F
Fortinet All Blogs
Cloudbric
Cloudbric
IT之家
IT之家
博客园 - 叶小钗
Microsoft Security Blog
Microsoft Security Blog
P
Proofpoint News Feed
博客园 - 司徒正美
Apple Machine Learning Research
Apple Machine Learning Research
PCI Perspectives
PCI Perspectives
AWS News Blog
AWS News Blog
H
Help Net Security
S
Security @ Cisco Blogs
酷 壳 – CoolShell
酷 壳 – CoolShell
Recent Announcements
Recent Announcements
Hacker News - Newest:
Hacker News - Newest: "LLM"
cs.CV updates on arXiv.org
cs.CV updates on arXiv.org
F
Full Disclosure
S
Schneier on Security
S
Security Affairs
T
Tenable Blog

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant Common SOC 2 Failures (Real World) Stop Vibe-Checking Your AI App: A Practical Guide to Evals How to Use SonarQube and SonarScanner Locally to Level Up Your Code Quality Your Next To-Do App Is Dead — I Replaced Mine with an OpenClaw AI Sign a Nostr event in 60 lines of Python using coincurve — no nostr-sdk, no nbxplorer, no rust toolchain ITGC Audit Explained Like You’re in Big 4 Patch Tuesday abril 2026: Microsoft parcha 163 vulnerabilidades y un zero-day en SharePoint Stop scraping everything: a better way to track competitor price changes Listing on MCPize + the Official MCP Registry while routing payments OUTSIDE the marketplace — how I kept 100% of my x402 revenue Building an AI-Powered Risk Intelligence System Using Serverless Architecture Why We Ripped Function Overloading Out of Our AI Toolchain Testing AI-Generated Code: How to Actually Know If It Works SaaS Churn Is Killing Your Business. Here Is What to Do About It (Without a Support Team) The Speed of AI Is No Longer Linear - And Self-Improving Models Are Why How to Implement RBAC for MCP Tools: A Practical Guide for Engineering Teams From Standard Quote to Persuasive Proposal: AI Automation for Arborists I built a CLI that scaffolds complete multi-tenant SaaS apps Axios CVE-2025–62718: The Silent SSRF Bug That Could Be Hiding in Your Node.js App Right Now The dashboard that ended our friendship Data Pipelines Explained Simply (and How to Build Them with Python) The Hidden Cost of AI Systems Nobody Talks About. undefined vs undeclared, and how typeof behaves Switching from file-based jobs to NATS/Kafka in Rust without changing code io_uring Adventures: Rust Servers That Love Syscalls Why Agentic AI is Killing the Traditional Database The POUR principles of web accessibility for developers and designers Quantum Neural Network 3D — A Deep Dive into Interactive WebGL Visualization How To Install Caveman In Codex On macOS And Windows Automation Pipeline Reliability: Why Your Workflow Breaks When Nobody Is Watching I Built an 'Open World' AI Coding Agent — It Works From ANY Folder From Freelancing to Product: A Tech Service Company's SaaS Transformation China's AI Giants: Adding Tencent Hunyuan & ByteDance Doubao to AI University (74 Providers) On the Vibe Coders and Their Lies clerk: Auto-Summarize Your Claude Code Sessions AI Weekly — 2026/04/10–04/17 | The Model Lockdown Is Here, but the Toolchain Is the Real Battleground AI 週報 — 2026/04/10–2026/04/17 模型封鎖潮來了,但工具鏈才是真戰場 Maybe this is how Open-Source apps are born... 🚀 Fine-Tune LLMs with LoRA and QLoRA: 2026 Guide tRPC v11 + Next.js App Router: End-to-End Type Safety Without the Boilerplate ShadCN UI in 2026: Why I Stopped Installing Component Libraries and Started Owning My Components SaaS Billing in React Server Components: Stripe + Supabase Without a Single `useEffect` Join our DEV Weekend Challenge — $1,000 in Prizes Across TEN winners! Submissions Due April 20 at 6:59 AM UTC. Implementing FSRS Spaced Repetition in Flutter + Supabase — Adding Memory Science to an AI Learning App "I Texted My Localhost From the Train — Claude Code Fixed the Bug Before I Got Home" I Built a Sales Prep AI and It Went Deeper Than Expected Design to Code #2: One JSON, Eleven Outputs Solving the 100M-Row Problem: A Summary Table Pattern for High-Volume Push Notification Logs Flutter Web With Wasm: What Actually Changes For Developers I Built 50 Royalty-Free Soundtracks for My Side Project in a Weekend Using AI Music Generation The Vibe Coding Security Checklist: 7 Things to Check Before You Ship Stop Letting Googlebot Guess Fix Your React App's SEO Right Desconstruindo o Streaming do LinkedIn: Como Criar um Engine de Extração de Vídeo de Alta Performance com HLS e FFmpeg (EDA Part-1) EDA (Exploratory Data Analysis) Explained With Real Life — Why Looking at Your Data Is the Most Important Step in Machine Learning Brand Relationship Management at Scale: Our 4-Touch Outreach System for 200+ Brands Why String.fromEnvironment() Might Return an Empty String in Dart JGuardrails 1.0.0 — Hardening Java LLM Apps Against Jailbreaks, Toxicity, and Prompt Injection Plan and Schedule a Full Week of Threads Content From One Claude Conversation Coding Cat Oran Ep3, Five Tables Changed Everything Updated: BFF Pattern I'm done watching freelancers get buried by 200 proposals. So I'm building the alternative. This is my first post BFS Algorithm in Java Step by Step Tutorial with Examples Tracking LLM Pricing Monthly: An Open Dataset for 22 AI Models How We Measure Content ROI on a Comparison Site: Revenue Attribution Without Perfect Data Introducing Nova AI Ops: The AI-Native Operating System for SRE Teams I built a free desktop video downloader for Windows — Grabbit How Talkie OCR Helps Vision-Impaired & Dyslexic Users Read the World Around Them VRCFaceTracking安装和iPhone面捕配置教程,有bug Even CrowdStrike Can't See Your Agents The Automation Gold Rush: What n8n Workflows and Claude Are Opening Up for Developers Right Now
Cleaning Messy Phone Numbers in PostgreSQL Using REGEXP_REPLACE
Sharon-nyabu · 2026-05-19 · via DEV Community

