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Spatial Indexes: Solr

In two previous articles I introduced the spherical Earth model, using SQLite as a geographical data storage and using MySQL as a geographical data storage. In this article we're going to have a look at importing the data into something else than a relational database: the search platform Solr. (Yes, I know I've skipped PostgreSQL, but I'll come back to that).


Solr is "the popular, blazing fast open source enterprise search platform from the Apache Lucene project." Since version 3.1, Solr has support for spatial search, including geospatial search. It can store coordinates in two different field types: solr.PointType for n-dimensional points, and solr.LatLonType for a two-dimensional point for geospatial search. The main difference is that with solr.PointType, calculations are done according to the flat Earth model, and solr.LatLonType does calculations for a spherical Earth model—which is just what we need.

In this example we will use the default Solr configuration, unless noted otherwise. First we download Solr 3.1 and then untar it with tar -xvzf apache-solr-3.1.0.tgz. Then we edit example/solr/conf/solrconfig.xml to comment out the section on Query Elevation Component so that it looks like:

<searchComponent name="elevator" class="solr.QueryElevationComponent" >
  pick a fieldType to analyze queries
        <str name="queryFieldType">string</str>
        <str name="config-file">elevate.xml</str>

See here for the reason.

Setting Up Solr

Before we can start using Solr, we need to define a schema. This schema is quite analogous to a database schema, except that Solr only has one table. By default Solr comes with a schema defining lots of fields in example/solr/conf/schema.xml. I've changed the whole <fields> section to look like:

        <field name="id" type="string" indexed="true" stored="true" />
        <field name="type" type="int" indexed="true" stored="true" />
        <field name="name" type="text" indexed="true" stored="true" />
        <field name="amenity" type="string" indexed="true" stored="true" />
        <field name="address" type="text" indexed="true" stored="true" />
        <field name="postcode" type="text" indexed="true" stored="true" />
        <field name="phone" type="text" indexed="true" stored="true" />
        <field name="cuisine" type="string" indexed="true" stored="true" />
        <field name="location" type="location" indexed="true" stored="true" />
        <field name="location_0_coordinate" type="double" indexed="true" stored="true" />
        <field name="location_1_coordinate" type="double" indexed="true" stored="true" />

I've defined specific fields for the type of source (1=node, 2=way/area), the name of the amenity (name of the pub, hospital, etc), what sort of amenity it is (cafe, bank, etc), the address and postcode, the phone number, the type of cuisine as well as the location. For the location I've also defined two subtypes: location_0_coordinate and location_1_coordinate. Solr requires this for multi-dimensional types such as solr.LatLonType.

The types for each field are also defined in schema.xml. text is for a string that is analysed and tokenized (broken down in smaller parts) so that you can search in only parts of the text, string is for a string of characters that is not analysed or tokenized, and location contains our latitude/longitude point. Each of those types are associated with a specific Java class with an entry such as:

<fieldType name="location" class="solr.LatLonType" subFieldSuffix="_coordinate"/>

The subFieldSuffix in this line configures the suffix in the fields location_0_coordinate and location_1_coordinate.

Now we have changed the default schema, we can start Solr with: cd example && java -jar start.jar.

On the PHP side, we need to do a bit of set-up as well. I've been using the Solr PECL extension to talk to Solr which can be installed with pecl install solr. Please do not forget to add the extension to php.ini with Check whether it is installed correctly with: php --ri solr.


For our import, we need to change the script we used previously quite substantially. Instead of just using London, I've also downloaded the whole of the UK from Geofabrik and extracted all amenities from the dataset with Osmosis:


./osmosis-0.39/bin/osmosis -v 5 \
--read-xml file=great_britain.osm.bz2 \
--tf reject-relations \
--tf accept-nodes amenity=* \
--tf reject-ways \
outPipe.0=POI \
--read-xml file=great_britain.osm.bz2 \
--tf reject-relations \
--tf accept-ways amenity=* \
--used-node outPipe.0=area \
--merge inPipe.0=POI inPipe.1=area \
--write-xml file=great_britain_amenity.osm

This is going to take quite some time, and will result in a 500MB XML file.

The main logic of the importing script remains the same, but instead of adding each item to a database, we now build a Solr import document and add it to the search index. I've also split the address and postcode into two separate fields, and added the amenity field to store all the different amenities. For now, I'm filtering out post boxes, parking areas and grave yards. The script, parsepoi-solr.php is available here.

After downloading, and renaming the downloaded file to parsepoi-solr.php we can run the script with:

php -dmemory_limit=1G parsepoi-solr.php great_britain_amenity.osm

When done, this should have imported more than 140.000 items into Solr. You can verify this, by going to http://localhost:8983/solr/select/?q=*:*&rows=0


As you can see, you can query Solr through its HTTP interface quite easily. In fact, all queries to Solr are done over HTTP. The Solr extension however abstracts this away from you for at least the import. I couldn't find any functionality in the extension to do spatial queries, so we'll do it manually.

If we look at the URL above, we can divide it into different parts:

  • http://localhost:8983/solr/: The base URL for this Solr instance.

  • select/?: The action for running search queries.

  • q=*:*: q is the search query parameter, and its value, *:* means all fields (the first *) and all possible values (the second *). In this case that means return everything.

  • rows=0: Do not return any rows, so that we just get a count.

We get back the result as an XML file. If we want JSON instead, we can simply append &wt=json. In case you want a CSV file, append &wt=csv.

In a first example, all we want to do is to return all cafes within 100 meter. We construct the search query as follows:

  • http://localhost:8983/solr/select/?: The base URL with search action.

