Yes, I am using Spark1.1.0 and have used rdd.registerTempTable().
I tried by adding sqlContext.cacheTable(), but it took 59 seconds (more than
earlier).
I also tried by changing schema to use Long data type in some fields but
seems conversion takes more time.
Is there any way to specify index ? Though I checked and didn't found any,
just want to confirm.
For your reference here is the snippet of code.
-----------------------------------------------------------------------------------------------------------------
case class EventDataTbl(EventUID: Long,
ONum: Long,
RNum: Long,
Timestamp: java.sql.Timestamp,
Duration: String,
Type: String,
Source: String,
OName: String,
RName: String)
val format = new java.text.SimpleDateFormat("yyyy-MM-dd
hh:mm:ss")
val cedFileName =
"hdfs://hadoophost:8020/demo/poc/JoinCsv/output_2"
val cedRdd = sc.textFile(cedFileName).map(_.split(",",
-1)).map(p =>
EventDataTbl(p(0).toLong, p(1).toLong, p(2).toLong, new
java.sql.Timestamp(format.parse(p(3)).getTime()), p(4), p(5), p(6), p(7),
p(8)))
cedRdd.registerTempTable("EventDataTbl")
sqlCntxt.cacheTable("EventDataTbl")
val t1 = System.nanoTime()
println("\n\n10 Most frequent conversations between the
Originators and
Recipients\n")
sql("SELECT COUNT(*) AS Frequency,ONum,OName,RNum,RName FROM
EventDataTbl
GROUP BY ONum,OName,RNum,RName ORDER BY Frequency DESC LIMIT
10").collect().foreach(println)
val t2 = System.nanoTime()
println("Time taken " + (t2-t1)/1000000000.0 + " Seconds")
-----------------------------------------------------------------------------------------------------------------
Thanks,
Shailesh
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