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The Three Cs: Concatenate, Compress, Cache

CSS Wizardry

Caching them at the other end: How long should we cache files on a user’s device? Cache This is the easy one. Caching is something I’ve been a little obsessed with lately , but for static assets as we’re discussing today, we don’t need to know much other than: cache everything as aggressively as possible.

Cache 338
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Elasticsearch Indexing Strategy in Asset Management Platform (AMP)

The Netflix TechBlog

It provides simple APIs for creating indices, indexing or searching documents, which makes it easy to integrate. One of the first decisions when integrating with Elasticsearch is designing the indices, their settings and mappings. Mapping is used to define how documents and their fields are supposed to be stored and indexed.

Strategy 266
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AWS serverless services: Exploring your options

Dynatrace

Instead of worrying about infrastructure management functions, such as capacity provisioning and hardware maintenance, teams can focus on application design, deployment, and delivery. The first benefit is simplicity. But which are the best fit for your business, and where do they make the most sense in your serverless application stack?

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Netflix’s Distributed Counter Abstraction

The Netflix TechBlog

By: Rajiv Shringi , Oleksii Tkachuk , Kartik Sathyanarayanan Introduction In our previous blog post, we introduced Netflix’s TimeSeries Abstraction , a distributed service designed to store and query large volumes of temporal event data with low millisecond latencies. Today, we’re excited to present the Distributed Counter Abstraction.

Latency 253
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Critical CSS? Not So Fast!

CSS Wizardry

Honestly, in this scenario, my advice is almost always: don’t bother trying to retrofit Critical CSS—just hash-n-cache 1 2 the living daylights out of your existing CSS bundles until you replatform and do it differently next time. This means that speeding up the file’s fetch is unlikely to help it be applied to the document any sooner.

Media 356
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Benchmark (YCSB) numbers for Redis, MongoDB, Couchbase2, Yugabyte and BangDB

High Scalability

Application example: user profile cache, where profiles are constructed elsewhere (e.g., The latency table shows that 99th percentile latency for Yugabyte is quite high compared to others (lower is better). Workload C: Read only. This workload is 100% read. However, Very high Read latency for MonoDB makes it the last db to finish the task.

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ABAC on SpiceDB: Enabling Netflix’s Complex Identity Types

The Netflix TechBlog

To do so Netflix’s design required: An event based mechanism that could ingest information about application autoscaling groups. At authZ check time, provide the attributes for the identity to check, e.g. “can app bar in us-west-2 access this document.” What was problematic about this design?

Cache 257