Explain shows the cached filter, but based on the time it takes (took: 528)
I doubt it's used efficiently
{
value: 2
description: Math.min of
details: [
{
value: 2
description: function score, score mode [sum]
details: [
{
value: 2
description: function score, product of:
details: [
{
value: 1
description: match filter: cache(BooleanFilter(+cache(olbValid:T)
+BooleanFilter(GeoPolygonFilter(location, [[34.081519, -118.3971081],
[34.081519, -118.3440351], [34.131086, -118.3440351], [34.131086,
-118.3971081], [34.081519, -118.3971081]]) GeoPolygonFilter(location,
[[34.05576, -118.3907139], [34.05576, -118.361375], [34.083811,
-118.361375], [34.083811, -118.3907139], [34.05576, -118.3907139]])
GeoPolygonFilter(location, [[34.0412758, -118.528682], [34.0412758,
-118.453384], [34.1324337, -118.453384], [34.1324337, -118.528682],
[34.0412758, -118.528682]]) GeoPolygonFilter(location, [[34.0518801,
-118.28436], [34.0518801, -118.2665471], [34.072782, -118.2665471],
[34.072782, -118.28436], [34.0518801, -118.28436]])
GeoPolygonFilter(location, [[34.017233, -118.46146], [34.017233,
-118.39865], [34.0532549, -118.39865], [34.0532549, -118.46146],
[34.017233, -118.46146]]) GeoPolygonFilter(location, [[33.973535,
-118.455804], [33.973535, -118.407918], [34.0268609, -118.407918],
[34.0268609, -118.455804], [33.973535, -118.455804]])
GeoPolygonFilter(location, [[34.0491411, -118.4215721], [34.0491411,
-118.406023], [34.0639819, -118.406023], [34.0639819, -118.4215721],
[34.0491411, -118.4215721]]) GeoPolygonFilter(location, [[34.1036901,
-118.3678789], [34.1036901, -118.3010089], [34.152483, -118.3010089],
[34.152483, -118.3678789], [34.1036901, -118.3678789]])
GeoPolygonFilter(location, [[34.0790469, -118.396506], [34.0790469,
-118.3669411], [34.1075319, -118.3669411], [34.1075319, -118.396506],
[34.0790469, -118.396506]]) GeoPolygonFilter(location, [[34.048743,
-118.259363], [34.048743, -118.250157], [34.056087, -118.250157],
[34.056087, -118.259363], [34.048743, -118.259363]])
GeoPolygonFilter(location, [[34.0734518, -118.4822519], [34.0734518,
-118.422647], [34.132908, -118.422647], [34.132908, -118.4822519],
[34.0734518, -118.4822519]]) GeoPolygonFilter(location, [[33.9658801,
-118.4404339], [33.9658801, -118.397569], [33.9839929, -118.397569],
[33.9839929, -118.4404339], [33.9658801, -118.4404339]])
GeoPolygonFilter(location, [[34.065094, -118.4423781], [34.065094,
-118.3867129], [34.1354771, -118.3867129], [34.1354771, -118.4423781],
[34.065094, -118.4423781]]) GeoPolygonFilter(location, [[34.057085,
-118.390748], [34.057085, -118.3720269], [34.072342, -118.3720269],
[34.072342, -118.390748], [34.057085, -118.390748]])
GeoPolygonFilter(location, [[34.0541081, -118.4163189], [34.0541081,
-118.389668], [34.069207, -118.389668], [34.069207, -118.4163189],
[34.0541081, -118.4163189]]) GeoPolygonFilter(location, [[33.9755489,
-118.419822], [33.9755489, -118.37006], [34.0173769, -118.37006],
[34.0173769, -118.419822], [33.9755489, -118.419822]])
GeoPolygonFilter(location, [[33.9016261, -118.4371209], [33.9016261,
-118.3735181], [33.931641, -118.3735181], [33.931641, -118.4371209],
[33.9016261, -118.4371209]]) GeoPolygonFilter(location, [[33.8504661,
-118.414124], [33.8504661, -118.384853], [33.878445, -118.384853],
[33.878445, -118.414124], [33.8504661, -118.414124]])
GeoPolygonFilter(location, [[33.9955069, -119.0699391], [33.9955069,
-118.557949], [34.1443899, -118.557949], [34.1443899, -119.0699391],
[33.9955069, -119.0699391]]) GeoPolygonFilter(location, [[33.8728038,
