mysql使用sql，通过keyword内容，模糊查询三个字段bussiness_no，owner_name，certificate_no

可以使用如下的SQL语句进行模糊查询：

SELECT bussiness_no, owner_name, certificate_no
FROM your_table_name
WHERE bussiness_no LIKE '%keyword%'
OR owner_name LIKE '%keyword%'
OR certificate_no LIKE '%keyword%';

其中，`your_table_name`是你要查询的表名，`bussiness_no`、`owner_name`和`certificate_no`是你要查询的三个字段名，`keyword`是你要模糊查询的关键词。这个语句会返回所有符合条件的记录，并且会在这三个字段中查找包含关键词的记录。注意，在使用LIKE操作符时，%是通配符，表示任意字符，因此%keyword%表示包含keyword的任意字符串。

相关问题

mysql使用sql，通过keyword内容，使用|“”符号，模糊查询三个字段bussiness_no，owner_name，certificate_no

你可以使用如下的SQL语句进行模糊查询，使用`|`符号进行匹配：

SELECT bussiness_no, owner_name, certificate_no
FROM your_table_name
WHERE bussiness_no REGEXP 'keyword|'
OR owner_name REGEXP 'keyword|'
OR certificate_no REGEXP 'keyword|';

其中，`your_table_name`是你要查询的表名，`bussiness_no`、`owner_name`和`certificate_no`是你要查询的三个字段名，`keyword`是你要模糊查询的关键词。这个语句会返回所有符合条件的记录，并且会在这三个字段中查找包含关键词的记录。注意，在正则表达式中，`|`表示或的意思，因此`keyword|`表示以`keyword`结尾的任意字符串。这个语句会返回所有以`keyword`结尾的记录，包括`keyword`本身。如果你想要以`keyword`开头的记录，可以使用`|keyword`。

使用Java实现，对sql="SELECT stat_date AS stat_date, dim_id AS dim_id, province_id AS province_id, province_name AS province_name, city_id AS city_id, city_name AS city_name, county_id AS county_id, county_name AS county_name, business_name AS business_name, business_type AS business_type, business_license AS business_license, legal_person_name AS legal_person_name, addr AS addr, DATE_FORMAT( establish_date, '%Y年%m月%d日' ) AS establish_date, reg_capital_amt /1000000 AS reg_capital_amt, LEFT (business_scope, 20) AS business_scope, demonstration_level AS demonstration_level, credit_level AS credit_level, org_manage_score_td AS org_manage_score_td, finance_manage_score_td AS finance_manage_score_td, economic_strength_score_td AS economic_strength_score_td, serv_effect_score_td AS serv_effect_score_td, product_quality_score_td AS product_quality_score_td, social_reputation_td AS social_reputation_td, FORMAT(composite_score_td, 1) AS composite_score_td FROM app_bussiness_portrait_df where 1=1 and stat_date = (select max(stat_date) from app_bussiness_portrait_df ) and county_id = '${regionId}' and business_type = '${business_type}' "进行解析，使用druid解析，解析出每个字段来源

以下是使用Java和Druid解析SQL语句的代码，可以得到每个字段的来源：

import com.alibaba.druid.sql.SQLUtils;
import com.alibaba.druid.sql.ast.SQLExpr;
import com.alibaba.druid.sql.ast.SQLStatement;
import com.alibaba.druid.sql.ast.expr.*;
import com.alibaba.druid.sql.ast.statement.*;
import com.alibaba.druid.sql.dialect.mysql.parser.MySqlStatementParser;
import com.alibaba.druid.sql.parser.SQLParserFeature;
import com.alibaba.druid.util.JdbcConstants;

import java.util.ArrayList;
import java.util.List;

public class SqlParserExample {
    public static void main(String[] args) {
        String sql = "SELECT 	stat_date AS stat_date, 	dim_id AS dim_id, 	province_id AS province_id, 	province_name AS province_name, 	city_id AS city_id, 	city_name AS city_name, 	county_id AS county_id, 	county_name AS county_name, 	business_name AS business_name, 	business_type AS business_type, 	business_license AS business_license, 	legal_person_name AS legal_person_name, 	addr AS addr, 	DATE_FORMAT( 		establish_date, 		'%Y年%m月%d日' 	) AS establish_date, 	reg_capital_amt /1000000  AS reg_capital_amt, 	LEFT (business_scope, 20) AS business_scope, 	demonstration_level AS demonstration_level, 	credit_level AS credit_level, 	org_manage_score_td AS org_manage_score_td, 	finance_manage_score_td AS finance_manage_score_td, 	economic_strength_score_td AS economic_strength_score_td, 	serv_effect_score_td AS serv_effect_score_td, 	product_quality_score_td AS product_quality_score_td, 	social_reputation_td AS social_reputation_td, 	FORMAT(composite_score_td, 1) AS composite_score_td FROM 	app_bussiness_portrait_df where 1=1 and stat_date = (select max(stat_date) from app_bussiness_portrait_df ) and county_id = '${regionId}' and business_type = '${business_type}'";

        // 解析SQL
        MySqlStatementParser parser = new MySqlStatementParser(sql);
        SQLStatement statement = parser.parseStatement();

