WAL (Write-Ahead Logging) - AI-Optimized Technical Reference

Core Functionality

Purpose: Prevents data loss during database crashes by logging changes before applying them to data files.

Mechanism: Sequential write to log file → fsync() to disk → asynchronous application to data files

Recovery Process: Replay log entries from last checkpoint to restore consistent state

Performance Specifications

Write Performance

• Sequential WAL writes: 2-5x faster than random data file writes
• Batch commits: Single fsync() can handle multiple transactions
• Production throughput: 8,000-50,000+ transactions/second on NVMe SSD with 32GB RAM
• Transaction latency: 1-5ms typical on decent hardware

Storage Requirements

• Typical overhead: 20-50% additional storage for WAL files
• High-write scenarios: Can reach 80% during bulk imports
• Formula: WAL overhead = write_rate × checkpoint_timeout
• Example: 100MB/sec writes + 5-minute checkpoints = ~30GB WAL minimum

Critical Configuration Settings

PostgreSQL WAL Settings

-- Essential parameters
wal_level = replica                    -- Enable replication
fsync = on                            -- NEVER disable in production
synchronous_commit = on               -- Set to 'off' only for analytics workloads
checkpoint_timeout = 300              -- 5 minutes default, tune based on workload
max_wal_size = 1GB                    -- Increase for write-heavy systems
wal_compression = on                  -- PostgreSQL 14+, saves 20-40% space

Performance Tuning Parameters

-- Advanced tuning
checkpoint_completion_target = 0.9    -- Spread checkpoint I/O over 90% of interval
wal_buffers = 16MB                    -- Usually auto-tuned correctly
commit_delay = 0                      -- Let PostgreSQL handle batching automatically
recovery_parallel_workers = 4        -- PostgreSQL 13+, speeds up recovery

Failure Modes and Solutions

WAL Disk Full

Symptoms: "could not write to WAL file: No space left on device"
Impact: Database stops accepting writes immediately
Prevention: Alert at 70% WAL disk usage, emergency at 90%
Recovery: Fix archive command or increase disk space

Checkpoint Configuration Issues

Problem: Misconfigured checkpoints cause I/O spikes or excessive WAL buildup
Symptoms:

• Too frequent: Constant I/O spikes killing performance
• Too infrequent: WAL buildup (30GB+ per hour), slow recovery times
  Solution: Balance checkpoint_timeout and max_wal_size based on workload

Replication Lag

Cause: WAL generation exceeds network bandwidth or replica apply speed
Impact: Replicas fall behind, potential data loss during failover
Monitoring: SELECT * FROM pg_stat_replication;
Solutions:

• Increase network bandwidth
• Upgrade replica hardware
• Switch from logical to physical replication

Archive Command Failures

Problem: Archive command fails silently, old WAL files accumulate
Detection: SELECT * FROM pg_stat_archiver;
Impact: WAL disk fills up, no point-in-time recovery capability
Prevention: Monitor archiver stats, test archive/restore regularly

Recovery Time Expectations

Recovery Performance

• Standard hardware: 1-3 minutes per GB of WAL to replay
• High-end NVMe: Sub-minute per GB
• Spinning disks: 3-5 minutes per GB
• Real example: 170GB WAL = 4+ hours recovery time with poor checkpoint config

Recovery Acceleration

• Enable parallel recovery workers (recovery_parallel_workers)
• Use faster storage for WAL and data files
• Optimize checkpoint frequency to reduce WAL volume

Database-Specific Implementations

PostgreSQL

• WAL location: pg_wal/ directory
• File size: 16MB segments
• Compression: Available PostgreSQL 14+
• Strengths: Single WAL system, excellent tooling, reliable
• Best for: Most OLTP production systems

MySQL InnoDB

• Dual-log system: Redo log (crash recovery) + Binary log (replication)
• Complexity: Requires both logs for complete recovery
• Performance: 1-3ms write latency
• Storage overhead: 25-45%
• Limitation: Dual-log management complexity

SQLite WAL Mode

• File: .wal extension
• Limitation: Single writer only
• Performance: Sub-millisecond writes
• Best for: Mobile apps, embedded systems
• Recovery: Seconds, not minutes

MongoDB Oplog

• Type: Operations log (not true WAL)
• Storage: Capped collection
• Complexity: Oplog sizing is critical - too small and replicas fall behind permanently
• Write latency: 5-15ms range

Production Monitoring Requirements

Critical Metrics

-- WAL generation rate
SELECT * FROM pg_stat_wal;

-- Replication status
SELECT * FROM pg_stat_replication;

-- Archive status
SELECT * FROM pg_stat_archiver;

-- Replication slots (can hold WAL files)
SELECT slot_name, restart_lsn FROM pg_replication_slots;

Alert Thresholds

• WAL disk usage: Alert at 70%, emergency at 90%
• Replication lag: Alert if behind by >100MB or 5 minutes
• Archive failures: Alert on any failed archive attempts
• Checkpoint duration: Alert if checkpoints take >60% of checkpoint_timeout

Resource Requirements

Hardware Considerations

• WAL storage: Fast sequential write performance more important than random I/O
• Network: Replication requires sustained bandwidth equal to WAL generation rate
• CPU: WAL compression trades CPU for I/O (PostgreSQL 14+)
• RAM: Larger shared_buffers reduces checkpoint frequency

Cloud Provider Costs

• Aurora: 3-4x RDS pricing for distributed WAL benefits
• AlloyDB: Similar premium pricing, faster analytical queries
• Standard RDS: Most cost-effective for typical workloads

Common Misconceptions

Dangerous Settings

• fsync = off: Disabling loses crash recovery entirely
• synchronous_commit = off: Can lose last second of transactions
• Manual WAL file deletion: Breaks replication, never do this
• WAL on NFS: Reliability issues, avoid in production

Performance Myths

• WAL doesn't slow down writes - it makes them safer and often faster through batching
• More WAL files doesn't mean worse performance - it means more write activity
• Checkpoint storms are configuration problems, not WAL problems

Essential Tools

Debugging and Analysis

• pg_waldump: Analyze WAL file contents during troubleshooting
• pgBadger: WAL performance analysis and checkpoint timing
• pg_stat_io: PostgreSQL 17+ I/O pattern visibility

Backup and Archiving

• pgBackRest: Reliable WAL archiving and point-in-time recovery
• WAL-G: Modern alternative to WAL-E with better error handling

Monitoring

• Prometheus postgres_exporter: Comprehensive WAL metrics
• Grafana dashboards: Visualize WAL generation, checkpoint timing, replication lag

Decision Criteria

When WAL Works Best

• OLTP workloads with frequent small transactions
• Systems requiring point-in-time recovery
• Read replica configurations
• Applications needing crash consistency guarantees

When to Consider Alternatives

• Pure read-only workloads (minimal WAL benefit)
• Systems with extreme storage constraints
• Applications that can tolerate data loss for performance gains
• Single-user embedded applications (SQLite WAL mode sufficient)