Merge branch 'master' of github.com:DTStack/dt-sql-parser

README-zh_CN.md

@@ -16,6 +16,7 @@ dt-sql-parser 是一个基于 [ANTLR4](https://github.com/antlr/antlr4) 开发
 - Spark SQL
 - Hive SQL
 - PL/SQL
+- Trino SQL
 
 > 提示：当前的 Parser 是 `Javascript` 语言版本，如果有必要，可以尝试编译 Grammar 文件到其他目标语言
 
@@ -103,7 +104,6 @@ console.log(tokens)
         tokenIndex: -1
         type: 137
         _text: null
-        text: "SELECT"
     },
     ...
 ]

README.md

@@ -26,6 +26,7 @@ Supported SQL:
 - Hive SQL
 - PL/SQL
 - PostgreSQL
+- Trino SQL
 
 >Tips: This project is the default for Javascript language, also you can try to compile it to other languages if you need.
 
@@ -121,7 +122,6 @@ console.log(tokens)
         tokenIndex: -1
         type: 137
         _text: null
-        text: "SELECT"
     },
     ...
 ]

src/lib/flinksql/FlinkSqlParserListener.ts

@@ -161,6 +161,7 @@ import { SearchedCaseContext } from "./FlinkSqlParser";
 import { PositionContext } from "./FlinkSqlParser";
 import { FirstContext } from "./FlinkSqlParser";
 import { FunctionNameContext } from "./FlinkSqlParser";
+import { FunctionParamContext } from "./FlinkSqlParser";
 import { DereferenceDefinitionContext } from "./FlinkSqlParser";
 import { CorrelationNameContext } from "./FlinkSqlParser";
 import { QualifiedNameContext } from "./FlinkSqlParser";
@@ -170,7 +171,6 @@ import { MultiUnitsIntervalContext } from "./FlinkSqlParser";
 import { ErrorCapturingUnitToUnitIntervalContext } from "./FlinkSqlParser";
 import { UnitToUnitIntervalContext } from "./FlinkSqlParser";
 import { IntervalValueContext } from "./FlinkSqlParser";
-import { IntervalTimeUnitContext } from "./FlinkSqlParser";
 import { ColumnAliasContext } from "./FlinkSqlParser";
 import { TableAliasContext } from "./FlinkSqlParser";
 import { ErrorCapturingIdentifierContext } from "./FlinkSqlParser";
@@ -198,11 +198,15 @@ import { BitOperatorContext } from "./FlinkSqlParser";
 import { MathOperatorContext } from "./FlinkSqlParser";
 import { UnaryOperatorContext } from "./FlinkSqlParser";
 import { ConstantContext } from "./FlinkSqlParser";
+import { TimePointLiteralContext } from "./FlinkSqlParser";
 import { StringLiteralContext } from "./FlinkSqlParser";
 import { DecimalLiteralContext } from "./FlinkSqlParser";
 import { BooleanLiteralContext } from "./FlinkSqlParser";
 import { SetQuantifierContext } from "./FlinkSqlParser";
-import { ReservedKeywordsUsedAsFunctionNameContext } from "./FlinkSqlParser";
+import { TimePointUnitContext } from "./FlinkSqlParser";
+import { TimeIntervalUnitContext } from "./FlinkSqlParser";
+import { ReservedKeywordsUsedAsFuncParamContext } from "./FlinkSqlParser";
+import { ReservedKeywordsUsedAsFuncNameContext } from "./FlinkSqlParser";
 import { ReservedKeywordsContext } from "./FlinkSqlParser";
 import { NonReservedKeywordsContext } from "./FlinkSqlParser";
 
@@ -1842,6 +1846,16 @@ export default class FlinkSqlParserListener extends ParseTreeListener {
 	 * @param ctx the parse tree
 	 */
 	exitFunctionName?: (ctx: FunctionNameContext) => void;
+	/**
+	 * Enter a parse tree produced by `FlinkSqlParser.functionParam`.
+	 * @param ctx the parse tree
+	 */
+	enterFunctionParam?: (ctx: FunctionParamContext) => void;
+	/**
+	 * Exit a parse tree produced by `FlinkSqlParser.functionParam`.
+	 * @param ctx the parse tree
+	 */
+	exitFunctionParam?: (ctx: FunctionParamContext) => void;
 	/**
 	 * Enter a parse tree produced by `FlinkSqlParser.dereferenceDefinition`.
 	 * @param ctx the parse tree
@@ -1932,16 +1946,6 @@ export default class FlinkSqlParserListener extends ParseTreeListener {
 	 * @param ctx the parse tree
 	 */
 	exitIntervalValue?: (ctx: IntervalValueContext) => void;
-	/**
-	 * Enter a parse tree produced by `FlinkSqlParser.intervalTimeUnit`.
-	 * @param ctx the parse tree
-	 */
-	enterIntervalTimeUnit?: (ctx: IntervalTimeUnitContext) => void;
-	/**
-	 * Exit a parse tree produced by `FlinkSqlParser.intervalTimeUnit`.
-	 * @param ctx the parse tree
-	 */
-	exitIntervalTimeUnit?: (ctx: IntervalTimeUnitContext) => void;
 	/**
 	 * Enter a parse tree produced by `FlinkSqlParser.columnAlias`.
 	 * @param ctx the parse tree
@@ -2222,6 +2226,16 @@ export default class FlinkSqlParserListener extends ParseTreeListener {
 	 * @param ctx the parse tree
 	 */
 	exitConstant?: (ctx: ConstantContext) => void;
+	/**
+	 * Enter a parse tree produced by `FlinkSqlParser.timePointLiteral`.
+	 * @param ctx the parse tree
+	 */
+	enterTimePointLiteral?: (ctx: TimePointLiteralContext) => void;
+	/**
+	 * Exit a parse tree produced by `FlinkSqlParser.timePointLiteral`.
+	 * @param ctx the parse tree
+	 */
+	exitTimePointLiteral?: (ctx: TimePointLiteralContext) => void;
 	/**
 	 * Enter a parse tree produced by `FlinkSqlParser.stringLiteral`.
 	 * @param ctx the parse tree
@@ -2263,15 +2277,45 @@ export default class FlinkSqlParserListener extends ParseTreeListener {
 	 */
 	exitSetQuantifier?: (ctx: SetQuantifierContext) => void;
 	/**
-	 * Enter a parse tree produced by `FlinkSqlParser.reservedKeywordsUsedAsFunctionName`.
+	 * Enter a parse tree produced by `FlinkSqlParser.timePointUnit`.
 	 * @param ctx the parse tree
 	 */
-	enterReservedKeywordsUsedAsFunctionName?: (ctx: ReservedKeywordsUsedAsFunctionNameContext) => void;
+	enterTimePointUnit?: (ctx: TimePointUnitContext) => void;
 	/**
-	 * Exit a parse tree produced by `FlinkSqlParser.reservedKeywordsUsedAsFunctionName`.
+	 * Exit a parse tree produced by `FlinkSqlParser.timePointUnit`.
 	 * @param ctx the parse tree
 	 */
-	exitReservedKeywordsUsedAsFunctionName?: (ctx: ReservedKeywordsUsedAsFunctionNameContext) => void;
+	exitTimePointUnit?: (ctx: TimePointUnitContext) => void;
+	/**
+	 * Enter a parse tree produced by `FlinkSqlParser.timeIntervalUnit`.
+	 * @param ctx the parse tree
+	 */
+	enterTimeIntervalUnit?: (ctx: TimeIntervalUnitContext) => void;
+	/**
+	 * Exit a parse tree produced by `FlinkSqlParser.timeIntervalUnit`.
+	 * @param ctx the parse tree
+	 */
+	exitTimeIntervalUnit?: (ctx: TimeIntervalUnitContext) => void;
+	/**
+	 * Enter a parse tree produced by `FlinkSqlParser.reservedKeywordsUsedAsFuncParam`.
+	 * @param ctx the parse tree
+	 */
+	enterReservedKeywordsUsedAsFuncParam?: (ctx: ReservedKeywordsUsedAsFuncParamContext) => void;
+	/**
+	 * Exit a parse tree produced by `FlinkSqlParser.reservedKeywordsUsedAsFuncParam`.
+	 * @param ctx the parse tree
+	 */
+	exitReservedKeywordsUsedAsFuncParam?: (ctx: ReservedKeywordsUsedAsFuncParamContext) => void;
+	/**
+	 * Enter a parse tree produced by `FlinkSqlParser.reservedKeywordsUsedAsFuncName`.
+	 * @param ctx the parse tree
+	 */
+	enterReservedKeywordsUsedAsFuncName?: (ctx: ReservedKeywordsUsedAsFuncNameContext) => void;
+	/**
+	 * Exit a parse tree produced by `FlinkSqlParser.reservedKeywordsUsedAsFuncName`.
+	 * @param ctx the parse tree
+	 */
+	exitReservedKeywordsUsedAsFuncName?: (ctx: ReservedKeywordsUsedAsFuncNameContext) => void;
 	/**
 	 * Enter a parse tree produced by `FlinkSqlParser.reservedKeywords`.
 	 * @param ctx the parse tree

