Modern distributed systems need to ensure reliability, durability, and consistency across crashes, delays, and replication. Whether you’re working with databases like PostgreSQL, message brokers like Kafka, or document stores like MongoDB, there’s one technique that underpins them all: the Write-Ahead Log (WAL).
We will quickly see, why they’re essential, and how they’re implemented across systems.
What Is a Write-Ahead Log?
A Write-Ahead Log (WAL) is a sequential file where systems log every change before applying it to the actual data store. This approach ensures two things:
- Durability: If a crash occurs, changes in the log can be replayed.
- Consistency: Partial changes don’t corrupt the state; replaying the log restores the last consistent version.
In practice, every modification (insert/update/delete) is first appended to this log, which is stored on disk. Only after logging, the change is applied to the actual data structure.
Why WALs Matter
Durability
WALs ensure that once a system acknowledges a change, it won’t be lost—even during crashes. Systems write changes sequentially, which is faster and more reliable than random disk writes.
Crash Recovery
On restart, a system reads the WAL and reapplies incomplete operations to restore a consistent state. This enables fast recovery even from sudden power failures.
Replication
Logs provide a linear, ordered view of changes, making them ideal for propagating data to replicas or consumers in real-time systems.
Performance
Appending to a log is lightweight and efficient. Modern hardware handles sequential I/O better, and logs simplify recovery, state sync, and backups.
WAL in Action: System-Specific Implementations
PostgreSQL
PostgreSQL uses WALs to guarantee ACID transactions, replication, and crash recovery.
- Log First: Every transaction is written to a WAL segment file before modifying the database.
- Apply Later: Actual data files are updated asynchronously, during checkpoints.
- Recovery: On restart, PostgreSQL scans from the last checkpoint and reapplies WAL entries.
PostgreSQL supports two replication models:
- Physical Replication: Byte-for-byte WAL segment streaming.
- Logical Replication: Decodes WAL entries into SQL-like operations.
Archived WALs can support point-in-time recovery (PITR), backup, and disaster recovery.
Kafka
Kafka takes WAL to the next level—it is a log system.
- Topics and Partitions: Each partition is an append-only WAL.
- Offsets: Each message gets a unique offset for ordering and replay.
- Log Persistence: Kafka flushes logs to disk before acknowledging writes.
- Consumer Flexibility: Consumers use offsets to track and replay messages.
Kafka’s strength lies in its horizontal scalability:
- Partitioning enables parallelism.
- Replication ensures fault tolerance.
- Leaders and Followers manage write/read flow across brokers.
MongoDB
MongoDB uses the oplog (operations log), a logical WAL, primarily for replication and crash recovery.
- Log First: Writes are logged as high-level JSON operations.
- Apply Later: Operations are applied asynchronously.
- Replica Sync: Secondaries read oplog entries and apply changes in order.
MongoDB’s oplog is fixed-size and works like a circular buffer. If a secondary node falls too far behind, it may miss older entries and need a full resync.
Shared Principles Across All Systems
Despite differences in structure and application, PostgreSQL, Kafka, and MongoDB share core WAL traits:
- Append-Only: Logs are immutable and sequential.
- Idempotency: Operations can be replayed safely.
- Recovery Mechanism: WALs act as a source of truth after crashes.
- Foundation for Replication: Logs simplify keeping replicas up to date.
- Efficiency: Sequential logging improves throughput and resilience.
Real-World Scenarios
- PostgreSQL: ACID compliance and point-in-time recovery.
- Kafka: Streaming pipelines with high-throughput logs and message replays.
- MongoDB: Asynchronous replication for HA in distributed clusters.
All follow the principle: log first, apply later.
Final Thoughts
WALs aren’t just a database trick—they’re the core of modern system design. If you’re building systems that need to scale, recover from failure, or replicate data reliably, you’re already depending on WALs.
Understanding how PostgreSQL, Kafka, and MongoDB implement them gives you a solid foundation for designing robust, fault-tolerant systems.