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| l---------[-rw-r--r--] | collectors/python.d.plugin/changefinder/README.md | 242 | ||||
| -rw-r--r-- | collectors/python.d.plugin/changefinder/integrations/python.d_changefinder.md | 216 | ||||
| -rw-r--r-- | collectors/python.d.plugin/changefinder/metadata.yaml | 170 |
3 files changed, 368 insertions, 260 deletions
diff --git a/collectors/python.d.plugin/changefinder/README.md b/collectors/python.d.plugin/changefinder/README.md index 0e9bab887..0ca704eb1 100644..120000 --- a/collectors/python.d.plugin/changefinder/README.md +++ b/collectors/python.d.plugin/changefinder/README.md @@ -1,241 +1 @@ -<!-- -title: "Online change point detection with Netdata" -description: "Use ML-driven change point detection to narrow your focus and shorten root cause analysis." -custom_edit_url: "https://github.com/netdata/netdata/edit/master/collectors/python.d.plugin/changefinder/README.md" -sidebar_label: "changefinder" -learn_status: "Published" -learn_topic_type: "References" -learn_rel_path: "Integrations/Monitor/QoS" ---> - -# Online change point detection with Netdata - -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. - -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). - -> As this is a somewhat unique collector and involves often subjective concepts like changepoints and anomalies, we would love to hear any feedback on it from the community. Please let us know on the [community forum](https://community.netdata.cloud/t/changefinder-collector-feedback/972) or drop us a note at [analytics-ml-team@netdata.cloud](mailto:analytics-ml-team@netdata.cloud) for any and all feedback, both positive and negative. This sort of feedback is priceless to help us make complex features more useful. - -## Charts - -Two charts are available: - -### ChangeFinder Scores (`changefinder.scores`) - -This chart shows the percentile of the score that is output from the ChangeFinder library (it is turned off by default -but available with `show_scores: true`). - -A high observed score is more likely to be a valid changepoint worth exploring, even more so when multiple charts or -dimensions have high changepoint scores at the same time or very close together. - -### ChangeFinder Flags (`changefinder.flags`) - -This chart shows `1` or `0` if the latest score has a percentile value that exceeds the `cf_threshold` threshold. By -default, any scores that are in the 99th or above percentile will raise a flag on this chart. - -The raw changefinder score itself can be a little noisy and so limiting ourselves to just periods where it surpasses -the 99th percentile can help manage the "[signal to noise ratio](https://en.wikipedia.org/wiki/Signal-to-noise_ratio)" -better. - -The `cf_threshold` parameter might be one you want to play around with to tune things specifically for the workloads on -your node and the specific charts you want to monitor. For example, maybe the 95th percentile might work better for you -than the 99th percentile. - -Below is an example of the chart produced by this collector. The first 3/4 of the period looks normal in that we see a -few individual changes being picked up somewhat randomly over time. But then at around 14:59 towards the end of the -chart we see two periods with 'spikes' of multiple changes for a small period of time. This is the sort of pattern that -might be a sign something on the system that has changed sufficiently enough to merit some investigation. - - - -## Requirements - -- 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 - -Install the Python requirements above, enable the collector and restart Netdata. - -```bash -cd /etc/netdata/ -sudo ./edit-config python.d.conf -# Set `changefinder: no` to `changefinder: yes` -sudo systemctl restart netdata -``` - -The configuration for the changefinder collector defines how it will behave on your system and might take some -experimentation with over time to set it optimally for your node. Out of the box, the config comes with -some [sane defaults](https://www.netdata.cloud/blog/redefining-monitoring-netdata/) to get you started that try to -balance the flexibility and power of the ML models with the goal of being as cheap as possible in term of cost on the -node resources. - -_**Note**: If you are unsure about any of the below configuration options then it's best to just ignore all this and -leave the `changefinder.conf` file alone to begin with. Then you can return to it later if you would like to tune things -a bit more once the collector is running for a while and you have a feeling for its performance on your node._ - -Edit the `python.d/changefinder.conf` configuration