src/lib/flinksql/FlinkSqlParserVisitor.ts

@@ -161,6 +161,7 @@ import { SearchedCaseContext } from "./FlinkSqlParser";
 import { PositionContext } from "./FlinkSqlParser";
 import { FirstContext } from "./FlinkSqlParser";
 import { FunctionNameContext } from "./FlinkSqlParser";
+import { FunctionParamContext } from "./FlinkSqlParser";
 import { DereferenceDefinitionContext } from "./FlinkSqlParser";
 import { CorrelationNameContext } from "./FlinkSqlParser";
 import { QualifiedNameContext } from "./FlinkSqlParser";
@@ -170,7 +171,6 @@ import { MultiUnitsIntervalContext } from "./FlinkSqlParser";
 import { ErrorCapturingUnitToUnitIntervalContext } from "./FlinkSqlParser";
 import { UnitToUnitIntervalContext } from "./FlinkSqlParser";
 import { IntervalValueContext } from "./FlinkSqlParser";
-import { IntervalTimeUnitContext } from "./FlinkSqlParser";
 import { ColumnAliasContext } from "./FlinkSqlParser";
 import { TableAliasContext } from "./FlinkSqlParser";
 import { ErrorCapturingIdentifierContext } from "./FlinkSqlParser";
@@ -198,11 +198,15 @@ import { BitOperatorContext } from "./FlinkSqlParser";
 import { MathOperatorContext } from "./FlinkSqlParser";
 import { UnaryOperatorContext } from "./FlinkSqlParser";
 import { ConstantContext } from "./FlinkSqlParser";
+import { TimePointLiteralContext } from "./FlinkSqlParser";
 import { StringLiteralContext } from "./FlinkSqlParser";
 import { DecimalLiteralContext } from "./FlinkSqlParser";
 import { BooleanLiteralContext } from "./FlinkSqlParser";
 import { SetQuantifierContext } from "./FlinkSqlParser";
-import { ReservedKeywordsUsedAsFunctionNameContext } from "./FlinkSqlParser";
+import { TimePointUnitContext } from "./FlinkSqlParser";
+import { TimeIntervalUnitContext } from "./FlinkSqlParser";
+import { ReservedKeywordsUsedAsFuncParamContext } from "./FlinkSqlParser";
+import { ReservedKeywordsUsedAsFuncNameContext } from "./FlinkSqlParser";
 import { ReservedKeywordsContext } from "./FlinkSqlParser";
 import { NonReservedKeywordsContext } from "./FlinkSqlParser";
 
@@ -1188,6 +1192,12 @@ export default class FlinkSqlParserVisitor<Result> extends ParseTreeVisitor<Resu
 	 * @return the visitor result
 	 */
 	visitFunctionName?: (ctx: FunctionNameContext) => Result;
+	/**
+	 * Visit a parse tree produced by `FlinkSqlParser.functionParam`.
+	 * @param ctx the parse tree
+	 * @return the visitor result
+	 */
+	visitFunctionParam?: (ctx: FunctionParamContext) => Result;
 	/**
 	 * Visit a parse tree produced by `FlinkSqlParser.dereferenceDefinition`.
 	 * @param ctx the parse tree
@@ -1242,12 +1252,6 @@ export default class FlinkSqlParserVisitor<Result> extends ParseTreeVisitor<Resu
 	 * @return the visitor result
 	 */
 	visitIntervalValue?: (ctx: IntervalValueContext) => Result;
-	/**
-	 * Visit a parse tree produced by `FlinkSqlParser.intervalTimeUnit`.
-	 * @param ctx the parse tree
-	 * @return the visitor result
-	 */
-	visitIntervalTimeUnit?: (ctx: IntervalTimeUnitContext) => Result;
 	/**
 	 * Visit a parse tree produced by `FlinkSqlParser.columnAlias`.
 	 * @param ctx the parse tree
@@ -1415,6 +1419,12 @@ export default class FlinkSqlParserVisitor<Result> extends ParseTreeVisitor<Resu
 	 * @return the visitor result
 	 */
 	visitConstant?: (ctx: ConstantContext) => Result;
+	/**
+	 * Visit a parse tree produced by `FlinkSqlParser.timePointLiteral`.
+	 * @param ctx the parse tree
+	 * @return the visitor result
+	 */
+	visitTimePointLiteral?: (ctx: TimePointLiteralContext) => Result;
 	/**
 	 * Visit a parse tree produced by `FlinkSqlParser.stringLiteral`.
 	 * @param ctx the parse tree
@@ -1440,11 +1450,29 @@ export default class FlinkSqlParserVisitor<Result> extends ParseTreeVisitor<Resu
 	 */
 	visitSetQuantifier?: (ctx: SetQuantifierContext) => Result;
 	/**
-	 * Visit a parse tree produced by `FlinkSqlParser.reservedKeywordsUsedAsFunctionName`.
+	 * Visit a parse tree produced by `FlinkSqlParser.timePointUnit`.
 	 * @param ctx the parse tree
 	 * @return the visitor result
 	 */
-	visitReservedKeywordsUsedAsFunctionName?: (ctx: ReservedKeywordsUsedAsFunctionNameContext) => Result;
+	visitTimePointUnit?: (ctx: TimePointUnitContext) => Result;
+	/**
+	 * Visit a parse tree produced by `FlinkSqlParser.timeIntervalUnit`.
+	 * @param ctx the parse tree
+	 * @return the visitor result
+	 */
+	visitTimeIntervalUnit?: (ctx: TimeIntervalUnitContext) => Result;
+	/**
+	 * Visit a parse tree produced by `FlinkSqlParser.reservedKeywordsUsedAsFuncParam`.
+	 * @param ctx the parse tree
+	 * @return the visitor result
+	 */
+	visitReservedKeywordsUsedAsFuncParam?: (ctx: ReservedKeywordsUsedAsFuncParamContext) => Result;
+	/**
+	 * Visit a parse tree produced by `FlinkSqlParser.reservedKeywordsUsedAsFuncName`.
+	 * @param ctx the parse tree
+	 * @return the visitor result
+	 */
+	visitReservedKeywordsUsedAsFuncName?: (ctx: ReservedKeywordsUsedAsFuncNameContext) => Result;
 	/**
 	 * Visit a parse tree produced by `FlinkSqlParser.reservedKeywords`.
 	 * @param ctx the parse tree

