Het Siddhapura’s Post

I published a new piece on a problem that had been slowing me down for a while: 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗔𝗴𝗮𝗶𝗻𝘀𝘁 𝗥𝗲𝗮𝗹 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 𝗪𝗮𝘀 𝗦𝗹𝗼𝘄𝗶𝗻𝗴 𝗠𝗲 𝗗𝗼𝘄𝗻 — 𝗦𝗼 𝗜 𝗕𝘂𝗶𝗹𝘁 𝗠𝘆 𝗢𝘄𝗻 𝗠𝗲𝗺𝗼𝗿𝘆 𝗦𝗲𝗿𝘃𝗲𝗿𝘀 In my workflow, integration tests against manually managed Postgres Docker containers had turned into a 30–40 minute cycle. That made realistic testing too expensive for normal development. So I built:  • postgres-memory-server for disposable real Postgres and ParadeDB  • redisjson-memory-server for disposable real Redis with RedisJSON support The goal was simple: keep the realism of testing against real infrastructure, but remove the mess of shared databases, contamination, and slow setup. In my setup, that brought the loop down to under 2 minutes. I wrote about the pain points, the approach, and shared Node.js examples here: https://lnkd.in/dchjqdPf Would love feedback from folks working with Node.js, Postgres, ParadeDB, Redis, or RedisJSON. #nodejs #postgres #testing #softwaretesting #unittesting #redis #redisjson

Check out our new article “Stop choosing between blobs and fixed data types in your distributed cache” in DeveloperTech News: https://lnkd.in/gMtCW_4s. The article explains how you can transform distributed caching from a passive data store into a powerful, in-memory computing platform with ScaleOut Active Caching™.   The article includes our explainer video in which you'll see how developers can deploy custom C# or Java data structures directly into ScaleOut's distributed cache using two types of modules. “API modules” let clients fetch or update only the data they need and move processing to the distributed cache.  "Message modules" connect to hubs like Apache Kafka to receive and process messages while accessing cached objects instead of persistent stores.   Active caching results in faster applications, reduced network traffic, and improved scalability by offloading processing to cache servers. Modules also improve maintainability by treating cached objects as object-oriented data instead of uninterpreted blobs. #DistributedCaching #MessageProcessing #CloudComputing

⚡ 6 Proven Strategies to Improve REST API Performance Building an API is easy. Building a fast and scalable API is the real challenge. Here are 6 proven techniques used by high-performance systems 👇 🚀 1. Implement Pagination Instead of returning thousands of records in one response, return data in smaller chunks. This reduces memory usage and improves response time. 🗄 2. Optimize Database Queries Poor queries slow everything down. Use indexes, avoid N+1 queries, and fetch only the required data. 📦 3. Enable Payload Compression Use Gzip or Brotli compression to reduce response size and speed up data transfer. ⚡ 4. Multi-Level Caching Combine HTTP caching + Redis caching to serve frequently requested data instantly. 🔄 5. Transition to Async APIs Use asynchronous processing to handle multiple requests efficiently and prevent blocking operations. 🔌 6. Use Connection Pooling Tools like HikariCP help reuse database connections, improving throughput and reducing latency. 💡 Simple rule: Fast APIs = Optimized queries + Smart caching + Efficient infrastructure Most performance issues in APIs come from database queries and missing caching layers. Which technique improved your API performance the most? 👇 BitFront Infotech #BackendDevelopment #APIDesign #SystemDesign #SoftwareEngineering #WebDevelopment #Redis #PerformanceOptimization #Developers #Programming

⚡ Why Your API Is Fast on Local but Slow in Production? This is one of the most common problems developers face. 👉 “It works perfectly on my machine… but production is slow.” I’ve faced this multiple times in .NET Core + SQL Server applications — and the issue is usually NOT what you think. 🔍 Common Reasons Behind This: 💥 1. Database Indexing Issues Your local DB has small data → queries are fast Production DB has millions of records → same query becomes slow 👉 Missing indexes = full table scans 💥 2. Different Execution Plans SQL Server may choose different query plans in production 👉 Parameter sniffing can break performance badly 💥 3. Network Latency Local = everything runs on same machine Production = API → DB → external services 👉 Every call adds delay 💥 4. Third-Party APIs On local, you might mock them In production, real APIs can be slow or unreliable 💥 5. Logging & Middleware Overhead Too much logging (especially sync logging) can slow down APIs 💥 6. Missing Caching No Redis / in-memory caching = repeated DB hits 🚀 How I Usually Fix It: ✔ Add proper indexes based on query usage ✔ Analyze queries using execution plans ✔ Use caching (Redis) for frequent data ✔ Optimize API calls (parallel where possible) ✔ Reduce unnecessary logging ✔ Monitor with tools (logs, APM, SQL profiler) 💡 Key Lesson: 👉 Performance issues are usually system-level, not just code-level. If your API is slow in production… Don’t just look at code — look at the whole architecture. 💬 Have you ever faced this issue? What was the root cause? 🔥 Hashtags: #DotNet #SQLServer #Performance #BackendDevelopment #WebAPI #SoftwareEngineering #SystemDesign #Caching #Redis #Developers #TechTips #Programming

