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The Three Cs: Concatenate, Compress, Cache

CSS Wizardry

Concatenating our files on the server: Are we going to send many smaller files, or are we going to send one monolithic file? Caching them at the other end: How long should we cache files on a user’s device? Caching them at the other end: How long should we cache files on a user’s device? That’s almost 22× more!

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Optimising for High Latency Environments

CSS Wizardry

This gives fascinating insights into the network topography of our visitors, and how much we might be impacted by high latency regions. Round-trip-time (RTT) is basically a measure of latency—how long did it take to get from one endpoint to another and back again? What is RTT? RTT isn’t a you-thing, it’s a them-thing.

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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. Bandwidth optimization: Caching reduces the amount of data transferred over the network, minimizing bandwidth usage and improving efficiency.

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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. We started seeing increased response latencies and leader servers running at dangerously high utilization.

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

High Scalability

Redis Server: 5.07, x86/64. MongoDB server: 4.4.2, BangDB server: 2.0.0, We note that for MongoDB update latency is really very low (low is better) compared to other dbs, however the read latency is on the higher side. Application example: user profile cache, where profiles are constructed elsewhere (e.g.,

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Migrating Netflix to GraphQL Safely

The Netflix TechBlog

Before GraphQL: Monolithic Falcor API implemented and maintained by the API Team Before moving to GraphQL, our API layer consisted of a monolithic server built with Falcor. A single API team maintained both the Java implementation of the Falcor framework and the API Server. To launch Phase 1 safely, we used AB Testing.

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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. Useful for keeping “n-newest” or prefix path deletion.

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