Optimizing PostgreSQL with Advanced Database Indexing

https://lnkd.in/gkriCMsh simple production story of a composite index where you miss the filter on the left and need to scan the entire column. before: Composite index was on: (entity, dbms, org_id, type) The leading column entity isn't in the WHERE clause. Postgres can't do a prefix lookup — it scans across every entity value, filtering as it goes. 300ms query time --- after: (org_id, dbms, entity, type) entity shifted back, org_id, dbms are always present and matched 38 micro seconds. voila!

My updates on DB(SQL) Learning this week! 'Foreign key' is a column in one table which relate to another table's 'primary key'. Managing the data using CASCADE(deletion upto connected references), SET NULL(soft deletion) and RESTRICT(no deletion at all). Learned and understood INNER , LEFT and FULL OUTER JOIN how they are useful based on the requirements. Then I come to know about 'EXPLAIN ANALYZE' diagnostic tool shows realtime statistics! further deep dive into INDEXING how we create index and how its reduce the query execution time to 'B-Tree , O(log n)' and something 'non-key value index' which store the value on its leaf level to reduce the lookup ! Next, I learnt about the Transactions which include Begin , Update , Commit or Rollback and got to know about the 'dirty read' is basically showing the values before commit which is not good! PostgreSQL don't have 'dirty read'. Finally Learnt ACID compliance in databases which has 4 things , ATOMICITY means its neither half or fraction, it will be full else Rollback ie, transactions must be fully commit, then we have CONSISTENCY which mean transaction brings form one valid state to another. ISOLATION , concurrent transactions don't interface with each other and last is DURABILITY which means once transaction is Committed it remains permanently recored even in the event of system crash or power outage!!!! #PostgreSQL #SQL #Database

𝐒𝐭𝐨𝐩 𝐭𝐫𝐞𝐚𝐭𝐢𝐧𝐠 𝐏𝐨𝐬𝐭𝐠𝐫𝐞𝐒𝐐𝐋 𝐥𝐢𝐤𝐞 𝐚 “𝐝𝐮𝐦𝐛” 𝐝𝐚𝐭𝐚 𝐬𝐭𝐨𝐫𝐞.🛑 At scale, the bottleneck is rarely Postgres itself — it’s how your application talks to it. 𝘗𝘰𝘴𝘵𝘨𝘳𝘦𝘚𝘘𝘓 𝘪𝘴 𝘱𝘰𝘸𝘦𝘳𝘧𝘶𝘭, 𝘣𝘶𝘵 𝘪𝘵𝘴 𝘱𝘳𝘰𝘤𝘦𝘴𝘴-𝘱𝘦𝘳-𝘤𝘰𝘯𝘯𝘦𝘤𝘵𝘪𝘰𝘯 𝘮𝘰𝘥𝘦𝘭 + 𝘳𝘰𝘸-𝘭𝘦𝘷𝘦𝘭 𝘭𝘰𝘤𝘬𝘪𝘯𝘨 𝘥𝘦𝘮𝘢𝘯𝘥 𝘢 𝘤𝘰𝘯𝘤𝘶𝘳𝘳𝘦𝘯𝘤𝘺-𝘧𝘪𝘳𝘴𝘵 𝘮𝘪𝘯𝘥𝘴𝘦𝘵. Here’s how I handle it using Go 👇 🧠 1. Eliminate Deadlocks with Deterministic Ordering Deadlocks happen when transactions lock rows in different orders. → Sort updates by primary key before the transaction → For hot rows, funnel writes through a single goroutine (via channels) Result: predictable execution, zero deadlocks. ⚡ 2. Fix Connection Fatigue Each Postgres connection = heavy OS process. → Use pgxpool / database/sql pooling → Keep pools small and efficient (e.g. 20 > 200) Result: lower context switching, better throughput under load. 🏗️ 3. Respect MVCC (Keep Transactions Tight) Postgres uses MVCC — but long transactions still increase contention. → Do computation before db.Begin() → Use context timeouts to kill slow queries Result: reduced lock time, higher concurrency. — The insight: 𝘎𝘰’𝘴 𝘨𝘰𝘳𝘰𝘶𝘵𝘪𝘯𝘦𝘴, 𝘤𝘩𝘢𝘯𝘯𝘦𝘭𝘴, 𝘢𝘯𝘥 𝘤𝘰𝘯𝘵𝘦𝘹𝘵𝘴 𝘢𝘳𝘦𝘯’𝘵 𝘫𝘶𝘴𝘵 𝘭𝘢𝘯𝘨𝘶𝘢𝘨𝘦 𝘧𝘦𝘢𝘵𝘶𝘳𝘦𝘴 — 𝘵𝘩𝘦𝘺’𝘳𝘦 𝘤𝘰𝘯𝘵𝘳𝘰𝘭 𝘴𝘺𝘴𝘵𝘦𝘮𝘴 𝘧𝘰𝘳 𝘥𝘢𝘵𝘢𝘣𝘢𝘴𝘦 𝘣𝘦𝘩𝘢𝘷𝘪𝘰𝘶𝘳. Master the orchestration, and Postgres won’t be your bottleneck. 🚀 #Golang #PostgreSQL #BackendEngineering #Concurrency #SystemDesign #Scalability

