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Designing Instagram

High Scalability

Design a photo-sharing platform similar to Instagram where users can upload their photos and share it with their followers. High Level Design. When the server receives a request for an action (post, like etc.) Component Design. API Design. We have provided the API design of posting an image on Instagram below.

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Timestone: Netflix’s High-Throughput, Low-Latency Priority Queueing System with Built-in Support…

The Netflix TechBlog

Timestone: Netflix’s High-Throughput, Low-Latency Priority Queueing System with Built-in Support for Non-Parallelizable Workloads by Kostas Christidis Introduction Timestone is a high-throughput, low-latency priority queueing system we built in-house to support the needs of Cosmos , our media encoding platform. Over the past 2.5

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Crucial Redis Monitoring Metrics You Must Watch

Scalegrid

You will need to know which monitoring metrics for Redis to watch and a tool to monitor these critical server metrics to ensure its health. Understanding Redis Performance Indicators Redis is designed to handle high traffic and low latency with its in-memory data store and efficient data structures.

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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. Again Yugabyte latency is quite high. The latency table for test D is as below.

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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? Plotted on the same horizontal axis of 1.6s, the waterfalls speak for themselves: 201ms of cumulative latency; 109ms of cumulative download. 4,362ms of cumulative latency; 240ms of cumulative download.

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Achieving 100Gbps intrusion prevention on a single server

The Morning Paper

Achieving 100 Gbps intrusion prevention on a single server , Zhao et al., With more nodes and more coordination comes more complexity, both in design and operation. Today’s paper choice is a wonderful example of pushing the state of the art on a single server. This makes the whole system latency sensitive. OSDI’20.

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SLOG: serializable, low-latency, geo-replicated transactions

The Morning Paper

SLOG: serializable, low-latency, geo-replicated transactions Ren et al., That’s where SLOG (Serializable LOw-latency, Geo-replicated transactions) comes in. Data is replicated across regions, but for every data item one of these regions is designated as its home replica. VLDB’19. Let’s make a plan. Multi-home transactions.

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