Prevent PostgreSQL Table Bloat for Optimal Performance

PostgreSQL micro-blog: Check your Cache Hit Ratio Ever wondered what your cache hit ratio is for your PostgreSQL database? Run this quick check: SELECT datname, blks_hit, blks_read, ROUND(100.0 * blks_hit / NULLIF(blks_hit + blks_read, 0), 2) AS hit_ratio FROM pg_stat_database WHERE datname = 'bluebox'; As a general rule, this value should be greater than 99% for well-performing systems. If it’s significantly lower, it may indicate: • insufficient shared buffers • queries scanning large portions of tables • missing or inefficient indexes • workload larger than available memory Memory is orders of magnitude faster than disk, keeping frequently accessed data in cache is critical for performance. #PostgreSQL #DatabasePerformance #DBA #PerformanceTuning #DatabaseSpa

What's the biggest PostgreSQL table you've ever managed? I'll go first. 41 hypertables. Biggest single hypertable: 800 GB compressed. Before compression it was 2.4 TB. TimescaleDB compression took a 3:1 ratio on most of our time-series data. The chunk interval is 1 day, compression kicks in after 1 hour, and retention drops anything older than 30 days. The numbers that surprised me: • Compression runs automatically and rarely takes more than a few seconds per chunk • Decompression for queries is fast enough that users don't notice • The real cost isn't storage -- it's the WAL generated during compression and decompression But I'm curious about what other people are running. Biggest table? Most rows? Weirdest schema? Let me know in the comments -- I genuinely want to hear the war stories. #PostgreSQL #TimescaleDB #Database #DataEngineering #DevOps

What's the biggest PostgreSQL table you've ever managed? I'll go first. 41 hypertables. Biggest single hypertable: 800 GB compressed. Before compression it was 2.4 TB. TimescaleDB compression took a 3:1 ratio on most of our time-series data. The chunk interval is 1 day, compression kicks in after 1 hour, and retention drops anything older than 30 days. The numbers that surprised me: • Compression runs automatically and rarely takes more than a few seconds per chunk • Decompression for queries is fast enough that users don't notice • The real cost isn't storage -- it's the WAL generated during compression and decompression But I'm curious about what other people are running. Biggest table? Most rows? Weirdest schema? Let me know in the comments -- I genuinely want to hear the war stories. #PostgreSQL #TimescaleDB #Database #DataEngineering #DevOps

Our PostgreSQL table hit 200M+ rows. Queries started timing out. Backups took hours. The fix? Database Partitioning. Here's what we did and why it worked 👇 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗽𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝗶𝗻𝗴? Instead of one giant table, PostgreSQL lets you split it into smaller, independent pieces — same schema, different data. 𝗪𝗵𝗮𝘁 𝘄𝗲 𝘂𝘀𝗲𝗱: → Range Partitioning by created_at (monthly partitions) → Queries now scan only the relevant month → Old partitions dropped in milliseconds instead of hours Here's what changed 👇 𝗕𝗲𝗳𝗼𝗿𝗲: → 200M rows in one table → Avg query time: 4.2 seconds → Slow backups, lock waits, hard to scale 𝗔𝗳𝘁𝗲𝗿 splitting by month: → Avg query time: 0.3 seconds → Faster maintenance → Old data deleted in seconds → Each chunk: ~16M rows 𝗪𝗵𝗲𝗻 𝘁𝗼 𝘂𝘀𝗲 𝗶𝘁: ✅ Table has 50M+ rows ✅ You filter by date, region, or user ✅ You need to delete old data fast Don't wait for the table to break. Partition early. One query. One chunk read. 14x faster. #PostgreSQL #DevOps #Database #SystemDesign #BackendEngineering #SRE #Infrastructure #SoftwareEngineering

500 million rows. Queries crawling. What's your move? Most engineers reach for a new database. The better answer is often already in PostgreSQL. I wrote a practical guide to table partitioning on HackerNoon — range, list, hash, pruning, and what to avoid in production. https://lnkd.in/d-gV_hUz #PostgreSQL #BackendEngineering #Databases

