Avoid Leaking Business Volume with permid64

Stop the Race: Solving Data Inconsistency in Concurrent Systems Building a "working" application is easy. Building a reliable one is hard. I recently spent time diving into the world of Concurrency and Data Integrity using Python and SQL. One of the most common (and dangerous) bugs in software is the "Race Condition"—where two processes try to update the same data at the same time, leading to "lost updates" and corrupted balances. I simulated a high-traffic banking system to see how data inconsistency happens and, more importantly, how to stop it. The Solution: A Two-Pronged Defense  Application-Level Locking: Using Python’s threading.Lock to create "Mutual Exclusion" (Mutex). This ensures that only one thread can access the critical "Read-Modify-Write" logic at a time.  Database-Level Integrity (ACID): Moving the logic into a relational database (PostgreSQL/SQLite) to leverage Atomicity and Isolation. By using BEGIN, FOR UPDATE, and COMMIT statements, the database acts as the ultimate gatekeeper for data truth. Key Takeaways: Transactions are Non-Negotiable: If it’s not Atomic (all-or-nothing), it’s not safe. The "with" Statement is a Lifesaver: Using context managers in Python ensures locks are released even if the code crashes, preventing deadlocks. Scalability Matters: While local locks work for one server, ACID-compliant databases are essential for distributed systems. Check out the snippet of my GitHub Codespaces setup below! https://lnkd.in/eguenR7g #Python #SoftwareEngineering #SQL #Database #Coding #DataIntegrity #BackendDevelopment #GitHub

Ever wondered why the same database shows different data to different users? 😳 This is not a bug. This is Isolation Levels in action. In this video, I break down one of the most important (and often misunderstood) concepts in databases 🔍 What you'll learn: • What isolation really means in databases • Why concurrency can silently break your system • Dirty Reads, Non-repeatable Reads & Phantom Reads • All 4 isolation levels explained with hands-on examples • How real databases balance performance vs consistency 💡 Key insight: Most dangerous bugs are not crashes… they are inconsistent data issues you can't easily reproduce. 🎥 Watch the full video here: https://lnkd.in/gJat4WMX If you're into backend, system design, or preparing for interviews — this is a must-know topic. #backend #systemdesign #databases #mysql #sql #programming #softwareengineering

I'd like to introduce HeeRanjId - a snowflake-like distributed database IDing system that is designed to be used by different ORMs such as Django and SeaORM seemlessly, while being easy to implement even during an MVP phase where there might only be a single distributed node. It uses a core written in rust with external bindings to C API, Python, Typescript, and .NET (C# but F# apps should be able to use it). Its core functionality is combining sortability with scalability - you start with a 64 bit ID field that just works, that can probably scale pretty far on its own - and then you can extend that to a custom UUiDv8 with a ready-made migration when you need more nodes, bigger timestamps (up to cosmic scale), or more rapid inserts. Currently supports PostgreSQL and MSSQL. Open to any suggestions, testing, reviews, or otherwise feedback as I intend to use this in a personal project. https://lnkd.in/gHZRhWsu #distributedsystems #softwareengineering #snowflakeid #django #rust #python #sql #postgres #mssql #postgressql #scalability

🚀 Built my first CRUD API using FastAPI + MySQL and deployed it on Render! 🌐 Live URL: https://lnkd.in/gHKJaCXx Today I created a REST API with full CRUD operations using Python (FastAPI) and MySQL as the database, and deployed it using Render. What I built: ✔ GET → Read data from MySQL   ✔ POST → Insert data into MySQL   ✔ PUT → Update existing records   ✔ DELETE → Remove records from MySQL  Deployment: 🌐 Hosted on Render This project helped me understand how backend systems work in real-world applications—from API design to database integration and deployment. Key learnings: - REST API design principles   - CRUD operations with MySQL   - FastAPI backend development   - Deploying applications on Render  It’s a simple project, but it reflects real-world backend architecture. Next step: add authentication and improve security. #Python #FastAPI #MySQL #CRUD #BackendDevelopment #Render #Deployment #RESTAPI

