CDC (Change Data Capture / Debezium) Offset Loss Causing Data Synchronization Gaps

System Design Interview•~30 min read•medium

Debezium CDC connector streaming PostgreSQL mutations to Kafka to synchronize an analytical data warehouse.

1. Understand Requirements & Scope

Debezium CDC connector streaming PostgreSQL mutations to Kafka to synchronize an analytical data warehouse. Failure cascade dynamic: I/O Latency → max.poll Exceeded → Eager Revoke → 💥 Lag.

Functional Requirements:

Operational Invariant: "CDC connector offset topics must be permanent and compacted; pair streaming replication with automated checksum audits."
Production Hardening: Revocations Configure Debezium offset topics with compacted log retention and infinite retention time ('retention.ms = -1'), back up offset topics daily, and implement automated da
Prevent cascading failure by isolating downstream degradation at the ingress boundary.

2. Back-of-the-Envelope Capacity Estimation

• Incident Scenario Baseline: Debezium CDC connector streaming PostgreSQL mutations to Kafka to synchronize an analytical data warehouse.

• Failure Cascade Path: I/O Latency → max.poll Exceeded → Eager Revoke → 💥 Lag

• Sub-millisecond rejection / shed latency for overloaded requests

3. High-Level Architecture Flow

Revocations Configure Debezium offset topics with compacted log retention and infinite retention time ('retention.ms = -1'), back up offset topics daily, and implement automated data reconciliation auditing jobs (e.g. daily checksum diffs). Invariant: "CDC connector offset topics must be permanent and compacted; pair streaming replication with automated checksum audits."

Ingress → Bounded Admission Controller → Adaptive Concurrency / Queue Gate → Protected Worker Engine; Overload Shed Path → HTTP 429 / Circuit Fallback (<0.1ms).

Fig 1. Architecture data flow and component topology.

4. Production Implementation & Code

// HARDENED: Fast local Bloom filter / Redis revocation list verification
Claims claims = Jwts.parser().setSigningKey(secret).parseClaimsJws(token).getBody();
if (revocationBloomFilter.mightContain(claims.getId()) && redis.exists("revoked:" + claims.getId())) {
    throw new SecurityException("Revoked token access attempt rejected");
}

Hardened Implementation Sequence

Ingress Boundary Defense: Revocations Configure Debezium offset topics with compacted log retention and infinite retention time ('retention.ms = -1'), back up offset topics dai
Failure Containment: Eliminate unconstrained buffering and propagate physical backpressure.
Invariant Enforcement: CDC connector offset topics must be permanent and compacted; pair streaming replication with automated checksum audits.

5. Key Decisions & Operational Mitigations

Architectural Decisions & Trade-Offs

•Ingress Admission: Naive unconstrained buffering causes queue bloat and GC exhaustion. Mitigation: Bounded ingress gate with fast non-blocking shedding.
•Resource Strategy: Static capacity allocations collapse under brownouts. Mitigation: Dynamic adaptive limits scaling to real-time physical bounds.
•Outage Impact: Cascading lock convoys and memory saturation. Mitigation: Isolated failure domains keep P99 latency flat and protected.

Operational Hazards & Mitigations

•Cascading Failure: Balloons to Millions Kafka Connect offset storage topic is accidentally deleted or reset. Debezium resumes streaming from the current database LSN, permanently skipping 12 hours of intermediate updates and leaving the warehouse permanently out of sync.
•Breakdown Chain: I/O Latency → max.poll Exceeded → Eager Revoke → 💥 Lag
•Shedding Failure: Without fast rejection at the boundary, queued dead work triggers multi-second GC pauses, CPU thrashing, and cascading 504 Gateway Timeouts.

Interactive Knowledge Check • Question 1 of 3↓

Interactive Practice Quiz•Question 1 of 3

intermediate

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