⛩️Martin Kleppmann - Collected Quotes

"A fault is usually defined as one component of the system deviating from its spec, where - as a failure is when the system as a whole stops providing the required service to the user. It is impossible to reduce the probability of a fault to zero; therefore it is usually best to design fault-tolerance mechanisms that prevent faults from causing failures." (Martin Kleppmann, "Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems", 2015)

"[…] a NoSQL system may find itself accidentally reinventing SQL, albeit in disguise."(Martin Kleppmann, "Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems", 2015)

"An architecture that scales well for a particular application is built around assumptions of which operations will be common and which will be rare - the load parameters. If those assumptions turn out to be wrong, the engineering effort for scaling is at best wasted, and at worst counterproductive." (Martin Kleppmann, "Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems", 2015)

"[…] as software engineers and architects, we also need to have a technically accurate and precise understanding of the various technologies and their trade-offs if we want to build good applications." (Martin Kleppmann, "Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems", 2015)

"[…] building for scale that you don’t need is wasted effort and may lock you into an inflexible design. In effect, it is a form of premature optimization. However, it’s also important to choose the right tool for the job, and different technologies each have their own strengths and weaknesses." (Martin Kleppmann, "Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems", 2015)

"Consensus is one of the most important and fundamental problems in distributed computing. On the surface, it seems simple: informally, the goal is simply to get several nodes to agree on something." (Martin Kleppmann, "Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems", 2015)

"Every legacy system is unpleasant in its own way, and so it is difficult to give general recommendations for dealing with them." (Martin Kleppmann, "Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems", 2015)

"Everybody has an intuitive idea of what it means for something to be reliable or unreliable. For software, typical expectations include: The application performs the function that the user expected. It can tolerate the user making mistakes or using the software in unexpected ways. Its performance is good enough for the required use case, under the expected load and data volume. The system prevents any unauthorized access and abuse." (Martin Kleppmann, "Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems", 2015)

"It would be unwise to assume that faults are rare and simply hope for the best. It is important to consider a wide range of possible faults - even fairly unlikely ones - and to artificially create such situations in your testing environment to see what happens. In distributed systems, suspicion, pessimism, and paranoia pay off." (Martin Kleppmann, "Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems", 2015)

"Reducing response times at very high percentiles is difficult because they are easily affected by random events outside of your control, and the benefits are diminishing." (Martin Kleppmann, "Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems", 2015) 

"Technology is a powerful force in our society. Data, software, and communication can be used for bad: to entrench unfair power structures, to undermine human rights, and to protect vested interests. But they can also be used for good: to make underrepresented people’s voices heard, to create opportunities for everyone, and to avert disasters." (Martin Kleppmann, "Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems", 2015)

"The architecture of systems that operate at large scale is usually highly specific to the application - there is no such thing as a generic, one-size-fits-all scalable architecture (informally known as magic scaling sauce)." (Martin Kleppmann, "Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems", 2015)

"The fact that SQL is more limited in functionality gives the database much more room for automatic optimizations." (Martin Kleppmann, "Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems", 2015)

"The need for data integration often only becomes apparent if you zoom out and consider the dataflows across an entire organization." (Martin Kleppmann, "Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems", 2015)

"This is a deliberate choice in the design of computers: if an internal fault occurs, we prefer a computer to crash completely rather than returning a wrong result, because wrong results are difficult and confusing to deal with. Thus, computers hide the fuzzy physical reality on which they are implemented and present an idealized system model that operates with mathematical perfection." (Martin Kleppmann, "Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems", 2015)

"[...] when measuring performance, it’s worth using percentiles rather than averages. The main advantage of the mean is that it’s easy to calculate, but percentiles are much more meaningful." (Martin Kleppmann, "Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems", 2015)

"When we develop predictive analytics systems, we are not merely automating a human’s decision by using software to specify the rules for when to say yes or no; we are even leaving the rules themselves to be inferred from data. However, the patterns learned by these systems are opaque: even if there is some correlation in the data, we may not know why. If there is a systematic bias in the input to an algorithm, the system will most likely learn and amplify that bias in its output." (Martin Kleppmann, "Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems", 2015)

"Working with distributed systems is fundamentally different from writing software on a single computer - and the main difference is that there are lots of new and exciting ways for things to go wrong." (Martin Kleppmann, "Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems", 2015)