Regenerate integrations.js (#18373)
Co-authored-by: ilyam8 <22274335+ilyam8@users.noreply.github.com>
Netdata bot committed
Aug 19, 2024 at 10:57 UTC
3b3a61396b675e87f81a0367e54ca241ecaab9be
3 files changed
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integrations/integrations.js
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@@ -19112,81 +19112,6 @@ export const integrations = [
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"edit_link": "https://github.com/netdata/netdata/blob/master/src/collectors/python.d.plugin/ceph/metadata.yaml",
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"related_resources": ""
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},
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- {
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- "meta": {
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- "plugin_name": "python.d.plugin",
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- "module_name": "changefinder",
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- "monitored_instance": {
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- "name": "python.d changefinder",
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- "link": "",
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- "categories": [
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- "data-collection.other"
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- ],
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- "icon_filename": ""
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- },
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- "related_resources": {
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- "integrations": {
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- "list": []
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- }
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- },
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- "info_provided_to_referring_integrations": {
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- "description": ""
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- },
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- "keywords": [
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- "change detection",
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- "anomaly detection",
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- "machine learning",
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- "ml"
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- ],
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- "most_popular": false
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- },
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- "overview": "# python.d changefinder\n\nPlugin: python.d.plugin\nModule: changefinder\n\n## Overview\n\nThis collector uses the Python [changefinder](https://github.com/shunsukeaihara/changefinder) library to\nperform [online](https://en.wikipedia.org/wiki/Online_machine_learning) [changepoint detection](https://en.wikipedia.org/wiki/Change_detection)\non your Netdata charts and/or dimensions.\n\n\nInstead 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).\n### Notes - It may take an hour or two (depending on your choice of `n_score_samples`) for the collector to 'settle' into it's\n typical behaviour in terms of the trained models and scores you will see in the normal running of your node. Mainly\n this is because it can take a while to build up a proper distribution of previous scores in over to convert the raw\n score returned by the ChangeFinder algorithm into a percentile based on the most recent `n_score_samples` that have\n already been produced. So when you first turn the collector on, it will have a lot of flags in the beginning and then\n should 'settle down' once it has built up enough history. This is a typical characteristic of online machine learning\n approaches which need some initial window of time before they can be useful.\n- As this collector does most of the work in Python itself, you may want to try it out first on a test or development\n system to get a sense of its performance characteristics on a node similar to where you would like to use it.\n- 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\n typical performance characteristics we saw from running this collector (with defaults) were:\n - A runtime (`netdata.runtime_changefinder`) of ~30ms.\n - Typically ~1% additional cpu usage.\n - About ~85mb of ram (`apps.mem`) being continually used by the `python.d.plugin` under default configuration.\n\n\nThis collector is supported on all platforms.\n\nThis collector supports collecting metrics from multiple instances of this integration, including remote instances.\n\n\n### Default Behavior\n\n#### Auto-Detection\n\nBy default this collector will work over all `system.*` charts.\n\n#### Limits\n\nThe default configuration for this integration does not impose any limits on data collection.\n\n#### Performance Impact\n\nThe default configuration for this integration is not expected to impose a significant performance impact on the system.\n",