test/parser/flinksql/lexer.test.ts

@@ -5,7 +5,6 @@ describe('FlinkSQL Lexer tests', () => {
 
     const sql = 'SELECT * FROM table1';
     const tokens = parser.getAllTokens(sql);
-
     test('token counts', () => {
         expect(tokens.length - 1).toBe(7);
     });

test/parser/flinksql/syntax/comprehensive.test.ts

@@ -0,0 +1,16 @@
+import fs from 'fs';
+import path from 'path';
+import FlinkSQL from "../../../../src/parser/flinksql";
+
+// 综合测试的 sql 不做切割
+const features = {
+    templates: fs.readFileSync(path.join(__dirname, 'fixtures', 'templates.sql'), 'utf-8')
+};
+
+describe('FlinkSQL Comprehensive Tests', () => {
+    const parser = new FlinkSQL();
+    test('Stream SQL templates', () => {
+        expect(parser.validate(features.templates).length).toBe(0);
+    });
+});
+

test/parser/flinksql/syntax/fixtures/templates.sql

@@ -0,0 +1,884 @@
+--********************************************************************--
+-- Flink SQL 快速入门示例 创建表
+-- 该模版仅支持使用"执行"功能。如需"提交"运行，需要您增加 INSERT 相关逻辑
+--********************************************************************--
+-- 执行创建临时表 DDL，不需要指定catalog.database
+CREATE TABLE orders (
+    order_uid  BIGINT,
+    product_id BIGINT,
+    price      DECIMAL(32, 2),
+    order_time TIMESTAMP(3)
+) WITH (
+    'connector' = 'datagen'
+);
+
+
+--********************************************************************--
+-- Flink SQL 快速入门示例 INSERT INTO
+--********************************************************************--
+-- 定义数据源表
+CREATE TABLE server_logs (
+    client_ip STRING,
+    client_identity STRING,
+    userid STRING,
+    user_agent STRING,
+    log_time TIMESTAMP(3),
+    request_line STRING,
+    status_code STRING,
+    size INT
+) WITH (
+    'connector' = 'faker',   
+    'fields.client_ip.expression' = '#{Internet.publicIpV4Address}',
+    'fields.client_identity.expression' =  '-',
+    'fields.userid.expression' =  '-',
+    'fields.user_agent.expression' = '#{Internet.userAgentAny}',
+    'fields.log_time.expression' =  '#{date.past ''15'',''5'',''SECONDS''}',
+    'fields.request_line.expression' = '#{regexify ''(GET|POST|PUT|PATCH){1}''} #{regexify ''(/search\.html|/login\.html|/prod\.html|cart\.html|/order\.html){1}''} #{regexify ''(HTTP/1\.1|HTTP/2|/HTTP/1\.0){1}''}',
+    'fields.status_code.expression' = '#{regexify ''(200|201|204|400|401|403|301){1}''}',
+    'fields.size.expression' = '#{number.numberBetween ''100'',''10000000''}'
+);
+
+-- 定义结果表，实际应用中会选择 Kafka、JDBC 等作为结果表
+CREATE TABLE client_errors (
+    log_time TIMESTAMP(3),
+    request_line STRING,
+    status_code STRING,
+    size INT
+) WITH (
+    'connector' = 'stream-x'
+);
+
+-- 写入数据到结果表
+INSERT INTO client_errors
+    SELECT
+       log_time,
+       request_line,
+       status_code,
+       size
+    FROM server_logs
+    WHERE
+       status_code SIMILAR TO '4[0-9][0-9]';
+
+
+--********************************************************************--
+-- Flink SQL 快速入门示例 Statement Set
+--********************************************************************--
+-- 定义数据源表
+CREATE TABLE server_logs (
+    client_ip       STRING,
+    client_identity STRING,
+    userid          STRING,
+    user_agent      STRING,
+    log_time        TIMESTAMP(3),
+    request_line    STRING,
+    status_code     STRING,
+    size            INT,
+    WATERMARK FOR log_time AS log_time - INTERVAL '30' SECONDS
+) WITH (
+    'connector' = 'faker',    -- Faker 连接器仅在 VVR-4.0.12 及以上支持
+    'fields.client_ip.expression' = '#{Internet.publicIpV4Address}',
+    'fields.client_identity.expression' =  '-',
+    'fields.userid.expression' =  '-',
+    'fields.user_agent.expression' = '#{Internet.userAgentAny}',
+    'fields.log_time.expression' =  '#{date.past ''15'',''5'',''SECONDS''}',
+    'fields.request_line.expression' = '#{regexify ''(GET|POST|PUT|PATCH){1}''} #{regexify ''(/search\.html|/login\.html|/prod\.html|cart\.html|/order\.html){1}''} #{regexify ''(HTTP/1\.1|HTTP/2|/HTTP/1\.0){1}''}',
+    'fields.status_code.expression' = '#{regexify ''(200|201|204|400|401|403|301){1}''}',
+    'fields.size.expression' = '#{number.numberBetween ''100'',''10000000''}'
+);
+
+-- 定义结果表1
+CREATE TABLE aggregations1 (
+    `browser`     STRING,
+    `status_code` STRING,
+    `end_time`    TIMESTAMP(3),
+    `requests`    BIGINT NOT NULL
+) WITH (
+    'connector' = 'blackhole'
+);
+
+-- 定义结果表2
+CREATE TABLE aggregations2 (
+    `browser`     STRING,
+    `status_code` STRING,
+    `requests`    BIGINT NOT NULL
+) WITH (
+       'connector' = 'stream-x'
+);
+
+-- This is a shared view that will be used by both insert into statements
+CREATE VIEW browsers AS 
+    SELECT
+        REGEXP_EXTRACT(user_agent,'[^\/]+') AS browser,
+        status_code,
+        log_time
+    FROM server_logs;
+
+-- 封装多个INSERT INTO语句到一个STATEMENT SET语句中
+BEGIN STATEMENT SET;
+-- 5min窗口粒度聚合
+INSERT INTO aggregations1
+    SELECT
+        browser,
+        status_code,
+        TUMBLE_ROWTIME(log_time, INTERVAL '5' MINUTE) AS end_time,
+        COUNT(*) requests
+FROM browsers
+GROUP BY
+    browser,
+    status_code,
+    TUMBLE(log_time, INTERVAL '5' MINUTE);
+-- 1h窗口粒度聚合
+INSERT INTO aggregations2
+    SELECT
+        browser,
+        status_code,
+        COUNT(*) requests
+    FROM browsers
+    GROUP BY
+        browser,
+        status_code,
+        TUMBLE(log_time, INTERVAL '1' HOUR);
+END;
+
+
+--********************************************************************--
