At least works for me though, temporarily disabled Kyro serilizer until
upgrade to 1.6.0. Appreciate for your update. :)
Luciano Resende <luckbr1...@gmail.com>于2016年2月9日 周二02:37写道：
> Sorry, same expected results with trunk and Kryo serializer
>
> On Mon, Feb 8, 2016 at 4:15 AM, SLiZn Liu <sliznmail...@gmail.com> wrote:
>
>> I’ve found the trigger of my issue: if I start my spark-shell or submit
>> by spark-submit with --conf
>> spark.serializer=org.apache.spark.serializer.KryoSerializer, the
>> DataFrame content goes wrong, as I described earlier.
>>
>>
>> On Mon, Feb 8, 2016 at 5:42 PM SLiZn Liu <sliznmail...@gmail.com> wrote:
>>
>>> Thanks Luciano, now it looks like I’m the only guy who have this issue.
>>> My options is narrowed down to upgrade my spark to 1.6.0, to see if this
>>> issue is gone.
>>>
>>> —
>>> Cheers,
>>> Todd Leo
>>>
>>>
>>>
>>> On Mon, Feb 8, 2016 at 2:12 PM Luciano Resende <luckbr1...@gmail.com>
>>> wrote:
>>>
>>>> I tried in both 1.5.0, 1.6.0 and 2.0.0 trunk and
>>>> com.databricks:spark-csv_2.10:1.3.0 with expected results, where the
>>>> columns seem to be read properly.
>>>>
>>>> +----------+----------------------+
>>>> |C0 |C1 |
>>>> +----------+----------------------+
>>>>
>>>> |1446566430 | 2015-11-04<SP>00:00:30|
>>>> |1446566430 | 2015-11-04<SP>00:00:30|
>>>> |1446566430 | 2015-11-04<SP>00:00:30|
>>>> |1446566430 | 2015-11-04<SP>00:00:30|
>>>> |1446566430 | 2015-11-04<SP>00:00:30|
>>>> |1446566431 | 2015-11-04<SP>00:00:31|
>>>> |1446566431 | 2015-11-04<SP>00:00:31|
>>>> |1446566431 | 2015-11-04<SP>00:00:31|
>>>> |1446566431 | 2015-11-04<SP>00:00:31|
>>>> |1446566431 | 2015-11-04<SP>00:00:31|
>>>> +----------+----------------------+
>>>>
>>>>
>>>>
>>>>
>>>> On Sat, Feb 6, 2016 at 11:44 PM, SLiZn Liu <sliznmail...@gmail.com>
>>>> wrote:
>>>>
>>>>> Hi Spark Users Group,
>>>>>
>>>>> I have a csv file to analysis with Spark, but I’m troubling with
>>>>> importing as DataFrame.
>>>>>
>>>>> Here’s the minimal reproducible example. Suppose I’m having a
>>>>> *10(rows)x2(cols)* *space-delimited csv* file, shown as below:
>>>>>
>>>>> 1446566430 2015-11-04<SP>00:00:30
>>>>> 1446566430 2015-11-04<SP>00:00:30
>>>>> 1446566430 2015-11-04<SP>00:00:30
>>>>> 1446566430 2015-11-04<SP>00:00:30
>>>>> 1446566430 2015-11-04<SP>00:00:30
>>>>> 1446566431 2015-11-04<SP>00:00:31
>>>>> 1446566431 2015-11-04<SP>00:00:31
>>>>> 1446566431 2015-11-04<SP>00:00:31
>>>>> 1446566431 2015-11-04<SP>00:00:31
>>>>> 1446566431 2015-11-04<SP>00:00:31
>>>>>
>>>>> the <SP> in column 2 represents sub-delimiter within that column, and
>>>>> this file is stored on HDFS, let’s say the path is hdfs:///tmp/1.csv
>>>>>
>>>>> I’m using *spark-csv* to import this file as Spark *DataFrame*:
>>>>>
>>>>> sqlContext.read.format("com.databricks.spark.csv")
>>>>> .option("header", "false") // Use first line of all files as
>>>>> header
>>>>> .option("inferSchema", "false") // Automatically infer data types
>>>>> .option("delimiter", " ")
>>>>> .load("hdfs:///tmp/1.csv")
>>>>> .show
>>>>>
>>>>> Oddly, the output shows only a part of each column:
>>>>>
>>>>> [image: Screenshot from 2016-02-07 15-27-51.png]
>>>>>
>>>>> and even the boundary of the table wasn’t shown correctly. I also used
>>>>> the other way to read csv file, by sc.textFile(...).map(_.split(" "))
>>>>> and sqlContext.createDataFrame, and the result is the same. Can
>>>>> someone point me out where I did it wrong?
>>>>>
>>>>> —
>>>>> BR,
>>>>> Todd Leo
>>>>>
>>>>>
>>>>
>>>>
>>>>
>>>> --
>>>> Luciano Resende
>>>> http://people.apache.org/~lresende
>>>> http://twitter.com/lresende1975
>>>> http://lresende.blogspot.com/
>>>>
>>>
>
>
> --
> Luciano Resende
> http://people.apache.org/~lresende
> http://twitter.com/lresende1975
> http://lresende.blogspot.com/
>
