PostgreSQL change data capture (CDC) presents a fundamental challenge: capturing every database modification while maintaining system performance and operational reliability. Organizations require real-time visibility into data changes across operational systems, yet traditional CDC implementations demand extensive custom development and ongoing maintenance overhead.

This guide examines every available CDC method for PostgreSQL, from basic approaches to enterprise-grade solutions, providing the technical foundation needed to implement reliable change capture for your data architecture.

Understanding PostgreSQL Change Data Capture

Change data capture is a process that detects every incremental change to a database, and then delivers those changes in a downstream system. When done correctly, the other system can process these changes to perfectly replicate the original data set. [1]

PostgreSQL CDC is driven by its Write-Ahead Logging (WAL), which maintains a record of all database updates, including when changes are made to database tables. Anytime data is inserted, updated, or deleted, it is logged in the WAL. [2]

The challenge lies in implementation complexity. Every insert, update, and delete in the Postgres database needs to be captured and delivered in order. No alteration can be missed. The physics of never missing a change and guaranteeing delivery makes building a reliable CDC system challenging. [1]

Listen/Notify Implementation

PostgreSQL's Listen/Notify provides the simplest CDC approach through its publish-subscribe mechanism. Sessions can "listen" to channels while database triggers "notify" those channels of changes.

Implementation

CREATE OR REPLACE FUNCTION notify_trigger() RETURNS TRIGGER AS $$
DECLARE
 payload json;
BEGIN
 payload := json_build_object('table', TG_TABLE_NAME, 'id', NEW.id, 'action', TG_OP);
 PERFORM pg_notify('table_changes', payload::text);
 RETURN new;
END;
$$ LANGUAGE plpgsql;

CREATE TRIGGER my_trigger
AFTER INSERT OR UPDATE OR DELETE ON my_table
FOR EACH ROW EXECUTE FUNCTION notify_trigger();

Technical Limitations

• At-most-once delivery semantics: Notifications are transient with no persistence
• 8,000-byte payload limit: Messages exceeding this size cause the notify command to fail
• Network dependency: Brief disconnections result in permanent message loss
• No durability: Listeners must be active when notifications are published

Listen/Notify suits basic change detection but fails reliability requirements for operational systems.

Poll the Table Method

Table polling relies on timestamp columns to identify recently modified records through periodic queries. This approach requires each monitored table to include an updated_at column that updates with every row modification.

Implementation

SELECT * FROM public.users
WHERE updated_at > 'TIMESTAMP_LAST_QUERY'
ORDER BY updated_at, id;

Applications maintain cursor state using the combination of updated_at and id values, polling the table at regular intervals to discover changes.

Technical Limitations

• Cannot capture DELETE events unless using soft deletions [3]
• No change diffs: You know records changed but not what specifically was modified
• Race conditions: Uses the query layer for extracting data, which puts additional load on PostgreSQL and requires recurring polling of the monitored table [3]
• Out-of-order commits: Postgres datetimes and sequences can commit out-of-order, leading to missed records

Polling works for simple scenarios but scales poorly and cannot guarantee complete change capture.

Audit Table Pattern

The audit table approach creates separate logging tables to track all database modifications through triggers. This method provides complete change history including old and new values.

Implementation

CREATE OR REPLACE FUNCTION changelog_trigger() RETURNS TRIGGER AS $$
DECLARE
 action text;
 table_name text;
 transaction_id bigint;
 timestamp timestamp;
 old_data jsonb;
 new_data jsonb;
BEGIN
 action := lower(TG_OP::text);
 table_name := TG_TABLE_NAME::text;
 transaction_id := txid_current();
 timestamp := current_timestamp;

 IF TG_OP = 'DELETE' THEN
   old_data := to_jsonb(OLD.*);
 ELSIF TG_OP = 'INSERT' THEN
   new_data := to_jsonb(NEW.*);
 ELSIF TG_OP = 'UPDATE' THEN
   old_data := to_jsonb(OLD.*);
   new_data := to_jsonb(NEW.*);
 END IF;

