real-time

name	real-time
description	Real-time systems and processing
license	MIT
metadata	{"audience":"developers","category":"software-development"}

What I do

• Build real-time data pipelines
• Implement WebSocket communication
• Design streaming architectures
• Handle time-sensitive processing
• Manage event-driven systems
• Optimize latency requirements

When to use me

When building applications requiring immediate data processing, live updates, or time-critical operations.

Key Concepts

Real-Time Classifications

Hard Real-Time: Missed deadline = failure
Firm Real-Time: Missed deadline = degraded quality
Soft Real-Time: Missed deadline = performance drop

WebSocket Implementation

// Server
const wss = new WebSocket.Server({ port: 8080 });
wss.on('connection', ws => {
  ws.on('message', message => {
    broadcast(message);
  });
});

// Client
const ws = new WebSocket('ws://localhost:8080');
ws.onmessage = (event) => {
  updateUI(JSON.parse(event.data));
};

Streaming Architectures

# Apache Kafka + Spark Streaming
from pyspark.streaming import StreamingContext
ssc = StreamingContext(sc, 1)
kafka_stream = KafkaUtils.createDirectStream(
    ssc, ['topic'], {'bootstrap.servers': 'localhost:9092'}
)

Latency Optimization

Target latencies:
- Hard real-time: < 1ms
- Trading systems: < 10ms
- Gaming: < 50ms
- Web updates: < 100ms

Event-Driven Patterns

// Event sourcing
const store = createStore((state, event) => {
  switch (event.type) {
    case 'USER_UPDATED':
      return { ...state, user: event.data };
  }
});

// CQRS pattern
Command: writeModel.execute(command)
Query: readModel.query(query)

Technologies

• WebSockets, SSE
• Apache Kafka, RabbitMQ
• Redis Pub/Sub
• gRPC streaming
• WebRTC
• Edge computing

Monitoring

# Latency tracking
from prometheus_client import Histogram
request_latency = Histogram('request_latency_seconds')