apache-spark - Spark S3A写省略上传部分无故障

标签 apache-spark hadoop

我使用 Spark 2.4.0 和 Hadoop 2.7，hadoop-aws 2.7.5 将数据集写入 S3A 上的 Parquet 文件。偶尔会丢失一个文件部分；即部分 00003 在这里:

> aws s3 ls my-bucket/folder/
2019-02-28 13:07:21          0 _SUCCESS
2019-02-28 13:06:58   79428651 part-00000-5789ebf5-b55d-4715-8bb5-dfc5c4e4b999-c000.snappy.parquet
2019-02-28 13:06:59   79586172 part-00001-5789ebf5-b55d-4715-8bb5-dfc5c4e4b999-c000.snappy.parquet
2019-02-28 13:07:00   79561910 part-00002-5789ebf5-b55d-4715-8bb5-dfc5c4e4b999-c000.snappy.parquet
2019-02-28 13:07:01   79192617 part-00004-5789ebf5-b55d-4715-8bb5-dfc5c4e4b999-c000.snappy.parquet
2019-02-28 13:07:07   79364413 part-00005-5789ebf5-b55d-4715-8bb5-dfc5c4e4b999-c000.snappy.parquet
2019-02-28 13:07:08   79623254 part-00006-5789ebf5-b55d-4715-8bb5-dfc5c4e4b999-c000.snappy.parquet
2019-02-28 13:07:10   79445030 part-00007-5789ebf5-b55d-4715-8bb5-dfc5c4e4b999-c000.snappy.parquet
2019-02-28 13:07:10   79474923 part-00008-5789ebf5-b55d-4715-8bb5-dfc5c4e4b999-c000.snappy.parquet
2019-02-28 13:07:11   79477310 part-00009-5789ebf5-b55d-4715-8bb5-dfc5c4e4b999-c000.snappy.parquet
2019-02-28 13:07:12   79331453 part-00010-5789ebf5-b55d-4715-8bb5-dfc5c4e4b999-c000.snappy.parquet
2019-02-28 13:07:13   79567600 part-00011-5789ebf5-b55d-4715-8bb5-dfc5c4e4b999-c000.snappy.parquet
2019-02-28 13:07:13   79388012 part-00012-5789ebf5-b55d-4715-8bb5-dfc5c4e4b999-c000.snappy.parquet
2019-02-28 13:07:14   79308387 part-00013-5789ebf5-b55d-4715-8bb5-dfc5c4e4b999-c000.snappy.parquet
2019-02-28 13:07:15   79455483 part-00014-5789ebf5-b55d-4715-8bb5-dfc5c4e4b999-c000.snappy.parquet
2019-02-28 13:07:17   79512342 part-00015-5789ebf5-b55d-4715-8bb5-dfc5c4e4b999-c000.snappy.parquet
2019-02-28 13:07:18   79403307 part-00016-5789ebf5-b55d-4715-8bb5-dfc5c4e4b999-c000.snappy.parquet
2019-02-28 13:07:18   79617769 part-00017-5789ebf5-b55d-4715-8bb5-dfc5c4e4b999-c000.snappy.parquet
2019-02-28 13:07:19   79333534 part-00018-5789ebf5-b55d-4715-8bb5-dfc5c4e4b999-c000.snappy.parquet
2019-02-28 13:07:20   79543324 part-00019-5789ebf5-b55d-4715-8bb5-dfc5c4e4b999-c000.snappy.parquet

