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Observing Derived State

At an abstract level, the dataflow systems discussed in the last section give you a process for creating derived datasets (such as search indexes, materialized views, and predictive models) and keeping them up to date. Let’s call that process the write path: whenever some piece of information is written to the system, it may go through multiple stages of batch and stream processing, and eventually every derived dataset is updated to incorporate the data that was written. Figure 12-1 shows an example of updating a search index.

In a search index, writes (document updates) meet reads (queries)

Figure 12-1. In a search index, writes (document updates) meet reads (queries).

But why do you create the derived dataset in the first place? Most likely because you want to query it again at a later time. This is the read path: when serving a user request you read from the derived dataset, perhaps perform some more processing on the results, and construct the response to the user.

Taken together, the write path and the read path encompass the whole journey of the data, from the point where it is collected to the point where it is consumed (probably by another human). The write path is the portion of the journey that is precomputed —i.e., that is done eagerly as soon as the data comes in, regardless of whether anyone has asked to see it. The read path is the portion of the journey that only happens when someone asks for it. If you are familiar with functional programming languages, you might notice that the write path is similar to eager evaluation, and the read path is similar to lazy evaluation.

The derived dataset is the place where the write path and the read path meet, as illustrated in Figure 12-1. It represents a trade-off between the amount of work that needs to be done at write time and the amount that needs to be done at read time.

 
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