-118.430009], [33.8728038, -118.3785991], [33.9061261, -118.3785991],
[33.9061261, -118.430009], [33.8728038, -118.430009]])
GeoPolygonFilter(location, [[34.024999, -118.5875051], [34.024999,
-118.495177], [34.13165, -118.495177], [34.13165, -118.5875051],
[34.024999, -118.5875051]]) GeoPolygonFilter(location, [[33.8041178,
-118.405002], [33.8041178, -118.3703771], [33.8580759, -118.3703771],
[33.8580759, -118.405002], [33.8041178, -118.405002]])
GeoPolygonFilter(location, [[34.041016, -118.651771], [34.041016,
-118.5512071], [34.145413, -118.5512071], [34.145413, -118.651771],
[34.041016, -118.651771]]) GeoPolygonFilter(location, [[33.9765188,
-118.4904589], [33.9765188, -118.4443409], [34.0147499, -118.4443409],
[34.0147499, -118.4904589], [33.9765188, -118.4904589]])
GeoPolygonFilter(location, [[33.95876, -118.471863], [33.95876,
-118.4241309], [33.994206, -118.4241309], [33.994206, -118.471863],
[33.95876, -118.471863]]) GeoPolygonFilter(location, [[33.9163209,
-118.4640851], [33.9163209, -118.41719], [33.974274, -118.41719],
[33.974274, -118.4640851], [33.9163209, -118.4640851]])
GeoPolygonFilter(location, [[33.993572, -118.508206], [33.993572,
-118.483473], [34.0256331, -118.483473], [34.0256331, -118.508206],
[33.993572, -118.508206]]) GeoPolygonFilter(location, [[34.018217,
-118.521865], [34.018217, -118.4837659], [34.05056, -118.4837659],
[34.05056, -118.521865], [34.018217, -118.521865]])
GeoPolygonFilter(location, [[34.0130348, -118.51226], [34.0130348,
-118.470385], [34.046699, -118.470385], [34.046699, -118.51226],
[34.0130348, -118.51226]]) GeoPolygonFilter(location, [[34.012045,
-118.490271], [34.012045, -118.4528569], [34.0409041, -118.4528569],
[34.0409041, -118.490271], [34.012045, -118.490271]])
GeoPolygonFilter(location, [[33.9954159, -118.4917131], [33.9954159,
-118.4435171], [34.0278161, -118.4435171], [34.0278161, -118.4917131],
[33.9954159, -118.4917131]]) GeoPolygonFilter(location, [[34.119054,
-118.4225019], [34.119054, -118.35193], [34.157517, -118.35193],
[34.157517, -118.4225019], [34.119054, -118.4225019]])
GeoPolygonFilter(location, [[34.063724, -118.4504281], [34.063724,
-118.437812], [34.076625, -118.437812], [34.076625, -118.4504281],
[34.063724, -118.4504281]]))))
}
{
value: 2
description: static boost factor
details: [
{
value: 2
description: boostFactor
}
]
}
]
}
]
}
On Thursday, February 12, 2015 at 1:47:37 PM UTC-6, ffor...@homeaway.com
wrote:
I am using a function score with a function filter to score a subset of
results higher. I specify to cache the function filter, but its performance
is slower and slower the more clauses I add to the filter.
A query with 2 geo_polygons clauses returns relatively fast (30ms), but
the more polygons clauses I add the slower it gets. With 50 polygons the
call slows to 1s. Does elasticsearch actually cache the filter?
Example query with 2 geo_polygons clauses:
{
"query": {
"function_score": {
"filter": {
"bool": {
"must": [
{
"term": {
"city": "LA"
}
}
]
}
},
"functions": [
{
"filter": {
"bool": {
"should": [
{
"geo_polygon": {
"location": {
"points": [
[
-118.3971081,
34.081519
],
[
-118.3440351,
34.081519
],
[
-118.3440351,
34.131086
],
[
-118.3971081,
34.131086
]
]
}
}
},
{
"geo_polygon": {
"location": {
"points": [
[
-118.3907139,
34.05576
],
[
-118.361375,
34.05576
],
[
-118.361375,
34.083811
],
[
-118.3907139,
34.083811
]
]
}
}
}
],
"_cache": true
}
},
"boost_factor": 2
}
],
"score_mode": "sum"
}
}
}
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