AI-Ready PostgreSQL 18: Building Intelligent Data Systems

Really excited to finally share something we’ve been working on - our new book is officially out now 📘 ✨   “AI-Ready PostgreSQL 18: Building Intelligent Data Systems with Transactions, Analytics, and Vectors.” by Vibhor Kumar and Marc Linster   If you’ve been building data-heavy or AI-enabled applications lately, you’ve probably felt this shift; everything is converging. Transactions, analytics, AI… they’re no longer separate concerns anymore 🔄   That’s exactly what this book explores.   It’s a practical guide to using PostgreSQL 18 as a unified platform not just for storing data, but for building intelligent systems that can handle real-time decisions, analytics, and even AI workloads like vector search and LLM integrations 🤖 📊   Inside, we’ve focused on: Real examples you can actually apply 🛠️ Clear guidance on architecture and trade-offs 🧠 Ways to get the most out of PostgreSQL 18’s latest features ⚡ When to stick with Postgres and when not to 🎯   Whether you’re a developer, data engineer, or architect, the goal is simple: help you build smarter systems without unnecessary complexity 🚀   📣 If this sounds relevant to your work, check it out and grab your copy here: https://packt.link/gQKTu   Curious to hear your take 👉 Are you already using PostgreSQL for AI use cases? 👉 Do you think one system can realistically handle OLTP, OLAP, and AI together?   Would love to hear what you’re seeing in your own work 👇 💬   #postgresql #aiengineering #dataarchitecture #realtimeanalytics #vectorsearch #techleadership #dataengineering #dataplatforms  

Been reading Designing Data-Intensive Applications lately and one thing is becoming very clear When you’re building a data-intensive system, most problems don’t come from “bad code” They come from wrong assumptions about data. For example: You pick PostgreSQL because “it’s reliable” Cool until: reads start going to replicas replica is 2s behind user logs in → “account not found” Nothing crashed. System is working exactly as designed. Your assumption was wrong. Or you pick MongoDB thinking: “no joins → faster” True until: your data isn’t actually “one document per request” you start doing app-level joins now latency just moved from DB → backend Or you go with Apache Cassandra Great write throughput. But now: compaction kicks in read latency becomes unpredictable and you’re tuning things you didn’t even know existed The pattern I’m starting to see is every system pushes complexity somewhere else SQL → schema + migrations NoSQL → application logic LSM trees → compaction replication → consistency issues partitioning → query complexity You don’t remove complexity. You just choose where you want to suffer. Another shift that clicked for me: Apache Kafka is not “just a queue” It’s basically: a log that other systems build their reality from Which means: your DB is no longer the only source of truth your system becomes state derived from events That changes how you think about everything: retries, failures, even debugging. Also, OLTP vs OLAP separation is not optional at scale. If you try to run analytics on your main DB: it will hurt. That’s why people bring in things like ClickHouse later not because they want to but because they have to. I think the biggest mindset shift is this: Stop asking: “which database should I use?” Start asking: “what kind of data + access pattern + failure can I tolerate?” Still wrapping my head around a lot of this, but yeah Feels like system design is less about tools and more about understanding trade-offs deeply enough to not get surprised in production #systemdesign #backend #databases #distributedsystems

