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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 247
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RabbitMQ vs. Kafka: Key Differences

Scalegrid

Message brokers handle validation, routing, storage, and delivery, ensuring efficient and reliable communication. Message Broker vs. Distributed Event Streaming Platform RabbitMQ functions as a message broker, managing message confirmation, routing, storage, and delivery within a queue. What is RabbitMQ?

Latency 147
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Building Netflix’s Distributed Tracing Infrastructure

The Netflix TechBlog

Now let’s look at how we designed the tracing infrastructure that powers Edgar. If we had an ID for each streaming session then distributed tracing could easily reconstruct session failure by providing service topology, retry and error tags, and latency measurements for all service calls.

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Optimizing data warehouse storage

The Netflix TechBlog

At this scale, we can gain a significant amount of performance and cost benefits by optimizing the storage layout (records, objects, partitions) as the data lands into our warehouse. We built AutoOptimize to efficiently and transparently optimize the data and metadata storage layout while maximizing their cost and performance benefits.

Storage 208
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Introducing Netflix’s Key-Value Data Abstraction Layer

The Netflix TechBlog

Vidhya Arvind , Rajasekhar Ummadisetty , Joey Lynch , Vinay Chella Introduction At Netflix our ability to deliver seamless, high-quality, streaming experiences to millions of users hinges on robust, global backend infrastructure. Data Model At its core, the KV abstraction is built around a two-level map architecture.

Latency 256
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Title Launch Observability at Netflix Scale

The Netflix TechBlog

The Challenge of Title Launch Observability As engineers, were wired to track system metrics like error rates, latencies, and CPU utilizationbut what about metrics that matter to a titlessuccess? This approach provides a few advantages: Low burden on existing systems: Log processing imposes minimal changes to existing infrastructure.

Traffic 168
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Comparing PostgreSQL DigitalOcean Performance & Pricing – ScaleGrid vs. DigitalOcean Managed Databases

Scalegrid

As an open source database, it’s a highly popular choice for enterprise applications looking to modernize their infrastructure and reduce their total cost of ownership, along with startup and developer applications looking for a powerful, flexible and cost-effective database to work with. Compare Latency. At a glance – TLDR.

Database 230