config.yaml.sample

postgresql:
  host: localhost
  port: 5432
  # multiple databases can be specified, and these are iterated
  # through to balance query loads. This is useful when connecting to
  # pgbouncer, which can dispatch to different back end databases
  # based on the name.
  dbnames: [osm]
  user: osm
  password:
queries:
  config: ../vector-datasource/queries.yaml
  template-path: ../vector-datasource/queries
  # whether to reload the jinja query templates. This should be off in
  # production.
  reload-templates: true

# format extensions to support
# buffered Mapbox Vector Tiles are also possible by specifying mvtb
formats: [json, topojson, mvt]
# buffer configuration, see tilequeue sample config for details
buffer: {}

server:
  host: localhost
  port: 8080
  debug: true
  reload: true
  threaded: false

http:
  # whether to include cors headers on responses
  cors: true
  # client cache time in seconds ("Cache-Control: max-age" header)
  max-age: 300

# optional for health checks. This can be useful for monitoring to
# verify that the service is still running and can connect to the
# database.
health:
  # request path to listen for health checks
  url: /_health

# to cache tiles locally, enable a cache. This can be useful when
# experiencing timeouts for low or mid zoom ranges.
# cache:
#   type: <redis|file|null, default 'null'>
#   redis:
#     url: redis://localhost:6379
#     options:
#       # prefix added onto saved keys for namespace purposes
#       key_prefix: tiles
#       # time in seconds to keep key in cache
#       expires: 900
#   file:
#     prefix: directory_prefix

# to support requesting tiles for different metatile sizes
# these should get prefixed to the beginning of the url path
path_tile_size:
  # NOTE: the keys should be strings
  "256": 1
  "512": 2

# requests for zoom levels higher than this will 404
max_interesting_zoom: 20

# control how python code from yaml is used
yaml:
  # dotted name or runtime
  type: parse
  parse:
    # this will parse the yaml files and compile them on startup
    # useful when iterating on updating the yaml files themselves
    path: ../vector-datasource/yaml
  callable:
    # this will call a function that should return a mapping from
    # layer name to function that generates the output properties
    # mapping returned should look like:
    # layer-name -> function(shape, properties, feature_id)
    # useful when the output functions have already been generated
    # and it's simply a matter of pointing to them
    dotted-name: path.to.function.returning.mapping
    args:
      - 'any-args-to-pass'