Heroku Labs: log-runtime-metrics
Last updated 14 November 2017
Table of Contents
This Heroku Labs
log-runtime-metrics feature adds experimental support for enabling visibility into load and memory usage for running dynos. Per-dyno stats on memory use, swap use, and load average are inserted into the app’s log stream where they can be seen via
heroku logs --tail, used for graphs or alerting via an add-on which consumes app logs, or sent to a log drain. There is no cost incurred by enabling this feature.
Features added through Heroku Labs are experimental and subject to change.
$ heroku labs:enable log-runtime-metrics Enabling log-runtime-metrics for myapp... done $ heroku restart
$ heroku labs:disable log-runtime-metrics Disabling log-runtime-metrics for myapp... done $ heroku restart
You may need to perform a deploy to fully disable the feature. You can do so with an empty commit:
git commit --allow-empty -m "Force Heroku build." followed by a git push.
How it works
The load and memory usage metrics are surfaced as system logs in the Logplex log stream. Metrics are emitted for each running dyno, at an approximate frequency of once every 20 seconds.
Runtime metrics logs have the following format:
source=web.1 dyno=heroku.2808254.d97d0ea7-cf3d-411b-b453-d2943a50b456 sample#load_avg_1m=2.46 sample#load_avg_5m=1.06 sample#load_avg_15m=0.99 source=web.1 dyno=heroku.2808254.d97d0ea7-cf3d-411b-b453-d2943a50b456 sample#memory_total=21.00MB sample#memory_rss=21.22MB sample#memory_cache=0.00MB sample#memory_swap=0.00MB sample#memory_pgpgin=348836pages sample#memory_pgpgout=343403pages
source field identifies a dyno in your dyno formation and is intended to be used by systems draining application logs. The
dyno field includes the app id and a UUID that unique identifies every distinct dyno run on the platform. Over the life of your app metrics, you will see the same
source value have many different
dyno values, related to when you deploy or restart dynos.
CPU load averages
The following fields are reported for CPU load average:
Load Average 1m (
load_avg_1m): The load average for the dyno in the last 1 minute. This reflects the number of CPU tasks that are in the ready queue (i.e. waiting to be processed). More details about how load averages are calculated can be found below.
Load Average 5m (
load_avg_5m): The load average for the dyno in the last 5 minutes. Computed in the same manner as 1m load average.
Load Average 15m (
load_avg_15m): The load average for the dyno in the last 15 minutes. Computed in the same manner as 1m load average.
Memory & swap
The following fields are reported for memory consumption and swap:
Resident Memory (
memory_rss): The portion of the dyno’s memory (megabytes) held in RAM.
Disk Cache Memory (
memory_cache): The portion of the dyno’s memory (megabytes) used as disk cache.
Swap Memory (
memory_swap): The portion of a dyno’s memory, in megabytes, stored on disk. It’s normal for an app to use a few megabytes of swap per dyno. Higher levels of swap usage though may indicate too much memory usage when compared to the dyno size. This can lead to slow response times and should be avoided.
Total Memory (
memory_total): The total memory (megabytes) being used by the dyno, equal to the sum of resident, cache, and swap memory.
Memory Quota (
memory_quota): The resident memory (
memory_rss) value (megabytes) at which an R14 is triggered. This metric is not yet available to Private Spaces users.
Pages Written to Disk (
memory_pgpgout): The cumulative total of the pages written to disk. Sudden high variations on this number can indicate short duration spikes in swap usage. The other memory related metrics are point in time snapshots and can miss short spikes.
Pages Read from Disk (
memory_pgpgin): The cumulative total of the pages read from disk. As with the previous metric, watch out for sudden variations.
Understanding load averages
The dyno manager takes the count of runnable tasks for each dyno roughly every 20 seconds. A runnable task is a process or thread that is either currently running on a CPU or is waiting for a CPU to run on, but otherwise has all the resources it needs to run. An exponentially damped moving average is computed with the count of runnable tasks from the previous 30 minutes in the following iterative algorithm:
expterm = Math.exp(-(count_of_runnable_tasks.time - avg.time) / (period)) newavg = (1 - expterm) * count_of_runnable_tasks.value + expterm * avg.value
period is either 1-, 5-, or 15-minutes (in seconds), the
count_of_runnable_tasks is an entry of the number of tasks in the queue at a given point in time, and the
avg is the previous calculated exponential load average from the last iteration.
Heroku’s dyno load calculation differs from LINUX’s load calculation as it does not include processes in uninterruptible sleep, making it similar to traditional UNIX load calculations.