Re: How to read gzip data in Spark - Simple question
I got it running by myself
On Wed, Aug 5, 2015 at 10:27 PM, Ganelin, Ilya
wrote:
> Have you tried reading the spark documentation?
>
> http://spark.apache.org/docs/latest/programming-guide.html
>
>
>
> Thank you,
> Ilya Ganelin
>
>
>
>
> -Original Message-
> *From: *ÐΞ€ρ@Ҝ (๏̯͡๏) [deepuj...@gmail.com]
> *Sent: *Thursday, August 06, 2015 12:41 AM Eastern Standard Time
> *To: *Philip Weaver
> *Cc: *user
> *Subject: *Re: How to read gzip data in Spark - Simple question
>
> how do i persist the RDD to HDFS ?
>
> On Wed, Aug 5, 2015 at 8:32 PM, Philip Weaver
> wrote:
>
>> This message means that java.util.Date is not supported by Spark
>> DataFrame. You'll need to use java.sql.Date, I believe.
>>
>> On Wed, Aug 5, 2015 at 8:29 PM, ÐΞ€ρ@Ҝ (๏̯͡๏)
>> wrote:
>>
>>> That seem to be working. however i see a new exception
>>>
>>> Code:
>>> def formatStringAsDate(dateStr: String) = new
>>> SimpleDateFormat("-MM-dd").parse(dateStr)
>>>
>>>
>>> //(2015-07-27,12459,,31242,6,Daily,-999,2099-01-01,2099-01-02,1,0,0.1,0,1,-1,isGeo,,,204,694.0,1.9236856708701322E-4,0.0,-4.48,0.0,0.0,0.0,)
>>> val rowStructText =
>>> sc.textFile("/user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-3.gz")
>>> case class Summary(f1: Date, f2: Long, f3: Long, f4: Integer, f5 :
>>> String, f6: Integer, f7 : Date, f8: Date, f9: Integer, f10: Integer, f11:
>>> Float, f12: Integer, f13: Integer, f14: String)
>>>
>>> val summary = rowStructText.map(s => s.split(",")).map(
>>> s => Summary(formatStringAsDate(s(0)),
>>> s(1).replaceAll("\"", "").toLong,
>>> s(3).replaceAll("\"", "").toLong,
>>> s(4).replaceAll("\"", "").toInt,
>>> s(5).replaceAll("\"", ""),
>>> s(6).replaceAll("\"", "").toInt,
>>> formatStringAsDate(s(7)),
>>> formatStringAsDate(s(8)),
>>> s(9).replaceAll("\"", "").toInt,
>>> s(10).replaceAll("\"", "").toInt,
>>> s(11).replaceAll("\"", "").toFloat,
>>> s(12).replaceAll("\"", "").toInt,
>>> s(13).replaceAll("\"", "").toInt,
>>> s(14).replaceAll("\"", "")
>>> )
>>> ).toDF()
>>> bank.registerTempTable("summary")
>>>
>>>
>>> //Output
>>> import java.text.SimpleDateFormat import java.util.Calendar import
>>> java.util.Date formatStringAsDate: (dateStr: String)java.util.Date
>>> rowStructText: org.apache.spark.rdd.RDD[String] =
>>> /user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-3.gz
>>> MapPartitionsRDD[105] at textFile at :60 defined class Summary x:
>>> org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[106] at map at
>>> :61 java.lang.UnsupportedOperationException: Schema for type
>>> java.util.Date is not supported at
>>> org.apache.spark.sql.catalyst.ScalaReflection$class.schemaFor(ScalaReflection.scala:188)
>>> at
>>> org.apache.spark.sql.catalyst.ScalaReflection$.schemaFor(ScalaReflection.scala:30)
>>> at
>>> org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$schemaFor$1.apply(ScalaReflection.scala:164)
>>>
>>> Any suggestions
>>>
>>> On Wed, Aug 5, 2015 at 8:18 PM, Philip Weaver
>>> wrote:
>>>
>>>> The parallelize method does not read the contents of a file. It simply
>>>> takes a collection and distributes it to the cluster. In this case, the
>>>> String is a collection 67 characters.
>>>>
>>>> Use sc.textFile instead of sc.parallelize, and it should work as you
>>>> want.
>>>>
>>>> On Wed, Aug 5, 2015 at 8:12 PM, ÐΞ€ρ@Ҝ (๏̯͡๏)
>>>> wrote:
>>>>
>>>>> I have csv data that is embedded in gzip format on HDFS.
