Storing 100.000 prices per SKU – part 1

One of the questions I have received, from time to time, is that how to store a lot of prices per SKU in Optimizely (B2C) Commerce Cloud. While this is usually a perfect candidate for Optimizely B2B Commerce, there are many customers invested in B2C and want to make the best out of it. Is it possible?

It’s important to understand the pricing system of Optimizely Commerce (which is, written in detail in my book – shameless plug). But in short:

  • There are two price systems, IPriceService and IPriceDetailService
  • One is handling prices in batch – i.e. prices per SKU (IPriceService), and one is handling prices per individual price (IPriceDetailService)
  • Both are cached in latest version (cache for IPriceDetailService was added in late 13.x version)

With that in mind, it would be very problematic if you use IPriceService for such high number of prices per SKU, because each time you save a price, you save a lot of prices at once (same as loading prices). This is how the default IPriceService implementation saves prices of a SKU

create procedure dbo.ecf_Pricing_SetCatalogEntryPrices
    @CatalogKeys udttCatalogKey readonly,
    @PriceValues udttCatalogEntryPrice readonly
    begin try
        declare @initialTranCount int = @@TRANCOUNT
        if @initialTranCount = 0 begin transaction

        delete pv
        from @CatalogKeys ck
        join dbo.PriceGroup pg on ck.CatalogEntryCode = pg.CatalogEntryCode
        join dbo.PriceValue pv on pg.PriceGroupId = pv.PriceGroupId

        merge into dbo.PriceGroup tgt
        using (select distinct CatalogEntryCode, MarketId, CurrencyCode, PriceTypeId, PriceCode from @PriceValues) src
        on (    tgt.CatalogEntryCode = src.CatalogEntryCode
            and tgt.MarketId = src.MarketId
            and tgt.CurrencyCode = src.CurrencyCode
            and tgt.PriceTypeId = src.PriceTypeId
            and tgt.PriceCode = src.PriceCode)
        when matched then update set Modified = GETUTCDATE()
        when not matched then insert (Created, Modified, CatalogEntryCode, MarketId, CurrencyCode, PriceTypeId, PriceCode)
            values (GETUTCDATE(), GETUTCDATE(), src.CatalogEntryCode, src.MarketId, src.CurrencyCode, src.PriceTypeId, src.PriceCode);

        insert into dbo.PriceValue (PriceGroupId, ValidFrom, ValidUntil, MinQuantity, MaxQuantity, UnitPrice)
        select pg.PriceGroupId, src.ValidFrom, src.ValidUntil, src.MinQuantity, src.MaxQuantity, src.UnitPrice
        from @PriceValues src
        left outer join PriceGroup pg
            on  src.CatalogEntryCode = pg.CatalogEntryCode
            and src.MarketId = pg.MarketId
            and src.CurrencyCode = pg.CurrencyCode
            and src.PriceTypeId = pg.PriceTypeId
            and src.PriceCode = pg.PriceCode

        delete tgt
        from dbo.PriceGroup tgt
        join @CatalogKeys ck on tgt.CatalogEntryCode = ck.CatalogEntryCode
        left join dbo.PriceValue pv on pv.PriceGroupId = tgt.PriceGroupId
        where pv.PriceGroupId is null

        if @initialTranCount = 0 commit transaction
    end try
    begin catch
        declare @msg nvarchar(4000), @severity int, @state int
        select @msg = ERROR_MESSAGE(), @severity = ERROR_SEVERITY(), @state = ERROR_STATE()
        if @initialTranCount = 0 rollback transaction
        raiserror(@msg, @severity, @state)
    end catch

If you have experience with SQL (which you probably should), you will see that it’s a deletion of rows in PriceValue that have CatalogEntryCode same as , then a merge, then a deletion of left over rows. To make matters worse, IPriceService system stores data on 3 tables: PriceValue, PriceGroup and PriceType. Imagine doing that with a few dozen of thousands rows.

Even if you change just one price, all prices of that specific SKU will be touched. It’d be fine if you have like ten prices, but if you have ten thousands prices, it’ll be a huge waste.

Not just that. To save one price, you would still need to load all prices of that specific SKU. That’s two layers of waste: the read operations at database layer, and then on application, a lot of price objects will need to be constructed, and then you need to recreate a datatable to send all the data back to the database to do the expensive operation above.

And wait, because the prices saved to IPriceService needs to be synchronized to IPriceDetailService (however, you can disable this). Prices that were changed (which is, all of them) need to be replicated to another table.

So in short, IPriceService was not designed to handle many prices per SKU. If you have less than a few hundred prices per SKU (on average), it’s fine. But if you have more than 1000 prices per SKU, it’s time to look at other options.

Price optimizing: to be or not to be

It can be quite confusing when you first edit prices in Episerver Commerce. To your surprises, some of the prices you imported or edited might disappear, or change, without your consent! What happened?

To answer that question, it’s essential to know there are two pricing system in Commerce: IPriceService and IPriceDetailService.

They have some different characteristics, and one of them is very important: the default implementation of IPriceDetailService saves prices as-is, while the default implementation of IPriceService does not: it optimizes prices before saving. Prices which are best for customers will be favored over the “less good” ones.

Continue reading “Price optimizing: to be or not to be”