History

Sérgio Ramos 59ca154fd1 use percentage in leak mem metrics also: move to a docker compose format that is compatible with triton		2017-03-16 16:15:07 +00:00
..
artillery	leak client code	2016-11-25 09:26:53 +00:00
prometheus	use percentage in leak mem metrics	2017-03-16 16:15:07 +00:00
scripts	use percentage in leak mem metrics	2017-03-16 16:15:07 +00:00
src	use percentage in leak mem metrics	2017-03-16 16:15:07 +00:00
static	leak client code	2016-11-25 09:26:53 +00:00
.babelrc	leak client code	2016-11-25 09:26:53 +00:00
.eslintignore	leak client code	2016-11-25 09:26:53 +00:00
.eslintrc	leak client code	2016-11-25 09:26:53 +00:00
.gitignore	leak spike initial commit	2016-11-21 13:18:12 +00:00
docker-compose.yml	use percentage in leak mem metrics	2017-03-16 16:15:07 +00:00
Dockerfile	consume prometheus API to display graphs	2016-11-25 09:26:54 +00:00
metrics.json	leak client code	2016-11-25 09:26:53 +00:00
package.json	fix leak<->prometheus integration	2017-03-06 17:15:16 +00:00
readme.md	improve readme	2016-11-25 10:34:08 +00:00
yarn.lock	fix leak<->prometheus integration	2017-03-06 17:15:16 +00:00

leak

1. Spawn a bunch of servers:
- another-fast: a node with a linear memory leak
- fast: a node with a linear memory leak
- slow: a node with a memory leak that grows very slowly
- plain: a node with no memory leak
1. Spawn an artillery for each node that loads it with a small but constant stream of requests
1. Spawn Prometheus that watches the cpu/memory of each node

Then, locally we start the same server and we can see the different instances and an aggregate of the metrics for each job.

usage

λ docker-compose up
λ node .