Solving Problems & Saving Time through Software and Crushing Entropy
Powered by Cecil & Hyde.
There are two major upsides to duplicating data. Make no mistake, when I say “duplicated” I mean it. I mean not-normalized data.
I can already hear the chorus: “One Source Of Truth”. And I agree, there should be one source of truth *in most cases. But sometimes that doesn’t apply.
Let me define the situation for which this does not apply; a complex, concurrent, multi-service, and multi-user system. If you’ve got one of those keep on reading.
I liken this to double-entry bookkeeping. There is not one source of truth. You only get to the truth when you’re comparing two (or more) records.
This notion of double-entry bookkeeping is the first major upside to “duplicating” data. Or at the very least, storing it in multiple forms, at multiple stages along its transformation. Why? Because when something goes wrong you have a breadcrumb trail to help figure out at which stage it went wrong. When you only have one source of truth that is constantly being overwritten—and something goes wrong, because something always goes wrong—you’ll never have any insight into what the state of the data was before the error. It’s gone. Overwritten. Inaccessible.
The second upside is optimization. Often when creating the One Source Of Truth it is designed strictly and ontologically. All the relationships conform to the platonic ideal according to the essence and relationships of the object (sorry, I studied philosophy). This design values ontology higher than the pragmatism of a running process. Duplicating data flies in the face of this thinking, and instead optimizes for access patterns; namely how you’re going to read and write this data.
My recommendation is that your initial source of truth is write-optimized. Make it easy and fast to write your data. The faster and more compact your writes are in a distributed system the lower the probability of losing (much) data when problems arise.
Duplicate and transform your data into as many different shapes you need to be in order to be able to read fast.
If you ever want to keep a system performing reliably and fast you have two options: keep it really really small, or embrace the CAP theorem and data duplication.