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

The Netflix TechBlog

This blog series will examine the tools, techniques, and strategies we have utilized to achieve this goal. In this testing strategy, we execute a copy (replay) of production traffic against a system’s existing and new versions to perform relevant validations. This approach has a handful of benefits.

Traffic 339
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Failure Modes and Continuous Resilience

Adrian Cockcroft

There are many possible failure modes, and each exercises a different aspect of resilience. Collecting some critical metrics at one second intervals, with a total observability latency of ten seconds or less matches the human attention span much better. A resilient system continues to operate successfully in the presence of failures.

Latency 52
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Failure Modes and Continuous Resilience

Adrian Cockcroft

There are many possible failure modes, and each exercises a different aspect of resilience. Collecting some critical metrics at one second intervals, with a total observability latency of ten seconds or less matches the human attention span much better. A resilient system continues to operate successfully in the presence of failures.

Latency 53
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A Decade of Dynamo: Powering the next wave of high-performance, internet-scale applications

All Things Distributed

Performant – DynamoDB consistently delivers single-digit millisecond latencies even as your traffic volume increases. DynamoDB automatically re-distributes your data to healthy servers to ensure there are always multiple replicas of your data without you needing to intervene. Auto Scaling is on by default for all new tables and indexes.

Internet 128
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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. Our object storage service splits objects into many parts and stores them in S3.

Media 214