Admin Panel

Banshee is a real-time anomalies(outliers) detection system for periodic metrics. We are using it to monitor our website and rpc services intefaces, including called frequency, response time and exception calls. Our services send statistics to statsd, statsd aggregates them every 10 seconds and broadcasts the results to its backends including banshee, banshee analyzes current metrics with history data, calculates the trending and alerts us if the trending behaves anomalous. For example, we have an api named get_user, this api's response time (in milliseconds) is reported to banshee from statsd every 10 seconds: Banshee will catch the latest metric 300 and report it as an anomaly. Why don't we just set a fixed threshold instead (i.e. 200ms)? This may also works but it is boring and hard to maintain a lot of thresholds. Banshee will analyze metric trendings automatically, it will find the "thresholds" automatically. 1. Designed for periodic metrics. Reality metrics are always with periodicity, banshee only peeks metrics with the same "phase" to detect. 2. Multiple alerting rule configuration options, to alert via fixed-thresholds or via anomalous trendings. 3. Coming with anomalies visualization webapp and alerting rules admin panels. 4. Require no extra storage services, banshee handles storage on disk by itself. 1. Go >= 1.5. 2. Node and gulp. 1. Clone the repo. 2. Build binary via `make`. 3. Build static files via `make static`. Usage: Flags: See package config. In order to forward metrics to banshee from statsd, we need to add the npm module statsd-banshee to statsd's banckends: 1. Install statsd-banshee on your statsd servers: 2. Add module statsd-banshee to statsd's backends in config.js: Require bell.js v2.0+ and banshee v0.0.7+: Banshee have 4 compontents and they are running in the same process: 1. Detector is to detect incoming metrics with history data and store the results. 2. Webapp is to visualize the detection results and provides panels to manage alerting rules, projects and users. 3. Alerter is to send sms and emails once anomalies are found. 4. Cleaner is to clean outdated metrics from storage. See package alerter and alerter/exampleCommand. Via fabric(http://www.fabfile.org/): See deploy.py docs for more. Just pull the latest code: Note that the admin storage sqlite3 schema will be auto-migrated. 1. Detection algorithms, see package detector. 2. Detector input net protocol, see package detector. 3. Storage, see package storage. 4. Filter, see package filter. Reference: https://github.com/eleme/banshee/blob/master/intro.md MIT (c) eleme, inc.

Banshee is a real-time anomalies(outliers) detection system for periodic metrics. We are using it to monitor our website and rpc services intefaces, including called frequency, response time and exception calls. Our services send statistics to statsd, statsd aggregates them every 10 seconds and broadcasts the results to its backends including banshee, banshee analyzes current metrics with history data, calculates the trending and alerts us if the trending behaves anomalous. For example, we have an api named get_user, this api's response time (in milliseconds) is reported to banshee from statsd every 10 seconds: Banshee will catch the latest metric 300 and report it as an anomaly. Why don't we just set a fixed threshold instead (i.e. 200ms)? This may also works but it is boring and hard to maintain a lot of thresholds. Banshee will analyze metric trendings automatically, it will find the "thresholds" automatically. 1. Designed for periodic metrics. Reality metrics are always with periodicity, banshee only peeks metrics with the same "phase" to detect. 2. Multiple alerting rule configuration options, to alert via fixed-thresholds or via anomalous trendings. 3. Coming with anomalies visualization webapp and alerting rules admin panels. 4. Require no extra storage services, banshee handles storage on disk by itself. 1. Go >= 1.5. 2. Node and gulp. 1. Clone the repo. 2. Build binary via `make`. 3. Build static files via `make static`. Usage: Flags: See package config. In order to forward metrics to banshee from statsd, we need to add the npm module statsd-banshee to statsd's banckends: 1. Install statsd-banshee on your statsd servers: 2. Add module statsd-banshee to statsd's backends in config.js: Require bell.js v2.0+ and banshee v0.0.7+: Banshee have 4 compontents and they are running in the same process: 1. Detector is to detect incoming metrics with history data and store the results. 2. Webapp is to visualize the detection results and provides panels to manage alerting rules, projects and users. 3. Alerter is to send sms and emails once anomalies are found. 4. Cleaner is to clean outdated metrics from storage. See package alerter and alerter/exampleCommand. Via fabric(http://www.fabfile.org/): See deploy.py docs for more. Just pull the latest code: Note that the admin storage sqlite3 schema will be auto-migrated. 1. Detection algorithms, see package detector. 2. Detector input net protocol, see package detector. 3. Storage, see package storage. 4. Filter, see package filter. MIT (c) eleme, inc.

