tencent cloud

TDSQL Boundless

Release Notes
Product Introduction
Overview
Scenarios
Product Architecture
Instance Types
Compatibility Notes
Kernel Features
Kernel Overview
Kernel Version Release Notes
Functionality Features
Performance Features
Billing
Billing Overview
Purchase Method
Pricing Details
Renewal
Overdue Payments
Refund
Getting Started
Creating an Instance
Connect to Instances
User Guide
Data Migration
Data Subscription
Instance Management
Configuration Change
Parameter Configuration
Account Management
Security Group
Backup and Restoration
Database Auditing
Tag Management
Use Cases
Technical Evolution and Usage Practices of Online DDL
Lock Mechanism Analysis and Troubleshooting Practices
Data Intelligent Scheduling and Related Practices for Performance Optimization
TDSQL Boundless Selection Guide and Practical Tutorial
Developer Guide
Developer Guide (MySQL Compatibility Mode)
Developer Guide (HBase Compatibility Mode)
Performance Tuning
Performance Tuning Overview
SQL Tuning
DDL Tuning
Performance White Paper
Performance Overview
TPC-C Test
Sysbench Test
API Documentation
History
Introduction
API Category
Making API Requests
Instance APIs
Security Group APIs
Task APIs
Backup APIs
Rollback APIs
Parameter APIs
Database APIs
Data Types
Error Codes
General Reference
System Architecture
SQL Reference
Database Parameter Description
TPC-H benchmark data model reference
Error Code Information
Security and Compliance
FAQs
Agreements
Service Level Agreement
Terms of Service
Privacy Policy
Data Processing And Security Agreement
Contact Us
Glossary
문서TDSQL BoundlessPerformance TuningPerformance Tuning Overview

Performance Tuning Overview

PDF
포커스 모드
폰트 크기
마지막 업데이트 시간: 2026-03-23 16:01:41

Performance Tuning Objectives

The ultimate goal of database system performance tuning is to fully utilize server hardware and software resources, enabling database software to deliver efficient data services. Specific metrics include:
QPS/TPS (Queries Per Second/Transactions Per Second): measure the system's request processing capability.
RT (Response Time): refers to the time required for a request to be processed from initiation to completion.
Improving QPS/TPS can fully utilize single-server resources, enhance cost-effectiveness, and reduce the Total Cost of Ownership (TCO). Reducing RT enhances user experience while further boosting the system's processing capability.

Performance Tuning Steps

Performance tuning is typically divided into three key steps:
1. Determine optimization direction: Analyze the current system's load characteristics and business requirements to clarify whether the primary focus is on increasing throughput or reducing latency.
2. Identify bottlenecks: Through monitoring tools and execution plan analysis, identify performance bottlenecks in the system, such as high CPU utilization, memory shortage, network latency, or hotspot row contention.
3. Develop optimization solutions: Take corresponding measures based on the causes of bottlenecks, such as adjusting parameter configurations, optimizing SQL execution paths, or modifying data distribution policies.

System-Level Performance Tuning

System tuning focuses on the operational efficiency of the entire system rather than the performance of individual SQL. The primary approaches include:
Comprehensively analyzing the execution plans of multiple SQL statements and system load characteristics;
Focus on global issues, such as:
hotspot row contention
Buffer Cache hit ratio
Reasonableness of partition table design
Optimize by adjusting access paths, execution order, logical restructuring, and other methods;
When the number of partitions is large, consider increasing query parallelism for higher performance, but requires careful consideration of resource consumption.


도움말 및 지원

문제 해결에 도움이 되었나요?

피드백