 INSERT INTO changelog (action, table_name, transaction_id, timestamp, old_data, new_data)
 VALUES (action, table_name, transaction_id, timestamp, old_data, new_data);

 RETURN null;
END;
$$ LANGUAGE plpgsql;

Processing Audit Records

BEGIN;

SELECT * FROM changelog
ORDER BY timestamp
LIMIT 100
FOR UPDATE SKIP LOCKED;

-- Process records then remove them
DELETE FROM changelog
WHERE id IN (list_of_processed_record_ids);

COMMIT;

Technical Limitations

• Write amplification: Single writes result in multiple audit table operations
• Manual back-pressure management: The audit table continues filling regardless of processing capacity
• Complex scaling considerations: Worker processes, queue management, and error handling require custom development
• Storage overhead: Audit tables can grow larger than source tables

Audit tables provide comprehensive change tracking but demand significant infrastructure development and operational management.

Logical Replication

Since version 9.4, PostgreSQL offers logical replication for efficiently and safely replicating data between different PostgreSQL instances. The write-ahead log holds all events that change the data of the PostgreSQL database, including INSERTs, UPDATEs, and DELETEs. [3]

Implementation

-- Create replication slot
SELECT * FROM pg_create_logical_replication_slot('your_slot_name', 'pgoutput');

-- Start replication (using appropriate client library)
-- cursor.start_replication(slot_name='your_slot_name', decode=True)
-- cursor.consume_stream(lambda msg: process_message(msg))

Logical replication uses replication slots to track WAL position and ensure no changes are missed. The pgoutput plugin formats output for PostgreSQL subscribers, while wal2json provides JSON output for external systems.

Technical Limitations

• Complex client implementation: Replication protocol differs from standard SQL queries
• Slot management: Replication slots must be monitored to prevent WAL accumulation
• Message acknowledgment: Clients must properly acknowledge processed messages
• Restart handling: Recovery procedures required for connection interruptions

Logical replication provides robust change capture but demands specialized expertise for implementation and maintenance.

Foreign Data Wrappers

Foreign Data Wrappers (FDWs) enable cross-database operations and can serve specialized CDC scenarios, particularly for PostgreSQL-to-PostgreSQL synchronization.

Implementation Example

-- Create foreign table connection
CREATE FOREIGN TABLE foreign_app_database (
   -- table definition
) SERVER foreign_server;

-- CDC trigger using FDW
CREATE OR REPLACE FUNCTION sync_trigger() RETURNS TRIGGER AS $$
BEGIN
 UPDATE foreign_app_database.account
 SET status = NEW.status
 WHERE email = NEW.email;
 RETURN NEW;
END;
$$ LANGUAGE plpgsql;

Technical Limitations

• Limited applicability: Primarily suited for database-to-database scenarios
• Performance considerations: Cross-network operations introduce latency
• Complex development: Custom FDWs require substantial implementation effort
• Transactional coupling: Changes can be rejected, causing rollbacks

FDWs address specific integration patterns but lack the flexibility needed for comprehensive CDC solutions.

Enterprise CDC Solution: Stacksync

Manual CDC implementations create technical debt and operational overhead that scales poorly with enterprise requirements. Custom-coded integrations are often complex, brittle, and resource-intensive to build and maintain, diverting valuable engineering talent from core product development. Generic iPaaS solutions, while offering a broad range of connectors, may not be optimized for true real-time, bi-directional synchronization.

Platforms like Stacksync are engineered specifically to solve the challenge of real-time, bi-directional data synchronization at scale. Unlike generic iPaaS tools or brittle custom code, Stacksync provides a managed, reliable, and stateful engine designed for mission-critical operational data.

Addressing Manual Method Limitations

Reliability Beyond Listen/Notify Where Listen/Notify provides at-most-once delivery, Stacksync delivers automated reliability with built-in error handling, comprehensive logging, and automated retries, guaranteeing data consistency and removing the maintenance burden from your engineering team.

Complete Change Capture Without Polling Unlike polling methods that miss deletions and create database load, Stacksync uses Change Data Capture (CDC) that monitors field-level changes without modifying source databases.

