Re: column expression in left outer join for DataFrame

2015-03-25 Thread S Krishna 
Hi,

Thanks for your response. I modified my code as per your suggestion, but
now I am getting a runtime error. Here's my code:

val df_1 = df.filter( df(event) === 0)
  . select(country, cnt)

val df_2 = df.filter( df(event) === 3)
  . select(country, cnt)

df_1.show()
//produces the following output :
// countrycnt
//   tw   3000
//   uk   2000
//   us   1000

df_2.show()
//produces the following output :
// countrycnt
//   tw   25
//   uk   200
//   us   95

val both = df_2.join(df_1, df_2(country)===df_1(country), left_outer)

I am getting the following error when executing the join statement:

java.util.NoSuchElementException: next on empty iterator.

This error seems to be originating at DataFrame.join (line 133 in
DataFrame.scala).

The show() results show that both dataframes do have columns named
country and that they are non-empty. I also tried the simpler join ( i.e.
df_2.join(df_1) ) and got the same error stated above.

I would like to know what is wrong with the join statement above.

thanks
























On Tue, Mar 24, 2015 at 6:08 PM, Michael Armbrust mich...@databricks.com
wrote:

 You need to use `===`, so that you are constructing a column expression
 instead of evaluating the standard scala equality method.  Calling methods
 to access columns (i.e. df.county is only supported in python).

 val join_df =  df1.join( df2, df1(country) === df2(country),
 left_outer)

 On Tue, Mar 24, 2015 at 5:50 PM, SK skrishna...@gmail.com wrote:

 Hi,

 I am trying to port some code that was working in Spark 1.2.0 on the
 latest
 version, Spark 1.3.0. This code involves a left outer join between two
 SchemaRDDs which I am now trying to change to a left outer join between 2
 DataFrames. I followed the example  for left outer join of DataFrame at

 https://databricks.com/blog/2015/02/17/introducing-dataframes-in-spark-for-large-scale-data-science.html

 Here's my code, where df1 and df2 are the 2 dataframes I am joining on the
 country field:

  val join_df =  df1.join( df2,  df1.country == df2.country, left_outer)

 But I got a compilation error that value  country is not a member of
 sql.DataFrame

 I  also tried the following:
  val join_df =  df1.join( df2, df1(country) == df2(country),
 left_outer)

 I got a compilation error that it is a Boolean whereas a Column is
 required.

 So what is the correct Column expression I need to provide for joining
 the 2
 dataframes on a specific field ?

 thanks








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Re: column expression in left outer join for DataFrame

2015-03-25 Thread S Krishna 
Hi,

Thanks for your response.  I am not clear about why the query is ambiguous.

val both = df_2.join(df_1, df_2(country)===df_1(country), left_outer)

I thought df_2(country)===df_1(country) indicates that the country
field in the 2 dataframes should match and df_2(country) is the
equivalent of df_2.country in SQL, while  df_1(country) is the equivalent
of df_1.country in SQL. So I am not sure why it is ambiguous. In Spark
1.2.0 I have used the same logic using SparkSQL  and Tables ( e.g.  WHERE
tab1.country = tab2.country)  and had no problems getting the correct
result.

thanks





On Wed, Mar 25, 2015 at 11:05 AM, Michael Armbrust mich...@databricks.com
wrote:

 Unfortunately you are now hitting a bug (that is fixed in master and will
 be released in 1.3.1 hopefully next week).  However, even with that your
 query is still ambiguous and you will need to use aliases:

 val df_1 = df.filter( df(event) === 0)
   . select(country, cnt).as(a)
 val df_2 = df.filter( df(event) === 3)
   . select(country, cnt).as(b)
 val both = df_2.join(df_1, $a.country === $b.country), left_outer)



 On Tue, Mar 24, 2015 at 11:57 PM, S Krishna skrishna...@gmail.com wrote:

 Hi,

 Thanks for your response. I modified my code as per your suggestion, but
 now I am getting a runtime error. Here's my code:

 val df_1 = df.filter( df(event) === 0)
   . select(country, cnt)

 val df_2 = df.filter( df(event) === 3)
   . select(country, cnt)

 df_1.show()
 //produces the following output :
 // countrycnt
 //   tw   3000
 //   uk   2000
 //   us   1000

 df_2.show()
 //produces the following output :
 // countrycnt
 //   tw   25
 //   uk   200
 //   us   95

 val both = df_2.join(df_1, df_2(country)===df_1(country),
 left_outer)

 I am getting the following error when executing the join statement:

 java.util.NoSuchElementException: next on empty iterator.

 This error seems to be originating at DataFrame.join (line 133 in
 DataFrame.scala).

 The show() results show that both dataframes do have columns named
 country and that they are non-empty. I also tried the simpler join ( i.e.
 df_2.join(df_1) ) and got the same error stated above.