>>>>>
>>>>> *With Pig*
>>>>>
>>>>> a = load
>>>>> '/user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-3.gz'
>>>>> using
>>>>> PigStorage();
>>>>>
>>>>> b = limit a 10
>>>>>
>>>>>
>>>>&
RE: How to read gzip data in Spark - Simple question
Have you tried reading the spark documentation?
http://spark.apache.org/docs/latest/programming-guide.html
Thank you,
Ilya Ganelin
-Original Message-
From: ÐΞ€ρ@Ҝ (๏̯͡๏) [deepuj...@gmail.com<mailto:deepuj...@gmail.com>]
Sent: Thursday, August 06, 2015 12:41 AM Eastern Standard Time
To: Philip Weaver
Cc: user
Subject: Re: How to read gzip data in Spark - Simple question
how do i persist the RDD to HDFS ?
On Wed, Aug 5, 2015 at 8:32 PM, Philip Weaver
mailto:philip.wea...@gmail.com>> wrote:
This message means that java.util.Date is not supported by Spark DataFrame.
You'll need to use java.sql.Date, I believe.
On Wed, Aug 5, 2015 at 8:29 PM, ÐΞ€ρ@Ҝ (๏̯͡๏)
mailto:deepuj...@gmail.com>> wrote:
That seem to be working. however i see a new exception
Code:
def formatStringAsDate(dateStr: String) = new
SimpleDateFormat("-MM-dd").parse(dateStr)
//(2015-07-27,12459,,31242,6,Daily,-999,2099-01-01,2099-01-02,1,0,0.1,0,1,-1,isGeo,,,204,694.0,1.9236856708701322E-4,0.0,-4.48,0.0,0.0,0.0,)
val rowStructText =
sc.textFile("/user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-3.gz")
case class Summary(f1: Date, f2: Long, f3: Long, f4: Integer, f5 : String, f6:
Integer, f7 : Date, f8: Date, f9: Integer, f10: Integer, f11: Float, f12:
Integer, f13: Integer, f14: String)
val summary = rowStructText.map(s => s.split(",")).map(
s => Summary(formatStringAsDate(s(0)),
s(1).replaceAll("\"", "").toLong,
s(3).replaceAll("\"", "").toLong,
s(4).replaceAll("\"", "").toInt,
s(5).replaceAll("\"", ""),
s(6).replaceAll("\"", "").toInt,
formatStringAsDate(s(7)),
formatStringAsDate(s(8)),
s(9).replaceAll("\"", "").toInt,
s(10).replaceAll("\"", "").toInt,
s(11).replaceAll("\"", "").toFloat,
s(12).replaceAll("\"", "").toInt,
s(13).replaceAll("\"", "").toInt,
s(14).replaceAll("\"", "")
)
).toDF()
bank.registerTempTable("summary")
//Output
import java.text.SimpleDateFormat import java.util.Calendar import
java.util.Date formatStringAsDate: (dateStr: String)java.util.Date
rowStructText: org.apache.spark.rdd.RDD[String] =
/user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-3.gz
MapPartitionsRDD[105] at textFile at :60 defined class Summary x:
org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[106] at map at :61
java.lang.UnsupportedOperationException: Schema for type java.util.Date is not
supported at
org.apache.spark.sql.catalyst.ScalaReflection$class.schemaFor(ScalaReflection.scala:188)
at
org.apache.spark.sql.catalyst.ScalaReflection$.schemaFor(ScalaReflection.scala:30)
at
org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$schemaFor$1.apply(ScalaReflection.scala:164)
Any suggestions
On Wed, Aug 5, 2015 at 8:18 PM, Philip Weaver
mailto:philip.wea...@gmail.com>> wrote:
The parallelize method does not read the contents of a file. It simply takes a
collection and distributes it to the cluster. In this case, the String is a
collection 67 characters.
Use sc.textFile instead of sc.parallelize, and it should work as you want.
On Wed, Aug 5, 2015 at 8:12 PM, ÐΞ€ρ@Ҝ (๏̯͡๏)
mailto:deepuj...@gmail.com>> wrote:
I have csv data that is embedded in gzip format on HDFS.
With Pig
a = load '/user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-3.gz'
using PigStorage();
b = limit a 10
(2015-07-27,12459,,31243,6,Daily,-999,2099-01-01,2099-01-02,4,0,0.1,0,1,203,4810370.0,1.4090459061723766,1.017458,-0.03,-0.11,0.05,0.468666,)
(2015-07-27,12459,,31241,6,Daily,-999,2099-01-01,2099-01-02,4,0,0.1,0,1,0,isGeo,,,203,7937613.0,1.1624841995932425,1.11562,-0.06,-0.15,0.03,0.233283,)
However with Spark
val rowStructText =
sc.parallelize("/user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-0.gz")
val x = rowStructText.map(s => {
println(s)
s}
)
x.count
Questions
1) x.count always shows 67 irrespective of the path i change in sc.parallelize
2) It shows x as RDD[Char] instead of String
3) println() never emits the rows.
