【flink番外篇】9、Flink Table API 支持的操作示例（1）-完整版
Flink 系列文章
一、Flink 专栏
Flink 专栏系统介绍某一知识点，并辅以具体的示例进行说明。
-
1、Flink 部署系列
本部分介绍Flink的部署、配置相关基础内容。 -
2、Flink基础系列
本部分介绍Flink 的基础部分，比如术语、架构、编程模型、编程指南、基本的datastream api用法、四大基石等内容。 -
3、Flik Table API和SQL基础系列
本部分介绍Flink Table Api和SQL的基本用法，比如Table API和SQL创建库、表用法、查询、窗口函数、catalog等等内容。 -
4、Flik Table API和SQL提高与应用系列
本部分是table api 和sql的应用部分，和实际的生产应用联系更为密切，以及有一定开发难度的内容。 -
5、Flink 监控系列
本部分和实际的运维、监控工作相关。
二、Flink 示例专栏
Flink 示例专栏是 Flink 专栏的辅助说明，一般不会介绍知识点的信息，更多的是提供一个一个可以具体使用的示例。本专栏不再分目录，通过链接即可看出介绍的内容。
两专栏的所有文章入口点击：Flink 系列文章汇总索引
文章目录
- Flink 系列文章
- 一、maven依赖
- 二、示例：基本的程序结构
- 三、示例：通过Table API和SQL创建表
- 四、示例：通过Table API 和 SQL 创建视图
-
- 1、示例：通过SQL创建视图
- 2、示例：通过Table API创建视图
- 五、示例：通过API查询表和使用窗口函数的查询
-
- 1、示例：基本的查询表
- 2、示例：Tumble窗口查询表
- 3、示例：Over窗口查询表
- 六、示例：Table API 对表的查询、过滤操作
- 七、示例：表的列操作
- 八、示例：表的聚合操作
-
- 1、示例代码公共部分
- 2、group by
- 3、GroupBy Window Aggregation
- 4、Over Window Aggregation
- 5、Distinct Aggregation
- 6、Distinct
- 九、示例：表的join操作
-
- 1、示例：join示例
- 2、示例：时态表的join（scala版本）
-
- 1）、统计需求对应的SQL
- 2）、Without connnector 实现代码
- 3）、With CSVConnector 实现代码
本文介绍了通过Table API和SQL创建表、视图以及查询示例，同时介绍了Table API 操作表的列、聚合、join操作。
如果需要了解更多内容，可以在本人Flink 专栏中了解更新系统的内容。
本文除了maven依赖外，没有其他依赖。
本示例依赖hive、hadoop、kafka环境好用，代码中示例的hive配置文件路径根据你自己的环境而设置。
本文更详细的内容可参考文章：
17、Flink 之Table API: Table API 支持的操作（1）
17、Flink 之Table API: Table API 支持的操作（2）
本专题分为以下几篇文章：
【flink番外篇】9、Flink Table API 支持的操作示例（1）-通过Table API和SQL创建表
【flink番外篇】9、Flink Table API 支持的操作示例（2）- 通过Table API 和 SQL 创建视图
【flink番外篇】9、Flink Table API 支持的操作示例（3）- 通过API查询表和使用窗口函数的查询
【flink番外篇】9、Flink Table API 支持的操作示例（4）- Table API 对表的查询、过滤操作
【flink番外篇】9、Flink Table API 支持的操作示例（5）- 表的列操作
【flink番外篇】9、Flink Table API 支持的操作示例（6）- 表的聚合(group by、Distinct、GroupBy/Over Window Aggregation)操作
【flink番外篇】9、Flink Table API 支持的操作示例（7）- 表的join操作（内联接、外联接以及联接自定义函数等）
【flink番外篇】9、Flink Table API 支持的操作示例（8）- 时态表的join（scala版本）
【flink番外篇】9、Flink Table API 支持的操作示例（9）- 表的union、unionall、intersect、intersectall、minus、minusall和in的操作
【flink番外篇】9、Flink Table API 支持的操作示例（10）- 表的OrderBy、Offset 和 Fetch、insert操作
【flink番外篇】9、Flink Table API 支持的操作示例（11）- Group Windows（tumbling、sliding和session）操作
【flink番外篇】9、Flink Table API 支持的操作示例（12）- Over Windows（有界和无界的over window）操作
【flink番外篇】9、Flink Table API 支持的操作示例（13）- Row-based（map、flatmap、aggregate、group window aggregate等）操作
【flink番外篇】9、Flink Table API 支持的操作示例（14）- 时态表的join（java版本）
【flink番外篇】9、Flink Table API 支持的操作示例（1）-完整版
【flink番外篇】9、Flink Table API 支持的操作示例（2）-完整版
一、maven依赖
本文或本专题依赖的maven，为了节省篇幅不再每个示例再单独列出，可能存在多余的maven依赖。
<properties>
<encoding>UTF-8encoding>
<project.build.sourceEncoding>UTF-8project.build.sourceEncoding>
<maven.compiler.source>1.8maven.compiler.source>
<maven.compiler.target>1.8maven.compiler.target>
<java.version>1.8java.version>
<scala.version>2.12scala.version>
<flink.version>1.17.0flink.version>
properties>
<dependencies>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-clientsartifactId>
<version>${flink.version}version>
<scope>providedscope>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-javaartifactId>
<version>${flink.version}version>
<scope>providedscope>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-table-commonartifactId>
<version>${flink.version}version>
<scope>providedscope>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-streaming-javaartifactId>
<version>${flink.version}version>
<scope>providedscope>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-table-api-java-bridgeartifactId>
