After several long discussions with Jan I finally wrapped my head around the document oriented concept. I was blown away, it was exactly what I would have liked to use on so many occasions at my one year internship at a geospatial company. Though CouchDB wasn't ready, I needed spatial indexing. One week later I had a first idea of how such an extension might look like.

And only 2 years later I'm really involved in CouchDB and people actually start using GeoCouch :) I'd like to use this blog post to thank the developers and the whole community, it's been a great time and the IRC channel just kicks ass. You all helped to make CouchDB 1.0 possible!

GeoCouch: Eine Erweiterung für CouchDB zur Abfrage räumlicher Daten

Er richtet sich an Geographen, wird also nicht zu sehr ins Detail der Implementierung gehen. Es sind auch keine Vorkenntnisse zum Thema CouchDB nötig. Wer also mehr über CouchDB und GeoCouch wissen will, ist herzlich dazu eingeladen. Danach stehe ich natürlich zu Fragen zur Verfügung.

Ich habe keine Ahnung wie groß die CouchDB Community im Raum Augsburg ist, aber sollte jemand dieser Einladung folgen, spricht auch nichts gegen ein anschließendes kleines CouchDB/GeoCouch/NoSQL Meetup. Am besten meldet ihr euch bei mir per Mail, denn wenn ein paar Leute sicher kommen, werden es sich andere bestimmt auch überlegen.

Sorry Planet CouchDB for writing in German, but this is about a talk in German.

I had the chance to tell people a bit more about CouchDB and Couchapps,

One project I haven't heard that much before of was Degree. They build the whole stack of OGC services you could imagine. For me it was of interest that they have a blob storage in their upcoming 3.0 release. The data isn't flattened into SQL tables but stored as blobs. This sounds like good use for a CouchDB backend in the future.

I was working with Simon Pigot on a GeoNetwork re-implementation based on CouchDB using Couchapp. We got the basic stuff like putting an XML document into the database, editing it and returning the new document, as well as fulltext indexing with couchdb-lucene work. Next steps are improving the JSON to XML mapping and integrating spatial search based on GeoCouch.

The event was really enjoyable, thanks Couchio for sponsoring the trip, thanks Jeroen for organizing it, and thanks all other hackers that made it such a awesome event. Hope to see you next year!

I did it! I'll speak at the FOSS4G Conference 2010 (Free and Open Source Software for Geospatial Conference), 6th–9th September in Barcelona about “GeoCouch: A spatial index for CouchDB”. As soon as the abstract is available online I'll link to it. Hope to see you there!

Current implementation

Thanks to the feedback after the FOSS4G 2009 and "GeoCouch: The future" blog entry" it was clear that people prefer a simple, yet powerful and tightly integrated approach, rather than having to many external dependencies (which was a showstopper for quite a few people).

I implemented an R-tree (I call it vtree as the implementation is subject to change a lot) from scratch. The reason why I haven't used the already existing R-Tree implementation available at Github is that I needed something to learn Erlang, it doesn't contain test or examples and that it is always a good idea to implement a data structure yourself to understand the details/problems. My implementation is far from being perfect but works good enough for now. The vtree is implemented as an append-only data structure just as CouchDB's B-trees are. Currently it doesn't support bulk insertion.

If you want to know details on how to create your own indexer, have a look at my Indexer tutorial.

Feature set

Following the "Release early, release often" philosophy currently only points can be inserted, the only supported query is a bounding box search. Though other geometries should follow soon.

Using GeoCouch

GeoCouch is now hosted at Github. Giving GeoCouch a go is easy:

git clone http://github.com/vmx/couchdb.git
cd couchdb
./bootstrap
./configure
make dev
./utils/run

To try the spatial features when it's up and running is easy as well. Just add a spatial property and a named function to your Design Document as you would to for show or list functions:

function(doc) {
    if (doc.loc) {
        emit(doc._id, {
            type: "Point",
            coordinates: [doc.loc[0], doc.loc[1]]
        });
    }
};

All you need to do is emitting GeoJSON as the value (Remember that point is the only supported geometry at the moment), the key is currently ignored.

curl -X PUT http://127.0.0.1:5984/places
curl -X PUT -d '{"spatial":{"points":"function(doc) {\n    if (doc.loc) {\n        emit(doc._id, {\n            type: \"Point\",\n            coordinates: [doc.loc[0], doc.loc[1]]\n        });\n    }};"}}' http://127.0.0.1:5984/places/_design/main

Before a bounding box query can return anything, you need to insert Documents that contain a location.

curl -X PUT -d '{"loc": [-122.270833, 37.804444]}' http://127.0.0.1:5984/places/oakland
curl -X PUT -d '{"loc": [10.898333, 48.371667]}' http://127.0.0.1:5984/places/augsburg

And finally you can make a bounding box request:

curl -X GET 'http://localhost:5984/places/_design/main/_spatial/points/%5B0,0,180,90%5D'

This one should return only augsburg:

{"query1":[{"id":"augsburg","loc":[10.898333,48.371667]}]}

Next steps

The development of GeoCouch was quite slow in the past, but it gets up to speed as my diploma thesis (comparable to a master's thesis) will be about GeoCouch. Additionally Couchio kindly supports the development.