Introduction

The first thing anyone that has worked with real world data will tell you is that rarely does data come in the clean, uniform format you expect. Phone numbers are one of the biggest culprits.

One person enters +254712345678, another writes 0712345678, someone else throws in 254-712-345-678, another (254)-712345678 and another 254 712 345 678. Before you know it, your database has a different version of phone numbers in every row.

In this article, we will explore how to fix that, using a powerful PostgreSQL function called regexp_replace.

By the end, you will understand what it does, how to build the pattern, and how to apply it to actually clean your data.


First, What Even Is regexp_replace?

First things first, we need to understand what this function is and what it does.

regexp_replace is a PostgreSQL function that finds a pattern inside a string and replaces whatever matches that pattern with something else you specify. The word regexp is short for regular expression.

The syntax looks like this:

regexp_replace(source, pattern, replacement_string, flag)

Enter fullscreen mode Exit fullscreen mode

Let us break each part down simply:

Part What It Means
source The string you are searching inside. In our case, this is the column passenger_phone
pattern What you are looking for : What do you want to find and remove or replace?
replacement_string What do you want to put in place of what you found? If you just want to delete it, you pass an empty string ''
flag Controls how the matching behaves. The two you will use most are i for case-insensitive matching and g for global, meaning it applies the replacement to everything it finds in the string, not just the first match

Now that we have the hang of it, let us move on to building the pattern.


Understanding the Special Characters in Patterns

A pattern is basically a set of rules that tells PostgreSQL what to look for. It is like saying "find any character that is not a digit" or "find this pattern, but only at the beginning" etc.

To write these rules/instructions, there are special characters that are used.

Here are the ones we will use today:

The ^ character

Depending on where it is placed, this will do either of 2 things;

When ^ is at the beginning of a pattern it means: start matching from the very beginning of the string.

^'hello'

Enter fullscreen mode Exit fullscreen mode

This means: only match hello if it appears at the start of the string.

^[0-9]

Enter fullscreen mode Exit fullscreen mode

This means: Only the first character of a string is checked and matched if it is a digit. Anything after that is ignored.

When ^ is inside square brackets [ ], it means: NOT these characters.

[^0-9]

Enter fullscreen mode Exit fullscreen mode

This means: Every character in the string is checked, any one that is not a digit gets matched.

Same symbol, two different purposes depending on its position.


The \ character (backslash)

The backslash is used to tell PostgreSQL: treat the next character as a character, not as a special operator.

For example, the + sign in regular expressions has a special meaning (we will cover it in a moment). So if you want to find a plus sign + in your data, like +254, you write it as \+ to tell PostgreSQL "I mean the actual plus sign, not the operator."


Square brackets [ ]

Square brackets let you define a group of characters to match. For example:

  • [0-9] means match any digit from 0 to 9
  • [a-z] means match any lowercase letter
  • [^0-9] means match anything that is NOT a digit (the ^ inside means NOT, as we've mentioned above)

The + operator

When used outside brackets, + means: keep matching one or more of the preceding character or group.

So [0-9]+ means: Match the digits as long as they are all next to each other with no other characters in between. (Without the + , it stops after the first match)


The $ character

Just like ^ marks the start of a string, $ marks the end. When you write $ at the end of your pattern, you are saying: the string must end here, nothing else allowed after this.


Putting them together: ^[0-9]+$

Now that you know each piece, read this pattern again:

^[0-9]+$

Enter fullscreen mode Exit fullscreen mode

  • ^ → start from the beginning of the string
  • [0-9] → the characters must be digits
  • + → keep going, there can be one or more of them
  • $ → and this must be the end of the string, nothing else after

All together: the entire string, from start to finish, must contain only digits.

