Synchronizing Databases with Real-Time Streams

Introduction

The relationship between databases and streams goes beyond simple asynchronous updates or messaging: they are deeply interconnected concepts. At the core, databases can be seen as the materialized state derived from an unbounded stream of change events. This subchapter explores how streams sync with databases, the role of change data capture (CDC) and event sourcing, and the advantages of thinking about state using an immutable event-driven model.

Keeping Systems in Sync

Modern applications use databases alongside systems like caches, search indexes, and warehouses, each optimized for a specific role. As data appears in multiple systems, they require synchronization to avoid diverging states:

1. Batch ETL: A traditional method, entire database snapshots are transformed (e.g., for analytics) and periodically uploaded to warehouses or other systems.
   • Problem: High latency between updates.
2. Dual Writes: Applications update both the database and external systems simultaneously.
   • Problem: Race conditions between concurrent update events lead to inconsistencies, as illustrated by interleaving writes reaching systems in different orders.

Change Data Capture (CDC)

Instead of relying on batch snapshots or error-prone dual writes, CDC tracks and extracts database changes in real-time:

1. Databases produce replication logs as they process write operations. By monitoring these logs and forwarding changes to downstream consumers (e.g., cache, search, analytics), CDC creates robust synchronization pipelines.

CDC Implementation

1. Log-Based Replication: Changes captured directly from the database’s write-ahead log (WAL), preserving write order.
   • Examples: Apache Kafka connectors, Debezium.
2. Trigger-Based Replication: Database triggers log changes manually, updating a CDC table for downstream retrieval.
   • Drawback: Fragile and high-performance overhead.

By ensuring downstream systems apply changes in the same sequence as the primary database, CDC eliminates problems tied to race conditions and update divergence.

Event Sourcing

While CDC tracks changes at the database level, event sourcing is an architectural pattern where the application itself stores domain-level events (not direct state changes) in an immutable log:

1. Events define what happened, independent of how that event affects stored state.
   • Example: Instead of mutating a table directly to reflect that a “seat was reserved,” event sourcing appends an “event log” entry saying, “Seat X reserved for User Y.”
2. Application state becomes a derived materialized view of the event stream. If new requirements arise (e.g., showing reservation history), reprocessing the stream suffices without modifying existing state.

State, Streams, and Immutability

Immutability complements both CDC and event sourcing by addressing data consistency and recovery challenges:

• Immutable Event Streams: Represent state changes over time, facilitating reproduction of any application state by replaying the log.
• Integration with Log Compaction: Deletes overwritten data versions, keeping only the most recent while maintaining derived states effectively.

Use Cases:

1. Database Recovery: Crash recovery by replaying event logs to rebuild the latest consistent state.
2. Debugging and Auditing: Immutable logs allow complete traceability of all historical actions, preventing silent overwrites of important updates.

Conclusion

By bridging the gap between databases and streams through innovations like CDC, event sourcing, and immutability, modern systems achieve real-time synchronization at scale. These techniques not only eliminate issues like race conditions but also provide resilience and flexibility for evolving system designs. Adopting this mindset decouples immediate application needs from fixed schemas, enabling rich downstream processing and integration capabilities.