PostgreSQL Performance Tuning Tool

Known for its reliability, feature robustness, and high performance, PostgreSQL is a highly scalable, open-source database management system. Due to the amount of data and users it can accommodate, tuning PostgreSQL is critical for ensuring performance. To effectively optimize performance, database tuning often involves monitoring for deeper visibility into query trends.

Tuning with a PostgreSQL tuning tool can help support your efforts to gain insights more easily with the following features:

• Monitoring: Tuning starts with gathering information through monitoring, so you can more easily pinpoint impacted resources. This includes insight into CPU, I/O, memory, network, and other key factors.
• Troubleshooting and diagnostics: Look at configuration and authentication, as well as resource logs and write-ahead logs, to get more insight into where problems could be occurring. 
• Query tuning: You’ll need to find slow queries and rewrite them using best practices.
• Reporting and logging: By using reports and logs, it’s possible to more easily pinpoint when and where something went wrong, so you can tune effectively.

When tuning PostgreSQL, the following are common causes of performance issues and potential opportunities where tuning can help drive significant database performance improvements:

• Check your indexes: Look at the number of indexes you’ve defined on your table and determine whether their potential query benefits outweigh the storage and insert overhead. Every system is different, so the number of indexes is up to your best judgment.
• Avoid using UNIQUE keys and foreign key (FK) constraints: If a UNIQUE constraint is otherwise defined, the insert can necessitate an index lookup to determine if the row already exists, which will adversely impact the speed of your INSERT. Similarly, when you have an FK constraint, every INSERT will typically then need to read from your referenced table, which can degrade performance. Consider denormalizing your data instead.
• Reconsider how you’re using disks: Data stored to the WAL can lead to slow disks that impact insert performance. In addition, to avoid bottlenecks, you can increase throughput by using a separate disk (tablespace) for the database's write-ahead log (WAL) and data.

Using a Postgres query tuning tool, you can more easily evaluate whether a table or indexes may not be following PostgreSQL best practices. SolarWinds Database Performance Analyzer (DPA) includes a built-in table tuning advisor designed to list violations and recommend next steps. Some table tuning best practices you can more easily investigate and help solve for with SolarWinds DPA include:

• Determining whether a foreign key is indexed - Indexing each FK can help improve the performance of queries joining the two tables.
• Identifying overlapping indexes - Examining overlapping indexes can help you determine if any can be removed.
• Removing trailing edge columns - Since wide indexes (five or more columns) require more storage, they can increase the cost of index maintenance. 
• Adding defined indexes for the table - However, if the table is very small, an index might not be more efficient than a full table scan.
• Making sure the table has a primary key (PK) - A PK can uniquely identify a record and is necessary to ensure data integrity. If no column or combination of columns provides a unique value, you can add an artificial PK, such as an ID column.

Database tuning can help optimize database environments, so your applications can quickly access the data. Database tuning can also affect your ability to maintain database and application performance, data integrity, and controls.

Since PostgreSQL is an open-source database, it can often be complex and time-consuming to optimize performance. When building out PostgreSQL database queries and applications, you need a way to ensure optimization without the process being time-consuming or difficult.

PostgreSQL database tuning and related tools can provide you with the insights you need to tune more effectively and ultimately ensure more efficient implementations.

With a PostgreSQL tuning tool, database admins can observe query performance trends, view code and query changes, and gain insight into impacts from test and production databases using powerful features designed to help pinpoint issues quickly.

PostgreSQL tuning tools can provide “before and after” views of key database elements to allow you to compare performance, understand trends, and identify issues, such as:

• Metrics on system and database health, including operating system-level and DBMS-focused metrics
• Current snapshots and historical data
• Insight into code and query changes
• Information on impacts within test and production databases
• Alerts for when a system goes down or when various thresholds for latency, counts, or resource consumption are crossed

Using a PostgreSQL performance tuning tool with a query analyzer, like SolarWinds Database Performance Analyzer (DPA), can also let you more easily visualize critical Postgres metrics using multi-dimensional query analysis to pinpoint performance issues.

SolarWinds Database Performance Analyzer (DPA) is essential for effective PostgreSQL query tuning, providing comprehensive metrics to understand resource usage like CPU, disk, memory, and more. These metrics, including memory, disk, network, session, wait, row, vacuum, checkpoint, replication, and cache eviction metrics, illuminate the environment during slowdowns, aiding in root cause identification.

Accessing these metrics in DPA is intuitive: select a database instance, navigate to the Resources tab to view charts with adjustable time ranges, and hover over data points for annotations or VMware event details. Beyond metrics, DPA's table tuning advisor identifies tables affected by inefficient queries, offering insights into query performance and existing indexes. This aggregated information empowers informed decision-making for optimized PostgreSQL performance.

The Table Tuning Advisor is a tool designed to optimize database performance by analyzing and addressing inefficient queries. These queries often read a lot of data but return few results, leading to unnecessary I/O and resource strain. The Advisor aids in tuning such queries by offering insight into whether adjustments to queries, indexes, or plans are needed.

It provides an aggregated view of a table's workload, identifying high-cost SQL and plan steps for targeted optimization. The Advisor breaks down table usage patterns, index design, and transactional activity to inform strategic decisions.

To access the Advisor, you can use the "Tuning" super-tab or find it within the Query Performance Analysis page. It presents three main sections: Inefficient SQL, SQL and Plan Details, and Table and Index Information.

The Advisor highlights inefficient plan steps, like full table scans, suggesting focus areas for improvement. It also displays all queries hitting a table, prioritizing them by their impact on the workload.

The tool also considers the current state of table indexes, suggesting whether to add new indexes or modify existing ones based on data churn and other metadata, ensuring that any changes optimize performance without introducing new issues.

When you run PostgreSQL on a cloud computing platform, performance optimization and tuning are key, since you’re pay for compute resources and inefficient, poorly written queries can cost you money—not to mention the impact on front-end application performance.

DPA delivers a holistic approach to PostgreSQL performance management, including:

• Real-time and historical views
• 24/7 down-to-the-second data collection
• Simple, agentless implementation with less than 1% overhead
• Postgres-specific reporting, ad-hoc or scheduled
• Anomaly detection powered by machine learning
• Multi-dimensional query wait time analysis
• Query advisors to pinpoint problem areas
• Both system and PostgreSQL metrics
• Drill down to the SQL text and run live plans
• Custom drag-and-drop email alert template
• RESTful API for automation integration

DPA can be implemented on a physical server or VM, in Azure, or as service in AWS.