article thumbnail

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 212
article thumbnail

Rebuilding Netflix Video Processing Pipeline with Microservices

The Netflix TechBlog

This architecture shift greatly reduced the processing latency and increased system resiliency. We expanded pipeline support to serve our studio/content-development use cases, which had different latency and resiliency requirements as compared to the traditional streaming use case.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Scalable Annotation Service?—?Marken

The Netflix TechBlog

The service should be able to serve real-time, aka UI, applications so CRUD and search operations should be achieved with low latency. A data model in Marken can be described using schema — just like how we create schemas for database tables etc. The databases we pick should be able to scale horizontally.

article thumbnail

MezzFS?—?Mounting object storage in Netflix’s media processing platform

The Netflix TechBlog

Mounting object storage in Netflix’s media processing platform By Barak Alon (on behalf of Netflix’s Media Cloud Engineering team) MezzFS (short for “Mezzanine File System”) is a tool we’ve developed at Netflix that mounts cloud objects as local files via FUSE. in Atlas , Netflix’s in-memory dimensional time series database.

Media 214
article thumbnail

Data ingestion pipeline with Operation Management

The Netflix TechBlog

by Varun Sekhri , Meenakshi Jindal , Burak Bacioglu Introduction At Netflix, to promote and recommend the content to users in the best possible way there are many Media Algorithm teams which work hand in hand with content creators and editors. But we cannot search or present low latency retrievals from files Etc.

Media 264
article thumbnail

Designing Instagram

High Scalability

User Feed Service, Media Counter Service) read the actions from the streaming data store and performs their specific tasks. media search index, locations search index, and so forth) in future. We will use a graph database such as Neo4j to store the information. Sample Queries supported by Graph Database. Component Design.

Design 334
article thumbnail

Data Reprocessing Pipeline in Asset Management Platform @Netflix

The Netflix TechBlog

By Meenakshi Jindal Overview At Netflix, we built the asset management platform (AMP) as a centralized service to organize, store and discover the digital media assets created during the movie production. Production assets operations are performed in parallel with older data reprocessing without any service downtime.

Media 237