Επεξεργασία

Κοινή χρήση μέσω

Recommendations for continuous performance optimization

  • Άρθρο

Applies to this Azure Well-Architected Framework Performance Efficiency checklist recommendation:

PE:12 Continuously optimize performance. Focus on components that show deteriorating performance over time, such as databases and networking features.

This guide describes the recommendations for continuous performance optimization. Continuous performance optimization is the process of constantly monitoring, analyzing, and improving performance efficiency. Performance efficiency adapts to increases and decreases in demand. Performance optimization needs to be an ongoing activity throughout the life of the workload. Workload performance often degrades or becomes excessive over time, and factors to consider include changes in usage patterns, demand, features, and technical debt.

Definitions

Term Definition
Data tiering A storage strategy that involves categorizing data based on its access frequency and storing it on storage tiers accordingly.
Technical debt The accumulated inefficiencies, suboptimal design choices, or shortcuts intentionally taken during the development process to deliver code faster.
Time-to-live A mechanism that sets an expiration time for data.

Key design strategies

Performance efficiency is when workload capacity aligns to actual usage. A workload that overperforms is as problematic as one that underperforms. The tradeoffs differ. Overperformance affects cost optimization. Poor performance affects users. The key to performance efficiency is monitoring, adjusting, and testing over time. You need to regularly review performance metrics and make adjustments as necessary to ensure that the workload is efficient. Testing all changes pre- and post-implementation is required to reach performance targets.

Develop a performance culture

A performance culture is an environment in which continuous improvement is expected and the team learns from production. Performance optimization requires specialized skills. Workload teams need the right skills and mindset to optimize their performance to meet increases and decreases in demand. You also need to allocate their time to support the required monitoring and remediation of performance issues as they arise. These teams need clear expectations. For example, performance targets, baselines, and deviation thresholds (how far from baseline is acceptable) need to be highly visible and socialized.

Tradeoff: Continuous performance optimizations require a team that has the right skills and time to find and fix performance issues. Dedicating personnel to performance adds operational cost. If you have limited personnel resources, continuous performance optimization could take time away from other operational tasks.

Evaluate new platform features

Evaluating new platform features involves examining the new functionalities and tools of a platform that can improve performance efficiency, such as optimized storage solutions, caching mechanisms, or resource management tools. New platform features can open avenues for enhancing performance efficiency. Keep your platform and tools up-to-date to ensure you're using the latest innovations and best practices. Consistently monitor feedback and performance metrics from these new additions to refine your approach.

Prioritize optimization efforts

Proactively optimizing performance means taking proactive measures to improve and enhance the performance of the workload before any performance issues arise. Using proactive measures involves identifying potential bottlenecks, monitoring performance metrics, and implementing optimizations to ensure that the workload operates efficiently and meets the desired performance goals. Based on the analysis of deteriorating components, critical flows, and technical debt, you can implement performance optimizations specific to each area. Improvements might involve code changes, infrastructure adjustments, or configuration updates.

Prioritize deteriorating components

A workload often has components such as databases and networking components that are prone to performance degradations over time. As the workload evolves and usage patterns change, these changes often affect the performance of individual components in the workload. Increased data in databases can lead to longer query run times and slower data retrieval. Changes in usage patterns might result in suboptimal query design. Queries that were once efficient can become inefficient as the workload evolves. Inefficient queries can consume excessive resources and degrade database performance. Increased workload usage can lead to higher network traffic, causing congestion and latency issues.

It's important to make continuous efforts to optimize the performance of these components. Proactively identify and address performance issues in your workload. By prioritizing known deteriorating components, you can proactively address potential performance issues and ensure the smooth operation of your workload. It might involve implementing performance tuning techniques, optimizing resource allocation, or upgrading hardware or software components as needed.

Prioritize critical flows

Critical flows are the most important and high-priority processes or workflows in the workload. By prioritizing these critical flows, you ensure that the most essential parts of the workload are optimized for performance. Knowing which flows are critical helps prioritize optimization efforts. Optimizing the performance efficiency of the most important areas of your application provides the highest return on investment. You should monitor critical flows and the most popular pages. Look for ways to make them more efficient.

Automate performance optimization

Automation can eliminate repetitive and time-consuming manual processes, allowing them to be performed efficiently. Automation reduces the chances of human error and ensures consistency in running optimization tasks. By automating these tasks, you can also free up people to focus on more complex activities and activities that add value. You can apply automation to various tasks, such as performance testing, deployment, and monitoring:

  • Automated performance testing: Use automated performance testing tools like JMeter, K6, or Selenium to simulate different workloads and scenarios.

  • Automated deployment: Implement automated deployment processes to ensure consistent and error-free deployments. Use CI/CD tools to automate the deployment process. These tools can help you identify performance bottlenecks as you use them to test against endpoints, check HTTP statuses, and even validate data quality and variations.

  • Monitoring and alerting: Set up automated monitoring and alerting systems to continuously monitor performance metrics and detect any deviations or anomalies. When performance issues are detected, automated alerts can be triggered to notify the appropriate teams or individuals.

  • Incident management: Implement an automated incident management system that can receive alerts, create tickets, and assign tickets to the appropriate teams for resolution. These steps help ensure that performance issues are promptly addressed and assigned to the right resources.

