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FIFO vs. LIFO: Which Queueing Strategy Is Better for Availability and Latency?

DZone

As an engineer, you probably know that server performance under heavy load is crucial for maintaining the availability and responsiveness of your services. But what happens when traffic bursts overwhelm your system? Queueing requests is a common solution, but what's the best approach: FIFO or LIFO?

Strategy 141
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Uber’s Big Data Platform: 100+ Petabytes with Minute Latency

Uber Engineering

To accomplish this, Uber relies heavily on making data-driven decisions at every level, from forecasting rider demand during high traffic events to identifying and addressing bottlenecks … The post Uber’s Big Data Platform: 100+ Petabytes with Minute Latency appeared first on Uber Engineering Blog.

Big Data 109
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Site reliability done right: 5 SRE best practices that deliver on business objectives

Dynatrace

Site reliability engineering (SRE) has recently become a critical discipline in recent years as the world has shifted in favor of web-based interactions. This shift is leading more organizations to hire site reliability engineers to guarantee the reliability and resiliency of their services. Mobile retail e-commerce spending in the U.

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Implementing service-level objectives to improve software quality

Dynatrace

SLOs can be a great way for DevOps and infrastructure teams to use data and performance expectations to make decisions, such as whether to release and where engineers should focus their time. Latency is the time that it takes a request to be served. SLOs aid decision making. SLOs promote automation. Define SLOs for each service.

Software 263
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SLOs done right: how DevOps teams can build better service-level objectives

Dynatrace

So how do development and operations (DevOps) teams and site reliability engineers (SREs) distinguish among good, great, and suboptimal SLOs? Monitors signals The first attribute of a good SLO is the ability to monitor the four “golden signals”: latency, traffic, error rates, and resource saturation.

DevOps 215
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Supporting Diverse ML Systems at Netflix

The Netflix TechBlog

Without these integrations, projects would be stuck at the prototyping stage, or they would have to be maintained as outliers outside the systems maintained by our engineering teams, incurring unsustainable operational overhead. Importantly, all the use cases were engineered by practitioners themselves.

Systems 226
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Seamlessly Swapping the API backend of the Netflix Android app

The Netflix TechBlog

This allowed Android engineers to have much more control and observability over how we get our data. For each route we migrated, we wanted to make sure we were not introducing any regressions: either in the form of missing (or worse, wrong) data, or by increasing the latency of each endpoint. Replay Testing Enter replay testing.

Latency 233