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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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RabbitMQ vs. Kafka: Key Differences

Scalegrid

Its partitioned log architecture supports both queuing and publish-subscribe models, allowing it to handle large-scale event processing with minimal latency. Apache Kafka uses a custom TCP/IP protocol for high throughput and low latency. Apache Kafka, designed for distributed event streaming, maintains low latency at scale.

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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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.

Latency 239
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Applying Netflix DevOps Patterns to Windows

The Netflix TechBlog

The challenges with service management included: Stale documentation OS Updates High cognitive overhead A lack of continuous testing Scaling Image Creation Our existing AMI baking tool Aminator does not support Windows so we had to leverage other tools. Services are more reliable, testable, and documented.

DevOps 210
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Automated observability, security, and reliability at scale

Dynatrace

Whether tracking internal, workload-centric indicators such as errors, duration, or saturation or focusing on the golden signals and other user-centric views such as availability, latency, traffic, or engagement, SLOs-as-code enables coherent and consistent monitoring throughout the environment at scale.

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PostgreSQL Connection Pooling: Part 1 – Pros & Cons

Scalegrid

While there is plenty of well-documented benefits to using a connection pooler, there are some arguments to be made against using one: Introducing a middleware in the communication inevitably introduces some latency. A connection pooler is an almost indispensable part of a production-ready PostgreSQL setup.