Geo_shape & 30M pois

After weeks of prototyping, I finally got around to giving es a more
serious workout then my simplistic tests. Since I have been posting and
commenting a lot on the geo_shape functionality here, I thought I'd share
some details on my setup.

I have about 30M pois, which are a big part of a bigger data set that we
are going to use. These are simple points, with a small amount of meta data
(name, category, source). This is excluding the open street map data that I
posted last week. When completed, that will be a bit bigger data set of up
to a couple of hundred of million pois, streets, buildings, neighborhoods,
etc.

So, on a completely generic, pretty much bog standard es running on my mac
book pro I threw the my data at it using a little ruby script that uses the
bulk api with 6 threads and 500 documents for each request. I gave es 1500M
of memory (my laptop has 8). I'm using a fairly recent snapshot that is
about a week old now (after lucene 4.2 was integrated).

I have a mapping that looks maps the geometry field in my json to geo_shape:

    "properties": {

        "geometry": {

        "tree": "quadtree",

        "tree_levels": 20,

            "type": "geo_shape",

        }

    }

I find this particular setting delivers good enough accuracy & index size.
The raw input is about 3GB of json. In my test setup, I have only one shard
currently.

It took about 1 hour to index the 3M documents. Index size is about 11GB.
That's about a bit less than 10K documents per second. I ran a couple of
test queries intersecting a small polygon (50m) at different places in the
world and they all came back in a very reasonable 20-30 ms. Even while it
was still indexing. The latter point I found particularly impressive. So
that is good performance on a cold index that is still being indexed at a
rate of 10K documents per second. On a laptop. Not bad.

This is one of the queries (randomly selected a coordinate in
london): {"from":0, "size":10, "version":true, "explain":true,
"query":{"filtered":{"query":{"match_all":{}},
"filter":{"geo_shape":{"geometry":{"shape":{"type":"Polygon",
"coordinates":[[[-0.11039300861473171, 51.51717889117429],
[-0.11045049097119652, 51.51731495268828], [-0.11057272644040506,
51.51743330129237], [-0.11074774976296656, 51.51752235220061],
[-0.11095842843660952, 51.51757338848964], [-0.11118413976487317,
51.517581414371904], [-0.1114027895503237, 51.51754564421813],
[-0.11159297482853864, 51.51746957946019], [-0.11173607893938753,
51.51736066584657], [-0.11181809385543551, 51.51722956460061],
[-0.1118309913852683, 51.51708910882571], [-0.11177350902880348,
51.516953047311716], [-0.11165127355959495, 51.516834698707626],
[-0.11147625023703345, 51.51674564779939], [-0.11126557156339048,
51.51669461151036], [-0.11103986023512684, 51.516686585628094],
[-0.1108212104496763, 51.516722355781866], [-0.11063102517146137,
51.51679842053981], [-0.11048792106061248, 51.51690733415343],
[-0.1104059061445645, 51.517038435399385], [-0.11039300861473171,
51.51717889117429]]]}, "relation":"intersects"}}}}}}

Overall I'm pretty happy with this level of performance & index size and
this totally validates usage of elastic search for geospatial search for
us.

So, a big thanks to all who helped me get to this stage here and offline.

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Very cool feedback. Thanks for the feedback.
Do you think you will blog on your user Story somewhere?

When in production, may I suggest that you send a PR on elasticsearch.github.com and add your case in users section?

Regards

David :wink:
Twitter : @dadoonet / @elasticsearchfr / @scrutmydocs

Le 20 mars 2013 à 17:33, Jilles van Gurp jillesvangurp@gmail.com a écrit :

After weeks of prototyping, I finally got around to giving es a more serious workout then my simplistic tests. Since I have been posting and commenting a lot on the geo_shape functionality here, I thought I'd share some details on my setup.

I have about 30M pois, which are a big part of a bigger data set that we are going to use. These are simple points, with a small amount of meta data (name, category, source). This is excluding the open street map data that I posted last week. When completed, that will be a bit bigger data set of up to a couple of hundred of million pois, streets, buildings, neighborhoods, etc.

So, on a completely generic, pretty much bog standard es running on my mac book pro I threw the my data at it using a little ruby script that uses the bulk api with 6 threads and 500 documents for each request. I gave es 1500M of memory (my laptop has 8). I'm using a fairly recent snapshot that is about a week old now (after lucene 4.2 was integrated).