The next steps are (in no particular order):

• Better R-tree (better splitting algorithm, bulk operations)
• Supporting more geometries
• Polygon search
• Improving CouchDB's plugin capabilities

Thanks

I'd like to thank all the people that kept me motivated over the past two years with their tremendous feedback. Special thanks go to Jan Lehnardt for getting me onto the Couch, Cameron Shorter for introducing me into the geospatial open source business and all people from Couchio for the great two weeks in Oakland.

Stripping it down

GeoCouch needs an external spatial index, at the moment I use SpatiaLite for it, but a PostGIS backend would be easily possible. My inital idea was that it is better to use the existing power of spatial databases, rather than reinventing the wheel. I though I could use all the power they have, that I can even use them for complex analytics, but I can't. As I only store the geometries, I need to “ask” CouchDB for the attributes (no, I don't want to store attributes in my spatial index).

If I don't use the full power of the spatial databases, but only a small fraction, there might be better solution. Therefore I propose that GeoCouch will use a simple spatial index for storing the geometries, not a full blown spatial database. I haven't decided yet which one it'll be, but I really think about moving this part to Erlang (I know that quite a few people would love that move).

You will loose functionality like reprojection. The spatial index won't know anything about projections. So GeoCouch won't be projection aware anymore, but you application still can be. For example if you want to return your data in a different projection than it was stored, you do the transformation after you've queried GeoCouch.

You would also loose fancy things for geometries, like boolean operations on them. But this is something I'd call complex analytics, and not simple querying.

GeoCouch would only support three simple queries: bounding search, polygon search and radius/distance search. If the search would be within a union of polygons, let's say all countries of the European Union, you would simply make the union operation before you query GeoCouch.

Complex analytics

What I call “complex analytics” is things like: “return all apple trees that are located with a 10km range around buildings that have are over 100m high, but only in countries with a population over 50 million people” is not possible with GeoCouch as you would need the attribute values as well. Those are stored in CouchDB, so you would need to request them. What GeoCouch only supports is a simple: give me all IDs within a bounding box/polygon/radius.

Conclusion

Simple requests are needed for everyday use, thus they should be incredibly fast. Complex analytics don't necessarily need to handle thousands of requests per second, in most cases they don't even need to be processed in real-time. I'd like to see some layer build above GeoCouch, so CouchDB can even be used for analytics (which is a thing I wanted to have right from the start).

This means that GeoCouch will be mainly for high performance and massive sized projects that need some simple spatial bits, what I think the majority of users need.

If you either think you really need only those simple queries, but you want them to be fast, or you think this is wrong, that you need dynamic reprojection I can only invite you to leave a comment below or drop a mail to volker.mische@gmail.com. Thanks.

• The raw slides as PDF (licensed under CC-BY-3.0-de).
• The slides with comments as HTML (licensed under CC-BY-3.0-de).
• The slides with audio (or at blib.tv). It’s the recording of the actual talk at the conference. Thanks Alex and FOSSLC for recording it (licensed under CC-BY-SA-3.0).

What is a tilecache

Everyone knows Google Maps and its small images, called tiles. Rendering those tiles for the whole world for every zoom level can be quite time consuming, therefore you can render them on demand and cache them once they are rendered. This is the business of a tilecache.

You can use the tilecache as a proxy to a remote tile server as well, that's what I did for this benchmark.

Coding

The implementation looks quite similar to the memcache one. I haven't implemented locking as I was just after something working, not a full-fledged backend.

When I finished coding, it was time to find out how it performs. That should be easy, as there's a tilecache_seeding script bundled with TileCache to fill the cache. So you fill the cache, then you switch the remote server off and test how long it takes if all requests are hits without any fails (i.e. all tiles are in your cache and don't need to be requested from a remote server).

The two contestants for the benchmark are the CouchDB backend and the one that stores the tiles directly on the filesystem.

Everyone loves numbers

We keep it simple and measure the time for seeding with time. How long will it take to request 780 tiles? The first number is the average (in seconds), the one in brackets the standard deviation.

• Filesystem:

  real 0.35 (0.04)
  user 0.16 (0.02)
  sys  0.05 (0.01)
  

• CouchDB:

  real 3.03 (0.18)
  user 0.96 (0.05)
  sys  0.21 (0.03)
  

Let's say CouchDB is 10 times slower that the file system based cache. Wow, CouchDB really sucks! Why would you use it as tile storage? Although you could:

• easily store metadata with every tile, like a date when it should expire.
• keep a history of tiles and show them as "travel through time layers" in your mapping application
• easy replication to other servers

You just don't want such a slow hog. And those CouchDB people try to tell me that CouchDB would be fast. Pha!

Really??

You might already wonder, where the details are, the software version numbers, the specification of the system and all that stuff? These things are missing with a good reason. This benchmark just isn't right, even if I would add these details. The problem lies some layers deeper.

This benchmark is way to far away from a real-life usage. You would request much more tiles and not the same 780 ones with every run. When I was benchmarking the filesystem cache, all tiles were already in the system's cache, therefore it was that fast.

Simple solution: clear the system cache and run the tests again. Here are the results after as echo 3 > /proc/sys/vm/drop_caches

• Filesystem:

  real 8.36 (0.71)
  user 0.29 (0.04)
  sys  0.18 (0.03)
  

• CouchDB:

  real 6.64 (0.15)
  user 1.13 (0.07)
  sys  0.29 (0.06)