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https://issues.apache.org/jira/browse/SPARK-21392?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16090159#comment-16090159
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Stuart Reynolds commented on SPARK-21392:
-----------------------------------------
So.... trying to look at the csv was helpful.
{code:none}
#root = "/network/folder" # succeeds
root = "" # fails
rdd.write.parquet(root+"mi", mode="overwrite")
rdd.write.csv(root+"minn.csv", mode="overwrite")
rdd2 = sqlc.read.parquet(root+"mi")
{code}
The above creates a folder on my local machine, but no data.
{code:none}
% ls -la mi minn.csv
mi:
total 12
drwxrwxr-x 2 builder builder 4096 Jul 17 10:42 .
drwxrwxr-x 5 builder builder 4096 Jul 17 10:42 ..
-rw-r--r-- 1 builder builder 0 Jul 17 10:42 _SUCCESS
-rw-r--r-- 1 builder builder 8 Jul 17 10:42 ._SUCCESS.crc
minn.csv/:
total 12
drwxrwxr-x 2 builder builder 4096 Jul 17 10:42 .
drwxrwxr-x 5 builder builder 4096 Jul 17 10:42 ..
-rw-r--r-- 1 builder builder 0 Jul 17 10:42 _SUCCESS
-rw-r--r-- 1 builder builder 8 Jul 17 10:42 ._SUCCESS.crc
{code}
Prepending the paths with network folder that's available to spark succeeds.
So, is this just a "file not found error", with a terrible error message?
> Unable to infer schema when loading large Parquet file
> ------------------------------------------------------
>
> Key: SPARK-21392
> URL: https://issues.apache.org/jira/browse/SPARK-21392
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 2.1.1, 2.2.0
> Environment: Spark 2.1.1. python 2.7.6
> Reporter: Stuart Reynolds
> Labels: parquet, pyspark
>
> The following boring code works up until when I read in the parquet file.
> {code:none}
> import numpy as np
> import pandas as pd
> import pyspark
> from pyspark import SQLContext, SparkContext, SparkConf
> print pyspark.__version__
> sc = SparkContext(conf=SparkConf().setMaster('local'))
> df = pd.DataFrame({"mi":np.arange(100), "eid":np.arange(100)})
> print df
> sqlc = SQLContext(sc)
> df = sqlc.createDataFrame(df)
> df = df.createOrReplaceTempView("outcomes")
> rdd = sqlc.sql("SELECT eid,mi FROM outcomes limit 5")
> print rdd.schema
> rdd.show()
> rdd.write.parquet("mi", mode="overwrite")
> rdd2 = sqlc.read.parquet("mi") # FAIL!
> {code}
> {code:none}
> # print pyspark.__version__
> 2.2.0
> # print df
> eid mi
> 0 0 0
> 1 1 1
> 2 2 2
> 3 3 3
> ...
> [100 rows x 2 columns]
> # print rdd.schema
> StructType(List(StructField(eid,LongType,true),StructField(mi,LongType,true)))
> # rdd.show()
> +---+---+
> |eid| mi|
> +---+---+
> | 0| 0|
> | 1| 1|
> | 2| 2|
> | 3| 3|
> | 4| 4|
> +---+---+
> {code}
>
> fails with:
> {code:none}
> rdd2 = sqlc.read.parquet("mixx")
> File "/usr/local/lib/python2.7/dist-packages/pyspark/sql/readwriter.py",
> line 291, in parquet
> return self._df(self._jreader.parquet(_to_seq(self._spark._sc, paths)))
> File "/usr/local/lib/python2.7/dist-packages/py4j/java_gateway.py", line
> 1133, in __call__
> answer, self.gateway_client, self.target_id, self.name)
> File "/usr/local/lib/python2.7/dist-packages/pyspark/sql/utils.py", line
> 69, in deco
> raise AnalysisException(s.split(': ', 1)[1], stackTrace)
> pyspark.sql.utils.AnalysisException: u'Unable to infer schema for Parquet. It
> must be specified manually.;'
> {code}
> The documentation for parquet says the format is self describing, and the
> full schema was available when the parquet file was saved. What gives?
> Works with master='local', but fails with my cluster is specified.
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