If you've ever wondered why your database survives crashes while your filesystem sometimes doesn't, WAL is the answer. It's dead simple: log changes before you apply them. That's it.
When your application writes data, the database doesn't immediately scatter those changes all over your disk like some kind of maniac. Instead, it writes a sequential record of what's about to happen to a log file first. Then, whenever it gets around to it (usually during checkpoints), it applies those changes to the actual data files.
Why does this matter? Because when your database crashes (not if, when), you can replay the log and get back to exactly where you were. Without WAL, your transactions vanish into the void and you get to explain to your boss why last hour's orders disappeared.
How it actually works: Your writes hit shared buffers first, then get logged to WAL, then eventually PostgreSQL gets around to writing them to disk when it feels like it. This keeps your data safe while not being slow as hell.
How WAL Actually Works in Practice
The protocol is straightforward but the implications run deep:
Write to WAL first: Every
INSERT,UPDATE, orDELETEgets logged with enough detail to either replay it or undo it. This includes the transaction ID, what table got hit, and the before/after values.Force it to disk: The database calls
fsync()or equivalent to make sure that log entry is actually on persistent storage. Nofsync(), no durability guarantee. PostgreSQL learned this the hard way in early versions.Apply changes later: The actual data pages get updated asynchronously by background writer processes. If you crash between steps 2 and 3, recovery just replays the WAL during startup.
This approach works because writing sequentially to the end of a file is way faster than random writes scattered across your database. Even on modern NVMe SSDs, sequential writes still outperform random writes by 2-5x for most workloads.
The Performance Reality Check
Here's why WAL isn't just about durability - it's about performance:
Sequential vs Random I/O: Traditional spinning disks could handle maybe 100-200 random IOPS but 100MB/s+ sequential writes. WAL turns your random database updates into sequential log writes. Even SSDs benefit from this pattern.
Batch Commits: Multiple transactions can commit with a single fsync() to the WAL. On decent hardware (NVMe SSD, 32GB RAM), PostgreSQL can hit 50,000+ small transactions per second because of this batching effect. On spinning rust, you're lucky to get 1/10th of that.
Reduced Lock Contention: Since data pages get updated in the background, your transactions don't block waiting for disk I/O on random data pages. They just wait for the WAL write, which is typically 5-10x faster.
Had this system pushing like 8k-12k writes per second - way higher than we planned for during the initial design phase. Direct data file updates would've been a disaster, but WAL kept transaction times under 5ms even when traffic spiked. PostgreSQL 17's parallel vacuum improvements help with the cleanup too.
The PostgreSQL docs cover WAL tuning pretty well. Key settings: wal_level, fsync, synchronous_commit, and checkpoint stuff. pgBadger helps analyze WAL performance when things go wrong.
The Dark Side Nobody Talks About
WAL isn't magic. Here's what can bite you:
Storage Requirements: WAL files grow like weeds. Usually 20-50% extra storage, but bulk imports can hit 80% when someone fucks up the checkpoint settings. Had one system pushing 30GB of WAL per hour during an import because someone set checkpoint_timeout to 45 minutes. Genius move. The PostgreSQL docs have some guidance on not letting this kill your disk space.
Recovery Time: Replaying WAL during recovery takes time. Big databases with lots of WAL to replay can take 2-3 minutes per GB on decent hardware. Plan accordingly. Tools like pg_waldump can help analyze WAL content during troubleshooting.
Replication Lag: If your WAL generation exceeds network throughput to replicas, you get replication lag. This is especially painful with logical replication in PostgreSQL which has higher overhead than physical replication. Monitor using pg_stat_replication views and consider WAL archiving strategies for large deployments.
Checkpoint Tuning Hell: Getting checkpoint frequency right is pure fucking voodoo. Too frequent and you get I/O spikes that kill performance. Too infrequent and WAL builds up like crazy, making recovery take forever. PostgreSQL 16+ helped with better checkpoint spreading, but most people still just stick with defaults because tuning this shit is black magic.
Checkpoint Process: The background writer and checkpointer work together to flush dirty pages from shared buffers to data files, allowing old WAL files to be recycled or archived.
The bottom line: WAL works great until it doesn't, and when it doesn't, you're usually dealing with it at 3am during an outage. Which brings us to the questions nobody wants to ask until they're staring at a full WAL disk...