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- "setup": "## Setup\n\n### Prerequisites\n\n#### Python Requirements\n\nThis collector will only work with Python 3 and requires the packages below be installed.\n\n```bash\n# become netdata user\nsudo su -s /bin/bash netdata\n# install required packages for the netdata user\npip3 install --user numpy==1.19.5 changefinder==0.03 scipy==1.5.4\n```\n\n**Note**: if you need to tell Netdata to use Python 3 then you can pass the below command in the python plugin section\nof your `netdata.conf` file.\n\n```yaml\n[ plugin:python.d ]\n # update every = 1\n command options = -ppython3\n```\n\n\n\n### Configuration\n\n#### File\n\nThe configuration file name for this integration is `python.d/changefinder.conf`.\n\n\nYou can edit the configuration file using the `edit-config` script from the\nNetdata [config directory](/docs/netdata-agent/configuration/README.md#the-netdata-config-directory).\n\n```bash\ncd /etc/netdata 2>/dev/null || cd /opt/netdata/etc/netdata\nsudo ./edit-config python.d/changefinder.conf\n```\n#### Options\n\nThere are 2 sections:\n\n* Global variables\n* One or more JOBS that can define multiple different instances to monitor.\n\nThe following options can be defined globally: priority, penalty, autodetection_retry, update_every, but can also be defined per JOB to override the global values.\n\nAdditionally, the following collapsed table contains all the options that can be configured inside a JOB definition.\n\nEvery configuration JOB starts with a `job_name` value which will appear in the dashboard, unless a `name` parameter is specified.\n\n\n{% details open=true summary=\"Config options\" %}\n| Name | Description | Default | Required |\n|:----|:-----------|:-------|:--------:|\n| charts_regex | what charts to pull data for - A regex like `system\\..*/` or `system\\..*/apps.cpu/apps.mem` etc. | system\\..* | yes |\n| 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'. | | no |\n| mode | get ChangeFinder scores 'per_dim' or 'per_chart'. | per_chart | yes |\n| cf_r | default parameters that can be passed to the changefinder library. | 0.5 | no |\n| cf_order | default parameters that can be passed to the changefinder library. | 1 | no |\n| cf_smooth | default parameters that can be passed to the changefinder library. | 15 | no |\n| cf_threshold | the percentile above which scores will be flagged. | 99 | no |\n| n_score_samples | the number of recent scores to use when calculating the percentile of the changefinder score. | 14400 | no |\n| 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) | no | no |\n\n{% /details %}\n#### Examples\n\n##### Default\n\nDefault configuration.\n\n```yaml\nlocal:\n name: 'local'\n host: '127.0.0.1:19999'\n charts_regex: 'system\\..*'\n charts_to_exclude: ''\n mode: 'per_chart'\n cf_r: 0.5\n cf_order: 1\n cf_smooth: 15\n cf_threshold: 99\n n_score_samples: 14400\n show_scores: false\n\n```\n",
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- "troubleshooting": "## Troubleshooting\n\n### Debug Mode\n\nTo troubleshoot issues with the `changefinder` collector, run the `python.d.plugin` with the debug option enabled. The output\nshould give you clues as to why the collector isn't working.\n\n- Navigate to the `plugins.d` directory, usually at `/usr/libexec/netdata/plugins.d/`. If that's not the case on\n your system, open `netdata.conf` and look for the `plugins` setting under `[directories]`.\n\n ```bash\n cd /usr/libexec/netdata/plugins.d/\n ```\n\n- Switch to the `netdata` user.\n\n ```bash\n sudo -u netdata -s\n ```\n\n- Run the `python.d.plugin` to debug the collector:\n\n ```bash\n ./python.d.plugin changefinder debug trace\n ```\n\n### Getting Logs\n\nIf you're encountering problems with the `changefinder` collector, follow these steps to retrieve logs and identify potential issues:\n\n- **Run the command** specific to your system (systemd, non-systemd, or Docker container).\n- **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.\n\n#### System with systemd\n\nUse the following command to view logs generated since the last Netdata service restart:\n\n```bash\njournalctl _SYSTEMD_INVOCATION_ID=\"$(systemctl show --value --property=InvocationID netdata)\" --namespace=netdata --grep changefinder\n```\n\n#### System without systemd\n\nLocate the collector log file, typically at `/var/log/netdata/collector.log`, and use `grep` to filter for collector's name:\n\n```bash\ngrep changefinder /var/log/netdata/collector.log\n```\n\n**Note**: This method shows logs from all restarts. Focus on the **latest entries** for troubleshooting current issues.\n\n#### Docker Container\n\nIf your Netdata runs in a Docker container named \"netdata\" (replace if different), use this command:\n\n```bash\ndocker logs netdata 2>&1 | grep changefinder\n```\n\n### Debug Mode\n\n\n\n### Log Messages\n\n\n\n",
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- "alerts": "## Alerts\n\nThere are no alerts configured by default for this integration.\n",
