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