+-- Flink SQL 快速入门示例 Watermark
+-- 该模版仅支持使用"执行"功能。如需"提交"运行，需要您增加 INSERT 相关逻辑
+--********************************************************************--
+CREATE TABLE dt_catalog.dt_db.doctor_sightings (
+    doctor        STRING,
+    sighting_time TIMESTAMP(3),
+    -- 通过watermark把sighting_time标识为事件时间，定义最大的乱序时间：期望所有的记录在目击发生后的15秒内都到达。
+    WATERMARK FOR sighting_time AS sighting_time - INTERVAL '15' SECONDS
+) WITH (
+    'connector' = 'faker',   
+    'fields.doctor.expression' = '#{dr_who.the_doctors}',
+    'fields.sighting_time.expression' = '#{date.past ''15'',''SECONDS''}'
+);
+
+SELECT
+    doctor,
+    -- 在滚动窗口中使用sighting_time字段
+    TUMBLE_ROWTIME(sighting_time, INTERVAL '1' MINUTE) AS sighting_time,
+    COUNT(*) AS sightings
+FROM dt_catalog.dt_db.doctor_sightings
+GROUP BY
+    TUMBLE(sighting_time, INTERVAL '1' MINUTE),
+    doctor;
+
+
+--********************************************************************--
+-- Flink SQL 快速入门示例 GROUP BY
+-- 该模版仅支持使用"执行"功能。如需"提交"运行，需要您增加 INSERT 相关逻辑
+--********************************************************************--
+-- 定义数据源表
+CREATE TABLE dt_catalog.dt_db.server_logs (
+    client_ip STRING,
+    client_identity STRING,
+    userid STRING,
+    user_agent STRING,
+    log_time TIMESTAMP(3),
+    request_line STRING,
+    status_code STRING,
+    size INT
+) WITH (
+    'connector' = 'faker',   
+    'fields.client_ip.expression' = '#{Internet.publicIpV4Address}',
+    'fields.client_identity.expression' =  '-',
+    'fields.userid.expression' =  '-',
+    'fields.user_agent.expression' = '#{Internet.userAgentAny}',
+    'fields.log_time.expression' =  '#{date.past ''15'',''5'',''SECONDS''}',
+    'fields.request_line.expression' = '#{regexify ''(GET|POST|PUT|PATCH){1}''} #{regexify ''(/search\.html|/login\.html|/prod\.html|cart\.html|/order\.html){1}''} #{regexify ''(HTTP/1\.1|HTTP/2|/HTTP/1\.0){1}''}',
+    'fields.status_code.expression' = '#{regexify ''(200|201|204|400|401|403|301){1}''}',
+    'fields.size.expression' = '#{number.numberBetween ''100'',''10000000''}'
+);
+
+-- 采样user_agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.75.14 (KHTML, like Gecko) Version/7.0.3 Safari/7046A194A
+-- 正则表达式: '[^\/]+' (匹配 '/' 之前的所有字符)
+SELECT
+    REGEXP_EXTRACT(user_agent,'[^\/]+') AS browser,
+    status_code,
+    COUNT(*) AS cnt_status
+FROM dt_catalog.dt_db.server_logs
+-- 按浏览器和状态码两个维度统计日志数量
+GROUP BY
+    REGEXP_EXTRACT(user_agent,'[^\/]+'),
+    status_code;
+
+
+--********************************************************************--
+-- Flink SQL 快速入门示例 滚动窗口聚合
+-- 该模版仅支持使用"执行"功能。如需"提交"运行，需要您增加 INSERT 相关逻辑
+--********************************************************************--
+CREATE TABLE dt_catalog.dt_db.server_logs (
+    client_ip STRING,
+    client_identity STRING,
+    userid STRING,
+    request_line STRING,
+    status_code STRING,
+    log_time AS PROCTIME() -- 使用当前系统处理时间作为表的时间字段
+) WITH (
+    'connector' = 'faker',  
+    'fields.client_ip.expression' = '#{Internet.publicIpV4Address}',
+    'fields.client_identity.expression' =  '-',
+    'fields.userid.expression' =  '-',
+    'fields.log_time.expression' =  '#{date.past ''15'',''5'',''SECONDS''}',
+    'fields.request_line.expression' = '#{regexify ''(GET|POST|PUT|PATCH){1}''} #{regexify ''(/search\.html|/login\.html|/prod\.html|cart\.html|/order\.html){1}''} #{regexify ''(HTTP/1\.1|HTTP/2|/HTTP/1\.0){1}''}',
+    'fields.status_code.expression' = '#{regexify ''(200|201|204|400|401|403|301){1}''}'
+);
+
+-- 按 window_start, window_end 维度计算每分钟窗口上不同的 ip 数量
+SELECT window_start, window_end, COUNT(DISTINCT client_ip) AS ip_addresses
+    FROM TABLE(
+        -- 定义1min滑动窗口
+        TUMBLE(TABLE dt_catalog.dt_db.server_logs, DESCRIPTOR(log_time), INTERVAL '1' MINUTE))
+    GROUP BY window_start, window_end;
+
+
+--********************************************************************--
+-- Flink SQL 快速入门示例 滑动窗口聚合
+-- 该模版仅支持使用"执行"功能。如需"提交"运行，需要您增加 INSERT 相关逻辑
+--********************************************************************--
+CREATE TABLE dt_catalog.dt_db.bids (
+    bid_id STRING,
+    currency_code STRING,
+    bid_price DOUBLE,
+    transaction_time TIMESTAMP(3),
+    WATERMARK FOR transaction_time AS transaction_time - INTERVAL '5' SECONDS  -- 定义事件时间，允许的最大窗口延迟为5s
+) WITH (
+    'connector' = 'faker',   
+    'fields.bid_id.expression' = '#{Internet.UUID}',
+    'fields.currency_code.expression' = '#{regexify ''(EUR|USD|CNY)''}',
+    'fields.bid_price.expression' = '#{Number.randomDouble ''2'',''1'',''150''}',
+    'fields.transaction_time.expression' = '#{date.past ''30'',''SECONDS''}',
+    'rows-per-second' = '100'
+);
+
+-- 定义1min 的滑动窗口，每隔 30s 滚动一次
+SELECT window_start, window_end, currency_code, ROUND(AVG(bid_price),2) AS MovingAverageBidPrice
+    FROM TABLE(
+        HOP(TABLE dt_catalog.dt_db.bids, DESCRIPTOR(transaction_time), INTERVAL '30' SECONDS, INTERVAL '1' MINUTE))
+    GROUP BY window_start, window_end, currency_code;
+
+
+--********************************************************************--
+-- Flink SQL 快速入门示例 累计窗口聚合
+-- 该模版仅支持使用"执行"功能。如需"提交"运行，需要您增加 INSERT 相关逻辑
+--********************************************************************--
+-- 商品销售订单表
+CREATE TABLE dt_catalog.dt_db.orders (
+    order_id    BIGINT, -- 订单ID
+    goods_id    BIGINT, -- 商品ID
+    goods_sales DOUBLE, -- 商品销售额
+    order_time  TIMESTAMP(3), -- 下单时间
