System.out.println(value.getField(0)); } }); Table myTable = tableEnvironment.fromDataStream
(stream, "myLong, myString"); 复制代码 这里通过StreamTableEnvironment.fromDataStream将DataStream转为Table Table转...(stream); // convert DataStream into Table with field "f1" only Table table = tableEnv.fromDataStream...Table table = tableEnv.fromDataStream(stream); // convert DataStream into Table with renamed fields..."myAge", "myName" (name-based) Table table = tableEnv.fromDataStream(stream, "age as myAge, name as myName...(stream); // convert DataStream into Table with field name "myLong" Table table = tableEnv.fromDataStream
(stream, "myLong, myString"); 这里通过StreamTableEnvironment.fromDataStream将DataStream转为Table Table转DataStream...(stream); // convert DataStream into Table with field "f1" only Table table = tableEnv.fromDataStream...Table table = tableEnv.fromDataStream(stream); // convert DataStream into Table with renamed fields..."myAge", "myName" (name-based) Table table = tableEnv.fromDataStream(stream, "age as myAge, name as...(stream); // convert DataStream into Table with field name "myLong" Table table = tableEnv.fromDataStream
本文主要研究一下flink Table的Time Attributes apache-flink-training-table-api-sql-33-638.jpg Processing time 通过fromDataStream...stream = ...; // declare an additional logical field as a processing time attribute Table table = tEnv.fromDataStream...table.window(Tumble.over("10.minutes").on("UserActionTime").as("userActionWindow")); 从DataStream创建Table的话,可以在fromDataStream...is no longer necessary // replace first field with a logical event time attribute Table table = tEnv.fromDataStream...时可以指定Time Attributes,指定了之后就可以作为field来使用或者参与time-based的操作 针对Processing time,如果从DataStream创建Table的话,可以在fromDataStream
序 本文主要研究一下flink Table的Time Attributes Processing time 通过fromDataStream定义 DataStream<Tuple2<String, String...stream = ...; // declare an additional logical field as a processing time attribute Table table = tEnv.fromDataStream...table.window(Tumble.over("10.minutes").on("UserActionTime").as("userActionWindow")); 从DataStream创建Table的话,可以在fromDataStream...is no longer necessary // replace first field with a logical event time attribute Table table = tEnv.fromDataStream...时可以指定Time Attributes,指定了之后就可以作为field来使用或者参与time-based的操作 针对Processing time,如果从DataStream创建Table的话,可以在fromDataStream
另外,还可以在 fromDataStream()方法里增加参数,用来重新命名列字段。...// 将数据流转换成动态表,动态表只有一个字段,重命名为 myLong Table table = tableEnv.fromDataStream(stream, $(“myLong”)); 2....,f0 命名为 myLong Table table = tableEnv.fromDataStream(stream, $("f1").as("myInt"), $("f0").as("myLong"...Table table = tableEnv.fromDataStream(stream); Table table = tableEnv.fromDataStream(stream, $("user"...)); Table table = tableEnv.fromDataStream(stream, $("user").as("myUser"), $("url").as("myUrl")); 4.
fromDataStream(DataStream, Schema):将仅插入更改和任意类型的流解释为表。可选模式允许丰富列数据类型并添加时间属性、水印策略、其他计算列或主键。...它是 createTemporaryView(String, fromDataStream(DataStream)) 的快捷方式。...它是 createTemporaryView(String, fromDataStream(DataStream, Schema)) 的快捷方式。...fromDataStream 例子 下面的代码展示了如何将 fromDataStream 用于不同的场景。...因为 fromChangelogStream 的行为与 fromDataStream 类似,我们建议在继续之前阅读上一节。 此虚拟连接器还支持读取和写入流记录的行时元数据。
. // convert the DataStream into a Table with default fields '_1, '_2 val table1: Table = tableEnv.fromDataStream...// convert the DataStream into a Table with fields 'myLong, 'myString val table2: Table = tableEnv.fromDataStream...// convert DataStream into Table with field names 'myLong, 'myString val table1: Table = tableEnv.fromDataStream...... // convert DataStream into Table with default field names 'name, 'age val tableCC1 = tableEnv.fromDataStream...] = ... // convert DataStream into Table with field names 'name, 'age val table1: Table = tableEnv.fromDataStream
Integer.parseInt(split[2])); } }); // 将流转化为表 Table table = tableEnv.fromDataStream
