Usually, TaBi processes BGP messages that are archived in MRT files. Then, in order to use it, you will then need to install a MRT parser. Its favorite companion is MaBo, but it is also compatible with CAIDA’s bgpreader. Internally, TaBi translates BGP messages into its own representation. Therefore, its is possible to implement new inputs depending on your needs.
Nicolas Vivet firstname.lastname@example.org
Guillaume Valadon email@example.com
Julie Rossi firstname.lastname@example.org
François Contat email@example.com
## Building TaBi
TaBi depends on two external Python modules. The easiest method to install them is to use virtualenv and pip.
If you use a Debian-like system you can install these dependencies using:
apt-get install python-dev python-pip python-virtualenv
Then install TaBi in a virtual environment:
virtualenv ve_tabisource ve_tabi/bin/activatepip install py-radix python-dateutilpython setup.py install
Removing TaBi and its dependencies is therefore as simple as removing the cloned repository.
Historically TaBi was designed to process MRT dump files from the collectors of the RIPE RIS.
### Grabbing MRT dumps
You will then need to retrieve some MRT dumps. Copying and pasting the following commands in a terminal will grab a full BGP view and some updates.
tabi – the command line tool
The tabi command is the legacy tool that uses TaBi to build technical indicators for the Observatory reports. It uses mabo to parse MRT dumps.
Given the name of the BGP collector, an output directory and MRT dumps using the RIS naming convention, tabi will follow the evolution of routes seen in MRT dumps (or provided with the –ases option), and detect BGP IP prefixes conflicts.
Several options can be used to control tabi behavior:
$ tabi –helpUsage: tabi [options] collector_id output_directory filenames*Options: -h, –help show this help message and exit -f, –file files content comes from mabo -p PIPE, –pipe=PIPE Read the MRT filenames used as input from this pipe -d, –disable disable checks of the filenames RIS format -j JOBS, –jobs=JOBS Number of jobs that will process the files -a ASES, –ases=ASES File containing the ASes to monitor -s, –stats Enable code profiling -m OUTPUT_MODE, –mode=OUTPUT_MODE Select the output mode: legacy, combined or live -v, –verbose Turn on verbose output -l, –log Messages are written to a log file.
Among this options, two are very interesting:
-j that forks several tabi processes to process the MRT dumps faster
-a that can be used to limit the output to a limited list of ASes
Note that the legacy output mode will likely consume all file descriptors as it creates two files per processed AS (i.e. around 100k opened files). The default is the combined output mode.
Here is an example call to tabi:
tabi -j 8 rrc01 results/ bview.20160101.0000.gz updates.20160101.0000.gz
After around 5 minutes of processing, you will find the following files in results/2016.01/:
all.defaults.json.gz that contains all default routes seen by TaBi
all.routes.json.gz that contains all routes monitored
all.hijacks.json.gz that contains all BGP prefix conflicts
## Using TaBi as a Python module
TaBi could also be used as a regular Python module in order to use it in your own tool.
The example provided in this repository enhance BGP prefix conflicts detection, with possible hijacks classification. To do so, it relies on external data sources such as RPKI ROA, route objects and other IRR objects.
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