Scala Spark中的groupBy函数需要Lzocodec吗？
Scala Spark中的groupBy函数需要Lzocodec吗？
提问于 2018-01-31 09:09:47
我在Scala Spark中做了一个函数，看起来像这样。
def prepareSequences(data: RDD[String], splitChar: Char = '\t') = {
val x = data.map(line => {
val Array(id, se, offset, hour) = line.split(splitChar)
(id + "-" + se,
Step(offset = if (offset == "NULL") {
-5
} else {
offset.toInt
},
hour = hour.toInt))
})
val y = x.groupBy(_._1)}我需要groupBy，但是一旦我添加它，我就会得到一个错误。请求Lzocodec时出现错误。
Exception in thread "main" java.lang.RuntimeException: Error in configuring object
at org.apache.hadoop.util.ReflectionUtils.setJobConf(ReflectionUtils.java:112)
at org.apache.hadoop.util.ReflectionUtils.setConf(ReflectionUtils.java:78)
at org.apache.hadoop.util.ReflectionUtils.newInstance(ReflectionUtils.java:136)
at org.apache.spark.rdd.HadoopRDD.getInputFormat(HadoopRDD.scala:188)
at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:201)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
at org.apache.spark.Partitioner$$anonfun$defaultPartitioner$2.apply(Partitioner.scala:66)
at org.apache.spark.Partitioner$$anonfun$defaultPartitioner$2.apply(Partitioner.scala:66)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.immutable.List.foreach(List.scala:381)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.immutable.List.map(List.scala:285)
at org.apache.spark.Partitioner$.defaultPartitioner(Partitioner.scala:66)
at org.apache.spark.rdd.RDD$$anonfun$groupBy$1.apply(RDD.scala:687)
at org.apache.spark.rdd.RDD$$anonfun$groupBy$1.apply(RDD.scala:687)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
at org.apache.spark.rdd.RDD.groupBy(RDD.scala:686)
at com.savagebeast.mypackage.DataPreprocessing$.prepareSequences(DataPreprocessing.scala:42)
at com.savagebeast.mypackage.activity_mapper$.main(activity_mapper.scala:31)
at com.savagebeast.mypackage.activity_mapper.main(activity_mapper.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:738)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:187)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:212)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:126)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: java.lang.reflect.InvocationTargetException
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.hadoop.util.ReflectionUtils.setJobConf(ReflectionUtils.java:109)
... 44 more
Caused by: java.lang.IllegalArgumentException: Compression codec com.hadoop.compression.lzo.LzoCodec not found.
at org.apache.hadoop.io.compress.CompressionCodecFactory.getCodecClasses(CompressionCodecFactory.java:139)
at org.apache.hadoop.io.compress.CompressionCodecFactory.<init>(CompressionCodecFactory.java:180)
at org.apache.hadoop.mapred.TextInputFormat.configure(TextInputFormat.java:45)
... 49 more
Caused by: java.lang.ClassNotFoundException: Class com.hadoop.compression.lzo.LzoCodec not found
at org.apache.hadoop.conf.Configuration.getClassByName(Configuration.java:2101)
at org.apache.hadoop.io.compress.CompressionCodecFactory.getCodecClasses(CompressionCodecFactory.java:132)
... 51 more我按照这个Class com.hadoop.compression.lzo.LzoCodec not found for Spark on CDH 5?安装了lzo和其他所需的东西
我是不是遗漏了什么？
更新:找到解决方案。
像这样对RDD进行分区解决了这个问题。
val y = x.groupByKey(50)50是我想要用于RDD的分区数量。它可以是任何数字。
然而，我不确定为什么这是可行的。如果有人能解释的话我会很感激。
更新-2:到目前为止，下面的工作更明智，而且是稳定的。
我将hadoop-lzo-0.4.21-SNAPSHOT.jar从/Users/<username>/hadoop-lzo/target复制到/usr/local/Cellar/apache-spark/2.1.0/libexec/jars。实质上是将jar复制到spark的类路径中。
回答 1
Stack Overflow用户
发布于 2018-01-31 09:19:15
不是的。这不是groupBy所必需的。如果你看一下堆栈跟踪(用于发布它的kudos)，你会发现它在输入格式中的某个地方失败了：
org.apache.hadoop.mapred.TextInputFormat.configure(TextInputFormat.java:45)上的
这表明您的输入是压缩的。当您调用groupBy时，它会失败，因为这是Spark必须决定分区数量并触摸输入的地方。
在实践中-是的，似乎你需要lzo编解码器来执行你的工作。
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原文链接：
https://stackoverflow.com/questions/48532951
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