+    WATERMARK FOR order_time AS order_time - INTERVAL '5' SECONDS  -- 定义事件时间，允许的最大窗口延迟为5s
+) WITH (
+    'connector' = 'faker',   
+    'fields.order_id.expression' = '#{number.numberBetween ''0'',''1000000000''}',
+    'fields.goods_id.expression' = '#{number.numberBetween ''0'',''1000000000''}',
+    'fields.goods_sales.expression' = '#{Number.randomDouble ''2'',''1'',''150''}',
+    'fields.order_time.expression' = '#{date.past ''30'',''SECONDS''}',
+    'rows-per-second' = '100'
+);
+
+-- 每分钟更新一次从零点开始截止到当前时刻的累计销售额
+SELECT
+    window_start,
+    window_end, 
+    SUM(goods_sales) AS cumulate_gmv -- 当天累计销售额
+FROM TABLE(
+    -- 定义窗口最大长度为一天的累计窗口，窗口滚动步长为1分钟
+    CUMULATE(
+        TABLE dt_catalog.dt_db.orders,
+        DESCRIPTOR(order_time),
+        INTERVAL '1' MINUTES,
+        INTERVAL '1' DAY))
+GROUP BY window_start, window_end;
+
+
+--********************************************************************--
+-- Flink SQL 快速入门示例 会话窗口聚合
+-- 该模版仅支持使用"执行"功能。如需"提交"运行，需要您增加 INSERT 相关逻辑
+--********************************************************************--
+CREATE TABLE dt_catalog.dt_db.server_logs (
+    client_ip STRING,
+    client_identity STRING,
+    userid STRING,
+    log_time TIMESTAMP(3),
+    request_line STRING,
+    status_code STRING,
+    WATERMARK FOR log_time AS log_time - INTERVAL '5' SECONDS -- 定义 watermark
+) WITH (
+    'connector' = 'faker',  
+    'rows-per-second' = '5',
+    'fields.client_ip.expression' = '#{Internet.publicIpV4Address}',
+    'fields.client_identity.expression' =  '-',
+    'fields.userid.expression' =  '#{regexify ''(morsapaes|knauf|sjwiesman){1}''}',
+    'fields.log_time.expression' =  '#{date.past ''5'',''SECONDS''}',
+    'fields.request_line.expression' = '#{regexify ''(GET|POST|PUT|PATCH){1}''} #{regexify ''(/search\.html|/login\.html|/prod\.html|cart\.html|/order\.html){1}''} #{regexify ''(HTTP/1\.1|HTTP/2|/HTTP/1\.0){1}''}',
+    'fields.status_code.expression' = '#{regexify ''(200|201|204|400|401|403|301){1}''}'
+);
+
+SELECT 
+    userid,
+    SESSION_START(log_time, INTERVAL '10' SECOND) AS session_beg,
+    SESSION_ROWTIME(log_time, INTERVAL '10' SECOND) AS session_end,
+    COUNT(request_line) AS request_cnt
+FROM dt_catalog.dt_db.server_logs
+    WHERE status_code = '403'
+    GROUP BY
+        userid,
+    -- 会话窗口的最大空闲间隔为10s，当10s内该窗口没有接收到新的请求，会关闭当前窗口
+    SESSION(log_time, INTERVAL '10' SECOND);
+
+
+--********************************************************************--
+-- Flink SQL 快速入门示例 OVER窗口聚合
+-- 该模版仅支持使用"执行"功能。如需"提交"运行，需要您增加 INSERT 相关逻辑
+--********************************************************************--
+CREATE TABLE dt_catalog.dt_db.temperature_measurements (
+    measurement_time TIMESTAMP(3),
+    city STRING,
+    temperature FLOAT,
+    WATERMARK FOR measurement_time AS measurement_time - INTERVAL '15' SECONDS -- 定义时间属性字段，OVER窗口排序时使用
+) WITH (
+    'connector' = 'faker',    -- Faker 连接器仅在 VVR-4.0.12 及以上支持
+    'fields.measurement_time.expression' = '#{date.past ''15'',''SECONDS''}',
+    'fields.temperature.expression' = '#{number.numberBetween ''0'',''50''}',
+    'fields.city.expression' = '#{regexify ''(Chicago|Munich|Berlin|Portland|Hangzhou|Seatle|Beijing|New York){1}''}'
+);
+
+SELECT
+    measurement_time,
+    city,
+    temperature,
+    AVG(CAST(temperature AS FLOAT)) OVER last_minute AS avg_temperature_minute, -- 计算平均值
+    MAX(temperature) OVER last_minute AS min_temperature_minute, -- 计算最大值
+    MIN(temperature) OVER last_minute AS max_temperature_minute, -- 计算最小值
+    STDDEV(CAST(temperature AS FLOAT)) OVER last_minute AS stdev_temperature_minute -- 计算标准差
+FROM dt_catalog.dt_db.temperature_measurements
+    WINDOW last_minute AS ( -- 定义1min时间间隔的OVER窗口，按城市粒度分区，温度测量值排序，每个元素都会触发一次计算
+        PARTITION BY city
+        ORDER BY measurement_time
+        RANGE BETWEEN INTERVAL '1' MINUTE PRECEDING AND CURRENT ROW
+    );
+
+--********************************************************************--
+-- Flink SQL 快速入门示例 级联窗口聚合
+--********************************************************************--
+CREATE TEMPORARY TABLE server_logs (
+    log_time TIMESTAMP(3),
+    client_ip STRING,
+    client_identity STRING,
+    userid STRING,
+    request_line STRING,
+    status_code STRING,
+    size INT,
+    WATERMARK FOR log_time AS log_time - INTERVAL '15' SECONDS -- 定义watermark
+) WITH (
+    'connector' = 'faker',   
+    'fields.log_time.expression' =  '#{date.past ''15'',''5'',''SECONDS''}',
+    'fields.client_ip.expression' = '#{Internet.publicIpV4Address}',
+    'fields.client_identity.expression' =  '-',
+    'fields.userid.expression' =  '-',
+    'fields.request_line.expression' = '#{regexify ''(GET|POST|PUT|PATCH){1}''} #{regexify ''(/search\.html|/login\.html|/prod\.html|cart\.html|/order\.html){1}''} #{regexify ''(HTTP/1\.1|HTTP/2|/HTTP/1\.0){1}''}',
+    'fields.status_code.expression' = '#{regexify ''(200|201|204|400|401|403|301){1}''}',
+    'fields.size.expression' = '#{number.numberBetween ''100'',''10000000''}'
+);
+
+-- 1min聚合结果表
+CREATE TEMPORARY TABLE avg_request_size_1m (
+    window_start TIMESTAMP(3),
+    window_end TIMESTAMP(3),
+    avg_size BIGINT
+) WITH (
+    'connector' = 'blackhole'
+);
+
+-- 5min聚合结果表
+CREATE TEMPORARY TABLE avg_request_size_5m (
+    window_start TIMESTAMP(3),
+    window_end TIMESTAMP(3),
+    avg_size BIGINT
+) WITH (
+    'connector' = 'blackhole'
+);
+
+-- 1min窗口查询结果
+CREATE VIEW server_logs_window_1m AS
+SELECT 
