PostgreSQL Performance Tuning Tips for Cloud Environments

Introduction

The growth of cloud computing has drastically changed the competitive landscape regarding PostgreSQL database performance. The cloud offers unique scalability and flexibility opportunities for businesses; however, with the growth also come distinctive challenges that companies must learn to manage and optimize.

This blog will discuss ten ways your cloud-based PostgreSQL deployment can be propelled to maximum performance, efficiency, and cost-effectiveness. Hopefully, your database can become a high-performing, cost-effective model that your business can depend on.

1. Resource Management in the Cloud

Resource management is the foundation for maximizing PostgreSQL performance in the cloud. Unlike on-premise environments, cloud providers come with dynamic scaling capabilities, which can be both helpful and problematic.

What to Do:

• Conduct periodic health checks to monitor resource consumption
• Use auto-scaling features available to you
• Utilize cloud provider monitoring to see how you consume resources
• Make a decision based on performance versus cost

What Can You Gain?

• Overall infrastructure cost reductions of 30%
• Reduce application response times by 25%
• Optimize resource consumption based on workload

2. Network Latency Mitigation Practices

Your network can bring your cloud PostgreSQL implementations to their knees if something goes wrong. Latency absolutely needs to be mitigated so your database performs based on your users’ expectations.

Methods for reducing latency: 

• Utilize connection pooling techniques that work better for your application
• Utilize regional read replicas appropriately
• Take advantage of your cloud provider’s internal networking implementation

Pro tip: You can reduce network round-trip times by carefully structuring your cloud network architecture.

3. PostgreSQL configuration tuning in cloud environments

Your PostgreSQL configuration needs to be optimized for your respective cloud environment. Optimizing parameters is not one-size-fits-all when deploying in a cloud environment; you will need to consider how to optimize each deployment separately.

The following parameters are critical to ensure optimal performance:

•  shared_buffers: Ensure the value is reasonable as the size of the available memory

•  effective_cache_size: Make sure to estimate based on total size of memory in the system

•  maintenance_work_mem: Size your maintenance_work_mem for common maintenance operations

Expert opinion: Periodically review your configurations as workloads may change.

4. Intelligent Indexing

Throughput based on the design of indexes influences the performance of a PostgreSQL database. With shared access to cloud resources; indexing effectively is critical.

Indexing best practices include

• Periodically review query patterns
• Create indexes for frequently accessed stored data
• Drop unused or redundant indexes
• Use partial indexes and expression indexes for complex queries.

Performance note: A well-designed index can potentially increase query performance by 200-500% for complex queries.

5. Using PostgreSQL Partitioning

Table partitioning is a strong optimization strategy, especially in cloud environments, where scans increase costs and slow things down.

Ways to partition include

• Partitioning data types by time sequence
• Mapping partitions using range or list
• Improving performance for large data set queries
• Lowering total compute and storage expenses

Cloud Specific: Capitalizing on the reduction of data scans saves cloud infrastructure costs.

6. Cloud Native Monitoring and Observability

Advanced or “cloud-native” platforms can give incredible monitoring products to deep dive into a database’s performance.

Keeping a pulse on performance includes

• Creating overall dashboards to see performance broadly
• Implementing smart alerting
• Recording metrics like: Usage of CPU, Usage of RAM, Disk IO performance, Connection stats in a pool

Pro-Active: Build predictive before it becomes reactive.

7. VACUUM/ANALYZE Improvement

The health of the overall database/meta state in a high-write cloud environment depends on the regular and effective working of its maintenance operations.

Best Maintenance Techniques:

• Auto vacuum configuration to provide optimization action automatically
• Manual VACUUM during down times each week
• Monitor bloat in tables and devise an actionable clean-up
• Find an overhead balance to keep performance from slipping.

8. Advanced Caching Implementations

Cache to lessen the load on the DB server and increase response times by implementing a multi-tiered caching process.

9. Optimize Read-Heavy Workload

If your application is read-heavy, the write load is less important. A fast and smart read-replica strategy is very important.

Read-replica strategies may be

• Distributing read queries evenly across read replicas.
• Using geo-distributed read replicas, being sure they are consistent and routing to the nearest.
• Using some connection poolers to route queries based on proximity or metadata.
• Monitoring load and balancing reads by read-replica.

10. Continuous Evolution of Performance

We can never attain fully optimized performance. Audits need to be performed regularly and adjusted accordingly.

Continuous Evolution Framework:

• Assess and audit performance quarterly.
• Be aware of the latest PostgreSQL or cloud provider developments.