<version>${flink.version}version>
<scope>providedscope>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-sql-gatewayartifactId>
<version>${flink.version}version>
<scope>providedscope>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-csvartifactId>
<version>${flink.version}version>
<scope>providedscope>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-jsonartifactId>
<version>${flink.version}version>
<scope>providedscope>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-table-planner_2.12artifactId>
<version>${flink.version}version>
<scope>providedscope>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-table-api-java-uberartifactId>
<version>${flink.version}version>
<scope>providedscope>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-table-runtimeartifactId>
<version>${flink.version}version>
<scope>providedscope>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-connector-jdbcartifactId>
<version>3.1.0-1.17version>
dependency>
<dependency>
<groupId>mysqlgroupId>
<artifactId>mysql-connector-javaartifactId>
<version>5.1.38version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-connector-hive_2.12artifactId>
<version>1.17.0version>
dependency>
<dependency>
<groupId>org.apache.hivegroupId>
<artifactId>hive-execartifactId>
<version>3.1.2version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-connector-kafkaartifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-sql-connector-kafkaartifactId>
<version>${flink.version}version>
<scope>providedscope>
dependency>
<dependency>
<groupId>org.apache.commonsgroupId>
<artifactId>commons-compressartifactId>
<version>1.24.0version>
dependency>
<dependency>
<groupId>org.projectlombokgroupId>
<artifactId>lombokartifactId>
<version>1.18.2version>
dependency>
dependencies>
二、示例：基本的程序结构
本示例着重展示基本的程序结构，不涉及复杂的source、transformation和sink。
为了节省篇幅，本示例包含本专题所需要的所有import，下面的示例不再引入。
import static org.apache.flink.table.api.Expressions.$;
import static org.apache.flink.table.api.Expressions.and;
import static org.apache.flink.table.api.Expressions.lit;
import static org.apache.flink.table.expressions.ApiExpressionUtils.unresolvedCall;
import java.sql.Timestamp;
import java.time.Duration;
import java.util.Arrays;
import java.util.Collections;
import java.util.HashMap;
import java.util.List;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.typeinfo.TypeHint;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.table.KafkaConnectorOptions;
import org.apache.flink.table.api.DataTypes;
import org.apache.flink.table.api.EnvironmentSettings;
import org.apache.flink.table.api.Over;
import org.apache.flink.table.api.Schema;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.TableDescriptor;
import org.apache.flink.table.api.TableEnvironment;
import org.apache.flink.table.api.Tumble;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.catalog.CatalogDatabaseImpl;
import org.apache.flink.table.catalog.CatalogView;
import org.apache.flink.table.catalog.Column;
import org.apache.flink.table.catalog.ObjectPath;
import org.apache.flink.table.catalog.ResolvedCatalogView;
import org.apache.flink.table.catalog.ResolvedSchema;
import org.apache.flink.table.catalog.hive.HiveCatalog;
import org.apache.flink.table.functions.BuiltInFunctionDefinitions;
import org.apache.flink.types.Row;
import com.google.common.collect.Lists;