This is the pattern we will use to identify which phone numbers are already clean and which ones need fixing.


Our Problem: What Does the Messy Data Look Like?

Let us say when we run this:

SELECT passenger_phone FROM dirty_safari_data;

Enter fullscreen mode Exit fullscreen mode

We get back a mix of things like:

+254712345678
254712345678
0712345678
0712-345-678
+254 712 345 678
(0) 712345678

Enter fullscreen mode Exit fullscreen mode

The goal is to standardize all of these into the local format starting with 0, containing only digits, like:

0712345678

Enter fullscreen mode Exit fullscreen mode

Two problems to solve:

  1. Some numbers start with +254 or 254 instead of 0
  2. Some numbers have extra characters like brackets, spaces, dashes, or plus signs

Step 1: Replace +254 or 254 at the Start With 0

The first thing we want to do is find any phone number that starts with +254 or 254 and swap that prefix out for 0.

SELECT regexp_replace(passenger_phone, '^(\+254|254)', '0', 'g')
FROM contacts;

Enter fullscreen mode Exit fullscreen mode

Let us read the pattern ^(\+254|254) carefully:

  • ^ → look at the start of the string only
  • ( ) → group what is inside together
  • \+254 → literally the characters +254 (the backslash makes + a literal character)
  • | → OR
  • 254 → literally the characters 254

So the full pattern says: at the start of the string, find either +254 or 254.

The replacement is '0', so wherever that prefix is found, replace it with 0.

The flag is 'g' for global.

After this step, +254712345678 becomes 0712345678 and 254712345678 also becomes 0712345678.


Step 2: Remove Any Character That Is Not a Digit

Some numbers still have spaces, dashes, or other characters sitting in them. The second regexp_replace cleans all of that up.

SELECT regexp_replace(passenger_phone, '[^0-9]', '', 'g')
FROM contacts;

Enter fullscreen mode Exit fullscreen mode

The pattern [^0-9] means: any character that is NOT a digit.

The replacement is '' (an empty string), so we are just deleting those characters.

The 'g' flag means do this for every non-digit character found in the string, not just the first one.

So 0712-345-678 becomes 0712345678 and 0712 345 678 also becomes 0712345678.


Step 3: The WHERE Clause — Only Update What Needs Fixing

We do not want to touch phone numbers that are already clean. That is where our ^[0-9]+$ pattern comes in handy. We use it in the WHERE clause to filter for records where the phone number is not already a clean string of digits.

You might be tempted to write it like this:

WHERE passenger_phone != '^[0-9]+$'

Enter fullscreen mode Exit fullscreen mode

But this is not correct. The != operator just compares strings literally, so this would check if the phone number is literally equal to the text ^[0-9]+$, which is not what we want.

In PostgreSQL, to check whether a string does NOT match a regular expression, you use the !~ operator. The corrected version should be:

WHERE passenger_phone !~ '^[0-9]+$'

Enter fullscreen mode Exit fullscreen mode

This now correctly says: only return (or update) rows where the phone number does not match the pattern of being purely digits from start to finish.


Putting It All Together

The SELECT query (to preview your cleaned data before changing anything)

Always preview before you update. This is a good habit.

SELECT 
    passenger_phone AS original,
    regexp_replace(
        regexp_replace(passenger_phone, '^(\+254|254)', '0', 'g'),
        '[^0-9]', '', 'g'
    ) AS cleaned_phone
FROM contacts
WHERE passenger_phone !~ '^[0-9]+$';

Enter fullscreen mode Exit fullscreen mode

Notice how we nested one regexp_replace inside another. The inner one runs first (fixing the prefix), and then the outer one runs on the result (removing non-digits). It is like running two cleaning steps in one go.


The UPDATE query (to actually apply the changes)

Once you are happy with the preview, run the update:

UPDATE contacts
SET passenger_phone = regexp_replace(
    regexp_replace(passenger_phone, '^(\+254|254)', '0', 'g'),
    '[^0-9]', '', 'g'
)
WHERE passenger_phone !~ '^[0-9]+$';

Enter fullscreen mode Exit fullscreen mode

This updates only the rows that have messy phone numbers, leaving the already clean ones untouched.


Quick Recap

Here is a summary of everything covered:

Pattern What It Does
^ at the start of a pattern Anchors the match to the start of the string
^ inside [ ] Means NOT — match anything except what follows
\+ Treat + as a literal plus sign, not an operator
[0-9] Match any digit from 0 to 9
[^0-9] Match any character that is NOT a digit
+ One or more of the preceding character/group
$ Anchors the match to the end of the string
^[0-9]+$ The entire string must be digits only, nothing else
g flag Apply the replacement globally (every match, not just the first)

Final Thoughts

Regular expressions can look intimidating and confusing at first, but once you understand what each character means and why it is there, they start making a lot of sense. The key takeaway here is to build your pattern step by step, rather than trying to write the whole thing at once.

Also, always test with a SELECT before running an UPDATE. You can never be too careful when modifying data directly in a table.