Good Use case for Kafka , Flink Streaming - Building a Ad server

 https://www.kai-waehner.de/blog/2023/09/15/how-to-build-a-real-time-advertising-platform-with-apache-kafka-and-flink/

Intergrating Kafka with your API

 Integrating Kafka with your application API without making direct changes to the API itself can be achieved by using various techniques and tools. This approach typically involves setting up an intermediary service or using a middleware that intercepts API requests and forwards the necessary data to Kafka. Here are a few strategies to achieve this:

Example - You want to know what are the ad request you are making . Example want to know which songs users are playing on spotify

Strategy 1: Sidecar Pattern

The sidecar pattern involves running a companion service alongside your main application that handles Kafka integration. This can be particularly useful in containerized environments like Kubernetes.

Steps to Implement Sidecar Pattern

1. Create a Sidecar Service:

   • Develop a separate service that consumes API logs or data and sends it to Kafka.

2. Deploy the Sidecar:

   • Deploy the sidecar service alongside your main application.

Example Implementation

Sidecar Service (Python):

python
import requests
from confluent_kafka import Producer
import json

# Kafka Producer configuration
conf = {
    'bootstrap.servers': 'localhost:9092',
    'client.id': 'sidecar-producer'
}
producer = Producer(**conf)

def send_to_kafka(topic, message):
    producer.produce(topic, json.dumps(message).encode('utf-8'))
    producer.flush()
    print(f"Message sent to Kafka topic {topic}")

def intercept_and_forward(url, headers, params=None):
    response = requests.get(url, headers=headers, params=params)
    data = response.json()
    send_to_kafka('your_topic', data)
    return data

# Example usage
if __name__ == "__main__":
    url = "http://example.com/api/data"
    headers = {"Authorization": "Bearer token"}
    data = intercept_and_forward(url, headers)
    print(data)

Kubernetes Deployment Example

Kubernetes Pod Definition:

yaml
apiVersion: v1
kind: Pod
metadata:
  name: api-with-sidecar
spec:
  containers:
  - name: main-app
    image: your-main-app-image
    ports:
    - containerPort: 80
  - name: sidecar
    image: your-sidecar-image
    env:
    - name: KAFKA_BOOTSTRAP_SERVERS
      value: "localhost:9092"

Strategy 2: API Gateway

Using an API gateway allows you to manage, secure, and monitor API traffic, as well as intercept and forward data to Kafka without modifying the backend API.

Steps to Implement API Gateway Integration

1. Set Up API Gateway:

   • Use an API gateway solution like Kong, Apigee, or Amazon API Gateway.

2. Configure Plugins or Middleware:

   • Use plugins or middleware to intercept requests and forward data to Kafka.

Example with Kong Gateway

1. Install Kong and Kafka Plugin:

   • Follow the Kong installation guide.
   • Install the Kafka logging plugin for Kong.

2. Configure Kafka Plugin:

yaml
services:
- name: example-service
  url: http://example.com
  routes:
  - name: example-route
    paths:
    - /api

plugins:
- name: kafka-log
  config:
    bootstrap_servers: "localhost:9092"
    topic: "your_topic"
    producer_config:
      acks: "all"

3. Apply Configuration:

bash
# Assuming you have a Docker-based setup
docker-compose up -d

Strategy 3: Log-Based Integration

You can also use a log-based approach where application logs are collected and processed to send data to Kafka.

Steps to Implement Log-Based Integration

1. Set Up Log Forwarder:

   • Use a log forwarder like Filebeat, Fluentd, or Logstash.

2. Configure Log Forwarder to Send Data to Kafka:

Filebeat Configuration Example:

yaml
filebeat.inputs:
- type: log
  paths:
    - /var/log/myapp/*.log

output.kafka:
  hosts: ["localhost:9092"]
  topic: "your_topic"
  codec.json:
    pretty: true

3. Run Log Forwarder:

bash
filebeat -e -c filebeat.yml

Summary

These strategies allow you to integrate Kafka with your application API without making direct changes to the API code:

1. Sidecar Pattern: Run a companion service alongside your application.
2. API Gateway: Use an API gateway to intercept and forward data.
3. Log-Based Integration: Use log forwarders to process and send logs to Kafka.

Choose the strategy that best fits your architecture and operational requirements.