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Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2016/11/02 14:23:59 UTC
[jira] [Comment Edited] (SPARK-12677) Lazy file discovery for
parquet
[ https://issues.apache.org/jira/browse/SPARK-12677?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15629114#comment-15629114 ]
Hyukjin Kwon edited comment on SPARK-12677 at 11/2/16 2:23 PM:
---------------------------------------------------------------
Ah, I see. IMHO, it might not be an issue as long as it gives a clear message for the similar reason with [~reactormonk].
I first thought you meant schema inference. In case of this, it does launch another job for schema inference[1] and I submitted a PR[2] to deal with this problem by allowing to read them in driver when they are few.
[1] https://github.com/apache/spark/blob/77a98162d1ec28247053b8b3ad4af28baa950797/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFileFormat.scala#L585-L593
[2] https://github.com/apache/spark/pull/14660
was (Author: hyukjin.kwon):
Ah, I see. IMHO, it might not be an issue as long as it gives a clear message as the similar reason with [~reactormonk].
I first thought you meant schema inference. In case of this, it does launch another job for schema inference[1] and I submitted a PR[2] to deal with this problem by allowing to read them in driver when they are few.
[1]https://github.com/apache/spark/blob/77a98162d1ec28247053b8b3ad4af28baa950797/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFileFormat.scala#L585-L593
[2]https://github.com/apache/spark/pull/14660
> Lazy file discovery for parquet
> -------------------------------
>
> Key: SPARK-12677
> URL: https://issues.apache.org/jira/browse/SPARK-12677
> Project: Spark
> Issue Type: Wish
> Components: SQL
> Reporter: Tiago Albineli Motta
> Priority: Minor
> Labels: features
>
> When using sqlContext.read.parquet(files: _*) the driver verifyies if everything is ok with the files. But reading those files is lazy, so when it starts maybe the files are not there anymore, or they have changed, so we receive this error message:
> {quote}
> 16/01/06 10:52:43 ERROR yarn.ApplicationMaster: User class threw exception: org.apache.spark.SparkException: Job aborted due to stage failure: Task 4 in stage 0.0 failed 4 times, most recent failure: Lost task 4.3 in stage 0.0 (TID 16, riolb586.globoi.com): java.io.FileNotFoundException: File does not exist: hdfs://mynamenode.com:8020/rec/prefs/2016/01/06/part-r-00003-27a100b0-ff49-45ad-8803-e6cc77286661.gz.parquet
> at org.apache.hadoop.hdfs.DistributedFileSystem$22.doCall(DistributedFileSystem.java:1309)
> at org.apache.hadoop.hdfs.DistributedFileSystem$22.doCall(DistributedFileSystem.java:1301)
> at org.apache.hadoop.fs.FileSystemLinkResolver.resolve(FileSystemLinkResolver.java:81)
> at org.apache.hadoop.hdfs.DistributedFileSystem.getFileStatus(DistributedFileSystem.java:1317)
> at parquet.hadoop.ParquetFileReader.readFooter(ParquetFileReader.java:381)
> at parquet.hadoop.ParquetRecordReader.initializeInternalReader(ParquetRecordReader.java:155)
> at parquet.hadoop.ParquetRecordReader.initialize(ParquetRecordReader.java:138)
> at org.apache.spark.sql.sources.SqlNewHadoopRDD$$anon$1.<init>(SqlNewHadoopRDD.scala:153)
> at org.apache.spark.sql.sources.SqlNewHadoopRDD.compute(SqlNewHadoopRDD.scala:124)
> at org.apache.spark.sql.sources.SqlNewHadoopRDD.compute(SqlNewHadoopRDD.scala:66)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
> at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:69)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:242)
> at org.apache.spark.rdd.UnionRDD.compute(UnionRDD.scala:87)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
> at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:70)
> at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
> at org.apache.spark.scheduler.Task.run(Task.scala:70)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
> 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:745)
> {quote}
> Maybe if sqlContext.read.parquet could receive a Function to discover the files instead it could be avoided. Like this: sqlContext.read.parquet( () => files )
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