fluentd-plugin-juniper-telemetry

Fluentd plugin for Juniper telemetry

Installation

From gems repository

gem install fluent-plugin-juniper-telemetry

From source

git clone https://github.com/JNPRAutomate/fluent-plugin-juniper-telemetry.git
cd fluent-plugin-juniper-telemetry
rake install

Usage

This plugin include 2 parsers, one for each type of Juniper Devices data streaming type.

Juniper Telemetry Interface (jvision)

Supported devices : MX/PTX (up to 16.1) format juniper_jti

Supported Sensors

• Physical Interface > /junos/system/linecard/interface/
• Logical Interface > /junos/system/linecard/interface/logical/usage
• Firewall Filter > /junos/system/linecard/firewall/

Experimental Sensors

• LSP Statistics > /junos/services/label-switched-path/usage/

analyticsd

Supported devices : EX4300 & QFX5100 (add version info) format juniper_analyticsd

Supported devices are listed as of December 2016, please refer to Juniper website for accurate support list

Options

output_format: The format of the data send to the output plugin : structured*, statsd, flat

structured

All information are in key/value pair, the list of keys depend of the type of data send.

{
    "device":"WFD-QFX5100-48T-1",
    "type":"traffic-stats.txmcpkt",  
    "interface":"xe-0_0_3",  
    "value":838
}

Format "structured" is compatible with output_plugin for influxdb

flat

{
    "device.wfd-qfx5100-48t-2.interface.xe-0_0_1.queue.latency" : 3231155
}

Format "flat" is compatible with output_plugin for graphite

Statsd

{
    "statsd_type" : "gauge",
    "statsd_key" : "interface.et-0_0_52.type.txucpkt",
    "statsd_gauge" : 37673515243
}

Format "statsd" is compatible with output_plugin for statsd

Configuration Example

<source>
    @type udp
    tag jnpr.jti
    format juniper_jti
    port 40000
    bind 0.0.0.0
</source>

<source>
    @type udp
    tag jnpr.analyticsd
    format juniper_analyticsd
    port 40020
    bind 0.0.0.0
</source>

Full configuration example is available here

Docker container for test & development

The project include a docker container for test and development. The container is preconfigured with fluentd for all plugins.
Configuration file is available here

By default, everything is going to stdout in /var/log/fluentd.log

You first need to build the container

docker build -t fluent-plugin-juniper-telemetry .

And then you can launch it

docker run --rm -t -i fluent-plugin-juniper-telemetry /sbin/my_init -- bash -l

There are 2 scripts provided : docker.build.sh and docker.debug.sh, to simplify these steps.

Build ruby library based on proto files

The container has all tools needed to generate ruby library from proto files.
It's possible to regenerate all ruby files by executing the command below.

It will mount the project under the directory /gpb inside the container and compile all files Newly created .rb will be stored under lib/

docker run --rm -t -v $(pwd):/gpb -i fluent-plugin-juniper-telemetry /bin/sh /home/fluent/compile_protofile.sh

Packet samples for development and troubleshooting

The directory packet_examples include some capture files from different devices. These can easily be replay and send to another container for development and testing purpose using a third party container with tcpreplay

In order to be able to send these files to any container, we need to change the destition IP and MAc addresses. In a docker environment, it's possible to use the broadcast address of the internal network 172.17/16 and a generic mac address.
See instruction below on how to update these information

Replay a capture (while being in the packet_examples directory)

docker run --rm -t -v $(pwd):/data -i dgarros/tcpreplay /usr/bin/tcpreplay --intf1=eth0 --pps=100 jvision_phy_int.pcap

Rewrite destination Mac and IP addresses

docker run --rm -t -v $(pwd):/data -i dgarros/tcpreplay /usr/bin/tcprewrite --infile=jvision_phy_int.pcap --outfile=jvision_phy_int_fixed.pcap --dstipmap=10.92.71.225:172.17.255.255 --enet-dmac=01:00:05:11:00:06 --fixcsum

Build gem

make gems-build
make gems-push

Contributing

1. Fork it
2. Create your feature branch (git checkout -b my-new-feature)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin my-new-feature)
5. Create new Pull Request