I have a mapping that looks maps the geometry field in my json to geo_shape:

    "properties": {

        "geometry": {

        "tree": "quadtree",

        "tree_levels": 20,

            "type": "geo_shape",

        }

    }

I find this particular setting delivers good enough accuracy & index size. The raw input is about 3GB of json. In my test setup, I have only one shard currently.

It took about 1 hour to index the 3M documents. Index size is about 11GB. That's about a bit less than 10K documents per second. I ran a couple of test queries intersecting a small polygon (50m) at different places in the world and they all came back in a very reasonable 20-30 ms. Even while it was still indexing. The latter point I found particularly impressive. So that is good performance on a cold index that is still being indexed at a rate of 10K documents per second. On a laptop. Not bad.

This is one of the queries (randomly selected a coordinate in london): {"from":0, "size":10, "version":true, "explain":true, "query":{"filtered":{"query":{"match_all":{}}, "filter":{"geo_shape":{"geometry":{"shape":{"type":"Polygon", "coordinates":[[[-0.11039300861473171, 51.51717889117429], [-0.11045049097119652, 51.51731495268828], [-0.11057272644040506, 51.51743330129237], [-0.11074774976296656, 51.51752235220061], [-0.11095842843660952, 51.51757338848964], [-0.11118413976487317, 51.517581414371904], [-0.1114027895503237, 51.51754564421813], [-0.11159297482853864, 51.51746957946019], [-0.11173607893938753, 51.51736066584657], [-0.11181809385543551, 51.51722956460061], [-0.1118309913852683, 51.51708910882571], [-0.11177350902880348, 51.516953047311716], [-0.11165127355959495, 51.516834698707626], [-0.11147625023703345, 51.51674564779939], [-0.11126557156339048, 51.51669461151036], [-0.11103986023512684, 51.516686585628094], [-0.1108212104496763, 51.516722355781866], [-0.11063102517146137, 51.51679842053981], [-0.11048792106061248, 51.51690733415343], [-0.1104059061445645, 51.517038435399385], [-0.11039300861473171, 51.51717889117429]]]}, "relation":"intersects"}}}}}}

Overall I'm pretty happy with this level of performance & index size and this totally validates usage of elastic search for geospatial search for us.

So, a big thanks to all who helped me get to this stage here and offline.

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Sure, I'll probably post something to my blog about this in a couple or
days. I'll drop the url in this thread.

My startup (localstre.am) is still stealth mode. When the time comes there
will be plenty more blogging about what we are doing and how, and we'd love
to be mentioned in the users section as well. That might take us quite a
bit of time still.

Jilles

On Wednesday, March 20, 2013 5:41:37 PM UTC+1, David Pilato wrote:

Very cool feedback. Thanks for the feedback.
Do you think you will blog on your user Story somewhere?

When in production, may I suggest that you send a PR on
elasticsearch.github.com and add your case in users section?

Regards

David :wink:
Twitter : @dadoonet / @elasticsearchfr / @scrutmydocs

Le 20 mars 2013 à 17:33, Jilles van Gurp <jilles...@gmail.com<javascript:>>
a écrit :

After weeks of prototyping, I finally got around to giving es a more
serious workout then my simplistic tests. Since I have been posting and
commenting a lot on the geo_shape functionality here, I thought I'd share
some details on my setup.

I have about 30M pois, which are a big part of a bigger data set that we
are going to use. These are simple points, with a small amount of meta data
(name, category, source). This is excluding the open street map data that I
posted last week. When completed, that will be a bit bigger data set of up
to a couple of hundred of million pois, streets, buildings, neighborhoods,
etc.

So, on a completely generic, pretty much bog standard es running on my mac
book pro I threw the my data at it using a little ruby script that uses the
bulk api with 6 threads and 500 documents for each request. I gave es 1500M
of memory (my laptop has 8). I'm using a fairly recent snapshot that is
about a week old now (after lucene 4.2 was integrated).

I have a mapping that looks maps the geometry field in my json to
geo_shape:

    "properties": {

        "geometry": {

        "tree": "quadtree",

        "tree_levels": 20,

            "type": "geo_shape",

        }

    }

I find this particular setting delivers good enough accuracy & index size.
The raw input is about 3GB of json. In my test setup, I have only one shard
currently.

It took about 1 hour to index the 3M documents. Index size is about 11GB.
That's about a bit less than 10K documents per second. I ran a couple of
test queries intersecting a small polygon (50m) at different places in the
world and they all came back in a very reasonable 20-30 ms. Even while it
was still indexing. The latter point I found particularly impressive. So
that is good performance on a cold index that is still being indexed at a
rate of 10K documents per second. On a laptop. Not bad.