+    TUMBLE_START(log_time, INTERVAL '1' MINUTE) AS window_start,
+    TUMBLE_ROWTIME(log_time, INTERVAL '1' MINUTE) AS window_end,
+    SUM(size) AS total_size,
+    COUNT(*) AS num_requests
+FROM server_logs
+    GROUP BY
+    TUMBLE(log_time, INTERVAL '1' MINUTE);
+
+-- 基于1min窗口查询结果，进行5min粒度窗口聚合
+CREATE VIEW server_logs_window_5m AS
+SELECT
+    TUMBLE_START(window_end, INTERVAL '5' MINUTE) AS window_start,
+    TUMBLE_ROWTIME(window_end, INTERVAL '5' MINUTE) AS window_end,
+    SUM(total_size) AS total_size,
+    SUM(num_requests) AS num_requests
+FROM server_logs_window_1m
+    GROUP BY
+    TUMBLE(window_end, INTERVAL '5' MINUTE);
+
+BEGIN STATEMENT SET;
+-- 写入结果到1min窗口粒度结果表
+INSERT INTO avg_request_size_1m SELECT
+    window_start,
+    window_end,
+    total_size / num_requests AS avg_size
+FROM server_logs_window_1m;
+
+-- 写入结果到5min窗口粒度结果表
+INSERT INTO avg_request_size_5m SELECT
+    window_start,
+    window_end,
+    total_size / num_requests AS avg_size
+FROM server_logs_window_5m;
+END;
+
+
+--********************************************************************--
+-- Flink SQL 快速入门示例 去重
+-- 该模版仅支持使用"执行"功能。如需"提交"运行，需要您增加 INSERT 相关逻辑
+--********************************************************************--
+CREATE TABLE dt_catalog.dt_db.orders (
+  id INT,
+  order_time AS CURRENT_TIMESTAMP,
+  WATERMARK FOR order_time AS order_time - INTERVAL '5' SECONDS
+)
+WITH (
+  'connector' = 'datagen',
+  'rows-per-second'='10',
+  'fields.id.kind'='random',
+  'fields.id.min'='1',
+  'fields.id.max'='100'
+);
+
+-- 对于每个order_id，按事件时间去重，只保留最新时间的记录即可实现去重
+SELECT
+  order_id,
+  order_time
+FROM (
+    SELECT id AS order_id,
+        order_time,
+                -- 按事件时间升序排序
+        ROW_NUMBER() OVER (PARTITION BY id ORDER BY order_time) AS rownum
+    FROM dt_catalog.dt_db.orders)
+WHERE rownum = 1; -- 只取排名第一的记录，去重是Top-N的一种特例
+
+
+--********************************************************************--
+-- Flink SQL 快速入门示例 Top-N
+-- 该模版仅支持使用"执行"功能。如需"提交"运行，需要您增加 INSERT 相关逻辑
+--********************************************************************--
+CREATE TABLE dt_catalog.dt_db.spells_cast (
+    wizard STRING,
+    spell  STRING
+) WITH (
+  'connector' = 'faker',   
+  'fields.wizard.expression' = '#{harry_potter.characters}',
+  'fields.spell.expression' = '#{harry_potter.spells}'
+);
+
+-- 找出每个巫师最喜欢的两个法术
+SELECT wizard, spell, times_cast
+FROM (
+    SELECT *,
+    ROW_NUMBER() OVER (PARTITION BY wizard ORDER BY times_cast DESC) AS row_num -- 按法术次数降序排序
+    FROM (SELECT wizard, spell, COUNT(*) AS times_cast FROM dt_catalog.dt_db.spells_cast GROUP BY wizard, spell) -- 计算每个巫师施展的各种法术的次数
+)
+WHERE row_num <= 2;
+
+
+--********************************************************************--
+-- Flink SQL 快速入门示例 窗口Top-N
+-- 该模版仅支持使用"执行"功能。如需"提交"运行，需要您增加 INSERT 相关逻辑
+--********************************************************************--
+CREATE TABLE dt_catalog.dt_db.orders (
+    bidtime TIMESTAMP(3),
+    price DOUBLE,
+    item STRING,
+    supplier STRING,
+    WATERMARK FOR bidtime AS bidtime - INTERVAL '5' SECONDS  -- 定义watermark
+) WITH (
+    'connector' = 'faker',  
+    'fields.bidtime.expression' = '#{date.past ''30'',''SECONDS''}',
+    'fields.price.expression' = '#{Number.randomDouble ''2'',''1'',''150''}',
+    'fields.item.expression' = '#{Commerce.productName}',
+    'fields.supplier.expression' = '#{regexify ''(Alice|Bob|Carol|Alex|Joe|James|Jane|Jack)''}',
+    'rows-per-second' = '100'
+);
+
+-- 取出销售排名前三的供应商
+SELECT *
+    FROM (
+        -- 按窗口时间分区，按价格降序排序
+        SELECT *, ROW_NUMBER() OVER (PARTITION BY window_start, window_end ORDER BY price DESC) AS rownum
+        FROM (
+            -- 计算每个窗口内各个供应商的销售额
+            SELECT window_start, window_end, supplier, SUM(price) AS price, COUNT(*) AS cnt
+            FROM TABLE(
+                TUMBLE(TABLE dt_catalog.dt_db.orders, DESCRIPTOR(bidtime), INTERVAL '10' MINUTES))
+            GROUP BY window_start, window_end, supplier
+        )
+    ) 
+WHERE rownum <= 3;
+
+
+--********************************************************************--
+-- Flink SQL 快速入门示例 模式检测CEP
+-- 该模版仅支持使用"执行"功能。如需"提交"运行，需要您增加 INSERT 相关逻辑
+--********************************************************************--
+CREATE TABLE dt_catalog.dt_db.subscriptions (
+    id STRING,
+    user_id INT,
+    type STRING,
+    start_date TIMESTAMP(3),
+    end_date TIMESTAMP(3),
+    payment_expiration TIMESTAMP(3),
+    proc_time AS PROCTIME()
+) WITH (
+    'connector' = 'faker',   
+    'fields.id.expression' = '#{Internet.uuid}',
+    'fields.user_id.expression' = '#{number.numberBetween ''1'',''50''}',
+    'fields.type.expression'= '#{regexify ''(basic|premium|platinum){1}''}',
+    'fields.start_date.expression' = '#{date.past ''30'',''DAYS''}',
+    'fields.end_date.expression' = '#{date.future ''15'',''DAYS''}',
+    'fields.payment_expiration.expression' = '#{date.future ''365'',''DAYS''}'
+);
+
+SELECT *
+FROM dt_catalog.dt_db.subscriptions
+    MATCH_RECOGNIZE ( -- 按user_id分区，按处理时间proc_time升序排序
+        PARTITION BY user_id
+        ORDER BY proc_time
+        MEASURES
+            LAST(PREMIUM.type) AS premium_type,
+            AVG(TIMESTAMPDIFF(DAY,PREMIUM.start_date,PREMIUM.end_date)) AS premium_avg_duration,
+            BASIC.start_date AS downgrade_date
+        AFTER MATCH SKIP PAST LAST ROW
+        --模式: 一个或多个‘premium‘或’platinum‘订阅事件
+        --对于相同的'user_id'，后面跟着一个'basic'订阅事件
