![]() In most cases, logs end up being used for auditing and, from a monitoring perspective, fault diagnosis. Some consider logging to be a subset of events and in many ways that definition fits. Logging is pretty much a staple of any monitoring system. The disadvantage is related to the volume of data being “pushed.” It too must be optimized as not to overwhelm the network by pushing too much data. Like most things, there are some drawbacks and push-based monitoring is no different. Another advantage to push-based monitoring is the communication channel is unidirectional as the emitters do not listen for remote connections which in turn reduces the complexity of the security model for the network. This approach typically favors a decentralized structure and has the advantage of not requiring the monitoring system to “pre-register” the monitored component as the emitter pushes data to the configured destination upon start. Push-based monitoring works differently in that the application being monitoring becomes an emitter of data “pushing” metrics and events on some time interval or a constraint violation. One of the drawbacks to this approach is that the more applications and systems there are being monitored, the more explicit checks that need configuration. ![]() Primarily in pull-based monitoring, the system asks or queries a monitored component, for example, pinging a host, and usually emphasize availability as the primary concern. Pull/polling based solutions are quite common in monitoring and historically favor a centralized organizational structure, although this is not a requirement. Sometimes referred to as “Blackbox” and “Whitebox” monitoring, pull vs. Part of “collection” is the means or direction in which the data flows for both aggregation and reporting of which there are two styles pull and push. Upon collection, the agent does some aggregation and then pushes the results to the intended recipient. In our case, we have chosen a decentralized agent-based method for the collection of data with a local agent running on each host that instruments a given application or service. Both models have pro’s and con’s which the discussion of is beyond the scope of this document. There are two camps when it comes to collection, just as there are two ways to build organizational structure centralized and decentralized. In our sandbox solution, we use Elasticsearch built atop Apache Lucene a full-text search engine library. Also, they are commonly stored in text format which if indexed properly provides a powerful search mechanism for analytics. Each one of these events triggers as a result of some behavior that occurs within the application or system.Įvents are very useful for monitoring as they provide a behavioral footprint of how the system is performing. For example, in Lightbend Monitoring we have several different types of events such as Dead Letter events, Unhandled Message events, Circuit Breaker Open events, and Mailbox Size Limit events to name a few. Following are some common transformations for metrics:Īn event is a historical record that something has happened such as an action or behavior of a system. Examples of different types of metrics are as follows:Ī single metric in and of itself is often not that useful, but when visualized over time especially in conjunction with a mathematical transformation, metrics can give great insight into a system. The canonical example for metrics gathering is website hits, where we regularly collect various data points like the number of times someone visits a particular page and the location or source of the visitor for a given site. These observations are some value or a series of related values combined with a timestamp that describes the observations, the output of which is commonly called time-series data. Metrics make an observation by keeping track of the state of an object, through recording temporal points of data. At its core, a metric is essentially a measurement of a property of a portion of an application or system. Metrics are one of the “go-to” standards for any monitoring system of which there are a variety of different types. ![]() Metrics and events are the sources of truth from which we derive the answers to two fundamental questions: At the core of any mature monitoring systems lies the instrumentation components, metrics, and events.
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