Speed? Wrt what? Db reads/writes? Db queries are vanishingly small time wise,
I've found, especially compared to the ajax POST or GET.
To answer the OP's question, for graphing I use jqplot. This way my client does
all the processing, and they can customize their plots on the fly rather than
serving a static image. There are other packages that perform similar
functions.
For the data acquisition, I just run a python daemon with sqlite databases.
Nice part here is that you can store acquisition parameters in tables and
change them as you see fit, rather than hard-coding them. This also totally
decouples acquisition and visualization.
To get data to the client there are also many options, but I use ajax/wsgi,
either with mod_wsgi on Apache or uwsgi on nginx.
C
> On Mar 30, 2015, at 10:05 AM, Stefano Miccoli <mo...@icloud.com> wrote:
>
> The only reason I can see is speed, and compatibility with (big) apps like
> cacti, nagios, or ganglia.
>
> S.
>
>> On 30 Mar 2015, at 01:00, Colin Reese <colin.re...@gmail.com> wrote:
>>
>> Out of curiosity, why would you use this? Using a database with accurate
>> time stamps and log sizing is trivial, and apis for beautiful html plots are
>> easy to come by.
>>
>> C
>>
>>
>>
>>> On Mar 29, 2015, at 3:42 PM, Stefano Miccoli <mo...@icloud.com> wrote:
>>>
>>>
>>>> On 29 Mar 2015, at 15:32, mike.kal...@gmail.com wrote:
>>>>
>>>> rrdtool create house.rrd --start N --step 300 \DS:garageIN:GAUGE:600:U:U \
>>>> DS:garageRTN:GAUGE:600:U:U \
>>>> DS:bedroomIN:GAUGE:600:U:U \
>>>> DS:bedroomRTN:GAUGE:600:U:U \
>>>> DS:floorIN:GAUGE:600:U:U \
>>>> DS:floorRTN:GAUGE:600:U:U \
>>>> DS:watertankIN:GAUGE:600:U:U \
>>>> DS:watertankRTN:GAUGE:600:U:U \
>>>> DS:loopIN:GAUGE:600:U:U \
>>>> DS:loopRTN:GAUGE:600:U:U \
>>>> DS:headerIN:GAUGE:600:U:U \
>>>> DS:headerRTN:GAUGE:600:U:U \
>>>> DS:basementIN:GAUGE:600:U:U \
>>>> DS:basementRTN:GAUGE:600:U:U \
>>>> DS:mechanicalRM:GAUGE:600:U:U \
>>>> RRA:AVERAGE:0.5:1:12 \
>>>> RRA:AVERAGE:0.5:1:288 \
>>>> RRA:AVERAGE:0.5:12:168 \
>>>> RRA:AVERAGE:0.5:12:720 \
>>>> RRA:AVERAGE:0.5:288:365
>>>
>>> In fact having rrdtool working is quite complicated.
>>>
>>> Thirst thing to note: you should run “rrdtool update” at about a 300s
>>> interval (—step 300). If you fail to run ‘rrdupdate’ for 600s (see 600 in
>>> 'DS:garageIN:GAUGE:600:U:U’) the data point will be marked ‘unknown’ (and
>>> nothing will show up in your graphs.)
>>>
>>> Second thing: RRA:AVERAGE:0.5:1:12 and RRA:AVERAGE:0.5:12:168 are
>>> redundant, since you save also longer RRA’s at the same frequency
>>> (RRA:AVERAGE:0.5:1:288 and RRA:AVERAGE:0.5:12:720).
>>>
>>> Third thing: if you are interested in plotting MIN/MAX graphs, you should
>>> also add RRA:MIN… and RRA:MAX definitions in your rrd.
>>>
>>> Now suppose that the values in OWFS are correct. The best way to check what
>>> got into your rrd is “rrdtool dump house.rrd” which will output and XML
>>> file with the exact content of your rrd. If you are interested in the last
>>> 24h of data, you can also issue ‘rrdtool fetch house.rrd AVERAGE —start
>>> -24h and so on. Once you confirm that data is correctly stored in rrd, then
>>> you can start plotting graphs.
>>>
>>> A final comment: AFAIK, while having a single rrd file for all your sensors
>>> is fine, this is rather an uncommon choice. Usually you define a file for
>>> each sensor, in order to break the read/update loop in smaller chunks; e.g.
>>>
>>> rrdtool update Garage_Return.rrd N:`cat
>>> /mnt/owfs/uncached/Garage_Return/temperature`
>>>
>>> and so on… possibly in a bash for-loop over all sensors.
>>>
>>> Bye,
>>>
>>> Stefano
>>>
>>>
>>>
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>> ------------------------------------------------------------------------------
>> Dive into the World of Parallel Programming The Go Parallel Website,
>> sponsored
>> by Intel and developed in partnership with Slashdot Media, is your hub for
>> all
>> things parallel software development, from weekly thought leadership blogs to
>> news, videos, case studies, tutorials and more. Take a look and join the
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> ------------------------------------------------------------------------------
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> things parallel software development, from weekly thought leadership blogs to
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