import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.NoArgsConstructor;
/**
* @author alanchan
*
*/
public class TestTableAPIDemo {
final static List<User> userList = Arrays.asList(
new User(1L, "alan", 18, 1698742358391L),
new User(2L, "alan", 19, 1698742359396L),
new User(3L, "alan", 20, 1698742360407L),
new User(4L, "alanchan", 28, 1698742361409L),
new User(5L, "alanchan", 29, 1698742362424L)
);
/**
* @param args
* @throws Exception
*/
public static void main(String[] args) throws Exception {
// 0、运行环境
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
StreamTableEnvironment tenv = StreamTableEnvironment.create(env);
// 2、数据源
DataStream<User> users = env.fromCollection(userList);
// 3、DataStream 转 Table
Table sourceTable = tenv.fromDataStream(users);
// 4、查询
Table resultQuery = sourceTable.groupBy($("name")).select($("name"), $("age").sum().as("age_sum"));
// 5、Table 转 DataStream
DataStream<Tuple2<Boolean, Row>> resultDS = tenv.toRetractStream(resultQuery, Row.class);
// 6、sink
resultDS.print();
// 7、执行
env.execute();
}
static void testTableEnvironment() throws Exception {
// TableEnvironment 的主要职能包括：
// 注册 Catlog
// 在内部 Catlog 中注册表
// 加载可插拔模块
// 执行 SQL 查询
// 注册用户自定义函数
// DataStream 和 Table 之间的转换（在 StreamTableEnvironment 的情况下）
// 提供更详细的配置选项
// 设置方式一
// inStreamingMode/inBatchMode，批处理或流式，默认流式
// useBlinkPlanner/useOldPlanner/useAnyPlanner，执行计划，默认blink（和flink版本有关），anyplanner不需要显式设置
EnvironmentSettings env = EnvironmentSettings.newInstance().inStreamingMode().build();
// EnvironmentSettings.newInstance().useBlinkPlanner().inBatchMode().build();
// EnvironmentSettings.newInstance().useOldPlanner().inStreamingMode().build();
// EnvironmentSettings.newInstance().useAnyPlanner().inStreamingMode().build();
TableEnvironment tenv = TableEnvironment.create(env);
// 设置方式二,基于 StreamExecutionEnvironment 创建 StreamTableEnvironment 来与 DataStream
// API 进行相互转换
// StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
// StreamTableEnvironment tenv = StreamTableEnvironment.create(env);
// 表 Table 有两种类型的表，一种是连接器表(Connector Tables) Table，一种是虚拟表(Virtual Tables)
// VIEW。连接器表一般用来描述外部数据，例如文件、数据库表或者消息队列。虚拟表通常是 Table API 或 SQL 查询的结果，可以基于现有的连接器表
// Table 对象来创建。
// 1、连接器 Connector 表
// 创建 Table 最直观的方式，就是通过连接器(Connector)连接到一个外部系统，然后定义出对应的表结构。
// 例如我们可以连接到 Kafka 或者文件系统，将存储在这些外部系统的数据以表 Table 的形式定义出来，
// 这样对表 Table的读写就可以通过连接器转换成对外部系统的读写。连接器表可以直接通过 SQL DDL 方式创建：
// 2、虚拟表
// 。。。。。。
}
@Data
@NoArgsConstructor
@AllArgsConstructor
public static class User {
private long id;
private String name;
private int age;
private Long rowtime;
}
}
三、示例：通过Table API和SQL创建表
本示例是通过Table API 和 SQL 两种方式创建一张kafka表，其中表结构涉及使用kafka的元数据，即kafka的事件时间、分区编号和kafka数据offset。
同时，提供验证方法，即输入数据和预期输出数据。
/**
* @author alanchan
*
*/
public class TestTableAPIDemo {
/**
* @param args
* @throws Exception
*/
public static void main(String[] args) throws Exception {
// testCreateTableByAPI();
testCreateTableBySQL();
}
static void testCreateTableByAPI() throws Exception {
// EnvironmentSettings env = EnvironmentSettings.newInstance().inStreamingMode().build();
// TableEnvironment tenv = TableEnvironment.create(env);
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
StreamTableEnvironment tenv = StreamTableEnvironment.create(env);
//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])
}
static void testCreateTableBySQL() throws Exception {
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);
//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])
