Flink 系列文章
1、Flink 部署、概念介绍、source、transformation、sink使用示例、四大基石介绍和示例等系列综合文章链接
13、Flink 的table api与sql的基本概念、通用api介绍及入门示例
14、Flink 的table api与sql之数据类型: 内置数据类型以及它们的属性
15、Flink 的table api与sql之流式概念-详解的介绍了动态表、时间属性配置（如何处理更新结果）、时态表、流上的join、流上的确定性以及查询配置
16、Flink 的table api与sql之连接外部系统: 读写外部系统的连接器和格式以及FileSystem示例（1）
16、Flink 的table api与sql之连接外部系统: 读写外部系统的连接器和格式以及Elasticsearch示例（2）
16、Flink 的table api与sql之连接外部系统: 读写外部系统的连接器和格式以及Apache Kafka示例（3）
16、Flink 的table api与sql之连接外部系统: 读写外部系统的连接器和格式以及JDBC示例（4）
16、Flink 的table api与sql之连接外部系统: 读写外部系统的连接器和格式以及Apache Hive示例（6）
17、Flink 之Table API: Table API 支持的操作（1）
20、Flink SQL之SQL Client: 不用编写代码就可以尝试 Flink SQL，可以直接提交 SQL 任务到集群上
22、Flink 的table api与sql之创建表的DDL
24、Flink 的table api与sql之Catalogs（介绍、类型、java api和sql实现ddl、java api和sql操作catalog）-1
24、Flink 的table api与sql之Catalogs（java api操作数据库、表）-2
24、Flink 的table api与sql之Catalogs（java api操作视图）-3
24、Flink 的table api与sql之Catalogs（java api操作分区与函数）-4
26、Flink 的SQL之概览与入门示例
27、Flink 的SQL之SELECT (select、where、distinct、order by、limit、集合操作和去重)介绍及详细示例（1）
27、Flink 的SQL之SELECT (SQL Hints 和 Joins)介绍及详细示例（2）
27、Flink 的SQL之SELECT (窗口函数)介绍及详细示例（3）
27、Flink 的SQL之SELECT (窗口聚合)介绍及详细示例（4）
27、Flink 的SQL之SELECT (Group Aggregation分组聚合、Over Aggregation Over聚合 和 Window Join 窗口关联)介绍及详细示例（5）
27、Flink 的SQL之SELECT (Top-N、Window Top-N 窗口 Top-N 和 Window Deduplication 窗口去重)介绍及详细示例（6）
27、Flink 的SQL之SELECT (Pattern Recognition 模式检测)介绍及详细示例（7）
28、Flink 的SQL之DROP 、ALTER 、INSERT 、ANALYZE 语句
29、Flink SQL之DESCRIBE、EXPLAIN、USE、SHOW、LOAD、UNLOAD、SET、RESET、JAR、JOB Statements、UPDATE、DELETE（1）
29、Flink SQL之DESCRIBE、EXPLAIN、USE、SHOW、LOAD、UNLOAD、SET、RESET、JAR、JOB Statements、UPDATE、DELETE（2）
30、Flink SQL之SQL 客户端（通过kafka和filesystem的例子介绍了配置文件使用-表、视图等）
32、Flink table api和SQL 之用户自定义 Sources & Sinks实现及详细示例
41、Flink之Hive 方言介绍及详细示例
42、Flink 的table api与sql之Hive Catalog
43、Flink之Hive 读写及详细验证示例
44、Flink之module模块介绍及使用示例和Flink SQL使用hive内置函数及自定义函数详细示例–网上有些说法好像是错误的
文章目录
本文通过示例介绍了如何使用table api进行表、视图、窗口函数的操作，同时也介绍了table api对表的查询、过滤、列、聚合以及join操作。
关于表的set、order by、insert、group window、over window等相关操作详见下篇文章：17、Flink 之Table API: Table API 支持的操作（2）。
本文依赖flink、kafka、hive集群能正常使用。
本文示例java api的实现是通过Flink 1.17版本做的示例，SQL是在Flink 1.17版本的环境中运行的。
本文分为5个部分，即入门示例、表的查询与过滤、表的列操作、表的聚合操作和表的join操作。
一、Table API介绍
Table API 是批处理和流处理的统一的关系型 API。Table API 的查询不需要修改代码就可以采用批输入或流输入来运行。Table API 是 SQL 语言的超集，并且是针对 Apache Flink 专门设计的。Table API 集成了 Scala，Java 和 Python 语言的 API。Table API 的查询是使用 Java，Scala 或 Python 语言嵌入的风格定义的，有诸如自动补全和语法校验的 IDE 支持，而不是像普通 SQL 一样使用字符串类型的值来指定查询。
Table API 和 Flink SQL 共享许多概念以及部分集成的 API。通过查看公共概念 & API来学习如何注册表或如何创建一个表对象。流概念页面讨论了诸如动态表和时间属性等流特有的概念。
具体内容参照下文：
15、Flink 的table api与sql之流式概念-详解的介绍了动态表、时间属性配置（如何处理更新结果）、时态表、流上的join、流上的确定性以及查询配置
1、入门示例
1）、maven依赖
本文中所有示例，若无特别说明，均使用如下maven依赖。
<properties><encoding>UTF-8</encoding><project.build.sourceEncoding>UTF-8</project.build.sourceEncoding><maven.compiler.source>1.8</maven.compiler.source><maven.compiler.target>1.8</maven.compiler.target><java.version>1.8</java.version><scala.version>2.12</scala.version><flink.version>1.17.0</flink.version></properties><dependencies><!-- https://mvnrepository.com/artifact/org.apache.flink/flink-clients --><dependency><groupId>org.apache.flink</groupId><artifactId>flink-clients</artifactId><version>${flink.version}</version><scope>provided</scope></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-java</artifactId><version>${flink.version}</version><scope>provided</scope></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-table-common</artifactId><version>${flink.version}</version><scope>provided</scope></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-streaming-java</artifactId><version>${flink.version}</version><scope>provided</scope></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-table-api-java-bridge</artifactId><version>${flink.version}</version><scope>provided</scope></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-sql-gateway</artifactId><version>${flink.version}</version><scope>provided</scope></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-csv</artifactId><version>${flink.version}</version><scope>provided</scope></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-json</artifactId><version>${flink.version}</version><scope>provided</scope></dependency><!-- https://mvnrepository.com/artifact/org.apache.flink/flink-table-planner --><dependency><groupId>org.apache.flink</groupId><artifactId>flink-table-planner_2.12</artifactId><version>${flink.version}</version><scope>provided</scope></dependency><!-- https://mvnrepository.com/artifact/org.apache.flink/flink-table-api-java-uber --><dependency><groupId>org.apache.flink</groupId><artifactId>flink-table-api-java-uber</artifactId><version>${flink.version}</version><scope>provided</scope></dependency><!-- https://mvnrepository.com/artifact/org.apache.flink/flink-table-runtime --><dependency><groupId>org.apache.flink</groupId><artifactId>flink-table-runtime</artifactId><version>${flink.version}</version><scope>provided</scope></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-connector-jdbc</artifactId><version>3.1.0-1.17</version></dependency><dependency><groupId>mysql</groupId><artifactId>mysql-connector-java</artifactId><version>5.1.38</version></dependency><!-- https://mvnrepository.com/artifact/org.apache.flink/flink-connector-hive --><dependency><groupId>org.apache.flink</groupId><artifactId>flink-connector-hive_2.12</artifactId><version>1.17.0</version></dependency><dependency><groupId>org.apache.hive</groupId><artifactId>hive-exec</artifactId><version>3.1.2</version></dependency><!-- flink连接器 --><!-- https://mvnrepository.com/artifact/org.apache.flink/flink-connector-kafka --><dependency><groupId>org.apache.flink</groupId><artifactId>flink-connector-kafka</artifactId><version>${flink.version}</version></dependency><!-- https://mvnrepository.com/artifact/org.apache.flink/flink-sql-connector-kafka --><dependency><groupId>org.apache.flink</groupId><artifactId>flink-sql-connector-kafka</artifactId><version>${flink.version}</version><scope>provided</scope></dependency><!-- https://mvnrepository.com/artifact/org.apache.commons/commons-compress --><dependency><groupId>org.apache.commons</groupId><artifactId>commons-compress</artifactId><version>1.24.0</version></dependency><dependency><groupId>org.projectlombok</groupId><artifactId>lombok</artifactId><version>1.18.2</version></dependency></dependencies>
2）、入门示例1-通过SQL和API创建表