  • Automated diagnostics: Develop automated diagnostic tools or scripts that can analyze performance data and identify the root causes of performance issues. These tools can help pinpoint specific areas or components of the system that are causing performance problems.

  • Automated remediation actions: Define and implement automated remediation actions that can be triggered when specific performance issues are detected. These actions can include restarting services, adjusting resource allocation, clearing caches, or implementing other performance optimization techniques.

  • Self-healing systems: Build self-healing capabilities into your system by automating the recovery process for known performance issues. This capability can involve automatically fixing or adjusting the system configuration to restore optimal performance.

Address technical debt

Technical debt refers to the accumulated inefficiencies, suboptimal design choices, or shortcuts taken during the development process that can affect performance. Technical debt, unclear code, and overly complex implementations can make performance efficiency more difficult to attain. Addressing technical debt involves identifying and resolving these issues to improve the overall performance and maintainability of the workload. This work might include refactoring code, optimizing database queries, improving architectural design, or implementing best practices. Perhaps you introduced technical debt to meet a deadline, but you need to address the technical debt as you optimize performance efficiency over time.

Optimize databases

Continuously optimizing databases involves identifying and implementing optimizations to ensure that databases can handle loads, deliver fast response times, and minimize resource utilization. By regularly optimizing databases, you can improve application performance, reduce downtime, and enhance the overall user experience.

  • Optimize database queries: Poorly written SQL statements can degrade database performance. Inefficient JOIN conditions can cause unneeded data processing. Complex subqueries, nested queries, and excessive functions can reduce running speed. Queries that retrieve too much data should be rewritten. You should identify your most common or critical database queries and optimize them. The optimization helps ensure faster queries.

  • Maintain indexes: Evaluate your indexing strategy to ensure that indexes are properly designed and maintained. Index maintenance includes identifying unused or redundant indexes and creating indexes that align with the query patterns. Database indexes help accelerate data retrieval operations. For relational databases, you need to monitor index fragmentation. You should rebuild or reorganize indexes regularly. For nonrelational databases, you need to pick the correct indexing policy for your workload. Use automatic tuning on databases where available. These features include automatically creating missing indexes, dropping unused indexes, and plan correction. For more information, see Maintaining indexes to improve performance.

  • Review model design: Review the data model to ensure you optimize it for the specific requirements of the application. Improving query performance and data retrieval might involve denormalization, partitioning, or other techniques.

  • Optimize database configuration: Optimize database configuration settings such as memory allocation, disk I/O, and concurrency settings to maximize performance and resource utilization.

Optimize data efficiency

Optimizing data efficiency is the process of ensuring that data is stored, processed, and accessed in the most efficient way possible. Data tiering and using time-to-live (TTL) are techniques that can be used to optimize data efficiency. You can apply these techniques in various data storage scenarios, such as databases, file systems, or object storage.

  • Use data tiering: Data tiering involves categorizing data based on its importance or frequency of access and storing data in different tiers accordingly. Setting up data tiering allows for more efficient use of storage resources and improves performance. Frequently accessed or critical data can be stored in high-performance tiers, while less frequently accessed or less critical data can be stored in lower-cost tiers. The goal is to review data usage over time to ensure data is in the correct tier. As data priorities change, data should move from one tier to another.

  • Implement time-to-live: Time-to-live is a mechanism that sets an expiration time for data. Time-to-live allows data to be automatically deleted or archived after a certain period, reducing storage requirements and improving data management. By setting an appropriate time-to-live, you allow unnecessary data to be removed, freeing up storage space and improving overall efficiency. Session data, temporary files, and cache data are frequent targets for the time-to-live. Database entries can also have a time-to-live.

Risk: A time-to-live that's too short can create performance issues.

Azure facilitation

Automating performance optimization: Azure Advisor provides automatic performance recommendations based on workload telemetry. You should review and address these recommendations regularly. Azure Monitor provides real-time insights into the performance of your system and allows you to set up alerts based on specific performance metrics. Azure Log Analytics provides automated diagnostics and analysis on collected logs and metrics. Tools like Azure Application Insights provide insights and recommendations for optimizing performance.

To automate remediation, use automation tools or scripts to execute remediation actions automatically when the alerts are triggered. You can use Azure Automation, Azure Functions, or custom automation solutions.

Azure lets performance testing to simulate different user scenarios and workloads. Automated testing can help you identify performance bottlenecks and optimize your system accordingly. Tools like Azure DevOps can automate performance testing.

Optimizing databases: The SQL family of products has many built-in features that allow you to monitor and remediate SQL database performance. You should use these features to maintain database performance. Azure SQL Database has an automatic tuning feature that continuously monitors and improves queries. You should use this feature to improve SQL queries automatically.

You can customize your indexing policies by using the features of Azure Cosmos DB. Customize the policies to meet the performance needs of your workload.

Optimizing data efficiency: Data tiering allows you to store data in different tiers based on its access frequency and importance. It helps optimize storage costs and performance. Azure provides different storage tiers, such as hot, cool, and archive tiers for blob data. Hot tiers are optimized for frequently accessed data, cool tiers are for infrequently accessed data, and archive tiers are for rarely accessed data. By using the storage access tier best suited to your data, you can ensure efficient data storage and retrieval.

Performance Efficiency checklist

Refer to the complete set of recommendations.

Performance Efficiency checklist