I am happy to announce release 0.20.0 of Hawkular Metrics.This is a major release anchored by the inclusion of Hawkular Alerting in the distribution, a brand new compression algorithm, and numerous API enhancements and fixes.
Hawkular Metrics + Alerting
Hawkular Metrics now includes Hawkular Alerting out of the box; it's no longer necessary to install and manage two separate components. Hawkular Metrics delivers a single EAR file containing components from both projects. Incoming metric data is efficiently filtered and evaluated, generating alerts and events for defined triggers. The combined EAR artifact is the primary binary distribution of Hawkular Metrics going forward. For more details: HWKMETRICS-491
Hawkular Alerting Details
- Deployment Naming
- The new EAR should be deployed as 'hawkular-metrics.ear' in WildFly. This naming is required to allow for proper dependencies to be established by the EAR components.
The following snippet must be added to
standalone.xml to support the Hawkular Alerting component. Add this additional cache-container entry after the existing cache-container entries:
<cache-container name="hawkular-alerts" module="org.jboss.as.clustering.infinispan">
To use Hawkular Alerting with Hawkular Metrics there is a naming convention when defining trigger conditions. For a metric with name 'X', the alerting DataId to reference it will be '_X', where the depends on the metric's type. For example, let's consider a metric with name 'HeapUsed' and type 'gauge'. This example defines a trigger condition to test if the used heap exceeded 80%:
hm_g_HeapUsed > .8. The 'hm_g_' prefix indicates that HeapUsed is a gauge.
Because Hawkular Metrics allows the same metric name for different types, the prefix is needed to make clear the target metric. The prefixes are:
- hm_a: availability
- hm_c: counter
- hm_cr: counter rate
- hm_g: gauge
- hm_gr: gauge rate
- hm_s: string
Hawkular Metrics will now compress all the data with a compression scheme based on Facebook's Gorilla paper. A job will be run every two hours that compresses data received during the 2 hour block since last compression (there's one hour time for out-of-order writes to be written to the Cassandra). These data points are then written to a new storage format that's designed after the Gorilla compression and also compressed with the LZ4 algorithm to give even better compression ratio. Benefits include a faster reading time of larger block of datapoints and very large disk space savings. For more details: HWKMETRICS-464
Other major changes
Hawkular Metrics Clients
A big "Thank you" goes to John Sanda, Matt Wringe, Michael Burman, Joel Takvorian, Jay Shaughnessy, Lucas Ponce, and Heiko Rupp for their project contributions.
Published by Stefan Negrea on 06 October 2016