importstaticorg.apache.flink.table.api.Expressions.$;importstaticorg.apache.flink.table.api.Expressions.and;importstaticorg.apache.flink.table.api.Expressions.lit;importstaticorg.apache.flink.table.expressions.ApiExpressionUtils.unresolvedCall;importjava.sql.Timestamp;importjava.time.Duration;importjava.util.Arrays;importjava.util.Collections;importjava.util.HashMap;importjava.util.List;importorg.apache.flink.api.common.eventtime.WatermarkStrategy;importorg.apache.flink.api.common.typeinfo.TypeHint;importorg.apache.flink.api.common.typeinfo.TypeInformation;importorg.apache.flink.api.java.tuple.Tuple2;importorg.apache.flink.api.java.tuple.Tuple3;importorg.apache.flink.streaming.api.datastream.DataStream;importorg.apache.flink.streaming.api.environment.StreamExecutionEnvironment;importorg.apache.flink.streaming.connectors.kafka.table.KafkaConnectorOptions;importorg.apache.flink.table.api.DataTypes;importorg.apache.flink.table.api.EnvironmentSettings;importorg.apache.flink.table.api.Over;importorg.apache.flink.table.api.Schema;importorg.apache.flink.table.api.Table;importorg.apache.flink.table.api.TableDescriptor;importorg.apache.flink.table.api.TableEnvironment;importorg.apache.flink.table.api.Tumble;importorg.apache.flink.table.api.bridge.java.StreamTableEnvironment;importorg.apache.flink.table.catalog.CatalogDatabaseImpl;importorg.apache.flink.table.catalog.CatalogView;importorg.apache.flink.table.catalog.Column;importorg.apache.flink.table.catalog.ObjectPath;importorg.apache.flink.table.catalog.ResolvedCatalogView;importorg.apache.flink.table.catalog.ResolvedSchema;importorg.apache.flink.table.catalog.hive.HiveCatalog;importorg.apache.flink.table.functions.BuiltInFunctionDefinitions;importorg.apache.flink.types.Row;importcom.google.common.collect.Lists;importlombok.AllArgsConstructor;importlombok.Data;importlombok.NoArgsConstructor;/**
* @author alanchan
*
*/publicclassTestTableAPIDemo{/**
* @param args
* @throws Exception
*/publicstaticvoidmain(String[] args)throwsException{testCreateTableBySQLAndAPI();}staticvoidtestCreateTableBySQLAndAPI()throwsException{// EnvironmentSettings env = EnvironmentSettings.newInstance().inStreamingMode().build();// TableEnvironment tenv = TableEnvironment.create(env);StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();StreamTableEnvironment tenv =StreamTableEnvironment.create(env);// SQL 创建输入表// String sourceSql = "CREATE TABLE Alan_KafkaTable (\r\n" + // " `event_time` TIMESTAMP(3) METADATA FROM 'timestamp',\r\n" + // " `partition` BIGINT METADATA VIRTUAL,\r\n" + // " `offset` BIGINT METADATA VIRTUAL,\r\n" + // " `user_id` BIGINT,\r\n" + // " `item_id` BIGINT,\r\n" + // " `behavior` STRING\r\n" + // ") WITH (\r\n" + // " 'connector' = 'kafka',\r\n" + // " 'topic' = 'user_behavior',\r\n" + // " 'properties.bootstrap.servers' = '192.168.10.41:9092,192.168.10.42:9092,192.168.10.43:9092',\r\n" + // " 'properties.group.id' = 'testGroup',\r\n" + // " 'scan.startup.mode' = 'earliest-offset',\r\n" + // " 'format' = 'csv'\r\n" + // ");";// tenv.executeSql(sourceSql);//API创建表Schema schema =Schema.newBuilder().columnByMetadata("event_time",DataTypes.TIME(3),"timestamp").columnByMetadata("partition",DataTypes.BIGINT(),true).columnByMetadata("offset",DataTypes.BIGINT(),true).column("user_id",DataTypes.BIGINT()).column("item_id",DataTypes.BIGINT()).column("behavior",DataTypes.STRING()).build();TableDescriptor kafkaDescriptor =TableDescriptor.forConnector("kafka").comment("kafka source table").schema(schema).option(KafkaConnectorOptions.TOPIC,Lists.newArrayList("user_behavior")).option(KafkaConnectorOptions.PROPS_BOOTSTRAP_SERVERS,"192.168.10.41:9092,192.168.10.42:9092,192.168.10.43:9092").option(KafkaConnectorOptions.PROPS_GROUP_ID,"testGroup").option("scan.startup.mode","earliest-offset").format("csv").build();
tenv.createTemporaryTable("Alan_KafkaTable", kafkaDescriptor);//查询String sql ="select * from Alan_KafkaTable ";Table resultQuery = tenv.sqlQuery(sql);DataStream<Tuple2<Boolean,Row>> resultDS = tenv.toRetractStream(resultQuery,Row.class);// 6、sink
resultDS.print();// 7、执行
env.execute();//kafka中输入测试数据// 1,1001,login// 1,2001,p_read//程序运行控制台输入如下// 11> (true,+I[16:32:19.923, 0, 0, 1, 1001, login])// 11> (true,+I[16:32:32.258, 0, 1, 1, 2001, p_read])}@Data@NoArgsConstructor@AllArgsConstructorpublicstaticclassUser{privatelong id;privateString name;privateint age;privateLong rowtime;}}
上面例子是通过SQL和API两种方式创建一张名称为Alan_KafkaTable 的连接器为kafka的表，然后查询其数据。如需要需要进行聚合操作，直接编写sql即可。
3）、入门示例2-通过SQL和API创建视图
程序的整体框架使用入门示例1的，此处仅仅列出创建视图的方法
staticvoidtestCreateViewByAPI()throwsException{StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();StreamTableEnvironment tenv =StreamTableEnvironment.create(env);// SQL 创建输入表String sourceSql ="CREATE TABLE Alan_KafkaTable (\r\n"+" `event_time` TIMESTAMP(3) METADATA FROM 'timestamp',\r\n"+" `partition` BIGINT METADATA VIRTUAL,\r\n"+" `offset` BIGINT METADATA VIRTUAL,\r\n"+" `user_id` BIGINT,\r\n"+" `item_id` BIGINT,\r\n"+" `behavior` STRING\r\n"+") WITH (\r\n"+" 'connector' = 'kafka',\r\n"+" 'topic' = 'user_behavior',\r\n"+" 'properties.bootstrap.servers' = '192.168.10.41:9092,192.168.10.42:9092,192.168.10.43:9092',\r\n"+" 'properties.group.id' = 'testGroup',\r\n"+" 'scan.startup.mode' = 'earliest-offset',\r\n"+" 'format' = 'csv'\r\n"+");";
tenv.executeSql(sourceSql);// 创建视图String catalogName ="alan_hive";String defaultDatabase ="default";String databaseName ="viewtest_db";String hiveConfDir ="/usr/local/bigdata/apache-hive-3.1.2-bin/conf";HiveCatalog hiveCatalog =newHiveCatalog(catalogName, defaultDatabase, hiveConfDir);
tenv.registerCatalog(catalogName, hiveCatalog);
tenv.useCatalog(catalogName);
hiveCatalog.createDatabase(databaseName,newCatalogDatabaseImpl(newHashMap(), hiveConfDir){},true);
tenv.useDatabase(databaseName);String viewName ="Alan_KafkaView";String originalQuery ="select user_id , behavior from Alan_KafkaTable group by user_id ,behavior ";String expandedQuery ="SELECT user_id , behavior FROM "+databaseName+"."+"Alan_KafkaTable group by user_id ,behavior ";String comment ="this is a comment";ObjectPath path=newObjectPath(databaseName, viewName);createView(originalQuery,expandedQuery,comment,hiveCatalog,path);// 查询视图String queryViewSQL =" select * from Alan_KafkaView ";Table queryViewResult = tenv.sqlQuery(queryViewSQL);DataStream<Tuple2<Boolean,Row>> resultDS = tenv.toRetractStream(queryViewResult,Row.class);// 6、sink
resultDS.print();// 7、执行
env.execute();//kafka中输入测试数据// 1,1001,login// 1,2001,p_read//程序运行控制台输入如下// 3> (true,+I[1, login])// 14> (true,+I[1, p_read])}staticvoidcreateView(String originalQuery,String expandedQuery,String comment,HiveCatalog hiveCatalog,ObjectPath path)throwsException{ResolvedSchema resolvedSchema =newResolvedSchema(Arrays.asList(Column.physical("user_id",DataTypes.INT()),Column.physical("behavior",DataTypes.STRING())),Collections.emptyList(),null);CatalogView origin =CatalogView.of(Schema.newBuilder().fromResolvedSchema(resolvedSchema).build(),
comment,
originalQuery,
expandedQuery,Collections.emptyMap());CatalogView view =newResolvedCatalogView(origin, resolvedSchema);
