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The Three Cs: Concatenate, Compress, Cache

CSS Wizardry

Compressing them over the network: Which compression algorithm, if any, will we use? Caching them at the other end: How long should we cache files on a user’s device? This is because, at present, algorithms like Gzip and Brotli become more effective the more historical data they have to play with. main.af8a22.css

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Cache Grab: How Much Are You Leaving on the Table?

CSS Wizardry

For the longest time now, I have been obsessed with caching. I think every developer of any discipline would agree that caching is important, but I do tend to find that, particularly with web developers, gaps in knowledge leave a lot of opportunities for optimisation on the table. Want to know everything (and more) about HTTP cache?

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The Power of Caching: Boosting API Performance and Scalability

DZone

Caching is the process of storing frequently accessed data or resources in a temporary storage location, such as memory or disk, to improve retrieval speed and reduce the need for repetitive processing.

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Consistent caching mechanism in Titus Gateway

The Netflix TechBlog

We introduce a caching mechanism in the API gateway layer, allowing us to offload processing from singleton leader elected controllers without giving up strict data consistency and guarantees clients observe. Active data includes jobs and tasks that are currently running. Titus Gateway handles user requests.

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Introducing Netflix’s Key-Value Data Abstraction Layer

The Netflix TechBlog

Second, developers had to constantly re-learn new data modeling practices and common yet critical data access patterns. To overcome these challenges, we developed a holistic approach that builds upon our Data Gateway Platform. Data Model At its core, the KV abstraction is built around a two-level map architecture.

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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.

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Bloom Filters: Efficient Data Filtering With Practical Applications

DZone

Bloom filters are probabilistic data structures that allow for efficient testing of an element's membership in a set. They effectively filter out unwanted items from extensive data sets while maintaining a small probability of false positives. Since their invention in 1970 by Burton H.