Eliminating Audit Table Complexity Instead of custom audit infrastructure, Stacksync's two-way sync engine detects and propagates changes in milliseconds, with conflict resolution logic to handle simultaneous updates.

Logical Replication Without Operational Overhead Stacksync provides effortless scalability designed to handle enterprise data volumes, allowing you to connect CRMs, ERPs, and databases with millions of records without compromising performance.

Implementation Without Custom Development

No-Code Setup Process:

1. Connect PostgreSQL using secure credentials or OAuth
2. Select tables and target systems for synchronization
3. Configure field mappings with automatic suggestions
4. Set sync direction and conflict resolution rules
5. Activate real-time monitoring

Configure and sync data within minutes without code. Whether you sync 50k or 100M+ records, Stacksync handles all the dirty plumbing of infrastructure, queues and code so you don't have to. Integrations are non-invasive and do not require any setup on your systems.

Customer Implementation Examples

Nautilus Solar: PostgreSQL to Multi-System Sync

Nautilus Solar operate solar farms and powers energy to 16k+ households integrating in real-time NetSuite, Postgres and HubSpot. Traditional polling would have required custom development across three systems. Stacksync eliminated the complexity while providing real-time operational data consistency.

Acertus: Enterprise Data Consolidation

Acertus delivers in real-time enriched data to Salesforce and consolidates enterprise insights connecting Salesforce, NetSuite, Snowflake product databases. Manual logical replication implementation would have required months of specialized development. By implementing Stacksync, they achieved real-time data synchronization, reducing manual effort and saving over $30,000 annually.

PostgreSQL CDC Best Practices

Delivery Guarantees

A good change data capture system will provide delivery guarantees around reliability, performance, and data integrity. In many CDC implementations, it's possible for a message to be dropped (at-most-once delivery) or delivered more than once (at-least-once delivery). For Postgres CDC use cases where consistency is critical, exactly-once processing is ideal. [1]

Performance Considerations

Manual implementations require increasing the max_wal_senders and max_replication_slots settings in the postgresql.conf file and allowing more concurrent WAL sender processes. [4] Automated platforms handle these optimizations without manual database configuration.

Operational Monitoring

Maintaining optimal performance requires adherence to best practices including regular maintenance tasks such as vacuuming and analyzing tables to maintain database health and performance for reliable and efficient real-time data synchronization. [4]

Choosing the Right CDC Approach

For Basic Requirements:

• Listen/Notify works for non-critical change notifications
• Polling suits simple scenarios with low data volumes

For Moderate Complexity:

• Audit tables provide comprehensive change tracking with manual processing
• Requires careful design for scaling and error handling

For Enterprise Requirements:

• Logical replication offers complete change capture but demands specialized expertise
• Automated platforms eliminate complexity while providing enterprise-grade reliability

For Production Systems: Traditional methods like custom code and generic iPaaS platforms have served as temporary fixes, but they fail to provide the reliability, real-time performance, and scalability required for mission-critical operations. Achieving a truly unified data ecosystem requires a purpose-built solution. Platforms like Stacksync deliver the necessary foundation by providing real-time, bi-directional synchronization that is both powerful and easy to manage.

Conclusion

PostgreSQL change data capture spans from basic notification patterns to enterprise-grade automated synchronization. While manual implementations provide control, they require substantial engineering investment and ongoing operational overhead.

Automated data sync between applications, real-time data synchronization, and database synchronization capabilities transform CDC from a technical challenge into an operational advantage. Bi-directional sync tools and low code integration platforms eliminate the traditional trade-offs between reliability, performance, and implementation complexity.

Platforms like Stacksync are at the forefront of this transformation, providing the purpose-built technology to achieve instant data consistency across complex enterprise ecosystems. By abstracting the complexities of integration and focusing on reliability, scalability, and real-time performance, such solutions empower organizations to take control of their data flows and unlock the full potential of their interconnected systems.

Ready to eliminate PostgreSQL CDC complexity? Explore Stacksync's automated synchronization platform and discover how leading organizations achieve reliable change data capture without custom development.