Sep 15, 2022
rabbitmq
RabbitMQ enables reliable message queuing. Here’s how to use it effectively.
Basic Setup Producer const amqp = require('amqplib'); async function sendMessage() { const connection = await amqp.connect('amqp://localhost'); const channel = await connection.createChannel(); const queue = 'tasks'; const message = 'Hello RabbitMQ!'; await channel.assertQueue(queue, { durable: true }); channel.sendToQueue(queue, Buffer.from(message), { persistent: true }); console.log("Sent:", message); await channel.close(); await connection.close(); } Consumer async function receiveMessage() { const connection = await amqp.connect('amqp://localhost'); const channel = await connection.createChannel(); const queue = 'tasks'; await channel.assertQueue(queue, { durable: true }); channel.consume(queue, (msg) => { if (msg) { console.log("Received:", msg.content.toString()); channel.ack(msg); } }); } Patterns Work Queues // Distribute tasks among workers channel.prefetch(1); // Fair dispatch Pub/Sub // Exchange for broadcasting await channel.assertExchange('logs', 'fanout', { durable: false }); channel.publish('logs', '', Buffer.from(message)); Best Practices Use durable queues Acknowledge messages Handle errors Monitor queues Use exchanges for routing Conclusion Implement reliable message queuing with RabbitMQ! 🐰
ReadAug 20, 2022
postgresql
PostgreSQL performance tuning requires understanding configuration and queries. Here’s a guide.
Configuration Tuning postgresql.conf # Memory settings shared_buffers = 256MB effective_cache_size = 1GB work_mem = 16MB # Connection settings max_connections = 100 # Query planner random_page_cost = 1.1 Query Optimization Use EXPLAIN ANALYZE EXPLAIN ANALYZE SELECT * FROM users WHERE email = '[email protected]'; Indexing -- B-tree index CREATE INDEX idx_user_email ON users(email); -- Partial index CREATE INDEX idx_active_users ON users(email) WHERE status = 'active'; -- Composite index CREATE INDEX idx_user_status_created ON users(status, created_at); Best Practices Analyze query plans Create appropriate indexes Use connection pooling Monitor slow queries Vacuum regularly Conclusion Tune PostgreSQL for optimal performance! 🐘
ReadJul 20, 2022
python
Understanding Python’s asyncio architecture is crucial for writing efficient asynchronous code. Here’s a comprehensive guide.
Event Loop The event loop is the core of asyncio. It manages and distributes the execution of different tasks.
import asyncio async def main(): print("Hello") await asyncio.sleep(1) print("World") # Event loop runs the coroutine asyncio.run(main()) Coroutines Coroutines are functions defined with async def. They can be paused and resumed.
async def fetch_data(): await asyncio.sleep(1) return "data" # Coroutine object coro = fetch_data() Tasks Tasks wrap coroutines and schedule them on the event loop.
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ReadJun 15, 2022
python
Learn how to build a simple blog application using Python and Flask.
Setup from flask import Flask, render_template, request, redirect, url_for app = Flask(__name__) # Simple in-memory storage posts = [] Routes @app.route('/') def index(): return render_template('index.html', posts=posts) @app.route('/post', methods=['GET', 'POST']) def create_post(): if request.method == 'POST': title = request.form['title'] content = request.form['content'] posts.append({'title': title, 'content': content}) return redirect(url_for('index')) return render_template('create_post.html') Templates <!-- index.html --> {% for post in posts %} <article> <h2>{{ post.title }}</h2> <p>{{ post.content }}</p> </article> {% endfor %} Running flask run Conclusion Build your first Flask blog application! 🚀
ReadMay 10, 2022
rust
Ownership is Rust’s unique feature that ensures memory safety without garbage collection.