This is one of the queries (randomly selected a coordinate in
london): {"from":0, "size":10, "version":true, "explain":true,
"query":{"filtered":{"query":{"match_all":{}},
"filter":{"geo_shape":{"geometry":{"shape":{"type":"Polygon",
"coordinates":[[[-0.11039300861473171, 51.51717889117429],
[-0.11045049097119652, 51.51731495268828], [-0.11057272644040506,
51.51743330129237], [-0.11074774976296656, 51.51752235220061],
[-0.11095842843660952, 51.51757338848964], [-0.11118413976487317,
51.517581414371904], [-0.1114027895503237, 51.51754564421813],
[-0.11159297482853864, 51.51746957946019], [-0.11173607893938753,
51.51736066584657], [-0.11181809385543551, 51.51722956460061],
[-0.1118309913852683, 51.51708910882571], [-0.11177350902880348,
51.516953047311716], [-0.11165127355959495, 51.516834698707626],
[-0.11147625023703345, 51.51674564779939], [-0.11126557156339048,
51.51669461151036], [-0.11103986023512684, 51.516686585628094],
[-0.1108212104496763, 51.516722355781866], [-0.11063102517146137,
51.51679842053981], [-0.11048792106061248, 51.51690733415343],
[-0.1104059061445645, 51.517038435399385], [-0.11039300861473171,
51.51717889117429]]]}, "relation":"intersects"}}}}}}

Overall I'm pretty happy with this level of performance & index size and
this totally validates usage of elastic search for geospatial search for
us.

So, a big thanks to all who helped me get to this stage here and offline.

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Just noticed a typo "It took about 1 hour to index the 3M documents", that
should be 30M.

Jilles

On Wednesday, March 20, 2013 5:33:57 PM UTC+1, Jilles van Gurp wrote:

After weeks of prototyping, I finally got around to giving es a more
serious workout then my simplistic tests. Since I have been posting and
commenting a lot on the geo_shape functionality here, I thought I'd share
some details on my setup.

I have about 30M pois, which are a big part of a bigger data set that we
are going to use. These are simple points, with a small amount of meta data
(name, category, source). This is excluding the open street map data that I
posted last week. When completed, that will be a bit bigger data set of up
to a couple of hundred of million pois, streets, buildings, neighborhoods,
etc.

So, on a completely generic, pretty much bog standard es running on my mac
book pro I threw the my data at it using a little ruby script that uses the
bulk api with 6 threads and 500 documents for each request. I gave es 1500M
of memory (my laptop has 8). I'm using a fairly recent snapshot that is
about a week old now (after lucene 4.2 was integrated).

I have a mapping that looks maps the geometry field in my json to
geo_shape:

    "properties": {

        "geometry": {

        "tree": "quadtree",

        "tree_levels": 20,

            "type": "geo_shape",

        }

    }

I find this particular setting delivers good enough accuracy & index size.
The raw input is about 3GB of json. In my test setup, I have only one shard
currently.

It took about 1 hour to index the 3M documents. Index size is about 11GB.
That's about a bit less than 10K documents per second. I ran a couple of
test queries intersecting a small polygon (50m) at different places in the
world and they all came back in a very reasonable 20-30 ms. Even while it
was still indexing. The latter point I found particularly impressive. So
that is good performance on a cold index that is still being indexed at a
rate of 10K documents per second. On a laptop. Not bad.

This is one of the queries (randomly selected a coordinate in
london): {"from":0, "size":10, "version":true, "explain":true,
"query":{"filtered":{"query":{"match_all":{}},
"filter":{"geo_shape":{"geometry":{"shape":{"type":"Polygon",
"coordinates":[[[-0.11039300861473171, 51.51717889117429],
[-0.11045049097119652, 51.51731495268828], [-0.11057272644040506,
51.51743330129237], [-0.11074774976296656, 51.51752235220061],
[-0.11095842843660952, 51.51757338848964], [-0.11118413976487317,
51.517581414371904], [-0.1114027895503237, 51.51754564421813],
[-0.11159297482853864, 51.51746957946019], [-0.11173607893938753,
51.51736066584657], [-0.11181809385543551, 51.51722956460061],
[-0.1118309913852683, 51.51708910882571], [-0.11177350902880348,
51.516953047311716], [-0.11165127355959495, 51.516834698707626],
[-0.11147625023703345, 51.51674564779939], [-0.11126557156339048,
51.51669461151036], [-0.11103986023512684, 51.516686585628094],
[-0.1108212104496763, 51.516722355781866], [-0.11063102517146137,
51.51679842053981], [-0.11048792106061248, 51.51690733415343],
[-0.1104059061445645, 51.517038435399385], [-0.11039300861473171,
51.51717889117429]]]}, "relation":"intersects"}}}}}}

Overall I'm pretty happy with this level of performance & index size and
this totally validates usage of elastic search for geospatial search for
us.

