tapalcatl-py
A simpler, less configurable Python port of Tapalcatl. Extracts vector tiles from metatiles stored on S3.
Why Python?
While looking at this repository about Go on AWS Lambda, I saw that the Python cold start time for Lambda was very fast and I thought I'd try writing it again in Python just for fun over the holidays.
Development
We use Pipenv to manage dependencies. To develop on this software, you'll need to get pipenv installed first. Once you have pipenv installed, you can install the dependencies:
pipenv install --dev
With the dependencies installed, you can enter the virtual environment so your dependencies are used:
pipenv shell
Configuration
The important bits of configuration are set in config.py using environment variables:
| Environment Variable Name | Description |
|---|---|
S3_BUCKET |
Specifies the S3 bucket to use when requesting metatiles. |
S3_PREFIX |
Specifies the (optional) S3 key prefix to use when requesting metatiles. Should not include trailing slash. |
METATILE_SIZE |
The metatile size used when creating the metatiles you're reading from. |
METATILE_MAX_DETAIL_ZOOM |
(Optional) The zoom of the most detailed metatiles available. If present, this can be used to satisfy requests for larger tile sizes at zooms higher than are actually present by transparently falling back to "smaller" tile sizes. |
REQUESTER_PAYS |
A boolean flag in configuration for REQUESTER_PAYS. Set it to true to use a requester pays bucket for metatiles. |
Running locally
Once you have the dependencies installed as described above, you can use the Flask command line tool to run the server locally.
FLASK_DEBUG=true FLASK_APP=server.py flask run
The FLASK_ environment variables specified before the flask run command are used to enable debug mode (FLASK_DEBUG) and to tell Flask's command line helper where the app code is (FLASK_APP). You can also include the other environment variables from the Configuration section here, too. When I run this locally to develop I run:
AWS_PROFILE=nextzen \
FLASK_DEBUG=true \
FLASK_APP=server.py \
S3_PREFIX=20171221 \
S3_BUCKET=redacted-bucket-name \
METATILE_SIZE=4 \
flask run
Deploying
This server can run in a normal WSGI environment (on Heroku, with gunicorn, etc.) but it was designed with Lambda in mind. We use Zappa to coordinate the package and deploy to Lambda. To get this to lambda, I ran:
-
Setup Zappa for your account and AWS environment:
zappa init -
Ask Zappa to deploy to your environment:
zappa deploy dev -
Once Zappa deploys your code, it will not work until you set the configuration variables mentioned in the Configuration section above. You can set those via your Zappa configuration file or on the AWS Lambda console.