file using `edit-config` from the your -agent's [config directory](https://github.com/netdata/netdata/blob/master/docs/configure/nodes.md), which is usually at `/etc/netdata`. - -```bash -cd /etc/netdata # Replace this path with your Netdata config directory, if different -sudo ./edit-config python.d/changefinder.conf -``` - -The default configuration should look something like this. Here you can see each parameter (with sane defaults) and some -information about each one and what it does. - -```yaml -# - -# JOBS (data collection sources) - -# Pull data from local Netdata node. -local: - - # A friendly name for this job. - name: 'local' - - # What host to pull data from. - 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\..*' - - # Charts to exclude, useful if you would like to exclude some specific charts. - # Note: should be a ',' separated string like 'chart.name,chart.name'. - charts_to_exclude: '' - - # Get ChangeFinder scores 'per_dim' or 'per_chart'. - mode: 'per_chart' - - # Default parameters that can be passed to the changefinder library. - cf_r: 0.5 - cf_order: 1 - cf_smooth: 15 - - # The percentile above which scores will be flagged. - cf_threshold: 99 - - # The number of recent scores to use when calculating the percentile of the changefinder score. - n_score_samples: 14400 - - # 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. - show_scores: false -``` - -## Troubleshooting - -To see any relevant log messages you can use a command like below. - -```bash -grep 'changefinder' /var/log/netdata/error.log -``` - -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 -``` - -## 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. - -## Useful links and further reading - -- [PyPi changefinder](https://pypi.org/project/changefinder/) reference page. -- [GitHub repo](https://github.com/shunsukeaihara/changefinder) for the changefinder library. -- Relevant academic papers: - - Yamanishi K, Takeuchi J. A unifying framework for detecting outliers and change points from nonstationary time - series data. 8th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD02. 2002: - 676. ([pdf](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.12.3469&rep=rep1&type=pdf)) - - Kawahara Y, Sugiyama M. Sequential Change-Point Detection Based on Direct Density-Ratio Estimation. SIAM - International Conference on Data Mining. 2009: - 389–400. ([pdf](https://onlinelibrary.wiley.com/doi/epdf/10.1002/sam.10124)) - - Liu S, Yamada M, Collier N, Sugiyama M. Change-point detection in time-series data by relative density-ratio - estimation. Neural Networks. Jul.2013 43:72–83. [PubMed: 23500502] ([pdf](https://arxiv.org/pdf/1203.0453.pdf)) - - T. Iwata, K. Nakamura, Y. Tokusashi, and H. Matsutani, “Accelerating Online Change-Point Detection Algorithm using - 10 GbE FPGA NIC,” Proc. International European Conference on Parallel and Distributed Computing (Euro-Par’18) - Workshops, vol.11339, pp.506–517, Aug. - 2018 ([pdf](https://www.arc.ics.keio.ac.jp/~matutani/papers/iwata_heteropar2018.pdf)) -- The [ruptures](https://github.com/deepcharles/ruptures) python package is also a good place to learn more about - changepoint detection (mostly offline as opposed to online but deals with similar concepts). -- A nice [blog post](https://techrando.com/2019/08/14/a-brief-introduction-to-change-point-detection-using-python/) - showing some of the other options and libraries for changepoint detection in Python. -- [Bayesian changepoint detection](https://github.com/hildensia/bayesian_changepoint_detection) library - we may explore - implementing a collector for this or integrating this approach into this collector at a future date if there is - interest and it proves computationaly feasible. -- You might also find the - Netdata [anomalies collector](https://github.com/netdata/netdata/tree/master/collectors/python.d.plugin/anomalies) - interesting. -- [Anomaly Detection](https://en.wikipedia.org/wiki/Anomaly_detection) wikipedia page. -- [Anomaly Detection YouTube playlist](https://www.youtube.com/playlist?list=PL6Zhl9mK2r0KxA6rB87oi4kWzoqGd5vp0) - maintained by [andrewm4894](https://github.com/andrewm4894/) from Netdata. -- [awesome-TS-anomaly-detection](https://github.com/rob-med/awesome-TS-anomaly-detection) Github list of useful tools, - libraries and resources. -- [Mendeley public group](https://www.mendeley.com/community/interesting-anomaly-detection-papers/) with some - interesting anomaly detection papers we have been reading. -- Good [blog post](https://www.anodot.com/blog/what-is-anomaly-detection/) from Anodot on time series anomaly detection. - Anodot also have some great whitepapers in this space too that some may find useful. -- Novelty and outlier detection in - the [scikit-learn documentation](https://scikit-learn.org/stable/modules/outlier_detection.html). - -### Troubleshooting - -To troubleshoot issues with the `changefinder` 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 `changefinder` module in debug mode: - -```bash -./python.d.plugin changefinder debug trace -``` - +integrations/python.d_changefinder.md
