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InfluxDB Integration Build Status

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Reporting Metrics to InfluxDB

InfluxDB is a high performance open source database for handling time series data. It receives metrics over HTTP or UDP using its own line protocol. It's a high availability and high performance database and provides an excellent backend for monitoring.

Installation

Add the kamon-influxdb dependency to your project and ensure that it is in your classpath at runtime. Kamon's module loader will detect that the InfluxDB module is in the classpath and automatically starts it.

Getting Started

Kamon akka-spm module is currently available for Scala 2.10, 2.11 and 2.12.

Supported releases and dependencies are shown below.

kamon-influxdb status jdk scala
0.6.7 stable 1.8+ 2.10, 2.11, 2.12

To get started with SBT, simply add the following to your build.sbt file:

libraryDependencies += "io.kamon" %% "kamon-influxdb" % "0.6.7"

Configuration

At the very basic level, you will certainly want to use the kamon.influxdb.hostname and kamon.influxdb.port configuration keys to ensure your data is being sent to wherever your InfluxDB database is running. Additionally to that, you can configure the metric categories to which this module will subscribe using the kamon.influxdb.subscriptions key. By default, the following subscriptions are included:

kamon.influxdb {
  subscriptions {
    histogram       = [ "**" ]
    min-max-counter = [ "**" ]
    gauge           = [ "**" ]
    counter         = [ "**" ]
    trace           = [ "**" ]
    trace-segment   = [ "**" ]
    akka-actor      = [ "**" ]
    akka-dispatcher = [ "**" ]
    akka-router     = [ "**" ]
    system-metric   = [ "**" ]
    http-server     = [ "**" ]
  }
}

By default, the module sends data to InfluxDB through its HTTP interface. While the HTTP instance has a richer API compared to UDP, it is much slower to send data through it. It is possible to optimize the sends by setting a higher kamon.influxdb.max-packet-size, the default is 16 kB.

Using the HTTP protocol it's possible to define the database where the data is stored. You can change the value by modifying the kamon.influxdb.database configuration. With UDP this setting has to be done in the InfluxDB configuration. To switch the protocol, change the kamon.influxdb.protocol to udp.

If the server requires authentication, define the write user and password by setting the values for kamon.influxdb.authentication.user and kamon.influxdb.authentication.password. The retention policy can be defined by changing the kamon.influxdb.authentication.retention-policy. When using UDP, change these values from InfluxDB configuration.

Metric Key Generators

The kamon.influxdb.application-name affects to the measurement names stored in InfluxDB. By default, the value is kamon. The data is stored into two measurements in the selected database: kamon-counters and kamon-timers, where kamon is replaced by the chosen application-name. The measurements are sent with the following tags:

  • category: The entity's category.
  • entity: The entity's name.
  • metric: The metric name assigned in the entity recorder.
  • hostname: Uses the local host name or kamon.influxdb.hostname-override, if given.

The counter metrics store their current value to the value field. For histograms, the following fields are stored:

  • lower: The lowest value.
  • mean: The average value.
  • upper: The highest value.

Additionally, it's possible to define the percentiles to be stored to InfluxDB for every tick. To change these, modify the kamon.influxdb.percentiles configuration. Default percentiles are [50.0, 70.0, 90.0, 95.0, 99.0, 99.9]. For example, the configuration could be [50.0, 70.5, 90.0], so the following fields would be stored:

  • p50: 50th percentile
  • p70.5: 70.5th percentile
  • p90: 90th percentile

Using the InfluxDB data

InfluxDB is a fast, powerful and reliable database. The stored measurements can be queried using an SQL-like language. There are many good interfaces for visualizing the data, including Grafana and Chronograf. InfluxDB can handle huge amounts of data and scales easily with your project. It is very fast even with large datasets, resulting fast and beautiful dashboards for monitoring.

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