real-time
Real-time systems and processing
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- ffsshhttiikk/opencode-agents-skills
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- 28 février 2026 à 22:54
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SKILL.md
Instructions source · Aperçu en lecture seule- 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
```javascript
// 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
```python
# 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
```javascript
// 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
```python
# Latency tracking
from prometheus_client import Histogram
request_latency = Histogram('request_latency_seconds')
```
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