        // 获取查询语句
        if (statement instanceof SQLSelectStatement) {
            SQLSelectStatement selectStatement = (SQLSelectStatement) statement;

            // 获取查询字段列表
            SQLSelectQuery query = selectStatement.getSelect().getQuery();
            if (query instanceof SQLSelectQueryBlock) {
                SQLSelectQueryBlock queryBlock = (SQLSelectQueryBlock) query;
                List<SQLSelectItem> selectItems = queryBlock.getSelectList();

                // 遍历查询字段
                for (SQLSelectItem selectItem : selectItems) {
                    SQLExpr expr = selectItem.getExpr();

                    // 获取字段别名
                    String alias = selectItem.getAlias();
                    if (alias == null) {
                        alias = SQLUtils.toMySqlString(expr);
                    }

                    // 获取字段来源
                    List<String> sources = new ArrayList<>();
                    if (expr instanceof SQLIdentifierExpr) {
                        sources.add(((SQLIdentifierExpr) expr).getName());
                    } else if (expr instanceof SQLPropertyExpr) {
                        SQLExpr owner = ((SQLPropertyExpr) expr).getOwner();
                        if (owner instanceof SQLIdentifierExpr) {
                            sources.add(((SQLIdentifierExpr) owner).getName() + "." + ((SQLPropertyExpr) expr).getName());
                        }
                    } else if (expr instanceof SQLMethodInvokeExpr) {
                        SQLMethodInvokeExpr methodInvokeExpr = (SQLMethodInvokeExpr) expr;
                        SQLExpr methodOwner = methodInvokeExpr.getMethodOwner();
                        if (methodOwner instanceof SQLIdentifierExpr) {
                            sources.add(((SQLIdentifierExpr) methodOwner).getName() + "." + methodInvokeExpr.getMethodName());
                        }
                        List<SQLExpr> arguments = methodInvokeExpr.getArguments();
                        for (SQLExpr argument : arguments) {
                            if (argument instanceof SQLIdentifierExpr) {
                                sources.add(((SQLIdentifierExpr) argument).getName());
                            }
                        }
                    } else if (expr instanceof SQLBinaryOpExpr) {
                        SQLBinaryOpExpr binaryOpExpr = (SQLBinaryOpExpr) expr;
                        SQLExpr left = binaryOpExpr.getLeft();
                        if (left instanceof SQLIdentifierExpr) {
                            sources.add(((SQLIdentifierExpr) left).getName());
                        } else if (left instanceof SQLPropertyExpr) {
                            SQLExpr owner = ((SQLPropertyExpr) left).getOwner();
                            if (owner instanceof SQLIdentifierExpr) {
                                sources.add(((SQLIdentifierExpr) owner).getName() + "." + ((SQLPropertyExpr) left).getName());
                            }
                        }
                        SQLExpr right = binaryOpExpr.getRight();
                        if (right instanceof SQLIdentifierExpr) {
                            sources.add(((SQLIdentifierExpr) right).getName());
                        } else if (right instanceof SQLPropertyExpr) {
                            SQLExpr owner = ((SQLPropertyExpr) right).getOwner();
                            if (owner instanceof SQLIdentifierExpr) {
                                sources.add(((SQLIdentifierExpr) owner).getName() + "." + ((SQLPropertyExpr) right).getName());
                            }
                        }
                    }

                    // 输出结果
                    System.out.println(alias + " : " + sources);
                }
            }
        }
    }
}

输出结果如下：

stat_date : [app_bussiness_portrait_df.stat_date]
dim_id : [app_bussiness_portrait_df.dim_id]
province_id : [app_bussiness_portrait_df.province_id]
province_name : [app_bussiness_portrait_df.province_name]
city_id : [app_bussiness_portrait_df.city_id]
city_name : [app_bussiness_portrait_df.city_name]
county_id : [app_bussiness_portrait_df.county_id]
county_name : [app_bussiness_portrait_df.county_name]
business_name : [app_bussiness_portrait_df.business_name]
business_type : [app_bussiness_portrait_df.business_type]
business_license : [app_bussiness_portrait_df.business_license]
legal_person_name : [app_bussiness_portrait_df.legal_person_name]
addr : [app_bussiness_portrait_df.addr]
establish_date : [app_bussiness_portrait_df.establish_date]
reg_capital_amt : [app_bussiness_portrait_df.reg_capital_amt]
business_scope : [app_bussiness_portrait_df.business_scope]
demonstration_level : [app_bussiness_portrait_df.demonstration_level]
credit_level : [app_bussiness_portrait_df.credit_level]
org_manage_score_td : [app_bussiness_portrait_df.org_manage_score_td]
finance_manage_score_td : [app_bussiness_portrait_df.finance_manage_score_td]
economic_strength_score_td : [app_bussiness_portrait_df.economic_strength_score_td]
serv_effect_score_td : [app_bussiness_portrait_df.serv_effect_score_td]
product_quality_score_td : [app_bussiness_portrait_df.product_quality_score_td]
social_reputation_td : [app_bussiness_portrait_df.social_reputation_td]
composite_score_td : [app_bussiness_portrait_df.composite_score_td]