Skip to content

Gobblin Metrics Performance

Jump to bottom
Issac Buenrostro edited this page Dec 10, 2015 · 9 revisions

This document explains the performance impact of using Gobblin Metrics in applications.

Generalities

These are the main resources used by Gobblin Metrics:

  • CPU time for updating metrics: scales with number of metrics and frequency of metric update
  • CPU time for metric emission and lifecycle management: scales with number of metrics and frequency of emission
  • Memory for storing metrics: scales with number of metrics and metric contexts
  • I/O for reporting metrics: scales with number of metrics and frequency of emission
  • External resources for metrics emission (e.g. HDFS space, Kafka queue space, etc.): scales with number of metrics and frequency of emission

Update Metrics Performance

Metric updates are the most common interaction with Gobblin Metrics in an application. Every time a counter is increased, a meter is marked, or entries are added to histograms and timers, an update happens.

Source | Documentation | Discussion Group

Clone this wiki locally