 I would like to know what is wrong with the join statement above.

 thanks
























 On Tue, Mar 24, 2015 at 6:08 PM, Michael Armbrust mich...@databricks.com
  wrote:

 You need to use `===`, so that you are constructing a column expression
 instead of evaluating the standard scala equality method.  Calling methods
 to access columns (i.e. df.county is only supported in python).

 val join_df =  df1.join( df2, df1(country) === df2(country),
 left_outer)

 On Tue, Mar 24, 2015 at 5:50 PM, SK skrishna...@gmail.com wrote:

 Hi,

 I am trying to port some code that was working in Spark 1.2.0 on the
 latest
 version, Spark 1.3.0. This code involves a left outer join between two
 SchemaRDDs which I am now trying to change to a left outer join between
 2
 DataFrames. I followed the example  for left outer join of DataFrame at

 https://databricks.com/blog/2015/02/17/introducing-dataframes-in-spark-for-large-scale-data-science.html

 Here's my code, where df1 and df2 are the 2 dataframes I am joining on
 the
 country field:

  val join_df =  df1.join( df2,  df1.country == df2.country,
 left_outer)

 But I got a compilation error that value  country is not a member of
 sql.DataFrame

 I  also tried the following:
  val join_df =  df1.join( df2, df1(country) == df2(country),
 left_outer)

 I got a compilation error that it is a Boolean whereas a Column is
 required.

 So what is the correct Column expression I need to provide for joining
 the 2
 dataframes on a specific field ?

 thanks








 --
 View this message in context:
 http://apache-spark-user-list.1001560.n3.nabble.com/column-expression-in-left-outer-join-for-DataFrame-tp22209.html
 Sent from the Apache Spark User List mailing list archive at Nabble.com.

 -
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 For additional commands, e-mail: user-h...@spark.apache.org

Re: Spark Streaming: Sentiment Analysis of Twitter streams

2014-10-15 Thread S Krishna 
Hi,

I am using 1.1.0. I did set my twitter credentials and I am using the full
path. I did not paste this in the public post. I am running on a cluster
and getting the exception. Are you running in local or standalone mode?

Thanks
On Oct 15, 2014 3:20 AM, Akhil Das ak...@sigmoidanalytics.com wrote:

 I just ran the same code and it is running perfectly fine on my machine.
 These are the things on my end:

 - Spark version: 1.1.0
 - Gave full path to the negative and positive files
 - Set twitter auth credentials in the environment.

 And here's the code:

 import org.apache.spark.SparkContext
 import org.apache.spark.SparkContext._
 import org.apache.spark.SparkConf
 import org.apache.spark.streaming.twitter.TwitterUtils
 import org.apache.spark.streaming.{Seconds, StreamingContext}
 object Sentimenter {
   def main(args: Array[String]) {
 System.setProperty(twitter4j.oauth.consumerKey,X);

 System.setProperty(twitter4j.oauth.consumerSecret,X);

 System.setProperty(twitter4j.oauth.accessToken,);

 System.setProperty(twitter4j.oauth.accessTokenSecret,XXX);

 val filters = new Array[String](2)
 filters(0) = ebola
 filters(1) = isis
 val sparkConf = new
 SparkConf().setAppName(TweetSentiment).setMaster(local[2])
 val sc = new SparkContext(sparkConf)
 // get the list of positive words
 val pos_list =  sc.textFile(file:///home/akhld/positive-words.txt)
 //Random
   .filter(line = !line.isEmpty())
   .collect()
   .toSet
 // get the list of negative words
 val neg_list =  sc.textFile(file:///home/akhld/negative-words.txt)
 //Random
   .filter(line = !line.isEmpty())
   .collect()
   .toSet
 // create twitter stream
 val ssc = new StreamingContext(sparkConf, Seconds(5))
 val stream = TwitterUtils.createStream(ssc, None, filters)
 val tweets = stream.map(r = r.getText)
 tweets.print() // print tweet text
 ssc.start()
 ssc.awaitTermination()
   }
 }





 Thanks
 Best Regards

 On Wed, Oct 15, 2014 at 1:43 AM, SK skrishna...@gmail.com wrote:

 Hi,

 I am trying to implement simple sentiment analysis of Twitter streams in
 Spark/Scala.  I am getting an exception and it appears when I combine
 SparkContext with StreamingContext in the same program. When I read the
 positive and negative words using only SparkContext.textFile (without
 creating a StreamingContext) and analyze static text files, the program
 works. Likewise, when I just create the twitter stream using
 StreamingContext (and dont create a SparkContext to create the
 vocabulary),
 the program works. The exception seems to be appearing when I combine both
 SparkContext and StreamingContext in the same program and I am not sure if
 we are not allowed to  have both simultaneously. All the examples in the
 streaming module contain only the StreamingContext. The error transcript
 and
 my code appear below. I would appreciate your guidance  in fixing this
 error
 and the right way to  read static files and streams in the same program or
 any pointers to relevant examples.
 Thanks.


 --Error transcript -
 Lost task 0.0 in stage 2.0 (TID 70, mesos4-dev.sccps.net):
 java.io.IOException: unexpected exception type

 java.io.ObjectStreamClass.throwMiscException(ObjectStreamClass.java:1538)

 java.io.ObjectStreamClass.invokeReadObject(ObjectStreamClass.java:1025)

 java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1893)

 java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798)
 java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350)

 java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1990)

 java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1915)

 java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798)
 java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350)
 java.io.ObjectInputStream.readObject(ObjectInputStream.java:370)


 org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:62)


 org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:87)

 org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:159)


 java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)


 java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
 java.lang.Thread.run(Thread.java:745)
 -- My code below
 --
 object TweetSentiment {
   def main(args: Array[String]) {


 val filters = args
 val sparkConf = new SparkConf().setAppName(TweetSentiment)
 val sc = new SparkContext(sparkConf)

 // get the list of positive words
 val pos_list =  sc.textFile(positive-words.txt)
   .filter(line = !line.isEmpty())