> 在 2019年8月26日，11:53，Zili Chen <
[hidden email]> 写道：
>
> 不应该呀，我看到仍然有
>
> def registerDataStream[T](name: String, dataStream: DataStream[T], fields: Expression*): Unit
>
> 这个方法的，你能提供完整一点的上下文和报错吗？
>
> Best,
> tison.
>
>
> ddwcg <
[hidden email] <mailto:
[hidden email]>> 于2019年8月26日周一 上午11:38写道：
> 感谢您的回复，确实是这个原因。但是后面注册表的时候不能使用 Expression
> 总是感觉 java api 和scala api有点混乱了
>
>
>
>> 在 2019年8月26日，11:22，Zili Chen <
[hidden email] <mailto:
[hidden email]>> 写道：
>>
>> 试试把
>>
>> import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment
>>
>> 换成
>>
>> import org.apache.flink.table.api.scala.StreamExecutionEnvironment
>>
>> 应该是意外 import 了不同包下的同名类的缘故
>>
>> Best,
>> tison.
>>
>>
>> ddwcg <
[hidden email] <mailto:
[hidden email]>> 于2019年8月26日周一 上午11:12写道：
>>
>>> 大家好，
>>> 我周末升级到了1.9.0，但是在初始化table env的时候编译不通过，请大家帮忙看看是什么原因，
>>>
>>> import org.apache.flink.streaming.api.CheckpointingMode
>>> import org.apache.flink.streaming.api.environment.CheckpointConfig.ExternalizedCheckpointCleanup
>>> import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment
>>> import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer
>>> import org.apache.flink.streaming.util.serialization.SimpleStringSchema
>>> import org.apache.flink.table.api.scala.StreamTableEnvironment
>>> import org.apache.flink.table.planner.expressions.StddevPop
>>> import org.apache.kafka.clients.consumer.ConsumerConfig
>>> import org.apache.kafka.clients.producer.ProducerConfig
>>>
>>> object StreamingJob {
>>> def main(args: Array[String]) {
>>> val kafkaTopic = "source.kafka.topic"
>>> val jobName ="test"
>>> val parallelism =1
>>> val checkPointPath ="checkpoint/"
>>> val kafkaBrokers =""
>>>
>>> // set up the streaming execution environment
>>> val env = StreamExecutionEnvironment.getExecutionEnvironment
>>> env.setParallelism(parallelism)
>>> env.enableCheckpointing(10000)
>>> env.getCheckpointConfig.setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE)
>>> env.getCheckpointConfig.setMaxConcurrentCheckpoints(1)
>>> env.getCheckpointConfig.enableExternalizedCheckpoints(ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION)
>>> //env.setStateBackend(new FsStateBackend(checkPointPath))
>>>
>>>
>>> val tableEnv = StreamTableEnvironment.create(env)
>>>
>>>
>>> 提示有多个实现：
>>>
>>> 下面是pom文件：
>>>
>>> <dependency>
>>> <groupId>org.apache.flink</groupId>
>>> <artifactId>flink-scala_${scala.binary.version}</artifactId>
>>> <version>${flink.version}</version>
>>> <scope>compile</scope>
>>> </dependency>
>>> <dependency>
>>> <groupId>org.apache.flink</groupId>
>>> <artifactId>flink-streaming-scala_${scala.binary.version}</artifactId>
>>> <version>${flink.version}</version>
>>> <scope>compile</scope>
>>> </dependency>
>>> <dependency>
>>> <groupId>org.apache.flink</groupId>
>>> <artifactId>flink-table-planner-blink_${scala.binary.version}</artifactId>
>>> <version>${flink.version}</version>
>>> <scope>provided</scope>
>>> </dependency>
>>> <!--
https://mvnrepository.com/artifact/org.apache.flink/flink-table-runtime-blink <
https://mvnrepository.com/artifact/org.apache.flink/flink-table-runtime-blink> -->
>>> <dependency>
>>> <groupId>org.apache.flink</groupId>
>>> <artifactId>flink-table-runtime-blink_2.11</artifactId>
>>> <version>1.9.0</version>
>>> </dependency>
>>> <dependency>
>>> <groupId>org.apache.flink</groupId>
>>> <artifactId>flink-connector-kafka_2.11</artifactId>
>>> <version>1.9.0</version>
>>> </dependency>
>>> <dependency>
>>> <groupId>org.apache.flink</groupId>
>>> <artifactId>flink-table-common</artifactId>
>>> <version>${flink.version}</version>
>>> <scope>provided</scope>
>>> </dependency>
>>>
>>>
>>>
>>>
>
Locked
