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Performance Tuning Java Applications in Linux

DZone

You may also like: How to Properly Plan JVM Performance Tuning. While Performance Tuning an application both Code and Hardware running the code should be accounted for. Polling threads are an example where you might want to do this. For low latency, applications use Concurrent Mark and Sweep Algorithm — CMS or G1 GC.

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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. For example, if some fields in the responses are timestamps, those will differ.

Traffic 347
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Introducing Netflix’s Key-Value Data Abstraction Layer

The Netflix TechBlog

These include challenges with tail latency and idempotency, managing “wide” partitions with many rows, handling single large “fat” columns, and slow response pagination. It also serves as central configuration of access patterns such as consistency or latency targets.

Latency 261
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Optimizing your Kubernetes clusters without breaking the bank

Dynatrace

Kubernetes microservices applications are a striking example of the complexity of today’s modern application and IT stacks. Tuning thousands of parameters has become an impossible task to achieve via a manual and time-consuming approach. SREcon21 – Automating Performance Tuning with Machine Learning. lower than 2%.).

Latency 211
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Bending pause times to your will with Generational ZGC

The Netflix TechBlog

Reduced tail latencies In both our GRPC and DGS Framework services, GC pauses are a significant source of tail latencies. For a given CPU utilization target, ZGC improves both average and P99 latencies with equal or better CPU utilization when compared to G1. No explicit tuning has been required to achieve these results.

Latency 238
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Introducing Netflix TimeSeries Data Abstraction Layer

The Netflix TechBlog

Rajiv Shringi Vinay Chella Kaidan Fullerton Oleksii Tkachuk Joey Lynch Introduction As Netflix continues to expand and diversify into various sectors like Video on Demand and Gaming , the ability to ingest and store vast amounts of temporal data — often reaching petabytes — with millisecond access latency has become increasingly vital.

Latency 240
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Bulldozer: Batch Data Moving from Data Warehouse to Online Key-Value Stores

The Netflix TechBlog

For example, in order to enhance our user experience, one online application fetches subscribers’ preferences data to recommend movies and TV shows. The data warehouse is not designed to serve point requests from microservices with low latency. Let’s look at an example of a Bulldozer YAML configuration (Figure 3).

Latency 252