Metric Middleware

Crystal Metric Middleware is a middleware for OpenStack Swift that dynamically manages monitoring metrics.

Crystal Metric Middleware is an extension point of Crystal: new metrics can be added in order to provide more information to the controller. The github repository includes some metric samples like the number of GET/PUT operations, the number of active operations, the bandwidth used or the request performance.

The code below is an example of a simple metric that counts GET requests:

from crystal_metric_middleware.metrics.abstract_metric import AbstractMetric

# The metric class inherits from AbstractMetric
class GetOps(AbstractMetric):

    # This method must be implemented because is the main interception point.
    def execute(self):
        # Setting the type of the metric. The metric internal value is reset when it is
        # published (at periodic intervals)
        self.type = 'stateless'

        # Checking if it's a GET operation and an object request
        if self.method == "GET" and self._is_object_request():
            # register_metric(key, value) increments by one the current metric (GetOps)
            # for the current target tenant
            self.register_metric(self.account, 1)

        return self.response

The code below is an example of a bandwidth metric for GET object requests:

class GetBw(AbstractMetric):

    def execute(self):
        self.type = 'stateless'

        if self.method == "GET" and self._is_object_request():
            # By calling _intercept_get(), all read chunks will enter on_read method

        # If the request is intercepted with _intercept_get() it is necessary to return the response
        return self.response

    def on_read(self, chunk):
        # In this case, the metric counts the number of bytes
        mbytes = (len(chunk)/1024.0)/1024
        self.register_metric(self.account, mbytes)

The next example shows a metric that counts active PUT requests:

class PutActiveRequests(AbstractMetric):

    def execute(self):
        # The type is stateful: the internal value is never reset
        self.type = 'stateful'

        if self.method == "PUT" and self._is_object_request():
            # Incrementing the metric (a new active PUT request has been intercepted)
            self.register_metric(self.account, 1)

        return self.request

    def on_finish(self):
        # Decrementing the metric (the PUT request has just finished)
        self.register_metric(self.account, -1)

Metric classes (inheriting from AbstractMetric) must implement the execute() method and can implement the optional on_read() and on_finish() methods.

  • execute(): This method is the main interception point. If the metric needs access to the data flow or needs to know when the request has finished, it has to call self._intercept_get() or self._intercept_put().
  • on_read(): this method is called if the metric has previously called self._intercept_get() or self._intercept_put(). All read chunks will enter this method.
  • on_finish(): this method is called if the metric has previously called self._intercept_get() or self._intercept_put(). This method is called once the request has been completed.

There are three types of metrics supported:

  • stateless: the default type. When the metric is published (typically at one second intervals), the internal value is reset to 0.
  • stateful: when the metric is published, the internal value is not reset. Thus, the value can be incremented/decremented during the next intervals. In a previous example, a stateful metric is used to count the active PUT requests: whenever a request is intercepted the metric value is incremented, and when the request finishes the metric value is decremented. At periodic intervals the metric value is published, showing how many concurrent requests are being served at a given instant.
  • force: this type of metric is published directly after the call to register_metric instead of being published at intervals.

Metric classes must be registered and enabled through Crystal controller API. A convenient web dashboard is also available to simplify these API calls.