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Beautiful Data by Toby Segaran, Jeff Hammerbacher

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Conclusion

When we embarked on the PNUTS project, we had in mind a system that could seamlessly scale to thousands of servers and multiple continents. Building such a system required more than clever engineering; it required us to reopen many settled debates in the database field. Although it was a relatively easy decision to jettison ACID, we soon realized we had to develop something to replace it, and thus developed the timeline consistency model. Although the model is relatively simple by design, handling complex corner cases, developing an efficient implementation mechanism, and mapping application use cases to the model required deep thinking and many iterations. Another point to note is that at first our customers and we were relatively blasé about restricting ourselves to a simple query language. However, as developers began trying to build real applications on top of PNUTS, we realized that the small fraction of the query workload that was more complex than we could handle would be a major stumbling block to the system's adoption. If we did not develop a mechanism to handle these queries, developers would have to resort to complicated workarounds, either implementing expensive operations (such as nested loop joins) in their application logic or frequently exporting data to external indexes to support their workload.

The field is in the early stages of cloud data management, and this is reflected in the many alternative system designs being built and deployed. We hope the ideas embodied in the PNUTS system can help us get closer to the goal of easily manageable, broadly applicable, multitenanted cloud database systems that provide applications with elastic, efficient, globally available, and extremely robust data backends.

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