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PolyScale.ai – Scaling MySQL & PostgreSQL with Global Caching

Scalegrid

Data-driven applications span a wide breadth of complexity, from simple microservices to real-time event-driven systems under significant load. Guest post by Ben Hagan from PolyScale.ai However, as any development and/or DevOps team tasked with performance improvements will attest, making data-driven apps fast globally is “non-trivial”.

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Performance Game Changer: Browser Back/Forward Cache

Smashing Magazine

Performance Game Changer: Browser Back/Forward Cache. Performance Game Changer: Browser Back/Forward Cache. With that caveat out of the way, let’s get to the guts of the article: What is the Back/Forward Cache and why does it matter so much? Didn’t The HTTP Cache Do All That Anyway? Barry Pollard.

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Which Query Used the Most CPU? Implementing Extended Events

DZone

While you can look at what's in cache through the DMVs to see the queries there, you don't get any real history and you don't get any detail of when the executions occurred. If you really want a detailed analysis of which query used the most CPU, you need to first set up an Extended Events session and then consume that data.

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Supporting Diverse ML Systems at Netflix

The Netflix TechBlog

Explainer flow is event-triggered by an upstream flow, such Model A, B, C flows in the illustration. A hugely important detail that often goes overlooked is event-triggering : it allows a team to integrate their Metaflow flows to surrounding systems upstream (e.g. ETL workflows), as well as downstream (e.g.

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Dynatrace accelerates business transformation with new AI observability solution

Dynatrace

The RAG process begins by summarizing and converting user prompts into queries that are sent to a search platform that uses semantic similarities to find relevant data in vector databases, semantic caches, or other online data sources.

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Improved Alerting with Atlas Streaming Eval

The Netflix TechBlog

Moreover, common database optimizations like caching recently queried data don’t really work for alerting queries because, generally speaking, the last received datapoint is required for correctness. It became clear to us that we needed to solve the scalability problem with a fundamentally different approach.

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Hashnode Creates Scalable Feed Architecture on AWS with Step Functions, EventBridge and Redis

InfoQ

Hashnode created a scalable event-driven architecture (EDA) for composing feed data for thousands of users. The company used serverless services on AWS, including Lambda, Step Functions, EventBridge, and Redis Cache. The solution leverages Step Functions' distributed maps feature that enables high-concurrency processing.