The last two mssql-python releases shipped big features: Bulk Copy in 1.4 for high-throughput data loading, and Apache Arrow in 1.5 for zero-copy analytics. Version 1.6 is about what happens next: you take those features into production, scale up your thread pool, and find out where the driver was quietly holding you back. https://lnkd.in/d-snaHPW

3 SQL red flags to avoid: 1️⃣ Hardcoding: Make it dynamic or it won't last. 2️⃣ No Insight: If the data doesn't drive a decision, it’s just noise. 3️⃣ Join Guessing: Know the "why" behind your logic, not just the "what." Stop just pulling data—start providing value. 📊 Which one are you guilty of? ⬇️ #SQL #DataAnalytics #TechTips #DataScience #JavaScript

Database transactions don't prevent two requests from reading the same row simultaneously. Wrapping code in atomic() makes it safe from concurrent modification. That assumption is wrong in a specific and destructive way. atomic() alone doesn't prevent race conditions. It only guarantees atomicity - all or nothing. It says nothing about what other transactions can read while yours is running. select_for_update() is what actually locks the row. How select_for_update() works - 1. When Django executes SELECT ... FOR UPDATE - the database places a lock on that row. 2. Any other transaction attempting to read the same row with select_for_update() blocks - it waits until the first transaction commits or rolls back. 3. Only then does the second transaction proceed - with the updated value. No simultaneous reads. No conflicting writes. The real Catch! select_for_update() must be inside an atomic() block. Always. Outside a transaction - most databases silently ignore the lock entirely. No error. No warning. No lock. nowait and skip_locked can be used for controlling wait behaviour. nowait=True -> if the row is already locked, raise an exception immediately instead of waiting skip_locked=True -> if the row is locked, skip it and move on. Useful for task queues where any available row is acceptable. Takeaway - -> Transactions tell the database - treat these operations as one. -> Locks tell the database - treat this row as mine until I'm done. -> Both are needed. Neither replaces the other. What concurrency bug has cost you the most debugging time - race condition, deadlock, or something else entirely? #Python #Django #BackendDevelopment #SoftwareEngineering

We almost blamed FastAPI for a performance issue that had nothing to do with FastAPI. A client reported that one endpoint was “randomly slow.” Same code path, same server, same payload shape, but some requests returned in 90ms and others in 2.5 seconds. At first, the team looked at the framework: maybe async overhead, maybe too many dependencies, maybe Pydantic validation. Wrong direction. The real problem was in PostgreSQL. One query used a filter that looked harmless in code, but under load it triggered a sequential scan because the index did not match the actual access pattern. The endpoint only became slow when the table crossed a certain size, which made it feel like an application bug. What fixed it: 1. Logging the slow query, not just the slow endpoint 2. Running `EXPLAIN ANALYZE` on real production-like data 3. Replacing a generic index with a composite index that matched the query 4. Returning less data from the endpoint instead of “just in case” fields Result: response time dropped from 2.5s to 120ms. My takeaway: when an API gets slow, don’t start by blaming the framework. In many cases, the bottleneck is the database, the query shape, or the amount of data you send back. Have you ever spent hours debugging the app layer and then found the real issue in SQL? #FastAPI #PostgreSQL #Python #BackendDevelopment #APIDesign

I’ve seen 5-year "Senior" resumes that haven't ever touched a raw Execution Plan. 📉 It’s Friday at 4 PM. The web servers are idling, but the SQL Database is screaming at 99% CPU. Your code is "clean," your ORM is "standard," but your production environment is melting. We’re building a generation of engineers with "Hollow Experience"—senior-level output with junior-level foundational roots. We treat the database as a "Black Box" that just stores JSON. But when the abstraction leaks, and the N+1 queries start firing like a machine gun, do you know how to look under the hood? Real seniority is developed in the intuition of failure. It's the ability to "smell" a table scan before it hits production. SQL isn't an "implementation detail"—it is the backbone of your system. If you can't read an Execution Plan, you aren't driving the car; you're just a passenger hoping the AI co-pilot doesn't crash. Stop guessing and start indexing. I’ve written a deep dive into why your ORM might be killing your performance. Read the full post here: https://lnkd.in/dD-SHnQW #SQLServer #DatabasePerformance #BackendEngineering #SoftwareArchitecture #Codehouse #SeniorEngineer