🚀 Scaling Smart: Using Bloom Filters to Eliminate Unnecessary DB Hits In my previous post, I talked about building resilient systems with Kafka + DLQs. Today, let’s zoom into a powerful optimization technique that quietly boosts performance at scale: Bloom Filters. 💡 The Problem: In high-traffic systems, databases often get flooded with repetitive existence checks: Does this user exist? Is this email already registered? Is this token valid? Even with caching, these checks can become a bottleneck under heavy load. ⚡ The Solution: Bloom Filters A Bloom Filter is a space-efficient probabilistic data structure that helps answer: 👉 Is this element definitely NOT in the set, or MAYBE in the set? ✔️ If it says NOT present → 100% accurate (skip DB call) ✔️ If it says MAYBE present → fallback to DB/Redis check This simple layer drastically reduces unnecessary database queries. 🔧 Where It Fits in Architecture: Placed before DB or cache lookups Works great with Redis-backed systems Ideal for auth systems (user/email existence checks) Can be shared across services in distributed environments 📈 Why It Matters: ⚡ Reduces DB load significantly 🚀 Improves response times 💰 Saves infrastructure cost at scale 🔄 Perfect for read-heavy systems ⚠️ Trade-off: Bloom Filters can have false positives, but never false negatives. 👉 That’s why they’re used as a first-pass filter, not a source of truth. 🧠 Pro Tip: Tune your hash functions and bit array size carefully to balance memory vs accuracy. ✨ In the next post, we will talk about Redis + JWT token and refresh token #SystemDesign #BackendEngineering #Scalability #BloomFilter #DistributedSystems #PerformanceOptimization #nodejs #javascript

Our API response time jumped from 120ms → 600ms overnight. No code deployed. No infra change. No incidents reported. Just... slower. Here’s how I debugged it in 40 minutes 👇 Step 1: Isolate the symptom CloudWatch showed the spike started at 11:42 PM. But here’s the interesting part: P95 latency spiked P50 stayed normal That usually means: Large payloads Heavy queries Edge-case traffic Not a full-system slowdown. Step 2: Eliminate the usual suspects I checked the obvious first: Lambda cold starts? ❌ Warm instances were also slow DB connection pool? ❌ Only 42% utilized External APIs? ❌ Not in the request path That narrowed it down to one likely culprit: ➡️ The database query itself. Step 3: Inspect the query plan Ran EXPLAIN ANALYZE on the main trade lookup query. Result: Sequential scan on 2.1M rows Estimated cost: 48,000 Index was no longer being chosen Why? As the table grew, PostgreSQL recalculated cost estimates and changed the execution plan automatically. Silent. Invisible. Expensive. Step 4: Fix it Added a composite index on: (user_id, created_at DESC) Immediately after: Query planner switched to Index Scan P95 dropped from 600ms → 89ms The real lesson Your system can break without deployments. Because performance bugs often come from: Data growth Query planner decisions Traffic shape changes Hidden thresholds EXPLAIN ANALYZE isn’t just an optimization tool. It’s a production survival tool. And if you’re not tracking P95 latency, you’re blind to what power users are experiencing. My takeaway As systems scale, the code may stay the same — but behavior changes. That’s where engineering gets interesting. Curious: what’s the sneakiest production bug you’ve debugged? Drop it in the comments 👇 (Real stories only — those are always the best lessons.) If this was useful, repost it so more engineers see it. #PostgreSQL #BackendEngineering #NodeJS #SystemDesign #SoftwareEngineering #Debugging #AWS #Performance #DevOps

