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

Microsoft Azure Blog
Microsoft Azure Blog
Engineering at Meta
Engineering at Meta
CTFtime.org: upcoming CTF events
CTFtime.org: upcoming CTF events
F
Fortinet All Blogs
博客园 - 叶小钗
T
Tailwind CSS Blog
Security Archives - TechRepublic
Security Archives - TechRepublic
T
The Exploit Database - CXSecurity.com
Blog — PlanetScale
Blog — PlanetScale
T
Tenable Blog
人人都是产品经理
人人都是产品经理
D
DataBreaches.Net
A
Arctic Wolf
P
Proofpoint News Feed
S
SegmentFault 最新的问题
C
CERT Recently Published Vulnerability Notes
T
Threatpost
Y
Y Combinator Blog
WordPress大学
WordPress大学
L
LINUX DO - 热门话题
酷 壳 – CoolShell
酷 壳 – CoolShell
V
Vulnerabilities – Threatpost
AWS News Blog
AWS News Blog
小众软件
小众软件
U
Unit 42
云风的 BLOG
云风的 BLOG
美团技术团队
S
Securelist
C
Cybersecurity and Infrastructure Security Agency CISA
L
LangChain Blog
G
GRAHAM CLULEY
P
Proofpoint News Feed
I
Intezer
Security Latest
Security Latest
L
Lohrmann on Cybersecurity
NISL@THU
NISL@THU
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
The Hacker News
The Hacker News
Cisco Talos Blog
Cisco Talos Blog
T
The Blog of Author Tim Ferriss
H
Heimdal Security Blog
T
Tor Project blog
MongoDB | Blog
MongoDB | Blog
The Cloudflare Blog
T
Troy Hunt's Blog
Know Your Adversary
Know Your Adversary
博客园 - 司徒正美
Google Online Security Blog
Google Online Security Blog
V
V2EX
Application and Cybersecurity Blog
Application and Cybersecurity 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
Why PostgreSQL and ClickHouse Work So Well Together
Mohamed Huss · 2026-05-11 · via DEV Community

A lot of people compare PostgreSQL and ClickHouse like they are competing databases.

They really are not.

In fact, modern data systems often use both together.

And once you understand what each database is optimized for, the reason becomes pretty obvious.


PostgreSQL and ClickHouse Solve Different Problems

The biggest mistake people make is expecting both databases to behave similarly.

They are built for entirely different workloads.

PostgreSQL is primarily an OLTP database.

ClickHouse is primarily an OLAP database.

That single difference changes almost everything about how they think internally.


PostgreSQL Thinks About Transactions First

PostgreSQL is extremely good at handling transactional workloads.

Things like:

  • user data
  • payments
  • inventory
  • banking records
  • order systems
  • application state

These are systems where:

  • consistency matters
  • updates happen frequently
  • rows are modified constantly
  • transactions must be reliable

For example:

UPDATE inventory
SET stock = stock - 1
WHERE product_id = 101;

Enter fullscreen mode Exit fullscreen mode

This kind of workload is where PostgreSQL shines.

You want:

  • ACID guarantees
  • reliable transactions
  • row-level updates
  • strong consistency

PostgreSQL is designed around exactly that.


ClickHouse Thinks About Analytics First

ClickHouse approaches data very differently.

Instead of optimizing for frequent row updates, it optimizes for analytical queries across massive datasets.

Things like:

  • metrics
  • observability
  • logs
  • event streams
  • analytical dashboards
  • time-series workloads

For example:

SELECT
    service_name,
    avg(response_time_ms)
FROM metrics
WHERE timestamp >= now() - INTERVAL 1 HOUR
GROUP BY service_name;

Enter fullscreen mode Exit fullscreen mode

This is a completely different style of workload.

Instead of:

  • modifying small numbers of rows

ClickHouse is optimized for:

  • scanning huge amounts of data efficiently
  • aggregating billions of records
  • compressing analytical datasets
  • fast columnar reads

PostgreSQL Stores the Business. ClickHouse Explains It.

This is honestly the simplest way I think about it now.

PostgreSQL usually stores:

  • current application state
  • transactional business data
  • operational records

ClickHouse usually stores:

  • analytical history
  • events
  • metrics
  • large-scale queryable telemetry

One powers the application.

The other explains what the application is doing.


Why They Commonly Exist Together

This is where things get interesting.

In many modern architectures, PostgreSQL becomes the operational source of truth.

Then data flows into ClickHouse for analytics.

Something like this:

Application
    ↓
PostgreSQL
    ↓
CDC / Airbyte / Kafka
    ↓
ClickHouse
    ↓
Dashboards / Analytics / Observability

Enter fullscreen mode Exit fullscreen mode

This pattern is far more common than many people realize.

Because each database is doing what it is best at.


Why Not Just Use PostgreSQL for Analytics?

PostgreSQL can do analytical queries.

But analytical workloads behave very differently from transactional workloads.

For example:

  • scanning billions of rows
  • large aggregations
  • observability queries
  • real-time analytics
  • historical trend analysis

These workloads stress databases differently.

ClickHouse is optimized around:

  • columnar storage
  • vectorized execution
  • aggressive compression
  • analytical query execution

That is why queries over huge datasets often feel dramatically faster in ClickHouse.


Why Not Just Use ClickHouse for Everything?

This is another common misunderstanding.

ClickHouse is incredible for analytics.

But transactional systems require things like:

  • frequent updates
  • transactional consistency
  • row-level modifications
  • operational application state

That is not the primary design goal of ClickHouse.

You generally do not want your:

  • user authentication system
  • banking transactions
  • inventory updates
  • operational business logic

to depend entirely on analytical database behavior.


The Interesting Part Is the Separation of Responsibilities

What I personally find interesting is how these systems complement each other instead of replacing each other.

PostgreSQL handles:

  • operational correctness

ClickHouse handles:

  • analytical scale

That separation creates much cleaner architectures.

Instead of forcing one database to solve every problem, each system handles the workload it was designed for.


CDC Is What Connects Them

One thing that makes this architecture powerful is CDC (Change Data Capture).

Instead of manually exporting data repeatedly, systems can stream changes from PostgreSQL into ClickHouse continuously.

Tools like:

  • Debezium
  • Airbyte
  • Kafka pipelines

make this pattern extremely practical now.

The operational system continues running normally while analytical systems receive data almost in real time.


They Even Think Differently Internally

The differences go deeper than just "transactions vs analytics".

PostgreSQL thinks heavily about:

  • rows
  • transactional consistency
  • updates
  • locking
  • relational integrity

ClickHouse thinks heavily about:

  • columns
  • compression
  • merges
  • partitions
  • analytical scans
  • aggregation efficiency

Even their storage engines reflect completely different priorities.


This Is Why Modern Data Stacks Often Use Both

Once you stop viewing databases as competitors and instead view them as workload-specific systems, the architecture starts making much more sense.

PostgreSQL handles the operational side.

ClickHouse handles the analytical side.

Together, they create systems that can:

  • process transactions reliably
  • scale analytical workloads efficiently
  • support observability
  • power dashboards
  • retain huge historical datasets

without forcing a single database to do everything.


Final Thought

The more I learn about databases, the more I realize that most modern architectures are really about separation of responsibilities.

PostgreSQL and ClickHouse work well together because they optimize for fundamentally different problems.

One is built to preserve business state reliably.

The other is built to analyze massive amounts of history efficiently.

And when combined properly, they complement each other extremely well.