我最关心的是 Spark 应用成功。

  • 对于驱动程序和执行程序，stderr 看起来都非常正常
  • stdout 对于驱动程序来说看起来完全正常
  • 只有执行者的 stdout 给出了出错的迹象:
2019-02-28 21:05:39 INFO  AmazonHttpClient:448 - Unable to execute HTTP request: Read timed out
java.net.SocketTimeoutException: Read timed out
    at java.net.SocketInputStream.socketRead0(Native Method)
    at java.net.SocketInputStream.socketRead(SocketInputStream.java:116)
    at java.net.SocketInputStream.read(SocketInputStream.java:171)
    at java.net.SocketInputStream.read(SocketInputStream.java:141)
    at org.apache.http.impl.io.AbstractSessionInputBuffer.fillBuffer(AbstractSessionInputBuffer.java:161)
    at org.apache.http.impl.io.SocketInputBuffer.fillBuffer(SocketInputBuffer.java:82)
    at org.apache.http.impl.io.AbstractSessionInputBuffer.readLine(AbstractSessionInputBuffer.java:278)
    at org.apache.http.impl.conn.DefaultHttpResponseParser.parseHead(DefaultHttpResponseParser.java:138)
    at org.apache.http.impl.conn.DefaultHttpResponseParser.parseHead(DefaultHttpResponseParser.java:56)
    at org.apache.http.impl.io.AbstractMessageParser.parse(AbstractMessageParser.java:259)
    at org.apache.http.impl.AbstractHttpClientConnection.receiveResponseHeader(AbstractHttpClientConnection.java:286)
    at org.apache.http.impl.conn.DefaultClientConnection.receiveResponseHeader(DefaultClientConnection.java:257)
    at org.apache.http.impl.conn.ManagedClientConnectionImpl.receiveResponseHeader(ManagedClientConnectionImpl.java:207)
    at org.apache.http.protocol.HttpRequestExecutor.doReceiveResponse(HttpRequestExecutor.java:273)
    at com.amazonaws.http.protocol.SdkHttpRequestExecutor.doReceiveResponse(SdkHttpRequestExecutor.java:66)
    at org.apache.http.protocol.HttpRequestExecutor.execute(HttpRequestExecutor.java:125)
    at org.apache.http.impl.client.DefaultRequestDirector.tryExecute(DefaultRequestDirector.java:684)
    at org.apache.http.impl.client.DefaultRequestDirector.execute(DefaultRequestDirector.java:486)
    at org.apache.http.impl.client.AbstractHttpClient.doExecute(AbstractHttpClient.java:835)
    at org.apache.http.impl.client.CloseableHttpClient.execute(CloseableHttpClient.java:83)
    at org.apache.http.impl.client.CloseableHttpClient.execute(CloseableHttpClient.java:56)
    at com.amazonaws.http.AmazonHttpClient.executeHelper(AmazonHttpClient.java:384)
    at com.amazonaws.http.AmazonHttpClient.execute(AmazonHttpClient.java:232)
    at com.amazonaws.services.s3.AmazonS3Client.invoke(AmazonS3Client.java:3528)
    at com.amazonaws.services.s3.AmazonS3Client.invoke(AmazonS3Client.java:3480)
    at com.amazonaws.services.s3.AmazonS3Client.listObjects(AmazonS3Client.java:604)
    at org.apache.hadoop.fs.s3a.S3AFileSystem.getFileStatus(S3AFileSystem.java:960)
    at org.apache.hadoop.fs.s3a.S3AFileSystem.deleteUnnecessaryFakeDirectories(S3AFileSystem.java:1144)
    at org.apache.hadoop.fs.s3a.S3AFileSystem.finishedWrite(S3AFileSystem.java:1133)
    at org.apache.hadoop.fs.s3a.S3AOutputStream.close(S3AOutputStream.java:142)
    at org.apache.hadoop.fs.FSDataOutputStream$PositionCache.close(FSDataOutputStream.java:72)
    at org.apache.hadoop.fs.FSDataOutputStream.close(FSDataOutputStream.java:106)
    at org.apache.parquet.hadoop.util.HadoopPositionOutputStream.close(HadoopPositionOutputStream.java:64)
    at org.apache.parquet.hadoop.ParquetFileWriter.end(ParquetFileWriter.java:685)
    at org.apache.parquet.hadoop.InternalParquetRecordWriter.close(InternalParquetRecordWriter.java:122)
    at org.apache.parquet.hadoop.ParquetRecordWriter.close(ParquetRecordWriter.java:165)
    at org.apache.spark.sql.execution.datasources.parquet.ParquetOutputWriter.close(ParquetOutputWriter.scala:42)
    at org.apache.spark.sql.execution.datasources.FileFormatDataWriter.releaseResources(FileFormatDataWriter.scala:57)
    at org.apache.spark.sql.execution.datasources.FileFormatDataWriter.commit(FileFormatDataWriter.scala:74)
    at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask$3.apply(FileFormatWriter.scala:244)
    at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask$3.apply(FileFormatWriter.scala:239)
    at org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1394)
    at org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:245)
    at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:169)
    at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:168)
...

(此堆栈跟踪重复 6 次)

我正在调整 Hadoop S3A 配置以查看是否可以降低这种情况的发生频率，但我真正想要的是应用程序在这种情况发生时失败。事实上，下游应用程序启动，期望数据存在，并由于数据丢失而产生不正确的结果。

在这种情况下，如何更改 Spark/Hadoop 的行为？

最佳答案

似乎无法解决这个问题(至少在 Hadoop 2.7 中)，所以现在我在每次 Spark S3 写入后添加了一个断言，确保文件部分的数量与数据集中的分区数量相匹配研发:

  def overwriteParquetS3(
    ds: Dataset[_],
    bucket: String,
    folder: String
  ): Unit = {
    val numPartitions = ds.rdd.getNumPartitions
    val destination = GeneralUtils.joinPaths("s3a://", bucket, folder)

    ds
        .write
        .mode(SaveMode.Overwrite)
        .parquet(destination)

    val fs = FileSystem.get(
      URI.create(s"s3a://$bucket/"),
      ds.sparkSession.sparkContext.hadoopConfiguration
    )
    val writtenFiles = fs.listFiles(new Path(destination), false)
    val parts = new ArrayBuffer[LocatedFileStatus]()
    while (writtenFiles.hasNext) {
      val next = writtenFiles.next()
      val name = next.getPath.getName
      if (name.startsWith("part-") && name.endsWith(".parquet")) {
        parts += next
      }
    }

    val filePartStr = parts
        .sortBy(_.getPath.getName)
        .map((fileStatus) => s"${fileStatus.getModificationTime} ${fileStatus.getBlockSize} ${fileStatus.getPath.getName}")
        .mkString("\n\t")
    assert(
      parts.length == numPartitions,
      s"Expected to write dataframe with $numPartitions partitions in $destination but instead " +
          s"found ${parts.length} written parts!\n\t$filePartStr"
    )

    println(s"Confirmed that there numPartitions $numPartitions = ${parts.length} written parts")
  }

这似乎捕获了写入应该出错但没有出错的所有情况。

关于apache-spark - Spark S3A写省略上传部分无故障，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/54935822/

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