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 >>>>>> >>>>>> >>>>> >>>> >>> >> >