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Here's just a couple of notes about Compression.
You will notice that some sets of data, just by their very
nature, they are, they are highly compressible. I mean, just take a look at this
Trend right here. We are seeing just constant
spikes up and down, up and down. There's
not a lot of data that is going to be able
to be compressed out of that because it's just
varying so much from one iteration to the next.
We are probably going to be generating Compression Events,
you know, Archive Events, for each value that comes
in. So, in this case, using the Swinging
Door Algorithm, all the values would be archived for this. Now, compare
that to this data over here. This data is, you know,
fairly uninteresting. It,
it does not change that much, and it's obviously
not going to be violating the deadband a great deal, so it's probably going to be
very highly compressible. Another way of describing
this is we, when we, when we look at
things like scan rate, the amount of times
you scan a value does not really impact
how much you store because, well,
scan rate is simply how often you look.
You can look at a dull value all you want, and if it does not change
we are simply not going to store it. We are going to store it based
on the Compression Deviation. Now, some other
things. Remember, we do store only actual
Process Values. So, every single thing you see in the Archive, you know,
these values right here. They are actual values. We do not ever make
up data. Any kind of data that might
be out of sequence -- if data comes in,
oh, let's say for some reason, strange reason, in this
particular sequence, we have
values that come in, you know, at this time, and then
again at this time, and then for some reason we get a value over
here at that time -- and it actually
comes in after this value over here comes in. Well,
if that happens, this value is going to go in without
Compression. We call that out-of-sequence data.
And, all out-of-sequence data like that would
be archived. It's rare that we see an interface that gets
its data so out of order that it does that, but
if it does, then that data will be written without Compression.
Also, a point to note, as I have said
many times now. Each and every tag has its own
Compression setting. So, I am only looking at the Compression for the Tag
CDT185 right now.
So that is something to consider,
Each Tag has its own. And, again, you can always opt out by turning
Compressing off.
I do want to reiterate that it is absolutely
a trade-off when you set Data Compression. So, and, and we
understand it and admit it. It is a trade-off, so I can
understand the, the impulse to set
Compression off, to turn it off entirely.
If you want raw data, you can do that by simply
setting Compressing Equal to Off. So, the trade-off
is between the accuracy of the data and the speed
of retrieval. It's really not the
disk space that we are concerned about. It's mostly
performance, especially performance when people are
using a live tool like ProcessBook. You know, we have learned
over the years, if it takes longer than a second or two for people
to retrieve their data, they just stop going to PI.
They, they will not use it as much if it takes
a long time. So, you know,
we, we would like for everybody to have
well-tuned data so that their performance is very, very fast as they
retrieve this data. So, and the trade-off, basically,
is between the raw data -- you know, here's the raw data
complete with all the line noise. If you trail this,
you know, there's a tremendous amount of line noise that you see in here
that really, probably, does not even represent useful information
to anybody. That line
noise is eliminated during the Exception Test.
So, if you look at this point here, now we are looking at data that has
passed that Exception Test, so the line noise is gone.
And, after that line noise
has been removed, we still have the opportunity to
tighten up even more. A good example would be to
look at this sequence right here. That sequence of values --
it all fits fairly
closely to a certain vector, where if we simply
record, you know, the beginning
and the mid-point and the end, we can
store all these values using a lot fewer
values. So the Compression Test is --
the point of the Compression Test is to take this data and
to see how efficiently we can store that
in the Archives without losing any accuracy. But,
admittedly, it is a trade-off.