Getting started¶

Installation¶

Tensor can be installed from PyPi with pip

$ pip install tensor

This will also install Twisted, protobuf and PyYAML

Or you can use the .deb package. Let the latest release from https://github.com/calston/tensor/releases/latest

$ aptitude install python-twisted python-protobuf python-yaml
$ wget https://github.com/calston/tensor/releases/download/0.3.0/tensor_0.3.0_amd64.deb
$ dpkg -i tensor_0.3.0_amd64.deb

This also gives you an init script and default config in /etc/tensor/

Creating a configuration file¶

Tensor uses a simple YAML configuration file

The first basic requirements are the Riemann server and port (defaults to localhost:5555) and the queue interval:

server: localhost
port: 5555
interval: 1.0
proto: udp

Tensors checks are Python classes (called sources) which are instantiated with the configuration block which defines them. Rather than being one-shot scripts, a source object remains in memory with its own timer which adds events to a queue. The interval defined above is the rate at which these events are rolled up into a message and sent to Riemann.

It is important then that interval is set to a value appropriate to how frequently you want to see them in Riemann, as well as the rate at which they collect metrics from the system. All interval attributes are floating point in seconds, this means you can check (and send to Riemann) at rates well below 1 second.

Using outputs¶

You can configure multiple outputs which receive a copy of every message for example

outputs:
    - output: tensor.outputs.riemann.RiemannTCP
      server: localhost
      port: 5555

If you enable multiple outputs then the server, port and proto options will go un-used and the default Riemann TCP transport won’t start.

You can configure as many outputs as you like, or create your own.

Using sources¶

To configure the basic CPU usage source add it to the sources list in the config file

sources:
    - service: cpu
      source: tensor.sources.linux.basic.CPU
      interval: 1.0
      warning: {
        cpu: "> 0.5"
      }
      critical: {
        cpu: "> 0.8"
      }

This will measure the CPU from /proc/stat every second, with a warning state if the value exceeds 50%, and a critical state if it exceeds 80%

The service attribute can be any string you like, populating the service field in Riemann. The logical expression to raise the state of the event is (eg. critical) is assigned to a key which matches the service name.

Sources may return more than one metric, in which case it will add a prefix to the service. The state expression must correspond to that as well.

For example, the Ping check returns both latency and packet loss:

service: googledns
source: tensor.sources.network.Ping
interval: 60.0
destination: 8.8.8.8
critical: {
    googledns.latency: "> 100",
    googledns.loss: "> 0"
}

This will ping 8.8.8.8 every 60 seconds and raise a critical alert for the latency metric if it exceeds 100ms, and the packet loss metric if there is any at all.

Configuration¶

Sources can contain any number of configuration attributes which vary between them. All sources take the following options though

service	mandatory	Service name after which extra metric names are appended, dot separated
interval	depends	Clock tick interval, for sources which implement a polling clock
ttl	optional	TTL for metric expiry in Riemann
hostname	optional	Hostname to tag this service with. Defaults to system FQDN but can be overriden.
tags	optional	Comma separated list of tags for metrics

State triggers¶

critical and warning matches can also be a regular expression for sources which output keys for different devices and metrics:

service: network
source: tensor.sources.linux.basic.Network
...
critical: {
    network.\w+.tx_packets: "> 1000",
}

Routing sources¶

Since multiple outputs can be added, Tensor events can be routed from sources to specific outputs or multiple outputs. By default events are routed to all outputs.

To enable routing, outputs need a unique name attribute:

outputs:
    - output: tensor.outputs.riemann.RiemannTCP
      name: riemann1
      server: riemann1.acme.com
      port: 5555

    - output: tensor.outputs.riemann.RiemannTCP
      name: riemann2
      server: riemann2.acme.com
      port: 5555

    - output: tensor.outputs.riemann.RiemannUDP
      name: riemannudp
      server: riemann1.acme.com
      port: 5555

sources:
    - service: cpu1
      source: tensor.sources.linux.basic.CPU
      interval: 1.0
      route: riemannudp

    - service: cpu2
      source: tensor.sources.linux.basic.CPU
      interval: 1.0
      route:
        - riemann1
        - riemann2

The route attribute can also accept a list of output names. The above configuration would route cpu1 metrics to the UDP output, and the cpu2 metrics to both riemann1 and riemann2 TCP outputs.

Starting Tensor¶

To start Tensor, simply use twistd to run the service and pass a config file:

twistd -n tensor -c tensor.yml

If you’re using the Debian package then an init script is included.