Hi,

Got it.  Staging the queries that way probably would be faster and more
efficient.

cheers,

Kem

> Hi Kem,
>
> I am in complete agreement!   I guess I wasn't clear enough in what I
> was proposing.   I am not suggesting that we ignore the deep detection
> data in alerting at base.   What I *am* suggesting is that it may be
> more efficient to query that data for the relatively small number of
> detected transients rather than to blindly load it all into memory for
> every frame.
>
> Tim
>
> Kem Cook wrote:
>
>>Hi,
>>
>>So, we may not need all the static data, but I thought that it would be
>>value added to be able to say that a new transient occured at the
>> position
>>of a known, but non-variable object (say a distant, barely resolved
>>galaxy), or that  a new transient appeared x arcsec offset from the
>>nucleus of a rather well resolved galaxy. or that there was a transient
>> at
>>the position of a known M dwarf (perviously non-variable).
>>
>>Kem
>>
>>
>>
>>>Hi,
>>>
>>>A few thoughts following our telecon:
>>>
>>>Jacek Becla wrote:
>>>
>>>
>>>
>>>>Keywords: DataAccWG
>>>>
>>>>Hi,
>>>>
>>>>I just had a quick word with Kem, here is the summary plus
>>>>some estimates/comments I added.
>>>>
>>>>
>>>>1) What is the expected size of pre-cached archived data
>>>>at the base camp?
>>>>We are expecting to have roughly 100TB of image data,
>>>>corresponding catalog data will probably be ~10% of that
>>>>So it is ~10TB (upper bound).
>>>>
>>>>2) How much of that data will be needed on average per image?
>>>>~5GB (upper bound)
>>>>
>>>>
>>>>
>>>I think we need a combination of deep object data, which does not have
>>>time history (it is constructed from deep stacks of images over a long
>>>time span), and shallower object data which does have time history.  For
>>>the deep object data, if we assume 50 galaxies per arcmin**2, we have
>>>about 2e6 objects per image.   We need to work on the object data
>>>definition, but let's suppose that for each filter we keep three
>>>different kinds of magnitudes and their associated errors.    We will
>>>have some kind of classification, and a variety of data quality flags.
>>>There will also be detailed shape information, but we likely don't need
>>>this at base, so I ignore that here.   If each of those mags/errors is a
>>>32 bit float, the flags take 16 bits, and the classification 8 bits, and
>>>we have 5 bands (U will not go deep), then we need 2e6*5 filters*(3
>>>mags*2 quantities/mag * 4 bytes/quantity + 3 bytes for flags) = 2.7 GB.
>>>
>>>The time dependent data will basically be limited to stars at roughly
>>>25th in V.  The density on the sky is highly variable, but an average
>>>value of about 2 per arcmin**2 is probably not too far off.   This gives
>>>us roughly 1e5 stars per image on average.  I think we will need summary
>>>information for each star, plus perhaps the most recent ten measurements
>>>(in whichever bands we have them).   Again, we suffer from not having
>>>defined our object data well.   But suppose we summarize the star with
>>>10 floats per band, and then have for each time point 2 floats plus 2
>>>bytes of quality flags.   Then for each star we need 5*10*4 + 10*(2*4 +
>>>2) = 300 bytes.   The total for stars is then 0.07 GB.
>>>
>>>So, given the assumptions about what we need,  I certainly think that
>>>Kem's number is in the right ballpark.  However, I wonder if we really
>>>need to prestage all this data for every image.    After all, we are
>>>operating on difference images at base, and even granted that the
>>>detection are supplemented by some list of objects to always measure, we
>>>will be needing object information for only a tiny fraction of the stars
>>>in the image - perhaps a few percent.   So, we need to fetch info for a
>>>few * 1e3 objects from the total of 2e6 total (mostly deep) objects for
>>>which we have data.   Does this change our strategy?
>>>
>>>Tim
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>>>
>>>
>>>
>>
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