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- "metrics": "## Metrics\n\nMetrics grouped by *scope*.\n\nThe scope defines the instance that the metric belongs to. An instance is uniquely identified by a set of labels.\n\n\n\n### Per python.d changefinder instance\n\n\n\nThis scope has no labels.\n\nMetrics:\n\n| Metric | Dimensions | Unit |\n|:------|:----------|:----|\n| changefinder.scores | a dimension per chart | score |\n| changefinder.flags | a dimension per chart | flag |\n\n",
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- "integration_type": "collector",
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- "id": "python.d.plugin-changefinder-python.d_changefinder",
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- "edit_link": "https://github.com/netdata/netdata/blob/master/src/collectors/python.d.plugin/changefinder/metadata.yaml",
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- "related_resources": ""
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- },
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- {
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- "meta": {
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- "plugin_name": "python.d.plugin",
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- "module_name": "example",
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- "monitored_instance": {
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- "name": "Example collector",
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- "link": "/src/collectors/python.d.plugin/example/README.md",
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- "categories": [
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- "data-collection.other"
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- ],
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- "icon_filename": ""
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- },
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- "related_resources": {
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- "integrations": {
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- "list": []
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- }
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- },
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- "info_provided_to_referring_integrations": {
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- "description": ""
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- },
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- "keywords": [
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- "example",
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- "netdata",
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- "python"
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- ],
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- "most_popular": false
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- },
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- "overview": "# Example collector\n\nPlugin: python.d.plugin\nModule: example\n\n## Overview\n\nExample collector that generates some random numbers as metrics.\n\nIf you want to write your own collector, read our [writing a new Python module](/src/collectors/python.d.plugin/README.md#how-to-write-a-new-module) tutorial.\n\n\nThe `get_data()` function uses `random.randint()` to generate a random number which will be collected as a metric.\n\n\nThis collector is supported on all platforms.\n\nThis collector supports collecting metrics from multiple instances of this integration, including remote instances.\n\n\n### Default Behavior\n\n#### Auto-Detection\n\nThis integration doesn't support auto-detection.\n\n#### Limits\n\nThe default configuration for this integration does not impose any limits on data collection.\n\n#### Performance Impact\n\nThe default configuration for this integration is not expected to impose a significant performance impact on the system.\n",
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- "setup": "## Setup\n\n### Prerequisites\n\nNo action required.\n\n### Configuration\n\n#### File\n\nThe configuration file name for this integration is `python.d/example.conf`.\n\n\nYou can edit the configuration file using the `edit-config` script from the\nNetdata [config directory](/docs/netdata-agent/configuration/README.md#the-netdata-config-directory).\n\n```bash\ncd /etc/netdata 2>/dev/null || cd /opt/netdata/etc/netdata\nsudo ./edit-config python.d/example.conf\n```\n#### Options\n\nThere are 2 sections:\n\n* Global variables\n* One or more JOBS that can define multiple different instances to monitor.\n\nThe following options can be defined globally: priority, penalty, autodetection_retry, update_every, but can also be defined per JOB to override the global values.\n\nAdditionally, the following collapsed table contains all the options that can be configured inside a JOB definition.