Search Results for "netflix"

Metaflow is a human-friendly Python library that helps scientists and engineers build and manage real-life data science projects. Metaflow was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning.

Micrometer provides a simple facade over the instrumentation clients for the most popular observability systems, allowing you to instrument your JVM-based application code without vendor lock-in. Think SLF4J, but for observability. Micrometer provides vendor-neutral interfaces for timers, gauges, counters, distribution summaries, and long task timers with a dimensional data model that, when paired with a dimensional monitoring system, allows for efficient access to a particular named metric...

...And if we compare it to Redux, MST offers better performance and much less boilerplate code than Redux! MobX is one of the most popular Redux alternatives and is used (along with MobX-State-Tree) by companies all over the world, including Netflix, Grow, IBM, DAZN, Baidu, and more. It supports a full set of features for a modern state management system, all in a package with zero dependencies other than MobX itself.

...Ribbon comprises of multiple components some of which are used in production internally and some of which were replaced by non-OSS solutions over time. This is because Netflix started moving into a more componentized architecture for RPC with a focus on single-responsibility modules. So each Ribbon component gets a different level of attention at this moment. APIs that integrate load balancing, fault tolerance, caching/batching on top of other ribbon modules and Hystrix. Load balancer APIs that can be used independently or with other modules. ...

Defend your app with Hystrix, a latency and fault tolerance library developed by the Netflix API team. It works by isolating points of access to remote systems, services and 3rd party libraries, thereby stopping cascading failure and providing fallback options, and making complex distributed systems more resilient in cases where failure is inevitable. In a distributed environment, failure is simply inevitable for some service dependencies.