The vectortiles package

[Tags:apache, benchmark, library, test]

GIS Vector Tiles, as defined by Mapbox.

This library implements version 2.1 of the official Mapbox spec, as defined here:

Note that currently this library ignores top-level protobuf extensions, Value extensions, and UNKNOWN geometries.

The order in which to explore the modules of this library is as follows:

1. Geography.VectorTile.VectorTile (high-level types)

2. Geography.VectorTile.Geometry (typical GIS geometry types)

3. Geography.VectorTile.Protobuf (mid-level representation of parsed protobuf data with conversion functions)

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Versions 1.0.0, 1.1.0, 1.1.1, 1.2.0,,,, (info)
Change log
Dependencies base (==4.9.*), bytestring, cereal (==0.5.*), containers, deepseq (==1.4.*), protobuf (>= && <0.3), text (==1.2.*), transformers (==0.5.*), vector (>=0.11 && <0.13) [details]
License Apache-2.0
Author Colin Woodbury
Category Geography
Home page
Uploaded Fri Mar 24 15:18:02 UTC 2017 by fosskers
Distributions LTSHaskell:, NixOS:, Stackage:, Tumbleweed:
Downloads 326 total (37 in the last 30 days)
0 []
Status Docs available [build log]
Last success reported on 2017-03-24 [all 1 reports]




Maintainer's Corner

For package maintainers and hackage trustees

Readme for vectortiles

Readme for vectortiles-


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What are VectorTiles?

Invented by Mapbox, they are a combination of the ideas of finite-sized tiles and vector geometries. Mapbox maintains the official implementation spec for VectorTile codecs.

VectorTiles are advantageous over raster tiles in that:

  1. They are typically smaller to store
  2. They can be easily transformed (rotated, etc.) in real time
  3. They allow for continuous (as opposed to step-wise) zoom in Slippy Maps.

Raw VectorTile data is stored in the protobuf format. Any codec implementing the spec must decode and encode data according to this .proto schema.

What is this library?

vectortiles is a minimum viable implementation of Version 2.1 of the VectorTile spec. It aims to be a solid reference from which to implement other codecs. It exposes a small API of conversion functions between raw protobuf data and a higher-level VectorTile type that is more condusive to further processing. It also exposes fairly simplistic (yet sensible) implementations of the typical GIS Geometry types:

  • Point
  • LineString
  • Polygon

For ease of encoding and decoding, each Geometry type and its Multi counterpart (i.e. Multipoint) are considered the same thing, a Vector of that Geometry.


This library is not micro-optimized, but does leverage some "for-free" aspects of Haskell to remain usable:

  • Point is implemented as a Record Pattern Synonym to hide the fact it's just a vanilla tuple of Ints. This allows us to use the more efficient unboxed Vectors with it:
-- | Access a Point's values with the `x` and `y` functions.
type Point = (Int,Int)
pattern Point :: Int -> Int -> (Int, Int)
pattern Point{x, y} = (x, y)
  • Some types (like LineString) are implemented as a newtype for its compile-time unboxing:
import qualified Data.Vector.Unboxed as U

newtype LineString = LineString { lsPoints :: U.Vector Point }
  • All lenses are INLINEd.


You can run the benchmarks with stack bench, provided you have the stack tool. The following results are from a 2016 Lenovo ThinkPad Carbon X1 with an Intel Core i7 processor, comparing this library with a Python library of similar functionality. All benchmarking code is available in the bench directory.

Note: 1 ms = 1000 μs


| | One Point | One LineString | One Polygon | roads.mvt (40kb, 15 layers) | --- | --- | --- | --- | --- | | CPython 3.5.2 | 63 μs | 70 μs | 84 μs | 76 ms | | PyPy 5.3 | 116 μs | 210 μs | 211 μs | 12 ms | | Haskell | 3.6 μs | 5 μs | 5.8 μs | 17.1 ms

The Haskell times are measuring data evaluation to their Normal Form (fully evaluated form).