In 2016, our university's file infrastructure had a problem that was only going to get worse. Every file across all institutional systems lived inside PostgreSQL — fragmented into 50KB compressed chunks that the application server had to reassemble on every single request. The most accessed endpoint in the entire system was photo retrieval. And it was doing all that work, millions of times a month, on a database machine shared across three different systems. Under load testing with 1,000 simultaneous users, the error rate hit 22%. We evaluated our options. Object storage solutions existed, but our infrastructure at the time was constrained — we didn't have the capacity to operate and maintain that kind of tooling with the reliability and operational requirements that a production environment demands. So we designed something that fit what we actually had, and built it to last. The new architecture introduced a dedicated content server, fully decoupled from the database, with two-stage encrypted file upload, persistent connection pooling to avoid handshake overhead, and Sköld — a dedicated security layer that authenticates, audits, and logs every file access in real time, integrated into our observability stack. One requirement shaped the whole design: files are immutable by year. If you upload a profile photo in 2025 and change it in 2026, a new file is created — the 2025 version is never touched. Historical snapshots stay intact, backups are clean per-year, and audit trails are unambiguous. The migration ran for months in hybrid mode. A Python daemon synced files from the database to disk every minute, so by the time we switched each application over, the content server was already in sync. Not a single file was lost. Not a single request failed at cutover. The server has an active replica with 3-minute sync, full yearly backups, and daily incrementals. Nearly ten years later: → Zero unplanned downtime → Zero data loss → 16.2 million files, 18.5 TB under management → 2.7 million monthly access requests → 37.4 million audit operations logged since June 2025 alone → Storage expansion underway from 18 TB to 23 TB The machine running all of this: 8 cores, 16GB RAM. The hardest part wasn't the code. It was designing a migration that tens of thousands of users would never notice — and holding the line on doing it right. #SoftwareArchitecture #SystemDesign #Migration #Java #PublicSector #Engineering

Designing a URL Shortener sounds simple. But it actually covers some powerful system design fundamentals. Here’s a simple way to think about it: 1️⃣ Requirements: - Convert long URL → short URL - Redirect users using short URL - Support expiry and optional custom alias 2️⃣ High-Level Design: - Load balancer + multiple application servers - Cache (Redis) for fast redirection (read-heavy system) - Database to store URL mappings 3️⃣ Low-Level Design: Database: - short_code (Primary Key) - original_url - created_at - expiry_at APIs: - POST /shorten → generate short URL - GET /{short_code} → redirect (HTTP 302) 💡 Key Insight: Good system design is not about complexity. It’s about breaking problems into: Requirements → Data → APIs → Flow Simple thinking leads to scalable systems. #systemdesign #backenddevelopment #softwareengineer #scalablearchitecture #javadeveloper

𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗦𝗰𝗵𝗲𝗺𝗮 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 𝗠𝗼𝗿𝗲 𝗧𝗵𝗮𝗻 𝗬𝗼𝘂 𝗧𝗵𝗶𝗻𝗸 Most engineers treat database schema like an afterthought. That’s a mistake. You can throw caching, indexing, and scaling tricks at a bad schema… and it will still bleed performance. Here’s the uncomfortable truth: 👉 A bad schema forces complexity everywhere else 👉 A good schema removes the need for “clever” optimizations If your tables are poorly designed: You’ll write ugly joins You’ll duplicate data without realizing You’ll fight consistency bugs You’ll depend on cache as a crutch And worst of all — every new feature becomes slower to build. A clean schema, on the other hand: Makes queries predictable Reduces unnecessary reads/writes Keeps data consistent by design Scales naturally without hacks People love to say: “Just add Redis” “Just optimize queries” No. Fix your schema first. Because if your foundation is trash, every layer above it is just well-engineered garbage. Great backend engineers don’t just write APIs. They design data. And that’s where the real leverage is.

Sometimes everything in your system works fine. Then one day, traffic spikes… and multiple requests try to update the same data at the same time. Now you get weird issues: Duplicate orders Overbooked seats Negative inventory Not because of bugs. Because of concurrent updates. --- This is where Distributed Locking comes in The idea is simple: Only one process should modify a resource at a time. Everyone else has to wait. --- What actually happens Let’s say two requests try to update the same product stock. Without locking: Both read stock = 10 Both reduce it Final value becomes wrong With locking: First request gets the lock Second request waits Updates happen safely --- Where this is used Payment processing Inventory management Booking systems Scheduled jobs Anywhere consistency matters. --- Common ways to implement Database locks Simple, but can affect performance. Redis locks (like Redisson) Fast and commonly used in distributed systems. Zookeeper / etcd Used in large-scale systems. --- Why this matters In distributed systems: Multiple instances run in parallel Race conditions are common Data can get corrupted silently Locks help keep things consistent. --- But be careful Locks can slow things down. If not handled properly, they can even cause deadlocks. Use them only where necessary. --- Simple takeaway When multiple processes touch the same data, coordination becomes essential. --- Where in your system could two requests clash at the same time without you noticing? #Java #SpringBoot #Programming #SoftwareDevelopment #Cloud #AI #Coding #Learning #Tech #Technology #WebDevelopment #Microservices #API #Database #SpringFramework #Hibernate #MySQL #BackendDevelopment #CareerGrowth #ProfessionalDevelopment #RDBMS #PostgreSQL #backend