Цитаты из книги Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems

The Industrial Revolution came about through major technological and agricultural advances, and it brought sustained economic growth and significantly improved living standards in the long run. Yet it also came with major problems: pollution of the air (due to smoke and chemical processes) and the water (from industrial…

When services become good at predicting what content users want to see, they may end up showing people only opinions they already agree with, leading to echo chambers in which stereotypes, misinformation, and polarization can breed. We are already seeing the impact of social media echo chambers on election campaigns.

We will also need to figure out how to prevent data being used to harm people, and realize its positive potential instead. For example, analytics can reveal financial and social characteristics of people’s lives. On the one hand, this power could be used to focus aid and support to help those people who most need it. On…

I could imagine integrity-checking and auditing algorithms, like those of certificate transparency and distributed ledgers, becoming more widely used in data systems in general. Some work will be needed to make them equally scalable as systems without cryptographic auditing, and to keep the performance penalty as low as…

I think it is worth exploring fault-tolerance abstractions that make it easy to provide application-specific end-to-end correctness properties, but also maintain good performance and good operational characteristics in a large-scale distributed environment.

The challenge is that the assumption of stateless clients and request/response interactions is very deeply ingrained in our databases, libraries, frameworks, and protocols. Many datastores support read and write operations where a request returns one response, but much fewer provide an ability to subscribe to…

However, in most programming languages you cannot subscribe to changes in a mutable variable—you can only read it periodically. <...> Databases have inherited this passive approach to mutable data: if you want to find out whether the content of the database has changed, often your only option is to poll (i.e., to repeat…

The tools for composing data systems are getting better, but I think one major part is missing: we don’t yet have the unbundled-database equivalent of the Unix shell (i.e., a high-level language for composing storage and processing systems in a simple and declarative way).

Perhaps, over time, patterns for application development will emerge that allow causal dependencies to be captured efficiently, and derived state to be maintained correctly, without forcing all events to go through the bottleneck of total order broadcast.