\n\nEvery configuration JOB starts with a `job_name` value which will appear in the dashboard, unless a `name` parameter is specified.\n\n\n{% details open=true summary=\"Config options\" %}\n| Name | Description | Default | Required |\n|:----|:-----------|:-------|:--------:|\n| num_lines | The number of lines to create. | 4 | no |\n| lower | The lower bound of numbers to randomly sample from. | 0 | no |\n| upper | The upper bound of numbers to randomly sample from. | 100 | no |\n| update_every | Sets the default data collection frequency. | 1 | no |\n| priority | Controls the order of charts at the netdata dashboard. | 60000 | no |\n| autodetection_retry | Sets the job re-check interval in seconds. | 0 | no |\n| penalty | Indicates whether to apply penalty to update_every in case of failures. | yes | no |\n| name | Job name. This value will overwrite the `job_name` value. JOBS with the same name are mutually exclusive. Only one of them will be allowed running at any time. This allows autodetection to try several alternatives and pick the one that works. | | no |\n\n{% /details %}\n#### Examples\n\n##### Basic\n\nA basic example configuration.\n\n```yaml\nfour_lines:\n name: \"Four Lines\"\n update_every: 1\n priority: 60000\n penalty: yes\n autodetection_retry: 0\n num_lines: 4\n lower: 0\n upper: 100\n\n```\n",
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- "troubleshooting": "## Troubleshooting\n\n### Debug Mode\n\nTo troubleshoot issues with the `example` collector, run the `python.d.plugin` with the debug option enabled. The output\nshould give you clues as to why the collector isn't working.\n\n- Navigate to the `plugins.d` directory, usually at `/usr/libexec/netdata/plugins.d/`. If that's not the case on\n your system, open `netdata.conf` and look for the `plugins` setting under `[directories]`.\n\n ```bash\n cd /usr/libexec/netdata/plugins.d/\n ```\n\n- Switch to the `netdata` user.\n\n ```bash\n sudo -u netdata -s\n ```\n\n- Run the `python.d.plugin` to debug the collector:\n\n ```bash\n ./python.d.plugin example debug trace\n ```\n\n### Getting Logs\n\nIf you're encountering problems with the `example` collector, follow these steps to retrieve logs and identify potential issues:\n\n- **Run the command** specific to your system (systemd, non-systemd, or Docker container).\n- **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.\n\n#### System with systemd\n\nUse the following command to view logs generated since the last Netdata service restart:\n\n```bash\njournalctl _SYSTEMD_INVOCATION_ID=\"$(systemctl show --value --property=InvocationID netdata)\" --namespace=netdata --grep example\n```\n\n#### System without systemd\n\nLocate the collector log file, typically at `/var/log/netdata/collector.log`, and use `grep` to filter for collector's name:\n\n```bash\ngrep example /var/log/netdata/collector.log\n```\n\n**Note**: This method shows logs from all restarts. Focus on the **latest entries** for troubleshooting current issues.\n\n#### Docker Container\n\nIf your Netdata runs in a Docker container named \"netdata\" (replace if different), use this command:\n\n```bash\ndocker logs netdata 2>&1 | grep example\n```\n\n",
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- "alerts": "## Alerts\n\nThere are no alerts configured by default for this integration.\n",
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- "metrics": "## Metrics\n\nMetrics grouped by *scope*.\n\nThe scope defines the instance that the metric belongs to. An instance is uniquely identified by a set of labels.\n\n\n\n### Per Example collector instance\n\nThese metrics refer to the entire monitored application.\n\n\nThis scope has no labels.\n\nMetrics:\n\n| Metric | Dimensions | Unit |\n|:------|:----------|:----|\n| example.random | random | number |\n\n",
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- "integration_type": "collector",
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- "id": "python.d.plugin-example-Example_collector",
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- "edit_link": "https://github.com/netdata/netdata/blob/master/src/collectors/python.d.plugin/example/metadata.yaml",
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- "related_resources": ""
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- },
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{
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"meta": {
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"plugin_name": "python.d.plugin",
integrations/integrations.json
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@@ -19110,81 +19110,6 @@
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"edit_link": "https://github.com/netdata/netdata/blob/master/src/collectors/python.d.plugin/ceph/metadata.yaml",
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"related_resources": ""
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},
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- {
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- "meta": {
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- "plugin_name": "python.d.plugin",