hiveCatalog.createTable(path, view,false);}staticvoidtestCreateViewBySQL()throwsException{StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();StreamTableEnvironment tenv =StreamTableEnvironment.create(env);// SQL 创建输入表String sourceSql ="CREATE TABLE Alan_KafkaTable (\r\n"+" `event_time` TIMESTAMP(3) METADATA FROM 'timestamp',\r\n"+" `partition` BIGINT METADATA VIRTUAL,\r\n"+" `offset` BIGINT METADATA VIRTUAL,\r\n"+" `user_id` BIGINT,\r\n"+" `item_id` BIGINT,\r\n"+" `behavior` STRING\r\n"+") WITH (\r\n"+" 'connector' = 'kafka',\r\n"+" 'topic' = 'user_behavior',\r\n"+" 'properties.bootstrap.servers' = '192.168.10.41:9092,192.168.10.42:9092,192.168.10.43:9092',\r\n"+" 'properties.group.id' = 'testGroup',\r\n"+" 'scan.startup.mode' = 'earliest-offset',\r\n"+" 'format' = 'csv'\r\n"+");";
tenv.executeSql(sourceSql);//String sql ="select user_id , behavior from Alan_KafkaTable group by user_id ,behavior ";Table resultQuery = tenv.sqlQuery(sql);
tenv.createTemporaryView("Alan_KafkaView", resultQuery);String queryViewSQL =" select * from Alan_KafkaView ";Table queryViewResult = tenv.sqlQuery(queryViewSQL);DataStream<Tuple2<Boolean,Row>> resultDS = tenv.toRetractStream(queryViewResult,Row.class);// 6、sink
resultDS.print();// 7、执行
env.execute();//kafka中输入测试数据// 1,1001,login// 1,2001,p_read//程序运行控制台输入如下// 3> (true,+I[1, login])// 14> (true,+I[1, p_read])}
本示例通过sql和api创建视图，视图是user_id和behavior分组的结果，如果需要聚合直接使用sql即可。
4）、入门示例-通过API查询表（使用窗口函数）
本示例实现了Tumble和Over窗口。
如果使用sql的窗口函数参考：
27、Flink 的SQL之SELECT (Group Aggregation分组聚合、Over Aggregation Over聚合 和 Window Join 窗口关联)介绍及详细示例（5）
staticvoidtestQueryTableWithWindwosByAPI()throwsException{StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();StreamTableEnvironment tenv =StreamTableEnvironment.create(env);DataStream<User> users = env.fromCollection(userList).assignTimestampsAndWatermarks(WatermarkStrategy.<User>forBoundedOutOfOrderness(Duration.ofSeconds(1)).withTimestampAssigner((user, recordTimestamp)-> user.getRowtime()));Table usersTable = tenv.fromDataStream(users, $("id"), $("name"), $("age"),$("rt").rowtime());// tumbleTable result = usersTable
.filter(and(// $("name").equals("alanchan"),// $("age").between(1, 20),
$("name").isNotNull(),
$("age").isGreaterOrEqual(19))).window(Tumble.over(lit(1).hours()).on($("rt")).as("hourlyWindow"))// 定义滚动窗口并给窗口起一个别名.groupBy($("name"),$("hourlyWindow"))// 窗口必须出现的分组字段中.select($("name"),$("name").count().as("count(*)"), $("hourlyWindow").start(), $("hourlyWindow").end());
result.printSchema();DataStream<Tuple2<Boolean,Row>> resultDS = tenv.toRetractStream(result,Row.class);
resultDS.print();// over
usersTable
.window(Over.partitionBy($("name")).orderBy($("rt")).preceding(unresolvedCall(BuiltInFunctionDefinitions.UNBOUNDED_RANGE)).as("hourlyWindow")).select($("id"), $("rt"), $("id").count().over($("hourlyWindow")).as("count_t")).execute().print();
env.execute();}
Table API 支持 Scala, Java 和 Python 语言。Scala 语言的 Table API 利用了 Scala 表达式，Java 语言的 Table API 支持 DSL 表达式和解析并转换为等价表达式的字符串，Python 语言的 Table API 仅支持解析并转换为等价表达式的字符串。
整体来看，使用API操作Flink 的表，可能会比较麻烦，大多数还是使用sql比较简单，如果sql不满足的情况下，api是一个补充。
2、表的查询、过滤操作
Table API支持如下操作。请注意不是所有的操作都可以既支持流也支持批；这些操作都具有相应的标记。
具体示例如下，运行结果在源文件中
importstaticorg.apache.flink.table.api.Expressions.$;importstaticorg.apache.flink.table.api.Expressions.row;importstaticorg.apache.flink.table.api.Expressions.and;importorg.apache.flink.api.java.tuple.Tuple2;importorg.apache.flink.streaming.api.datastream.DataStream;importorg.apache.flink.streaming.api.environment.StreamExecutionEnvironment;importorg.apache.flink.table.api.DataTypes;importorg.apache.flink.table.api.Table;importorg.apache.flink.table.api.bridge.java.StreamTableEnvironment;importorg.apache.flink.types.Row;/**
* @author alanchan
*
*/publicclassTestTableAPIOperationDemo{staticString sourceSql ="CREATE TABLE Alan_KafkaTable (\r\n"+" `event_time` TIMESTAMP(3) METADATA FROM 'timestamp',\r\n"+" `partition` BIGINT METADATA VIRTUAL,\r\n"+" `offset` BIGINT METADATA VIRTUAL,\r\n"+" `user_id` BIGINT,\r\n"+" `item_id` BIGINT,\r\n"+" `behavior` STRING\r\n"+") WITH (\r\n"+" 'connector' = 'kafka',\r\n"+" 'topic' = 'user_behavior',\r\n"+" 'properties.bootstrap.servers' = '192.168.10.41:9092,192.168.10.42:9092,192.168.10.43:9092',\r\n"+" 'properties.group.id' = 'testGroup',\r\n"+" 'scan.startup.mode' = 'earliest-offset',\r\n"+" 'format' = 'csv'\r\n"+");";/**
* @param args
* @throws Exception
*/publicstaticvoidmain(String[] args)throwsException{// test1();// test2();test3();}staticvoidtest3()throwsException{StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();StreamTableEnvironment tenv =StreamTableEnvironment.create(env);// 建表
tenv.executeSql(sourceSql);Table table1 = tenv.from("Alan_KafkaTable");// 重命名字段。Table result = table1.as("a","b","c","d","e","f");DataStream<Tuple2<Boolean,Row>> resultDS = tenv.toRetractStream(result,Row.class);
resultDS.print();//11> (true,+I[2023-11-01T11:00:30.183, 0, 2, 1, 1002, login])//和 SQL 的 WHERE 子句类似。 过滤掉未验证通过过滤谓词的行。Table table2 = result.where($("f").isEqual("login"));DataStream<Tuple2<Boolean,Row>> result2DS = tenv.toRetractStream(table2,Row.class);
result2DS.print();//11> (true,+I[2023-11-01T11:00:30.183, 0, 2, 1, 1002, login])Table table3 = result.where($("f").isNotEqual("login"));DataStream<Tuple2<Boolean,Row>> result3DS = tenv.toRetractStream(table3,Row.class);
result3DS.print();// 没有匹配条件的记录，无输出Table table4 = result
.filter(and(
$("f").isNotNull(),// $("d").isGreater(1)
$("e").isNotNull()));DataStream<Tuple2<Boolean,Row>> result4DS = tenv.toRetractStream(table4,Row.class);
result4DS.print("test filter:");//test filter::11> (true,+I[2023-11-01T11:00:30.183, 0, 2, 1, 1002, login])
env.execute();}/**
* 和 SQL 查询中的 VALUES 子句类似。 基于提供的行生成一张内联表。
*
* 你可以使用 row(...) 表达式创建复合行：
*
* @throws Exception
*/staticvoidtest2()throwsException{StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();StreamTableEnvironment tenv =StreamTableEnvironment.create(env);Table table = tenv.fromValues(row(1,"ABC"),row(2L,"ABCDE"));
table.printSchema();// (// `f0` BIGINT NOT NULL,// `f1` VARCHAR(5) NOT NULL// )DataStream<Tuple2<Boolean,Row>> resultDS = tenv.toRetractStream(table,Row.class);
resultDS.print();// 1> (true,+I[2, ABCDE])// 2> (true,+I[1, ABC])Table table2 = tenv.fromValues(DataTypes.ROW(DataTypes.FIELD("id",DataTypes.DECIMAL(10,2)),DataTypes.FIELD("name",DataTypes.STRING())),row(1,"ABCD"),row(2L,"ABCDEF"));
table2.printSchema();// (// `id` DECIMAL(10, 2),// `name` STRING// )DataStream<Tuple2<Boolean,Row>> result2DS = tenv.toRetractStream(table2,Row.class);
result2DS.print();// 15> (true,+I[2.00, ABCDEF])// 14> (true,+I[1.00, ABCD])
env.execute();}/**
* 和 SQL 查询的 FROM 子句类似。 执行一个注册过的表的扫描。
*
* @throws Exception
*/staticvoidtest1()throwsException{StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();StreamTableEnvironment tenv =StreamTableEnvironment.create(env);// 建表