I’ve been reviewing the new features in PostgreSQL 18, and a few improvements really stood out from a database architecture perspective. In many real production environments, we spend a lot of time optimizing I/O performance and managing indexes efficiently. PostgreSQL 18 seems to be addressing some of these long-standing challenges. A few features that I’m particularly interested in testing: • Asynchronous I/O – Could significantly improve sequential scan performance and heavy workloads. • Skip Scan for Multi-Column Indexes – This is a big one. It may reduce the need for creating multiple indexes just to support different query patterns. • UUID v7 Support – Time-ordered UUIDs that can improve index locality and write performance. • Better Upgrade Process – Preserving query planner statistics during upgrades can help avoid performance surprises after migration. PostgreSQL has been evolving rapidly over the last few versions, and it’s becoming an even stronger option for both high-throughput OLTP systems and analytical workloads. I’m curious to see how these improvements perform in real workloads. For those already exploring PostgreSQL 18 — which feature are you most excited about? #PostgreSQL #DBA #DatabaseEngineering #DataArchitecture #OpenSource #PerformanceTuning

PostgreSQL DB - List all Databases To list out all the existing databases in PostgreSQL: 1. Execute the SQL query from the system Catalog.    SELECT datname FROM pg_database; 2. The psql program's \l meta-command * \l or \list - Lists all databases, their owners, and character encodings in the psql interface. * \l+ or \list+ - Provides more detailed information, including database size and default tablespace. * -l command-line option are also useful for listing the existing databases. (Ex: $ psql -l ) 3. \c or \conninfo - displays the currently connected database. Watch Video Format: https://lnkd.in/eYSdHTkS

5 PostgreSQL Features Every Backend Developer Should Know PostgreSQL is more than just a database. Many developers start using Postgres like a simple storage engine. Tables. Queries. CRUD. But Postgres can do much more. Here are 5 features that make PostgreSQL incredibly powerful: • JSONB – store and query semi-structured data • Indexes – B-tree, GIN, GiST for high performance queries • CTEs – write complex queries in a clean way • Extensions – PostGIS, pgvector, TimescaleDB • Full-text search – built directly into the database In many cases Postgres can replace entire parts of your stack. Search engine. Vector database. Analytics engine. The real power of PostgreSQL isn’t just storing data. It’s processing data efficiently inside the database. What PostgreSQL feature do you use the most? #PostgreSQL #Backend #Databases #SoftwareEngineering

Did you know? When you run INSERT, UPDATE or DELETE in PostgreSQL, you don't need a separate SELECT to see what changed. The RETURNING clause gives you back the modified rows instantly — in the same query. A small but powerful detail that saves you an unnecessary database round-trip every single time. #SQL #PostgreSQL #RETURNING #DML #LearnSQL #BackendDevelopment #GitHub #100DaysOfCode

Changed just one word in a PostgreSQL function… and got a 3,000× speedup ⚡ Here’s what happened 👇 I had a simple UPDATE on a primary key — something that should take <1ms. Reality: ~498ms per call 9,500 calls ~80 minutes wasted 😅 After digging deeper, the culprit was surprisingly subtle: 👉 The function parameter was declared as NUMERIC 👉 The actual column type was BIGINT PostgreSQL was silently casting types on every call, which prevented the planner from reusing the optimal index plan inside PL/pgSQL. No errors. No warnings. Just a performance killer hiding in plain sight. The fix? One line: NUMERIC → BIGINT Result: Before: ~502ms/call After: 0.12ms/call 🚀 Key takeaways: 1️⃣ Enable pg_stat_statements in production, whenever required → You won’t see issues like this otherwise 2️⃣ Always match function parameter types with column types → Implicit casts are not “free” 3️⃣ Don’t rely only on EXPLAIN ANALYZE for ad-hoc queries → The planner behaves differently inside functions Sometimes, performance problems aren’t about complex queries or missing indexes… They’re about tiny mismatches with massive impact. iMocha Vishal Madan Sujit Karpe #PostgreSQL #Performance #DatabaseOptimization #BackendEngineering #TechLessons #Engineering