Spark 上的 Google Cloud Storage 连接器问题

Question

我正在尝试在 Mac OS 上的 Spark 上安装 Google Cloud Storage，以对我的 Spark 应用进行本地测试。我已阅读以下文档 (https://cloud.google.com/hadoop/google-cloud-storage-connector)。我在 spark/lib 文件夹中添加了“gcs-connector-latest-hadoop2.jar”。我还在 spark/conf 目录中添加了 core-data.xml 文件。

当我运行我的 pyspark shell 时，我得到一个错误：

>>> sc.textFile("gs://mybucket/test.csv").count()
    Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/poiuytrez/Documents/DataBerries/programs/spark/python/pyspark/rdd.py", line 847, in count
    return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum()
  File "/Users/poiuytrez/Documents/DataBerries/programs/spark/python/pyspark/rdd.py", line 838, in sum
    return self.mapPartitions(lambda x: [sum(x)]).reduce(operator.add)
  File "/Users/poiuytrez/Documents/DataBerries/programs/spark/python/pyspark/rdd.py", line 759, in reduce
    vals = self.mapPartitions(func).collect()
  File "/Users/poiuytrez/Documents/DataBerries/programs/spark/python/pyspark/rdd.py", line 723, in collect
    bytesInJava = self._jrdd.collect().iterator()
  File "/Users/poiuytrez/Documents/DataBerries/programs/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py", line 538, in __call__
  File "/Users/poiuytrez/Documents/DataBerries/programs/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py", line 300, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o26.collect.
: java.lang.RuntimeException: java.lang.ClassNotFoundException: Class com.google.cloud.hadoop.fs.gcs.GoogleHadoopFileSystem not found
    at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:1895)
    at org.apache.hadoop.fs.FileSystem.getFileSystemClass(FileSystem.java:2379)
    at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:2392)
    at org.apache.hadoop.fs.FileSystem.access$200(FileSystem.java:89)
    at org.apache.hadoop.fs.FileSystem$Cache.getInternal(FileSystem.java:2431)
    at org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:2413)
    at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:368)
    at org.apache.hadoop.fs.Path.getFileSystem(Path.java:296)
    at org.apache.hadoop.mapred.FileInputFormat.singleThreadedListStatus(FileInputFormat.java:256)
    at org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInputFormat.java:228)
    at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:304)
    at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:179)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:204)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:202)
    at scala.Option.getOrElse(Option.scala:120)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:202)
    at org.apache.spark.rdd.MappedRDD.getPartitions(MappedRDD.scala:28)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:204)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:202)
    at scala.Option.getOrElse(Option.scala:120)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:202)
    at org.apache.spark.api.python.PythonRDD.getPartitions(PythonRDD.scala:56)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:204)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:202)
    at scala.Option.getOrElse(Option.scala:120)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:202)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1135)
    at org.apache.spark.rdd.RDD.collect(RDD.scala:774)
    at org.apache.spark.api.java.JavaRDDLike$class.collect(JavaRDDLike.scala:305)
    at org.apache.spark.api.java.JavaRDD.collect(JavaRDD.scala:32)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:606)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)
    at py4j.Gateway.invoke(Gateway.java:259)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:207)
    at java.lang.Thread.run(Thread.java:744)
Caused by: java.lang.ClassNotFoundException: Class com.google.cloud.hadoop.fs.gcs.GoogleHadoopFileSystem not found
    at org.apache.hadoop.conf.Configuration.getClassByName(Configuration.java:1801)
    at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:1893)
    ... 40 more

我不知道下一步该去哪里。

Answer 1

要求 它可能在 Spark 的版本之间有所不同，但如果您查看 bdutil-0.35.2/extensions/spark/install_spark.sh 内部，您会看到我们使用 bdutil 设置的“Spark + Hadoop on GCE”是如何工作的；它包括您提到的项目，将连接器添加到 spark/lib 文件夹中，并将 core-site.xml 文件添加到 spark/conf 目录中，但另外在spark/conf/spark-env.sh 中添加了该行：

export SPARK_CLASSPATH=\$SPARK_CLASSPATH:${LOCAL_GCS_JAR}

${LOCAL_GCS_JAR} 是您添加到 spark/lib 的 jarfile 的绝对路径。尝试将其添加到您的 spark/conf/spark-env.sh 中，ClassNotFoundException 应该会消失。