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- "module_name": "changefinder",
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- "monitored_instance": {
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- "name": "python.d changefinder",
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- "link": "",
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- "categories": [
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- "data-collection.other"
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- ],
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- "icon_filename": ""
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- },
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- "related_resources": {
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- "integrations": {
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- "list": []
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- }
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- },
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- "info_provided_to_referring_integrations": {
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- "description": ""
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- },
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- "keywords": [
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- "change detection",
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- "anomaly detection",
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- "machine learning",
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- "ml"
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- ],
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- "most_popular": false
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- },
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- "overview": "# python.d changefinder\n\nPlugin: python.d.plugin\nModule: changefinder\n\n## Overview\n\nThis collector uses the Python [changefinder](https://github.com/shunsukeaihara/changefinder) library to\nperform [online](https://en.wikipedia.org/wiki/Online_machine_learning) [changepoint detection](https://en.wikipedia.org/wiki/Change_detection)\non your Netdata charts and/or dimensions.\n\n\nInstead 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).\n### Notes - It may take an hour or two (depending on your choice of `n_score_samples`) for the collector to 'settle' into it's\n typical behaviour in terms of the trained models and scores you will see in the normal running of your node. Mainly\n this is because it can take a while to build up a proper distribution of previous scores in over to convert the raw\n score returned by the ChangeFinder algorithm into a percentile based on the most recent `n_score_samples` that have\n already been produced. So when you first turn the collector on, it will have a lot of flags in the beginning and then\n should 'settle down' once it has built up enough history. This is a typical characteristic of online machine learning\n approaches which need some initial window of time before they can be useful.\n- As this collector does most of the work in Python itself, you may want to try it out first on a test or development\n system to get a sense of its performance characteristics on a node similar to where you would like to use it.\n- 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\n typical performance characteristics we saw from running this collector (with defaults) were:\n - A runtime (`netdata.runtime_changefinder`) of ~30ms.\n - Typically ~1% additional cpu usage.\n - About ~85mb of ram (`apps.mem`) being continually used by the `python.d.plugin` under default configuration.\n\n\nThis collector is supported on all platforms.\n\nThis collector supports collecting metrics from multiple instances of this integration, including remote instances.\n\n\n### Default Behavior\n\n#### Auto-Detection\n\nBy default this collector will work over all `system.*` charts.\n\n#### Limits\n\nThe default configuration for this integration does not impose any limits on data collection.\n\n#### Performance Impact\n\nThe default configuration for this integration is not expected to impose a significant performance impact on the system.\n",
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- "setup": "## Setup\n\n### Prerequisites\n\n#### Python Requirements\n\nThis collector will only work with Python 3 and requires the packages below be installed.\n\n```bash\n# become netdata user\nsudo su -s /bin/bash netdata\n# install required packages for the netdata user\npip3 install --user numpy==1.19.5 changefinder==0.03 scipy==1.5.4\n```\n\n**Note**: if you need to tell Netdata to use Python 3 then you can pass the below command in the python plugin section\nof your `netdata.conf` file.\n\n```yaml\n[ plugin:python.d ]\n # update every = 1\n command options = -ppython3\n```\n\n\n\n### Configuration\n\n#### File\n\nThe configuration file name for this integration is `python.d/changefinder.conf`.