Scala 虽然使用了import sqlContext.implicits.\uuu,但toDF不会编译
当我想使用Scala 虽然使用了import sqlContext.implicits.\uuu,但toDF不会编译,scala,apache-spark,dataframe,rdd,Scala,Apache Spark,Dataframe,Rdd,当我想使用toDF将RDD传递给DataFrame时,我在编译Spark Scala代码时遇到了一些问题。我检查了Spark 2.0.0和1.6.2,问题一直都是一样的。 下面我提供我的POM文件和一段代码: POM.xml <?xml version="1.0" encoding="UTF-8"?> <project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/
toDF
将RDD传递给DataFrame时,我在编译Spark Scala代码时遇到了一些问题。我检查了Spark 2.0.0和1.6.2,问题一直都是一样的。
下面我提供我的POM文件和一段代码:
POM.xml
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>org.test</groupId>
<artifactId>test_service</artifactId>
<version>1.0-SNAPSHOT</version>
<properties>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
<java.version>1.8</java.version>
<scala.version>2.10.6</scala.version>
<spark.version>2.0.0</spark.version>
<jackson.version>2.8.3</jackson.version>
</properties>
<dependencies>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>${scala.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming-kafka_2.10</artifactId>
<version>1.6.2</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_2.10</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.10</artifactId>
<version>${spark.version}</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming_2.10</artifactId>
<version>${spark.version}</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-mllib_2.10</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.module</groupId>
<artifactId>jackson-module-scala_2.10</artifactId>
<version>${jackson.version}</version>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.core</groupId>
<artifactId>jackson-databind</artifactId>
<version>${jackson.version}</version>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.core</groupId>
<artifactId>jackson-annotations</artifactId>
<version>${jackson.version}</version>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.core</groupId>
<artifactId>jackson-core</artifactId>
<version>${jackson.version}</version>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.module</groupId>
<artifactId>jackson-module-scala_2.10</artifactId>
<version>2.8.3</version>
</dependency>
<dependency>
<groupId>org.sedis</groupId>
<artifactId>sedis_2.10</artifactId>
<version>1.2.2</version>
</dependency>
<dependency>
<groupId>com.lambdaworks</groupId>
<artifactId>jacks_2.10</artifactId>
<version>2.3.3</version>
</dependency>
<dependency>
<groupId>com.github.nscala-time</groupId>
<artifactId>nscala-time_2.10</artifactId>
<version>2.12.0</version>
</dependency>
<dependency>
<groupId>com.typesafe</groupId>
<artifactId>config</artifactId>
<version>1.3.1</version>
</dependency>
<dependency>
<groupId>com.amazonaws</groupId>
<artifactId>aws-java-sdk-s3</artifactId>
<version>1.11.53</version>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<artifactId>maven-assembly-plugin</artifactId>
<executions>
<execution>
<id>build-a</id>
<configuration>
<archive>
<manifest>
<mainClass>org.test.Runner1</mainClass>
</manifest>
</archive>
<descriptorRefs>
<descriptorRef>jar-with-dependencies</descriptorRef>
</descriptorRefs>
<finalName>runner1</finalName>
</configuration>
<phase>package</phase>
<goals>
<goal>assembly</goal>
</goals>
</execution>
<execution>
<id>build-b</id>
<configuration>
<archive>
<manifest>
<mainClass>org.test.Runner2</mainClass>
</manifest>
</archive>
<descriptorRefs>
<descriptorRef>jar-with-dependencies</descriptorRef>
</descriptorRefs>
<finalName>runner2</finalName>
</configuration>
<phase>package</phase>
<goals>
<goal>assembly</goal>
</goals>
</execution>
</executions>
</plugin>
<!-- Configure maven-compiler-plugin to use the desired Java version -->
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.1</version>
<configuration>
<source>${java.version}</source>
<target>${java.version}</target>
</configuration>
</plugin>
<!-- Use build-helper-maven-plugin to add Scala source and test source directories -->
<plugin>
<groupId>org.codehaus.mojo</groupId>
<artifactId>build-helper-maven-plugin</artifactId>
<version>1.10</version>
<executions>
<execution>
<id>add-source</id>
<phase>generate-sources</phase>
<goals>
<goal>add-source</goal>
</goals>
<configuration>
<sources>
<source>src/main/scala</source>
</sources>
</configuration>
</execution>
<execution>
<id>add-test-source</id>
<phase>generate-test-sources</phase>
<goals>
<goal>add-test-source</goal>
</goals>
<configuration>
<sources>
<source>src/test/scala</source>
</sources>
</configuration>
</execution>
</executions>
</plugin>
<!-- Use scala-maven-plugin for Scala support -->
<plugin>
<groupId>net.alchim31.maven</groupId>
<artifactId>scala-maven-plugin</artifactId>
<version>3.2.2</version>
<executions>
<execution>
<goals>
<!-- Need to specify this explicitly, otherwise plugin won't be called when doing e.g. mvn compile -->
<goal>compile</goal>
<goal>testCompile</goal>
</goals>
</execution>
</executions>
</plugin>
</plugins>
</build>
</project>
这是意料之中的
RDD[Map[String,Any]
无法转换为DataFrame
:
- 没有隐式转换$implicits$
不支持DataFrame
Any
对象
或案例类
,其中的元素同时包含键和值。现在映射变量功能的结果
,这样您就可以将RDD[“case\u class\u name”]
传递到。toDF
函数好了,我更新了代码,现在它是RDD[map[String,String]
,但问题仍然是一样的!此警告是否会导致此问题<代码>[警告]预期所有依赖项都需要Scala版本:2.10.6[警告]org.测试:runner1:1.0-SNAPSHOT需要Scala版本:2.10.6[警告]org.apache.kafka:kafka_2.10:0.8.2.1需要Scala版本:2.10.4[警告]检测到多个版本的Scala库代码>
import org.apache.spark.sql.SQLContext
import org.apache.spark.streaming.kafka.KafkaUtils
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.{SparkConf, SparkContext}
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
import sqlContext.implicits._
myDStream.foreachRDD { rdd =>
val features = rdd.map(line => line.keySet).first().toList
val myDF = filtered.toDF(features: _*) // toDF is not recognized!!!
}