}
}
四、示例：通过Table API 和 SQL 创建视图
1、示例：通过SQL创建视图
本示例是通过sql创建一个简单的表，然后再通过sql创建一个视图，最后查询视图并输出结果。
/**
* @author alanchan
*
*/
public class TestTableAPIDemo {
/**
* @param args
* @throws Exception
*/
public static void main(String[] args) throws Exception {
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])
}
}
2、示例：通过Table API创建视图
本示例是通过Table API创建一个hive的表，将数据写入hive，然后再创建视图，最后查询视图输出。
本示例依赖hive、hadoop、kafka环境好用，代码中示例的hive配置文件路径根据你自己的环境而设置。
/**
* @author alanchan
*
*/
public class TestTableAPIDemo {
/**
* @param args
* @throws Exception
*/
public static void main(String[] args) throws Exception {
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 = new HiveCatalog(catalogName, defaultDatabase, hiveConfDir);
tenv.registerCatalog(catalogName, hiveCatalog);
tenv.useCatalog(catalogName);
hiveCatalog.createDatabase(databaseName, new CatalogDatabaseImpl(new HashMap(), 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 = new ObjectPath(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])
}
static void createView(String originalQuery, String expandedQuery, String comment, HiveCatalog hiveCatalog, ObjectPath path) throws Exception {
ResolvedSchema resolvedSchema = new ResolvedSchema(
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 = new ResolvedCatalogView(origin, resolvedSchema);
hiveCatalog.createTable(path, view, false);
}
}
五、示例：通过API查询表和使用窗口函数的查询
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）
1、示例：基本的查询表
本示例仅仅是基本的查询表用法，包含2种方式，即Table API 与 SQL的方式，关于SQL的方式上面的示例中有介绍，即【三、示例：通过Table API和SQL创建表】中的testCreateTableBySQL()方法。
/**
* @author alanchan
*
*/
public class TestTableAPIDemo {
/**
* @param args
* @throws Exception
*/
public static void main(String[] args) throws Exception {
// testQueryTableBySQL();
testQueryTableByAPI();
}
static void testQueryTableByAPI() throws Exception {
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);
// 2、将流转成table
String sql = "select * from Alan_KafkaTable ";
Table streamTable = tenv.sqlQuery(sql);
// 3、API查询
Table resultQuery = streamTable
.groupBy($("user_id"), $("behavior"))
.select($("user_id"), $("behavior"), $("behavior").count().as("count(*)"));
// 4、将流转成table
DataStream<Tuple2<Boolean, Row>> resultDS = tenv.toRetractStream(resultQuery, Row.class);
// 5、sink
resultDS.print();
// 6、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
// 14> (true,+I[1, p_read, 1])
// 3> (true,+I[1, login, 1])
}
static void testQueryTableBySQL() throws Exception {
// 参考 testCreateTableBySQLAndAPI()
}
}
2、示例：Tumble窗口查询表
/**
* @author alanchan
*
*/
public class TestTableAPIDemo {
final static List<User> userList = Arrays.asList(
new User(1L, "alan", 18, 1698742358391L),
new User(2L, "alan", 19, 1698742359396L),
new User(3L, "alan", 20, 1698742360407L),
new User(4L, "alanchan", 28, 1698742361409L),
new User(5L, "alanchan", 29, 1698742362424L));
/**
* @param args
* @throws Exception
*/
public static void main(String[] args) throws Exception {
testQueryTableWithWindwosByAPI();
}
static void testQueryTableWithWindwosByAPI() throws Exception {
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());
// tumble
Table 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();
env.execute();
}
@Data
@NoArgsConstructor
@AllArgsConstructor
public static class User {
private long id;
private String name;
private int age;
private Long rowtime;
}
}
3、示例：Over窗口查询表
/**
* @author alanchan
*
*/
public class TestTableAPIDemo {
