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GENI Monitoring Alerts

The GENI monitoring alerts system is based on the detection of events based on metric data that polled from remote systems. Raw data is published to a queueing system, which allows multiple complex event queries to operate on the same data stream in parallel. Output of complex queries can generate Nagios alerts, log results to a database, or both.

Poll to raw metric stream

As part of the polling process raw data is both recorded in a database and pushed to a queue. The queue serves as a fanout interface for a one-to-many raw metric subscription service.*

In the previous figure P represents our polling agent, which publishes data to a queue exchange represented by X. Clients, designated as C1 and C2, subscribe to exchanges by binding their own queues to exchanges. In the example, data published by P is replicated by X to client queues amq.gen-RQ6.. for client C1 and amq.gen-As8... for client C2.

Stream query of metric stream

The publish/subscribe queuing system allows streams of raw metric data to be replicated between many processes in parallel. This allows us to instantiate one or more complex event processing engines CEPE per replicated data stream and one or more queries inside of each CEPE. We make use of the Esper CEPE.

Esper complex event processing engine

Esper allows us to analyze large volumes of incoming messages or events, regardless of whether incoming messages are historical or real-time in nature. Esper filters and analyzes events in various ways, and respond to conditions of interest. An example of the Esper CEPE architecture is shown in the figure below.

Simply, CEPE queries are pattern-based (matching) subscriptions describing a possible future event. If the described event occurs, a described output is emitted from the CEPE.

Esper Queries

In a typical database we query existing data based on some declarative language. We can think of and Esper query like an upside down SQL, where if events occur in the future, results will be emitted. The Using the ESPER query language, EPL (similar to SQL) complex events can are described.

Consider the following EPL query:

select count(*) from MyEvent(somefield = 10).win:time(3 min) having count(*) >= 5
  • There exist a stream of events named MyEvent.
  • In the MyEvent stream there are events that contain a field named: somefield
  • In a 3 minute window, if somefield = 10 five or more times, emit data.

Just as traditional relational databases, and their related SQL queries, use specific data type operations based on column data types, data streams processed by Esper are defined by strongly typed object classes. In the previous EPL query the somefield field would have to defined as a numeric time in order for mathematical comparison to work.

For GENI Monitoring alerts, we use the LogTick class shown in the code block below:

public static class LogTick 
        String urn;
        String metric;
        long ts;
        double value;
        public LogTick(String urn, String metric, long ts, double value) 
            this.urn = urn;
            this.metric = metric;
            this.ts = ts;
            this.value = value;
        public String getUrn() {return urn;}
        public String getMetric() {return metric;}
        public long getTs() {return ts;}
        public double getValue() {return value;}
        public String toString() 
        	return "urn:" + urn + " metric:" + metric + " timestamp:" + ts + " value:" + value;

*Image from RabbitMQ tutorial *Image from Esper

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