𝗪𝗵𝘆 𝘁𝗵𝗲 “𝗢𝗿𝗶𝗴𝗶𝗻𝗮𝗹” 𝗪𝗶𝗻𝘀: 𝗧𝗵𝗲 𝗣𝗼𝘀𝘁𝗴𝗿𝗲𝘀 𝗙𝗼𝗿𝗸 𝗙𝗮𝗹𝗹𝗮𝗰𝘆 In the database world, there’s a growing trend: a rapid rise in specialized Postgres forks. Whether for AI, Analytics, or Security, new forks appear every week claiming to fill PostgreSQL’s “gaps.” However, here’s the challenge: forks often introduce long-term complexity. They require ongoing maintenance, fall behind on core updates, and can create forms of vendor dependency that teams don’t always anticipate. Postgres became the “World’s Most Loved Database” precisely because it keeps the core lean and relies on a powerful extension ecosystem. If you want to stay with stable Community Postgres instead of adopting a fork, here are proven paths: 𝟭. 𝗧𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝘁 𝗗𝗮𝘁𝗮 𝗘𝗻𝗰𝗿𝘆𝗽𝘁𝗶𝗼𝗻 (𝗧𝗗𝗘) 𝗧𝗵𝗲 𝗙𝗼𝗿𝗸 𝗧𝗲𝗺𝗽𝘁𝗮𝘁𝗶𝗼𝗻: Some enterprises choose EDB EPAS primarily for native TDE and Oracle-like security features. 𝗔 𝗕𝗲𝘁𝘁𝗲𝗿 𝗣𝗮𝘁𝗵: The pg_tde extension provides on-disk encryption while staying fully within the community ecosystem — no proprietary licensing required. 𝟮. 𝗖𝗼𝗹𝘂𝗺𝗻𝗮𝗿 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 𝗳𝗼𝗿 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀  𝗧𝗵𝗲 𝗚𝗮𝗽: Postgres is row-oriented (OLTP), so large-scale analytical queries can hit performance limits. 𝗔 𝗕𝗲𝘁𝘁𝗲𝗿 𝗣𝗮𝘁𝗵: Extensions like Citus or Hydra add columnar storage for analytical tables, often with significant compression benefits. 𝟯. 𝗩𝗲𝗰𝘁𝗼𝗿 𝗦𝗲𝗮𝗿𝗰𝗵 𝗳𝗼𝗿 𝗔𝗜/𝗟𝗟𝗠𝘀  𝗧𝗵𝗲 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻: “Do we need a specialized vector database like Pinecone?” 𝗔 𝗕𝗲𝘁𝘁𝗲𝗿 𝗣𝗮𝘁𝗵: pgvector has become a widely adopted standard, keeping vectors and metadata together in an ACID-compliant environment. 𝗧𝗵𝗲 𝗘𝘅𝗽𝗲𝗿𝘁 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆 Forks can introduce operational and financial overhead over time — from maintenance to migrations to licensing. Community Postgres, combined with the right extensions, offers a flexible ecosystem rather than a fixed product. 𝗠𝘆 𝗔𝗱𝘃𝗶𝗰𝗲 Before adopting a fork, explore whether an extension already solves your need. Many teams find that staying with “vanilla” Postgres gives them stability, predictability, and room to innovate where it truly matters. Where do you stand? Have you stayed with Community Postgres, or have forks like EDB delivered value in your environment? Looking forward to your experiences in the comments #Spectral Core

You probably don't need a separate vector database. If you're already running #PostgreSQL, you can add AI-powered semantic search with one extension: pgvector. Here's how it works in 5 steps: - Enable the extension → CREATE EXTENSION vector; - Add a VECTOR column to your existing tables - Store embeddings from any ML model (OpenAI, Hugging Face, etc.) - Query by meaning using similarity operators in plain SQL - Add an HNSW index for fast nearest-neighbor search at scale No new infrastructure. No sync jobs. No new vendor. Just SQL and vectors. The best part? You can combine vector search with everything PostgreSQL already does joins, filters, transactions, full-text search in a single query. Use cases teams are building right now: → Smart product search that understands intent, not just keywords → FAQ chatbots that match questions by meaning → Content recommendation engines Vibhor Kumar and Marc Linster wrote a great step-by-step walkthrough covering all of this from setup to production use cases. Full article here - https://lnkd.in/gBKF2APe Follow our Substack page for more such how to tutorials straight to your inbox - Data Engineering Byte

