I would suggest using http://search-hadoop.com/ to find literature on the empty
partitions directory problem.
If there is no answer there, please start a new thread with the following
information:
release of Spark
release of hadoop
code snippet
symptom
Cheers
On Mon, Oct 12, 2015 at 12:08 PM, Umesh Kacha <umesh.ka...@gmail.com> wrote:
> Hi Ted thanks much are you saying above code will work in only 1.5.1? I
> tried upgrading to 1.5.1 but I have found potential bug my Spark job
> creates hive partitions using hiveContext.sql("insert into partitions")
> when I use Spark 1.5.1 I cant see any partitions files orc files getting
> created in HDFS I can see empty partitions directory under Hive table along
> with many staging files created by spark.
>
> On Tue, Oct 13, 2015 at 12:34 AM, Ted Yu <yuzhih...@gmail.com> wrote:
>
>> SQL context available as sqlContext.
>>
>> scala> val df = Seq(("id1", 1), ("id2", 4), ("id3", 5)).toDF("id",
>> "value")
>> df: org.apache.spark.sql.DataFrame = [id: string, value: int]
>>
>> scala> df.select(callUDF("percentile_approx",col("value"),
>> lit(0.25))).show()
>> +------------------------------+
>> |'percentile_approx(value,0.25)|
>> +------------------------------+
>> | 1.0|
>> +------------------------------+
>>
>> Can you upgrade to 1.5.1 ?
>>
>> Cheers
>>
>> On Mon, Oct 12, 2015 at 11:55 AM, Umesh Kacha <umesh.ka...@gmail.com>
>> wrote:
>>
>>> Sorry forgot to tell that I am using Spark 1.4.1 as callUdf is available
>>> in Spark 1.4.0 as per JAvadocx
>>>
>>> On Tue, Oct 13, 2015 at 12:22 AM, Umesh Kacha <umesh.ka...@gmail.com>
>>> wrote:
>>>
>>>> Hi Ted thanks much for the detailed answer and appreciate your efforts.
>>>> Do we need to register Hive UDFs?
>>>>
>>>> sqlContext.udf.register("percentile_approx");???//is it valid?
>>>>
>>>> I am calling Hive UDF percentile_approx in the following manner which
>>>> gives compilation error
>>>>
>>>> df.select("col1").groupby("col1").agg(callUdf("percentile_approx",col("col1"),lit(0.25)));//compile
>>>> error
>>>>
>>>> //compile error because callUdf() takes String and Column* as arguments.
>>>>
>>>> Please guide. Thanks much.
>>>>
>>>> On Mon, Oct 12, 2015 at 11:44 PM, Ted Yu <yuzhih...@gmail.com> wrote:
>>>>
>>>>> Using spark-shell, I did the following exercise (master branch) :
>>>>>
>>>>>
>>>>> SQL context available as sqlContext.
>>>>>
>>>>> scala> val df = Seq(("id1", 1), ("id2", 4), ("id3", 5)).toDF("id",
>>>>> "value")
>>>>> df: org.apache.spark.sql.DataFrame = [id: string, value: int]
>>>>>
>>>>> scala> sqlContext.udf.register("simpleUDF", (v: Int, cnst: Int) => v *
>>>>> v + cnst)
>>>>> res0: org.apache.spark.sql.UserDefinedFunction =
>>>>> UserDefinedFunction(<function2>,IntegerType,List())
>>>>>
>>>>> scala> df.select($"id", callUDF("simpleUDF", $"value", lit(25))).show()
>>>>> +---+--------------------+
>>>>> | id|'simpleUDF(value,25)|
>>>>> +---+--------------------+
>>>>> |id1| 26|
>>>>> |id2| 41|
>>>>> |id3| 50|
>>>>> +---+--------------------+
>>>>>
>>>>> Which Spark release are you using ?
>>>>>
>>>>> Can you pastebin the full stack trace where you got the error ?
>>>>>
>>>>> Cheers
>>>>>
>>>>> On Fri, Oct 9, 2015 at 1:09 PM, Umesh Kacha <umesh.ka...@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> I have a doubt Michael I tried to use callUDF in the following code
>>>>>> it does not work.
>>>>>>
>>>>>> sourceFrame.agg(callUdf("percentile_approx",col("myCol"),lit(0.25)))
>>>>>>
>>>>>> Above code does not compile because callUdf() takes only two
>>>>>> arguments function name in String and Column class type. Please guide.
>>>>>>
>>>>>> On Sat, Oct 10, 2015 at 1:29 AM, Umesh Kacha <umesh.ka...@gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> thanks much Michael let me try.
