Tiago Albineli Motta created SPARK-12677:
--------------------------------------------
Summary: 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
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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