Any suggestions
-Deepak
--
Deepak
--
Deepak
--
Deepak
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Re: How to read gzip data in Spark - Simple question
I encourage you to find the answer this this on your own :).
On Wed, Aug 5, 2015 at 9:43 PM, ÐΞ€ρ@Ҝ (๏̯͡๏) wrote:
> Code:
>
> val summary = rowStructText.map(s => s.split(",")).map(
> {
> s =>
> Summary(formatStringAsDate(s(0)),
> s(1).replaceAll("\"", "").toLong,
> s(3).replaceAll("\"", "").toLong,
> s(4).replaceAll("\"", "").toInt,
> s(5).replaceAll("\"", ""),
> s(6).replaceAll("\"", "").toInt,
> formatStringAsDate(s(7)),
> formatStringAsDate(s(8)),
> s(9).replaceAll("\"", "").toInt,
> s(10).replaceAll("\"", "").toInt,
> s(11).replaceAll("\"", "").toFloat,
> s(12).replaceAll("\"", "").toInt,
> s(13).replaceAll("\"", "").toInt,
> s(14).replaceAll("\"", "")
> )
> }
> )
> summary.saveAsTextFile("sparkO")
>
> Exception:
> import java.text.SimpleDateFormat import java.util.Calendar import
> java.sql.Date import org.apache.spark.storage.StorageLevel
> formatStringAsDate: (dateStr: String)java.sql.Date rowStructText:
> org.apache.spark.rdd.RDD[String] =
> /user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-3.gz
> MapPartitionsRDD[263] at textFile at :154 defined class Summary
> summary: org.apache.spark.rdd.RDD[Summary] = MapPartitionsRDD[265] at map
> at :159 sumDF: org.apache.spark.sql.DataFrame = [f1: date, f2:
> bigint, f3: bigint, f4: int, f5: string, f6: int, f7: date, f8: date, f9:
> int, f10: int, f11: float, f12: int, f13: int, f14: string]
> org.apache.spark.SparkException: Job aborted due to stage failure: Task 0
> in stage 45.0 failed 4 times, most recent failure: Lost task 0.3 in stage
> 45.0 (TID 1872, datanode-6-3486.phx01.dev.ebayc3.com):
> java.lang.ArrayIndexOutOfBoundsException: 1 at
> $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$2.apply(:163)
> at
> $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$2.apply(:161)
> at scala.collection.Iterator$$anon
>
> On Wed, Aug 5, 2015 at 9:40 PM, ÐΞ€ρ@Ҝ (๏̯͡๏) wrote:
>
>> how do i persist the RDD to HDFS ?
>>
>> On Wed, Aug 5, 2015 at 8:32 PM, Philip Weaver
>> wrote:
>>
>>> This message means that java.util.Date is not supported by Spark
>>> DataFrame. You'll need to use java.sql.Date, I believe.
