import os
from flask import request
from mlflow.pyfunc import scoring_server, load_model
from prometheus_flask_exporter.multiprocess import GunicornInternalPrometheusMetrics
app = scoring_server.init(load_model(os.getenv('MODEL_PATH')))
metrics = GunicornInternalPrometheusMetrics(app, defaults_prefix=os.getenv('STATSD_PREFIX'))
metrics.register_default(
metrics.counter(
'by_path_counter', 'Request count by request paths',
labels={'path': lambda: request.path}
)
)
@app.after_request
def append_request_id(response):
response.headers.add("X-REQUEST-ID", current_request_id())
return response
@app.before_request
def start_timer():
g.ogc_start_time = monotonic()
@app.after_request
def log_time_and_request_response(response):
time_taken = int((monotonic() - g.ogc_start_time) * 1000)
_LOG.info("request: %s returned status: %d and took: %d ms", request.url, response.status_code, time_taken)
return response
# Note: register your default metrics after all routes have been set up.
# Also note, that Gauge metrics registered as default will track the /metrics endpoint, and this can't be disabled at the moment.
if os.environ.get("prometheus_multiproc_dir", False):
metrics.register_default(
metrics.summary(
'flask_ows_request_full_url', 'Request summary by request url',
labels={
'query_request': lambda: request.args.get('request'),
'query_service': lambda: request.args.get('service'),
'query_layers': lambda: request.args.get('layers'),
'query_url': lambda: request.full_path
}
)
)