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This tutorial

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discusses multi-part CSV
pricing capabilities

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within generic pricing script.

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So for this example, I'll be utilizing

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a sample product that we've set up
on one of our storefronts.

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You do have the ability to access this.

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If you want something to reference, if
you're trying to replicate this behavior.

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Just go to
https://training.infigosoftware.com/pricingscripts

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and the product you're looking for
is multipart

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CSV pricing scripts.

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This product
contains a few different parts.

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One of them of the top section
here is a description of what

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this product covers and how it's utilized,
along with a couple of images.

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Then you've got a sample configuration
of the pricing scripts.

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So the generic pricing script
will go into this in more detail later on.

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You've

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got visual representations
of the CSV documents

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that have been utilized
in this particular product.

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And then right at the bottom,
you've got this actually in action.

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So the ability to switch between

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different attribute combinations

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and draw in different prices.

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So our sample CSVs

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for this particular demonstration
only contain a few entries each.

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However, this functionality is intended
for use when possible

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number of configurations
for a product number in the thousands.

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So the way this works,
when the configuration

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of product attributes such as A and B
that we've got in here or changed

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the Infigo system has to look up
the updated price information

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from the provided CSV for very large CSVs.

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And we're talking here
about over a thousand entries.

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For example, as a general rule of thumb.

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This can take a noticeable amount of time.

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So by splitting one large CSV
into multiple smaller

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CSV documents, we can ensure that
this loading time is decreased.

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And this is what we'll be showing you
how to do in this tutorial.

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Okay.

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So what we actually mean

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by this functionality,
as we've mentioned, is splitting our data

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into multiple CSVs for easier access
in our particular example.

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It's been split into six different
CSV document, and each of those CSV

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documents covers the data
for one selection of the attribute B

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So this means that for each of the CSVs,

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the value for the B attribute
will be the same throughout.

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So I've got two example files open here.

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One of them has B one selected
for every option

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and one has B two
with every other configuration of A.

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So as a result, we've got six CSVs

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covering B values from 1 to 6.

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Now we must upload these CSVs
into a subdirectory

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within our global additional data section.

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So we utilize global additional data
in previous tutorials,

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but that was just for a single CSV
in order to actually

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utilize multiple CSVs
grouped together on one product.

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We do need to put them in a subdirectory.

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It's simply a case of typing the name
of the subdirectory that you want here

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and then clicking on Create

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that will create one of these

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large icons here
which represent the subdirectories.

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You can click on one of those
that is essentially just a file

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within a file where you can then upload
those subsequent multiple CSV documents.

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So you can see here, for example,
I quoted six CSV documents

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within this subdirectory.

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Now, in order to utilize the generic
pricing script in this fashion,

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the uploaded CSVs
all need to have a matching naming format.

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And there's a few things
you need to consider here.

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So they all need to have a common file
prefix.

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So you can see in this example, they've
all got the prefix of subdirectory,

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which is quite confusing
given the name of the subdirectory itself.

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You then need a common separator.

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So in this case we've got a dash.

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It could be an underscore.

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It could be an ampersand,
whatever you like.

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And then we need a unique suffix

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which matches the attribute value
that it's going to be associated with.

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So as we've mentioned in our example,
we have A, CSV for each value of B

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and that B value can be 1 to 6.

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So each file has a different suffix
ranging from 1 to 6.

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So for example, subdirectory dash one

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will cover all the values

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where B is equal to one.

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Now we need to continue
to the configuration

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in the product variant settings
of the product in question.

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So this is the same location
we've seen in previous

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tutorials about configuring
the generic pricing script.

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And you can see there's a few lines
that we need to consider here from the

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example script configuration.

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in several entries
in the pricing script configuration.

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First of all,
we have the subdirectory line.

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So this is a folder that we've created
within Infigo's global

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additional data area
in which your partial CSVs will be placed.

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So you seen that?
We created that a few moments ago.

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You just need to put the name
of that subdirectory in here.

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We then have the base file name.

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So this is the common prefix

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of all of those partial CSVs
that we've uploaded into the subdirectory.

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So just put the common prefix here.

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We then got the separator.

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So this is a common delimiter
located in the name

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of all of your partial CSV documents.

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And then we have split mapping.

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So this is the identification
of the attributes

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by which the CSVs are split.

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So in our example,
the split mapping is set to be

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and as we saw earlier
on, there is a different partial CSV

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for each possible value of B attribute.

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We also need a line in here
to do with the file path.

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Just to make sure
we override this default value

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because we're not actually
using one specific CSV here.

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We just need to make sure
that that is specified as blank.

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So with this in place,
we just need to make sure

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we save that configuration

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and then we can go ahead and test

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that procedure.

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Now Test Mode runs
the default configuration of the product

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within the admin area and provides
feedback on the data that's returned.

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As you can see at the top,
in order to utilize this,

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you will need a default configuration
which is expected to return a price.

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So for example,
you may have to preselect an

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attribute values.

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Let's take another look at this
on the front

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end of our storefront
just to see it in action.

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And what we can see
is a seamless experience.

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So there's no indication to the user
that it's using different

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CSV files here
or is any different to our other setup.

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However,
we do have a faster user experience

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because it's now
referencing smaller datasets.