So, a big thanks to all who helped me get to this stage here and offline.

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Here's the blog post:

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thanks jilles for all this info an help, voice, chats etc.!!
much appreciated!

simon

On Wednesday, March 20, 2013 8:09:06 PM UTC+1, Jilles van Gurp wrote:

Here's the blog post:
http://www.jillesvangurp.com/2013/03/20/using-elastic-search-for-geo-spatial-search/

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I love such writeups - simply amazing stuff

Jörg

Am 20.03.13 20:09, schrieb Jilles van Gurp:

Here's the blog post:
http://www.jillesvangurp.com/2013/03/20/using-elastic-search-for-geo-spatial-search/

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Hi Jilles,

Some questions: How much RAM are you using for this setup for ES? And what
information are you storing for the POIs - only a name and a list of
lat+lon? Also check out the osmosis plugin:


Will your feeder be as well open source? I would like to have a
better+easier Nominatim based on ElasticSearch for my graphhopper project!

Peter.

On Wednesday, March 20, 2013 8:09:06 PM UTC+1, Jilles van Gurp wrote:

Here's the blog post:
http://www.jillesvangurp.com/2013/03/20/using-elastic-search-for-geo-spatial-search/

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I would also love to hear the index size with only the POI data (or only
the geo shapes)!
To get an impression of the efficiency of lucene's spatial index.

Thanks,
Peter.

On Thursday, March 21, 2013 11:10:03 AM UTC+1, Karussell wrote:

Hi Jilles,

Some questions: How much RAM are you using for this setup for ES? And what
information are you storing for the POIs - only a name and a list of
lat+lon? Also check out the osmosis plugin:
https://github.com/ncolomer/elasticsearch-osmosis-plugin
Will your feeder be as well open source? I would like to have a
better+easier Nominatim based on ElasticSearch for my graphhopper project!

Peter.

On Wednesday, March 20, 2013 8:09:06 PM UTC+1, Jilles van Gurp wrote:

Here's the blog post:
http://www.jillesvangurp.com/2013/03/20/using-elastic-search-for-geo-spatial-search/

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I indexed with 1500MB of RAM yesterday. My mac has 8GB and I had probably
about 60% or so in use for other processes. My laptop ran pretty hot during
the indexing and it certainly felt like it was utilizing several cores. I
didn't profile or check top though.

The poi data is indeed a point, title, source, category, and id. So, very
simple.

I do have the osm data for Brandenburg in there as well, that's about 120K
pois, streets, and polygons. And I have a small dataset with country
borders. These live in separate indexes but use the same settings for quad
tree.

My project for converting the osm data to geojson is open
source; https://github.com/jillesvangurp/osm2geojson. That is pretty much
the hardest part of the geocoding. Once you have nice geojson features it
is just a matter of writing the right queries. Simply querying for streets
and sorting them by distance manually pretty much does what you would
expect. If people are interested, I have a method for calculating the
distance between a point and a path or polygon in my geogeometry project on
github.

The big technical challenge with this project is basically joining ways
with nodes and relations with ways and nodes. I'm sort of faking having an
index with my persistentcachingmap project, which works well enough for
smaller areas but probably won't scale all the way to the full data set.
Once joined, the rest is easy. The project already reconstructs multi
polygons from ways and converts the osm tags into a bit more consistent and
flattened category system. That last part is going to take a lot of
tweaking to get right since practices in osm are somewhat sloppy and
inconsistent for this.

Given the license of the data, I will of course provide the full osm
geojson when I get my project in a state where it can actually handle the 3
billion or so nodes. That might be some time though. I did this pretty much
as a spike to find out if I can come up with a good enough reverse geocoder
using raw osm data and elastic search.

Nominatim is interesting but a bit of an architectural mismatch for what we
are doing. I'd hate to have to worry about scaling postgresql with a
dataset like that and the business logic is written in languages I'd like
to stay far away from (php, C).

