Saving dataframe to local file system results in empty results

问题

We are running spark 2.3.0 on AWW EMR. The following DataFrame "df" is non empty and of modest size:

scala> df.count
res0: Long = 4067

The following code works fine for writing df to hdfs:

   scala> val hdf = spark.read.parquet("/tmp/topVendors")
hdf: org.apache.spark.sql.DataFrame = [displayName: string, cnt: bigint]

scala> hdf.count
res4: Long = 4067

However using the same code to write to a local parquet or csv file end up with empty results:

df.repartition(1).write.mode("overwrite").parquet("file:///tmp/topVendors")

scala> val locdf = spark.read.parquet("file:///tmp/topVendors")
org.apache.spark.sql.AnalysisException: Unable to infer schema for Parquet. It must be specified manually.;
  at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$9.apply(DataSource.scala:207)
  at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$9.apply(DataSource.scala:207)
  at scala.Option.getOrElse(Option.scala:121)

We can see why it fails:

 ls -l /tmp/topVendors
total 0
-rw-r--r-- 1 hadoop hadoop 0 Jul 30 22:38 _SUCCESS

So there is no parquet file being written.

I have tried this maybe twenty times and for both csv and parquet and on two different EMR Servers: this same behavior is exhibited in all cases.

Is this an EMR specific bug? A more general EC2 bug? Something else? This code works on spark on macos.

In case it matters - here is the versioning info:

Release label:emr-5.13.0
Hadoop distribution:Amazon 2.8.3
Applications:Spark 2.3.0, Hive 2.3.2, Zeppelin 0.7.3

回答1:

That is not a bug and it is the expected behavior. Spark does not really support writes to non-distributed storage (it will work in local mode, just because you have shared file system).

Local path is not interpreted (only) as a path on the driver (this would require collecting the data) but local path on each executor. Therefore each executor will write its own chunk to its own local file system.

Not only output is no readable back (to load data each executor and the driver should see the same state of the file system), but depending on the commit algorithm, might not be even finalized (move from the temporary directory).

回答2:

This error usually occurs when you try to read an empty directory as parquet. You could check 1. if the DataFrame is empty with outcome.rdd.isEmpty() before write it. 2. Check the if the path you are giving is correct

Also in what mode you are running your application? Try running it in client mode if you are running in cluster mode.

来源：https://stackoverflow.com/questions/51603404/saving-dataframe-to-local-file-system-results-in-empty-results

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apache-spark

amazon-emr