- Docs Home
- About TiDB Cloud
- Get Started
- Develop Applications
- Overview
- Quick Start
- Build a TiDB Developer Cluster
- CRUD SQL in TiDB
- Build a Simple CRUD App with TiDB
- Example Applications
- Connect to TiDB
- Design Database Schema
- Write Data
- Read Data
- Transaction
- Optimize
- Troubleshoot
- Reference
- Cloud Native Development Environment
- Manage Cluster
- Plan Your Cluster
- Create a TiDB Cluster
- Connect to Your TiDB Cluster
- Set Up VPC Peering Connections
- Use an HTAP Cluster with TiFlash
- Scale a TiDB Cluster
- Upgrade a TiDB Cluster
- Delete a TiDB Cluster
- Use TiDB Cloud API (Beta)
- Migrate Data
- Import Sample Data
- Migrate Data into TiDB
- Configure Amazon S3 Access and GCS Access
- Migrate from MySQL-Compatible Databases
- Migrate Incremental Data from MySQL-Compatible Databases
- Migrate from Amazon Aurora MySQL in Bulk
- Import or Migrate from Amazon S3 or GCS to TiDB Cloud
- Import CSV Files from Amazon S3 or GCS into TiDB Cloud
- Import Apache Parquet Files from Amazon S3 or GCS into TiDB Cloud
- Troubleshoot Access Denied Errors during Data Import from Amazon S3
- Export Data from TiDB
- Back Up and Restore
- Monitor and Alert
- Overview
- Built-in Monitoring
- Built-in Alerting
- Third-Party Monitoring Integrations
- Tune Performance
- Overview
- Analyze Performance
- SQL Tuning
- Overview
- Understanding the Query Execution Plan
- SQL Optimization Process
- Overview
- Logic Optimization
- Physical Optimization
- Prepare Execution Plan Cache
- Control Execution Plans
- TiKV Follower Read
- Coprocessor Cache
- Garbage Collection (GC)
- Tune TiFlash performance
- Manage User Access
- Billing
- Reference
- TiDB Cluster Architecture
- TiDB Cloud Cluster Limits and Quotas
- TiDB Limitations
- SQL
- Explore SQL with TiDB
- SQL Language Structure and Syntax
- SQL Statements
ADD COLUMN
ADD INDEX
ADMIN
ADMIN CANCEL DDL
ADMIN CHECKSUM TABLE
ADMIN CHECK [TABLE|INDEX]
ADMIN SHOW DDL [JOBS|QUERIES]
ALTER DATABASE
ALTER INDEX
ALTER TABLE
ALTER TABLE COMPACT
ALTER USER
ANALYZE TABLE
BATCH
BEGIN
CHANGE COLUMN
COMMIT
CHANGE DRAINER
CHANGE PUMP
CREATE [GLOBAL|SESSION] BINDING
CREATE DATABASE
CREATE INDEX
CREATE ROLE
CREATE SEQUENCE
CREATE TABLE LIKE
CREATE TABLE
CREATE USER
CREATE VIEW
DEALLOCATE
DELETE
DESC
DESCRIBE
DO
DROP [GLOBAL|SESSION] BINDING
DROP COLUMN
DROP DATABASE
DROP INDEX
DROP ROLE
DROP SEQUENCE
DROP STATS
DROP TABLE
DROP USER
DROP VIEW
EXECUTE
EXPLAIN ANALYZE
EXPLAIN
FLASHBACK TABLE
FLUSH PRIVILEGES
FLUSH STATUS
FLUSH TABLES
GRANT <privileges>
GRANT <role>
INSERT
KILL [TIDB]
MODIFY COLUMN
PREPARE
RECOVER TABLE
RENAME INDEX
RENAME TABLE
REPLACE
REVOKE <privileges>
REVOKE <role>
ROLLBACK
SELECT
SET DEFAULT ROLE
SET [NAMES|CHARACTER SET]
SET PASSWORD
SET ROLE
SET TRANSACTION
SET [GLOBAL|SESSION] <variable>
SHOW ANALYZE STATUS
SHOW [GLOBAL|SESSION] BINDINGS
SHOW BUILTINS
SHOW CHARACTER SET
SHOW COLLATION
SHOW [FULL] COLUMNS FROM
SHOW CREATE SEQUENCE
SHOW CREATE TABLE
SHOW CREATE USER
SHOW DATABASES
SHOW DRAINER STATUS
SHOW ENGINES
SHOW ERRORS
SHOW [FULL] FIELDS FROM
SHOW GRANTS
SHOW INDEX [FROM|IN]
SHOW INDEXES [FROM|IN]
SHOW KEYS [FROM|IN]
SHOW MASTER STATUS
SHOW PLUGINS
SHOW PRIVILEGES
SHOW [FULL] PROCESSSLIST
SHOW PROFILES
SHOW PUMP STATUS
SHOW SCHEMAS
SHOW STATS_HEALTHY
SHOW STATS_HISTOGRAMS
SHOW STATS_META
SHOW STATUS
SHOW TABLE NEXT_ROW_ID
SHOW TABLE REGIONS
SHOW TABLE STATUS
SHOW [FULL] TABLES
SHOW [GLOBAL|SESSION] VARIABLES
SHOW WARNINGS
SHUTDOWN
SPLIT REGION
START TRANSACTION
TABLE
TRACE
TRUNCATE
UPDATE
USE
WITH
- Data Types
- Functions and Operators
- Overview
- 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
- Locking Functions
- Information Functions
- JSON Functions
- Aggregate (GROUP BY) Functions
- Window Functions
- Miscellaneous Functions
- Precision Math
- Set Operations
- List of Expressions for Pushdown
- TiDB Specific Functions
- Clustered Indexes
- Constraints
- Generated Columns
- SQL Mode
- Table Attributes
- Transactions
- Views
- Partitioning
- Temporary Tables
- Cached Tables
- Character Set and Collation
- Read Historical Data
- System Tables
mysql
- INFORMATION_SCHEMA
- Overview
ANALYZE_STATUS
CLIENT_ERRORS_SUMMARY_BY_HOST
CLIENT_ERRORS_SUMMARY_BY_USER
CLIENT_ERRORS_SUMMARY_GLOBAL
CHARACTER_SETS
CLUSTER_INFO
COLLATIONS
COLLATION_CHARACTER_SET_APPLICABILITY
COLUMNS
DATA_LOCK_WAITS
DDL_JOBS
DEADLOCKS
ENGINES
KEY_COLUMN_USAGE
PARTITIONS
PROCESSLIST
REFERENTIAL_CONSTRAINTS
SCHEMATA
SEQUENCES
SESSION_VARIABLES
SLOW_QUERY
STATISTICS
TABLES
TABLE_CONSTRAINTS
TABLE_STORAGE_STATS
TIDB_HOT_REGIONS_HISTORY
TIDB_INDEXES
TIDB_SERVERS_INFO
TIDB_TRX
TIFLASH_REPLICA
TIKV_REGION_PEERS
TIKV_REGION_STATUS
TIKV_STORE_STATUS
USER_PRIVILEGES
VIEWS
- System Variables
