Hi,
I am indexing some pretty big ammount of positions in ES (like 150M ) in
monthy based indexes (201312 , 201311 etc)
One document has a timestamp and location.
My queries are like :
Give me all positions inside this boundig box... etc
I have 2 types of indexes with exaclty the same mapping except the location
fields.
Ex:
loc: {
type: geo_point
}
loc: {
tree: quadtree
type: geo_shape
}
It seems to me that there is big difference in the speed of the queries
agains the two types of indexes.
The index with location of type geo_shape is MUCH faster that the index
with geo_point.
With cold caches the query with geo_point runs for aout 26 seconds , where
the query with geo_shape runs for like 2 seconds.
Also the query with geo_point type loads huge ammount of data in field
cache (8GB for just one month data). With geo_shape field data is much less.
The geo_shape mapping is with default precision and qudtree type.
Both queries have the same logic.
I would like to undestand why it is much fatser with geo_shape than
geo_point.
Can someone shade some light on this matter ?
Ofc the index with geo_shape is like 30% bigger in size.
Example query for index type geo_shape
{
"query": {
"bool": {
"must": [
{
"range": {
"ts": {
"from": "2013-11-01",
"to": "2013-12-30"
}
}
},
{
"geo_shape": {
"loc": {
"shape": {
"type": "envelope",
"coordinates": [
[ 1.6754645,53.786 ],
[14.345234, 51.3453 ]
]
}
}
}
}
],
}
},
"aggregations": {
"agg1": {
"terms": {
"field": "e_id"
}
}
},
"size": 0
}
Example query for index type geo_point
{
"query": {
"bool": {
"must": [
{
"range": {
"ts": {
"from": "2013-11-01",
"to": "2013-12-30"
}
}
},
{
"geo_bounding_box" : {
"loc" : {
"top_left" : {
"lat" : 40.73,
"lon" : -74.1
},
"bottom_right" : {
"lat" : 40.01,
"lon" : -71.12
}
}
}
}
],
}
},
"aggregations": {
"agg1": {
"terms": {
"field": "e_id"
}
}
},
"size": 0
}
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