From a836a244a3d2bdd4da1ee2641e3e957850668cea Mon Sep 17 00:00:00 2001
From: Daniel Baumann <daniel.baumann@progress-linux.org>
Date: Mon, 8 May 2023 18:27:04 +0200
Subject: Adding upstream version 1.39.0.

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
---
 collectors/python.d.plugin/zscores/README.md | 36 ++++++++++++++++++----------
 1 file changed, 23 insertions(+), 13 deletions(-)

(limited to 'collectors/python.d.plugin/zscores/README.md')

diff --git a/collectors/python.d.plugin/zscores/README.md b/collectors/python.d.plugin/zscores/README.md
index d89aa6a0f..dcb685c98 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
+```
+
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