>>>
>>> On Wed, Aug 5, 2015 at 8:29 PM, ÐΞ€ρ@Ҝ (๏̯͡๏)
>>> wrote:
>>>
That seem to be working. however i see a new exception
Code:
def formatStringAsDate(dateStr: String) = new
SimpleDateFormat("-MM-dd").parse(dateStr)
//(2015-07-27,12459,,31242,6,Daily,-999,2099-01-01,2099-01-02,1,0,0.1,0,1,-1,isGeo,,,204,694.0,1.9236856708701322E-4,0.0,-4.48,0.0,0.0,0.0,)
val rowStructText =
sc.textFile("/user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-3.gz")
case class Summary(f1: Date, f2: Long, f3: Long, f4: Integer, f5 :
String, f6: Integer, f7 : Date, f8: Date, f9: Integer, f10: Integer, f11:
Float, f12: Integer, f13: Integer, f14: String)
val summary = rowStructText.map(s => s.split(",")).map(
s => Summary(formatStringAsDate(s(0)),
s(1).replaceAll("\"", "").toLong,
s(3).replaceAll("\"", "").toLong,
s(4).replaceAll("\"", "").toInt,
s(5).replaceAll("\"", ""),
s(6).replaceAll("\"", "").toInt,
formatStringAsDate(s(7)),
formatStringAsDate(s(8)),
s(9).replaceAll("\"", "").toInt,
s(10).replaceAll("\"", "").toInt,
s(11).replaceAll("\"", "").toFloat,
s(12).replaceAll("\"", "").toInt,
s(13).replaceAll("\"", "").toInt,
s(14).replaceAll("\"", "")
)
).toDF()
bank.registerTempTable("summary")
//Output
import java.text.SimpleDateFormat import java.util.Calendar import
java.util.Date formatStringAsDate: (dateStr: String)java.util.Date
rowStructText: org.apache.spark.rdd.RDD[String] =
/user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-3.gz
MapPartitionsRDD[105] at textFile at :60 defined class Summary x:
org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[106] at map at
:61 java.lang.UnsupportedOperationException: Schema for type
java.util.Date is not supported at
org.apache.spark.sql.catalyst.ScalaReflection$class.schemaFor(ScalaReflection.scala:188)
at
org.apache.spark.sql.catalyst.ScalaReflection$.schemaFor(ScalaReflection.scala:30)
at
org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$schemaFor$1.apply(ScalaReflection.scala:164)
Any suggestions
On Wed, A
Re: How to read gzip data in Spark - Simple question
Code:
val summary = rowStructText.map(s => s.split(",")).map(
{
s =>
Summary(formatStringAsDate(s(0)),
s(1).replaceAll("\"", "").toLong,
s(3).replaceAll("\"", "").toLong,
s(4).replaceAll("\"", "").toInt,
s(5).replaceAll("\"", ""),
s(6).replaceAll("\"", "").toInt,
formatStringAsDate(s(7)),
formatStringAsDate(s(8)),
s(9).replaceAll("\"", "").toInt,
s(10).replaceAll("\"", "").toInt,
s(11).replaceAll("\"", "").toFloat,
s(12).replaceAll("\"", "").toInt,
s(13).replaceAll("\"", "").toInt,
s(14).replaceAll("\"", "")
)
}
)
summary.saveAsTextFile("sparkO")
Exception:
import java.text.SimpleDateFormat import java.util.Calendar import
java.sql.Date import org.apache.spark.storage.StorageLevel
formatStringAsDate: (dateStr: String)java.sql.Date rowStructText:
org.apache.spark.rdd.RDD[String] =
/user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-3.gz
MapPartitionsRDD[263] at textFile at :154 defined class Summary
summary: org.apache.spark.rdd.RDD[Summary] = MapPartitionsRDD[265] at map
at :159 sumDF: org.apache.spark.sql.DataFrame = [f1: date, f2:
bigint, f3: bigint, f4: int, f5: string, f6: int, f7: date, f8: date, f9:
int, f10: int, f11: float, f12: int, f13: int, f14: string]
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0
in stage 45.0 failed 4 times, most recent failure: Lost task 0.3 in stage
45.0 (TID 1872, datanode-6-3486.phx01.dev.ebayc3.com):
java.lang.ArrayIndexOutOfBoundsException: 1 at
$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$2.apply(:163)
at
$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$2.apply(:161)
at scala.collection.Iterator$$anon
On Wed, Aug 5, 2015 at 9:40 PM, ÐΞ€ρ@Ҝ (๏̯͡๏) wrote:
> how do i persist the RDD to HDFS ?
>
> On Wed, Aug 5, 2015 at 8:32 PM, Philip Weaver
> wrote:
>
>> This message means that java.util.Date is not supported by Spark
>> DataFrame. You'll need to use java.sql.Date, I believe.
