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Netflix’s Distributed Counter Abstraction

The Netflix TechBlog

By: Rajiv Shringi , Oleksii Tkachuk , Kartik Sathyanarayanan Introduction In our previous blog post, we introduced Netflix’s TimeSeries Abstraction , a distributed service designed to store and query large volumes of temporal event data with low millisecond latencies. Today, we’re excited to present the Distributed Counter Abstraction.

Latency 251
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The Three Cs: Concatenate, Compress, Cache

CSS Wizardry

Given that 66% of all websites (and 77% of all requests ) are running HTTP/2, I will not discuss concatenation strategies for HTTP/1.1 Plotted on the same horizontal axis of 1.6s, the waterfalls speak for themselves: 201ms of cumulative latency; 109ms of cumulative download. 4,362ms of cumulative latency; 240ms of cumulative download.

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RabbitMQ vs. Kafka: Key Differences

Scalegrid

With its exchange feature, RabbitMQ enables advanced routing strategies, making it well-suited for workflows that require controlled message flow and guaranteed delivery. Its partitioned log architecture supports both queuing and publish-subscribe models, allowing it to handle large-scale event processing with minimal latency.

Latency 147
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OpenTelemetry 101: A nontechnical guide for IT leaders and enthusiasts

Dynatrace

Traces are used for performance analysis, latency optimization, and root cause analysis. The OpenTelemetry website provides detailed documentation for each language to guide you through the necessary steps to set up your environment. Capture critical performance indicators such as request latency, error rates, and resource usage.

Latency 264
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AI-driven analysis of Spring Micrometer metrics in context, with typology at scale

Dynatrace

Every company has its own strategy as to which technologies to use. It exports any pre-instrumented metrics for JVM, CPU Usage, Spring MVC, and WebFlux request latencies, cache utilization, data source utilization as well as custom metrics to the Dynatrace Metrics API v2. To learn more, see our documentation. Dynatrace news.

Metrics 246
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Migrating Critical Traffic At Scale with No Downtime?—?Part 2

The Netflix TechBlog

By collecting and analyzing key performance metrics of the service over time, we can assess the impact of the new changes and determine if they meet the availability, latency, and performance requirements. Migrating Persistent Stores Stateful APIs pose unique challenges that require different strategies.

Traffic 285
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Introducing Netflix TimeSeries Data Abstraction Layer

The Netflix TechBlog

Rajiv Shringi Vinay Chella Kaidan Fullerton Oleksii Tkachuk Joey Lynch Introduction As Netflix continues to expand and diversify into various sectors like Video on Demand and Gaming , the ability to ingest and store vast amounts of temporal data — often reaching petabytes — with millisecond access latency has become increasingly vital.

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