Hi Anchit,
this is not my expected, because you specified the HDFS directory in your code.
I've solved like this:
val text = sc.hadoopFile(Args.input,
classOf[TextInputFormat], classOf[LongWritable],
classOf[Text], 2)
val hadoopRdd = text.asInstanceOf[HadoopRDD[LongWritable, Text]]
hadoopRdd.mapPartitionsWithInputSplit((inputSplit, iterator) => {
val file = inputSplit.asInstanceOf[FileSplit]
terator.map ( tp => {tp._1, new Text(file.toString + “,” +
tp._2.toString)})
}
> On Sep 25, 2015, at 13:12, Anchit Choudhry <anchit.choud...@gmail.com> wrote:
>
> Hi Fengdong,
>
> So I created two files in HDFS under a test folder.
>
> test/dt=20100101.json
> { "key1" : "value1" }
>
> test/dt=20100102.json
> { "key2" : "value2" }
>
> Then inside PySpark shell
>
> rdd = sc.wholeTextFiles('./test/*')
> rdd.collect()
> [(u'hdfs://localhost:9000/user/hduser/test/dt=20100101.json', u'{ "key1" :
> "value1" }), (u'hdfs://localhost:9000/user/hduser/test/dt=20100102.json', u'{
> "key2" : "value2" })]
> import json
> def editMe(y, x):
> j = json.loads(y)
> j['source'] = x
> return j
>
> rdd.map(lambda (x,y): editMe(y,x)).collect()
> [{'source': u'hdfs://localhost:9000/user/hduser/test/dt=20100101.json',
> u'key1': u'value1'}, {u'key2': u'value2', 'source':
> u'hdfs://localhost:9000/user/hduser/test/dt=20100102.json'}]
>
> Similarly you could modify the function to return 'source' and 'date' with
> some string manipulation per your requirements.
>
> Let me know if this helps.
>
> Thanks,
> Anchit
>
>
> On 24 September 2015 at 23:55, Fengdong Yu <fengdo...@everstring.com
> <mailto:fengdo...@everstring.com>> wrote:
>
> yes. such as I have two data sets:
>
> date set A: /data/test1/dt=20100101
> data set B: /data/test2/dt=20100202
>
>
> all data has the same JSON format , such as:
> {“key1” : “value1”, “key2” : “value2” }
>
>
> my output expected:
> {“key1” : “value1”, “key2” : “value2” , “source” : “test1”, “date” :
> “20100101"}
> {“key1” : “value1”, “key2” : “value2” , “source” : “test2”, “date” :
> “20100202"}
>
>
>> On Sep 25, 2015, at 11:52, Anchit Choudhry <anchit.choud...@gmail.com
>> <mailto:anchit.choud...@gmail.com>> wrote:
>>
>> Sure. May I ask for a sample input(could be just few lines) and the output
>> you are expecting to bring clarity to my thoughts?
>>
>> On Thu, Sep 24, 2015, 23:44 Fengdong Yu <fengdo...@everstring.com
>> <mailto:fengdo...@everstring.com>> wrote:
>> Hi Anchit,
>>
>> Thanks for the quick answer.
>>
>> my exact question is : I want to add HDFS location into each line in my JSON
>> data.
>>
>>
>>
>>> On Sep 25, 2015, at 11:25, Anchit Choudhry <anchit.choud...@gmail.com
>>> <mailto:anchit.choud...@gmail.com>> wrote:
>>>
>>> Hi Fengdong,
>>>
>>> Thanks for your question.
>>>
>>> Spark already has a function called wholeTextFiles within sparkContext
>>> which can help you with that:
>>>
>>> Python
>>> hdfs://a-hdfs-path/part-00000
>>> hdfs://a-hdfs-path/part-00001
>>> ...
>>> hdfs://a-hdfs-path/part-nnnnn
>>> rdd = sparkContext.wholeTextFiles(“hdfs://a- <>hdfs-path”)
>>> (a-hdfs-path/part-00000, its content)
>>> (a-hdfs-path/part-00001, its content)
>>> ...
>>> (a-hdfs-path/part-nnnnn, its content)
>>> More info: http://spark
>>> <http://spark/>.apache.org/docs/latest/api/python/pyspark.html?highlight=wholetext#pyspark.SparkContext.wholeTextFiles
>>>
>>> ------------
>>>
>>> Scala
>>>
>>> val rdd = sparkContext.wholeTextFile("hdfs://a-hdfs-path")
>>>
>>> More info:
>>> https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.SparkContext@wholeTextFiles(String,Int):RDD[(String,String)]
>>>
>>> Let us know if this helps or you need more help.
>>>
>>> Thanks,
>>> Anchit Choudhry
>>>
>>> On 24 September 2015 at 23:12, Fengdong Yu <fengdo...@everstring.com
>>> <mailto:fengdo...@everstring.com>> wrote:
>>> Hi,
>>>
>>> I have multiple files with JSON format, such as:
>>>
>>> /data/test1_data/sub100/test.data
>>> /data/test2_data/sub200/test.data
>>>
>>>
>>> I can sc.textFile(“/data/*/*”)
>>>
>>> but I want to add the {“source” : “HDFS_LOCATION”} to each line, then save
>>> it the one target HDFS location.
>>>
>>> how to do it, Thanks.
>>>
>>>
>>>
>>>
>>>
>>>
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>>
>
>
