[atlas] Processing RIPE Atlas data as Big Data

Hi,

I assume you're referring to the daily dumps that we release here:
https://data-store.ripe.net/datasets/atlas-daily-dumps/

There are a couple of things that I find are relatively slow to deal 
with on the command line: standard bzip2 tooling, and jq for json 
parsing. So I lean on a couple of other tools to speed things up for me:

- the lbzip2 suite parallelises parts of the compress/decompress pipeline
- GNU parallel can split data in a pipe onto one process per core

So, for example, on my laptop I can reasonably quickly pull out all of 
the traceroutes my own probe ran:
lbzcat traceroute-2018-07-23T0700.bz2 | parallel -q --pipe jq '. | 
select(.prb_id == 14277)'

Stéphane has written about using jq to parse Atlas results on 
labs.ripe.net also: 
https://labs.ripe.net/Members/stephane_bortzmeyer/processing-ripe-atlas-results-with-jq

Happy to hear from others what tools they use for data processing!

Cheers,

S.



On 21/07/2018 19:09, BELLAFKIH hayat wrote:
> Dear RIPE Atlas users,
>
> I am studying the processing of the data collected by the probes as a 
> Big Data problem. For instance, one hour of traceroute data count for 
> 500 Mo (bzip2), so 7 Go of data in text format. Can you share with me 
> how you deal with these data in practice.
> are you using a super machine, Big Data tools?
>
> ​best regards,
> Hayat​
>
>

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