\n\n\nYou can edit the configuration file using the `edit-config` script from the\nNetdata [config directory](/docs/netdata-agent/configuration/README.md#the-netdata-config-directory).\n\n```bash\ncd /etc/netdata 2>/dev/null || cd /opt/netdata/etc/netdata\nsudo ./edit-config python.d/changefinder.conf\n```\n#### Options\n\nThere are 2 sections:\n\n* Global variables\n* One or more JOBS that can define multiple different instances to monitor.\n\nThe following options can be defined globally: priority, penalty, autodetection_retry, update_every, but can also be defined per JOB to override the global values.\n\nAdditionally, the following collapsed table contains all the options that can be configured inside a JOB definition.\n\nEvery configuration JOB starts with a `job_name` value which will appear in the dashboard, unless a `name` parameter is specified.\n\n\n| Name | Description | Default | Required |\n|:----|:-----------|:-------|:--------:|\n| charts_regex | what charts to pull data for - A regex like `system\\..*/` or `system\\..*/apps.cpu/apps.mem` etc. | system\\..* | yes |\n| 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'. | | no |\n| mode | get ChangeFinder scores 'per_dim' or 'per_chart'. | per_chart | yes |\n| cf_r | default parameters that can be passed to the changefinder library. | 0.5 | no |\n| cf_order | default parameters that can be passed to the changefinder library. | 1 | no |\n| cf_smooth | default parameters that can be passed to the changefinder library. | 15 | no |\n| cf_threshold | the percentile above which scores will be flagged. | 99 | no |\n| n_score_samples | the number of recent scores to use when calculating the percentile of the changefinder score. | 14400 | no |\n| 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) | no | no |\n\n#### Examples\n\n##### Default\n\nDefault configuration.\n\n```yaml\nlocal:\n name: 'local'\n host: '127.0.0.1:19999'\n charts_regex: 'system\\..*'\n charts_to_exclude: ''\n mode: 'per_chart'\n cf_r: 0.5\n cf_order: 1\n cf_smooth: 15\n cf_threshold: 99\n n_score_samples: 14400\n show_scores: false\n\n```\n",
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- "troubleshooting": "## Troubleshooting\n\n### Debug Mode\n\nTo troubleshoot issues with the `changefinder` collector, run the `python.d.plugin` with the debug option enabled. The output\nshould give you clues as to why the collector isn't working.\n\n- Navigate to the `plugins.d` directory, usually at `/usr/libexec/netdata/plugins.d/`. If that's not the case on\n your system, open `netdata.conf` and look for the `plugins` setting under `[directories]`.\n\n ```bash\n cd /usr/libexec/netdata/plugins.d/\n ```\n\n- Switch to the `netdata` user.\n\n ```bash\n sudo -u netdata -s\n ```\n\n- Run the `python.d.plugin` to debug the collector:\n\n ```bash\n ./python.d.plugin changefinder debug trace\n ```\n\n### Getting Logs\n\nIf you're encountering problems with the `changefinder` collector, follow these steps to retrieve logs and identify potential issues:\n\n- **Run the command** specific to your system (systemd, non-systemd, or Docker container).\n- **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.\n\n#### System with systemd\n\nUse the following command to view logs generated since the last Netdata service restart:\n\n```bash\njournalctl _SYSTEMD_INVOCATION_ID=\"$(systemctl show --value --property=InvocationID netdata)\" --namespace=netdata --grep changefinder\n```\n\n#### System without systemd\n\nLocate the collector log file, typically at `/var/log/netdata/collector.log`, and use `grep` to filter for collector's name:\n\n```bash\ngrep changefinder /var/log/netdata/collector.log\n```\n\n**Note**: This method shows logs from all restarts. Focus on the **latest entries** for troubleshooting current issues.\n\n#### Docker Container\n\nIf your Netdata runs in a Docker container named \"netdata\" (replace if different), use this command:\n\n```bash\ndocker logs netdata 2>&1 | grep changefinder\n```\n\n### Debug Mode\n\n\n\n### Log Messages\n\n\n\n",
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- "alerts": "## Alerts\n\nThere are no alerts configured by default for this integration.\n",
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- "metrics": "## Metrics\n\nMetrics grouped by *scope*.\n\nThe scope defines the instance that the metric belongs to. An instance is uniquely identified by a set of labels.\n\n\n\n### Per python.d changefinder instance\n\n\n\nThis scope has no labels.\n\nMetrics:\n\n| Metric | Dimensions | Unit |\n|:------|:----------|:----|\n| changefinder.scores | a dimension per chart | score |\n| changefinder.flags | a dimension per chart | flag |\n\n",
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- "integration_type": "collector",
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- "id": "python.d.plugin-changefinder-python.d_changefinder",
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- "edit_link": "https://github.com/netdata/netdata/blob/master/src/collectors/python.d.plugin/changefinder/metadata.yaml",
