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

The Netflix TechBlog

Migrating Critical Traffic At Scale with No Downtime — Part 1 Shyam Gala , Javier Fernandez-Ivern , Anup Rokkam Pratap , Devang Shah Hundreds of millions of customers tune into Netflix every day, expecting an uninterrupted and immersive streaming experience. This approach has a handful of benefits.

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Migrating Critical Traffic At Scale with No Downtime?—?Part 2

The Netflix TechBlog

Migrating Critical Traffic At Scale with No Downtime — Part 2 Shyam Gala , Javier Fernandez-Ivern , Anup Rokkam Pratap , Devang Shah Picture yourself enthralled by the latest episode of your beloved Netflix series, delighting in an uninterrupted, high-definition streaming experience. This is where large-scale system migrations come into play.

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Title Launch Observability at Netflix Scale

The Netflix TechBlog

Accurately Reflecting Production Behavior A key part of our solution is insights into production behavior, which necessitates our requests to the endpoint result in traffic to the real service functions that mimics the same pathways the traffic would take if it came from the usualcallers. We call this capability TimeTravel.

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Title Launch Observability at Netflix Scale

The Netflix TechBlog

To detect issues proactively, we need to simulate traffic and predict system behavior in advance. Once artificial traffic is generated, discarding the response object and relying solely on logs becomes inefficient. Stay tuned for a closer look at the innovation behind thescenes!

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Introducing Impressions at Netflix

The Netflix TechBlog

This approach ensures high availability by isolating regions, so if one becomes degraded, others remain unaffected, allowing traffic to be shifted between regions to maintain service continuity. Automating Performance Tuning with Autoscalers Tuning the performance of our Apache Flink jobs is currently a manual process.

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Migrating Netflix to GraphQL Safely

The Netflix TechBlog

The control group’s traffic utilized the legacy Falcor stack, while the experiment population leveraged the new GraphQL client and was directed to the GraphQL Shim. This helped us successfully migrate 100% of the traffic on the mobile homepage canvas to GraphQL in 6 months.

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TCP: Out of Memory — Consider Tuning TCP_Mem

DZone

All other application instances were handling the traffic properly. The application was running on a GNU/Linux OS, Java 8, Tomcat 8 application server. All of a sudden, one of the application instances became unresponsive. Proxy Error The proxy server received an invalid response from an upstream server.

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