Cookie Consent by Free Privacy Policy Generator 📌 Query Store Part 2- Regression Detection and Execution Plan Analysis: A Guide


✅ Query Store Part 2- Regression Detection and Execution Plan Analysis: A Guide


💡 Newskategorie: Programmierung
🔗 Quelle: dev.to

In the realm of SQL Server performance tuning, the Query Store is an indispensable feature that serves as a flight data recorder, providing a wealth of information about query execution and performance. This article explores how to leverage the Query Store to detect query regressions and how to compare and choose the right execution plan to ensure optimal performance.

Step 1: Detecting Query Regressions

A regression occurs when a query's performance degrades over time. To detect regressions, follow these steps:

  1. Determine the Metrics: Decide which performance metrics are crucial for your environment, be it CPU time, duration, logical reads, or another metric.

  2. Choose the Aggregation Method: While the average can provide a general idea of performance, using the maximum value of the metric will help you pinpoint the worst-case scenarios, which are often the most critical to address.

  3. Define Time Intervals: Select appropriate "Recent" and "History" intervals for comparison, making sure they do not overlap to avoid conflating data.

  4. Analyze the Data: Use the Query Store reports in SSMS to identify queries with significant regressions based on your chosen metrics.

Step 2: Comparing Execution Plans

Once you've identified a regressed query, compare its execution plans:

  1. Locate the Plans: In the Query Store UI, find the execution plans for the query of interest.

  2. Plan Analysis: Look for differences in the plans. Are there changes in join types, index usage, or does one plan involve parallelism while the other does not?

  3. Performance Metrics Review: Examine the runtime statistics for each plan, paying attention to metrics such as duration, CPU time, and memory consumption.

Step 3: Choosing the Right Plan

Choosing the correct plan requires a careful analysis of the plans available:

  1. Identify Stable Plans: Look for plans that show consistent performance over time.

  2. Avoid Outliers: Be wary of plans that only show good performance under specific conditions or at certain times.

  3. Consider All Variables: Think about the data distribution, parameterization, and whether the plan is likely to remain optimal as the database grows or as usage patterns change.

  4. Test in a Non-Production Environment: If possible, simulate workloads and test the chosen plan in a controlled environment.

Step 4: Forcing a Plan

If you decide that a specific plan is best for the query, you can force that plan using Query Store:

  1. Force the Plan: In the Query Store UI, you can select the desired plan and click "Force Plan" to ensure that SQL Server uses this plan for future executions.

  2. Monitor the Outcome: After forcing a plan, monitor the query's performance to ensure that it has improved and no new regressions occur.

  3. Be Prepared to Revert: If the forced plan does not deliver the expected improvements, be ready to unforce the plan and reassess.

Considerations and Best Practices

  • Regular Monitoring: The dynamics of a database can change. Regularly review Query Store data to stay ahead of potential issues.

  • Comprehensive Analysis: Remember that forcing a plan is a temporary measure. Always seek to understand and address the root cause of the regression.

  • Plan Forcing Alternatives: Compare Query Store plan forcing with other SQL Server features like plan guides or the USE PLAN hint to determine the best approach for your situation.

Conclusion

Query Store is an essential tool for any DBA looking to manage query performance in SQL Server. By understanding how to detect regressions, compare execution plans, and judiciously force plans, you can ensure your database runs efficiently, providing fast and reliable data access to your users. Keep your skills sharp, and your SQL Server will be all the better for it.

...

✅ CodeSOD: Query Query Query


📈 31.66 Punkte

✅ How SQL Query works? SQL Query Execution Order for Tech Interview


📈 25.84 Punkte

✅ Malware Analysis: Agent Tesla Part 2/2 Final Payload Analysis


📈 22.23 Punkte

✅ Efficient Object Detection with SSD and YoLO Models — A Comprehensive Beginner’s Guide (Part 3)


📈 21.89 Punkte

✅ Trend Micro ScanMail for Exchange 12.0 Log Query/Quarantine Query Cross Site Scripting


📈 21.1 Punkte

✅ Trend Micro ScanMail for Exchange 12.0 Log Query/Quarantine Query cross site scripting


📈 21.1 Punkte

✅ More DNS over HTTPS: Become One With the Packet. Be the Query. See the Query, (Thu, Dec 19th)


📈 21.1 Punkte

✅ Nelson Open Source ERP 6.3.1 db/utils/query/data.xml query sql injection


📈 21.1 Punkte

✅ GitHub Security Lab: ihsinme: CPP add query for: CPP Add query for CWE-20 Improper Input Validation


📈 21.1 Punkte

✅ CVE-2016-15020 | liftkit database up to 2.13.1 src/Query/Query.php processOrderBy sql injection


📈 21.1 Punkte

✅ TANStack Query: How It Changes the Way You Query APIs


📈 21.1 Punkte

✅ What is react-query? Why should we use react-query?


📈 21.1 Punkte

✅ New Css Media Query Syntax(Media Query Level 4 or MQ4)


📈 21.1 Punkte

✅ Catch me if you can: detection of injection exploitation by validating query and API integrity


📈 20.42 Punkte

✅ Object Detection Basics — A Comprehensive Beginner’s Guide (Part 1)


📈 20.36 Punkte

✅ Exploring Object Detection with R-CNN Models — A Comprehensive Beginner’s Guide (Part 2)


📈 20.36 Punkte

✅ MySQL Performance Monitoring and Query Analysis


📈 20.03 Punkte

✅ Network Detection and Response (NDR) vs. Endpoint Detection and Response (EDR): A Comparison


📈 19.73 Punkte

✅ Google Adds New Developer Fees As Part of Play Store's DMA Compliance Plan


📈 19.39 Punkte

✅ Outlier Detection Using Principal Component Analysis and Hotelling’s T2 and SPE/DmodX Methods


📈 19.34 Punkte

✅ Low CVE-2020-11499: Firmware analysis and comparison tool project Firmware analysis and comparison tool


📈 18.95 Punkte

✅ GitHub Security Lab: Java: Fix NashornScriptEngine detection in ScriptEngine query


📈 18.88 Punkte

✅ 🚀 Boost Your Laravel Performance with Real-Time Laravel N+1 Query Detection! 🛠️


📈 18.88 Punkte

✅ Breaking Down the Chrome Web Store: An exploratory analysis of extensions (part 1)


📈 18.84 Punkte

✅ US Mobile Unlimited All plan hands-on and Pooled Plan launch


📈 18.53 Punkte

✅ The key differences between a business continuity plan and a disaster recovery plan


📈 18.53 Punkte

✅ Plan and Non-plan Expenditure


📈 18.53 Punkte

✅ Can Machines Plan Like Us? NATURAL PLAN Sheds Light on the Limits and Potential of Large Language Models


📈 18.53 Punkte











matomo

Datei nicht gefunden!