我试图使用托管在EC2上的Spark，使用这个指南对常见爬取数据执行简单的转换。 我的代码如下：
package ccminer
import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
object ccminer {
val english = "english|en|eng"
val spanish = "es|esp|spa|spanish|espanol"
val turkish = "turkish|tr|tur|turc"
val greek = "greek|el|ell"
val italian = "italian|it|ita|italien"
val all = (english :: spanish :: turkish :: greek :: italian :: Nil).mkString("|")
def langIndep(s: String) = s.toLowerCase().replaceAll(all, "*")
def main(args: Array[String]): Unit = {
if (args.length != 3) {
System.err.println("Bad command line")
System.exit(-1)
}
val cluster = "spark://???"
val sc = new SparkContext(cluster, "Common Crawl Miner",
System.getenv("SPARK_HOME"), Seq("/root/spark/ccminer/target/scala-2.10/cc-miner_2.10-1.0.jar"))
sc.sequenceFile[String, String](args(0)).map {
case (k, v) => (langIndep(k), v)
}
.groupByKey(args(2).toInt)
.filter {
case (_, vs) => vs.size > 1
}
.saveAsTextFile(args(1))
}
}
我正在使用以下命令运行它：
sbt/sbt "run-main ccminer.ccminer s3n://aws-publicdatasets/common-crawl/parse-output/segment/1341690165636/textData-* s3n://parallelcorpus/out/ 2000"
但很快就会出现以下错误：
java.lang.OutOfMemoryError: Java heap space
at com.ning.compress.BufferRecycler.allocEncodingBuffer(BufferRecycler.java:59)
at com.ning.compress.lzf.ChunkEncoder.<init>(ChunkEncoder.java:93)
at com.ning.compress.lzf.impl.UnsafeChunkEncoder.<init>(UnsafeChunkEncoder.java:40)
at com.ning.compress.lzf.impl.UnsafeChunkEncoderLE.<init>(UnsafeChunkEncoderLE.java:13)
at com.ning.compress.lzf.impl.UnsafeChunkEncoders.createEncoder(UnsafeChunkEncoders.java:31)
at com.ning.compress.lzf.util.ChunkEncoderFactory.optimalInstance(ChunkEncoderFactory.java:44)
at com.ning.compress.lzf.LZFOutputStream.<init>(LZFOutputStream.java:61)
at org.apache.spark.io.LZFCompressionCodec.compressedOutputStream(CompressionCodec.scala:60)
at org.apache.spark.storage.BlockManager.wrapForCompression(BlockManager.scala:803)
at org.apache.spark.storage.BlockManager$$anonfun$5.apply(BlockManager.scala:471)
at org.apache.spark.storage.BlockManager$$anonfun$5.apply(BlockManager.scala:471)
at org.apache.spark.storage.DiskBlockObjectWriter.open(BlockObjectWriter.scala:117)
at org.apache.spark.storage.DiskBlockObjectWriter.write(BlockObjectWriter.scala:174)
at org.apache.spark.scheduler.ShuffleMapTask$$anonfun$runTask$1.apply(ShuffleMapTask.scala:164)
at org.apache.spark.scheduler.ShuffleMapTask$$anonfun$runTask$1.apply(ShuffleMapTask.scala:161)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:161)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:102)
at org.apache.spark.scheduler.Task.run(Task.scala:53)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$run$1.apply$mcV$sp(Executor.scala:213)
at org.apache.spark.deploy.SparkHadoopUtil.runAsUser(SparkHadoopUtil.scala:49)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:178)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:744)
所以我的基本问题是，如何编写一个可以按键分组并具有几乎无限量输入但不会耗尽内存的Spark任务？