diff options
Diffstat (limited to 'collectors/python.d.plugin/changefinder/metadata.yaml')
| -rw-r--r-- | collectors/python.d.plugin/changefinder/metadata.yaml | 170 |
1 files changed, 151 insertions, 19 deletions
diff --git a/collectors/python.d.plugin/changefinder/metadata.yaml b/collectors/python.d.plugin/changefinder/metadata.yaml index 6dcd903e7..170d9146a 100644 --- a/collectors/python.d.plugin/changefinder/metadata.yaml +++ b/collectors/python.d.plugin/changefinder/metadata.yaml @@ -5,55 +5,187 @@ modules: module_name: changefinder monitored_instance: name: python.d changefinder - link: '' + link: "" categories: - data-collection.other - icon_filename: '' + icon_filename: "" related_resources: integrations: list: [] info_provided_to_referring_integrations: - description: '' - keywords: [] + description: "" + keywords: + - change detection + - anomaly detection + - machine learning + - ml most_popular: false overview: data_collection: - metrics_description: '' - method_description: '' + metrics_description: | + This collector uses the Python [changefinder](https://github.com/shunsukeaihara/changefinder) library to + perform [online](https://en.wikipedia.org/wiki/Online_machine_learning) [changepoint detection](https://en.wikipedia.org/wiki/Change_detection) + on your Netdata charts and/or dimensions. + method_description: > + Instead of this collector just _collecting_ data, it also does some computation on the data it collects to return a + changepoint score for each chart or dimension you configure it to work on. This is + an [online](https://en.wikipedia.org/wiki/Online_machine_learning) machine learning algorithm so there is no batch step + to train the model, instead it evolves over time as more data arrives. That makes this particular algorithm quite cheap + to compute at each step of data collection (see the notes section below for more details) and it should scale fairly + well to work on lots of charts or hosts (if running on a parent node for example). + + ### Notes + - It may take an hour or two (depending on your choice of `n_score_samples`) for the collector to 'settle' into it's + typical behaviour in terms of the trained models and scores you will see in the normal running of your node. Mainly + this is because it can take a while to build up a proper distribution of previous scores in over to convert the raw + score returned by the ChangeFinder algorithm into a percentile based on the most recent `n_score_samples` that have + already been produced. So when you first turn the collector on, it will have a lot of flags in the beginning and then + should 'settle down' once it has built up enough history. This is a typical characteristic of online machine learning + approaches which need some initial window of time before they can be useful. + - As this collector does most of the work in Python itself, you may want to try it out first on a test or development + system to get a sense of its performance characteristics on a node similar to where you would like to use it. + - On a development n1-standard-2 (2 vCPUs, 7.5 GB memory) vm running Ubuntu 18.04 LTS and not doing any work some of the + typical performance characteristics we saw from running this collector (with defaults) were: + - A runtime (`netdata.runtime_changefinder`) of ~30ms. + - Typically ~1% additional cpu usage. + - About ~85mb of ram (`apps.mem`) being continually used by the `python.d.plugin` under default configuration. supported_platforms: include: [] exclude: [] multi_instance: true additional_permissions: - description: '' + description: "" default_behavior: auto_detection: - description: '' + description: "By default this collector will work over all `system.*` charts." limits: - description: '' + description: "" performance_impact: - description: '' + description: "" setup: prerequisites: - list: [] + list: + - title: Python Requirements + description: | + This collector will only work with Python 3 and requires the packages below be installed. + + ```bash + # become netdata user + sudo su -s /bin/bash netdata + # install required packages for the netdata user + pip3 install --user numpy==1.19.5 changefinder==0.03 scipy==1.5.4 + ``` + + **Note**: if you need to tell Netdata to use Python 3 then you can pass the below command in the python plugin section + of your `netdata.conf` file. + + ```yaml + [ plugin:python.d ] + # update every = 1 + command options = -ppython3 + ``` configuration: file: - name: '' - description: '' + name: python.d/changefinder.conf + description: "" options: - description: '' + description: | + 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. folding: - title: '' + title: "Config options" enabled: true - list: [] + list: + - name: charts_regex + description: what charts to pull data for - A regex like `system\..*|` or `system\..*|apps.cpu|apps.mem` etc. + default_value: "system\\..*" + required: true + - name: charts_to_exclude + description: | + charts to exclude, useful if you would like to exclude some specific charts. + note: should be a ',' separated string like 'chart.name,chart.name'. + default_value: "" + required: false + - name: mode + description: get ChangeFinder scores 'per_dim' or 'per_chart'. + default_value: "per_chart" + required: true + - name: cf_r + description: default parameters that can be passed to the changefinder library. + default_value: 0.5 + required: false + - name: cf_order + description: default parameters that can be passed to the changefinder library. + default_value: 1 + required: false + - name: cf_smooth + description: default parameters that can be passed to the changefinder library. + default_value: 15 + required: false + - name: cf_threshold + description: the percentile above which scores will be flagged. + default_value: 99 + required: false + - name: n_score_samples + description: the number of recent scores to use when calculating the percentile of the changefinder score. + default_value: 14400 + required: false + - name: show_scores + description: | + set to true if you also want to chart the percentile scores in addition to the flags. (mainly useful for debugging or if you want to dive deeper on how the scores are evolving over time) + default_value: false + required: false examples: folding: enabled: true - title: '' - list: [] + title: "Config" + list: + - name: Default + description: Default configuration. + folding: + enabled: false + config: | + local: + name: 'local' + host: '127.0.0.1:19999' + charts_regex: 'system\..*' + charts_to_exclude: '' + mode: 'per_chart' + cf_r: 0.5 + cf_order: 1 + cf_smooth: 15 + cf_threshold: 99 + n_score_samples: 14400 + show_scores: false troubleshooting: problems: - list: [] + list: + - name: "Debug Mode" + description: | + If you would like to log in as `netdata` user and run the collector in debug mode to see more detail. + + ```bash + # become netdata user + sudo su -s /bin/bash netdata + # run collector in debug using `nolock` option if netdata is already running the collector itself. + /usr/libexec/netdata/plugins.d/python.d.plugin changefinder debug trace nolock + ``` + - name: "Log Messages" + description: | + To see any relevant log messages you can use a command like below. + + ```bash + grep 'changefinder' /var/log/netdata/error.log + grep 'changefinder' /var/log/netdata/collector.log + ``` alerts: [] metrics: folding: |