tenv.executeSql(sourceSql);// 查询// tenv.from("Alan_KafkaTable").execute().print();// kafka输入数据// 1,1002,login// 应用程序控制台输出如下// +----+-------------------------+----------------------+----------------------+----------------------+----------------------+--------------------------------+// | op | event_time | partition | offset | user_id | item_id | behavior |// +----+-------------------------+----------------------+----------------------+----------------------+----------------------+--------------------------------+// | +I | 2023-11-01 11:00:30.183 | 0 | 2 | 1 | 1002 | login |Table temp = tenv.from("Alan_KafkaTable");//和 SQL 的 SELECT 子句类似。 执行一个 select 操作Table result1 = temp.select($("user_id"), $("item_id").as("behavior"), $("event_time"));DataStream<Tuple2<Boolean,Row>> result1DS = tenv.toRetractStream(result1,Row.class);// result1DS.print();// 11> (true,+I[1, 1002, 2023-11-01T11:00:30.183])//选择星号（*）作为通配符，select 表中的所有列。Table result2 = temp.select($("*"));DataStream<Tuple2<Boolean,Row>> result2DS = tenv.toRetractStream(result2,Row.class);
result2DS.print();// 11> (true,+I[2023-11-01T11:00:30.183, 0, 2, 1, 1002, login])
env.execute();}}
3、表的列操作
具体示例如下，运行结果在源文件中
importstaticorg.apache.flink.table.api.Expressions.$;importstaticorg.apache.flink.table.api.Expressions.row;importstaticorg.apache.flink.table.api.Expressions.and;importstaticorg.apache.flink.table.api.Expressions.concat;importorg.apache.flink.api.java.tuple.Tuple2;importorg.apache.flink.streaming.api.datastream.DataStream;importorg.apache.flink.streaming.api.environment.StreamExecutionEnvironment;importorg.apache.flink.table.api.DataTypes;importorg.apache.flink.table.api.Table;importorg.apache.flink.table.api.bridge.java.StreamTableEnvironment;importorg.apache.flink.types.Row;/**
* @author alanchan
*
*/publicclassTestTableAPIOperationDemo{staticString sourceSql ="CREATE TABLE Alan_KafkaTable (\r\n"+" `event_time` TIMESTAMP(3) METADATA FROM 'timestamp',\r\n"+" `partition` BIGINT METADATA VIRTUAL,\r\n"+" `offset` BIGINT METADATA VIRTUAL,\r\n"+" `user_id` BIGINT,\r\n"+" `item_id` BIGINT,\r\n"+" `behavior` STRING\r\n"+") WITH (\r\n"+" 'connector' = 'kafka',\r\n"+" 'topic' = 'user_behavior',\r\n"+" 'properties.bootstrap.servers' = '192.168.10.41:9092,192.168.10.42:9092,192.168.10.43:9092',\r\n"+" 'properties.group.id' = 'testGroup',\r\n"+" 'scan.startup.mode' = 'earliest-offset',\r\n"+" 'format' = 'csv'\r\n"+");";/**
* @param args
* @throws Exception
*/publicstaticvoidmain(String[] args)throwsException{// test1();// test2();test3();}staticvoidtest3()throwsException{StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();StreamTableEnvironment tenv =StreamTableEnvironment.create(env);// 建表
tenv.executeSql(sourceSql);Table table1 = tenv.from("Alan_KafkaTable");// 重命名字段。Table result = table1.as("a","b","c","d","e","f");DataStream<Tuple2<Boolean,Row>> resultDS = tenv.toRetractStream(result,Row.class);
resultDS.print();//11> (true,+I[2023-11-01T11:00:30.183, 0, 2, 1, 1002, login])//和 SQL 的 WHERE 子句类似。 过滤掉未验证通过过滤谓词的行。Table table2 = result.where($("f").isEqual("login"));DataStream<Tuple2<Boolean,Row>> result2DS = tenv.toRetractStream(table2,Row.class);
result2DS.print();//11> (true,+I[2023-11-01T11:00:30.183, 0, 2, 1, 1002, login])Table table3 = result.where($("f").isNotEqual("login"));DataStream<Tuple2<Boolean,Row>> result3DS = tenv.toRetractStream(table3,Row.class);
result3DS.print();// 没有匹配条件的记录，无输出Table table4 = result
.filter(and(
$("f").isNotNull(),// $("d").isGreater(1)
$("e").isNotNull()));DataStream<Tuple2<Boolean,Row>> result4DS = tenv.toRetractStream(table4,Row.class);
result4DS.print("test filter:");//test filter::11> (true,+I[2023-11-01T11:00:30.183, 0, 2, 1, 1002, login])
env.execute();}/**
* 和 SQL 查询中的 VALUES 子句类似。 基于提供的行生成一张内联表。
*
* 你可以使用 row(...) 表达式创建复合行：
*
* @throws Exception
*/staticvoidtest2()throwsException{StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();StreamTableEnvironment tenv =StreamTableEnvironment.create(env);Table table = tenv.fromValues(row(1,"ABC"),row(2L,"ABCDE"));
table.printSchema();// (// `f0` BIGINT NOT NULL,// `f1` VARCHAR(5) NOT NULL// )DataStream<Tuple2<Boolean,Row>> resultDS = tenv.toRetractStream(table,Row.class);
resultDS.print();// 1> (true,+I[2, ABCDE])// 2> (true,+I[1, ABC])Table table2 = tenv.fromValues(DataTypes.ROW(DataTypes.FIELD("id",DataTypes.DECIMAL(10,2)),DataTypes.FIELD("name",DataTypes.STRING())),row(1,"ABCD"),row(2L,"ABCDEF"));
table2.printSchema();// (// `id` DECIMAL(10, 2),// `name` STRING// )DataStream<Tuple2<Boolean,Row>> result2DS = tenv.toRetractStream(table2,Row.class);
result2DS.print();// 15> (true,+I[2.00, ABCDEF])// 14> (true,+I[1.00, ABCD])
env.execute();}/**
* 和 SQL 查询的 FROM 子句类似。 执行一个注册过的表的扫描。
*
* @throws Exception
*/staticvoidtest1()throwsException{StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();StreamTableEnvironment tenv =StreamTableEnvironment.create(env);// 建表
tenv.executeSql(sourceSql);// 查询// tenv.from("Alan_KafkaTable").execute().print();// kafka输入数据// 1,1002,login// 应用程序控制台输出如下// +----+-------------------------+----------------------+----------------------+----------------------+----------------------+--------------------------------+// | op | event_time | partition | offset | user_id | item_id | behavior |// +----+-------------------------+----------------------+----------------------+----------------------+----------------------+--------------------------------+// | +I | 2023-11-01 11:00:30.183 | 0 | 2 | 1 | 1002 | login |Table temp = tenv.from("Alan_KafkaTable");//和 SQL 的 SELECT 子句类似。 执行一个 select 操作Table result1 = temp.select($("user_id"), $("item_id").as("behavior"), $("event_time"));DataStream<Tuple2<Boolean,Row>> result1DS = tenv.toRetractStream(result1,Row.class);// result1DS.print();// 11> (true,+I[1, 1002, 2023-11-01T11:00:30.183])//选择星号（*）作为通配符，select 表中的所有列。Table result2 = temp.select($("*"));DataStream<Tuple2<Boolean,Row>> result2DS = tenv.toRetractStream(result2,Row.class);
result2DS.print();// 11> (true,+I[2023-11-01T11:00:30.183, 0, 2, 1, 1002, login])
env.execute();}staticvoidtest5()throwsException{StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();StreamTableEnvironment tenv =StreamTableEnvironment.create(env);// 建表
tenv.executeSql(sourceSql);Table table = tenv.from("Alan_KafkaTable");//和 SQL 的 GROUP BY 子句类似。 使用分组键对行进行分组，使用伴随的聚合算子来按照组进行聚合行。Table result = table.groupBy($("user_id")).select($("user_id"), $("user_id").count().as("count(user_id)"));DataStream<Tuple2<Boolean,Row>> resultDS = tenv.toRetractStream(result,Row.class);
resultDS.print();// 12> (true,+I[1, 1])
env.execute();}staticvoidtest4()throwsException{StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();StreamTableEnvironment tenv =StreamTableEnvironment.create(env);// 建表
tenv.executeSql(sourceSql);Table table = tenv.from("Alan_KafkaTable");//执行字段添加操作。 如果所添加的字段已经存在，将抛出异常。Table result2 = table.addColumns($("behavior").plus(1).as("t_col1"));
result2.printSchema();// (// `event_time` TIMESTAMP(3),// `partition` BIGINT,// `offset` BIGINT,// `user_id` BIGINT,// `item_id` BIGINT,// `behavior` STRING,// `t_col1` STRING// )Table result = table.addColumns($("behavior").plus(1).as("t_col3"),concat($("behavior"),"alanchan").as("t_col4"));
