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Diffstat (limited to 'collectors/python.d.plugin/zscores/README.md')
| -rw-r--r-- | collectors/python.d.plugin/zscores/README.md | 12 |
1 files changed, 6 insertions, 6 deletions
diff --git a/collectors/python.d.plugin/zscores/README.md b/collectors/python.d.plugin/zscores/README.md index 0b4472374..7fb189f6a 100644 --- a/collectors/python.d.plugin/zscores/README.md +++ b/collectors/python.d.plugin/zscores/README.md @@ -43,7 +43,7 @@ looking at first (for more background information on why 3 stddev see [here](https://en.wikipedia.org/wiki/68%E2%80%9395%E2%80%9399.7_rule#:~:text=In%20the%20empirical%20sciences%20the,99.7%25%20probability%20as%20near%20certainty.)) . -In the example below we basically took a sledge hammer to our system so its not suprising that lots of charts light up +In the example below we basically took a sledge hammer to our system so its not surprising that lots of charts light up after we run the stress command. In a more realistic setting you might just see a handful of charts with strange zscores and that could be a good indication of where to look first. @@ -101,9 +101,9 @@ information about each one and what it does. host: '127.0.0.1:19999' # What charts to pull data for - A regex like 'system\..*|' or 'system\..*|apps.cpu|apps.mem' etc. charts_regex: 'system\..*' -# length of time to base calulcations off for mean and stddev +# length of time to base calculations off for mean and stddev train_secs: 14400 # use last 4 hours to work out the mean and stddev for the zscore -# offset preceeding latest data to ignore when calculating mean and stddev +# offset preceding latest data to ignore when calculating mean and stddev offset_secs: 300 # ignore last 5 minutes of data when calculating the mean and stddev # recalculate the mean and stddev every n steps of the collector train_every_n: 900 # recalculate mean and stddev every 15 minutes @@ -114,11 +114,11 @@ z_clip: 10 # cap each zscore at 10 so as to avoid really large individual zscore # set z_abs: 'true' to make all zscores be absolute values only. z_abs: 'true' # burn in period in which to initially calculate mean and stddev on every step -burn_in: 2 # on startup of the collector continually update the mean and stddev in case any gaps or inital calculations fail to return +burn_in: 2 # on startup of the collector continually update the mean and stddev in case any gaps or initial calculations fail to return # mode can be to get a zscore 'per_dim' or 'per_chart' mode: 'per_chart' # 'per_chart' means individual dimension level smoothed zscores will be aggregated to one zscore per chart per time step # per_chart_agg is how you aggregate from dimension to chart when mode='per_chart' -per_chart_agg: 'mean' # 'absmax' will take the max absolute value accross all dimensions but will maintain the sign. 'mean' will just average. +per_chart_agg: 'mean' # 'absmax' will take the max absolute value across all dimensions but will maintain the sign. 'mean' will just average. ``` ## Notes @@ -128,7 +128,7 @@ per_chart_agg: 'mean' # 'absmax' will take the max absolute value accross all di calls to the netdata rest api to get the required data for each chart when calculating the mean and stddev. - It may take a few hours or so for the collector to 'settle' into it's typical behaviour in terms of the scores you will see in the normal running of your system. -- The zscore you see for each chart when using `mode: 'per_chart'` as actually an aggregated zscore accross all the +- The zscore you see for each chart when using `mode: 'per_chart'` as actually an aggregated zscore across all the dimensions on the underlying chart. - If you set `mode: 'per_dim'` then you will see a zscore for each dimension on each chart as opposed to one per chart. - As this collector does some calculations itself in python you may want to try it out first on a test or development |