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- "related_resources": ""
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- },
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- {
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- "meta": {
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- "plugin_name": "python.d.plugin",
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- "module_name": "example",
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- "monitored_instance": {
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- "name": "Example collector",
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- "link": "/src/collectors/python.d.plugin/example/README.md",
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- "categories": [
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- "data-collection.other"
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- ],
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- "icon_filename": ""
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- },
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- "related_resources": {
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- "integrations": {
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- "list": []
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- }
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- },
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- "info_provided_to_referring_integrations": {
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- "description": ""
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- },
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- "keywords": [
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- "example",
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- "netdata",
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- "python"
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- ],
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- "most_popular": false
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- },
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- "overview": "# Example collector\n\nPlugin: python.d.plugin\nModule: example\n\n## Overview\n\nExample collector that generates some random numbers as metrics.\n\nIf you want to write your own collector, read our [writing a new Python module](/src/collectors/python.d.plugin/README.md#how-to-write-a-new-module) tutorial.\n\n\nThe `get_data()` function uses `random.randint()` to generate a random number which will be collected as a metric.\n\n\nThis collector is supported on all platforms.\n\nThis collector supports collecting metrics from multiple instances of this integration, including remote instances.\n\n\n### Default Behavior\n\n#### Auto-Detection\n\nThis integration doesn't support auto-detection.\n\n#### Limits\n\nThe default configuration for this integration does not impose any limits on data collection.\n\n#### Performance Impact\n\nThe default configuration for this integration is not expected to impose a significant performance impact on the system.\n",
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- "setup": "## Setup\n\n### Prerequisites\n\nNo action required.\n\n### Configuration\n\n#### File\n\nThe configuration file name for this integration is `python.d/example.conf`.\n\n\nYou can edit the configuration file using the `edit-config` script from the\nNetdata [config directory](/docs/netdata-agent/configuration/README.md#the-netdata-config-directory).\n\n```bash\ncd /etc/netdata 2>/dev/null || cd /opt/netdata/etc/netdata\nsudo ./edit-config python.d/example.conf\n```\n#### Options\n\nThere are 2 sections:\n\n* Global variables\n* One or more JOBS that can define multiple different instances to monitor.\n\nThe following options can be defined globally: priority, penalty, autodetection_retry, update_every, but can also be defined per JOB to override the global values.\n\nAdditionally, the following collapsed table contains all the options that can be configured inside a JOB definition.\n\nEvery configuration JOB starts with a `job_name` value which will appear in the dashboard, unless a `name` parameter is specified.\n\n\n| Name | Description | Default | Required |\n|:----|:-----------|:-------|:--------:|\n| num_lines | The number of lines to create. | 4 | no |\n| lower | The lower bound of numbers to randomly sample from. | 0 | no |\n| upper | The upper bound of numbers to randomly sample from. | 100 | no |\n| update_every | Sets the default data collection frequency. | 1 | no |\n| priority | Controls the order of charts at the netdata dashboard. | 60000 | no |\n| autodetection_retry | Sets the job re-check interval in seconds. | 0 | no |\n| penalty | Indicates whether to apply penalty to update_every in case of failures. | yes | no |\n| name | Job name. This value will overwrite the `job_name` value. JOBS with the same name are mutually exclusive. Only one of them will be allowed running at any time. This allows autodetection to try several alternatives and pick the one that works. | | no |\n\n#### Examples\n\n##### Basic\n\nA basic example configuration.\n\n```yaml\nfour_lines:\n name: \"Four Lines\"\n update_every: 1\n priority: 60000\n penalty: yes\n autodetection_retry: 0\n num_lines: 4\n lower: 0\n upper: 100\n\n```\n",