  • q=amenity:cafe: We search in the field amenity and look for the value cafe.

  • fq={!geofilt}: We set a query filter to geofilt_. This filter restricts the result set according to the location (pt) and the maximum distance from this location (d) in km.

  • sfield=location: The field to use for location information.

  • pt=51.5375,-0.1934: The point that we center our search around.

  • d=0.1: The maximum distance in kilometers.

  • wt=csv: The format to return, in our case, CSV.

Together this makes the full GET request: http://localhost:8983/solr/select/?q=amenity:cafe&fq={!geofilt}&sfield=location&pt=51.5375,-0.1934&d=0.1&wt=csv

And the result is:

w62088838,,,51.53750834,-0.19329616,75 Kilburn High Road,-0.19329616,Costa,cafe,2,51.53750834,
w78337118,,,51.53828298,-0.19410346,101 Kilburn High Road,-0.19410346,Caffè Nero,cafe,2,51.53828298,NW6 6JE
w105467205,,,51.537555925,-0.192445525,274 Belsize Road,-0.192445525,Belsize Cafe,cafe,2,51.537555925,NW6 4BT
w105467209,+44 207 3724002,,51.537624071429,-0.19228221428571,270 Belsize Road,-0.19228221428571,Lord Jim,cafe,2,51.537624071429,NW6 4BT
w107710475,+44 20 76245736,,51.53695276,-0.19263662,2 Kilburn Bridge,-0.19263662,Famished Cafe,cafe,2,51.53695276,NW6 6HT
w107710482,,,51.53717868,-0.19288912,Kilburn Bridge,-0.19288912,Mike's,cafe,2,51.53717868,NW6 6HT
w107710490,+44 20 76246942,,51.53732946,-0.1930577,12 Kilburn Bridge,-0.1930577,La Dolce Vita,cafe,2,51.53732946,NW6 6HT

Which fields are returned can be configured. In this case, we are only interested in the location and name of each amenity, so we restrict the number of returned fields with: fl=id,location,name. The result now becomes:

w78337118,51.53828298,-0.19410346,Caffè Nero
w105467205,51.537555925,-0.192445525,Belsize Cafe
w105467209,51.537624071429,-0.19228221428571,Lord Jim
w107710475,51.53695276,-0.19263662,Famished Cafe
w107710490,51.53732946,-0.1930577,La Dolce Vita

Sadly, the results do not come back ordered by distance from our starting point. In order to do that, we need to add one more query parameter: sort=geodist() asc. The full query is now: http://localhost:8983/solr/select/?q=amenity:cafe&fq={!geofilt}&sfield=location&pt=51.5375,-0.1934&d=0.10&wt=csv&fl=id,location,name&sort=geodist()+asc

And the result:

w107710490,51.53732946,-0.1930577,La Dolce Vita
w105467205,51.537555925,-0.192445525,Belsize Cafe
w105467209,51.537624071429,-0.19228221428571,Lord Jim
w107710475,51.53695276,-0.19263662,Famished Cafe
w78337118,51.53828298,-0.19410346,Caffè Nero

It is however not possible to return the distance from our starting point directly with Solr. With the exception being that if you use only the geodist() function as query argument. In this case, make sure to include the special field score in the fl= argument as well. An URL showing this is: http://localhost:8983/solr/select/?q={!func}geodist()&fq={!geofilt}&sfield=location&pt=51.5375,-0.1934&d=0.10&wt=csv&fl=id,location,name,score&sort=geodist()+asc Of course, in this case you can not filter for specific amenities.


In currently released versions of Solr (3.1 at the time of writing), it is not possible to return the distance to the starting point for the search as part of the result set. The Solr developers have already implemented the capabilities to add the results of function queries to result documents for version 4.0. You can verify this by downloading a nightly build and running the query: http://localhost:8983/solr/select/?q=amenity:cafe&fq={!geofilt}&sfield=location&pt=51.5375,-0.1934&d=0.10&fl=id,location,name,score,geodist()&sort=geodist()+asc In this query I've added geodist() to the fl= parameter. The result now includes an extra field called geodist():

<?xml version="1.0" encoding="UTF-8"?>
  <!-- missing header -->
  <result name="response" numFound="7" start="0" maxScore="4.352648">
          <str name="id">w62088838</str>
          <str name="name">Costa</str>
          <str name="location">51.53750834,-0.19329616</str>
          <float name="score">4.352648</float>
          <double name="geodist()">0.0072415726934058865</double>
          <str name="id">w107710490</str>
          <str name="name">La Dolce Vita</str>
          <str name="location">51.53732946,-0.1930577</str>
          <float name="score">4.352648</float>
          <double name="geodist()">0.030333097323281075</double>

I could not manage to alias it to a different field, or get it to work with the CSV format (wt=csv).

If the return format is xml (the default) or json, then the result includes, besides the number of found items, the total search time for this query. Look for this information in the QTime field. In all of the examples here, the QTime has been less than 5, meaning 5 milliseconds. Solr is extremely fast, even with huge amounts of data. I will try to import the amenities of the whole "planet" at some point, and report back with some benchmarking information.

In the next installment of this series on storing geospatial data, I will be looking at using MongoDB as data store for geographical information.


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Great serie of articles ! Would be awesome to have some comparison sheet between the different systems, like : 1.Benchmarks 2.Ease-of-use 3.Problems encountered 4.Noticeable points of interests

Thanks for this new great article !

Thanks for this Derick, just what I was looking for with a recent project.


This is exactly what I was looking out for!!

Thank you so much! :)

Thanks Derick, this helped us a lot.

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