As far as I know, only yarn mode can set --num-executors, someone proved to
set more number-execuotrs for will perform better than set only 1 or 2
executor with large mem and core. sett
http://apache-spark-user-list.1001560.n3.nabble.com/executor-cores-vs-num-executors-td9878.html
Why
Thanks, I got it !
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I don't understand why worker need a master lock when sending heartbeat.
Caused by master HA ? Who can explain this in detail? Thanks~
Please refer:
http://stackoverflow.com/questions/25173219/why-does-the-spark-worker-actor-use-a-masterlock
case SendHeartbeat =
masterLock.synchronized {
Hi, Yin Huai
I test again with your snippet code.
It works well in spark-1.0.1
Here is my code:
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
case class Record(data_date: String, mobile: String, create_time: String)
val mobile = Record(2014-07-20,1234567,2014-07-19)
Hi,Kevin
I tried it on spark1.0.0, it works fine.
It's a bug in spark1.0.1 ...
Thanks,
Victor
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Hi, Michael
I only modified the default hadoop version to 0.20.2-cdh3u5, and
DEFAULT_HIVE=true in SparkBuild.scala.
Then sbt/sbt assembly.
I just run in the local standalone mode by using sbin/start-all.sh.
Hadoop version is 0.20.2-cdh3u5.
Then use spark-shell to execute the spark
Hi,Svend
Your reply is very helpful to me. I'll keep an eye on that ticket.
And also... Cheers :)
Best Regards,
Victor
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when I run a query to a hadoop file.
mobile.registerAsTable(mobile)
val count = sqlContext.sql(select count(1) from mobile)
res5: org.apache.spark.sql.SchemaRDD =
SchemaRDD[21] at RDD at SchemaRDD.scala:100
== Query Plan ==
ExistingRdd [data_date#0,mobile#1,create_time#2], MapPartitionsRDD[4] at
Hi, I encountered a weird problem in spark sql.
I use sbt/sbt hive/console to go into the shell.
I test the filter push down by using catalyst.
scala val queryPlan = sql(select value from (select key,value from src)a
where a.key=86 )
scala queryPlan.baseLogicalPlan
res0:
I use queryPlan.queryExecution.analyzed to get the logical plan.
it works.
And What you explained to me is very useful.
Thank you very much.
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