ByteNomads

Slow SQL queries are one of the most common causes of performance problems in backend applications. The challenge is not just knowing that a query is slow — it’s understanding why . This step-by-step guide shows how to debug slow SQL queries in a systematic and practical way, using PostgreSQL as an example. Step 1: Identify the Slow Query The first step is knowing which queries are slow. Common ways to identify slow queries: Application logs Database monitoring tools User reports In PostgreSQL, you can enable slow query logging to capture problematic queries. Step 2: Reproduce the Query Once you have the slow query, run it manually in the database. SELECT * FROM orders WHERE user_id = 42; Always test queries using realistic data volumes. Queries that are fast on small datasets may fail at scale. Step 3: Use EXPLAIN The EXPLAIN command shows how PostgreSQL plans to execute a query. EXPLAIN SELECT * FROM orders WHERE user_id = 42; This output sh...

PostgreSQL is known for being reliable and powerful. Yet, many developers experience the same issue: queries that were once fast become slower over time. This usually doesn’t happen by accident. In most cases, performance degradation has clear and fixable causes. In this article, we’ll explore why PostgreSQL queries become slow over time and what you can do to fix them. Why PostgreSQL Performance Degrades PostgreSQL databases evolve constantly: Tables grow Indexes become outdated Data distribution changes If the database is not maintained, query performance naturally suffers. Problem 1: Missing or Inefficient Indexes Indexes are critical for performance. Without them, PostgreSQL must scan entire tables. Example of a Slow Query SELECT * FROM orders WHERE user_id = 42; If user_id is not indexed, PostgreSQL performs a sequential scan. Solution CREATE INDEX idx_orders_user_id ON orders(user_id); Indexes dramatically reduce query time for large ta...

Most people use n8n for simple data transfers. But the real power of this tool lies in its ability to handle complex logic and multi-step integrations. Today, we’re building a Self-Healing Notification System : an automated workflow that identifies a code culprit after a crash and sends a detailed technical report via email. The Logic Flow Our workflow doesn't just send an email; it enriches data. Here is the pipeline: Webhook: Receives an error payload from your production server. HTTP Request (GitHub API): Fetches the last commit metadata for the failing service. Function Node: A custom JavaScript block to merge error logs with developer info. HTML Template: Generating a responsive, technical email body. SMTP/SendGrid: Dispatching the high-priority alert. 1. Handling the Webhook Payload Your server sends a POST request with the service_name and error_stack . In n8n, we use the Webhook Node . Ensure you set the HTTP Method to POST and the...

The role of a Product Owner is often bogged down by the manual task of converting vague ideas into structured tickets. What if we could automate the "thinking" process? In this article, we will build a Technical AI Agent that takes a simple prompt and transforms it into a professional User Story directly inside Jira . The Architecture: How the AI Agent Works Our solution consists of three main layers: the Intelligence Layer (OpenAI), the Integration Layer (Python + Jira API), and the Project Management Layer (Jira Software). 1. Setting Up the Jira API To communicate with Jira, you need an API Token. Go to your Atlassian account security settings and generate one. You will need your Domain , Email , and the Token . # Requirements pip install jira openai 2. The Intelligence Layer (The "PO Prompt") The secret to a good AI Product Owner is the System Prompt . We need to instruct the AI to output data in a specific format (Gherkin or standard User Sto...

Building an API that works is easy. Building an API that is secure is a completely different challenge. With the rise of microservices, APIs have become the primary target for hackers. In this guide, we will explore the most critical security flaws and, more importantly, how to fix them before they are exploited. 1. Broken Object Level Authorization (BOLA) BOLA is currently the #1 threat to APIs. It occurs when a user can access or modify data belonging to another user just by changing an ID in the URL. The Attack: A user logged in as ID 123 sends a request to GET /api/orders/456 and successfully sees someone else's order. The Fix: Never trust the ID provided in the request alone. Always validate that the resource belongs to the authenticated user in your database query. // BAD: Trusting the URL ID var order = db.Orders.Find(id); // GOOD: Checking ownership var userEmail = User.Identity.Name; var order = db.Orders.FirstOrDefault(o => o.Id == id && o.UserE...

Many developers dream of working on a clean, decoupled microservices architecture. However, the reality for most of us is a "Big Ball of Mud"—a massive, aging monolith that is hard to scale and even harder to test. But how do you move to microservices without stopping all feature development for a year? The answer is the Strangler Fig Pattern . The Problem: The "Big Bang" Failure A few years ago, my team tried to rewrite a complete e-commerce monolith from scratch. We spent six months coding the "new version" while the "old version" kept getting new features. We could never catch up. This is the "Big Bang" migration trap, and it fails 90% of the time. The Solution: The Strangler Fig Pattern The name comes from a vine that grows around a tree, eventually replacing it entirely. In software architecture, this means building new functionality in microservices and slowly "strangling" the monolith by redirecting traffic away...

Python is loved for its simplicity, but it’s often criticized for being slow. Recently, I was tasked with processing a 5GB CSV file containing millions of rows of user transaction data. My first attempt was a disaster—it was projected to take 4 hours to complete. Here is how I refactored it to run in under 10 minutes. The Initial Problem: The "Row-by-Row" Trap The original script used a standard for loop to iterate through the data. In Python, iterating over millions of rows using a native loop is incredibly expensive because of the overhead of the Python interpreter for each iteration. # What NOT to do with large data import csv with open('transactions.csv', 'r') as f: reader = csv.DictReader(f) for row in reader: # Complex calculation here tax = float(row['amount']) * 0.23 save_to_db(row['id'], tax) This approach was slow because of constant Disk I/O and the lack of memory management. The script was...

Transitioning a large codebase from synchronous to asynchronous execution in C# sounds straightforward, but it hides a dangerous trap. Last week, I spent six hours debugging why a legacy ASP.NET Web API simply stopped responding under load. The culprit? A classic Async Deadlock . The Scenario: The "Silent Hang" We were integrating a new cloud-based logging service into an older .NET Framework application. To keep things simple, one of our developers called an asynchronous method from a synchronous controller action using .Result . In isolation, it worked. Under the slightest bit of concurrency, the entire thread pool starved, and the API froze. The Problematic Code Here is a simplified version of what caused the disaster: // The Legacy Controller public class DataController : ApiController { public string Get() { // BLOCKED HERE: Calling async from sync var data = Service.GetDataAsync().Result; return data; } } // The Service Metho...

We've all been there: the code passes every unit test, the logic is flawless, but in production, the memory usage climbs like a mountain until the process crashes. Recently, I faced a "silent killer" in a Node.js microservice. Here is how I found and fixed it. The Problem: The Creeping RAM I was processing a large batch of API migrations using a simple forEach loop with async/await . On my local machine with 100 records, it was fast. In production with 100,000 records, the container kept hitting the 2GB RAM limit and restarting (OOM Error). // What I thought was fine data.forEach(async (item) => { await processItem(item); // This fired thousands of promises simultaneously! }); The Discovery The issue wasn't the processing itself, but concurrency control . By using forEach , I wasn't waiting for each promise to finish; I was spawning thousands of parallel operations, each holding a chunk of memory. Node's event loop was overwhelmed. The Sol...