Understanding Write-Ahead Logging in PostgreSQL: The Foundation of Modern CDC

Introduction to Write-Ahead Logging

Write-Ahead Logging, commonly known as WAL, represents one of the most fundamental concepts in database engineering. If you have ever wondered how PostgreSQL maintains data integrity during crashes, enables point-in-time recovery, or supports real-time Change Data Capture, the answer lies in understanding WAL.

In this comprehensive guide, we will explore the inner workings of PostgreSQL's WAL mechanism, examine its role in modern CDC systems, and provide practical guidance for engineers building data pipelines that require real-time change tracking.

What is Write-Ahead Logging?

At its core, WAL is a technique where all modifications to data must be written to a log before the actual data files are modified. This seemingly simple principle provides extraordinary guarantees:

Durability: When PostgreSQL confirms a transaction is committed, the WAL record for that transaction has been flushed to disk. Even if the server crashes immediately after, the committed transaction can be recovered.

Atomicity: Because changes are first written to WAL as a complete unit, transactions either fully complete or fully rollback—there is no in-between state.

Consistency: The sequential nature of WAL ensures that changes are applied in the correct order, maintaining database consistency even after recovery.

How WAL Works Under the Hood

When you execute an INSERT, UPDATE, or DELETE statement in PostgreSQL, here is what happens behind the scenes:

Step 1: Transaction Begins

PostgreSQL assigns a transaction ID (XID) to your transaction. This ID will be used to track all changes made within this transaction.

Step 2: Changes Written to WAL Buffer

As your statement modifies data, PostgreSQL does not immediately write to the actual table files. Instead, it writes WAL records to an in-memory buffer. These records contain all the information needed to replay the changes.

Step 3: WAL Buffer Flushed to Disk

When you commit the transaction, PostgreSQL flushes the WAL buffer to disk. Only after this flush completes does PostgreSQL confirm the commit to your application.

Step 4: Background Writer Updates Data Files

Asynchronously, PostgreSQL's background writer and checkpointer processes write the actual changes to the table files. This can happen well after the commit, but that is safe because the WAL contains all necessary recovery information.

WAL Segments and Configuration

PostgreSQL organizes WAL into segments, typically 16MB each by default. Understanding key configuration parameters is essential for production deployments:

-- View current WAL configuration
SHOW wal_level;           -- Controls what information is logged
SHOW max_wal_senders;     -- Maximum concurrent streaming connections
SHOW wal_keep_size;       -- Minimum WAL to retain for standbys
SHOW max_slot_wal_keep_size; -- Maximum WAL to keep for replication slots

wal_level Settings

The wal_level parameter determines how much information is written to WAL:

• minimal: Only enough for crash recovery. No replication possible.
• replica: Enables physical streaming replication. Sufficient for standby servers.
• logical: Full logical decoding support. Required for CDC and logical replication.

For any CDC implementation, you must set wal_level = logical.

Logical Decoding: The Key to CDC

Logical decoding transforms low-level WAL records into a logical stream of changes that applications can consume. This is the foundation of modern CDC systems.

Creating a Replication Slot

Replication slots ensure that PostgreSQL retains WAL segments needed by consumers:

-- Create a logical replication slot
SELECT pg_create_logical_replication_slot(
    'savegress_slot',  -- Slot name
    'pgoutput'         -- Output plugin
);

-- View existing slots
SELECT slot_name, plugin, slot_type, active, restart_lsn
FROM pg_replication_slots;

Consuming Changes

Once you have a replication slot, you can read changes:

-- Peek at changes without advancing the slot
SELECT * FROM pg_logical_slot_peek_changes(
    'savegress_slot',
    NULL,  -- Start from current position
    NULL   -- No limit on number of changes
);

-- Read and advance (changes are consumed)
SELECT * FROM pg_logical_slot_get_changes(
    'savegress_slot',
    NULL,
    NULL
);

WAL and CDC Performance Considerations

When implementing CDC based on WAL, several performance factors require attention:

Replication Slot Management

Inactive replication slots can cause WAL to accumulate indefinitely, filling your disk. Always monitor slot lag:

SELECT slot_name,
       pg_size_pretty(pg_wal_lsn_diff(pg_current_wal_lsn(), restart_lsn)) as lag
FROM pg_replication_slots;

Publication Filtering

For large databases, publish only the tables you need to replicate:

-- Create a publication for specific tables
CREATE PUBLICATION savegress_pub FOR TABLE orders, customers, products;

-- Or use row filters (PostgreSQL 15+)
CREATE PUBLICATION filtered_pub FOR TABLE orders WHERE (status = 'pending');

Real-World CDC Architecture

In production CDC systems, the architecture typically involves:

• Source Database: PostgreSQL with logical replication enabled
• CDC Connector: Software like Savegress that consumes the replication stream
• Message Queue: Kafka, Kinesis, or similar for buffering and distribution
• Consumers: Data warehouses, search indexes, caches, or other databases

Best Practices for Production WAL/CDC

Based on operating CDC systems at scale, here are essential recommendations:

• Always set appropriate max_slot_wal_keep_size to prevent unbounded WAL growth
• Monitor replication slot lag and alert when it exceeds thresholds
• Use dedicated replication users with minimal required privileges
• Implement proper slot cleanup when decommissioning CDC consumers
• Test failover scenarios—ensure your CDC can handle PostgreSQL primary switches

Conclusion

Write-Ahead Logging is the foundation that makes reliable Change Data Capture possible in PostgreSQL. By understanding how WAL works, how logical decoding transforms it into consumable change streams, and how to properly configure and monitor these systems, you can build robust data pipelines that capture every change in real-time.

Whether you are synchronizing data to a data warehouse, maintaining search indexes, or building event-driven architectures, WAL-based CDC provides the reliability and performance characteristics that modern data infrastructure demands.