The Python class decoded to is the builtin dict class.


| | One Point | One LineString | One Polygon | roads.mvt | --- | --- | --- | --- | --- | | CPython 3.5.2 | 218 μs | 278 μs | 703 μs | N/A | | Haskell | 3.2 μs | 4.4 μs | 5 μs | 11.1 ms

Certain encoding benchmarks for Python were not possible.

Data Access (Fetching first Polygon)

| | One Polygon | roads.mvt (water layer) | --- | --- | --- | | CPython 3.5.2 | 84 μs | 78 ms | | PyPy 5.3 | 31 μs | 7.9 ms | | Haskell | 3.4 μs | 6.8 ms |

The operation being benchmarked is ByteString -> Polygon, meaning we include the decoding time to account for speed gains afforded by laziness.

  • Laziness pays off. In Haskell, just fetching some specific data field is faster than decoding the entire structure.
  • Python data fetches are fast. They are based on the dict class, so fetch operations will be as fast as dict is.
  • PyPy results are enigmatic. Python3 seems to do much better "off the block", but given time the PyPy JIT overtakes it. Fetching layer names also seems to be faster than decoding the entire object, somehow. This may be due to the JIT being clever, noticing we aren't using the rest of the structure.

Questions & Issues

Simply parsing raw protobuf data is not enough to work with VectorTiles, since the spec also defines how said data is to be interpreted once parsed. In writing a codec, there are a number of things one must consider:

Hand written schema code vs protoc use

Many languages have a "protobuf compiler" which can take a .proto file and generate schema code to access parsed data. There are PROs and CONs to taking this approach.

PROs for using a protoc-like program:

  • All accessor code is written for you
  • Update process when new official .proto is released is clearer

PROs for writing your own schema:

  • Its your code, so you have more control. Bugs are easier to chase
  • The code will likely be much shorter

In the case of two Haskell protobuf libraries which were compared, the hand-written one allowed for a 50-line schema, while the other auto-generated a 550-line one.

Extension Support

The protobuf spec leaves room for additional:

  • Value types in the key-value metadata maps
  • fields in a Layer
  • fields in a Tile
  • use of the UNKNOWN geometry type

In writing a codec, you are completely free to ignore these. However, they are permitted by the spec, and some tools may encode data using them.

Feature/Geometry Polymorphism

At the protobuf level, Features of Points, LineStrings, and Polygons are all mixed into a single list, distinguished only by a GeomType label. At a high level, you may wish to separate these specifically. This library does just that:

data Layer = Layer { _version :: Int
                   , _name :: Text
                   , _points :: V.Vector (Feature Point)
                   , _linestrings :: V.Vector (Feature LineString)
                   , _polygons :: V.Vector (Feature Polygon)
                   , _extent :: Int

As opposed to having a single field named features, which contains all features unified by some superclass / trait / generic.

Claim: Having separate accessors for each geometry type yields a "heavier" API, but gives more power, is more performant, and less complex.

Layer / Feature Coupling

Layers and Features have coupled data at the protobuf level. In order to achieve higher compression ratios, Layers contain all metadata in key/value lists to be shared across their Features, while those Features store only indices into those lists. As a result, functions converting protobuf-level Feature objects into a high-level type need to be passed those key/value lists from the parent Layer. and a more isomorphic:

feature :: Geometry g => RawFeature -> Either Text (Feature g)

is not possible.

Polygon Definition

Version 2 of the spec mainly clarified language surrounding how polygons should be decoded. This Github issue reports another "gotcha" associated with the definition of polygons.

Sanity Checks / Error Handling

The protobuf data can be malformed in a number of ways. How much sanity checking you wish to do while decoding depends on how much performance you can sacrifice. For instance, here is a constraint found in the spec regarding feature metadata:

Every key index MUST be unique within that feature such that no other attribute pair within that feature has the same key index. A feature MUST have an even number of tag fields. A feature tag field MUST NOT contain a key index or value index greater than or equal to the number of elements in the layer's keys or values set, respectively.


Decode what you encode, and encode what you decode.

Your encoding and decoding functions should be as close to isomorphisms as possible.

(0,0) is in the top-left corner.

Know your binary arithmetic.

Lists of Geometry commands/values are Z-encoded. See the zig and unzig functions in Geometry.VectorTile.Geometry.