What's the next big thing in Postgres? Two years ago it was vector. Last year it was Iceberg. Right now it's graph. So what is a graph, exactly? A graph is a way of modeling relationships. Instead of rows and columns, you have nodes / things and edges / connections between things. Think healthcare: A patient sees a doctor. That doctor refers to a specialist. That specialist bills an insurer. Each of those is a node. Each relationship is an edge. And unlike a traditional table join, with graph you can traverse those connections to arbitrary depth -- "show me every provider in this referral chain, no matter how many hops" -- without writing recursive CTEs. Graphs show up everywhere: fraud rings, supply chains, social networks, org charts, dependency trees. Anywhere the relationships between things matter as much as the things themselves. Postgres has quietly handled graphs for a while -- small networks, social connections, spatial routing with pgRouting. But those were always limited to data already living inside Postgres. Now that Postgres is part of the object storage world, graphs can work across many different files to combine data from disparate sources. Two powerhouse extensions make this work: pg_lake -- Iceberg tables, Parquet, CSV, S3 access. DuckDB engine underneath. From Snowflake Labs. Apache AGE -- Full graph database with openCypher. Nodes, edges, variable-depth traversals. What makes it interesting: they both run in the same Postgres instance. So you can: - Query Iceberg data on S3 with standard SQL - Build a graph from that data using Cypher - Join graph results back to lake tables in the same query For some fun testing, I built sample code for a healthcare referral network analysis. Cypher finds out-of-network referral chains (variable depth -- try doing that with recursive CTEs). Then a CTE joins those graph results back to the Iceberg claims table for the actual cost impact. One query. One database. All open source. #postgres #iceberg #graphdatabase #datalake #apache

🚀 𝗽𝗴-𝗮𝗱𝘃𝗶𝘀𝗼𝗿 v0.2.0 is live. A CLI tool that scans your 𝗣𝗼𝘀𝘁𝗴𝗿𝗲𝗦𝗤𝗟 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲 and tells you what to fix — with ready-to-use SQL queries. No AI, pure 𝗿𝘂𝗹𝗲-𝗯𝗮𝘀𝗲𝗱 𝘀𝘆𝘀𝘁𝗲𝗺. No AI. No guessing. Pure rules. ━━━━━━━━━━━━━━━━━━━━━━━ 🆕 What's new in 𝘃𝟬.𝟮.𝟬 ━━━━━━━━━━━━━━━━━━━━━━━ 🔍 𝗽𝗴_𝘀𝘁𝗮𝘁_𝗮𝗰𝘁𝗶𝘃𝗶𝘁𝘆 monitoring   → Catches long-running queries (30s+)   → Detects idle-in-transaction connections holding locks   → Flags lock waits — shows you the blocker AND the blocked query   → Warns when connection pool hits 80%+ capacity 🧪 𝗵𝘆𝗽𝗼𝗽𝗴 — Hypothetical Index Testing   → Doesn't just say "add an index"   → Actually SIMULATES the index and measures cost reduction   → Only flags it if it's proven to help ⚙️ 𝗽𝗴-𝗮𝗱𝘃𝗶𝘀𝗼𝗿 𝘀𝗲𝘁𝘂𝗽 (new command)   → Run this FIRST before analyze   → Checks which extensions are available on your DB   → Tells you exactly how to install what's missing   → No more silent skips or confusing errors ━━━━━━━━━━━━━━━━━━━━━━━ ▶ 𝗛𝗼𝘄 𝘁𝗼 𝗴𝗲𝘁 𝘀𝘁𝗮𝗿𝘁𝗲𝗱 ━━━━━━━━━━━━━━━━━━━━━━━ 𝗦𝘁𝗲𝗽 𝟭 — Install: pip install pg-advisor 𝗦𝘁𝗲𝗽 𝟮 — Check your extensions: pg-advisor setup postgresql://user:pass@localhost/mydb 𝗦𝘁𝗲𝗽 𝟯 — Run the full scan: pg-advisor analyze postgresql://user:pass@localhost/mydb 𝗦𝘁𝗲𝗽 𝟰 — Save your report: pg-advisor analyze ... --save-report → Generates a timestamped Markdown report automatically ━━━━━━━━━━━━━━━━━━━━━━━ 𝗪𝗵𝗮𝘁 𝗶𝘁 𝗰𝗵𝗲𝗰𝗸𝘀: ❌ Missing PKs and FK indexes ❌ FLOAT used for money columns ❌ Slow queries and SELECT * ❌ Duplicate and unused indexes ❌ Live lock waits and idle connections ❌ Connection pool pressure ✅ All with ready-to-run SQL fixes 📦 𝗣𝘆𝗣𝗜: https://lnkd.in/gP5Jvvfz 🐙 𝗚𝗶𝘁𝗛𝘂𝗯: https://lnkd.in/gfqbY3Xh Drop a ⭐ if this is useful. Feedback welcome 🙏 #Python #PostgreSQL #OpenSource #DevTools #Backend #Database #CLI #SoftwareEngineering

