[jira] [Comment Edited] (SPARK-21392) Unable to infer schema when loading large Parquet file
[
https://issues.apache.org/jira/browse/SPARK-21392?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16090159#comment-16090159
]
Stuart Reynolds edited comment on SPARK-21392 at 7/18/17 5:30 PM:
--
So trying to look at the csv was helpful.
{code:none}
#root = "/network/folder/mi" # succeeds
root = "mi" # 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 builder0 Jul 17 10:42 _SUCCESS
-rw-r--r-- 1 builder builder8 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 builder0 Jul 17 10:42 _SUCCESS
-rw-r--r-- 1 builder builder8 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?
was (Author: stuartreynolds):
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 builder0 Jul 17 10:42 _SUCCESS
-rw-r--r-- 1 builder builder8 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 builder0 Jul 17 10:42 _SUCCESS
-rw-r--r-- 1 builder builder8 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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[jira] [Comment Edited] (SPARK-21392) Unable to infer schema when loading large Parquet file
[
https://issues.apache.org/jira/browse/SPARK-21392?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16084837#comment-16084837
]
Stuart Reynolds edited comment on SPARK-21392 at 7/13/17 4:55 PM:
--
I've simplified the example a little -more and also found the limiting the
query size to 100 rows succeeds, whereas if I select all 500k rows * 2 columns,
it fails-.
In case it helps, my utility function get_sparkSQLContextWithTables loaded the
full table 'outcomes' from postgres into spark, with 10 partitions with:
{code:none}
index="eid"
index_min=min(eid)
index_max=max(eid)
{code}
was (Author: stuartreynolds):
I've simplified the example a little more and also found the limiting the query
size to 100 rows succeeds, whereas if I select all 500k rows * 2 columns, it
fails.
In case it helps, my utility function get_sparkSQLContextWithTables loaded the
full table 'outcomes' from postgres into spark, with 10 partitions with:
{code:none}
index="eid"
index_min=min(eid)
index_max=max(eid)
{code}
> 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
> Environment: Spark 2.1.1. python 2.7.6
>Reporter: Stuart Reynolds
> Labels: parquet, pyspark
>
> The following boring code works
> {code:none}
> response = "mi_or_chd_5"
> sc = get_spark_context() # custom
> sqlc = get_sparkSQLContextWithTables(sc, tables=["outcomes"]) # custom
> rdd = sqlc.sql("SELECT eid,mi_or_chd_5 FROM outcomes")
> print rdd.schema
> #>>
> StructType(List(StructField(eid,IntegerType,true),StructField(mi_or_chd_5,ShortType,true)))
> rdd.show()
> #+---+---+
> #|eid|mi_or_chd_5|
> #+---+---+
> #|226| null|
> #|442| null|
> #|978| 0|
> #|851| 0|
> #|428| 0|
> rdd.write.parquet(response, mode="overwrite") # success!
> rdd2 = sqlc.read.parquet(response) # fail
> {code}
>
> fails with:
> {code:none}AnalysisException: u'Unable to infer schema for Parquet. It must
> be specified manually.;'
> {code}
> in
> {code:none}
> /usr/local/lib/python2.7/dist-packages/pyspark-2.1.0+hadoop2.7-py2.7.egg/pyspark/sql/utils.pyc
> in deco(*a, **kw)
> {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?
> The error doesn't happen if I add "limit 10" to the sql query. The whole
> selected table is 500k rows with an int and short column.
> Seems related to: https://issues.apache.org/jira/browse/SPARK-16975, but
> which claims it was fixed in 2.0.1, 2.1.0. (Current bug is 2.1.1)
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[jira] [Comment Edited] (SPARK-21392) Unable to infer schema when loading large Parquet file
[
https://issues.apache.org/jira/browse/SPARK-21392?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16084837#comment-16084837
]
Stuart Reynolds edited comment on SPARK-21392 at 7/12/17 10:27 PM:
---
I've simplified the example a little more and also found the limiting the query
size to 100 rows succeeds, whereas if I select all 500k rows * 2 columns, it
fails.
In case it helps, my utility function get_sparkSQLContextWithTables loaded the
full table 'outcomes' from postgres into spark, with 10 partitions with:
{code:non}
index="eid"
index_min=min(eid)
index_max=max(eid)
{code}
was (Author: stuartreynolds):
I've simplified the example a little more and also found the limiting the query
size to 100 rows succeeds, whereas if I select all 500k rows * 2 columns, it
fails.