/**
* @param args
* @throws Exception
*/
public static void main(String[] args) throws Exception {
testQueryTableWithWindwosByAPI();
}
static void testQueryTableWithWindwosByAPI() throws Exception {
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());
// 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();
}
}
六、示例：Table API 对表的查询、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
import static org.apache.flink.table.api.Expressions.$;
import static org.apache.flink.table.api.Expressions.row;
import static org.apache.flink.table.api.Expressions.and;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.DataTypes;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.types.Row;
/**
* @author alanchan
*
*/
public class TestTableAPIOperationDemo {
static String sourceSql = "CREATE TABLE Alan_KafkaTable (\r\n"
+ " `event_time` TIMESTAMP(3) METADATA FROM 'timestamp',\r\n"
+ " `partition` BIGINT METADATA VIRTUAL,\r\n"
+ " `offset` BIGINT METADATA VIRTUAL,\r\n"
+ " `user_id` BIGINT,\r\n"
+ " `item_id` BIGINT,\r\n"
+ " `behavior` STRING\r\n"
+ ") WITH (\r\n"
+ " 'connector' = 'kafka',\r\n"
+ " 'topic' = 'user_behavior',\r\n"
+ " 'properties.bootstrap.servers' = '192.168.10.41:9092,192.168.10.42:9092,192.168.10.43:9092',\r\n"
+ " 'properties.group.id' = 'testGroup',\r\n"
+ " 'scan.startup.mode' = 'earliest-offset',\r\n"
+ " 'format' = 'csv'\r\n"
+ ");";
/**
* @param args
* @throws Exception
*/
public static void main(String[] args) throws Exception {
// test1();
// test2();
test3();
}
static void test3() throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
StreamTableEnvironment tenv = StreamTableEnvironment.create(env);
// 建表
tenv.executeSql(sourceSql);
Table table1 = tenv.from("Alan_KafkaTable");
// 重命名字段。
Table result = table1.as("a","b","c","d","e","f");
DataStream<Tuple2<Boolean, Row>> resultDS = tenv.toRetractStream(result, Row.class);
resultDS.print();
//11> (true,+I[2023-11-01T11:00:30.183, 0, 2, 1, 1002, login])
//和 SQL 的 WHERE 子句类似。 过滤掉未验证通过过滤谓词的行。
Table table2 = result.where($("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
*/
static void test2() throws Exception {
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
*/
static void test1() throws Exception {
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();
}
}
七、示例：表的列操作
针对表的字段进行操作，具体示例如下，运行结果在源文件中。
import static org.apache.flink.table.api.Expressions.$;
import static org.apache.flink.table.api.Expressions.row;
import static org.apache.flink.table.api.Expressions.and;
import static org.apache.flink.table.api.Expressions.concat;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.DataTypes;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.types.Row;
/**
* @author alanchan
*
*/
public class TestTableAPIOperationDemo {
static String sourceSql = "CREATE TABLE Alan_KafkaTable (\r\n"
+ " `event_time` TIMESTAMP(3) METADATA FROM 'timestamp',\r\n"
+ " `partition` BIGINT METADATA VIRTUAL,\r\n"
+ " `offset` BIGINT METADATA VIRTUAL,\r\n"
+ " `user_id` BIGINT,\r\n"
+ " `item_id` BIGINT,\r\n"
+ " `behavior` STRING\r\n"
+ ") WITH (\r\n"
+ " 'connector' = 'kafka',\r\n"
+ " 'topic' = 'user_behavior',\r\n"
+ " 'properties.bootstrap.servers' = '192.168.10.41:9092,192.168.10.42:9092,192.168.10.43:9092',\r\n"
+ " 'properties.group.id' = 'testGroup',\r\n"
+ " 'scan.startup.mode' = 'earliest-offset',\r\n"
+ " 'format' = 'csv'\r\n"
+ ");";
/**
* @param args
* @throws Exception
