- 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
- Get Started
- MySQL Compatibility
- SQL Language Structure
- Data Types
- Functions and Operators
- Function and Operator Reference
- Type Conversion in Expression Evaluation
- 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
- Window Functions
- Miscellaneous Functions
- Precision Math
- List of Expressions for Pushdown
- SQL Statements
ADMIN CANCEL DDL
ADMIN CHECKSUM TABLE
ADMIN CHECK [TABLE|INDEX]
ADMIN SHOW DDL [JOBS|QUERIES]
CREATE TABLE LIKE
SET DEFAULT ROLE
SET [NAMES|CHARACTER SET]
SET [GLOBAL|SESSION] <variable>
SHOW ANALYZE STATUS
SHOW CHARACTER SET
SHOW [FULL] COLUMNS FROM
SHOW CREATE TABLE
SHOW CREATE USER
SHOW [FULL] FIELDS FROM
SHOW INDEXES [FROM|IN]
SHOW INDEX [FROM|IN]
SHOW KEYS [FROM|IN]
SHOW [FULL] PROCESSSLIST
SHOW [FULL] TABLES
SHOW TABLE REGIONS
SHOW TABLE STATUS
SHOW [GLOBAL|SESSION] VARIABLES
- Generated Columns
- Character Set
- SQL Mode
- System Databases
- Errors Codes
- Supported Client Drivers
- Garbage Collection (GC)
- Understanding the Query Execution Plan
- The Blocklist of Optimization Rules and Expression Pushdown
- Introduction to Statistics
- TopN and Limit Push Down
- Optimizer Hints
- Check the TiDB Cluster Status Using SQL Statements
- Execution Plan Binding
- Statement Summary Table
- Tune TiKV
- Operating System Tuning
- Column Pruning
- Key Monitoring Metrics
- Alert Rules
- Best Practices
- TiDB Binlog
- Use Cases
- TiDB Operator
- Table Filter
- TiDB Data Migration
- TiDB Lightning
- PD Control
- PD Recover
- TiKV Control
- TiDB Control
- TiDB in Kubernetes
- All Releases
You are viewing the documentation of an older version of the TiDB database (TiDB v3.0).
As a NewSQL database, TiDB excels in the best features of the traditional relational database and the scalability of the NoSQL database and is of course, highly available across data centers (hereinafter referred to as DC). This document is to introduce different deployment solutions in cross-DC environment.
TiDB, TiKV and PD are distributed among 3 DCs, which is the most common deployment solution with the highest availability.
All the replicas are distributed among 3 DCs. Even if one DC is down, the other 2 DCs will initiate leader election and resume service within a reasonable amount of time (within 20s in most cases) and no data is lost. See the following diagram for more information:
The performance is greatly limited by the network latency.
- For writes, all the data has to be replicated to at least 2 DCs. Because TiDB uses 2-phase commit for writes, the write latency is at least twice the latency of the network between two DCs.
- The read performance will also suffer if the leader is not in the same DC as the TiDB node with the read request.
- Each TiDB transaction needs to obtain TimeStamp Oracle (TSO) from the PD leader. So if TiDB and PD leader are not in the same DC, the performance of the transactions will also be impacted by the network latency because each transaction with write request will have to get TSO twice.
If not all of the three DCs need to provide service to the applications, you can dispatch all the requests to one DC and configure the scheduling policy to migrate all the TiKV Region leader and PD leader to the same DC, as what we have done in the following test. In this way, neither obtaining TSO or reading TiKV Regions will be impacted by the network latency between DCs. If this DC is down, the PD leader and Region leader will be automatically elected in other surviving DCs, and you just need to switch the requests to the DC that are still online.
This solution is similar to the previous 3-DC deployment solution and can be considered as an optimization based on the business scenario. The difference is that the distance between the 2 DCs within the same city is short and thus the latency is very low. In this case, we can dispatch the requests to the two DCs within the same city and configure the TiKV leader and PD leader to be in the 2 DCs in the same city.
Compared with the 3-DC deployment, the 3-DC in 2 cities deployment has the following advantages:
- Better write performance.
- Better usage of the resources because 2 DCs can provide services to the applications.
- Even if one DC is down, the TiDB cluster will be still available and no data is lost.
However, the disadvantage is that if the 2 DCs within the same city goes down, whose probability is higher than that of the outage of 2 DCs in 2 cities, the TiDB cluster will not be available and some of the data will be lost.
The 2-DC + Binlog replication is similar to the MySQL Source-Replica solution. 2 complete sets of TiDB clusters (each complete set of the TiDB cluster includes TiDB, PD and TiKV) are deployed in 2 DCs, one acts as the primary and one as the secondary. Under normal circumstances, the primary DC handles all the requests and the data written to the primary DC is asynchronously written to the secondary DC via Binlog.
If the primary DC goes down, the requests can be switched to the secondary cluster. Similar to MySQL, some data might be lost. But different from MySQL, this solution can ensure the high availability within the same DC: if some nodes within the DC are down, the online workloads won’t be impacted and no manual efforts are needed because the cluster will automatically re-elect leaders to provide services.
Some of our production users also adopt the 2-DC multi-active solution, which means:
- The application requests are separated and dispatched into 2 DCs.
- Each DC has 1 cluster and each cluster has two databases: A primary database to serve part of the application requests and a secondary database to act as the backup of the other DC’s primary database. Data written into the primary database is replicated via Binlog to the secondary database in the other DC, forming a loop of backup.
Please be noted that for the 2-DC + Binlog replication solution, data is asynchronously replicated via Binlog. If the network latency between 2 DCs is too high, the data in the secondary cluster will fall much behind of the primary cluster. If the primary cluster goes down, some data will be lost and it cannot be guaranteed the lost data is within 5 minutes.
For the 3-DC deployment solution and 3-DC in 2 cities solution, we can guarantee that the cluster will automatically recover, no human interference is needed and that the data is strongly consistent even if any one of the 3 DCs goes down. All the scheduling policies are to tune the performance, but availability is the top 1 priority instead of performance in case of an outage.
For 2-DC + Binlog replication solution, we can guarantee that the cluster will automatically recover, no human interference is needed and that the data is strongly consistent even if any some of the nodes within the primary cluster go down. When the entire primary cluster goes down, manual efforts will be needed to switch to the secondary and some data will be lost. The amount of the lost data depends on the network latency and is decided by the network condition.