[Hadoop] Slow performance of Elasticsearch-Hadoop + Spark SQL

Elastic Stack Elasticsearch
1

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)

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2

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)

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--
Costin

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