Ownership Rules Each value has a single owner Only one owner at a time When owner goes out of scope, value is dropped let s = String::from("hello"); // s owns the string Moving let s1 = String::from("hello"); let s2 = s1; // s1 is moved to s2 // s1 is no longer valid Borrowing fn calculate_length(s: &String) -> usize { s.len() } let s = String::from("hello"); let len = calculate_length(&s); // Borrowing Mutable References fn change(s: &mut String) { s.push_str(", world"); } let mut s = String::from("hello"); change(&mut s); Best Practices Use references when possible Understand ownership rules Avoid unnecessary moves Use clone() sparingly Leverage the borrow checker Conclusion Master Rust ownership for memory-safe systems programming! 🦀
ReadApr 15, 2022
rust
Rust’s error handling is elegant and type-safe. Here’s how to use it effectively.
Option Type fn find_user(id: u32) -> Option<String> { if id > 0 { Some(format!("User {}", id)) } else { None } } match find_user(1) { Some(name) => println!("Found: {}", name), None => println!("Not found"), } Result Type use std::fs::File; fn read_file(path: &str) -> Result<String, std::io::Error> { std::fs::read_to_string(path) } match read_file("data.txt") { Ok(content) => println!("Content: {}", content), Err(e) => println!("Error: {}", e), } Error Propagation fn read_config() -> Result<String, std::io::Error> { let content = std::fs::read_to_string("config.txt")?; Ok(content) } Custom Errors use std::fmt; #[derive(Debug)] struct CustomError { message: String, } impl fmt::Display for CustomError { fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result { write!(f, "{}", self.message) } } Best Practices Use Result for recoverable errors Use Option for optional values Propagate errors with ? Create custom errors when needed Handle errors explicitly Conclusion Handle errors elegantly in Rust! 🦀
ReadMar 20, 2022
python
Using SQLAlchemy with async Python requires understanding async patterns. Here’s how.
Setup from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession from sqlalchemy.orm import sessionmaker engine = create_async_engine("postgresql+asyncpg://user:pass@localhost/db") AsyncSessionLocal = sessionmaker(engine, class_=AsyncSession, expire_on_commit=False) Async Models from sqlalchemy.ext.declarative import declarative_base from sqlalchemy import Column, Integer, String Base = declarative_base() class User(Base): __tablename__ = "users" id = Column(Integer, primary_key=True) name = Column(String) Async Operations async def create_user(name: str): async with AsyncSessionLocal() as session: user = User(name=name) session.add(user) await session.commit() return user async def get_user(user_id: int): async with AsyncSessionLocal() as session: result = await session.get(User, user_id) return result Best Practices Use async context managers Commit transactions explicitly Handle exceptions properly Use connection pooling Close sessions correctly Conclusion Build efficient async database applications with SQLAlchemy! 🐍
ReadFeb 15, 2022
rust
Rust WebAssembly can significantly improve React application performance. Here’s how to integrate it.
Setup cargo install wasm-pack wasm-pack build --target web Rust Code use wasm_bindgen::prelude::*; #[wasm_bindgen] pub fn add(a: i32, b: i32) -> i32 { a + b } React Integration import init, { add } from './pkg/rust_wasm.js'; async function loadWasm() { await init(); console.log(add(2, 3)); // 5 } Performance Benefits Faster computation for heavy operations Memory efficient Type safe Near-native performance Best Practices Use for CPU-intensive tasks Minimize data transfer Profile performance Handle errors properly Bundle efficiently Conclusion Boost React performance with Rust WebAssembly! ⚡
ReadJan 20, 2022
python
Pandas provides powerful tools for joining DataFrames. Here’s a comprehensive guide.
Merge Types Inner Join import pandas as pd df1 = pd.DataFrame({'key': ['A', 'B'], 'value1': [1, 2]}) df2 = pd.DataFrame({'key': ['B', 'C'], 'value2': [3, 4]}) result = pd.merge(df1, df2, on='key', how='inner') Left Join result = pd.merge(df1, df2, on='key', how='left') Right Join result = pd.merge(df1, df2, on='key', how='right') Outer Join result = pd.merge(df1, df2, on='key', how='outer') Multiple Keys result = pd.merge(df1, df2, on=['key1', 'key2']) Best Practices Choose the right join type Handle missing values Use appropriate keys Check for duplicates Optimize for large datasets Conclusion Master Pandas joins for efficient data manipulation! 📊
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