result.printSchema();// (// `event_time` TIMESTAMP(3),// `partition` BIGINT,// `offset` BIGINT,// `user_id` BIGINT,// `item_id` BIGINT,// `behavior` STRING,// `t_col3` STRING,// `t_col4` STRING// )Table result3 = table.addColumns(concat($("behavior"),"alanchan").as("t_col4"));
result3.printSchema();// (// `event_time` TIMESTAMP(3),// `partition` BIGINT,// `offset` BIGINT,// `user_id` BIGINT,// `item_id` BIGINT,// `behavior` STRING,// `t_col4` STRING// )//执行字段添加操作。 如果添加的列名称和已存在的列名称相同，则已存在的字段将被替换。 此外，如果添加的字段里面有重复的字段名，则会使用最后一个字段。Table result4 = result3.addOrReplaceColumns(concat($("t_col4"),"alanchan").as("t_col"));
result4.printSchema();// (// `event_time` TIMESTAMP(3),// `partition` BIGINT,// `offset` BIGINT,// `user_id` BIGINT,// `item_id` BIGINT,// `behavior` STRING,// `t_col4` STRING,// `t_col` STRING// )Table result5 = result4.dropColumns($("t_col4"), $("t_col"));
result5.printSchema();// (// `event_time` TIMESTAMP(3),// `partition` BIGINT,// `offset` BIGINT,// `user_id` BIGINT,// `item_id` BIGINT,// `behavior` STRING// )//执行字段重命名操作。 字段表达式应该是别名表达式，并且仅当字段已存在时才能被重命名。Table result6 = result4.renameColumns($("t_col4").as("col1"), $("t_col").as("col2"));
result6.printSchema();// (// `event_time` TIMESTAMP(3),// `partition` BIGINT,// `offset` BIGINT,// `user_id` BIGINT,// `item_id` BIGINT,// `behavior` STRING,// `col1` STRING,// `col2` STRING// )DataStream<Tuple2<Boolean,Row>> resultDS = tenv.toRetractStream(table,Row.class);
resultDS.print();// 11> (true,+I[2023-11-01T11:00:30.183, 0, 2, 1, 1002, login])
env.execute();}}
4、表的聚合操作
1）、示例代码公共部分
本部分仅仅就是用的公共对象，比如User的定义，和需要引入的包
importstaticorg.apache.flink.table.api.Expressions.$;importstaticorg.apache.flink.table.api.Expressions.lit;importstaticorg.apache.flink.table.expressions.ApiExpressionUtils.unresolvedCall;importjava.time.Duration;importjava.util.Arrays;importjava.util.List;importorg.apache.flink.api.common.eventtime.WatermarkStrategy;importorg.apache.flink.api.java.tuple.Tuple2;importorg.apache.flink.streaming.api.datastream.DataStream;importorg.apache.flink.streaming.api.environment.StreamExecutionEnvironment;importorg.apache.flink.table.api.Over;importorg.apache.flink.table.api.Table;importorg.apache.flink.table.api.Tumble;importorg.apache.flink.table.api.bridge.java.StreamTableEnvironment;importorg.apache.flink.table.functions.BuiltInFunctionDefinitions;importorg.apache.flink.types.Row;importlombok.AllArgsConstructor;importlombok.Data;importlombok.NoArgsConstructor;/**
* @author alanchan
*
*/publicclassTestTableAPIOperationDemo2{finalstaticList<User> userList =Arrays.asList(newUser(1L,"alan",18,1698742358391L),newUser(2L,"alan",19,1698742359396L),newUser(3L,"alan",25,1698742360407L),newUser(4L,"alanchan",28,1698742361409L),newUser(5L,"alanchan",29,1698742362424L));/**
* @param args
* @throws Exception
*/publicstaticvoidmain(String[] args)throwsException{// test1();// test2();// test3();test4();}@Data@NoArgsConstructor@AllArgsConstructorpublicstaticclassUser{privatelong id;privateString name;privateint balance;privateLong rowtime;}}
2）、group by
本示例仅仅展示了group by操作，比较简单。
staticvoidtest2()throwsException{StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();StreamTableEnvironment tenv =StreamTableEnvironment.create(env);// 建表
tenv.executeSql(sourceSql);Table table = tenv.from("Alan_KafkaTable");//和 SQL 的 GROUP BY 子句类似。 使用分组键对行进行分组，使用伴随的聚合算子来按照组进行聚合行。Table result = table.groupBy($("user_id")).select($("user_id"), $("user_id").count().as("count(user_id)"));DataStream<Tuple2<Boolean,Row>> resultDS = tenv.toRetractStream(result,Row.class);
resultDS.print();// 12> (true,+I[1, 1])
env.execute();}
3）、GroupBy Window Aggregation
使用分组窗口结合单个或者多个分组键对表进行分组和聚合。
staticvoidtest2()throwsException{StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();StreamTableEnvironment tenv =StreamTableEnvironment.create(env);DataStream<User> users = env.fromCollection(userList).assignTimestampsAndWatermarks(WatermarkStrategy.<User>forBoundedOutOfOrderness(Duration.ofSeconds(1)).withTimestampAssigner((user, recordTimestamp)-> user.getRowtime()));Table usersTable = tenv.fromDataStream(users, $("id"), $("name"), $("balance"),$("rowtime").rowtime());//使用分组窗口结合单个或者多个分组键对表进行分组和聚合。Table result = usersTable
.window(Tumble.over(lit(5).minutes()).on($("rowtime")).as("w"))// 定义窗口.groupBy($("name"), $("w"))// 按窗口和键分组// 访问窗口属性并聚合.select(
$("name"),
$("w").start(),
$("w").end(),
$("w").rowtime(),
$("balance").sum().as("sum(balance)"));DataStream<Tuple2<Boolean,Row>> resultDS = tenv.toRetractStream(result,Row.class);
resultDS.print();// 2> (true,+I[alan, 2023-10-31T08:50, 2023-10-31T08:55, 2023-10-31T08:54:59.999, 62])// 16> (true,+I[alanchan, 2023-10-31T08:50, 2023-10-31T08:55, 2023-10-31T08:54:59.999, 57])
env.execute();}
4）、Over Window Aggregation
和 SQL 的 OVER 子句类似。
staticvoidtest3()throwsException{StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();StreamTableEnvironment tenv =StreamTableEnvironment.create(env);DataStream<User> users = env.fromCollection(userList).assignTimestampsAndWatermarks(WatermarkStrategy.<User>forBoundedOutOfOrderness(Duration.ofSeconds(1)).withTimestampAssigner((user, recordTimestamp)-> user.getRowtime()));Table usersTable = tenv.fromDataStream(users, $("id"), $("name"), $("balance"),$("rowtime").rowtime());// 所有的聚合必须定义在同一个窗口上，比如同一个分区、排序和范围内。目前只支持 PRECEDING 到当前行范围（无界或有界）的窗口。//尚不支持 FOLLOWING 范围的窗口。ORDER BY 操作必须指定一个单一的时间属性。Table result = usersTable
// 定义窗口.window(Over.partitionBy($("name")).orderBy($("rowtime")).preceding(unresolvedCall(BuiltInFunctionDefinitions.UNBOUNDED_RANGE)).following(unresolvedCall(BuiltInFunctionDefinitions.CURRENT_RANGE)).as("w"))// 滑动聚合.select(
$("id"),
$("balance").avg().over($("w")),
$("balance").max().over($("w")),
$("balance").min().over($("w")));DataStream<Tuple2<Boolean,Row>> resultDS = tenv.toRetractStream(result,Row.class);
resultDS.print();// 2> (true,+I[1, 18, 18, 18])// 16> (true,+I[4, 28, 28, 28])// 2> (true,+I[2, 18, 19, 18])// 16> (true,+I[5, 28, 29, 28])// 2> (true,+I[3, 20, 25, 18])
env.execute();}
5）、Distinct Aggregation
/**
* 和 SQL DISTINCT 聚合子句类似，例如 COUNT(DISTINCT a)。
* Distinct 聚合声明的聚合函数（内置或用户定义的）仅应用于互不相同的输入值。
* Distinct 可以应用于 GroupBy Aggregation、GroupBy Window Aggregation 和 Over Window Aggregation。
* @throws Exception
*/staticvoidtest4()throwsException{StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();StreamTableEnvironment tenv =StreamTableEnvironment.create(env);DataStream<User> users = env.fromCollection(userList).assignTimestampsAndWatermarks(WatermarkStrategy.<User>forBoundedOutOfOrderness(Duration.ofSeconds(1)).withTimestampAssigner((user, recordTimestamp)-> user.getRowtime()));Table usersTable = tenv.fromDataStream(users, $("id"), $("name"), $("balance"),$("rowtime").rowtime());// 按属性分组后的的互异（互不相同、去重）聚合Table groupByDistinctResult = usersTable