*/
public static void main(String[] args) throws Exception {
// test1();
// test2();
test3();
}
static void test3() throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
StreamTableEnvironment tenv = StreamTableEnvironment.create(env);
// 建表
tenv.executeSql(sourceSql);
Table table1 = tenv.from("Alan_KafkaTable");
// 重命名字段。
Table result = table1.as("a","b","c","d","e","f");
DataStream<Tuple2<Boolean, Row>> resultDS = tenv.toRetractStream(result, Row.class);
resultDS.print();
//11> (true,+I[2023-11-01T11:00:30.183, 0, 2, 1, 1002, login])
//和 SQL 的 WHERE 子句类似。 过滤掉未验证通过过滤谓词的行。
Table table2 = result.where($("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
*/
static void test2() throws Exception {
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
*/
static void test1() throws Exception {
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();
}
static void test5() throws Exception {
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"), $("user_id").count().as("count(user_id)"));
DataStream<Tuple2<Boolean, Row>> resultDS = tenv.toRetractStream(result, Row.class);
resultDS.print();
// 12> (true,+I[1, 1])
env.execute();
}
static void test4() throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
StreamTableEnvironment tenv = StreamTableEnvironment.create(env);
// 建表
tenv.executeSql(sourceSql);
Table table = tenv.from("Alan_KafkaTable");
//执行字段添加操作。 如果所添加的字段已经存在，将抛出异常。
Table result2 = table.addColumns($("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"), $("t_col").as("col2"));
result6.printSchema();
// (
// `event_time` TIMESTAMP(3),
// `partition` BIGINT,
// `offset` BIGINT,
// `user_id` BIGINT,
// `item_id` BIGINT,
// `behavior` STRING,
// `col1` STRING,
// `col2` STRING
// )
DataStream<Tuple2<Boolean, Row>> resultDS = tenv.toRetractStream(table, Row.class);
resultDS.print();
// 11> (true,+I[2023-11-01T11:00:30.183, 0, 2, 1, 1002, login])
env.execute();
}
}
八、示例：表的聚合操作
本示例较多，下文是本部分示例的公共代码部分。
1、示例代码公共部分
本部分仅仅就是用的公共对象，比如User的定义，和需要引入的包。
import static org.apache.flink.table.api.Expressions.$;
import static org.apache.flink.table.api.Expressions.lit;
import static org.apache.flink.table.expressions.ApiExpressionUtils.unresolvedCall;
import java.time.Duration;
import java.util.Arrays;
import java.util.List;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Over;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.Tumble;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.functions.BuiltInFunctionDefinitions;
import org.apache.flink.types.Row;
import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.NoArgsConstructor;
/**
* @author alanchan
*
*/
public class TestTableAPIOperationDemo2 {
final static List<User> userList = Arrays.asList(
new User(1L, "alan", 18, 1698742358391L),
new User(2L, "alan", 19, 1698742359396L),
new User(3L, "alan", 25, 1698742360407L),
new User(4L, "alanchan", 28, 1698742361409L),
new User(5L, "alanchan", 29, 1698742362424L)
);
/**
* @param args
* @throws Exception
*/
public static void main(String[] args) throws Exception {
}
@Data
@NoArgsConstructor
@AllArgsConstructor
public static class User {
private long id;
private String name;
private int balance;
private Long rowtime;
}
}
2、group by
本示例仅仅展示了group by操作，比较简单。
static void test2() throws Exception {
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。
static void test3() throws Exception {
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。
