There is an issue when using long instead of strings. 
Cache is not used.

Try with something like "from":"2014-06-01","to":"2015-01-01" for example.
Or explicitly set cache to true.

--
David ;-)
Twitter : @dadoonet / @elasticsearchfr / @scrutmydocs


Le 1 août 2014 à 06:25, Wang Yong <[email protected]> a écrit :

Thank you david 😊
most of my query looks like:

{
"filtered": {
      "query": {
        "match_all": {}
      },
      "filter": {
                   "range": {
                "timestamp": {
                  "from": 1403567280,
                  "to": 1403567340,
                  "include_lower": true,
                  "include_upper": false
                }
              }
        }
  },
 
 
 
    "facets" : {
"val": {
      "statistical": {
        "field": "val"
      }
    }
    }
}


Sent from Surface

From: David Pilato
Sent: ‎Friday‎, ‎August‎ ‎1‎, ‎2014 ‎10‎:‎43‎ ‎AM
To: [email protected]

Well. I guess it depends on your query. What does it look like?

--
David ;-)
Twitter : @dadoonet / @elasticsearchfr / @scrutmydocs


Le 1 août 2014 à 04:14, "Wang Yong" <[email protected]> a écrit :

Hi folks, I have an index storing lots of time serial data. The data are put 
into index by :
 
curl -XPUT 'localhost:9200/testindex/action1/1?pretty' -d '
{
    "val": 23,
    "timestamp": 1406822400
}'
 
And the only thing I search in this index is histogram facet in a very short 
time range, like “recent 5 min”. I found that the performance was pretty good 
at first. But when the index get bigger, the performance dropped to 
unacceptable. I found the IO maybe the bottleneck by checking the result of 
iostat.
 
My question is, even I only facet in a very short time range, why the size of 
index has so big impact on the performance of such query? Do I have to use 
daily index, just like logstash?
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