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- "troubleshooting": "## Troubleshooting\n\n### Debug Mode\n\nTo troubleshoot issues with the `example` collector, run the `python.d.plugin` with the debug option enabled. The output\nshould give you clues as to why the collector isn't working.\n\n- Navigate to the `plugins.d` directory, usually at `/usr/libexec/netdata/plugins.d/`. If that's not the case on\n your system, open `netdata.conf` and look for the `plugins` setting under `[directories]`.\n\n ```bash\n cd /usr/libexec/netdata/plugins.d/\n ```\n\n- Switch to the `netdata` user.\n\n ```bash\n sudo -u netdata -s\n ```\n\n- Run the `python.d.plugin` to debug the collector:\n\n ```bash\n ./python.d.plugin example debug trace\n ```\n\n### Getting Logs\n\nIf you're encountering problems with the `example` collector, follow these steps to retrieve logs and identify potential issues:\n\n- **Run the command** specific to your system (systemd, non-systemd, or Docker container).\n- **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.\n\n#### System with systemd\n\nUse the following command to view logs generated since the last Netdata service restart:\n\n```bash\njournalctl _SYSTEMD_INVOCATION_ID=\"$(systemctl show --value --property=InvocationID netdata)\" --namespace=netdata --grep example\n```\n\n#### System without systemd\n\nLocate the collector log file, typically at `/var/log/netdata/collector.log`, and use `grep` to filter for collector's name:\n\n```bash\ngrep example /var/log/netdata/collector.log\n```\n\n**Note**: This method shows logs from all restarts. Focus on the **latest entries** for troubleshooting current issues.\n\n#### Docker Container\n\nIf your Netdata runs in a Docker container named \"netdata\" (replace if different), use this command:\n\n```bash\ndocker logs netdata 2>&1 | grep example\n```\n\n",
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- "alerts": "## Alerts\n\nThere are no alerts configured by default for this integration.\n",
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- "metrics": "## Metrics\n\nMetrics grouped by *scope*.\n\nThe scope defines the instance that the metric belongs to. An instance is uniquely identified by a set of labels.\n\n\n\n### Per Example collector instance\n\nThese metrics refer to the entire monitored application.\n\n\nThis scope has no labels.\n\nMetrics:\n\n| Metric | Dimensions | Unit |\n|:------|:----------|:----|\n| example.random | random | number |\n\n",
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- "integration_type": "collector",
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- "id": "python.d.plugin-example-Example_collector",
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- "edit_link": "https://github.com/netdata/netdata/blob/master/src/collectors/python.d.plugin/example/metadata.yaml",
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- "related_resources": ""
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- },
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{
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"meta": {
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"plugin_name": "python.d.plugin",
src/collectors/COLLECTORS.md
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@@ -887,16 +887,12 @@ If you don't see the app/service you'd like to monitor in this list:
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### Other
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-- [Example collector](https://github.com/netdata/netdata/blob/master/src/collectors/python.d.plugin/example/integrations/example_collector.md)
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-
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- [Files and directories](https://github.com/netdata/netdata/blob/master/src/go/plugin/go.d/modules/filecheck/integrations/files_and_directories.md)
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- [GitHub API rate limit](https://github.com/netdata/netdata/blob/master/src/go/plugin/go.d/modules/prometheus/integrations/github_api_rate_limit.md)
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- [GitHub repository](https://github.com/netdata/netdata/blob/master/src/go/plugin/go.d/modules/prometheus/integrations/github_repository.md)
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-- [python.d changefinder](https://github.com/netdata/netdata/blob/master/src/collectors/python.d.plugin/changefinder/integrations/python.d_changefinder.md)
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- [python.d zscores](https://github.com/netdata/netdata/blob/master/src/collectors/python.d.plugin/zscores/integrations/python.d_zscores.md)
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### Processes and System Services