static void test4() throws Exception {
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
*/
static void test5() throws Exception {
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", new MyUdagg());
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。
static void test6() throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
StreamTableEnvironment tenv = StreamTableEnvironment.create(env);
List<User> userList = Arrays.asList(
new User(1L, "alan", 18, 1698742358391L),
new User(2L, "alan", 19, 1698742359396L),
new User(3L, "alan", 25, 1698742360407L),
new User(4L, "alanchan", 28, 1698742361409L),
new User(5L, "alanchan", 29, 1698742362424L),
new User(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();
}
九、示例：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、示例：join示例
import static org.apache.flink.table.api.Expressions.$;
import static org.apache.flink.table.api.Expressions.and;
import static org.apache.flink.table.api.Expressions.call;
import java.util.Arrays;
import java.util.List;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.functions.TableFunction;
import org.apache.flink.table.functions.TemporalTableFunction;
import org.apache.flink.types.Row;
import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.NoArgsConstructor;
/**
* @author alanchan
*
*/
public class TestTableAPIJoinOperationDemo {
@Data
@NoArgsConstructor
@AllArgsConstructor
public static class User {
private long id;
private String name;
private double balance;
private Long rowtime;
}
@Data
@NoArgsConstructor
@AllArgsConstructor
public static class Order {
private long id;
private long user_id;
private double amount;
private Long rowtime;
}
final static List<User> userList = Arrays.asList(
new User(1L, "alan", 18, 1698742358391L),
new User(2L, "alan", 19, 1698742359396L),
new User(3L, "alan", 25, 1698742360407L),
new User(4L, "alanchan", 28, 1698742361409L),
new User(5L, "alanchan", 29, 1698742362424L)
);
final static List<Order> orderList = Arrays.asList(
new Order(1L, 1, 18, 1698742358391L),
new Order(2L, 2, 19, 1698742359396L),
new Order(3L, 1, 25, 1698742360407L),
new Order(4L, 3, 28, 1698742361409L),
new Order(5L, 1, 29, 1698742362424L),
new Order(6L, 4, 49, 1698742362424L)
);
static void testInnerJoin() throws Exception {
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
*/
static void testOuterJoin() throws Exception {
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
*/
static void testIntervalJoin() throws Exception {
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
*/
static void testInnerJoinWithUDTF() throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
StreamTableEnvironment tenv = StreamTableEnvironment.create(env);
// 注册 User-Defined Table Function
TableFunction<Tuple3<String,String,String>> split = new SplitFunction();
tenv.registerFunction("split", split);
// join
DataStream<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
*/
static void testLeftOuterJoinWithUDTF() throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
StreamTableEnvironment tenv = StreamTableEnvironment.create(env);
// 注册 User-Defined Table Function
TableFunction<Tuple3<String,String,String>> split = new SplitFunction();
tenv.registerFunction("split", split);
// join
DataStream<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
*/
static void testJoinWithTemporalTable() throws Exception {
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 function
