📡 CDC in Cache – Change Data Capture

Definition:

CDC (Change Data Capture) is a technique to detect and capture changes in a database (inserts, updates, deletes) and propagate those changes to other systems, like caches or downstream services.

In caching systems, CDC ensures the cache stays consistent with the database without having to frequently poll the DB.

1️⃣ Why CDC is Needed for Cache

2️⃣ How It Works

  1. DB change occurs (insert/update/delete).
  2. CDC system captures the change (via logs, triggers, or streaming).
  3. Cache is updated automatically with the new value.

Flow (Text Diagram):

Database ---> CDC ---> Cache ---> Applications
   |            |
   |      Change detected
   |            |
Update cache automatically

3️⃣ Benefits

4️⃣ Common Implementations

5️⃣ Analogy

Imagine a whiteboard in an office:

💡 Key takeaway:

📡 Change Data Capture (CDC) in Database

Definition:

CDC is a technique used to detect and capture changes in a database—like inserts, updates, and deletes—and make those changes available to other systems in real-time or near real-time.

It’s widely used for replicating data, syncing caches, event streaming, and analytics pipelines.

1️⃣ How CDC Works

  1. Data changes in the source database (insert/update/delete).
  2. CDC detects these changes (via logs, triggers, or database hooks).
  3. Changes are published to a stream or message system (e.g., Kafka).
  4. Target systems (analytics, cache, other DBs, microservices) consume these changes.

Flow (Text Diagram):

Source DB → CDC → Message Queue / Stream → Target System (Cache, DB, Analytics)

2️⃣ Implementation Methods

Method How It Works
Database Logs (Log-Based CDC) Reads transaction logs (e.g., MySQL binlog, Postgres WAL)
Triggers (Trigger-Based CDC) Executes triggers on table changes
Timestamp/Version Column Checks rows with updated timestamps or version numbers

3️⃣ Benefits

4️⃣ Use Cases

5️⃣ Analogy

Imagine a journalist taking notes at a live event:

💡 Key takeaway:

1️⃣ What is CDC (Change Data Capture)?

Change Data Capture (CDC) is a technique that tracks changes in a database — like inserts, updates, or deletes — and makes them available to other systems in real time.

🔹 Purpose:

🔹 How It Works:

  1. CDC monitors the database’s transaction log (like MySQL binlog, PostgreSQL WAL).
  2. Captures every change (insert, update, delete).
  3. Converts it into a stream of events that other systems can consume.

🔹 Example:

Suppose you have a MySQL table orders:

order_id customer status
1 Alice pending

If a new order is inserted:

INSERT INTO orders VALUES (2, 'Bob', 'pending');

A CDC system will capture this change and send an event like:

{
  "operation": "insert",
  "table": "orders",
  "data": {
    "order_id": 2,
    "customer": "Bob",
    "status": "pending"
  }
}

2️⃣ What is Debezium?

Debezium is an open-source CDC platform that:

🔹 Key Features of Debezium:

🔹 How Debezium Fits in Architecture:

[Database] → [Debezium Connector] → [Kafka Topic] → [Microservices / Data Warehouse / Analytics]

🔹 Example Use Case

Scenario: You have a MySQL database and want your search index (Elasticsearch) to update in real time when data changes.

Without CDC:

With Debezium CDC:

🔹 Event Example from Debezium:

{
  "before": {"order_id": 1, "status": "pending"},
  "after": {"order_id": 1, "status": "shipped"},
  "source": {"db": "shop", "table": "orders"},
  "op": "u",   // u = update, c = create, d = delete
  "ts_ms": 1696930000000
}

🔹 Benefits of Using Debezium / CDC:

✅ Real-time data replication & analytics ✅ Low impact on the source database (reads logs, not tables) ✅ Enables event-driven architectures ✅ Scales well for multiple consumers ✅ Keeps microservices event-driven and synchronized

In short: