The best way is to use a profiler to understand where time is spent.
Spark while it is significantly faster than Hadoop, cannot compete with CULR.
The latter is a simple REST connection - the former triggers a JVM, Scala, 
akka, Spark,
which triggers es-hadoop which does the parallel call against all the nodes, 
retries the data in JSON format,
converts it into Scala/Java and applies on schema on top for Spark SQL to run 
with.

If you turn on logging, you'll see in fact there are multiple REST/CURL calls 
done by es-hadoop.
With a JVM/Scala warmed up, you should see less than 15s however it depends on 
how much hardware you have available.
Note that the curl comparison is not really fair - adding a SQL layer on top of 
that is bound to cost you something.


On 6/1/15 8:47 PM, Dmitriy Fingerman wrote:
Hi,

I see a big difference in performance of the same query expressed via Spark SQL 
and CURL.
In CURL the query runs less then a second, and in Spark SQL it runs 15 seconds.
The index/type which I am querying contains 1M documents.
Can you please explain why there is so big difference in performance?
Are there any ways to tune performance of Elasticsearch + Spark SQL?

Environment: (everything is running on the same box):
     Elasticsearch 1.4.4
     elasticsearch-hadoop 2.1.0.BUILD-SNAPSHOT
     Spark 1.3.0.

CURL:

curl -XPOST "http://localhost:9200/summary/intervals/_search"; -d'
{
     "query" : {
         "filtered" : {
             "query" : { "match_all" : {}},
              "filter" : {
                 "bool" : {
                     "must" : [
                         {
                             "term" : { "User" : "Robert Greene" }
                         },
                         {
                             "term" : { "DataStore" : "PROD_HK_HR" }
                         },
                         {
                             "term" : { "EventAffectedCount" : 56 }
                         }
                     ]
                 }
             }
         }
     }
}'

Spark:

     val sparkConf = new SparkConf().setAppName("Test1")

     // increasing scroll size to 5000 from the default 50 improved performance 
by 2.5 times
     sparkConf.set("es.scroll.size", "5000")

     val sc =  new SparkContext(sparkConf)
     val sqlContext = new SQLContext(sc)

     val intv = sqlContext.esDF("summary/intervals")
     intv.registerTempTable("INTERVALS")

     val intv2 = sqlContext.sql("select EventCount, Hour      " +
                                       "from intervals               " +
                                       "where User = 'Robert Greene' " +
                                       "and DataStore = 'PROD_HK_HR' " +
                                       "and EventAffectedCount = 56  ")
     intv2.show(1000)

--
Please update your bookmarks! We have moved to https://discuss.elastic.co/
---
You received this message because you are subscribed to the Google Groups 
"elasticsearch" group.
To unsubscribe from this group and stop receiving emails from it, send an email 
to
elasticsearch+unsubscr...@googlegroups.com 
<mailto:elasticsearch+unsubscr...@googlegroups.com>.
To view this discussion on the web visit
https://groups.google.com/d/msgid/elasticsearch/8fc0d384-23bd-4807-8eae-a2ef2011f6ed%40googlegroups.com
<https://groups.google.com/d/msgid/elasticsearch/8fc0d384-23bd-4807-8eae-a2ef2011f6ed%40googlegroups.com?utm_medium=email&utm_source=footer>.
For more options, visit https://groups.google.com/d/optout.

--
Costin

--
Please update your bookmarks! We have moved to https://discuss.elastic.co/
--- You received this message because you are subscribed to the Google Groups "elasticsearch" group.
To unsubscribe from this group and stop receiving emails from it, send an email 
to elasticsearch+unsubscr...@googlegroups.com.
To view this discussion on the web visit 
https://groups.google.com/d/msgid/elasticsearch/556CD145.3010203%40gmail.com.
For more options, visit https://groups.google.com/d/optout.

Reply via email to