Introduction
In the previous blog, we explored the fundamentals of logical decoding and logical replication. Now, let’s go one step further into the real-world challenges and advanced strategies that make logical replication a powerful tool for modern architectures. From conflict resolution to performance tuning and integration with platforms like Kafka or Snowflake, this guide covers what you need to know to put logical replication into production.
In this blog, we will explore:
- Conflict resolution mechanisms (native and custom)
- WAL tuning and slot management
- Integration patterns (Kafka, Snowflake, Flink)
- Active-active multi-region topologies
- Operational gotchas and how to prepare for them
Conflict Handling in Logical Replication
Logical replication works at the row level, but conflicts can arise when multiple nodes try to modify the same data.
Common Conflict Scenarios
- Update Conflicts: Two nodes are updating the same row.
UPDATE vs. UPDATE Conflicts
Example: Two writers concurrently update the same customer row with differing field values.
The subscriber applies changes using standard UPDATE statements. Without a last-writer-wins model or conflict-resolution hook, replication fails. - Delete/Update Conflicts: One node deletes a row while another updates it.
DELETE vs. UPDATE Conflicts
A DELETE on the subscriber may cause an UPDATE from the publisher to fail with ERROR: could not find tuple for update. - Latency-driven Conflicts: Subscriber lags, but newer changes arrive.
Latency-Driven Race Conditions
Replication lag (measured via pg_stat_replication.replay_lsn) leads to inconsistent row states across nodes.
Handling Conflicts
- Application-side handling: Let your app logic resolve conflicts.
- Custom conflict handlers (via extensions like pglogical).
- Design for conflict avoidance: Shard data, or make nodes authoritative for a specific table
- Regular monitoring
Performance Tuning Tips for Logical Replication
Logical replication adds flexibility, but it also requires tuning.
- Increase WAL sender processes
ALTER SYSTEM SET max_wal_senders = 20;
ALTER SYSTEM SET max_replication_slots = 20;
Requires restart:
For example:sudo systemctl restart postgresql - Tune work_mem and maintenance_work_mem for large replication sets.
- Batch replication by grouping tables into publications/subscriptions wisely.
- Monitor lag:
SELECT * FROM pg_stat_replication;
SELECT * FROM pg_replication_slots; - Avoid long-running slots — unconsumed WAL can bloat the disk.
Real-World Architecture Patterns
Microservices with Shared Data
Use logical replication to push subsets of data to different microservices. Each service only subscribes to the tables it needs.
Hybrid Cloud Migration
Replicate from on-prem PostgreSQL → AWS RDS or GCP CloudSQL with near-zero downtime during migration.
Streaming to Analytics Platforms
wal2json + Kafka → Real-time pipelines to Spark, Flink, or data lakes.
decoderbufs → Compact binary format for high-throughput streaming
Multi-Region Active-Active
Combine logical replication with conflict-handling to allow regional write activity (with careful design).
Limitations You Must Plan For
- Schema (DDL) changes still need manual sync.
- Logical replication won’t replicate roles, extensions, or sequences automatically.
- High write volume can stress replication slots; monitor lag carefully.
- Avoid LOBs (OID data types) not supported natively by logical decoding.
Best Practices
- Primary Keys on All Tables: required for logical replication
- Monitor pg_stat_replication: use alerts on replay lag
- Drop unused replication slots: prevent WAL accumulation
- Test failover/catch-up paths: especially in DR/HA scenarios
- Define Conflict policies early: plug-ins or custom handlers
- Partition Smartly: avoid cross-node writes
Closing Notes
Logical replication is a powerful PostgreSQL feature that unlocks streaming, hybrid cloud, active-active, and microservice architectures. With proper conflict management and tuning, it can scale horizontally across diverse systems, but it demands forethought.
If you’re working with complex topologies, custom decoding plugins, or multi-region systems, or integrating with Kafka/Snowflake, consider pairing logical replication with monitoring (e.g., pg_stat_monitor, Prometheus exporters), observability (like pg_waldump, pg_walinspect), and strong CI/CD practices.