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 ilya.gane...@capitalone.com
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 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 (๏̯͡๏) 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 console:60 defined class Summary x:
org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[106] at map at
console: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 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 (๏̯͡๏) 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
--
The information contained in this e-mail is confidential and/or
proprietary to Capital One and/or its affiliates and may only be used
solely in performance of work or services for Capital One. The information
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to which it is addressed. If the reader of this message is not the intended
recipient, you are hereby notified that any review, retransmission,
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action in reliance upon this information is strictly prohibited. If you
have received this communication in error, please contact the sender and
delete the material from your
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
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 (๏̯͡๏) 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 console:60 defined class Summary x:
org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[106] at map at
console: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 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 (๏̯͡๏) 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
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.commailto: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
philip.wea...@gmail.commailto: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 (๏̯͡๏)
deepuj...@gmail.commailto: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 console:60 defined class Summary x:
org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[106] at map at console: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
philip.wea...@gmail.commailto: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 (๏̯͡๏)
deepuj...@gmail.commailto: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
The information contained in this e-mail is confidential and/or proprietary to
Capital One and/or its affiliates and may only be used solely in performance of
work or services for Capital One. The information transmitted herewith is
intended only for use by the individual or entity to which it is addressed. If
the reader of this message is not the intended recipient, you are hereby
notified that any review, retransmission, dissemination, distribution, copying
or other use of, or taking of any action in reliance upon this information is
strictly prohibited. If you have received this communication in error, please
contact the sender and delete the material from your computer.
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 (๏̯͡๏) 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
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 console:60 defined class Summary x:
org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[106] at map at
console: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 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 (๏̯͡๏) 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
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 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 (๏̯͡๏) 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 console:60 defined class Summary x:
org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[106] at map at
console: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 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 (๏̯͡๏) 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
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 console:154 defined class Summary
summary: org.apache.spark.rdd.RDD[Summary] = MapPartitionsRDD[265] at map
at console: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$$anonfun$2.apply(console:163)
at
$iwC$$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$$anonfun$2.apply(console:161)
at scala.collection.Iterator$$anon
On Wed, Aug 5, 2015 at 9:40 PM, ÐΞ€ρ@Ҝ (๏̯͡๏) deepuj...@gmail.com wrote:
how do i persist the RDD to HDFS ?
On Wed, Aug 5, 2015 at 8:32 PM, Philip Weaver 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 (๏̯͡๏) 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 console:60 defined class Summary x:
org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[106] at map at
console: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 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 (๏̯͡๏) deepuj...@gmail.com
wrote:
I have csv data that is embedded in gzip format on HDFS.
