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Diffstat (limited to 'collectors/python.d.plugin/zscores/README.md')
| -rw-r--r-- | collectors/python.d.plugin/zscores/README.md | 36 |
1 files changed, 23 insertions, 13 deletions
diff --git a/collectors/python.d.plugin/zscores/README.md b/collectors/python.d.plugin/zscores/README.md index d89aa6a0..dcb685c9 100644 --- a/collectors/python.d.plugin/zscores/README.md +++ b/collectors/python.d.plugin/zscores/README.md @@ -1,16 +1,6 @@ -<!-- -title: "zscores" -description: "Use statistical anomaly detection to narrow your focus and shorten root cause analysis." -custom_edit_url: "https://github.com/netdata/netdata/edit/master/collectors/python.d.plugin/zscores/README.md" -sidebar_label: "zscores" -learn_status: "Published" -learn_topic_type: "References" -learn_rel_path: "References/Collectors references/Uncategorized" ---> +# Basic anomaly detection using Z-scores -# Z-Scores - basic anomaly detection for your key metrics and charts - -Smoothed, rolling [Z-Scores](https://en.wikipedia.org/wiki/Standard_score) for selected metrics or charts. +By using smoothed, rolling [Z-Scores](https://en.wikipedia.org/wiki/Standard_score) for selected metrics or charts you can narrow down your focus and shorten root cause analysis. This collector uses the [Netdata rest api](https://github.com/netdata/netdata/blob/master/web/api/README.md) to get the `mean` and `stddev` for each dimension on specified charts over a time range (defined by `train_secs` and `offset_secs`). For each dimension @@ -87,7 +77,7 @@ the `zscores.conf` files alone to begin with. Then you can return to it later if more once the collector is running for a while. Edit the `python.d/zscores.conf` configuration file using `edit-config` from the your -agent's [config directory](https://learn.netdata.cloud/guides/step-by-step/step-04#find-your-netdataconf-file), which is +agent's [config directory](https://github.com/netdata/netdata/blob/master/docs/configure/nodes.md#the-netdata-config-directory), which is usually at `/etc/netdata`. ```bash @@ -146,3 +136,23 @@ per_chart_agg: 'mean' # 'absmax' will take the max absolute value across all dim - If you activate this collector on a fresh node, it might take a little while to build up enough data to calculate a proper zscore. So until you actually have `train_secs` of available data the mean and stddev calculated will be subject to more noise. +### Troubleshooting + +To troubleshoot issues with the `zscores` module, run the `python.d.plugin` with the debug option enabled. The +output will give you the output of the data collection job or error messages on why the collector isn't working. + +First, navigate to your plugins directory, usually they are located under `/usr/libexec/netdata/plugins.d/`. If that's +not the case on your system, open `netdata.conf` and look for the setting `plugins directory`. Once you're in the +plugin's directory, switch to the `netdata` user. + +```bash +cd /usr/libexec/netdata/plugins.d/ +sudo su -s /bin/bash netdata +``` + +Now you can manually run the `zscores` module in debug mode: + +```bash +./python.d.plugin zscores debug trace +``` + |