python.d zscores
python.d zscores
Plugin: python.d.plugin Module: zscores
Overview
By using smoothed, rolling Z-Scores for selected metrics or charts you can narrow down your focus and shorten root cause analysis.
This collector uses the Netdata rest api 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 it will calculate a Z-Score as z = (x - mean) / stddev (clipped at z_clip). Scores are then smoothed over
time (z_smooth_n) and, if mode: 'per_chart', aggregated across dimensions to a smoothed, rolling chart level Z-Score at each time step.
This collector is supported on all platforms.
This collector supports collecting metrics from multiple instances of this integration, including remote instances.
Default Behavior
Auto-Detection
This integration doesn’t support auto-detection.
Limits
The default configuration for this integration does not impose any limits on data collection.
Performance Impact
The default configuration for this integration is not expected to impose a significant performance impact on the system.
Setup
Prerequisites
Python Requirements
This collector will only work with Python 3 and requires the below packages be installed.
# become netdata user
sudo su -s /bin/bash netdata
# install required packages
pip3 install numpy pandas requests netdata-pandas==0.0.38
Configuration
File
The configuration file name for this integration is python.d/zscores.conf.
You can edit the configuration file using the edit-config script from the
Netdata config directory.
cd /etc/netdata 2>/dev/null || cd /opt/netdata/etc/netdata
sudo ./edit-config python.d/zscores.conf
Options
There are 2 sections:
- Global variables
- One or more JOBS that can define multiple different instances to monitor.
The following options can be defined globally: priority, penalty, autodetection_retry, update_every, but can also be defined per JOB to override the global values.
Additionally, the following collapsed table contains all the options that can be configured inside a JOB definition.
Every configuration JOB starts with a job_name value which will appear in the dashboard, unless a name parameter is specified.
| Name | Description | Default | Required |
|---|---|---|---|
| charts_regex | what charts to pull data for - A regex like system\..*/ or system\..*/apps.cpu/apps.mem etc. |
system..* | yes |
| train_secs | length of time (in seconds) to base calculations off for mean and stddev. | 14400 | yes |
| offset_secs | offset (in seconds) preceding latest data to ignore when calculating mean and stddev. | 300 | yes |
| train_every_n | recalculate the mean and stddev every n steps of the collector. | 900 | yes |
| z_smooth_n | smooth the z score (to reduce sensitivity to spikes) by averaging it over last n values. | 15 | yes |
| z_clip | cap absolute value of zscore (before smoothing) for better stability. | 10 | yes |
| z_abs | set z_abs: ‘true’ to make all zscores be absolute values only. | true | yes |
| burn_in | burn in period in which to initially calculate mean and stddev on every step. | 2 | yes |
| mode | mode can be to get a zscore ‘per_dim’ or ‘per_chart’. | per_chart | yes |
| per_chart_agg | per_chart_agg is how you aggregate from dimension to chart when mode=‘per_chart’. | mean | yes |
| update_every | Sets the default data collection frequency. | 5 | no |
| priority | Controls the order of charts at the netdata dashboard. | 60000 | no |
| autodetection_retry | Sets the job re-check interval in seconds. | 0 | no |
| penalty | Indicates whether to apply penalty to update_every in case of failures. | yes | no |
Examples
Default
Default configuration.
local:
name: 'local'
host: '127.0.0.1:19999'
charts_regex: 'system\..*'
charts_to_exclude: 'system.uptime'
train_secs: 14400
offset_secs: 300
train_every_n: 900
z_smooth_n: 15
z_clip: 10
z_abs: 'true'
burn_in: 2
mode: 'per_chart'
per_chart_agg: 'mean'
Metrics
Metrics grouped by scope.
The scope defines the instance that the metric belongs to. An instance is uniquely identified by a set of labels.
Per python.d zscores instance
These metrics refer to the entire monitored application.
This scope has no labels.
Metrics:
| Metric | Dimensions | Unit |
|---|---|---|
| zscores.z | a dimension per chart or dimension | z |
| zscores.3stddev | a dimension per chart or dimension | count |
Alerts
There are no alerts configured by default for this integration.
Troubleshooting
Debug Mode
To troubleshoot issues with the zscores collector, run the python.d.plugin with the debug option enabled. The output
should give you clues as to why the collector isn’t working.
-
Navigate to the
plugins.ddirectory, usually at/usr/libexec/netdata/plugins.d/. If that’s not the case on your system, opennetdata.confand look for thepluginssetting under[directories].cd /usr/libexec/netdata/plugins.d/ -
Switch to the
netdatauser.sudo -u netdata -s -
Run the
python.d.pluginto debug the collector:./python.d.plugin zscores debug trace
Getting Logs
If you’re encountering problems with the zscores collector, follow these steps to retrieve logs and identify potential issues:
- Run the command specific to your system (systemd, non-systemd, or Docker container).
- Examine the output for any warnings or error messages that might indicate issues. These messages should provide clues about the root cause of the problem.
System with systemd
Use the following command to view logs generated since the last Netdata service restart:
journalctl _SYSTEMD_INVOCATION_ID="$(systemctl show --value --property=InvocationID netdata)" --namespace=netdata --grep zscores
System without systemd
Locate the collector log file, typically at /var/log/netdata/collector.log, and use grep to filter for collector’s name:
grep zscores /var/log/netdata/collector.log
Note: This method shows logs from all restarts. Focus on the latest entries for troubleshooting current issues.
Docker Container
If your Netdata runs in a Docker container named “netdata” (replace if different), use this command:
docker logs netdata 2>&1 | grep zscores