Скачать или смотреть Expanded Real-Time Streaming MySQL Pipeline for ML Inference Kafka + Spark + AI Automation Explained

Let’s break down a complete real-time ML streaming pipeline powered by MySQL + Kafka + Spark/Flink + ML model + Dashboard/Storage — a favorite topic in ML system design and data engineering interviews 🔥

1. Key Components of the Real-Time Architecture

A typical streaming pipeline includes:

MySQL (Source DB) → stores transactions, events, or sensor data.

Debezium (CDC Connector) → captures database changes in real-time.

Kafka (Message Broker) → streams these change events to subscribers.

Spark/Flink (Stream Processor) → transforms raw data into ML features.

Python ML Model (Deployed) → consumes the stream and makes predictions.

MySQL/NoSQL/Dashboard (Sink) → stores predictions or displays them live.

🧠 Example Interview Explanation:

When a new transaction is added in MySQL, Debezium publishes it to Kafka, Spark processes it into features, the ML model predicts fraud likelihood, and the result is written back to MySQL or a monitoring dashboard in seconds.

2. CDC (Change Data Capture) from MySQL to Kafka

CDC ensures every data change is captured without polling.
Debezium listens to the MySQL binlog and pushes events into Kafka topics.

Example Debezium connector configuration
{
"name": "mysql-source-connector",
"config": {
"connector.class": "io.debezium.connector.mysql.MySqlConnector",
"database.hostname": "localhost",
"database.port": "3306",
"database.user": "debezium",
"database.password": "dbz",
"database.server.id": "184054",
"table.include.list": "bank.transactions",
"database.history.kafka.bootstrap.servers": "kafka:9092",
"database.history.kafka.topic": "schema-changes.mysql"
}
}


This configuration ensures real-time MySQL updates flow into Kafka topics instantly.

3. Feature Engineering in Spark or Flink (Streaming ETL)

Spark Structured Streaming or Flink processes Kafka topics continuously.

from pyspark.sql import SparkSession, functions as F

spark = SparkSession.builder.appName("RealTimeFeatures").getOrCreate()
transactions = spark.readStream.format("kafka").option("subscribe", "mysql.transactions").load()

Feature creation
features = transactions.groupBy("user_id").agg(
F.count("transaction_id").alias("txn_count"),
F.avg("amount").alias("avg_amount"),
F.max("amount").alias("max_amount")
)


These real-time features can be pushed to the ML model for inference.

4. Model Scoring and Prediction Flow

The trained model (say, a fraud detection model) runs as a microservice or Spark UDF:

from joblib import load
model = load("fraud_model.joblib")

def predict_fraud(txn_count, avg_amount):
return model.predict([[txn_count, avg_amount]])[0]


Predictions can be streamed back into another Kafka topic or written into MySQL.

Dashboards (Power BI, Streamlit, Grafana) visualize live predictions.

5. Monitoring, Scaling, and Security

Monitoring: Use Kafka monitoring tools or Prometheus + Grafana for metrics.

Scaling: Increase Kafka partitions, deploy Spark clusters, or use Kubernetes autoscaling.

Security: Use SSL/TLS between MySQL, Kafka, and ML services to protect data.

🧠 Example Interview Insight:

Scaling the real-time pipeline requires partitioning Kafka topics by entity (like user_id) and parallelizing Spark jobs while maintaining state consistency for ML predictions.

✅ Together, this pipeline turns MySQL into a streaming data hub — enabling AI-driven insights within seconds of data generation.