>>
>> On Wed, Aug 5, 2015 at 8:29 PM, ÐΞ€ρ@Ҝ (๏̯͡๏)
>> wrote:
>>
>>> That seem to be working. however i see a new exception
>>>
>>> Code:
>>> def formatStringAsDate(dateStr: String) = new
>>> SimpleDateFormat("-MM-dd").parse(dateStr)
>>>
>>>
>>> //(2015-07-27,12459,,31242,6,Daily,-999,2099-01-01,2099-01-02,1,0,0.1,0,1,-1,isGeo,,,204,694.0,1.9236856708701322E-4,0.0,-4.48,0.0,0.0,0.0,)
>>> val rowStructText =
>>> sc.textFile("/user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-3.gz")
>>> case class Summary(f1: Date, f2: Long, f3: Long, f4: Integer, f5 :
>>> String, f6: Integer, f7 : Date, f8: Date, f9: Integer, f10: Integer, f11:
>>> Float, f12: Integer, f13: Integer, f14: String)
>>>
>>> val summary = rowStructText.map(s => s.split(",")).map(
>>> s => Summary(formatStringAsDate(s(0)),
>>> s(1).replaceAll("\"", "").toLong,
>>> s(3).replaceAll("\"", "").toLong,
>>> s(4).replaceAll("\"", "").toInt,
>>> s(5).replaceAll("\"", ""),
>>> s(6).replaceAll("\"", "").toInt,
>>> formatStringAsDate(s(7)),
>>> formatStringAsDate(s(8)),
>>> s(9).replaceAll("\"", "").toInt,
>>> s(10).replaceAll("\"", "").toInt,
>>> s(11).replaceAll("\"", "").toFloat,
>>> s(12).replaceAll("\"", "").toInt,
>>> s(13).replaceAll("\"", "").toInt,
>>> s(14).replaceAll("\"", "")
>>> )
>>> ).toDF()
>>> bank.registerTempTable("summary")
>>>
>>>
>>> //Output
>>> import java.text.SimpleDateFormat import java.util.Calendar import
>>> java.util.Date formatStringAsDate: (dateStr: String)java.util.Date
>>> rowStructText: org.apache.spark.rdd.RDD[String] =
>>> /user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-3.gz
>>> MapPartitionsRDD[105] at textFile at :60 defined class Summary x:
>>> org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[106] at map at
>>> :61 java.lang.UnsupportedOperationException: Schema for type
>>> java.util.Date is not supported at
>>> org.apache.spark.sql.catalyst.ScalaReflection$class.schemaFor(ScalaReflection.scala:188)
>>> at
>>> org.apache.spark.sql.catalyst.ScalaReflection$.schemaFor(ScalaReflection.scala:30)
>>> at
>>> org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$schemaFor$1.apply(ScalaReflection.scala:164)
>>>
>>>
>>> Any suggestions
>>>
>>> On Wed, Aug 5, 2015 at 8:18 PM, Philip Weaver
>>> wrote:
>>>
The parallelize method does not read the contents of a file. It simply
takes a collection and distributes it to the cluster. In this case, the
String is a collection 67 characters.
Use sc.tex
Re: How to read gzip data in Spark - Simple question
how do i persist the RDD to HDFS ?
On Wed, Aug 5, 2015 at 8:32 PM, Philip Weaver
wrote:
> This message means that java.util.Date is not supported by Spark
> DataFrame. You'll need to use java.sql.Date, I believe.
>
> On Wed, Aug 5, 2015 at 8:29 PM, ÐΞ€ρ@Ҝ (๏̯͡๏) wrote:
>
>> That seem to be working. however i see a new exception
>>
>> Code:
>> def formatStringAsDate(dateStr: String) = new
>> SimpleDateFormat("-MM-dd").parse(dateStr)
>>
>>
>> //(2015-07-27,12459,,31242,6,Daily,-999,2099-01-01,2099-01-02,1,0,0.1,0,1,-1,isGeo,,,204,694.0,1.9236856708701322E-4,0.0,-4.48,0.0,0.0,0.0,)
>> val rowStructText =
>> sc.textFile("/user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-3.gz")
>> case class Summary(f1: Date, f2: Long, f3: Long, f4: Integer, f5 :
>> String, f6: Integer, f7 : Date, f8: Date, f9: Integer, f10: Integer, f11:
>> Float, f12: Integer, f13: Integer, f14: String)
>>
>> val summary = rowStructText.map(s => s.split(",")).map(
>> s => Summary(formatStringAsDate(s(0)),
>> s(1).replaceAll("\"", "").toLong,
>> s(3).replaceAll("\"", "").toLong,
>> s(4).replaceAll("\"", "").toInt,
>> s(5).replaceAll("\"", ""),
>> s(6).replaceAll("\"", "").toInt,
>> formatStringAsDate(s(7)),
>> formatStringAsDate(s(8)),
>> s(9).replaceAll("\"", "").toInt,
>> s(10).replaceAll("\"", "").toInt,
>> s(11).replaceAll("\"", "").toFloat,
>> s(12).replaceAll("\"", "").toInt,
>> s(13).replaceAll("\"", "").toInt,
>> s(14).replaceAll("\"", "")
>> )
>> ).toDF()
>> bank.registerTempTable("summary")
>>
>>
>> //Output
>> import java.text.SimpleDateFormat import java.util.Calendar import
>> java.util.Date formatStringAsDate: (dateStr: String)java.util.Date
>> rowStructText: org.apache.spark.rdd.RDD[String] =
>> /user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-3.gz
>> MapPartitionsRDD[105] at textFile at :60 defined class Summary x:
>> org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[106] at map at
>> :61 java.lang.UnsupportedOperationException: Schema for type
>> java.util.Date is not supported at
>> org.apache.spark.sql.catalyst.ScalaReflection$class.schemaFor(ScalaReflection.scala:188)
>> at
>> org.apache.spark.sql.catalyst.ScalaReflection$.schemaFor(ScalaReflection.scala:30)
>> at
>> org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$schemaFor$1.apply(ScalaReflection.scala:164)
>>
>>
>> Any suggestions
>>
>> On Wed, Aug 5, 2015 at 8:18 PM, Philip Weaver
>> wrote:
>>
>>> The parallelize method does not read the contents of a file. It simply
>>> takes a collection and distributes it to the cluster. In this case, the
>>> String is a collection 67 characters.