- API Reference
- Storage Engines
- Dumpling
- Table Filter
- Troubleshoot Inconsistency Between Data and Indexes
- FAQs
- Release Notes
- Support
- Glossary
Avoid Implicit Type Conversions
This document introduces the rules and possible consequences of implicit type conversions in TiDB and how to avoid implicit type conversions.
Conversion rules
When the data types on the two sides of the predicate in a SQL statement do not match, TiDB implicitly convert the data types on one or both sides to a compatible data type for predicate operations.
The rules for implicit type conversion in TiDB are as follows:
- If one or both arguments are
NULL
, the result of the comparison isNULL
. The NULL-safe<=>
equivalent comparison operator does not require conversion, because NULL<=>
NULL results intrue
. - If both arguments in the comparison operation are strings, they are compared as strings.
- If both arguments are integers, they are compared as integers.
- If no comparison is made with numbers, the hexadecimal value is treated as a binary string.
- If one of the arguments is a decimal value, the comparison depends on the other argument. If the other argument is a decimal or integer value, the argument is compared with the decimal value. If the other argument is a floating-point value, the argument is compared with the floating-point value.
- If one of the arguments is a
TIMESTAMP
orDATETIME
column and the other argument is a constant, the constant is converted to a timestamp before the comparison is performed. - In all other cases, the arguments are compared as floating-point numbers (the
DOUBLE
type).
Consequences caused by implicit type conversion
Implicit type conversions increase the usability of human-computer interaction. However, avoid using implicit type conversions in application code, because they might lead to the following issues:
- Index invalidity
- Loss of precision
Index invalidity
In the following case, account_id
is the primary key and its data type is varchar
. In the execution plan, this SQL statement has an implicit type conversion and cannot use the index.
DESC SELECT * FROM `account` WHERE `account_id`=6010000000009801;
+-------------------------+----------------+-----------+---------------+------------------------------------------------------------+
| id | estRows | task | access object | operator info |
+-------------------------+----------------+-----------+---------------+------------------------------------------------------------+
| TableReader_7 | 8000628000.00 | root | | data:Selection_6 |
| └─Selection_6 | 8000628000.00 | cop[tikv] | | eq(cast(findpt.account.account_id), 6.010000000009801e+15) |
| └─TableFullScan_5 | 10000785000.00 | cop[tikv] | table:account | keep order:false |
+-------------------------+----------------+-----------+---------------+------------------------------------------------------------+
3 rows in set (0.00 sec)
Brief description of run results: From the above execution plan, the Cast
operator is visible.
Loss of precision
In the following case, the data type of the a
field is decimal(32,0)
. In the execution plan, an implicit type conversion occurs, and both the decimal field and the string constant are converted to the double type. Because the precision of the double type is not as high as decimal, there is a loss of precision. In this case, the SQL statement incorrectly filters the result set out of range.
DESC SELECT * FROM `t1` WHERE `a` BETWEEN '12123123' AND '1111222211111111200000';
+-------------------------+---------+-----------+---------------+-------------------------------------------------------------------------------------+
| id | estRows | task | access object | operator info |
+-------------------------+---------+-----------+---------------+-------------------------------------------------------------------------------------+
| TableReader_7 | 0.80 | root | | data:Selection_6 |
| └─Selection_6 | 0.80 | cop[tikv] | | ge(cast(findpt.t1.a), 1.2123123e+07), le(cast(findpt.t1.a), 1.1112222111111112e+21) |
| └─TableFullScan_5 | 1.00 | cop[tikv] | table:t1 | keep order:false, stats:pseudo |
+-------------------------+---------+-----------+---------------+-------------------------------------------------------------------------------------+
3 rows in set (0.00 sec)
Brief description of run results: From the above execution plan, the Cast
operator is visible.
SELECT * FROM `t1` WHERE `a` BETWEEN '12123123' AND '1111222211111111200000';
+------------------------+
| a |
+------------------------+
| 1111222211111111222211 |
+------------------------+
1 row in set (0.01 sec)
Brief description of run results: The above execution gives a wrong result.