Banshee is a real-time anomalies(outliers) detection system for periodic metrics. We are using it to monitor our website and rpc services intefaces, including called frequency, response time and exception calls. Our services send statistics to statsd, statsd aggregates them every 10 seconds and broadcasts the results to its backends including banshee, banshee analyzes current metrics with history data, calculates the trending and alerts us if the trending behaves anomalous. For example, we have an api named get_user, this api's response time (in milliseconds) is reported to banshee from statsd every 10 seconds: Banshee will catch the latest metric 300 and report it as an anomaly. Why don't we just set a fixed threshold instead (i.e. 200ms)? This may also works but it is boring and hard to maintain a lot of thresholds. Banshee will analyze metric trendings automatically, it will find the "thresholds" automatically. 1. Designed for periodic metrics. Reality metrics are always with periodicity, banshee only peeks metrics with the same "phase" to detect. 2. Multiple alerting rule configuration options, to alert via fixed-thresholds or via anomalous trendings. 3. Coming with anomalies visualization webapp and alerting rules admin panels. 4. Require no extra storage services, banshee handles storage on disk by itself. 1. Go >= 1.4 and godep. 2. Node and gulp. 1. Clone the repo. 2. Build binary via `make`. 3. Build static files via `make static`. Usage: Flags: See package config. In order to forward metrics to banshee from statsd, we need to add the npm module statsd-banshee to statsd's banckends: 1. Install statsd-banshee on your statsd servers: 2. Add module statsd-banshee to statsd's backends in config.js: Require bell.js v2.0+ and banshee v0.0.7+: Banshee have 4 compontents and they are running in the same process: 1. Detector is to detect incoming metrics with history data and store the results. 2. Webapp is to visualize the detection results and provides panels to manage alerting rules, projects and users. 3. Alerter is to send sms and emails once anomalies are found. 4. Cleaner is to clean outdated metrics from storage. See package alerter and alerter/exampleCommand. 1. Detection algorithms, see package detector. 2. Detector input net protocol, see package detector. 3. Storage, see package storage. 4. Filter, see package filter. MIT (c) eleme, inc.

Banshee is a real-time anomalies(outliers) detection system for periodic metrics. We are using it to monitor our website and rpc services intefaces, including called frequency, response time and exception calls. Our services send statistics to statsd, statsd aggregates them every 10 seconds and broadcasts the results to its backends including banshee, banshee analyzes current metrics with history data, calculates the trending and alerts us if the trending behaves anomalous. For example, we have an api named get_user, this api's response time (in milliseconds) is reported to banshee from statsd every 10 seconds: Banshee will catch the latest metric 300 and report it as an anomaly. Why don't we just set a fixed threshold instead (i.e. 200ms)? This may also works but it is boring and hard to maintain a lot of thresholds. Banshee will analyze metric trendings automatically, it will find the "thresholds" automatically. 1. Designed for periodic metrics. Reality metrics are always with periodicity, banshee only peeks metrics with the same "phase" to detect. 2. Multiple alerting rule configuration options, to alert via fixed-thresholds or via anomalous trendings. 3. Coming with anomalies visualization webapp and alerting rules admin panels. 4. Require no extra storage services, banshee handles storage on disk by itself. 1. Go >= 1.5. 2. Node and gulp. 1. Clone the repo. 2. Build binary via `make`. 3. Build static files via `make static`. Usage: Flags: See package config. In order to forward metrics to banshee from statsd, we need to add the npm module statsd-banshee to statsd's banckends: 1. Install statsd-banshee on your statsd servers: 2. Add module statsd-banshee to statsd's backends in config.js: Require bell.js v2.0+ and banshee v0.0.7+: Banshee have 4 compontents and they are running in the same process: 1. Detector is to detect incoming metrics with history data and store the results. 2. Webapp is to visualize the detection results and provides panels to manage alerting rules, projects and users. 3. Alerter is to send sms and emails once anomalies are found. 4. Cleaner is to clean outdated metrics from storage. See package alerter and alerter/exampleCommand. Via fabric(http://www.fabfile.org/): See deploy.py docs for more. Just pull the latest code: Note that the admin storage sqlite3 schema will be auto-migrated. 1. Detection algorithms, see package detector. 2. Detector input net protocol, see package detector. 3. Storage, see package storage. 4. Filter, see package filter. MIT (c) eleme, inc.