.groupBy($("name")).select($("name"), $("balance").sum().distinct().as("sum_balance"));DataStream<Tuple2<Boolean,Row>> resultDS = tenv.toRetractStream(groupByDistinctResult,Row.class);// resultDS.print();// 2> (true,+I[alan, 18])// 16> (true,+I[alanchan, 28])// 16> (false,-U[alanchan, 28])// 2> (false,-U[alan, 18])// 16> (true,+U[alanchan, 57])// 2> (true,+U[alan, 37])// 2> (false,-U[alan, 37])// 2> (true,+U[alan, 62])//按属性、时间窗口分组后的互异（互不相同、去重）聚合Table groupByWindowDistinctResult = usersTable
.window(Tumble.over(lit(5).minutes()).on($("rowtime")).as("w")).groupBy($("name"), $("w")).select($("name"), $("balance").sum().distinct().as("sum_balance"));DataStream<Tuple2<Boolean,Row>> result2DS = tenv.toRetractStream(groupByDistinctResult,Row.class);// result2DS.print();// 16> (true,+I[alanchan, 28])// 2> (true,+I[alan, 18])// 16> (false,-U[alanchan, 28])// 2> (false,-U[alan, 18])// 16> (true,+U[alanchan, 57])// 2> (true,+U[alan, 37])// 2> (false,-U[alan, 37])// 2> (true,+U[alan, 62])//over window 上的互异（互不相同、去重）聚合Table result = usersTable
.window(Over.partitionBy($("name")).orderBy($("rowtime")).preceding(unresolvedCall(BuiltInFunctionDefinitions.UNBOUNDED_RANGE)).as("w")).select(
$("name"), $("balance").avg().distinct().over($("w")),
$("balance").max().over($("w")),
$("balance").min().over($("w")));DataStream<Tuple2<Boolean,Row>> result3DS = tenv.toRetractStream(result,Row.class);
result3DS.print();// 16> (true,+I[alanchan, 28, 28, 28])// 2> (true,+I[alan, 18, 18, 18])// 2> (true,+I[alan, 18, 19, 18])// 16> (true,+I[alanchan, 28, 29, 28])// 2> (true,+I[alan, 20, 25, 18])
env.execute();}
用户定义的聚合函数也可以与 DISTINCT 修饰符一起使用。如果计算不同（互异、去重的）值的聚合结果，则只需向聚合函数添加 distinct 修饰符即可。
Table orders = tEnv.from("Orders");// 对 user-defined aggregate functions 使用互异（互不相同、去重）聚合
tEnv.registerFunction("myUdagg",newMyUdagg());
orders.groupBy("users").select(
$("users"),call("myUdagg", $("points")).distinct().as("myDistinctResult"));
6）、Distinct
和 SQL 的 DISTINCT 子句类似。 返回具有不同组合值的记录。
staticvoidtest5()throwsException{StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();StreamTableEnvironment tenv =StreamTableEnvironment.create(env);List<User> userList =Arrays.asList(newUser(1L,"alan",18,1698742358391L),newUser(2L,"alan",19,1698742359396L),newUser(3L,"alan",25,1698742360407L),newUser(4L,"alanchan",28,1698742361409L),newUser(5L,"alanchan",29,1698742362424L),newUser(5L,"alanchan",29,1698742362424L));DataStream<User> users = env.fromCollection(userList).assignTimestampsAndWatermarks(WatermarkStrategy.<User>forBoundedOutOfOrderness(Duration.ofSeconds(1)).withTimestampAssigner((user, recordTimestamp)-> user.getRowtime()));Table usersTable = tenv.fromDataStream(users, $("id"), $("name"), $("balance"),$("rowtime").rowtime());// Table orders = tableEnv.from("Orders");Table result = usersTable.distinct();DataStream<Tuple2<Boolean,Row>> resultDS = tenv.toRetractStream(result,Row.class);
resultDS.print();// 数据集有6条记录，并且有一条是重复的，故只输出5条// 9> (true,+I[2, alan, 19, 2023-10-31T08:52:39.396])// 1> (true,+I[1, alan, 18, 2023-10-31T08:52:38.391])// 13> (true,+I[3, alan, 25, 2023-10-31T08:52:40.407])// 7> (true,+I[4, alanchan, 28, 2023-10-31T08:52:41.409])// 13> (true,+I[5, alanchan, 29, 2023-10-31T08:52:42.424])
env.execute();}
5、表的join操作
本部分介绍了表的join主要操作，比如内联接、外联接以及联接自定义函数等，其中时态表的联接以scala的示例进行说明。
关于自定义函数的联接将在flink 自定义函数中介绍，因为使用函数和联接本身关系不是非常密切。
19、Flink 的Table API 和 SQL 中的自定义函数（2）
1）、关于join的示例
importstaticorg.apache.flink.table.api.Expressions.$;importstaticorg.apache.flink.table.api.Expressions.and;importstaticorg.apache.flink.table.api.Expressions.call;importjava.util.Arrays;importjava.util.List;importorg.apache.flink.api.java.tuple.Tuple2;importorg.apache.flink.api.java.tuple.Tuple3;importorg.apache.flink.streaming.api.datastream.DataStream;importorg.apache.flink.streaming.api.environment.StreamExecutionEnvironment;importorg.apache.flink.table.api.Table;importorg.apache.flink.table.api.bridge.java.StreamTableEnvironment;importorg.apache.flink.table.functions.TableFunction;importorg.apache.flink.table.functions.TemporalTableFunction;importorg.apache.flink.types.Row;importlombok.AllArgsConstructor;importlombok.Data;importlombok.NoArgsConstructor;/**
* @author alanchan
*
*/publicclassTestTableAPIJoinOperationDemo{@Data@NoArgsConstructor@AllArgsConstructorpublicstaticclassUser{privatelong id;privateString name;privatedouble balance;privateLong rowtime;}@Data@NoArgsConstructor@AllArgsConstructorpublicstaticclassOrder{privatelong id;privatelong user_id;privatedouble amount;privateLong rowtime;}finalstaticList<User> userList =Arrays.asList(newUser(1L,"alan",18,1698742358391L),newUser(2L,"alan",19,1698742359396L),newUser(3L,"alan",25,1698742360407L),newUser(4L,"alanchan",28,1698742361409L),newUser(5L,"alanchan",29,1698742362424L));finalstaticList<Order> orderList =Arrays.asList(newOrder(1L,1,18,1698742358391L),newOrder(2L,2,19,1698742359396L),newOrder(3L,1,25,1698742360407L),newOrder(4L,3,28,1698742361409L),newOrder(5L,1,29,1698742362424L),newOrder(6L,4,49,1698742362424L));staticvoidtestInnerJoin()throwsException{StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();StreamTableEnvironment tenv =StreamTableEnvironment.create(env);DataStream<User> users = env.fromCollection(userList);Table usersTable = tenv.fromDataStream(users, $("id"), $("name"),$("balance"),$("rowtime"));DataStream<Order> orders = env.fromCollection(orderList);Table ordersTable = tenv.fromDataStream(orders, $("id"), $("user_id"), $("amount"),$("rowtime"));Table left = usersTable.select($("id").as("userId"), $("name"), $("balance"),$("rowtime").as("u_rowtime"));Table right = ordersTable.select($("id").as("orderId"), $("user_id"), $("amount"),$("rowtime").as("o_rowtime"));Table result = left.join(right).where($("user_id").isEqual($("userId"))).select($("orderId"), $("user_id"), $("amount"),$("o_rowtime"),$("name"),$("balance"));DataStream<Tuple2<Boolean,Row>> resultDS = tenv.toRetractStream(result,Row.class);
resultDS.print();// 15> (true,+I[4, 3, 28.0, 1698742361409, alan, 25])// 12> (true,+I[1, 1, 18.0, 1698742358391, alan, 18])// 3> (true,+I[6, 4, 49.0, 1698742362424, alanchan, 28])// 12> (true,+I[2, 2, 19.0, 1698742359396, alan, 19])// 12> (true,+I[3, 1, 25.0, 1698742360407, alan, 18])// 12> (true,+I[5, 1, 29.0, 1698742362424, alan, 18])
env.execute();}/**
* 和 SQL LEFT/RIGHT/FULL OUTER JOIN 子句类似。 关联两张表。 两张表必须有不同的字段名，并且必须定义至少一个等式连接谓词。
* @throws Exception