WordCount("Ciao", 1L), new WordCount("Hello", 1L)); // DataStream 转 Table Table table = tEnv.fromDataStream...("Alice", 100), Row.of("Lucy", 50) ); // 将 DataStream 转换为 Table Table inputTable = tableEnv.fromDataStream...可以通过 fromDataStream 得到表的 Table 对象。得到 Table 对象之后,就可以调用 API 进行各种转换操作了。...("Alice", 100), Row.of("Lucy", 50) ); // 将 DataStream 转换为 Table Table inputTable = tableEnv.fromDataStream...("Alice", 100), Row.of("Lucy", 50) ); // 将 DataStream 转换为 Table Table inputTable = tableEnv.fromDataStream
代码表达 代码中实现非常简单,直接用tableEnv.fromDataStream()就可以了。...(dataStream) val sensorTable2 = tableEnv.fromDataStream(dataStream, 'id, 'timestamp as 'ts) 数据类型与Table...基于名称的对应: val sensorTable = tableEnv .fromDataStream(dataStream, $"timestamp" as "ts", $"id" as "myId...", "temperature") 基于位置的对应: val sensorTable = tableEnv .fromDataStream(dataStream, $"myId", $"ts")...代码如下: val stream = env.addSource(new SensorSource) val sensorTable = tableEnv .fromDataStream(stream
5.1、代码实现 代码中实现非常简单,直接用 tableEnv.fromDataStream() 就可以了。...DataStream[String] = readData.flatMap(_.split(" ")) // 将word 转为 table val table = tableEnv.fromDataStream...基于名称的对应: val userTable = tableEnv.fromDataStream(dataStream,'username as 'name,'id as 'myid) 基于位置的对应...: val userTable = tableEnv.fromDataStream(dataStream, 'name, 'id) Flink 的 DataStream 和 DataSet
使用 StreamTableEnvironment::fromDataStream API 将 DataStream 转为 Table Table inputTable = tableEnv.fromDataStream...John] 可以看到重点的接口就是: StreamTableEnvironment::toDataStream:将 Table 转为 DataStream StreamTableEnvironment::fromDataStream
将流(DataStream)转换成表(Table) (1)调用fromDataStream()方法 想要将一个DataStream转换成表也很简单,可以通过调用表环境的fromDataStream()方法来实现...另外,还可以在fromDataStream()方法里增加参数,用来重新命名列字段。...(stream); Table table = tableEnv.fromDataStream(stream, $("user")); Table table = tableEnv.fromDataStream...我们调用fromDataStream()方法创建表时,可以追加参数来定义表中的字段结构;这时可以给某个字段加上.rowtime() 后缀,就表示将当前字段指定为事件时间属性。...我们调用fromDataStream()方法创建表时,可以用.proctime()后缀来指定处理时间属性字段。
element: SensorReading): Long = element.timestamp * 1000L }) val sensorTable: Table = tableEnv.fromDataStream...element: SensorReading): Long = element.timestamp * 1000L }) val sensorTable: Table = tableEnv.fromDataStream...element: SensorReading): Long = element.timestamp * 1000L }) val sensorTable: Table = tableEnv.fromDataStream...SensorReading): Long = element.timestamp * 1000L }) // 将数据注册成一张临时表 val dataTable = tableEnv.fromDataStream
dataArray(0), dataArray(1).toLong, dataArray(2).toDouble) } ) val sensorTable: Table = tableEnv.fromDataStream...DataStream 中的数据类型,与表的 Schema 之间的对应关系,可以有两种:基于字段名称,或者基于字段位置 基于名称(name-based) val sensorTable = tableEnv.fromDataStream...因此,只能在schema定义的末尾定义它; 由 DataStream 转换成表时指定 val sensorTable = tableEnv.fromDataStream(dataStream,...DataStream 转换成 Table,使用 .rowtime 可以定义事件时间属性 // 将 DataStream转换为 Table,并指定时间字段 val sensorTable = tableEnv.fromDataStream...'timestamp.rowtime, 'temperature) // 或者,直接追加时间字段 val sensorTable = tableEnv.fromDataStream
.; Table table = tEnv.fromDataStream(stream, $("user_name"), $("data"), $("user_action_time").proctime......); // 声明一个额外逻辑字段作为事件时间属性 // 在 schema 的末尾使用 user_action_time.rowtime 定义事件时间属性 Table table = tEnv.fromDataStream...因此不在必须,可以直接使用事件时间属性替换这个字段 // replace first field with a logical event time attribute Table table = tEnv.fromDataStream
代码表达 代码中实现非常简单,直接用tableEnv.fromDataStream()就可以了。...(dataStream) val sensorTable2 = tableEnv.fromDataStream(dataStream, 'id, 'timestamp as 'ts) 数据类型与Table...基于名称的对应: val sensorTable = tableEnv .fromDataStream(dataStream, $"timestamp" as "ts", $"id" as "myId...", "temperature") 基于位置的对应: val sensorTable = tableEnv .fromDataStream(dataStream, $"myId", $"ts") Flink...代码如下: val stream = env.addSource(new SensorSource) val sensorTable = tableEnv .fromDataStream(stream
, "beer", 1))); //3.注册表 // convert DataStream to Table Table tableA = tEnv.fromDataStream
dataArray(0), dataArray(1).toLong, dataArray(2).toDouble) }) val sensorTable: Table = tableEnv.fromDataStream...).assignAscendingTimestamps(_.temperature * 1000L) // 方式一 val sensorTable: Table = tableEnv.fromDataStream...(dataStream, 'id, 'temperature.rowtime, 'timestamp) // 方式二 val sensorTable2 = tableEnv.fromDataStream
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