Banshee is a real-time anomalies(outliers) detection system for periodic metrics. We are using it to monitor our website and rpc services intefaces, including called frequency, response time and exception calls. Our services send statistics to statsd, statsd aggregates them every 10 seconds and broadcasts the results to its backends including banshee, banshee analyzes current metrics with history data, calculates the trending and alerts us if the trending behaves anomalous. For example, we have an api named get_user, this api's response time (in milliseconds) is reported to banshee from statsd every 10 seconds: Banshee will catch the latest metric 300 and report it as an anomaly. Why don't we just set a fixed threshold instead (i.e. 200ms)? This may also works but it is boring and hard to maintain a lot of thresholds. Banshee will analyze metric trendings automatically, it will find the "thresholds" automatically. 1. Designed for periodic metrics. Reality metrics are always with periodicity, banshee only peeks metrics with the same "phase" to detect. 2. Multiple alerting rule configuration options, to alert via fixed-thresholds or via anomalous trendings. 3. Coming with anomalies visualization webapp and alerting rules admin panels. 4. Require no extra storage services, banshee handles storage on disk by itself. 1. Go >= 1.5. 2. Node and gulp. 1. Clone the repo. 2. Build binary via `make`. 3. Build static files via `make static`. Usage: Flags: See package config. In order to forward metrics to banshee from statsd, we need to add the npm module statsd-banshee to statsd's banckends: 1. Install statsd-banshee on your statsd servers: 2. Add module statsd-banshee to statsd's backends in config.js: Require bell.js v2.0+ and banshee v0.0.7+: Banshee have 4 compontents and they are running in the same process: 1. Detector is to detect incoming metrics with history data and store the results. 2. Webapp is to visualize the detection results and provides panels to manage alerting rules, projects and users. 3. Alerter is to send sms and emails once anomalies are found. 4. Cleaner is to clean outdated metrics from storage. See package alerter and alerter/exampleCommand. Via fabric(http://www.fabfile.org/): See deploy.py docs for more. Just pull the latest code: Note that the admin storage sqlite3 schema will be auto-migrated. 1. Detection algorithms, see package detector. 2. Detector input net protocol, see package detector. 3. Storage, see package storage. 4. Filter, see package filter. MIT (c) eleme, inc.

This package implements the Cart abstract data type, a data type that could be used to collect items from around a program. For example: if you want to collect some items from some goroutines, you could create a Cart and pass its pointer to the goroutines. The goroutines can keep adding items to the cart, and when you are no longer interested in collecting new items, you can harvest the cart. Once you harvest the cart, no new item can be added to the cart. To prevent unauthorized routines from harvesting a cart, method harvest () can not be called directly on a cart. This is the reason why harvesting of a cart can only be done via the admin panel of the cart. Warning! When importing this package, import a specific version of the package because the master branch of its Github repository (https://github.com/qamarian-dtp/cart) is not guaranteed to be always backward compatible with older versions. Don't know how to import a specific version, you can read up the http://gopkg.in tool.

Banshee is a real-time anomalies(outliers) detection system for periodic metrics. We are using it to monitor our website and rpc services intefaces, including called frequency, response time and exception calls. Our services send statistics to statsd, statsd aggregates them every 10 seconds and broadcasts the results to its backends including banshee, banshee analyzes current metrics with history data, calculates the trending and alerts us if the trending behaves anomalous. For example, we have an api named get_user, this api's response time (in milliseconds) is reported to banshee from statsd every 10 seconds: Banshee will catch the latest metric 300 and report it as an anomaly. Why don't we just set a fixed threshold instead (i.e. 200ms)? This may also works but it is boring and hard to maintain a lot of thresholds. Banshee will analyze metric trendings automatically, it will find the "thresholds" automatically. 1. Designed for periodic metrics. Reality metrics are always with periodicity, banshee only peeks metrics with the same "phase" to detect. 2. Multiple alerting rule configuration options, to alert via fixed-thresholds or via anomalous trendings. 3. Coming with anomalies visualization webapp and alerting rules admin panels. 4. Require no extra storage services, banshee handles storage on disk by itself. 1. Go >= 1.4 and godep. 2. Node and gulp. 1. Clone the repo. 2. Build binary via `make`. 3. Build static files via `make static`. Usage: Flags: See package config. In order to forward metrics to banshee from statsd, we need to add the npm module statsd-banshee to statsd's banckends: 1. Install statsd-banshee on your statsd servers: 2. Add module statsd-banshee to statsd's backends in config.js: Require bell.js v2.0+ and banshee v0.0.7+: Banshee have 4 compontents and they are running in the same process: 1. Detector is to detect incoming metrics with history data and store the results. 2. Webapp is to visualize the detection results and provides panels to manage alerting rules, projects and users. 3. Alerter is to send sms and emails once anomalies are found. 4. Cleaner is to clean outdated metrics from storage. See package alerter and alerter/exampleCommand. 1. Detection algorithms, see package detector. 2. Detector input net protocol, see package detector. 3. Storage, see package storage. 4. Filter, see package filter. MIT (c) eleme, inc.

This package implements the Cart abstract data type, a data type that could be used to collect items from around a program. For example: if you want to collect some items from some goroutines, you could create a Cart and pass its pointer to the goroutines. The goroutines can keep adding items to the cart, and when you are no longer interested in collecting new items, you can harvest the cart. Once you harvest the cart, no new item can be added to the cart. To prevent unauthorized routines from harvesting a cart, method harvest () can not be called directly on a cart. This is the reason why harvesting of a cart can only be done via the admin panel of the cart. Warning! When importing this package, import a specific version of the package because the master branch of its Github repository (https://github.com/qamarian-dtp/cart) is not guaranteed to be always backward compatible with older versions. Don't know how to import a specific version, you can read up the http://gopkg.in tool.