Cut Cloud Costs with Smarter PostgreSQL CPU Core Allocation

Cut Cloud Costs with Smarter PostgreSQL CPU Core Allocation

Cloud costs can quickly spiral out of control if resources are not optimized. One of the most significant contributors to these costs is CPU core allocation, which forms the basis of the instance size with every major cloud provider. Many organizations over-provision cores for their PostgreSQL databases, paying for unused capacity, or under-provision them, leading to poor performance and missed SLAs.

This blog will explore strategies to allocate CPU cores effectively for PostgreSQL databases, ensuring optimal performance while keeping cloud expenses in check.

The Cost-Performance Tradeoff in the Cloud

Cloud providers charge based on resource usage, and CPU cores are among the most expensive components. Allocating too many cores leads to wasted costs, while too few can cause performance bottlenecks.

PostgreSQL databases are particularly sensitive to CPU allocation, as different workloads—OLTP (Online Transaction Processing) vs. OLAP (Online Analytical Processing)—place varying demands on processing power. Finding the right balance is essential to achieving both cost-efficiency and performance reliability.

Identifying the Sweet Spot: How Many Cores Do You Need?

Allocating the right number of CPU cores requires understanding your PostgreSQL workload. Here are key steps:

1. Workload Profiling

Use PostgreSQL’s built-in tools to analyze your workload:

  • pg_stat_activity: Identifies active queries and their resource usage.
  • pg_stat_statements: Highlights resource-intensive queries.

2. Benchmarking

Simulate your workload using tools like pgbench to measure performance under different core allocations. This helps identify the optimal configuration for your database.

3. Concurrency Analysis

Evaluate the number of concurrent users and queries. For highly concurrent workloads, more cores are generally beneficial, but you can mitigate resource contention with connection pooling.

Very roughly speaking, estimate the number of concurrent connections required to the database, assume 50% will be active at any time, and then calculate 3 connections per core. So, as an example, if 180 connections are required, assume that 90 will be active at any given time. 3 connections assigned to a core require 90/3 = 30 cores. 

Please note that this is a heuristic and the requirements can vary based on individual situations. 

Practical Strategies to Optimize CPU Core Allocation

1. Start Small and Scale Gradually

Begin with the minimum number of cores required to handle your workload and scale up based on performance monitoring. Many cloud platforms allow you to adjust core allocation dynamically.

2. Implement Connection Pooling

Tools like PgBouncer or Pgpool-II can reduce the number of active connections, minimizing CPU contention and improving resource utilization.

3. Optimize Queries

Poorly written queries are a common source of CPU inefficiency. Optimize queries by:

  • Adding proper indexes.
  • Avoiding cartesian product resulting from JOINs of large tables.
  • Using EXPLAIN and ANALYZE to identify and fix slow queries.

4. Tune PostgreSQL Parameters

Adjust parameters to maximize CPU efficiency:

  • work_mem: Optimize for query sorting and aggregation.
  • parallel_workers_per_gather: Control the number of workers for parallel queries.
  • max_parallel_workers: Set the total number of parallel workers allowed.

Tools and Metrics for Monitoring CPU Utilization

To ensure your CPU cores are optimally utilized, monitor performance regularly:

PostgreSQL Views:

  • pg_stat_activity: Monitor active sessions.
  • pg_stat_statements: Identify CPU-heavy queries.

OS-Level Tools:

  • Use htop or top to monitor CPU usage at the system level.

Cloud Dashboards:

  • AWS CloudWatch, GCP Monitoring, or Azure Monitor provide insights into CPU utilization trends and help spot over-allocation or under-utilization.

The Broader Impact: Aligning Performance and Cost

Optimizing CPU core allocation not only reduces costs but also ensures predictable performance. By avoiding over-provisioning, you can reinvest savings into other critical areas, such as storage or disaster recovery. Additionally, efficient resource use aligns with sustainability goals by reducing unnecessary compute power.

Actionable Tips for Reducing Cloud Costs

  1. Benchmark your PostgreSQL workload to understand resource requirements.
  2. Start with a minimal core allocation and scale up as needed.
  3. Use connection pooling and query optimization to minimize CPU demands.
  4. Regularly monitor CPU utilization using PostgreSQL views and cloud dashboards.

Conclusion

Reducing cloud costs does not require sacrificing database performance. By allocating CPU cores smartly, tuning your PostgreSQL setup, and leveraging the right tools, you can achieve significant cost savings while maintaining high availability and reliability.

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