TemporalTableFunction 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
*/
public static void main(String[] args) throws Exception {
// testInnerJoin();
// testOuterJoin();
// testIntervalJoin();
testInnerJoinWithUDTF();
}
static class SplitFunction extends TableFunction<Tuple3<String,String,String>>{
public void eval(Tuple3<String,String,String> tp) {
// for (String s : str.split(",")) {
// // use collect(...) to emit a row
collect(Row.of(s, s.length()));
// }
}
}
}
2、示例：时态表的join（scala版本）
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。
1）、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
2）、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TemporalTableFunction
val 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.Timestamp
import 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
}
}
3）、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 connector
new 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 connector
new CsvTableSource(
tempFilePath,
Array("ts","currency", "amount"),
Array(
Types.LONG,Types.STRING,Types.LONG
),
fieldDelim = "#",
rowDelim = CommonUtils.line,
ignoreFirstLine = true,
ignoreComments = "%"
)
}
3、主程序
import java.io.File
import org.apache.flink.api.common.typeinfo.{TypeInformation, Types}
import org.apache.flink.book.utils.{CommonUtils, FileUtils}
import org.apache.flink.table.sinks.{CsvTableSink, TableSink}
import org.apache.flink.table.sources.CsvTableSource
import org.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("$"), "csv_source_", "tmp")
// 创建Source connector
new CsvTableSource(
tempFilePath,
Array("words"),
Array(
Types.STRING
),
fieldDelim = "#",
rowDelim = "$",
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("$"), "csv_source_", "tmp")
// 创建Source connector
new CsvTableSource(
tempFilePath,
Array("rowtime","currency","rate"),
Array(
Types.STRING,Types.STRING,Types.STRING
),
fieldDelim = ",",
rowDelim = "$",
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 connector
new 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 connector
new 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，本文介绍了通过Table API和SQL创建表、视图以及查询示例，同时介绍了Table API 操作表的列、聚合、join操作。
如果需要了解更多内容，可以在本人Flink 专栏中了解更新系统的内容。
本文更详细的内容可参考文章：
17、Flink 之Table API: Table API 支持的操作（1）
17、Flink 之Table API: Table API 支持的操作（2）
本专题分为以下几篇文章：
【flink番外篇】9、Flink Table API 支持的操作示例（1）-通过Table API和SQL创建表
【flink番外篇】9、Flink Table API 支持的操作示例（2）- 通过Table API 和 SQL 创建视图
【flink番外篇】9、Flink Table API 支持的操作示例（3）- 通过API查询表和使用窗口函数的查询
【flink番外篇】9、Flink Table API 支持的操作示例（4）- Table API 对表的查询、过滤操作
【flink番外篇】9、Flink Table API 支持的操作示例（5）- 表的列操作
【flink番外篇】9、Flink Table API 支持的操作示例（6）- 表的聚合(group by、Distinct、GroupBy/Over Window Aggregation)操作
【flink番外篇】9、Flink Table API 支持的操作示例（7）- 表的join操作（内联接、外联接以及联接自定义函数等）
【flink番外篇】9、Flink Table API 支持的操作示例（8）- 时态表的join（scala版本）
【flink番外篇】9、Flink Table API 支持的操作示例（9）- 表的union、unionall、intersect、intersectall、minus、minusall和in的操作
【flink番外篇】9、Flink Table API 支持的操作示例（10）- 表的OrderBy、Offset 和 Fetch、insert操作
【flink番外篇】9、Flink Table API 支持的操作示例（11）- Group Windows（tumbling、sliding和session）操作
【flink番外篇】9、Flink Table API 支持的操作示例（12）- Over Windows（有界和无界的over window）操作
【flink番外篇】9、Flink Table API 支持的操作示例（13）- Row-based（map、flatmap、aggregate、group window aggregate等）操作
【flink番外篇】9、Flink Table API 支持的操作示例（14）- 时态表的join（java版本）
【flink番外篇】9、Flink Table API 支持的操作示例（1）-完整版
【flink番外篇】9、Flink Table API 支持的操作示例（2）-完整版