Publish Metrics




Installing StatsD Node

 - Linux

 - OSX

 - Windows

Configuring Services

Enabling Publish Metrics from OverOps

 - Metric Name Formats



Publishing metrics is an element of the Advant Stack feature that enables exporting OverOps data for use in Anomaly Detection, Visualization, Analytics, and Telemetry. The dynamic data exported to StatsD, enables you to use a variety of third-party tools, providing control over application data from OverOps.

OverOps supports sending metrics to third-party graphing tools via StatsD, is an open-source implementation protocol to capture, aggregate and send metrics to modern DevOps tools. It enables visualization of any combination of events within OverOps. StatsD receives data from various sources in different types of metrics according to which they are aggregated.

By default, StatsD is installed on the Collector machine and the data is sent to the chosen service from there using the StatsD protocol.


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Integrating OverOps with Graphing and Alerting Tools

To integrate with any of the graphing and alerting tools:

  1. Install StatsD node.
  2. Configure the services with which to integrate.
  3. Enable Publishing Metrics from the OverOps web application.


Installing StatsD Node

StatsD is a simple JavaScript application running inside Node.js. The StatsD node can be installed on each monitored OverOps machine with which the monitored nodes will communicate. Optionally, a central OverOps machine enables you to manage all your StatsD configurations through a centralized node. If your organization already has a StatsD server installed, you can connect directly to it and configure the host and port in the Publish Metric dialog box, described in Enabling Publish Metrics from OverOps below.


To install StatsD daemon on Linux provided by OverOps:

  1. Install Node.js and StatsD:
    sudo /opt/takipi/statsd/etc/takipi-statsd-install
  2. Configure the the services with which you want to integrate as described here.
  3. Launch StatsD:
    sudo /opt/takipi/statsd/etc/takipi-statsd-start


To install StatsD on OSX:

  1. Install Node.js using the .pkg installer.
  2. Download StatsD, or clone from GitHub.
  3. Download the Takipi config.json StatsD configuration file.
  4. Configure the services to which to send data as described here.
  5. Launch StatsD:
    node <PATH_TO_STATS.JS> <PATH_TO_CONFIG.JSON>The stats.js is located in your StatsD folder.


To install StatsD on Windows:

  1. Install Node.js using the .msi installer.
  2. Download StatsD, or clone from GitHub.
  3. Download the Takipi config.json StatsD configuration file.
  4. Configure the services to send data to as described here.
  5. Launch StatsD:
    node <path-to-stats.js> <path-to-config.json>
    The stats.js script is located in your StatsD folder.

Configuring Services

StatsD sends metrics into any graphing or alerting server application using backend extensions. Backend is a .js file that communicates with the target service, for example Graphite. The credentials required to connect with the remote service (i.e. address, API key, password..) is placed in the config.json file that is sent to StatsD as a parameter.

The config.json has pre-populated configuration samples for all the services, listed here. Uncomment the sections relevant to the services you want to use, and fill in your credentials. StatsD configuration data is machine-specific, and is protected.

When using the pre-packaged StatsD node (Linux only), all the relevant backend files are provided. When using your own StatsD node, make sure you’ve installed the appropriate StatsD backend for any of the following services:

Grafana, Datadog, InfluxDB, SplunkGraphite, Hosted Graphite, AppDynamics, AWS CloudWatch, Librato, Zabbix, Anodot, Sentry

For details on configuring these services, click here.


Enabling Publish Metrics from OverOps

When StatsD integration is enabled, collection and aggregation of metrics is fully automated. It provides access to all data displayed in OverOps, for each exception in any analysis or visualization tool, including the data from predefined Dashboard Views, Automated Root Causes, and number of active JVMs.

Note: Only services with support for StatsD protocol are able to read metrics from OverOps.

The export configuration is dynamic, which means the every change made to the OverOps Dashboard is reflected in the data sent to StatsD, providing a steady data flow.

In Views, when StatsD is configured, the metrics of every new View created is automatically sent to the defined backends through StatsD.

In the Events Stream, every event publishes its own statistics enabling the recreating and customizing the OverOps dashboard in the tool of your choice. In addition, every event publishes log-links to the event providing the Root Cause Analysis directly from the third-party graphic or alarm tool.

The JVM count sends query on how many JVMs are currently being monitored.

To publish metrics:

  1. From the OverOps dashboard, click Settings, and select Publish Metrics.

  2. In the Publish Metrics dialog box, turn on StatsD.
    The Publish Metrics dialog box is now active. Defaults can be changed if necessary. OverOps sends all the metrics recorded in the Views and Events panels to the folder defined in the Output Metric Format section.
  3. If the StatsD node is installed on a machine other than the Collector, enter the hostname or IP address and port of the StatsD server.
    The hostname and (optional) port of your StatsD server.
    - (
    The default StatsD port is 8125).
  4. Change the default folder path of the different metrics (optional).
    A default path is provided for the Views and the Events metrics as well as the JVM count.
    For more details on metric format, see Metric Name Formats
  5. When finished, click Save.

Once the Publish Metrics are set, metrics are sent from the Controller to the configured services.


Metric Name Formats

This section describes the formats of the metric name to use when publishing to StatsD. Different tools may require different patterns. 

The default patterns are listed below per metric type:

  1. Default patterns for Views metrics:
  2. Default patterns for Events metrics:
  3. Default pattern for Entry Points metrics:
  4. Default pattern for JVM count:

With tools such as InfluxDB use the pattern suggested below:

  2. Events:
  3. Entry Points:
  4. JVM Count:

The OverOps-specific variables that can be used by adding them to metric list are described below. In JVM count format, only {serviceid}, {application}, {applicationpid} and {server} may be used.

Metric Name

Used for




Entry Points
JVM Count

The name of the machine which is publishing the StatsD.

Text string 
(e.g. "prod04")


Entry Points
JVM Count

The unique numeric ID of the installation key on the machine.

(e.g. "S1234")


Entry Points
JVM Count

The process ID of the JVM to which the StatsD is related.

(e.g. "41884")


Entry Points
JVM Count

The name of the JVM to which the StatsD is related.

Text Sting 
(e.g. "worker", "my-webapp")


Entry Points

The name of the release to which the StatsD is related.

Text String
(e.g. "ver-2017/12/07")



The name of the View that is being published.

Text String
(e.g. "All Events", "DB Errors")



The name of the class to which the StatsD relates (not fully-qualified).

Text String



The name of the method to which the StatsD relates.

Text String



The unique identifier of the method to which the StatsD relates.




The type of the event to which the StatsD relates.




The name of the event  to which the StatsD relates.

Name of the exception or the event type, e.g.:



The path to the ARC screen of the event in OverOps. Add prefix according to the used 3rd party application (e.g: http://[<hostname>:8080/]<EVENTLINK>) and copy to browser.

Text String



The name of the first deployment in which the event was introduced.

Text String



The names of the labels to which the event is related.

Determined by configuration


Entry Points

The name of the entry point's class to which the StatsD metric relates (not fully-qualified).

Text String



The name of the entry point's method to which the StatsD metric relates.

Text String


Entry Points

Various entry point metrics.

entry_point_average_runtime (ms)
entry_point_total_runtime (ms)



The Jira issue key.

Custom key format


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