SQL Parser

经过不断迭代，已经解决了很多 hive解析的bug，比如 2020年的create tablebug
支持的db type 多，impala ,hive ，oracle 等等都支持 。
缺点就是捆绑销售，1个jar 包高大全的 datasource 全家桶。我们只是想要 parser 而已。
老版本的 sql 解析会有bug，但是新版本的已经解决。测试了 一些复杂 语句，都能正确 解析。
对比 hive 本身的 parser ast 的操作，还需要自己分析 token，实在是太那个了。
二者对比参考代码

    /*** hive-sql-parser versus druid-sql-parser */@Testpublic void testDruidSqlParser() throws ParseException {String sql = "FROM (SELECT p.datekey datekey, p.userid userid, c.clienttype  FROM detail.usersequence_client c JOIN fact.orderpayment p ON p.orderid = c.orderid  JOIN default.user du ON du.userid = p.userid WHERE p.datekey = 20131118 ) base  INSERT OVERWRITE TABLE `test`.`customer_kpi` SELECT base.datekey,   base.clienttype, count(distinct base.userid) buyer_count GROUP BY base.datekey, base.clienttype";final SQLStatementParser hive = SQLParserUtils.createSQLStatementParser(sql, DbType.hive);final SQLStatement statement = hive.parseStatement();System.out.println(statement);// as you can see , using this parseDriver will cause an errorParseDriver pd = new ParseDriver();ASTNode ast = pd.parse(sql);System.out.println(ast.dump());}/*** @return 解析树*/public static List<SQLStatement> parseStatements(String sql, String dbType) {try {return SQLUtils.parseStatements(sql, dbType);} catch (Exception e) {log.error(e.getMessage(), e);throw new RuntimeException("SQL格式错误");}}/** sql格式化*/public static void format(String sql, String dbType) {String sqlFormat = SQLUtils.format(sql, dbType);if (sql.equals(sqlFormat)) {throw new RuntimeException("SQL格式错误");}}

[Spark] https://spark.apache.org/docs/1.3.1/api/java/org/apache/spark/sql/SparkSQLParser.html

ChatGPT 生成case

<dependency><groupId>org.apache.spark</groupId><artifactId>spark-sql_2.12</artifactId><version>3.1.2</version>
</dependency>import org.apache.spark.sql.catalyst.AbstractSparkSQLParser;
import org.apache.spark.sql.catalyst.analysis.SimpleAnalyzer;
import org.apache.spark.sql.catalyst.parser.SqlParser;
import org.apache.spark.sql.catalyst.parser.SqlParserBase;
import org.apache.spark.sql.catalyst.symbols.Column;
import org.apache.spark.sql.catalyst.symbols.Symbol;import java.util.List;public class Main {public static void main(String[] args) {String sql = "SELECT a.id, b.name FROM table1 a JOIN table2 b ON a.id = b.id WHERE a.status = 'active'";// 创建 AbstractSparkSQLParser 实例AbstractSparkSQLParser parser = new AbstractSparkSQLParser(sql);// 解析 SQL 查询SqlParser sqlParser = parser.parseQuery();// 获取 SELECT 字段List<Symbol> selectList = sqlParser.getSelectList();// 打印 SELECT 字段for (Symbol symbol : selectList) {if (symbol instanceof Column) {System.out.println(((Column) symbol).getColumnName());}}// 获取 FROM 表List<Symbol> fromList = sqlParser.getFromList();// 打印 FROM 表for (Symbol symbol : fromList) {System.out.println(symbol.getName());}}
}