Jilles

On Thursday, March 21, 2013 11:10:03 AM UTC+1, Karussell wrote:

Hi Jilles,

Some questions: How much RAM are you using for this setup for ES? And what
information are you storing for the POIs - only a name and a list of
lat+lon? Also check out the osmosis plugin:
https://github.com/ncolomer/elasticsearch-osmosis-plugin
Will your feeder be as well open source? I would like to have a
better+easier Nominatim based on ElasticSearch for my graphhopper project!

Peter.

On Wednesday, March 20, 2013 8:09:06 PM UTC+1, Jilles van Gurp wrote:

Here's the blog post:
http://www.jillesvangurp.com/2013/03/20/using-elastic-search-for-geo-spatial-search/

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Here's the mapping for the index. There are a couple of fields in there
used by only two or three test objects. I also pasted an ls -lha of the
index dir with the poi data below. The non poi data is in a different index
directory so not included there.

{
"dynamic_templates": [
{
"analyzed_string": {
"mapping": {
"index": "analyzed",
"type": "string"
},
"match_mapping_type": "string",
"path_match": "name."
}
},
{
"non_analyzed_string": {
"mapping": {
"index": "not_analyzed",
"type": "string"
},
"match": "
",
"match_mapping_type": "string"
}
}
],
"_all": {
"enabled": false
},
"properties": {
"categories": {
"dynamic": "true",
"properties": {
"xxxxxx": {
"type": "string",
"index": "not_analyzed",
"omit_norms": true,
"index_options": "docs"
}
}
},
"geometry": {
"type": "geo_shape",
"tree": "quadtree",
"tree_levels": 20
},
"id": {
"type": "string",
"index": "not_analyzed",
"omit_norms": true,
"index_options": "docs"
},
"links": {
"dynamic": "true",
"properties": {
"rel": {
"type": "string",
"index": "not_analyzed",
"omit_norms": true,
"index_options": "docs"
},
"title": {
"type": "string",
"index": "not_analyzed",
"omit_norms": true,
"index_options": "docs"
},
"uri": {
"type": "string",
"index": "not_analyzed",
"omit_norms": true,
"index_options": "docs"
}
}
},
"name": {
"dynamic": "true",
"properties": {
"en": {
"type": "string"
}
}
},
"source": {
"type": "string",
"index": "not_analyzed",
"omit_norms": true,
"index_options": "docs"
},
"type": {
"type": "string",
"index": "not_analyzed",
"omit_norms": true,
"index_options": "docs"
}
}
}

drwxr-xr-x 465 jilles staff 15K Mar 21 10:49 .
drwxr-xr-x 5 jilles staff 170B Mar 20 15:47 ..
-rw-r--r-- 1 jilles staff 19K Mar 20 17:33 _checksums-1363797210191
-rw-r--r-- 1 jilles staff 2.4G Mar 20 16:29 _ds2.fdt
-rw-r--r-- 1 jilles staff 1.1M Mar 20 16:29 _ds2.fdx
-rw-r--r-- 1 jilles staff 1.5K Mar 20 16:45 _ds2.fnm
-rw-r--r-- 1 jilles staff 30M Mar 20 16:45 _ds2.nvd
-rw-r--r-- 1 jilles staff 57B Mar 20 16:45 _ds2.nvm
-rw-r--r-- 1 jilles staff 578B Mar 20 16:45 _ds2.si
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On Thursday, March 21, 2013 11:19:21 AM UTC+1, Karussell wrote:

I would also love to hear the index size with only the POI data (or only
the geo shapes)!
To get an impression of the efficiency of lucene's spatial index.

Thanks,
Peter.

On Thursday, March 21, 2013 11:10:03 AM UTC+1, Karussell wrote:

Hi Jilles,

Some questions: How much RAM are you using for this setup for ES? And
what information are you storing for the POIs - only a name and a list of
lat+lon? Also check out the osmosis plugin:
https://github.com/ncolomer/elasticsearch-osmosis-plugin
Will your feeder be as well open source? I would like to have a
better+easier Nominatim based on ElasticSearch for my graphhopper project!

Peter.

On Wednesday, March 20, 2013 8:09:06 PM UTC+1, Jilles van Gurp wrote:

Here's the blog post:
http://www.jillesvangurp.com/2013/03/20/using-elastic-search-for-geo-spatial-search/

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