Geo_polygon query in ES5.6 is slow?

Elastic Stack Elasticsearch
1

Hello!

We are in the process of migrating our cluster from ES 1.5.2 to ES 5.6.2. We are running some performance tests on both of the clusters and it looks like geo_polygon query is slow in ES 5.6.2. Both have the same index settings, 5 shards and 1 replica:

ES1.5 results:
Thread Stats Avg Stdev Max +/- Stdev
Latency 1.64s 544.52ms 4.82s 70.05%
Req/Sec 3.99 3.97 30.00 72.31%
Latency Distribution
50% 1.56s
75% 1.95s
90% 2.38s
99% 3.22s
25911 requests in 15.00m, 138.78MB read
Requests/sec: 28.79
Transfer/sec: 157.89KB

ES5.6 results:
Thread Stats Avg Stdev Max +/- Stdev
Latency 3.71s 1.71s 10.49s 71.56%
Req/Sec 2.65 3.73 30.00 86.56%
Latency Distribution
50% 3.39s
75% 4.63s
90% 6.05s
99% 9.09s
11628 requests in 15.00m, 68.58MB read
Requests/sec: 12.92
Transfer/sec: 78.03KB

The performance script reads from a file that has the queries specific to its version:

ES1.5 query sample:
{
"size": 3000,
"filter": {
"geo_polygon": {
"property.location": {
"points": [
{
"lat": -33.91248,
"lon": 151.15363
},
{
"lat": -33.91248,
"lon": 151.19965
},
{
"lat": -33.94949,
"lon": 151.19965
},
{
"lat": -33.94949,
"lon": 151.15363
},
{
"lat": -33.91248,
"lon": 151.15363
}
]
}
}
},
"fields": [
"id",
"ota_hotel_code",
"provider_code"
]
}

ES5.6 query sample:
{
"size": 3000,
"query": {
"bool": {
"must": {
"match_all": {}
},
"filter": {
"geo_polygon": {
"location": {
"points": [
{
"lat": -33.91248,
"lon": 151.15363
},
{
"lat": -33.91248,
"lon": 151.19965
},
{
"lat": -33.94949,
"lon": 151.19965
},
{
"lat": -33.94949,
"lon": 151.15363
},
{
"lat": -33.91248,
"lon": 151.15363
}
]
}
}
}
}
},
"_source": {
"includes": [
"id",
"ota_hotel_code",
"provider_code"
]
}
}

We have tried rerunning the performance script a couple of times and ES1.5 has a much more consistent result of 23req/s whereas ES5.6 some times fluctuates between 12req/s to 23req/s.

We are not sure whether our query in 5.6 is correct or maybe we are missing some configuration.

2

Some thoughts here:

  • The query is not exactly the same. 1.5 uses a top level filter which should have been rewritten as a post_filter instead of a bool query with a filter clause.
  • Could you try by not setting the size parameter to see if it actually comes from the geo part or the fetch phase?
3

Thanks for your response!

I tried rerunning the query using constant score filter:

{
"size": 3000,
"query": {
"constant_score": {
"filter": {
"geo_polygon": {
"location": {
"points": [
{
"lat": -33.91248,
"lon": 151.15363
},
{
"lat": -33.91248,
"lon": 151.19965
},
{
"lat": -33.94949,
"lon": 151.19965
},
{
"lat": -33.94949,
"lon": 151.15363
},
{
"lat": -33.91248,
"lon": 151.15363
}
]
}
}
}
}
},
"_source": {
"includes": [
"id",
"ota_hotel_code",
"provider_code"
]
}
}

And the results was pretty much the same at around 13req/s.

I took your suggestion and removed the size param and that made the difference, it's way more faster at 160.19 req/s for ES5 and 151.23 req/s for ES1.5.

However, we need to specify a huge size for this query to get all the possible docs within the polygon.

4

I’m not sure if there is any optimization in this case.
I’m surprised that reading _source is super different between 1.x and 5.x though.

@jpountz do you have an idea?

5

Does it make a difference if you keep size:3000 but disable source filtering and always get the whole source?

1 Like
6

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