> 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
> Environment: Spark 2.1.1. python 2.7.6
>Reporter: Stuart Reynolds
> Labels: parquet, pyspark
>
> The following boring code works
> {code:none}
> response = "mi_or_chd_5"
> sc = get_spark_context() # custom
> sqlc = get_sparkSQLContextWithTables(sc, tables=["outcomes"]) # custom
> rdd = sqlc.sql("SELECT eid,mi_or_chd_5 FROM outcomes")
> print rdd.schema
> #>>
> StructType(List(StructField(eid,IntegerType,true),StructField(mi_or_chd_5,ShortType,true)))
> rdd.show()
> #+---+---+
> #|eid|mi_or_chd_5|
> #+---+---+
> #|216| null|
> #|431| null|
> #|978| 0|
> #|852| 0|
> #|418| 0|
> rdd.write.parquet(response, mode="overwrite") # success!
> rdd2 = sqlc.read.parquet(response) # fail
> {code}
>
> fails with:
> {code:none}AnalysisException: u'Unable to infer schema for Parquet. It must
> be specified manually.;'
> {code}
> in
> {code:none}
> /usr/local/lib/python2.7/dist-packages/pyspark-2.1.0+hadoop2.7-py2.7.egg/pyspark/sql/utils.pyc
> in deco(*a, **kw)
> {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?
> The error doesn't happen if I add "limit 10" to the sql query. The whole
> selected table is 500k rows with an int and short column.
> Seems related to: https://issues.apache.org/jira/browse/SPARK-16975, but
> which claims it was fixed in 2.0.1, 2.1.0. (Current bug is 2.1.1)
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[jira] [Comment Edited] (SPARK-21392) Unable to infer schema when loading large Parquet file
[
https://issues.apache.org/jira/browse/SPARK-21392?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16084837#comment-16084837
]
Stuart Reynolds edited comment on SPARK-21392 at 7/12/17 10:27 PM:
---
I've simplified the example a little more and also found the limiting the query
size to 100 rows succeeds, whereas if I select all 500k rows * 2 columns, it
fails.
In case it helps, my utility function get_sparkSQLContextWithTables loaded the
full table 'outcomes' from postgres into spark, with 10 partitions with:
{code:none}
index="eid"
index_min=min(eid)
index_max=max(eid)
{code}
was (Author: stuartreynolds):
I've simplified the example a little more and also found the limiting the query
size to 100 rows succeeds, whereas if I select all 500k rows * 2 columns, it
fails.
In case it helps, my utility function get_sparkSQLContextWithTables loaded the
full table 'outcomes' from postgres into spark, with 10 partitions with:
{code:non}
index="eid"
index_min=min(eid)
index_max=max(eid)
{code}
> 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
> Environment: Spark 2.1.1. python 2.7.6
>Reporter: Stuart Reynolds
> Labels: parquet, pyspark
>
> The following boring code works
> {code:none}
> response = "mi_or_chd_5"
> sc = get_spark_context() # custom
> sqlc = get_sparkSQLContextWithTables(sc, tables=["outcomes"]) # custom
> rdd = sqlc.sql("SELECT eid,mi_or_chd_5 FROM outcomes")
> print rdd.schema
> #>>
> StructType(List(StructField(eid,IntegerType,true),StructField(mi_or_chd_5,ShortType,true)))
> rdd.show()
> #+---+---+
> #|eid|mi_or_chd_5|
> #+---+---+
> #|216| null|
> #|431| null|
> #|978| 0|
> #|852| 0|
> #|418| 0|
> rdd.write.parquet(response, mode="overwrite") # success!
> rdd2 = sqlc.read.parquet(response) # fail
> {code}
>
> fails with:
> {code:none}AnalysisException: u'Unable to infer schema for Parquet. It must
> be specified manually.;'
> {code}
> in
> {code:none}
> /usr/local/lib/python2.7/dist-packages/pyspark-2.1.0+hadoop2.7-py2.7.egg/pyspark/sql/utils.pyc
> in deco(*a, **kw)
> {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?
> The error doesn't happen if I add "limit 10" to the sql query. The whole
> selected table is 500k rows with an int and short column.
> Seems related to: https://issues.apache.org/jira/browse/SPARK-16975, but
> which claims it was fixed in 2.0.1, 2.1.0. (Current bug is 2.1.1)
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