There is no direct way of doing that. If you need a Single file for every batch duration, then you can repartition the data to 1 before saving. Another way would be to use hadoop's copy merge command/api(available from 2.0 versions) On 13 Jan 2015 01:08, "Su She" <[email protected]> wrote:
> Hello Everyone, > > Quick followup, is there any way I can append output to one file rather > then create a new directory/file every X milliseconds? > > Thanks! > > Suhas Shekar > > University of California, Los Angeles > B.A. Economics, Specialization in Computing 2014 > > On Thu, Jan 8, 2015 at 11:41 PM, Su She <[email protected]> wrote: > >> 1) Thank you everyone for the help once again...the support here is >> really amazing and I hope to contribute soon! >> >> 2) The solution I actually ended up using was from this thread: >> http://mail-archives.apache.org/mod_mbox/spark-user/201310.mbox/%3ccafnzj5ejxdgqju7nbdqy6xureq3d1pcxr+i2s99g5brcj5e...@mail.gmail.com%3E >> >> in case the thread ever goes down, the soln provided by Matei: >> >> plans.saveAsHadoopFiles("hdfs://localhost:8020/user/hue/output/completed","csv", >> String.class, String.class, (Class) TextOutputFormat.class); >> >> I had browsed a lot of similar threads that did not have answers, but >> found this one from quite some time ago, so apologize for posting a >> question that had been answered before. >> >> 3) Akhil, I was specifying the format as "txt", but it was not compatible >> >> Thanks for the help! >> >> >> On Thu, Jan 8, 2015 at 11:23 PM, Akhil Das <[email protected]> >> wrote: >> >>> saveAsHadoopFiles requires you to specify the output format which i >>> believe you are not specifying anywhere and hence the program crashes. >>> >>> You could try something like this: >>> >>> Class<? extends OutputFormat<?,?>> outputFormatClass = (Class<? extends >>> OutputFormat<?,?>>) (Class<?>) SequenceFileOutputFormat.class; >>> 46 >>> >>> yourStream.saveAsNewAPIHadoopFiles(hdfsUrl, >>> "/output-location",Text.class, Text.class, outputFormatClass); >>> >>> >>> >>> Thanks >>> Best Regards >>> >>> On Fri, Jan 9, 2015 at 10:22 AM, Su She <[email protected]> wrote: >>> >>>> Yes, I am calling the saveAsHadoopFiles on the Dstream. However, when I >>>> call print on the Dstream it works? If I had to do foreachRDD to >>>> saveAsHadoopFile, then why is it working for print? >>>> >>>> Also, if I am doing foreachRDD, do I need connections, or can I simply >>>> put the saveAsHadoopFiles inside the foreachRDD function? >>>> >>>> Thanks Yana for the help! I will play around with foreachRDD and convey >>>> my results. >>>> >>>> >>>> >>>> On Thu, Jan 8, 2015 at 6:06 PM, Yana Kadiyska <[email protected]> >>>> wrote: >>>> >>>>> are you calling the saveAsText files on the DStream --looks like it? >>>>> Look at the section called "Design Patterns for using foreachRDD" in the >>>>> link you sent -- you want to do dstream.foreachRDD(rdd => >>>>> rdd.saveAs....) >>>>> >>>>> On Thu, Jan 8, 2015 at 5:20 PM, Su She <[email protected]> wrote: >>>>> >>>>>> Hello Everyone, >>>>>> >>>>>> Thanks in advance for the help! >>>>>> >>>>>> I successfully got my Kafka/Spark WordCount app to print locally. >>>>>> However, I want to run it on a cluster, which means that I will have to >>>>>> save it to HDFS if I want to be able to read the output. >>>>>> >>>>>> I am running Spark 1.1.0, which means according to this document: >>>>>> https://spark.apache.org/docs/1.1.0/streaming-programming-guide.html >>>>>> >>>>>> I should be able to use commands such as saveAsText/HadoopFiles. >>>>>> >>>>>> 1) When I try saveAsTextFiles it says: >>>>>> cannot find symbol >>>>>> [ERROR] symbol : method >>>>>> saveAsTextFiles(java.lang.String,java.lang.String) >>>>>> [ERROR] location: class >>>>>> org.apache.spark.streaming.api.java.JavaPairDStream<java.lang.String,java.lang.Integer> >>>>>> >>>>>> This makes some sense as saveAsTextFiles is not included here: >>>>>> >>>>>> http://people.apache.org/~tdas/spark-1.1.0-temp-docs/api/java/org/apache/spark/streaming/api/java/JavaPairDStream.html >>>>>> >>>>>> 2) When I try >>>>>> saveAsHadoopFiles("hdfs://ip....us-west-1.compute.internal:8020/user/testwordcount", >>>>>> "txt") it builds, but when I try running it it throws this exception: >>>>>> >>>>>> Exception in thread "main" java.lang.RuntimeException: >>>>>> java.lang.RuntimeException: class scala.runtime.Nothing$ not >>>>>> org.apache.hadoop.mapred.OutputFormat >>>>>> at >>>>>> org.apache.hadoop.conf.Configuration.getClass(Configuration.java:2079) >>>>>> at >>>>>> org.apache.hadoop.mapred.JobConf.getOutputFormat(JobConf.java:712) >>>>>> at >>>>>> org.apache.spark.rdd.PairRDDFunctions.saveAsHadoopDataset(PairRDDFunctions.scala:1021) >>>>>> at >>>>>> org.apache.spark.rdd.PairRDDFunctions.saveAsHadoopFile(PairRDDFunctions.scala:940) >>>>>> at >>>>>> org.apache.spark.streaming.dstream.PairDStreamFunctions$$anonfun$8.apply(PairDStreamFunctions.scala:632) >>>>>> at >>>>>> org.apache.spark.streaming.dstream.PairDStreamFunctions$$anonfun$8.apply(PairDStreamFunctions.scala:630) >>>>>> at >>>>>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:42) >>>>>> at >>>>>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:40) >>>>>> at >>>>>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:40) >>>>>> at scala.util.Try$.apply(Try.scala:161) >>>>>> at org.apache.spark.streaming.scheduler.Job.run(Job.scala:32) >>>>>> at >>>>>> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:171) >>>>>> at >>>>>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) >>>>>> at >>>>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) >>>>>> at java.lang.Thread.run(Thread.java:724) >>>>>> Caused by: java.lang.RuntimeException: class scala.runtime.Nothing$ >>>>>> not org.apache.hadoop.mapred.OutputFormat >>>>>> at >>>>>> org.apache.hadoop.conf.Configuration.getClass(Configuration.java:2073) >>>>>> ... 14 more >>>>>> >>>>>> >>>>>> Any help is really appreciated! Thanks. >>>>>> >>>>>> >>>>> >>>> >>> >> >