Ever wondered why databases like Apache Cassandra and RocksDB handle massive write-heavy workloads so efficiently? 🤔 The answer often lies in LSM Trees (Log-Structured Merge Trees). 💡 What is an LSM Tree? An LSM Tree is a data structure designed to optimize write performance by turning random writes into sequential writes. 👉 Instead of updating data in place: 1. Writes go to an in-memory structure (MemTable) 2. Then flushed to disk as immutable files (SSTables) 3. Background compaction merges and cleans up data 🚀 Why LSM Trees are powerful: ✔ Sequential disk writes → much faster than random writes ✔ High write throughput ✔ Efficient for large-scale, distributed systems 🧠 When should you use LSM Trees? 👉 Write-heavy systems like Logging systems, Event ingestion pipelines, Time-series databases 👉 Systems where read-after-write consistency is flexible 👉 Large-scale distributed databases handling massive data ⚖️ LSM Trees vs B-Trees 🔹 Write Pattern LSM: Sequential writes (append + merge) B-Tree: Random in-place updates 🔹 Read Performance LSM: Slower reads (data may exist across multiple files) B-Tree: Faster point reads (single tree lookup) 🔹 Write Amplification LSM: Higher (due to compaction) B-Tree: Lower 🔹 Use Case Fit LSM: Write-heavy workloads B-Tree: Read-heavy or balanced workloads 🏗️ Real-world comparison: MySQL (InnoDB) → B-Tree based Apache Cassandra → LSM Tree based 🔥 Key insight: Choosing between LSM Trees and B-Trees is not about which is “better” — it’s about matching the data structure to your workload. If your system is dominated by writes → LSM Trees shine If you need fast reads with strong consistency → B-Trees win 💬 If you're diving into system design, understanding this tradeoff is a game-changer. #SystemDesign #Databases #LSMTree #BackendEngineering #Scalability

"We already have File Systems for persistence. Why do we need Databases?" In System Design, this isn't just an introductory riddle , it’s the fundamental architectural split between simply storing data and managing data. If your system requires ACID consistency, complex relationships, or massive concurrency, a "file" just won’t cut it. 🛠️ Day 21: Introduction to Databases – The System’s Core Memory When applications rely solely on File Systems: 📍 Chaos: Who manages concurrent access when 10,000 users try to update the same profile file? 📍 Security: How do you grant read access to one column but not another within a single CSV? 📍 Complexity: Searching for a specific user ID in a 50GB log file requires reading the entire thing sequentially (O(N)). ⏩ We introduce the DBMS (Database Management System). It’s the structured software layer that provides data abstraction, standard access protocols, complex indexing, and robust transaction management (ACID), solving all the limitations of the raw file layer. The Core Functions of a DBMS: ➡️ACID Transactions: Guaranteeing Atomicity, Consistency, Isolation, and Durability (essential for finance). ➡️ Indexing: Using specialized B-Tree or Hash data structures to achieve fast, O(log N) data access (essential for scale). ➡️ Concurrency Control: managing safe, concurrent reading and writing.  Impact: (a) The Foundation: Every major internet company (Meta, Google, Netflix) uses structured databases at its core for everything from inventory to user profiles. (b) The "File System" Truth: Spoiler Alert! Databases do not eliminate file systems. Every database (MySQL, PostgreSQL, Cassandra) still writes to the raw file system (ext4, NTFS) in the background. It just does so much more intelligently. The database is where your application’s logic meets its immutable truth. If you build it wrong, you won’t just get slow queries—you’ll get corrupt data. WEEK 3: COMPLETE! Next week, we move into WEEK 4: SQL vs NoSQL & Advanced Database Fundamentals. #SystemDesign #60DaysOfCode #DatabaseInternals #DBMS #Week4 #Databases #BackendEngineering #Persistence #SoftwareArchitecture #PlacementPrep #ComputerScience

pgvector turns PostgreSQL into a high-performance vector database, allowing you to store embeddings and perform similarity searches alongside your relational data with zero architectural overhead