Microsoft Blog for PostgreSQL

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Top 10 Performance Optimization Techniques for Azure Database for PostgreSQL Flexible Server

anugya-sinha

Microsoft

Aug 03, 2026

Introduction

Performance optimization is one of the most common challenges faced by organizations running business-critical workloads on Azure Database for PostgreSQL flexible server. As your workloads grow it’s common to encounter high CPU utilization, storage bottlenecks, autovacuum issues, excessive temporary file generation, and connection saturation.

The good news is that Azure PostgreSQL flexible server provides several built-in capabilities to help optimize performance, improve scalability, and reduce operational overhead. This article explores ten practical techniques that can significantly improve database performance and reliability.

1. Choose the Right Compute SKU

Performance starts with selecting the appropriate compute tier.

Azure PostgreSQL flexible server offers:

Pricing tier	Target workloads
Burstable	Designed for workloads that don't require full CPU performance continuously. Best suited for proof-of-concept environments, and development builds. Not recommended for production workloads.
General Purpose	Provides a balance between CPU and memory with scalable I/O throughput, making it suitable for most production workloads. Examples include servers for hosting web applications, mobile apps, and enterprise applications.
Memory Optimized	Suitable for high-performance database workloads that require in-memory performance for larger buffer cache sets, and higher concurrency. Examples include servers for processing real-time data and high-performance transactional or analytical apps.

Learn more about Compute Tiers here.

2.Enable and Use Query Store

Query Store is one of the most powerful performance tools available.

Query Store automatically captures the following and keeps them available for review:

Query execution statistics

Runtime metrics

Wait event information

Historical execution trends

It organizes the data into time windows, so you can spot database usage patterns. Data for all users, databases, and queries is stored in a database named azure_sys in the Azure Database for PostgreSQL instance.
It’s generally recommended to monitor query store from Azure tools, KQL, etc.
Learn more about Query store here.
You can also view some useful scenario for query store and some Best Practices for Query store

3.Leverage Built-In PgBouncer Connection Pooling

PostgreSQL uses a process-per-connection model, which means every connection consumes memory and CPU resources.

Azure PostgreSQL flexible server provides built-in PgBouncer support for eligible SKUs . PgBouncer allows multiple application sessions to reuse open backend connections and significantly reduces overhead.

Benefits include:

Lower memory consumption

Faster connection handling

Improved application scalability

Reduced CPU overhead

Learn more about PgBouncer here

4.Use Azure Troubleshooting Guides

One underutilized feature is the built-in troubleshooting experience available directly in the Azure portal.

Guides are available for:

CPU troubleshooting

Memory troubleshooting

IOPS analysis

Temporary files

Autovacuum monitoring

Autovacuum blockers

These tools provide actionable recommendations and visualizations without requiring external monitoring solutions.
Learn more about Troubleshooting Guides here.

5. Monitor and Tune Autovacuum

Autovacuum is critical for maintaining PostgreSQL performance.

Without proper vacuuming:

Dead tuples accumulate

Table bloat increases

Statistics are not refreshed regularly

Query performance degrades

Transaction ID wraparound risks emerge

Use Azure's built-in Autovacuum Monitoring TroubleshootingGuides to identify:

Vacuum lag

Blocked autovacuums

Table bloat

Inefficient cleanup operations

Azure now also offers adaptive tuning capabilities to optimize maintenance behavior.
Learn more about Autovacuum tuning here

6.Optimize Storage and IOPS Planning

Many performance incidents originate from insufficient storage planning rather than inefficient SQL.

In Azure PostgreSQL flexible server:

Storage and baseline IOPS are closely related. Learn more here.

Larger storage allocations provide higher baseline IOPS.

Auto-grow prevents storage-related outages
Note: Storage can only be scaled up and will always be double in size.
SSDv2 auto-grow will allow customized growth settings in future release.

For write-heavy workloads, monitoring storage utilization and IOPS is essential.

Best practice:

Enable Storage Auto-Grow

Monitor Read/Write IOPS regularly

Scale storage proactively

7.Investigate Temporary File Generation

Large sorts and hash operations that exceed available memory spill to disk and generate temporary files.

Symptoms include:

Sudden Latency Spikes

Increased IOPS

Slower query execution

Azure TroubleshootingGuides provide dedicated temporary-file analysis capabilities that help identify offending queries.

Frequent temp file generation often indicates:

Missing indexes

Undersized work_mem

Large sorting operations

8.Use Intelligent Tuning

Azure PostgreSQL flexible server includes Intelligent Tuning capabilities.

The service continuously observes workload behavior and automatically optimizes parameters related to write operations.

Examples of tuning include:

checkpoint_completion_target

max_wal_size

min_wal_size

bgwriter settings

This reduces administrative effort while helping maintain consistent performance.
Learn more about Intelligent Tuning here.

9.Optimize Checkpoints and Write Workloads

Checkpoint spikes frequently appear in escalations involving high IOPS and latency.

Aggressive checkpoint activity can:

Generate excessive disk writes

Increase latency

Consume IOPS capacity

Monitoring checkpoint behavior and ensuring WAL parameters are properly configured can significantly improve write-intensive workloads. Azure intelligent tuning can assist in this area as well.

10.Metric Monitoring

Optimization should always be data-driven.

You should track the following:

CPU utilization

Memory pressure

Active Connections

Oldest Query

IOPS consumption

Combining Azure Metrics, Query Store, and PostgreSQL statistic views allows teams to distinguish between normal workload spikes and true performance degradation.

PostgreSQL statistics views provide valuable workload insights. For example, pg_stat_activity can be used to identify long-running or blocking queries, pg_stat_user_tables helps track dead tuples, vacuum activity, and statistics refreshes, while pg_stat_statements (if enabled) help identify the most resource-intensive queries by execution time and frequency.

Learn more about Metric here

Conclusion

Performance optimization in Azure Database for PostgreSQL flexible server is not just about changing a few parameters and hoping for better results. It requires a structured approach that combines workload understanding, proactive monitoring, proper sizing, query optimization, and platform-native capabilities.

By leveraging Query Store, PgBouncer, Intelligent Tuning, Autovacuum Monitoring, Azure Metrics, and Troubleshooting Guides, you can significantly improve database efficiency while reducing operational effort.