>>>
>>> Use sc.textFile instead of sc.parallelize, and it should work as you
>>> want.
>>>
>>> On Wed, Aug 5, 2015 at 8:12 PM, ÐΞ€ρ@Ҝ (๏̯͡๏)
>>> wrote:
>>>
I have csv data that is embedded in gzip format on HDFS.
*With Pig*
a = load
'/user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-3.gz' using
PigStorage();
b = limit a 10
(2015-07-27,12459,,31243,6,Daily,-999,2099-01-01,2099-01-02,4,0,0.1,0,1,203,4810370.0,1.4090459061723766,1.017458,-0.03,-0.11,0.05,0.468666,)
(2015-07-27,12459,,31241,6,Daily,-999,2099-01-01,2099-01-02,4,0,0.1,0,1,0,isGeo,,,203,7937613.0,1.1624841995932425,1.11562,-0.06,-0.15,0.03,0.233283,)
However with Spark
val rowStructText =
sc.parallelize("/user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-0.gz")
val x = rowStructText.map(s => {
println(s)
s}
)
x.count
Questions
1) x.count always shows 67 irrespective of the path i change in
sc.parallelize
2) It shows x as RDD[Char] instead of String
3) println() never emits the rows.
Any suggestions
-Deepak
--
Deepak
>>>
>>
>>
>> --
>> Deepak
>>
>>
>
--
Deepak
Re: How to read gzip data in Spark - Simple question
This message means that java.util.Date is not supported by Spark DataFrame.
You'll need to use java.sql.Date, I believe.
On Wed, Aug 5, 2015 at 8:29 PM, ÐΞ€ρ@Ҝ (๏̯͡๏) wrote:
> That seem to be working. however i see a new exception
>
> Code:
> def formatStringAsDate(dateStr: String) = new
> SimpleDateFormat("-MM-dd").parse(dateStr)
>
>
> //(2015-07-27,12459,,31242,6,Daily,-999,2099-01-01,2099-01-02,1,0,0.1,0,1,-1,isGeo,,,204,694.0,1.9236856708701322E-4,0.0,-4.48,0.0,0.0,0.0,)
> val rowStructText =
> sc.textFile("/user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-3.gz")
> case class Summary(f1: Date, f2: Long, f3: Long, f4: Integer, f5 : String,
> f6: Integer, f7 : Date, f8: Date, f9: Integer, f10: Integer, f11: Float,
> f12: Integer, f13: Integer, f14: String)
>
> val summary = rowStructText.map(s => s.split(",")).map(
> s => Summary(formatStringAsDate(s(0)),
> s(1).replaceAll("\"", "").toLong,
> s(3).replaceAll("\"", "").toLong,
> s(4).replaceAll("\"", "").toInt,
> s(5).replaceAll("\"", ""),
> s(6).replaceAll("\"", "").toInt,
> formatStringAsDate(s(7)),
> formatStringAsDate(s(8)),
> s(9).replaceAll("\"", "").toInt,
> s(10).replaceAll("\"", "").toInt,
> s(11).replaceAll("\"", "").toFloat,
> s(12).replaceAll("\"", "").toInt,
> s(13).replaceAll("\"", "").toInt,
> s(14).replaceAll("\"", "")
> )
> ).toDF()
> bank.registerTempTable("summary")
>
>
> //Output
> import java.text.SimpleDateFormat import java.util.Calendar import
> java.util.Date formatStringAsDate: (dateStr: String)java.util.Date
> rowStructText: org.apache.spark.rdd.RDD[String] =
> /user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-3.gz
> MapPartitionsRDD[105] at textFile at :60 defined class Summary x:
> org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[106] at map at
> :61 java.lang.UnsupportedOperationException: Schema for type
> java.util.Date is not supported at
> org.apache.spark.sql.catalyst.ScalaReflection$class.schemaFor(ScalaReflection.scala:188)
> at
> org.apache.spark.sql.catalyst.ScalaReflection$.schemaFor(ScalaReflection.scala:30)
> at
> org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$schemaFor$1.apply(ScalaReflection.scala:164)
>
>
> Any suggestions
>
> On Wed, Aug 5, 2015 at 8:18 PM, Philip Weaver
> wrote:
>
>> The parallelize method does not read the contents of a file. It simply
>> takes a collection and distributes it to the cluster. In this case, the
>> String is a collection 67 characters.
