- Introduction
- Concepts
- Key Features
- Horizontal Scalability
- MySQL Compatible Syntax
- Replicate from and to MySQL
- Distributed Transactions with Strong Consistency
- Cloud Native Architecture
- Minimize ETL with HTAP
- Fault Tolerance & Recovery with Raft
- Automatic Rebalancing
- Deployment and Orchestration with Ansible, Kubernetes, Docker
- JSON Support
- Spark Integration
- Read Historical Data Without Restoring from Backup
- Fast Import and Restore of Data
- Hybrid of Column and Row Storage
- SQL Plan Management
- Open Source
- Online Schema Changes
- Key Features
- How-to
- Get Started
- Start a Local Cluster
- Deploy
- From Binary Tarball
- Orchestrated Deployment
- Geographic Redundancy
- Configure
- Secure
- Transport Layer Security (TLS)
- Monitor
- Migrate
- Migrate from MySQL
- Maintain
- Backup and Restore
- Scale
- Upgrade
- Get Started
- Reference
- SQL
- SQL Language Structure
- Data Types
- Numeric Types
- Date and Time Types
- String Types
- Functions and Operators
- Function and Operator Reference
- Type Conversion in Expression Evaluation
- Operators
- Control Flow Functions
- String Functions
- Numeric Functions and Operators
- Date and Time Functions
- Bit Functions and Operators
- Cast Functions and Operators
- Encryption and Compression Functions
- Information Functions
- JSON Functions
- Aggregate (GROUP BY) Functions
- Miscellaneous Functions
- Precision Math
- SQL Statements
ADD COLUMNADD INDEXADMINADMIN CANCEL DDLADMIN CHECKSUM TABLEADMIN CHECK [TABLE|INDEX]ADMIN SHOW DDL [JOBS|QUERIES]ALTER DATABASEALTER TABLEALTER USERANALYZE TABLEBEGINCHANGE COLUMNCOMMITCREATE DATABASECREATE INDEXCREATE TABLE LIKECREATE TABLECREATE USERDEALLOCATEDELETEDESCDESCRIBEDODROP COLUMNDROP DATABASEDROP INDEXDROP TABLEDROP USEREXECUTEEXPLAIN ANALYZEEXPLAINFLUSH PRIVILEGESFLUSH STATUSFLUSH TABLESGRANT <privileges>INSERTKILL [TIDB]LOAD DATALOAD STATSMODIFY COLUMNPREPARERENAME INDEXRENAME TABLEREPLACEREVOKE <privileges>ROLLBACKSELECTSET [NAMES|CHARACTER SET]SET PASSWORDSET TRANSACTIONSET [GLOBAL|SESSION] <variable>SHOW CHARACTER SETSHOW COLLATIONSHOW [FULL] COLUMNS FROMSHOW CREATE TABLESHOW DATABASESSHOW ENGINESSHOW ERRORSSHOW [FULL] FIELDS FROMSHOW GRANTSSHOW INDEXES [FROM|IN]SHOW INDEX [FROM|IN]SHOW KEYS [FROM|IN]SHOW PRIVILEGESSHOW [FULL] PROCESSSLISTSHOW SCHEMASSHOW STATUSSHOW [FULL] TABLESSHOW TABLE STATUSSHOW [GLOBAL|SESSION] VARIABLESSHOW WARNINGSSTART TRANSACTIONTRACETRUNCATEUPDATEUSE
- Configuration
- tidb-server
- pd-server
- tikv-server
- Security
- Transactions
- System Databases
- Performance
- Key Monitoring Metrics
- Best Practices
- TiDB Binlog
- Troubleshoot
- Tools
- TiDB Lightning
- sync-diff-inspector
- SQL
- FAQs
- Support
- Contribute
- Releases
- All Releases
- v2.1
- v2.0
- v1.0
- Glossary
TiDB Monitoring Framework Overview
The TiDB monitoring framework adopts two open source projects: Prometheus and Grafana. TiDB uses Prometheus to store the monitoring and performance metrics and Grafana to visualize these metrics.
About Prometheus in TiDB
As a time series database, Prometheus has a multi-dimensional data model and flexible query language. As one of the most popular open source projects, Prometheus has been adopted by many companies and organizations and has a very active community. PingCAP is one of the active developers and adopters of Prometheus for monitoring and alerting in TiDB, TiKV and PD.
Prometheus consists of multiple components. Currently, TiDB uses the following of them:
- The Prometheus Server to scrape and store time series data
- The client libraries to customize necessary metrics in the application
- An Alertmanager for the alerting mechanism
The diagram is as follows:
About Grafana in TiDB
Grafana is an open source project for analyzing and visualizing metrics. TiDB uses Grafana to display the performance metrics as follows:
