graphite-influxdb 0.3

Install and configure using docker

Using docker is an easy way to get graphite-api + graphite-influx up and running. See https://github.com/Dieterbe/graphite-api-influxdb-docker

Otherwise, follow instructions below. Graphite-api is the simplest to setup, though graphite-web might perform better. You can use the experimental statsd support in graphite-api to have this backend submit performance metrics (not supported with graphite-web)

Manual installation

pip install graphite_influxdb

don’t forget to run maintain_cache.py, which keeps the cache up to date in a loop

Using with graphite-api

You need the patched version from https://github.com/brutasse/graphite-api/pull/36 This adds support for caching, statsd instrumentation, and graphite-style templates

In your graphite-api config file:

finders:
  - graphite_influxdb.InfluxdbFinder
influxdb:
   host: localhost
   port: 8086
   user: graphite
   pass: graphite
   db:   graphite

Also enable the cache. memcache doesn’t seem to work well because the list of series is too big. filesystem seems to work well:

cache:
    CACHE_TYPE: 'filesystem'
    CACHE_DIR: '/tmp/graphite-api-cache'

Using with graphite-web

In graphite’s local_settings.py:

STORAGE_FINDERS = (
    'graphite_influxdb.InfluxdbFinder',
)
INFLUXDB_HOST = "localhost"
INFLUXDB_PORT = 8086
INFLUXDB_USER = "graphite"
INFLUXDB_PASS = "graphite"
INFLUXDB_DB =  "graphite"

Optional variable

In graphite-api, using parameter cheat_times: true means assume we have data since the beginning of time until now. Since we automatically yield None’s for unknown values, this doesn’t really make any difference, graphs look the same, but now we don’t query influx for the begin/end of each timeseries, which boosts performance. The default value of this is False. In graphite-web, the parameter is INFLUXDB_CHEAT_TIMES.