PostgreSQL Partitioning Speeds Up Queries by 14x

💡 PostgreSQL column limits — let’s talk real experience PostgreSQL tables have a column limit — usually somewhere between ~250 and 1600 columns, depending on the data types. But honestly… most well-designed tables shouldn’t get anywhere close to that 👀 Huge tables can mean: Poor schema design Harder maintenance Performance headaches Now I’m curious 👇 What’s the highest number of columns you’ve ever had in a table? Was it intentional… or did things just get out of hand? 😅 Drop your experience 👇 let’s compare notes. #PostgreSQL #DatabaseDesign #Backend #SoftwareEngineering #DataModeling

⚡ 5 PostgreSQL Performance Tuning Tips That Actually Work Many teams scale their applications… but forget to optimize the database powering everything. As a PostgreSQL DBA, I’ve seen the same performance issues appear again and again. Here are 5 PostgreSQL performance tuning tips that actually make a difference: 1️⃣ Use the Right Index Type Most people only use the default B-Tree index, but PostgreSQL provides several index types depending on the use case. Examples: B-Tree → equality and range queries GIN → full-text search and JSONB BRIN → very large tables GiST → geometric or spatial data Choosing the correct index can drastically improve query speed. 2️⃣ Analyze Queries with EXPLAIN ANALYZE Before optimizing a query, understand how PostgreSQL executes it. EXPLAIN ANALYZE shows: query execution plan time spent on each step whether indexes are being used It’s one of the most powerful tools for diagnosing slow queries. 3️⃣ Monitor Slow Queries One of the best PostgreSQL extensions for this is pg_stat_statements. It helps you identify: most frequently executed queries slowest queries total execution time Fixing just a few heavy queries can dramatically improve performance. 4️⃣ Tune Memory Settings Two important parameters to understand: • shared_buffers – memory used by PostgreSQL for caching data • work_mem – memory used for sorting and hash operations Proper tuning can reduce disk I/O and speed up complex queries. 5️⃣ Maintain Tables with VACUUM PostgreSQL uses MVCC, which creates dead tuples during updates and deletes. Running VACUUM helps: reclaim storage maintain table health prevent table bloat Most systems rely on autovacuum, but monitoring it is still essential. 💡 Final Thought Database performance issues are rarely caused by hardware. Most of the time, the problem is: inefficient queries missing indexes poor configuration Understanding how PostgreSQL works internally makes optimization much easier. #PostgreSQL #Database #DBA #DatabasePerformance #SQL #TechTips #DataEngineering

Partial Index in PostgreSQL. The main mistake with partial indexes is simple: 👉 You create the index… but your queries don’t match it. 👉 PostgreSQL will ignore a partial index if it can’t guarantee that all required rows are inside it. For example. If your index is: WHERE is_active = true then your query must also include: AND is_active = true It’s also important to understand: Conditions like: WHERE is_active != true or WHERE is_active = false Will NOT use this index. Because they target a different subset of data. Otherwise, PostgreSQL will ignore the index. That’s why even a “correct” index can behave like it doesn’t exist. Think of a partial index as a filtered dataset. It’s not a general-purpose index - it’s optimized for a specific access pattern. If your query doesn’t follow that pattern - no performance gain. When to use it: - large table - small, frequently accessed subset - consistent query pattern (the filter condition is always present and rarely changes) A partial index is only effective when your queries follow the same condition it was built on❗ #PostgreSQL #SQL #DatabaseOptimization #Performance #Indexing #Backend #SoftwareEngineering

I wrote a hands-on guide to PostgreSQL transactions and locks. If you've ever had queries that suddenly hang, or two concurrent UPDATEs that produce unexpected results — I tried to explain what's actually happening inside PostgreSQL. What's covered: → Why Serializable fails even without a logical conflict → A race condition puzzle where Alice and Bob both leave on-call duty — and nobody's left → How VACUUM reclaims space (and when it doesn't) → All demonstrated with SQL you can copy-paste and run https://lnkd.in/dijcP-8t #PostgreSQL #Database #SQL #Backend #SoftwareEngineering

Our PostgreSQL query executed 1,000 subqueries. It only needed 75. Imagine this: • 1,000 orders • Each referencing one of 75 products The original query fetched the product name per order using a correlated subquery. SELECT o.id, (SELECT name FROM product WHERE id = o.product_id) FROM orders o; PostgreSQL executed the product lookup 1,000 times. The fix? Rewrite it using a LATERAL join. SELECT o.id, p.name FROM orders o LEFT JOIN LATERAL ( SELECT name FROM product WHERE id = o.product_id ) p ON true; Now PostgreSQL introduces a Memoize node. It caches results by product_id. From the query plan  • 1000 rows processed • 75 cache misses (real product lookups) • 925 cache hits (reused results) That means PostgreSQL only queried the product table 75 times instead of 1000. Small query rewrite. Big difference in execution strategy. This works best when many rows reference a small set of keys. If every row has a unique key, Memoize has nothing to reuse. Sometimes performance improvements aren't about adding indexes — they're about changing the shape of the query plan. Note: Hash and Merge joins were disabled for demonstration. Normally PostgreSQL chooses the join strategy based on cost. #PostgreSQL #SQLPerformance #BackendEngineering