>>>>>>>
>>>>>>> On Sat, Oct 10, 2015 at 1:20 AM, Michael Armbrust <
>>>>>>> mich...@databricks.com> wrote:
>>>>>>>
>>>>>>>> This is confusing because I made a typo...
>>>>>>>>
>>>>>>>> callUDF("percentile_approx", col("mycol"), lit(0.25))
>>>>>>>>
>>>>>>>> The first argument is the name of the UDF, all other arguments need
>>>>>>>> to be columns that are passed in as arguments. lit is just saying to
>>>>>>>> make
>>>>>>>> a literal column that always has the value 0.25.
>>>>>>>>
>>>>>>>> On Fri, Oct 9, 2015 at 12:16 PM, <saif.a.ell...@wellsfargo.com>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>>> Yes but I mean, this is rather curious. How is def
>>>>>>>>> lit(literal:Any) --> becomes a percentile function lit(25)
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> Thanks for clarification
>>>>>>>>>
>>>>>>>>> Saif
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> *From:* Umesh Kacha [mailto:umesh.ka...@gmail.com]
>>>>>>>>> *Sent:* Friday, October 09, 2015 4:10 PM
>>>>>>>>> *To:* Ellafi, Saif A.
>>>>>>>>> *Cc:* Michael Armbrust; user
>>>>>>>>>
>>>>>>>>> *Subject:* Re: How to calculate percentile of a column of
>>>>>>>>> DataFrame?
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> I found it in 1.3 documentation lit says something else not percent
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> public static Column
>>>>>>>>> <https://spark.apache.org/docs/1.3.1/api/java/org/apache/spark/sql/Column.html>
>>>>>>>>> lit(Object literal)
>>>>>>>>>
>>>>>>>>> Creates a Column
>>>>>>>>> <https://spark.apache.org/docs/1.3.1/api/java/org/apache/spark/sql/Column.html>
>>>>>>>>> of
>>>>>>>>> literal value.
>>>>>>>>>
>>>>>>>>> The passed in object is returned directly if it is already a
>>>>>>>>> Column
>>>>>>>>> <https://spark.apache.org/docs/1.3.1/api/java/org/apache/spark/sql/Column.html>.
>>>>>>>>> If the object is a Scala Symbol, it is converted into a Column
>>>>>>>>> <https://spark.apache.org/docs/1.3.1/api/java/org/apache/spark/sql/Column.html>
>>>>>>>>> also.
>>>>>>>>> Otherwise, a new Column
>>>>>>>>> <https://spark.apache.org/docs/1.3.1/api/java/org/apache/spark/sql/Column.html>
>>>>>>>>> is
>>>>>>>>> created to represent the literal value.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On Sat, Oct 10, 2015 at 12:39 AM, <saif.a.ell...@wellsfargo.com>
>>>>>>>>> wrote:
>>>>>>>>>
>>>>>>>>> Where can we find other available functions such as lit() ? I
>>>>>>>>> can’t find lit in the api.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> Thanks
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> *From:* Michael Armbrust [mailto:mich...@databricks.com]
>>>>>>>>> *Sent:* Friday, October 09, 2015 4:04 PM
>>>>>>>>> *To:* unk1102
>>>>>>>>> *Cc:* user
>>>>>>>>> *Subject:* Re: How to calculate percentile of a column of
>>>>>>>>> DataFrame?
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> You can use callUDF(col("mycol"), lit(0.25)) to call hive UDFs
>>>>>>>>> from dataframes.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On Fri, Oct 9, 2015 at 12:01 PM, unk1102 <umesh.ka...@gmail.com>
>>>>>>>>> wrote:
>>>>>>>>>
>>>>>>>>> Hi how to calculate percentile of a column in a DataFrame? I cant
>>>>>>>>> find any
>>>>>>>>> percentile_approx function in Spark aggregation functions. For
>>>>>>>>> e.g. in Hive
>>>>>>>>> we have percentile_approx and we can use it in the following way
>>>>>>>>>
>>>>>>>>> hiveContext.sql("select percentile_approx("mycol",0.25) from
>>>>>>>>> myTable);
>>>>>>>>>
>>>>>>>>> I can see ntile function but not sure how it is gonna give results
>>>>>>>>> same as
>>>>>>>>> above query please guide.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> --
>>>>>>>>> View this message in context:
>>>>>>>>> http://apache-spark-user-list.1001560.n3.nabble.com/How-to-calculate-percentile-of-a-column-of-DataFrame-tp25000.html
>>>>>>>>> Sent from the Apache Spark User List mailing list archive at
>>>>>>>>> Nabble.com.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> ---------------------------------------------------------------------
>>>>>>>>> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org
>>>>>>>>> For additional commands, e-mail: user-h...@spark.apache.org
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>
>