+        PATTERN (PREMIUM+ BASIC)
+        DEFINE PREMIUM AS PREMIUM.type IN ('premium','platinum'),
+            BASIC AS BASIC.type = 'basic');
+
+
+--********************************************************************--
+-- Flink SQL 快速入门示例 Regular Join
+-- 该模版仅支持使用"执行"功能。如需"提交"运行，需要您增加 INSERT 相关逻辑
+--********************************************************************--
+CREATE TABLE dt_catalog.dt_db.NOC (
+    agent_id STRING,
+    codename STRING
+) WITH (
+    'connector' = 'faker',   
+    'fields.agent_id.expression' = '#{regexify ''(1|2|3|4|5){1}''}',
+    'fields.codename.expression' = '#{superhero.name}',
+    'number-of-rows' = '10'
+);
+
+CREATE TABLE dt_catalog.dt_db.RealNames (
+    agent_id STRING,
+    name STRING
+) WITH (
+    'connector' = 'faker',   
+    'fields.agent_id.expression' = '#{regexify ''(1|2|3|4|5){1}''}',
+    'fields.name.expression' = '#{Name.full_name}',
+    'number-of-rows' = '10'
+);
+
+-- 使用agent_id作为两张表关联的条件，左右两边任何一张表来了新数据都会触发join动作
+SELECT
+    name,
+    codename
+FROM dt_catalog.dt_db.NOC t1
+INNER JOIN dt_catalog.dt_db.RealNames t2 ON t1.agent_id = t2.agent_id;
+
+
+--********************************************************************--
+-- Flink SQL 快速入门示例 Interval Join
+-- 该模版仅支持使用"执行"功能。如需"提交"运行，需要您增加 INSERT 相关逻辑
+--********************************************************************--
+CREATE TABLE dt_catalog.dt_db.orders (
+    id INT,
+    order_time AS TIMESTAMPADD(DAY, CAST(FLOOR(RAND()*(1-5+1)+5)*(-1) AS INT), CURRENT_TIMESTAMP)
+) WITH (
+    'connector' = 'datagen',
+    'rows-per-second'='10',
+    'fields.id.kind'='sequence',
+    'fields.id.start'='1',
+    'fields.id.end'='1000'
+);
+
+CREATE TABLE dt_catalog.dt_db.shipments (
+    id INT,
+    order_id INT,
+    shipment_time AS TIMESTAMPADD(DAY, CAST(FLOOR(RAND()*(1-5+1)) AS INT), CURRENT_TIMESTAMP)
+) WITH (
+    'connector' = 'datagen',
+    'rows-per-second'='5',
+    'fields.id.kind'='random',
+    'fields.id.min'='0',
+    'fields.order_id.kind'='sequence',
+    'fields.order_id.start'='1',
+    'fields.order_id.end'='1000'
+);
+
+-- order表的每条数据会与shipments表过去三天至当前时刻时间范围内的数据进行join
+SELECT
+    o.id AS order_id,
+    o.order_time,
+    s.shipment_time,
+    TIMESTAMPDIFF(DAY,o.order_time,s.shipment_time) AS day_diff
+FROM dt_catalog.dt_db.orders o
+    JOIN dt_catalog.dt_db.shipments s ON o.id = s.order_id
+WHERE
+    -- 时间 join 条件：shipments.shipment_time - INTERVAL '3' DAY <= orders.order_time < shipments.shipment_time
+    o.order_time BETWEEN s.shipment_time - INTERVAL '3' DAY AND s.shipment_time;
+
+
+--********************************************************************--
+-- Flink SQL 快速入门示例 时态表Join
+-- 该模版仅支持使用"执行"功能。如需"提交"运行，需要您增加 INSERT 相关逻辑
+--********************************************************************--
+-- 使用主键约束和watermark来定义一张版本表，这张表可以是一个cdc表、upsert类型的kafka topic等
+CREATE TABLE dt_catalog.dt_db.currency_rates (
+    `currency_code` STRING,
+    `eur_rate` DECIMAL(6,4),
+    `rate_time` TIMESTAMP(3),
+    WATERMARK FOR `rate_time` AS rate_time - INTERVAL '15' SECONDS, -- 定义事件时间
+    PRIMARY KEY (currency_code) NOT ENFORCED -- 定义主键
+) WITH (
+    'connector' = 'faker',   
+    'fields.currency_code.expression' = '#{Currency.code}',
+    'fields.eur_rate.expression' = '#{Number.randomDouble ''4'',''0'',''10''}',
+    'fields.rate_time.expression' = '#{date.past ''15'',''SECONDS''}',
+    'rows-per-second' = '100'
+);
+
+-- 这是一个append-only类型的动态表，需要定义watermk
+CREATE TABLE dt_catalog.dt_db.transactions (
+    `id` STRING,
+    `currency_code` STRING,
+    `total` DECIMAL(10,2),
+    `transaction_time` TIMESTAMP(3),
+     WATERMARK FOR `transaction_time` AS transaction_time - INTERVAL '30' SECONDS --定义watermark
+) WITH (
+    'connector' = 'faker',   
+    'fields.id.expression' = '#{Internet.UUID}',
+    'fields.currency_code.expression' = '#{Currency.code}',
+    'fields.total.expression' = '#{Number.randomDouble ''2'',''10'',''1000''}',
+    'fields.transaction_time.expression' = '#{date.past ''30'',''SECONDS''}',
+    'rows-per-second' = '100'
+);
+
+-- 当左右两张表的watermark对齐时，才会触发join动作，左右两张表都需要定义watermark
+SELECT
+    t.id,
+    t.total * c.eur_rate AS total_eur,
+    t.total,
+    c.currency_code,
+    t.transaction_time
+FROM dt_catalog.dt_db.transactions t
+    -- transactions表每条记录都与currency_rates表transaction_time时刻的汇率进行join
+    JOIN dt_catalog.dt_db.currency_rates FOR SYSTEM_TIME AS OF t.transaction_time AS c
+    -- 指定join key
+    ON t.currency_code = c.currency_code;
+
+
+--********************************************************************--
+-- Flink SQL 快速入门示例 维表Join
+-- 该模版仅支持使用"执行"功能。如需"提交"运行，需要您增加 INSERT 相关逻辑
+--********************************************************************--
+CREATE TABLE dt_catalog.dt_db.subscriptions (
+    id STRING,
+    user_id INT,
+    type STRING,
+    start_date TIMESTAMP(3),
+    end_date TIMESTAMP(3),
+    payment_expiration TIMESTAMP(3),
+    proc_time AS PROCTIME() -- 这里需要定义处理时间属性
+) WITH (
+    'connector' = 'faker',   
+    'fields.id.expression' = '#{Internet.uuid}',
+    'fields.user_id.expression' = '#{number.numberBetween ''1'',''50''}',
+    'fields.type.expression'= '#{regexify ''(basic|premium|platinum){1}''}',
+    'fields.start_date.expression' = '#{date.past ''30'',''DAYS''}',
+    'fields.end_date.expression' = '#{date.future ''365'',''DAYS''}',
+    'fields.payment_expiration.expression' = '#{date.future ''365'',''DAYS''}'
+);
+
