Added below 2 lines just before the sql query line - *...* *file1_schema.count;* *file2_schema.count;* *...* and it started working. But I couldn't get the reason.
Can someone please explain me? What was happening earlier and what is happening with addition of these 2 lines? ~Sarath On Thu, Jul 17, 2014 at 1:13 PM, Sarath Chandra < sarathchandra.jos...@algofusiontech.com> wrote: > No Sonal, I'm not doing any explicit call to stop context. > > If you see my previous post to Michael, the commented portion of the code > is my requirement. When I run this over standalone spark cluster, the > execution keeps running with no output or error. After waiting for several > minutes I'm killing it by pressing Ctrl+C in the terminal. > > But the same code runs perfectly when executed from spark shell. > > ~Sarath > > > On Thu, Jul 17, 2014 at 1:05 PM, Sonal Goyal <sonalgoy...@gmail.com> > wrote: > >> Hi Sarath, >> >> Are you explicitly stopping the context? >> >> sc.stop() >> >> >> >> >> Best Regards, >> Sonal >> Nube Technologies <http://www.nubetech.co> >> >> <http://in.linkedin.com/in/sonalgoyal> >> >> >> >> >> On Thu, Jul 17, 2014 at 12:51 PM, Sarath Chandra < >> sarathchandra.jos...@algofusiontech.com> wrote: >> >>> Hi Michael, Soumya, >>> >>> Can you please check and let me know what is the issue? what am I >>> missing? >>> Let me know if you need any logs to analyze. >>> >>> ~Sarath >>> >>> >>> On Wed, Jul 16, 2014 at 8:24 PM, Sarath Chandra < >>> sarathchandra.jos...@algofusiontech.com> wrote: >>> >>>> Hi Michael, >>>> >>>> Tried it. It's correctly printing the line counts of both the files. >>>> Here's what I tried - >>>> >>>> *Code:* >>>> *package test* >>>> *object Test4 {* >>>> * case class Test(fld1: String, * >>>> * fld2: String, * >>>> * fld3: String, * >>>> * fld4: String, * >>>> * fld5: String, * >>>> * fld6: Double, * >>>> * fld7: String);* >>>> * def main(args: Array[String]) {* >>>> * val conf = new SparkConf()* >>>> * .setMaster(args(0))* >>>> * .setAppName("SQLTest")* >>>> * .setSparkHome(args(1))* >>>> * .set("spark.executor.memory", "2g");* >>>> * val sc = new SparkContext(conf);* >>>> * sc.addJar("test1-0.1.jar");* >>>> * val file1 = sc.textFile(args(2));* >>>> * println(file1.count());* >>>> * val file2 = sc.textFile(args(3));* >>>> * println(file2.count());* >>>> *// val sq = new SQLContext(sc);* >>>> *// import sq._* >>>> *// val file1_recs: RDD[Test] = file1.map(_.split(",")).map(l => >>>> Test(l(0), l(1), l(2), l(3), l(4), l(5).toDouble, l(6)));* >>>> *// val file2_recs: RDD[Test] = file2.map(_.split(",")).map(s => >>>> Test(s(0), s(1), s(2), s(3), s(4), s(5).toDouble, s(6)));* >>>> *// val file1_schema = sq.createSchemaRDD(file1_recs);* >>>> *// val file2_schema = sq.createSchemaRDD(file2_recs);* >>>> *// file1_schema.registerAsTable("file1_tab");* >>>> *// file2_schema.registerAsTable("file2_tab");* >>>> *// val matched = sq.sql("select * from file1_tab l join file2_tab s >>>> on " + * >>>> *// "l.fld7=s.fld7 where l.fld2=s.fld2 and " + * >>>> *// "l.fld3=s.fld3 and l.fld4=s.fld4 and " + * >>>> *// "l.fld6=s.fld6");* >>>> *// matched.collect().foreach(println);* >>>> * }* >>>> *}* >>>> >>>> *Execution:* >>>> *export CLASSPATH=$HADOOP_PREFIX/conf:$SPARK_HOME/lib/*:test1-0.1.jar* >>>> *export CONFIG_OPTS="-Dspark.jars=test1-0.1.jar"* >>>> *java -cp $CLASSPATH $CONFIG_OPTS test.Test4 spark://master:7077 >>>> "/usr/local/spark-1.0.1-bin-hadoop1" >>>> hdfs://master:54310/user/hduser/file1.csv >>>> hdfs://master:54310/user/hduser/file2.csv* >>>> >>>> ~Sarath >>>> >>>> On Wed, Jul 16, 2014 at 8:14 PM, Michael Armbrust < >>>> mich...@databricks.com> wrote: >>>> >>>>> What if you just run something like: >>>>> *sc.textFile("hdfs://localhost:54310/user/hduser/file1.csv").count()* >>>>> >>>>> >>>>> On Wed, Jul 16, 2014 at 10:37 AM, Sarath Chandra < >>>>> sarathchandra.jos...@algofusiontech.com> wrote: >>>>> >>>>>> Yes Soumya, I did it. >>>>>> >>>>>> First I tried with the example available in the documentation >>>>>> (example using people table and finding teenagers). After successfully >>>>>> running it, I moved on to this one which is starting point to a bigger >>>>>> requirement for which I'm evaluating Spark SQL. >>>>>> >>>>>> >>>>>> On Wed, Jul 16, 2014 at 7:59 PM, Soumya Simanta < >>>>>> soumya.sima...