>>
>> Use sc.textFile instead of sc.parallelize, and it should work as you want.
>>
>> On Wed, Aug 5, 2015 at 8:12 PM, ÐΞ€ρ@Ҝ (๏̯͡๏)
>> wrote:
>>
>>> I have csv data that is embedded in gzip format on HDFS.
>>>
>>> *With Pig*
>>>
>>> a = load
>>> '/user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-3.gz' using
>>> PigStorage();
>>>
>>> b = limit a 10
>>>
>>>
>>> (2015-07-27,12459,,31243,6,Daily,-999,2099-01-01,2099-01-02,4,0,0.1,0,1,203,4810370.0,1.4090459061723766,1.017458,-0.03,-0.11,0.05,0.468666,)
>>>
>>>
>>> (2015-07-27,12459,,31241,6,Daily,-999,2099-01-01,2099-01-02,4,0,0.1,0,1,0,isGeo,,,203,7937613.0,1.1624841995932425,1.11562,-0.06,-0.15,0.03,0.233283,)
>>>
>>>
>>> However with Spark
>>>
>>> val rowStructText =
>>> sc.parallelize("/user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-0.gz")
>>>
>>> val x = rowStructText.map(s => {
>>>
>>> println(s)
>>>
>>> s}
>>>
>>> )
>>>
>>> x.count
>>>
>>> Questions
>>>
>>> 1) x.count always shows 67 irrespective of the path i change in
>>> sc.parallelize
>>>
>>> 2) It shows x as RDD[Char] instead of String
>>>
>>> 3) println() never emits the rows.
>>>
>>> Any suggestions
>>>
>>> -Deepak
>>>
>>>
>>>
>>> --
>>> Deepak
>>>
>>>
>>
>
>
> --
> Deepak
>
>
Re: How to read gzip data in Spark - Simple question
That seem to be working. however i see a new exception
Code:
def formatStringAsDate(dateStr: String) = new
SimpleDateFormat("-MM-dd").parse(dateStr)
//(2015-07-27,12459,,31242,6,Daily,-999,2099-01-01,2099-01-02,1,0,0.1,0,1,-1,isGeo,,,204,694.0,1.9236856708701322E-4,0.0,-4.48,0.0,0.0,0.0,)
val rowStructText =
sc.textFile("/user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-3.gz")
case class Summary(f1: Date, f2: Long, f3: Long, f4: Integer, f5 : String,
f6: Integer, f7 : Date, f8: Date, f9: Integer, f10: Integer, f11: Float,
f12: Integer, f13: Integer, f14: String)
val summary = rowStructText.map(s => s.split(",")).map(
s => Summary(formatStringAsDate(s(0)),
s(1).replaceAll("\"", "").toLong,
s(3).replaceAll("\"", "").toLong,
s(4).replaceAll("\"", "").toInt,
s(5).replaceAll("\"", ""),
s(6).replaceAll("\"", "").toInt,
formatStringAsDate(s(7)),
formatStringAsDate(s(8)),
s(9).replaceAll("\"", "").toInt,
s(10).replaceAll("\"", "").toInt,
s(11).replaceAll("\"", "").toFloat,
s(12).replaceAll("\"", "").toInt,
s(13).replaceAll("\"", "").toInt,
s(14).replaceAll("\"", "")
)
).toDF()
bank.registerTempTable("summary")
//Output
import java.text.SimpleDateFormat import java.util.Calendar import
java.util.Date formatStringAsDate: (dateStr: String)java.util.Date
rowStructText: org.apache.spark.rdd.RDD[String] =
/user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-3.gz
MapPartitionsRDD[105] at textFile at :60 defined class Summary x:
org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[106] at map at
:61 java.lang.UnsupportedOperationException: Schema for type
java.util.Date is not supported at
org.apache.spark.sql.catalyst.ScalaReflection$class.schemaFor(ScalaReflection.scala:188)
at
org.apache.spark.sql.catalyst.ScalaReflection$.schemaFor(ScalaReflection.scala:30)
at
org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$schemaFor$1.apply(ScalaReflection.scala:164)
Any suggestions
On Wed, Aug 5, 2015 at 8:18 PM, Philip Weaver
wrote:
> The parallelize method does not read the contents of a file. It simply
> takes a collection and distributes it to the cluster. In this case, the
> String is a collection 67 characters.
