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Restrax #1074
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Restrax #1074
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Co-authored-by: Joran Angevaare <j.angevaare@nikhef.nl>
Thanks @mflierm and @cfuselli for the great reviews and excellent ideas. I've implemented all of them (unless explicitly stated otherwise). You can update the configs via (assuming you are on one of the ebs): from straxen import daq_core
db = daq_core.DataBases()
db.daq_db['restrax_config'].update_one({'name': 'restrax_config'},
{'$set': {'user': 'angevaare', 'last_modified': daq_core.now()}})
db.daq_db['restrax_config'].find_one() We could consider making a page on the website for this |
For bookkeeping: |
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Add restrax
In this PR, we change the bootstrax workflow. Instead of writing to disk directly, we will write the data twice, once using fast compressors, and once using arbitrary compressors.
We would change the workflow as follows:
Can you briefly describe how it works?
I've given a presentation here. Also see the dedicated documentation.
Current
Bootstrax rechunks certain datatypes live (i.e. in memory). As well does different levels of compression. Admix makes a 1:1 copy for rucio. In this scheme, we don’t rechunk the lowest level datatypes, those result in many files (one per chunk of live data). Therefore, we set the chunk size to 21s for background data.
Advantage:
Disadvantage:
Proposed
Bootstrax first write all datatypes to disk -> An extra process
restrax
to compresses and rechunk whatever data to whatever size. In this case we can set the chunk size to any duration (e.g. 3 seconds)Advantage:
Disadvantage:
target_size_mb
, there are no surprises here and memory usage is very predictible.Additional changes:
daq_core.py
to prevent duplication. Thereby making the way for Refactor bootstrax #479 to be implementedargs.execute
inajax
toargs.production
for consistency.Further requirements:
Restrax.get_compressor_and_size
more fancy after discussing with computing. It's current implementation should probably already improve their lives a lotblosc
(fastest) orlz4
in case of >2 GB chunks.