Why Your Database Indexes Might Be Hurting You Most engineers add indexes to speed things up. Few ever audit whether those indexes are actually being used. Here's what I've seen in production PostgreSQL systems: - Duplicate indexes silently eating write performance - Partial indexes ignored because the query planner chose a seq scan anyway - Composite indexes with columns in the wrong order, making them useless for 90% of queries Run this today: SELECT schemaname, relname, indexrelname, idx_scan, pg_relation_size(indexrelid) AS index_size FROM pg_stat_user_indexes WHERE idx_scan = 0 ORDER BY pg_relation_size(indexrelid) DESC; That query shows you every index that has never been used since the last stats reset, sorted by size. I've seen teams reclaim 30%+ storage and noticeably improve write throughput just by dropping dead indexes. The best engineers don't just add indexes. They treat them like code: review them, measure them, and delete them when they stop earning their keep. What's the worst "performance optimization" you've found that was actually making things slower? 🧑🏻💻 You can find the code snippet here: https://lnkd.in/eRz5vccD Don't worry, I am working on my URL Short-ner Service, it will be up soon. 😅 PS. Graphic generated by Claude. #postgresql #systemdesign #backend #softwareengineering

🔥 PostgreSQL Performance Optimization 🚀 Database performance isn’t achieved by throwing more hardware at the problem — it’s about making smarter tuning decisions. In real-world PostgreSQL environments, most performance bottlenecks stem from inefficient queries, poor indexing choices, or suboptimal configuration — not the database engine itself. ⚡ Core Areas to Focus On 1️⃣ Query Optimization * Minimize full table scans whenever possible * Use EXPLAIN ANALYZE to understand execution plans * Retrieve only necessary columns (avoid SELECT *) 2️⃣ Indexing Strategy * Leverage B-tree indexes for general use cases * Use GIN or GiST indexes for JSON and advanced search scenarios * Avoid excessive indexing, as it can negatively impact write performance 3️⃣ Memory & Configuration Tuning * Configure shared_buffers effectively for caching * Adjust work_mem for sorting and complex operations * Fine-tune WAL and checkpoint settings for better throughput 4️⃣ Vacuum & Routine Maintenance * Run VACUUM ANALYZE regularly to prevent table bloat * Ensure autovacuum is properly configured and active 5️⃣ Connection Management * Excessive connections can hurt performance * Use connection pooling solutions like PgBouncer or Pgpool-II 6️⃣ Continuous Monitoring * Identify and track slow-running queries * Monitor locks and blocking sessions * Regularly review execution plans for optimization opportunities 🎯 Final Takeaway Performance tuning isn’t a one-off activity — it’s an ongoing process of monitoring, analyzing, optimizing, and repeating. #postgresql #postgresdba #optimization #dba

I recently identified two production bugs that stemmed from the same silent root cause. A single pattern — DATE(updated_at) — was problematic in two ways: → Timezone math: DATE() truncates in UTC by default. A session completed at 08:30 in Sydney gets attributed to the previous day. No errors, no warnings — just incorrect data. → Index bypass: Wrapping a column in a function renders the predicate non-SARGable. PostgreSQL cannot utilize the index anymore, leading to full table scans. This results in timeouts on large tables. The fix is straightforward once recognized: ❌ WHERE DATE(updated_at) BETWEEN :start AND :end ✅ WHERE updated_at >= (:start AT TIME ZONE :tz) AND updated_at < (:end AT TIME ZONE :tz) + INTERVAL '1 day' This approach keeps the column bare, moves the timezone conversion to the bounds, restores index seeks, and ensures international users see the correct dates. For those working with timestamptz columns and multi-timezone data, this insight may be valuable. Additionally, this is my first blog post, published on Hashnode. I would appreciate it if you took a moment to check it out. 🙌 🔗 https://lnkd.in/gM9sQG8p #PostgreSQL #Backend #DatabaseEngineering #SoftwareEngineering #SQL

