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https://issues.apache.org/jira/browse/SPARK-21227?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16070062#comment-16070062
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Seydou Dia edited comment on SPARK-21227 at 6/30/17 1:07 PM:
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Our backend team confirmed your point [~srowen], our app is java based and
apparently there is a well known issue regarding locale-insensitive lower case
(cf.
https://stackoverflow.com/questions/11063102/using-locales-with-javas-tolowercase-and-touppercase)
A fix will be release on our side.
I was wondering, though, if using a "unicode aware" in spark would be a better
a more robust solution ?
was (Author: sdia):
Our backend team confirmed your point [~sro...@gmail.com], our app is java
based and apparently there is a well known issue regarding locale-insensitive
lower case (cf.
https://stackoverflow.com/questions/11063102/using-locales-with-javas-tolowercase-and-touppercase)
A fix will be release on our side.
I was wondering, though, if using a "unicode aware" in spark would be a better
a more robust solution ?
> Unicode in Json field causes AnalysisException when selecting from Dataframe
> ----------------------------------------------------------------------------
>
> Key: SPARK-21227
> URL: https://issues.apache.org/jira/browse/SPARK-21227
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 2.1.0
> Reporter: Seydou Dia
>
> Hi,
> please find below the step to reproduce the issue I am facing.
> First I create a json with 2 fields:
> * city_name
> * cıty_name
> The first one is valid ascii, while the second contains a unicode (ı, i
> without dot ).
> When I try to select from the dataframe I have an {noformat}
> AnalysisException {noformat}.
> {code:python}
> $ pyspark
> Python 3.4.3 (default, Sep 1 2016, 23:33:38)
> [GCC 4.8.3 20140911 (Red Hat 4.8.3-9)] on linux
> Type "help", "copyright", "credits" or "license" for more information.
> Setting default log level to "WARN".
> To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use
> setLogLevel(newLevel).
> 17/06/27 12:29:05 WARN Utils: Service 'SparkUI' could not bind on port 4040.
> Attempting port 4041.
> 17/06/27 12:29:05 WARN Utils: Service 'SparkUI' could not bind on port 4041.
> Attempting port 4042.
> 17/06/27 12:29:08 WARN Client: Neither spark.yarn.jars nor spark.yarn.archive
> is set, falling back to uploading libraries under SPARK_HOME.
> Welcome to
> ____ __
> / __/__ ___ _____/ /__
> _\ \/ _ \/ _ `/ __/ '_/
> /__ / .__/\_,_/_/ /_/\_\ version 2.1.0
> /_/
> Using Python version 3.4.3 (default, Sep 1 2016 23:33:38)
> SparkSession available as 'spark'.
> >>> sc=spark.sparkContext
> >>> js = ['{"city_name": "paris"}'
> ... , '{"city_name": "rome"}'
> ... , '{"city_name": "berlin"}'
> ... , '{"cıty_name": "new-york"}'
> ... , '{"cıty_name": "toronto"}'
> ... , '{"cıty_name": "chicago"}'
> ... , '{"cıty_name": "dubai"}']
> >>> myRDD = sc.parallelize(js)
> >>> myDF = spark.read.json(myRDD)
> >>> myDF.printSchema()
> >>>
> root
> |-- city_name: string (nullable = true)
> |-- cıty_name: string (nullable = true)
> >>> myDF.select(myDF['city_name'])
> Traceback (most recent call last):
> File "/usr/lib/spark/python/pyspark/sql/utils.py", line 63, in deco
> return f(*a, **kw)
> File "/usr/lib/spark/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py", line
> 319, in get_return_value
> py4j.protocol.Py4JJavaError: An error occurred while calling o226.apply.
> : org.apache.spark.sql.AnalysisException: Reference 'city_name' is ambiguous,
> could be: city_name#29, city_name#30.;
> at
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:264)
> at
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveQuoted(LogicalPlan.scala:168)
> at org.apache.spark.sql.Dataset.resolve(Dataset.scala:217)
> at org.apache.spark.sql.Dataset.col(Dataset.scala:1073)
> at org.apache.spark.sql.Dataset.apply(Dataset.scala:1059)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:498)
> at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
> at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
> at py4j.Gateway.invoke(Gateway.java:280)
> at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
> at py4j.commands.CallCommand.execute(CallCommand.java:79)
> at py4j.GatewayConnection.run(GatewayConnection.java:214)
> at java.lang.Thread.run(Thread.java:745)
> During handling of the above exception, another exception occurred:
> Traceback (most recent call last):
> File "<stdin>", line 1, in <module>
> File "/usr/lib/spark/python/pyspark/sql/dataframe.py", line 943, in
> __getitem__
> jc = self._jdf.apply(item)
> File "/usr/lib/spark/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py",
> line 1133, in __call__
> File "/usr/lib/spark/python/pyspark/sql/utils.py", line 69, in deco
> raise AnalysisException(s.split(': ', 1)[1], stackTrace)
> pyspark.sql.utils.AnalysisException: "Reference 'city_name' is ambiguous,
> could be: city_name#29, city_name#30.;"
> {code}
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