Applicability
When to Use
✓When systems need to react to database changes instantly
✓When building materialized views across databases
✓When migrating data between systems with zero downtime
Overview
How It Works
Change Data Capture monitors database transaction logs to detect inserts, updates, and deletes, then propagates those changes to downstream MCP servers in real-time. Unlike polling-based approaches, CDC captures every change with minimal database overhead.
In an MCP architecture, the agent subscribes to the CDC stream (via Kafka or direct log reading) and routes changes to the appropriate downstream servers. This enables real-time sync between databases, cache invalidation, search index updates, and event-driven workflows.
Implementation
Code Example
typescript
async function processCDCEvent(event) {
const { table, operation, before, after } = event;
switch (table) {
case "users":
if (operation === "INSERT" || operation === "UPDATE") {
await elasticsearch.index({ index: "users", id: after.id, body: after });
await redis.set(`user:${after.id}`, JSON.stringify(after));
}
if (operation === "DELETE") {
await elasticsearch.delete({ index: "users", id: before.id });
await redis.del(`user:${before.id}`);
}
break;
case "orders":
if (operation === "INSERT") {
await slack.sendMessage({ channel: "#orders", text: `New order: $${after.total}` });
}
break;
}
}Quick Info
Categorydata-pipeline
ComplexityHard