\ No newline at end of file diff --git a/collectors/python.d.plugin/changefinder/integrations/python.d_changefinder.md b/collectors/python.d.plugin/changefinder/integrations/python.d_changefinder.md new file mode 100644 index 000000000..2265d9620 --- /dev/null +++ b/collectors/python.d.plugin/changefinder/integrations/python.d_changefinder.md @@ -0,0 +1,216 @@ +<!--startmeta +custom_edit_url: "https://github.com/netdata/netdata/edit/master/collectors/python.d.plugin/changefinder/README.md" +meta_yaml: "https://github.com/netdata/netdata/edit/master/collectors/python.d.plugin/changefinder/metadata.yaml" +sidebar_label: "python.d changefinder" +learn_status: "Published" +learn_rel_path: "Data Collection/Other" +message: "DO NOT EDIT THIS FILE DIRECTLY, IT IS GENERATED BY THE COLLECTOR'S metadata.yaml FILE" +endmeta--> + +# python.d changefinder + +Plugin: python.d.plugin +Module: changefinder + +<img src="https://img.shields.io/badge/maintained%20by-Netdata-%2300ab44" /> + +## Overview + +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. + + +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. + + +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 + +By default this collector will work over all `system.*` charts. + +#### 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. + + +## 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 changefinder instance + + + +This scope has no labels. + +Metrics: + +| Metric | Dimensions | Unit | +|:------|:----------|:----| +| changefinder.scores | a dimension per chart | score | +| changefinder.flags | a dimension per chart | flag | + + + +## Alerts + +There are no alerts configured by default for this integration. + + +## Setup + +### Prerequisites + +#### Python Requirements + +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 + +The configuration file name for this integration is `python.d/changefinder.conf`. + + +You can edit the configuration file using the `edit-config` script from the +Netdata [config directory](https://github.com/netdata/netdata/blob/master/docs/configure/nodes.md#the-netdata-config-directory). + +```bash +cd /etc/netdata 2>/dev/null || cd /opt/netdata/etc/netdata +sudo ./edit-config python.d/changefinder.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. + + +<details><summary>Config options</summary> + +| Name | Description | Default | Required | +|:----|:-----------|:-------|:--------:| +| charts_regex | what charts to pull data for - A regex like `system\..*/` or `system\..*/apps.cpu/apps.mem` etc. | system\..* | True | +| charts_to_exclude | charts to exclude, useful if you would like to exclude some specific charts. note: should be a ',' separated string like 'chart.name,chart.name'. | | False | +| mode | get ChangeFinder scores 'per_dim' or 'per_chart'. | per_chart | True | +| cf_r | default parameters that can be passed to the changefinder library. | 0.5 | False | +| cf_order | default parameters that can be passed to the changefinder library. | 1 | False | +| cf_smooth | default parameters that can be passed to the changefinder library. | 15 | False | +| cf_threshold | the percentile above which scores will be flagged. | 99 | False | +| n_score_samples | the number of recent scores to use when calculating the percentile of the changefinder score. | 14400 | False | +| show_scores | 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) | False | False | + +</details> + +#### Examples + +##### Default + +Default configuration. + +```yaml +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 + +### Debug Mode + +To troubleshoot issues with the `changefinder` 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.d` directory, usually at `/usr/libexec/netdata/plugins.d/`. If that's not the case on + your system, open `netdata.conf` and look for the `plugins` setting under `[directories]`. + + ```bash + cd /usr/libexec/netdata/plugins.d/ + ``` + +- Switch to the `netdata` user. + + ```bash + sudo -u netdata -s + ``` + +- Run the `python.d.plugin` to debug the collector: + + ```bash + ./python.d.plugin changefinder debug trace + ``` + +### Debug Mode + + + +### Log Messages + + + + 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: |