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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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Consistent caching mechanism in Titus Gateway

The Netflix TechBlog

We introduce a caching mechanism in the API gateway layer, allowing us to offload processing from singleton leader elected controllers without giving up strict data consistency and guarantees clients observe. After the jobs are created, it monitors their execution progress. The cache is kept in sync with the current leader process.

Cache 233
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Observability vs. monitoring: What’s the difference?

Dynatrace

This trend is prompting advances in both observability and monitoring. But exactly what are the differences between observability vs. monitoring? Monitoring and observability provide a two-pronged approach. To get a better understanding of observability vs monitoring, we’ll explore the differences between the two.

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Migrating Netflix to GraphQL Safely

The Netflix TechBlog

The GraphQL shim enabled client engineers to move quickly onto GraphQL, figure out client-side concerns like cache normalization, experiment with different GraphQL clients, and investigate client performance without being blocked by server-side migrations. To launch Phase 1 safely, we used AB Testing.

Traffic 357
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Dynatrace accelerates business transformation with new AI observability solution

Dynatrace

The RAG process begins by summarizing and converting user prompts into queries that are sent to a search platform that uses semantic similarities to find relevant data in vector databases, semantic caches, or other online data sources. Observing AI models Running AI models at scale can be resource-intensive.

Cache 276
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Dynatrace supports SnapStart for Lambda as an AWS launch partner

Dynatrace

The new Amazon capability enables customers to improve the startup latency of their functions from several seconds to as low as sub-second (up to 10 times faster) at P99 (the 99th latency percentile). This can cause latency outliers and may lead to a poor end-user experience for latency-sensitive applications.

Lambda 246
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AI-driven analysis of Spring Micrometer metrics in context, with typology at scale

Dynatrace

Spring Boot 2 uses Micrometer as its default application metrics collector and automatically registers metrics for a wide variety of technologies, like JVM, CPU Usage, Spring MVC, and WebFlux request latencies, cache utilization, data source utilization, Rabbit MQ connection factories, and more. That’s a large amount of data to handle.

Metrics 246