The query was not slow. It was long-running. A client had a reporting query that took 40 minutes. The team had been tuning it for weeks. Indexes added. Plan rewrites attempted. Stats refreshed. Nothing moved the needle more than a rounding error. Then someone actually looked at what the query was doing. It was scanning 1.2 billion rows, aggregating across six dimensions, and writing results to an audit table. Forty minutes was not a performance problem. Forty minutes was the correct amount of time for that amount of work. Percona just published a piece making this distinction, and it is one of the most common misdiagnoses I see in the field. A slow query is one that should finish quickly and does not. A long-running query is one that is doing a large amount of legitimate work and taking a proportional amount of time to do it. Mixing them up costs teams weeks of engineering effort on the wrong problem. Meanwhile the actual slow queries, the ones hiding inside user-facing transactions, keep bleeding response time. Before you tune a query, diagnose it. Ask what the query is doing, not just how long it is taking. Ever had a tuning cycle that ended with the realization the query was never the problem? What did it end up being? #PostgreSQL #DatabasePerformance #QueryTuning #DataEngineering #DBA

Tired of guessing what is happening inside your PostgreSQL database? For too long, we’ve treated database observability as a reactive exercise: wait for the latency spike, wait for the transaction failures, and then start the fire drill. We are often flying blind until the dashboard finally catches up. I wanted to change that, so I built Pulse a real-time PostgreSQL observability experience built entirely in Rust. Pulse turns raw database telemetry into a live operational story, allowing you to watch database health evolve in near real-time (500ms intervals). I am attaching a demo of it running locally so you can see it in action. Here is what I am tracking in the video: 1) WAL Heartbeat: I am monitoring the live throughput rhythm, tracking bytes/sec and transactions/sec to spot checkpoint-related pressure. 2) Table Bloat Heatmap: A visual map of bloat severity that helps identify waste accumulation before it degrades performance. 3) Query Stream: A live feed that highlights duration, state, and criticality, using color semantics to flag blocked or long-running queries instantly. Why I chose this architecture: I wanted performance and low overhead. The backend is a Rust-based Axum server with SQLx collectors, while the frontend is built in Dioxus WebAssembly for low-latency rendering and a reactive dashboard state. Because it is built in Rust, the shared typed contracts between the backend and frontend make the whole system incredibly stable and fast. This project is a shift from reactive monitoring to a decision-support layer for database reliability, helping teams prioritize maintenance, catch scaling friction, and reduce incident diagnosis times. I’m currently focusing on the core experience, but the roadmap includes alerts, historical persistence, and role-based access control. I’d love to hear your thoughts on the approach. How are you currently managing "hidden" database risk? Is your team still reacting to alerts, or are you catching the signals first? Let’s discuss in the comments. #PostgreSQL #RustLang #SRE #Observability #DatabaseManagement #WebAssembly #Engineering #TechInnovation

An index is not something you add because a query feels slow. An index is a trade-off. It can make reads faster, but it also adds cost to writes, storage, and maintenance. That means every index should earn its place. My simple index checklist: Which query needs this? Which columns are filtered, joined, or sorted? How selective is the column? Will this help the actual execution plan? Is this index still useful after data grows? The mistake I made earlier was treating indexes like magic speed buttons. They are not magic. They are data structures. The better you understand your access patterns, the better your database decisions become. #PostgreSQL #DatabaseDesign #BackendDevelopment

When your data is small, everything feels fast. But as your table grows, queries start slowing down. That’s where indexes come in. Instead of scanning the entire table, PostgreSQL uses an index to jump directly to the required rows; just like a table of contents in a book. Creating one is simple: CREATE INDEX idx_users_email ON users(email); If you’re working in real projects (like FastAPI with Alembic), you should create it through migrations: op.create_index("idx_users_email", "users", ["email"]) Indexes can drastically improve read performance, but they also add overhead to writes. So don’t add them blindly; add them where queries actually need speed. That’s the difference between code that works… and systems that scale. #PostgreSQL #BackendDevelopment #DatabaseOptimization #FastAPI #SoftwareEngineering