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Why Your Postgres Analytics Are So Slow (And 3 Ways to Fix It)

2025/07/17

PostgreSQL excels at transactional workloads, but when it comes to postgres analytics, performance can grind to a halt. Those complex GROUP BY queries with aggregations that should take seconds often stretch to 30+ painful seconds. If you're struggling with slow analytical queries on your postgres replica or primary database, you're not alone.

The Root Problem: OLTP vs Analytics

PostgreSQL's row-oriented storage and MVCC design make it perfect for transactional operations—inserting users, updating orders, deleting expired sessions. But analytical questions like "What's our revenue by region over the last six months?" expose a fundamental mismatch. OLTP analytics workloads need columnar storage and specialized engines designed for aggregations and complex queries.

3 Ways to Supercharge Your PostgreSQL Analytics

1. DuckDB PostgreSQL Extension (The Quick Start)

Best for: Getting started quickly, ad-hoc analysis on smaller datasets

The DuckDB PostgreSQL extension uses the PostgreSQL wire protocol directly—it's literally plug and play. Simply create a PostgreSQL secret, use the attach command, and start querying your PostgreSQL tables as regular DuckDB tables. You can easily move data to MotherDuck (DuckDB in the cloud) using simple CREATE TABLE AS SELECT statements.

Pros:

  • Extremely simple setup
  • No changes to existing PostgreSQL setup
  • Great for moving data in batches
  • Works with any postgres read replica

Cons:

  • Network bottleneck for data transfer
  • Limited query pushdown capabilities
  • Puts compute pressure on PostgreSQL

2. PG_DuckDB (DuckDB Inside PostgreSQL)

Best for: Maximum performance without leaving PostgreSQL environment

This PostgreSQL extension embeds DuckDB directly inside PostgreSQL. You get access to all DuckDB features—reading Parquet files, Iceberg tables over S3, and more—while staying in your familiar PostgreSQL environment.

Pros:

  • No network overhead for existing PostgreSQL data
  • Insane speed improvements for analytical queries
  • Easy integration with object storage and MotherDuck
  • Can query external Parquet files efficiently

Cons:

  • Requires custom extension installation
  • Resource competition with transactional workloads
  • Needs careful monitoring (use a postgres replica!)

3. Change Data Capture (CDC)

Best for: Real-time analytics with complete workload separation

CDC provides near real-time synchronization between your PostgreSQL database and DuckDB/MotherDuck. Using tools like Debezium with Kafka or managed services like Airbyte, changes appear in your analytical database within seconds.

Pros:

  • Complete separation of postgres analytics from transactional workloads
  • Near real-time data synchronization
  • Automatic schema evolution
  • Zero impact on primary database performance

Cons:

  • High operational complexity
  • Increased storage requirements
  • Potential processing lag during high-volume periods

Best Practices for PostgreSQL Analytics

Regardless of which solution you choose, follow these operational best practices:

  • Always use postgres replicas for analytical workloads, especially with PG_DuckDB
  • Monitor resources closely - CPU, memory, I/O, and network usage
  • Profile your queries to identify bottlenecks and optimization opportunities
  • Implement proper workload separation to protect transactional performance

Choose Your Path

Each approach serves different needs:

  • DuckDB Extension: Perfect for quick wins and data migration
  • PG_DuckDB: Best performance while staying in PostgreSQL
  • CDC Pipeline: Ultimate solution for real-time postgres analytics at scale

Ready to transform your slow PostgreSQL analytics? Watch the full video above to see these solutions in action, complete with live demos and practical implementation tips.