Today we are going to be talking about how
future data is stored in the PI System. Future
data is typically data associated with a future
timestamp. It's things like predictions or
forecasts, projections or
goals. And PI Server 2015
now natively supports capturing and storing this
type of data. For example,
here I am able to compare my production rate forecast
with my actual production rate. This bar
indicates the current time here which
means that all of these values have
future timestamps. Now how does this
future data and the collection and storage of it
fit into the existing architecture of our
PI System? So in a typical
PI System, we have the
data source. And our PI Interface
is going to collect the data from the
data source and it's going to send that to the PI
Data Archive where the data is stored.
So how can we send data with future timestamps?
Well, there are specific PI Interfaces that can
send that type of data. There is the PI
Interface for RDMS. There is also the PI
Interface for UFL. And starting with the 2015
release, there is the PI to PI
Interface. So these interfaces all support sending data with future
timestamps. And they are going to be able to send the
data to the PI Data Archive and there
were a few changes that were make on the PI Data Archive side to
allow this. So what are those changes?
Well, there is the new PI
Point attribute and we are going to be looking at this later in the
video. And this PI Point attribute is basically just to
designate if this is a PI Point that is used for a
prediction or a forecast, so that type of thing.
And then, there are new
special archive files and these archive files are going to store the
data for these PI Points. And
these changes are great because if you'll think about
it, they aren't going to impact any of your existing
running PI Systems. They aren't going to impact the
existing PI Points or the existing archives because they are separate.
And let's talk a little bit about
these archive files. If we think a little bit
about before PI Server 2015,
we had a focus
really on collecting the real-time process
measurements. And this was the type of data
from our machines or our sensors and it was generally
coming in chronologically in real-time. And the data is really
dense. And we have archive files and
these files are going to store this type of
data. And these archives were super efficient at
storing this type of
data. Now with the PI
Data Archive 2015,
we introduced the ability to store predictions, forecasts and
targets. Now
how is this data different from that real-time process
data? The data can come in at
any time. It may not be chronological,
it could even be overwritten if a prediction is
updated. And the forecast data, that's typically
going to be much more sparse than the process
data that you are collecting. And as a
result, it makes sense to have a different
archive strategy because even just the sizing will be
different. So we've introduced
special separate archive files.
And these archive files are optimized for
storing this type of data. They are
dynamic to store as little or as much data as
necessary. And to distinguish them,
we call these future archives.
And that just means that these are the archive files
that we are going to be storing the predictions, forecasts,
targets -- that future data in. And to distinguish them from
the original archive files, we are going to call the original
archive files historical archives. So
as you can see, the separate archive
file strategy is going to allow you the
flexibility to have separate backup strategies or
separate archive retention policies. And it makes sure
that the storage of the data is optimized for the
type of data that it is. Now one of the
most common questions on this topic
is, what happens to the future
data when the timestamps are now
in the past? The data
continues to be stored in the future archives. We
don't transfer it to the
historical archives. Now that we understand that
there are these different archive files, let's start off with
first of all where they are stored.
Well there is a separate directory for future
archives that gets specified,
as an install or upgrade to PI Data Archive 2015.
Here, this was when I was updating my PI
Data Archive. And by default, it's just going
to make this recommendation to have a
sub folder where the sub folder name
is Future. And this is off of my
current existing archive file directory.
And the separate directory again allows you to have
that separate backup or retention policies.
And this directory can be changed in the
future. So now that we know where they are
stored, how can we manage these future
archives? Well these future archives, we can take a look at them in PI
System Management Tools. So I'm going to
open up PI System Management Tools on my PI Data
Archive. And starting with PI SMT 2015,
when I look at the archives
using the plug-in Operation
Archives, you'll see that there has been a new
look. And I can take a look at the future
archives by clicking on this tab.
Now one thing I really want to
emphasize is that the management
of future archives is pretty much non
existent because by default, the
future archives are automatically created whenever there is data
with a future timestamp. And every future
archive, you'll notice like this one here,
starts off with a size of 1 MB, really small, so it won't eat
up disk space. And it's going to grow
dynamically, so it'll be just the right size for the
forecast data that you are sending. And
the future data archive files are bound by a
month time range. So you can tell this
by the start and end time. So it's one calendar month.
And so for example, if I have a new
value that comes in for August
15th, my PI Data
Archive would automatically create a
new future archive file for that month for
me. Again, this is a super easy process and
it's really designed so that the only thing that I have to
manage is my disk space. Now which
data gets sent to the future archives versus the
historical archives? Well that's based on
the PI Point
attribute. So remember the PI Points have a new
attribute that specifies whether or not they are going to be used
for a forecast or a prediction, the future data. Now let's take a
look at that. So if I going to
Points and Point Builder,
as you can see, we now have a new
PI Point attribute. So stored values with a
default value being real-time
data. So let's take a look at the choice here. As you can
see, I can select future data and I would change this
to future data if I was going to create a PI Point to be
used for a forecast or prediction for
example. And this selection for
stored values, I just want to make sure to point out that this is
something that's a one time choice on point
creation. It can't be changed later after I hit save and
create the PI Point. And that's because this is
essentially designating where the data is going to be
stored and it's going to be optimized
for whether it's a prediction or a real-time
measurement. So as
you can see, it's really easy to use the PI System to capture and store your predictions, targets,
forecasts. You just designate your PI
Point for a forecast or a prediction
with a single attribute change here. And you can
use the PI Interfaces to send the data with the future
timestamps and
asset analytics can also be used to write
data with future timestamps as well. And this future
data gets stored on the PI Data Archive
in those separate future archive files which are
automatically created.