---
title: "Change Data Capture Replaces Polling for Mobile Sync"
published: true
description: "Build a CDC-powered sync pipeline using PostgreSQL logical replication and Debezium to replace expensive polling in offline-first mobile apps."
tags: postgresql, kotlin, architecture, mobile
canonical_url: https://blog.mvpfactory.co/change-data-capture-replaces-polling-for-mobile-sync
---
## What We Will Build
Let me show you a pattern I use in every project that needs real-time mobile sync. We will wire up a CDC (Change Data Capture) pipeline using PostgreSQL logical replication and Debezium to stream row-level changes directly to mobile clients via SSE — replacing polling with sub-second push-based invalidation.
I have built this in production. This architecture cut sync latency from 30+ seconds to under 500ms while reducing database load by 60-80%.
## Prerequisites
- PostgreSQL 10+ with logical replication enabled (`wal_level = logical`)
- Debezium (standalone server or Kafka Connect)
- An SSE-capable backend (Ktor, Spring, or similar)
- Kotlin Multiplatform client (or any SSE-capable mobile client)
## Step 1: Understand the Cost You Are Paying
Every client hitting `GET /sync?since=timestamp` every 30 seconds creates a compounding tax. Here is what that looks like at scale:
| Approach | Sync Latency | DB Queries/min (10K Users) | Server Load |
|---|---|---|---|
| Polling (30s interval) | 0–30s avg | ~20,000 | High |
| WebSocket with manual triggers | 1–5s | Event-driven | Medium |
| CDC via Debezium + SSE | < 500ms | 0 (reads WAL) | Low |
CDC does not query your tables at all. It reads the write-ahead log.
## Step 2: Set Up PostgreSQL Logical Replication
PostgreSQL's WAL records every row-level change before it hits disk. Logical replication decodes these binary entries into structured change events without touching your application tables.
sql
-- Create a logical replication slot
SELECT pg_create_logical_replication_slot('mobile_sync', 'pgoutput');
-- Create a publication scoped to sync-relevant tables
CREATE PUBLICATION mobile_changes FOR TABLE
users, documents, comments, attachments;
This adds zero overhead on your read path. The WAL is already being written; you are just tapping into it.
## Step 3: Connect Debezium as Your CDC Engine
Debezium connects to that replication slot and emits structured JSON events. Each event carries the before/after state, operation type, and transaction metadata:
json
{
"op": "u",
"before": { "id": 42, "title": "Draft", "tenant_id": "acme" },
"after": { "id": 42, "title": "Published", "tenant_id": "acme" },
"source": { "lsn": 234881024, "txId": 5891 }
}
## Step 4: Implement the Transactional Outbox Pattern
Here is the minimal setup to get this working safely. Raw CDC events leak your internal schema to consumers. The outbox pattern fixes this — write an explicit outbox record within the same transaction:
sql
BEGIN;
UPDATE documents SET title = 'Published' WHERE id = 42;
INSERT INTO outbox (aggregate_id, event_type, tenant_id, payload)
VALUES (42, 'document.updated', 'acme', '{"title":"Published"}');
COMMIT;
Debezium captures the outbox insert, routes it by `tenant_id`, and the outbox row gets deleted after capture. Your downstream consumers get a stable, versioned contract — not your internal column names.
## Step 5: Filter Events Per Tenant and Push via SSE
The docs do not mention this, but production requires tenant-scoped filtering. Build a lightweight event router between Kafka and your SSE gateway:
kotlin
fun routeEvent(event: OutboxEvent): List {
val tenantId = event.tenantId
val subscriberIds = subscriptionRegistry.getSubscribers(tenantId)
return subscriberIds // Each maps to an SSE channel
}
SSE is the pragmatic choice over WebSockets here — unidirectional, auto-reconnecting, and trivial behind standard load balancers.
| Transport | Direction | Reconnect | HTTP/2 Multiplexing | Complexity |
|---|---|---|---|---|
| WebSocket | Bidirectional | Manual | No | Higher |
| SSE | Server → Client | Built-in | Yes | Lower |
On the Kotlin Multiplatform side, the client listens and invalidates its local cache:
kotlin
sseClient.events("sync/$tenantId")
.collect { event ->
val change = json.decodeFromString(event.data)
localDatabase.applyChange(change)
}
No polling interval. No wasted queries. The server pushes exactly what changed, when it changes.
## Gotchas
Here is the gotcha that will save you hours:
- **Unmonitored replication slots will fill your disk.** If your consumer falls behind, the WAL accumulates. Watch `pg_replication_slots` and set `max_slot_wal_keep_size`. Treat this as a launch blocker, not a "we will add it later" item.
- **Delivery is at-least-once, not exactly-once.** Debezium gives you at-least-once guarantees, so your mobile client needs idempotent apply logic keyed on the LSN or event ID. This is not optional.
- **Start with the outbox pattern, not raw table CDC.** I skipped this step once and regretted it within a month. Schema evolution — column renames, table refactors — will break deployed mobile clients if you stream raw table changes.
- **Use SSE over WebSockets for unidirectional sync.** Built-in reconnection and HTTP/2 multiplexing make SSE the right default. You can always upgrade to WebSockets later if you need bidirectional communication.
## Wrapping Up
This pipeline replaces a brute-force polling loop with an event-driven stream that costs you zero additional database queries. Start with the outbox pattern, wire up Debezium, push through SSE, and monitor your replication slots from day one. Your database — and your on-call rotation — will thank you.
- [PostgreSQL Logical Replication Docs](https://www.postgresql.org/docs/current/logical-replication.html)
- [Debezium Documentation](https://debezium.io/documentation/)
- [Debezium Outbox Event Router](https://debezium.io/documentation/reference/transformations/outbox-event-router.html)
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