@gmail.com> wrote: >>>>>> >>>>>>> >>>>>>> >>>>>>> Can you try submitting a very simple job to the cluster. >>>>>>> >>>>>>> On Jul 16, 2014, at 10:25 AM, Sarath Chandra < >>>>>>> sarathchandra.jos...@algofusiontech.com> wrote: >>>>>>> >>>>>>> Yes it is appearing on the Spark UI, and remains there with state as >>>>>>> "RUNNING" till I press Ctrl+C in the terminal to kill the execution. >>>>>>> >>>>>>> Barring the statements to create the spark context, if I copy paste >>>>>>> the lines of my code in spark shell, runs perfectly giving the desired >>>>>>> output. >>>>>>> >>>>>>> ~Sarath >>>>>>> >>>>>>> On Wed, Jul 16, 2014 at 7:48 PM, Soumya Simanta < >>>>>>> soumya.sima...@gmail.com> wrote: >>>>>>> >>>>>>>> When you submit your job, it should appear on the Spark UI. Same >>>>>>>> with the REPL. Make sure you job is submitted to the cluster properly. >>>>>>>> >>>>>>>> >>>>>>>> On Wed, Jul 16, 2014 at 10:08 AM, Sarath Chandra < >>>>>>>> sarathchandra.jos...@algofusiontech.com> wrote: >>>>>>>> >>>>>>>>> Hi Soumya, >>>>>>>>> >>>>>>>>> Data is very small, 500+ lines in each file. >>>>>>>>> >>>>>>>>> Removed last 2 lines and placed this at the end >>>>>>>>> "matched.collect().foreach(println);". Still no luck. It's been more >>>>>>>>> than >>>>>>>>> 5min, the execution is still running. >>>>>>>>> >>>>>>>>> Checked logs, nothing in stdout. In stderr I don't see anything >>>>>>>>> going wrong, all are info messages. >>>>>>>>> >>>>>>>>> What else do I need check? >>>>>>>>> >>>>>>>>> ~Sarath >>>>>>>>> >>>>>>>>> On Wed, Jul 16, 2014 at 7:23 PM, Soumya Simanta < >>>>>>>>> soumya.sima...@gmail.com> wrote: >>>>>>>>> >>>>>>>>>> Check your executor logs for the output or if your data is not >>>>>>>>>> big collect it in the driver and print it. >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> On Jul 16, 2014, at 9:21 AM, Sarath Chandra < >>>>>>>>>> sarathchandra.jos...@algofusiontech.com> wrote: >>>>>>>>>> >>>>>>>>>> Hi All, >>>>>>>>>> >>>>>>>>>> I'm trying to do a simple record matching between 2 files and >>>>>>>>>> wrote following code - >>>>>>>>>> >>>>>>>>>> *import org.apache.spark.sql.SQLContext;* >>>>>>>>>> *import org.apache.spark.rdd.RDD* >>>>>>>>>> *object SqlTest {* >>>>>>>>>> * case class Test(fld1:String, fld2:String, fld3:String, >>>>>>>>>> fld4:String, fld4:String, fld5:Double, fld6:String);* >>>>>>>>>> * sc.addJar("test1-0.1.jar");* >>>>>>>>>> * val file1 = >>>>>>>>>> sc.textFile("hdfs://localhost:54310/user/hduser/file1.csv");* >>>>>>>>>> * val file2 = >>>>>>>>>> sc.textFile("hdfs://localhost:54310/user/hduser/file2.csv");* >>>>>>>>>> * val sq = new SQLContext(sc);* >>>>>>>>>> * val file1_recs: RDD[Test] = file1.map(_.split(",")).map(l => >>>>>>>>>> Test(l(0), l(1), l(2), l(3), l(4), l(5).toDouble, l(6)));* >>>>>>>>>> * val file2_recs: RDD[Test] = file2.map(_.split(",")).map(s => >>>>>>>>>> Test(s(0), s(1), s(2), s(3), s(4), s(5).toDouble, s(6)));* >>>>>>>>>> * val file1_schema = sq.createSchemaRDD(file1_recs);* >>>>>>>>>> * val file2_schema = sq.createSchemaRDD(file2_recs);* >>>>>>>>>> * file1_schema.registerAsTable("file1_tab");* >>>>>>>>>> * file2_schema.registerAsTable("file2_tab");* >>>>>>>>>> * val matched = sq.sql("select * from file1_tab l join file2_tab >>>>>>>>>> s on l.fld6=s.fld6 where l.fld3=s.fld3 and l.fld4=s.fld4 and >>>>>>>>>> l.fld5=s.fld5 >>>>>>>>>> and l.fld2=s.fld2");* >>>>>>>>>> * val count = matched.count();* >>>>>>>>>> * System.out.println("Found " + matched.count() + " matching >>>>>>>>>> records");* >>>>>>>>>> *}* >>>>>>>>>> >>>>>>>>>> When I run this program on a standalone spark cluster, it keeps >>>>>>>>>> running for long with no output or error. After waiting for few mins >>>>>>>>>> I'm >>>>>>>>>> forcibly killing it. >>>>>>>>>> But the same program is working well when executed from a spark >>>>>>>>>> shell. >>>>>>>>>> >>>>>>>>>> What is going wrong? What am I missing? >>>>>>>>>> >>>>>>>>>> ~Sarath >>>>>>>>>> >>>>>>>>>> >>>>>>>>> >>>>>>>> >>>>>>> >>>>>> >>>>> >>>> >>> >> >
