PostgreSQL 15+ Partial Replication Offers a Direct CDC Alternative

This review examines how PostgreSQL 15+ logical replication, using row filters and column lists, offers a direct alternative to complex CDC pipelines like Debezium and Kafka for selective data streaming.

The Answer Up Front

For teams operating 3-8 microservices that rely on a shared PostgreSQL primary and need selective data synchronization, PostgreSQL 15+ partial replication is a compelling, built-in alternative to external Change Data Capture (CDC) pipelines. It simplifies infrastructure by leveraging native database capabilities, reducing the need for separate Kafka or Debezium clusters. Skip this approach if your architecture requires CDC across heterogeneous database types, complex data transformations before replication, or if your services are not predominantly PostgreSQL-centric. The bottom line: it's a powerful simplification for specific PostgreSQL-heavy microservice architectures, streamlining data flow with minimal operational overhead.

Methodology

This v0 review draws exclusively on the architectural rationale and setup guide published by software_mvp-factory on dev.to, accessed on 2026-06-05. The post details how PostgreSQL 15+ logical replication, with its enhanced row filters and column lists, can be configured to replace traditional CDC tools like Debezium and Kafka Connect. This review covers the author's described setup, the specific SQL commands provided, and the architectural justification for using native PostgreSQL features for selective data streaming to microservices. It does not include independent performance benchmarks, long-term operational workflow analysis, or edge-case handling beyond what the source material addresses. Independent verification of performance claims and operational stability is pending. Update cadence: re-tested when claims diverge from observed behavior or new versions introduce significant changes.

What It Does

The core functionality described is PostgreSQL 15+'s ability to perform partial logical replication. This means a primary PostgreSQL instance can publish a subset of its data changes to one or more subscriber instances, based on granular filtering rules.

Selective Data Publication

PostgreSQL 15+ extends the CREATE PUBLICATION command to include WHERE clauses and column lists. This allows database administrators to define publications that only include rows matching a specific condition (e.g., WHERE status = 'paid') and only specific columns from a table (e.g., id, customer_id, amount). This capability enables a primary database to act as a direct change feed for downstream microservices, each receiving only the data relevant to its domain.

Simplified Infrastructure for Microservices

The author argues this approach eliminates the need for external CDC tools and their associated infrastructure. By using native PostgreSQL features, teams can avoid managing ZooKeeper or KRaft clusters, Kafka Connect workers, schema registries, and complex offset management. The setup involves configuring wal_level = logical on the primary, creating publications with filters, and then setting up subscriptions on downstream PostgreSQL instances. This reduces the number of moving parts in a microservice data synchronization pipeline.

WAL Bloat Prevention and Monitoring

The post also addresses practical operational concerns. It details how to prevent Write-Ahead Log (WAL) bloat by ensuring all replication slots are actively consumed. It also provides a monitoring query that can be integrated into an observability stack to track replication lag and slot status, ensuring the health and performance of the replication setup.

What's Interesting / What's Not

The most interesting aspect is the direct challenge to established CDC patterns. For years, the default answer for microservice data sharing has involved external systems like Debezium and Kafka. This post highlights how PostgreSQL's evolving native capabilities can bypass that complexity entirely for certain use cases. The ability to filter both rows and columns directly within CREATE PUBLICATION is a significant enhancement, moving beyond basic table-level replication to truly selective data streams. This shifts the burden of filtering from application logic or intermediate message queues back to the database, where the data originates.

What's less compelling, or at least requires careful consideration, is the inherent coupling to PostgreSQL. While simplifying the stack, this approach is only viable if both the primary and all subscribing services use PostgreSQL. It lacks the database-agnostic flexibility of a full-fledged CDC platform, which might be necessary in more heterogeneous environments. The post also doesn't provide performance benchmarks comparing the WAL overhead or replication latency against a Debezium/Kafka setup, which would be critical for high-throughput systems. The claim of reduced operational cost is plausible due to fewer components, but the operational burden shifts to deep PostgreSQL expertise for tuning and troubleshooting, rather than distributed systems knowledge.

Pricing

PostgreSQL is an open-source relational database, available for free under the PostgreSQL License. The partial replication features discussed are built-in capabilities of PostgreSQL 15+ and incur no additional licensing costs. Users are responsible for their own hosting, infrastructure, and operational expenses.

Verdict

PostgreSQL 15+ partial replication is a strong contender for simplifying data synchronization within a PostgreSQL-centric microservice architecture. It is best for teams with 3-8 services that need to share specific, filtered subsets of data from a single PostgreSQL primary. The native row and column filtering significantly reduce infrastructure complexity and operational overhead compared to external CDC pipelines. Teams should skip this if they operate in a multi-database environment, require extensive data transformation mid-stream, or need the robust guarantees and ecosystem of a dedicated message broker for other purposes. For its target niche, it offers a pragmatic, efficient, and cost-effective solution by leveraging existing database infrastructure.

What We'd Test Next

Our next steps would involve setting up a reproducible test environment to benchmark this approach against a Debezium/Kafka Connect pipeline. We would measure the performance impact on the primary database, specifically WAL generation rates and CPU utilization under varying loads. Latency from primary commit to subscriber update would be critical to quantify. We would also test the resilience of the replication setup to DDL changes on the primary, evaluating how schema modifications affect filtered publications and subscriptions. Finally, a detailed operational cost analysis, including monitoring complexity and troubleshooting effort, would provide a more complete picture of its long-term viability compared to external CDC solutions.

The investor read

This review highlights a significant trend: core database systems are absorbing functionalities previously handled by specialized tooling. PostgreSQL's enhanced logical replication directly competes with a segment of the CDC market, particularly for smaller, PostgreSQL-native microservice architectures. This signals potential pressure on vendors like Debezium (part of Confluent ecosystem) and other CDC-as-a-service providers, forcing them to differentiate on cross-database compatibility, advanced transformations, or enterprise features. For investors, this suggests that pure-play CDC solutions focused solely on PostgreSQL might face headwinds. Companies that build on PostgreSQL's native capabilities, offering managed services or extensions that enhance this built-in functionality (e.g., better monitoring, automated schema evolution handling), could be investable. However, a startup solely focused on replicating PostgreSQL data might be a deliberate small/bootstrapped play, as the core feature is now free and native.