+-- 定义维表，为了示例能直接运行，这里使用faker 作为维表，实际应用中一般会使用JDBC、Redis、Hbase等作为维表
+CREATE TABLE dt_catalog.dt_db.users (
+    user_id INT PRIMARY KEY, -- 定义主键
+    user_name VARCHAR(255) NOT NULL,
+    age INT NOT NULL
+) WITH (
+    'connector' = 'faker',   
+    'fields.user_id.expression' = '#{number.numberBetween ''1'',''10''}',
+    'fields.user_name.expression' = '#{regexify ''(ron|jark|danny){1}''}',
+    'fields.age.expression' = '#{number.numberBetween ''1'',''30''}'
+);
+
+SELECT
+    id AS subscription_id,
+    type AS subscription_type,
+    age AS user_age,
+    CASE
+        WHEN age < 18 THEN 1
+        ELSE 0
+    END AS is_minor
+FROM dt_catalog.dt_db.subscriptions usub
+    -- subscriptions每条记录都使用当前系统时间与维表users中的最新数据进行join
+    JOIN dt_catalog.dt_db.users FOR SYSTEM_TIME AS OF usub.proc_time AS u
+    -- 指定join key
+    ON usub.user_id = u.user_id;
+
+
+--********************************************************************--
+-- Flink SQL ODS 层
+-- 实际应用中，该任务应该是一个采集任务，源表为RDB
+--********************************************************************--
+CREATE  TABLE source_table (
+    account_number STRING,
+    channel_id INT,
+    account_open_datetime TIMESTAMP(3)
+) WITH (
+    'connector' = 'faker', -- 使用 Flink Faker Connector
+    'fields.account_number.expression' = '#{Finance.iban}', -- 随机生成银行账号
+    'fields.channel_id.expression' = '#{number.numberBetween ''1'',''4''}', -- 渠道ID随机生成1到3之间的数字
+    'fields.account_open_datetime.expression' =  '#{date.past ''15'',''5'',''SECONDS''}' -- 过去15天的日期，每5秒一条数据
+);
+
+-- 定义结果表，实际应用中应选择 Kafka等作为结果表
+CREATE TABLE sink_table (
+    account_number STRING,
+    channel_id INT,
+    account_open_datetime TIMESTAMP(3)
+) WITH (
+    'connector' = 'stream-x'
+);
+
+-- 写入数据到结果表
+INSERT INTO sink_table SELECT * FROM source_table
+
+--********************************************************************--
+-- Flink SQL DWD 层
+-- 实际应用中，源表为 ODS TOPIC
+--********************************************************************--
+CREATE  TABLE source_table (
+    account_number STRING,
+    channel_id INT,
+    account_open_datetime TIMESTAMP(3)
+) WITH (
+    'connector' = 'faker', -- 使用 Flink Faker Connector
+    'fields.account_number.expression' = '#{Finance.iban}', -- 随机生成银行账号
+    'fields.channel_id.expression' = '#{number.numberBetween ''1'',''4''}', -- 渠道ID随机生成1到3之间的数字
+    'fields.account_open_datetime.expression' =  '#{date.past ''15'',''5'',''SECONDS''}' -- 过去15天的日期，每5秒一条数据
+);
+
+-- 定义维表，实际应用中应选择RDB作为维表
+CREATE  TABLE dim_table (
+    channel_id INT,
+    channel_name STRING,
+    PRIMARY KEY (channel_id,channel_name) NOT ENFORCED
+) WITH (
+    'connector' = 'faker', -- 使用 Flink Faker Connector
+    'fields.channel_id.expression' = '#{number.numberBetween ''1'',''4''}', -- 渠道ID随机生成1到3之间的数字
+    'fields.channel_name.expression' =  '#{app.name}' -- 渠道名称
+);
+
+-- 定义结果表，实际应用中应选择 Kafka等作为结果表
+CREATE TABLE sink_table (
+    account_number STRING,
+    channel_id INT,
+    channel_name STRING,
+    account_open_datetime TIMESTAMP(3)
+) WITH (
+    'connector' = 'stream-x'
+);
+
+-- 写入数据到结果表
+INSERT INTO sink_table
+    SELECT
+        s1.account_number,
+        s1. channel_id ,
+        d1.channel_name,
+        s1.account_open_datetime
+    FROM source_table s1 
+    left JOIN dim_table d1 ON s1.channel_id=d1.channel_id
+
+
+--********************************************************************--
+-- Flink SQL DWS 层
+-- 实际应用中，源表为DWD TOPIC
+--********************************************************************--
+CREATE  TABLE source_table (
+    account_number STRING,
+    channel_id INT,
+    channel_name STRING,
+    account_open_datetime TIMESTAMP(3)
+) WITH (
+    'connector' = 'faker', -- 使用 Flink Faker Connector
+    'fields.account_number.expression' = '#{Finance.iban}', -- 随机生成银行账号
+    'fields.channel_id.expression' = '#{number.numberBetween ''1'',''4''}', -- 渠道ID随机生成1到3之间的数字
+    'fields.channel_name.expression' =  '#{app.name}' ,-- 渠道名称
+    'fields.account_open_datetime.expression' = '#{date.past ''15'',''5'',''SECONDS''}' -- 过去15天的日期，每5秒一条数据
+);
+DROP TABLE source_table
+SELECT * FROM source_table
+
+-- 定义结果表，实际应用中应选择Kafka作为结果表
+CREATE TABLE sink_table (
+    channel_id STRING,
+    open_date STRING,
+    cnt INT
+) WITH (
+    'connector' = 'stream-x'
+);
+DROP TABLE sink_table
+SELECT * FROM sink_table
+
+-- 写入数据到结果表
+INSERT INTO sink_table
+    SELECT 
+        channel_id,
+        DATE_FORMAT(account_open_datetime,'yyyy-MM-dd'),
+        count(account_number) 
+    FROM source_table 
+    GROUP BY channel_id,DATE_FORMAT(account_open_datetime,'yyyy-MM-dd')
+
+
+--********************************************************************--
+-- Flink SQL ADS 层
+-- 实际应用中，源表为DWS TOPIC
+--********************************************************************--
+CREATE TABLE source_table (
+    channel_id STRING,
+    open_time TIMESTAMP(3),
+    cnt INT
+) WITH (
+    'connector' = 'faker', -- 使用 Flink Faker Connector
+    'fields.channel_id.expression' = '#{number.numberBetween ''1'',''4''}', -- 渠道ID随机生成1到3之间的数字
+    'fields.open_time.expression' =  '#{Date.future ''15'',''5'',''SECONDS''}' ,-- 日期
+    'fields.cnt.expression' = '#{number.numberBetween ''1'',''100000000''}'-- 数量
+);
+
+-- 实际应用中，结果表为RDB
+CREATE TABLE sink_table (
+    open_date STRING,
+    cnt INT
+) WITH (
+    'connector' = 'stream-x'
+);
+
+-- 写入数据到结果表
+INSERT INTO sink_table
+SELECT DATE_FORMAT(open_time,'yyyy-MM-dd'),count(1);