*/staticvoidtestOuterJoin()throwsException{StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();StreamTableEnvironment tenv =StreamTableEnvironment.create(env);DataStream<User> users = env.fromCollection(userList);Table usersTable = tenv.fromDataStream(users, $("id"), $("name"),$("balance"),$("rowtime"));DataStream<Order> orders = env.fromCollection(orderList);Table ordersTable = tenv.fromDataStream(orders, $("id"), $("user_id"), $("amount"),$("rowtime"));Table left = usersTable.select($("id").as("userId"), $("name"), $("balance"),$("rowtime").as("u_rowtime"));Table right = ordersTable.select($("id").as("orderId"), $("user_id"), $("amount"),$("rowtime").as("o_rowtime"));Table leftOuterResult = left.leftOuterJoin(right, $("user_id").isEqual($("userId"))).select($("orderId"), $("user_id"), $("amount"),$("o_rowtime"),$("name"),$("balance"));DataStream<Tuple2<Boolean,Row>> leftOuterResultDS = tenv.toRetractStream(leftOuterResult,Row.class);// leftOuterResultDS.print();// 12> (true,+I[null, null, null, null, alan, 18])// 3> (true,+I[null, null, null, null, alanchan, 28])// 12> (false,-D[null, null, null, null, alan, 18])// 12> (true,+I[1, 1, 18.0, 1698742358391, alan, 18])// 15> (true,+I[4, 3, 28.0, 1698742361409, alan, 25])// 12> (true,+I[null, null, null, null, alan, 19])// 3> (false,-D[null, null, null, null, alanchan, 28])// 12> (false,-D[null, null, null, null, alan, 19])// 3> (true,+I[6, 4, 49.0, 1698742362424, alanchan, 28])// 12> (true,+I[2, 2, 19.0, 1698742359396, alan, 19])// 12> (true,+I[3, 1, 25.0, 1698742360407, alan, 18])// 3> (true,+I[null, null, null, null, alanchan, 29])// 12> (true,+I[5, 1, 29.0, 1698742362424, alan, 18])Table rightOuterResult = left.rightOuterJoin(right, $("user_id").isEqual($("userId"))).select($("orderId"), $("user_id"), $("amount"),$("o_rowtime"),$("name"),$("balance"));DataStream<Tuple2<Boolean,Row>> rightOuterResultDS = tenv.toRetractStream(rightOuterResult,Row.class);// rightOuterResultDS.print();// 12> (true,+I[1, 1, 18.0, 1698742358391, alan, 18])// 3> (true,+I[6, 4, 49.0, 1698742362424, alanchan, 28])// 15> (true,+I[4, 3, 28.0, 1698742361409, alan, 25])// 12> (true,+I[2, 2, 19.0, 1698742359396, alan, 19])// 12> (true,+I[3, 1, 25.0, 1698742360407, alan, 18])// 12> (true,+I[5, 1, 29.0, 1698742362424, alan, 18])Table fullOuterResult = left.fullOuterJoin(right, $("user_id").isEqual($("userId"))).select($("orderId"), $("user_id"), $("amount"),$("o_rowtime"),$("name"),$("balance"));DataStream<Tuple2<Boolean,Row>> fullOuterResultDS = tenv.toRetractStream(fullOuterResult,Row.class);
fullOuterResultDS.print();// 3> (true,+I[6, 4, 49.0, 1698742362424, null, null])// 12> (true,+I[1, 1, 18.0, 1698742358391, null, null])// 15> (true,+I[4, 3, 28.0, 1698742361409, null, null])// 12> (false,-D[1, 1, 18.0, 1698742358391, null, null])// 3> (false,-D[6, 4, 49.0, 1698742362424, null, null])// 12> (true,+I[1, 1, 18.0, 1698742358391, alan, 18])// 3> (true,+I[6, 4, 49.0, 1698742362424, alanchan, 28])// 3> (true,+I[null, null, null, null, alanchan, 29])// 12> (true,+I[2, 2, 19.0, 1698742359396, null, null])// 12> (false,-D[2, 2, 19.0, 1698742359396, null, null])// 12> (true,+I[2, 2, 19.0, 1698742359396, alan, 19])// 15> (false,-D[4, 3, 28.0, 1698742361409, null, null])// 12> (true,+I[3, 1, 25.0, 1698742360407, alan, 18])// 15> (true,+I[4, 3, 28.0, 1698742361409, alan, 25])// 12> (true,+I[5, 1, 29.0, 1698742362424, alan, 18])
env.execute();}/**
* Interval join 是可以通过流模式处理的常规 join 的子集。
* Interval join 至少需要一个 equi-join 谓词和一个限制双方时间界限的 join 条件。
* 这种条件可以由两个合适的范围谓词（<、<=、>=、>）或一个比较两个输入表相同时间属性（即处理时间或事件时间）的等值谓词来定义。
* @throws Exception
*/staticvoidtestIntervalJoin()throwsException{StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();StreamTableEnvironment tenv =StreamTableEnvironment.create(env);DataStream<User> users = env.fromCollection(userList);Table usersTable = tenv.fromDataStream(users, $("id"), $("name"),$("balance"),$("rowtime"));DataStream<Order> orders = env.fromCollection(orderList);Table ordersTable = tenv.fromDataStream(orders, $("id"), $("user_id"), $("amount"),$("rowtime"));Table left = usersTable.select($("id").as("userId"), $("name"), $("balance"),$("rowtime").as("u_rowtime"));Table right = ordersTable.select($("id").as("orderId"), $("user_id"), $("amount"),$("rowtime").as("o_rowtime"));Table result = left.join(right).where(and(
$("user_id").isEqual($("userId")),
$("user_id").isLess(3)// $("u_rowtime").isGreaterOrEqual($("o_rowtime").minus(lit(5).minutes())),// $("u_rowtime").isLess($("o_rowtime").plus(lit(10).minutes())))).select($("orderId"), $("user_id"), $("amount"),$("o_rowtime"),$("name"),$("balance"));
result.printSchema();DataStream<Tuple2<Boolean,Row>> resultDS = tenv.toRetractStream(result,Row.class);
resultDS.print();// 12> (true,+I[1, 1, 18.0, 1698742358391, alan, 18.0])// 12> (true,+I[2, 2, 19.0, 1698742359396, alan, 19.0])// 12> (true,+I[3, 1, 25.0, 1698742360407, alan, 18.0])// 12> (true,+I[5, 1, 29.0, 1698742362424, alan, 18.0])
env.execute();}/**
* join 表和表函数的结果。左（外部）表的每一行都会 join 表函数相应调用产生的所有行。
* 如果表函数调用返回空结果，则删除左侧（外部）表的一行。
* 该示例为示例性的，具体的验证将在自定义函数中进行说明
*
* @throws Exception
*/staticvoidtestInnerJoinWithUDTF()throwsException{StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();StreamTableEnvironment tenv =StreamTableEnvironment.create(env);// 注册 User-Defined Table FunctionTableFunction<Tuple3<String,String,String>> split =newSplitFunction();
tenv.registerFunction("split", split);// joinDataStream<Order> orders = env.fromCollection(orderList);Table ordersTable = tenv.fromDataStream(orders, $("id"), $("user_id"), $("amount"),$("rowtime"));Table result = ordersTable
.joinLateral(call("split", $("c")).as("s","t","v")).select($("a"), $("b"), $("s"), $("t"), $("v"));
env.execute();}/**
* join 表和表函数的结果。左（外部）表的每一行都会 join 表函数相应调用产生的所有行。
* 如果表函数调用返回空结果，则保留相应的 outer（外部连接）行并用空值填充右侧结果。
* 目前，表函数左外连接的谓词只能为空或字面（常量）真。
* 该示例为示例性的，具体的验证将在自定义函数中进行说明
*
* @throws Exception
*/staticvoidtestLeftOuterJoinWithUDTF()throwsException{StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();StreamTableEnvironment tenv =StreamTableEnvironment.create(env);// 注册 User-Defined Table FunctionTableFunction<Tuple3<String,String,String>> split =newSplitFunction();
tenv.registerFunction("split", split);// joinDataStream<Order> orders = env.fromCollection(orderList);Table ordersTable = tenv.fromDataStream(orders, $("id"), $("user_id"), $("amount"),$("rowtime"));Table result = ordersTable
.leftOuterJoinLateral(call("split", $("c")).as("s","t","v")).select($("a"), $("b"), $("s"), $("t"), $("v"));
env.execute();}/**
* Temporal table 是跟踪随时间变化的表。
* Temporal table 函数提供对特定时间点 temporal table 状态的访问。
* 表与 temporal table 函数进行 join 的语法和使用表函数进行 inner join 的语法相同。
* 目前仅支持与 temporal table 的 inner join。
*
* @throws Exception
*/staticvoidtestJoinWithTemporalTable()throwsException{StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();StreamTableEnvironment tenv =StreamTableEnvironment.create(env);Table ratesHistory = tenv.from("RatesHistory");// 注册带有时间属性和主键的 temporal table functionTemporalTableFunction rates = ratesHistory.createTemporalTableFunction(
$("r_proctime"),
$("r_currency"));
tenv.registerFunction("rates", rates);// 基于时间属性和键与“Orders”表关联Table orders = tenv.from("Orders");Table result = orders
.joinLateral(call("rates", $("o_proctime")), $("o_currency").isEqual($("r_currency")));
env.execute();}/**
* @param args
* @throws Exception
*/publicstaticvoidmain(String[] args)throwsException{// testInnerJoin();// testOuterJoin();// testIntervalJoin();testInnerJoinWithUDTF();}staticclassSplitFunctionextendsTableFunction<Tuple3<String,String,String>>{publicvoideval(Tuple3<String,String,String> tp){// for (String s : str.split(",")) {// // use collect(...) to emit a row collect(Row.of(s, s.length()));// }}}}
