Software Engineering Daily

Application performance monitoring helps an engineer understand what is going on with an application. An application on a single machine is often monitored by inserting bytecode instructions into the application after it has been interpreted. Distributed cloud applications with functionality broken up across multiple servers often use distributed tracing.

Andi Grabner from Dynatrace joins today’s show to explain how monitoring software is built, and how engineers use it to solve problems. Monitoring is core to every business, whether the goal is to understand top-level business processes of to dissect and debug a specific engineering problem. And because monitoring is important at every layer of the stack, there is a plethora of monitoring tools for sale.

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Online marketplaces and social networks often have a trust and safety team. The trust and safety team helps protect the platform from scams, fraud, and malicious actors. To detect these bad actors at scale requires building a system that classifies every transaction on the platform as safe or potentially malicious.

Since every social platform has to build something like this, Smyte decided to engineer trust and safety as a service. Josh Yudaken joins the show today to discuss how Smyte engineered its platform to provide machine learning models for any organization that wants to take advantage of Smyte for its trust and safety.

The tools we discuss include Kubernetes, RocksDB, and Kafka, and Smyte is solving some problems that have not been solved before, so this is a great episode for anyone interested in data engineering or fraud detection–or how to use cloud services and open source tools in unique ways.

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For many years, software companies have been breaking up their applications into individual services for the purpose of isolation and maintainability. In the early 2000s, we called this pattern “service-oriented architecture”. Today we call it “microservices”. Why did we change that terminology? Did the services get smaller? Not exactly.

Jonas Boner suggests that the movement towards cloud and the increased prevalence of mobile changes how we look at these services–so much that we needed to change the terminology necessary to even talk about them. And once the conversation has shifted to “microservices”, what steps do we need to take to implement them properly?

The reactive manifesto is a collection of principles for how to build applications. When the reactive manifesto is applied to the idea of microservices, we get reactive microservices, which Jonas and I discuss in today’s episode.

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Some tasks are simple, but cannot be performed by a computer. Audio transcription, image recognition, survey completion–these are simple procedures that almost any human could execute, but the machine learning models have not gotten consistent enough to do them accurately.

Scale is an API for human labor, created by Lucy Guo and Alexandr Wang. Similar to Amazon Mechanical Turk, Scale sends small, simple tasks to workers who can complete those tasks. Scale provides an interface that is easy for developers to use, unlike Mechanical Turk, which requires a dashboard.

Similar to how Stripe allows developers to build software off of payments systems easily, Scale allows developers to build human-driven, manual tasks into their code–which unlocks a wide range of potential applications, which Lucy and Alexandr discussed with me.

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Caching is a fundamental concept of computer science. When data is accessed frequently, we put that data in a place where it can be accessed more quickly–we put the data in a cache. When data is accessed less often, we leave it in a place where the access time is slow or expensive.

Netflix has a huge variety of data, and a huge variety of access patterns for how that data gets retrieved from storage. In today’s episode, Scott Mansfield gives an overview of Netflix’s caching architecture, including EVCache, the ephemeral, volatile cache built for Netflix’s cloud architecture.

As with other episodes about Netflix architecture, this show is a deeply technical case study.

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