>
> Use sc.textFile instead of sc.parallelize, and it should work as you want.
>
> On Wed, Aug 5, 2015 at 8:12 PM, ÐΞ€ρ@Ҝ (๏̯͡๏) wrote:
>
>> I have csv data that is embedded in gzip format on HDFS.
>>
>> *With Pig*
>>
>> a = load
>> '/user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-3.gz' using
>> PigStorage();
>>
>> b = limit a 10
>>
>>
>> (2015-07-27,12459,,31243,6,Daily,-999,2099-01-01,2099-01-02,4,0,0.1,0,1,203,4810370.0,1.4090459061723766,1.017458,-0.03,-0.11,0.05,0.468666,)
>>
>>
>> (2015-07-27,12459,,31241,6,Daily,-999,2099-01-01,2099-01-02,4,0,0.1,0,1,0,isGeo,,,203,7937613.0,1.1624841995932425,1.11562,-0.06,-0.15,0.03,0.233283,)
>>
>>
>> However with Spark
>>
>> val rowStructText =
>> sc.parallelize("/user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-0.gz")
>>
>> val x = rowStructText.map(s => {
>>
>> println(s)
>>
>> s}
>>
>> )
>>
>> x.count
>>
>> Questions
>>
>> 1) x.count always shows 67 irrespective of the path i change in
>> sc.parallelize
>>
>> 2) It shows x as RDD[Char] instead of String
>>
>> 3) println() never emits the rows.
>>
>> Any suggestions
>>
>> -Deepak
>>
>>
>>
>> --
>> Deepak
>>
>>
>
--
Deepak
Re: How to read gzip data in Spark - Simple question
The parallelize method does not read the contents of a file. It simply
takes a collection and distributes it to the cluster. In this case, the
String is a collection 67 characters.
Use sc.textFile instead of sc.parallelize, and it should work as you want.
On Wed, Aug 5, 2015 at 8:12 PM, ÐΞ€ρ@Ҝ (๏̯͡๏) wrote:
> I have csv data that is embedded in gzip format on HDFS.
>
> *With Pig*
>
> a = load
> '/user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-3.gz' using
> PigStorage();
>
> b = limit a 10
>
>
> (2015-07-27,12459,,31243,6,Daily,-999,2099-01-01,2099-01-02,4,0,0.1,0,1,203,4810370.0,1.4090459061723766,1.017458,-0.03,-0.11,0.05,0.468666,)
>
>
> (2015-07-27,12459,,31241,6,Daily,-999,2099-01-01,2099-01-02,4,0,0.1,0,1,0,isGeo,,,203,7937613.0,1.1624841995932425,1.11562,-0.06,-0.15,0.03,0.233283,)
>
>
> However with Spark
>
> val rowStructText =
> sc.parallelize("/user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-0.gz")
>
> val x = rowStructText.map(s => {
>
> println(s)
>
> s}
>
> )
>
> x.count
>
> Questions
>
> 1) x.count always shows 67 irrespective of the path i change in
> sc.parallelize
>
> 2) It shows x as RDD[Char] instead of String
>
> 3) println() never emits the rows.
>
> Any suggestions
>
> -Deepak
>
>
>
> --
> Deepak
>
>
How to read gzip data in Spark - Simple question
I have csv data that is embedded in gzip format on HDFS.
*With Pig*
a = load
'/user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-3.gz' using
PigStorage();
b = limit a 10
(2015-07-27,12459,,31243,6,Daily,-999,2099-01-01,2099-01-02,4,0,0.1,0,1,203,4810370.0,1.4090459061723766,1.017458,-0.03,-0.11,0.05,0.468666,)
(2015-07-27,12459,,31241,6,Daily,-999,2099-01-01,2099-01-02,4,0,0.1,0,1,0,isGeo,,,203,7937613.0,1.1624841995932425,1.11562,-0.06,-0.15,0.03,0.233283,)
However with Spark
val rowStructText =
sc.parallelize("/user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-0.gz")
val x = rowStructText.map(s => {
println(s)
s}
)
x.count
Questions
1) x.count always shows 67 irrespective of the path i change in
sc.parallelize
2) It shows x as RDD[Char] instead of String
3) println() never emits the rows.
Any suggestions
-Deepak
--
Deepak