2）、关于时态表的示例
该示例来源于：https://developer.aliyun.com/article/679659
假设有一张订单表Orders和一张汇率表Rates，那么订单来自于不同的地区，所以支付的币种各不一样，那么假设需要统计每个订单在下单时候Yen币种对应的金额。
- 统计需求对应的SQL
SELECT o.currency, o.amount, r.rate
o.amount * r.rate AS yen_amount
FROM
Orders AS o,
LATERAL TABLE(Rates(o.rowtime))AS r
WHERE r.currency = o.currency
- Without connnector 实现代码
object TemporalTableJoinTest {def main(args: Array[String]):Unit={val env = StreamExecutionEnvironment.getExecutionEnvironment
val tEnv = TableEnvironment.getTableEnvironment(env)
env.setParallelism(1)// 设置时间类型是 event-time env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)// 构造订单数据val ordersData =new mutable.MutableList[(Long,String, Timestamp)]
ordersData.+=((2L,"Euro",new Timestamp(2L)))
ordersData.+=((1L,"US Dollar",new Timestamp(3L)))
ordersData.+=((50L,"Yen",new Timestamp(4L)))
ordersData.+=((3L,"Euro",new Timestamp(5L)))//构造汇率数据val ratesHistoryData =new mutable.MutableList[(String,Long, Timestamp)]
ratesHistoryData.+=(("US Dollar",102L,new Timestamp(1L)))
ratesHistoryData.+=(("Euro",114L,new Timestamp(1L)))
ratesHistoryData.+=(("Yen",1L,new Timestamp(1L)))
ratesHistoryData.+=(("Euro",116L,new Timestamp(5L)))
ratesHistoryData.+=(("Euro",119L,new Timestamp(7L)))// 进行订单表 event-time 的提取val orders = env
.fromCollection(ordersData).assignTimestampsAndWatermarks(new OrderTimestampExtractor[Long,String]()).toTable(tEnv,'amount, 'currency,'rowtime.rowtime)// 进行汇率表 event-time 的提取val ratesHistory = env
.fromCollection(ratesHistoryData).assignTimestampsAndWatermarks(new OrderTimestampExtractor[String,Long]()).toTable(tEnv,'currency, 'rate,'rowtime.rowtime)// 注册订单表和汇率表
tEnv.registerTable("Orders", orders)
tEnv.registerTable("RatesHistory", ratesHistory)val tab = tEnv.scan("RatesHistory");// 创建TemporalTableFunctionval temporalTableFunction = tab.createTemporalTableFunction('rowtime, 'currency)//注册TemporalTableFunction
tEnv.registerFunction("Rates",temporalTableFunction)val SQLQuery ="""
|SELECT o.currency, o.amount, r.rate,
| o.amount * r.rate AS yen_amount
|FROM
| Orders AS o,
| LATERAL TABLE (Rates(o.rowtime)) AS r
|WHERE r.currency = o.currency
|""".stripMargin
tEnv.registerTable("TemporalJoinResult", tEnv.SQLQuery(SQLQuery))val result = tEnv.scan("TemporalJoinResult").toAppendStream[Row]// 打印查询结果
result.print()
env.execute()}}
OrderTimestampExtractor 实现如下
import java.SQL.Timestampimport org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor
import org.apache.flink.streaming.api.windowing.time.Time
class OrderTimestampExtractor[T1, T2]
extends BoundedOutOfOrdernessTimestampExtractor[(T1, T2,Timestamp)](Time.seconds(10)) {
override def extractTimestamp(element: (T1, T2,Timestamp)): Long = {
element._3.getTime
}
}
- With CSVConnector 实现代码
在实际的生产开发中，都需要实际的Connector的定义，下面我们以CSV格式的Connector定义来开发Temporal Table JOIN Demo。
1、genEventRatesHistorySource
def genEventRatesHistorySource: CsvTableSource ={val csvRecords = Seq("ts#currency#rate","1#US Dollar#102","1#Euro#114","1#Yen#1","3#Euro#116","5#Euro#119","7#Pounds#108")// 测试数据写入临时文件val tempFilePath =
FileUtils.writeToTempFile(csvRecords.mkString(CommonUtils.line),"csv_source_rate","tmp")// 创建Source connectornew CsvTableSource(
tempFilePath,
Array("ts","currency","rate"),
Array(
Types.LONG,Types.STRING,Types.LONG
),
fieldDelim ="#",
rowDelim = CommonUtils.line,
ignoreFirstLine =true,
ignoreComments ="%")}
2、genRatesOrderSource
def genRatesOrderSource: CsvTableSource ={val csvRecords = Seq("ts#currency#amount","2#Euro#10","4#Euro#10")// 测试数据写入临时文件val tempFilePath =
FileUtils.writeToTempFile(csvRecords.mkString(CommonUtils.line),"csv_source_order","tmp")// 创建Source connectornew CsvTableSource(
tempFilePath,
Array("ts","currency","amount"),
Array(
Types.LONG,Types.STRING,Types.LONG
),
fieldDelim ="#",
rowDelim = CommonUtils.line,
ignoreFirstLine =true,
ignoreComments ="%")}
3、主程序
importjava.io.File
importorg.apache.flink.api.common.typeinfo.{TypeInformation, Types}importorg.apache.flink.book.utils.{CommonUtils, FileUtils}importorg.apache.flink.table.sinks.{CsvTableSink, TableSink}importorg.apache.flink.table.sources.CsvTableSource
importorg.apache.flink.types.Row
object CsvTableSourceUtils {def genWordCountSource: CsvTableSource ={val csvRecords = Seq("words","Hello Flink","Hi, Apache Flink","Apache FlinkBook")// 测试数据写入临时文件val tempFilePath =
FileUtils.writeToTempFile(csvRecords.mkString("$"),"csv_source_","tmp")// 创建Source connectornew CsvTableSource(
tempFilePath,
Array("words"),
Array(
Types.STRING
),
fieldDelim ="#",
rowDelim ="$",
ignoreFirstLine =true,
ignoreComments ="%")}def genRatesHistorySource: CsvTableSource ={val csvRecords = Seq("rowtime ,currency ,rate","09:00:00 ,US Dollar , 102","09:00:00 ,Euro , 114","09:00:00 ,Yen , 1","10:45:00 ,Euro , 116","11:15:00 ,Euro , 119","11:49:00 ,Pounds , 108")// 测试数据写入临时文件val tempFilePath =
FileUtils.writeToTempFile(csvRecords.mkString("$"),"csv_source_","tmp")// 创建Source connectornew CsvTableSource(
tempFilePath,
Array("rowtime","currency","rate"),
Array(
Types.STRING,Types.STRING,Types.STRING
),
fieldDelim =",",
rowDelim ="$",
ignoreFirstLine =true,
ignoreComments ="%")}def genEventRatesHistorySource: CsvTableSource ={val csvRecords = Seq("ts#currency#rate","1#US Dollar#102","1#Euro#114","1#Yen#1","3#Euro#116","5#Euro#119","7#Pounds#108")// 测试数据写入临时文件val tempFilePath =
FileUtils.writeToTempFile(csvRecords.mkString(CommonUtils.line),"csv_source_rate","tmp")// 创建Source connectornew CsvTableSource(
tempFilePath,
Array("ts","currency","rate"),
Array(
Types.LONG,Types.STRING,Types.LONG
),
fieldDelim ="#",
rowDelim = CommonUtils.line,
ignoreFirstLine =true,
ignoreComments ="%")}def genRatesOrderSource: CsvTableSource ={val csvRecords = Seq("ts#currency#amount","2#Euro#10","4#Euro#10")// 测试数据写入临时文件val tempFilePath =
FileUtils.writeToTempFile(csvRecords.mkString(CommonUtils.line),"csv_source_order","tmp")// 创建Source connectornew CsvTableSource(
tempFilePath,
Array("ts","currency","amount"),
Array(
Types.LONG,Types.STRING,Types.LONG
),
fieldDelim ="#",
rowDelim = CommonUtils.line,
ignoreFirstLine =true,
ignoreComments ="%")}/**
* Example:
* genCsvSink(
* Array[String]("word", "count"),
* Array[TypeInformation[_] ](Types.STRING, Types.LONG))
*/def genCsvSink(fieldNames: Array[String], fieldTypes: Array[TypeInformation[_]]): TableSink[Row]={val tempFile = File.createTempFile("csv_sink_","tem")if(tempFile.exists()){
tempFile.delete()}new CsvTableSink(tempFile.getAbsolutePath).configure(fieldNames, fieldTypes)}}
4、运行结果
以上，通过示例介绍了如何使用table api进行表、视图、窗口函数的操作，同时也介绍了table api对表的查询、过滤、列、聚合以及join操作。关于表的set、order by、insert、group window、over window等相关操作详见下篇文章：17、Flink 之Table API: Table API 支持的操作（2）
本文转载自: https://blog.csdn.net/chenwewi520feng/article/details/131953473
版权归原作者 一瓢一瓢的饮 alanchan 所有， 如有侵权，请联系我们删除。
版权归原作者 一瓢一瓢的饮 alanchan 所有， 如有侵权，请联系我们删除。