Question

我正在尝试在scala中实现spark流应用程序。我想使用fileStream（）方法来处理新到达的文件以及hadoop目录中的旧文件。

我已经跟随了来自stackoverflow的两个线程的fileStream（）实现：

我正在使用fileStream（），如下所示：

val linesRDD = ssc.fileStream[LongWritable, Text, TextInputFormat](inputDirectory, (t: org.apache.hadoop.fs.Path) => true, false).map(_._2.toString)

但我收到如下错误消息：

type arguments [org.apache.hadoop.io.LongWritable,org.apache.hadoop.io.Text,
org.apache.hadoop.mapred.TextInputFormat] conform to the bounds of none of the overloaded alternatives of value fileStream: [K, V, F <: org.apache.hadoop.mapreduce.InputFormat[K,V]](directory: String, filter: org.apache.hadoop.fs.Path ⇒ Boolean, newFilesOnly: Boolean, conf: org.apache.hadoop.conf.Configuration)(implicit evidence$12: scala.reflect.ClassTag[K], implicit evidence$13: scala.reflect.ClassTag[V], implicit evidence$14: scala.reflect.ClassTag[F])
org.apache.spark.streaming.dstream.InputDStream[(K, V)] <and> 
[K, V, F <: org.apache.hadoop.mapreduce.InputFormat[K,V]](directory:
String, filter: org.apache.hadoop.fs.Path ⇒ Boolean, newFilesOnly: Boolean)(implicit evidence$9: scala.reflect.ClassTag[K], implicit evidence$10: scala.reflect.ClassTag[V], 
implicit evidence$11: scala.reflect.ClassTag[F])
org.apache.spark.streaming.dstream.InputDStream[(K, V)] <and> [K, V, F <: org.apache.hadoop.mapreduce.InputFormat[K,V]](directory: String)(implicit evidence$6: scala.reflect.ClassTag[K], implicit evidence$7: scala.reflect.ClassTag[V], implicit evidence$8: scala.reflect.ClassTag[F])
org.apache.spark.streaming.dstream.InputDStream[(K, V)]

wrong number of type parameters for overloaded method value fileStream with alternatives: 
[K, V, F <: org.apache.hadoop.mapreduce.InputFormat[K,V]](directory: String, filter: org.apache.hadoop.fs.Path ⇒ Boolean, newFilesOnly: Boolean, conf: org.apache.hadoop.conf.Configuration)(implicit evidence$12: scala.reflect.ClassTag[K], implicit evidence$13: scala.reflect.ClassTag[V], implicit evidence$14: scala.reflect.ClassTag[F])
org.apache.spark.streaming.dstream.InputDStream[(K, V)] <and> [K, V, F <:     org.apache.hadoop.mapreduce.InputFormat[K,V]](directory: String, filter: org.apache.hadoop.fs.Path ⇒ Boolean, newFilesOnly: Boolean)(implicit evidence$9: scala.reflect.ClassTag[K], implicit evidence$10: scala.reflect.ClassTag[V], implicit evidence$11: scala.reflect.ClassTag[F])
org.apache.spark.streaming.dstream.InputDStream[(K, V)] <and> 
[K, V, F <: org.apache.hadoop.mapreduce.InputFormat[K,V]](directory: String)(implicit evidence$6: scala.reflect.ClassTag[K], implicit evidence$7: scala.reflect.ClassTag[V], implicit evidence$8: scala.reflect.ClassTag[F])
org.apache.spark.streaming.dstream.InputDStream[(K, V)]

我正在使用 spark 1.4.1 和 hadoop 2.7.1 。在发布这个问题之前，我已经在stackoverflow上讨论了不同的实现，并且还提供了文档，但没有任何帮助。任何帮助将不胜感激。

由于罗杰尼希。

Answer 1

请在下面找到示例java代码，使用正确的导入，它对我来说工作正常

import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.VoidFunction;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.streaming.Duration;
import org.apache.spark.streaming.api.java.JavaDStream;
import org.apache.spark.streaming.api.java.JavaPairInputDStream;
import org.apache.spark.streaming.api.java.JavaStreamingContext;

JavaStreamingContext jssc = SparkUtils.getStreamingContext("key", jsc);
//      JavaDStream<String> rawInput = jssc.textFileStream(inputPath);

        JavaPairInputDStream<LongWritable, Text> inputStream = jssc.fileStream(
                inputPath, LongWritable.class, Text.class,
                TextInputFormat.class, new Function<Path, Boolean>() {
                    @Override
                    public Boolean call(Path v1) throws Exception {
                        if ( v1.getName().contains("COPYING") ) {
                            // This eliminates staging files.
                            return Boolean.FALSE;
                        }
                        return Boolean.TRUE;
                    }
                }, true);
        JavaDStream<String> rawInput = inputStream.map(
                  new Function<Tuple2<LongWritable, Text>, String>() {
                    @Override
                    public String call(Tuple2<LongWritable, Text> v1) throws Exception {
                      return v1._2().toString();
                    }
                });
        log.info(tracePrefix + "Created the stream, Window Interval: " + windowInterval + ", Slide interval: " + slideInterval);
        rawInput.print();

SparkStreaming：fileStream（）中的错误

1 个答案: