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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

This article outlines the key differences in architecture, performance, and use cases to help determine the best fit for your workload. RabbitMQ follows a message broker model with advanced routing, while Kafkas event streaming architecture uses partitioned logs for distributed processing. What is RabbitMQ? What is Apache Kafka?

Latency 147
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Single-core memory bandwidth: Latency, Bandwidth, and Concurrency

John McCalpin

The Multicore Era Over the past ~15 years, server processors from Intel and AMD have evolved from the early quad-core processors to the current monsters with over 50 cores per socket. The example below is for a 2005-era processor with 60 ns memory latency and 6.4 If we want to sustain full bandwidth, we need 64/2 =32 cache lines.

Latency 71
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Introducing Impressions at Netflix

The Netflix TechBlog

Architecture Overview The first pivotal step in managing impressions begins with the creation of a Source-of-Truth (SOT) dataset. These events are promptly relayed from the client side to our servers, entering a centralized event processing queue. This queue ensures we are consistently capturing raw events from our global userbase.

Tuning 166
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Timestone: Netflix’s High-Throughput, Low-Latency Priority Queueing System with Built-in Support…

The Netflix TechBlog

Timestone: Netflix’s High-Throughput, Low-Latency Priority Queueing System with Built-in Support for Non-Parallelizable Workloads by Kostas Christidis Introduction Timestone is a high-throughput, low-latency priority queueing system we built in-house to support the needs of Cosmos , our media encoding platform. Over the past 2.5

Latency 221
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Migrating Critical Traffic At Scale with No Downtime?—?Part 1

The Netflix TechBlog

When undertaking system migrations, one of the main challenges is establishing confidence and seamlessly transitioning the traffic to the upgraded architecture without adversely impacting the customer experience. It provides a good read on the availability and latency ranges under different production conditions.

Traffic 347
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Introducing Netflix’s Key-Value Data Abstraction Layer

The Netflix TechBlog

These include challenges with tail latency and idempotency, managing “wide” partitions with many rows, handling single large “fat” columns, and slow response pagination. Data Model At its core, the KV abstraction is built around a two-level map architecture. Useful for keeping “n-newest” or prefix path deletion.

Latency 260