Python库 | nubium-utils-0.1.1.tar.gz

## Monitoring The monitoring utils enable metrics to be surfaced from the kafka applications so the Prometheus server can scrape them. The Prometheus server can't dynamically figure out pod IPs and scrape the services directly, so we're using a metrics cache instead. The metrics cache is a StatefulSet with 2 services assigned to it. One service is a normal service, with a unique cluster IP address. The prometheus server scrapes this service endpoint. The other service doesn't have a cluster IP, which means that the monitoring utility can find the IP addresses of each of the backing pods, and send metrics to all of the pods. This setup gives us high-availability guarantees. The Monitoring utils are organized into two classes, `MetricsPusher` and `MetricsManager`. The `MetricsManager` is a container for all of the metrics for the app, and contains convenience methods for the 3 standardized metrics. These metrics are - `messages_consumed`: The number of messages consumed by the app - `messages_produced`: The number of messages produced by the app - `messages_errors`: The number of exceptions caught in the app (labeled by type) The `MetricsPusher` handles pushing the applications metrics to the metrics cache. It determines the list of IP addresses for all of the metrics cache pods, and sends the current metrics values for all of the metrics. ### Monitoring Setup Examples The initialization and update loop for application monitoring will differ from application to application based on their architecture. The following examples should cover the standard designs we use. #### Default Kafka Client Application A Kafka application that directly relies on interacting with Producer or Consumer clients should have it's monitoring classes set up and its pushing thread started in the main run function and passed to the loop, as follows: ```python import os from confluent_kafka import Consumer, Producer from nubium_utils.metrics import MetricsManager, MetricsPusher, start_pushing_metrics def run_function(): consumer = Consumer() producer = Producer() metrics_pusher = MetricsPusher( os.environ['HOSTNAME'], os.environ['METRICS_SERVICE_NAME'], os.environ['METRICS_SERVICE_PORT'], os.environ['METRICS_POD_PORT']) metrics_manager = MetricsManager(job=os.environ['HOSTNAME'], app=os.environ['APP_NAME'], metrics_pusher=metrics_pusher) start_pushing_metrics(metrics_manager, int(os.environ['METRICS_PUSH_RATE'])) try: while True: loop_function(consumer, producer, metrics_manager=metrics_manager) finally: consumer.close() ``` The `consume_message()` function from this library expects a metrics_manager object as an argument, so that it can increment the `messages_consumed` metric. The application itself needs to increment the `messages_produced` metric needs to be incremented as necessary by the application itself whenever a Kafka message is produced. The convenience method on the metrics_manager `inc_messages_produced()` makes this easier, since it automatically adds the necessary labels to the metric. The application also needs to be set to increment the `message_errors` metric whenever an exception is caught. An example loop function might look like this: ```python import os import logging from nubium_utils import consume_message from nubium_utils.custom_exceptions import NoMessageError def loop_function(consumer, producer, metrics_manager): try: message = consume_message(consumer, int(os.environ['POLL_TIMEOUT']), metrics_manager) outgoing_key = message.value()['email_address'] producer.produce(topic='outgoing_topic',key=outgoing_key,value=message.value()) metrics_manager.inc_messages_produced(1) except NoMessageError: pass except KeyError as error: metrics_manager.inc_message_errors(error) logging.debug('Message missing email address') ``` #### Flask Kafka Application The setup becomes a little bit different with a Flask application. The metrics_manager should be accessible through the app's configuration, so that it can be accessed in route functions. The preferred method for error monitoring is to hook into the built in flask error handling loop, using the `@app.errorhandler` decorator. Here is an example `create_app()` function ```python import flask from werkzeug.exceptions import HTTPException from .forms_producer_app import forms_producer from .util_blueprint import app_util_bp def create_app(config): """ Creates app from config and needed blueprints :param config: (Config) object used to configure the flask app :return: (flask.App) the application object """ app = flask.Flask(__name__) app.config.from_object(config) app.register_blueprint(forms_producer) app.register_blueprint(app_util_bp) @app.errorhandler(HTTPException) def handle_exception(e): """ Increment error gauge on metrics_manager before returning error message """ response = e.get_response() response.data = f'{e.code}:{e.name} - {e.description}' app.config['MONITOR'].inc_message_errors(e) return response @app.errorhandler(Exception) def unhandled_exception(error): app.logger.error(f'Unhandled exception: {error}') app.config['MONITOR'].inc_message_errors(error) return f'Unhandled exception: {error}', 500 return app ``` The route functions for produced messages should increase the `messages_produced` metric when necessary. Example: ```python @forms_producer.route('/', methods=["POST"]) @AUTH.login_required def handle_form(): """ Ingests a dynamic form from Eloqua and produces it to the topic """ values = request.json string_values = {key: str(value) for key, value in values.items()} LOGGER.debug(f'Processing form: {values}') current_app.config['PRODUCER'].produce( topic=current_app.config['TOPIC'], key=values['C_EmailAddress'], value={'form_data': string_values}, on_delivery=produce_message_callback ) current_app.config['MONITOR'].inc_messages_produced(1) return jsonify(success=True) ``` #### Faust Streams Application Monitoring for the Faust streams application is much simpler. The `FaustAppWrapper` base class in this library has builtin metrics_manager integration. It sets metrics to the values of Faust's internally tracked metrics, and increments the exception metric using Faust's error handling. The standalone metric pushing thread isn't needed here; instead, the `app.timer` decorator is used to define a pushing thread that runs asynchronously as part of the Faust App Simply import the `FaustAppWrapper` and define an app wrapper class from it with your app's agents. The metrics_manager and metrics pusher need to be initialized in the run file, like this example: ```python import os from nubium_utils import get_ssl_context, MetricsManager, MetricsPusher from nubium_utils.faust_utils import get_config from duplicates_filter.duplicates_filter_app import FaustApp from utilities.avro_utils import get_avro_client ssl_context = get_ssl_context() app_config = get_config() metrics_pusher = MetricsPusher(job=os.environ['HOSTNAME'], metrics_service_name=os.environ['METRICS_SERVICE_NAME'], metrics_service_port=os.environ['METRICS_SERVICE_PORT'], metrics_pod_port=os.environ['METRICS_POD_PORT']) metrics_manager = MetricsManager(job=metrics_pusher.job, app=app_config['id'], metrics_pusher=metrics_pusher) duplicate_filter = FaustApp( app_config, avro_client=get_avro_client(), metrics_manager=metrics_manager, metrics_pusher=metrics_pusher ) duplicate_filter.app.main() ```