🚨 What Actually Happens When You Run VACUUM in PostgreSQL? Most developers think VACUUM is just “cleanup”. That’s not even half the story. PostgreSQL doesn’t overwrite data when you UPDATE or DELETE. Because of MVCC (Multi-Version Concurrency Control), it creates new row versions and leaves the old ones behind. These old rows are called dead tuples. 🧠 So what does VACUUM really do? When you run VACUUM, PostgreSQL: ✔️ Scans the table page by page ✔️ Identifies dead tuples (no longer visible to any transaction) ✔️ Marks that space as reusable (not returned to OS) ✔️ Updates visibility map for faster future scans Important: 👉🏻 VACUUM does NOT shrink your table size ⚠️ Why this matters in production If you don’t vacuum regularly: • Dead tuples keep accumulating • Table bloat increases • Indexes become inefficient • Queries slow down • Storage usage grows Eventually… performance degrades badly. 🔥 VACUUM vs VACUUM FULL 👉🏻 VACUUM • Non-blocking • Reuses space internally • Safe for production 👉🏻VACUUM FULL • Rewrites the entire table • Returns space to OS • Requires exclusive lock (blocks reads/writes) • Use VACUUM FULL only when absolutely necessary. ⚙️ What about AUTOVACUUM? PostgreSQL already runs an autovacuum in the background. But here’s the catch: 👉🏻 Default settings are often NOT enough for high-write workloads As a DBA, you should monitor: pg_stat_user_tables Dead tuple count Autovacuum frequency 💡 Real takeaway. VACUUM is not optional maintenance. It’s part of how PostgreSQL stays alive under load. Ignore it, and your database won’t crash… It will just get slower and slower until users start complaining. #PostgreSQL #Database #DBA #PerformanceTuning #Backend #DataEngineering #SQL #DevOps

What if thousands of UPDATEs and DELETEs are hitting your table every second? Your data looks correct. But your table size keeps growing. 📈 This is called Table Bloat. And it's a direct cost of MVCC. Here's why it happens: → PostgreSQL never overwrites a row on UPDATE or DELETE → It always creates a new version of the row instead → The old version stays behind as a dead tuple → At thousands of writes per second, dead tuples pile up fast The result? → Tables grow even when actual data hasn't changed → Queries slow down scanning through dead tuples → Indexes keep pointing to rows that no longer exist So what's the solution? VACUUM. 🧹 What VACUUM does: → Scans the table for dead tuples → Removes them and marks space as reusable → Updates the visibility map so queries stay fast → Prevents transaction ID wraparound — ignore this and PostgreSQL will shut itself down 🚨 One thing VACUUM does NOT do: → It does not shrink the file size on disk → For that you need VACUUM FULL — but it locks the table, use carefully And the best part? You don't have to run it manually. PostgreSQL's autovacuum does this in the background automatically. But autovacuum isn't magic. On high-write tables it can fall behind — tuning it for your workload is where the real DBA work begins. MVCC gave PostgreSQL speed and clean isolation. VACUUM is what keeps that trade-off from breaking you. 💡 #PostgreSQL #Database #DBA #VACUUM #TableBloat #DataEngineering

If your PostgreSQL databases involve complex search or filtering logic, use GIN indexes instead of btree indexes to scale performance 💪 #postgresql #gin https://lnkd.in/dXSf-HeE

You just ran pg_upgrade from PostgreSQL 15 to 16. Everything looks clean. The databases are up, applications reconnect, queries execute. Then someone runs a spatial query and gets: "could not access file: No such file or directory." pg_upgrade copies catalog metadata faithfully. Too faithfully. It migrates the extension entries that say "PostGIS 3.4.0 is installed" without checking whether PostGIS 3.4.0 actually exists in the new cluster's library path. The old .so files were compiled against PG 15. They are gone or incompatible. Every C-implemented PostGIS function -- ST_Contains, ST_Intersects, ST_Buffer, ST_DWithin -- breaks on the first call. The treacherous part: not everything breaks. Pure SQL wrapper functions and metadata functions like PostGIS_Full_Version() still work. A quick smoke test passes. The application loads. It is only when a user triggers a real spatial query that the error surfaces. Even when you install the correct PostGIS packages for PG 16, there is a second problem. The catalog still says "3.4.0 installed" while the library on disk is 3.5.0. PostGIS functions work, but you are running with outdated SQL definitions, missing new functions, and potentially incompatible behavior between the library and catalog versions. The fix after every pg_upgrade: 1. Install PostGIS packages matching the new PG major version BEFORE upgrading 2. Run ALTER EXTENSION postgis UPDATE in EVERY database 3. Verify with PostGIS_Full_Version() -- SQL and library versions must match 4. Test a C function: SELECT ST_AsText(ST_MakePoint(0, 0)) The full detection queries, fix procedures, and a scripted post-upgrade checklist: https://lnkd.in/e8Zzrt4p #PostgreSQL #PostGIS #DatabaseUpgrade #DBA