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Architectural Insights: Designing Efficient Multi-Layered Caching With Instagram Example

DZone

Caching is a critical technique for optimizing application performance by temporarily storing frequently accessed data, allowing for faster retrieval during subsequent requests. Multi-layered caching involves using multiple levels of cache to store and retrieve data.

Cache 161
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The Three Cs: Concatenate, Compress, Cache

CSS Wizardry

When serving and storing files on the web, there are a number of different things we need to take into consideration in order to balance ergonomics, performance, and effectiveness. Caching them at the other end: How long should we cache files on a user’s device? 4,362ms of cumulative latency; 240ms of cumulative download.

Cache 291
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Best practices and key metrics for improving mobile app performance

Dynatrace

Mobile applications (apps) are an increasingly important channel for reaching customers, but the distributed nature of mobile app platforms and delivery networks can cause performance problems that leave users frustrated, or worse, turning to competitors. What is mobile app performance? Issue remediation.

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Crucial Redis Monitoring Metrics You Must Watch

Scalegrid

Redis® is an in-memory database that provides blazingly fast performance. This makes it a compelling alternative to disk-based databases when performance is a concern. You might already use ScaleGrid hosting for Redis hosting to power your performance-sensitive applications.

Metrics 130
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Dynatrace accelerates business transformation with new AI observability solution

Dynatrace

This blog post explores how AI observability enables organizations to predict and control costs, performance, and data reliability. For example, a Stanford University and UC Berkeley team noted in a research study that ChatGPT behavior deteriorates over time. Consequently, AI model drift and hallucinations emerge as primary concerns.

Cache 201
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Supporting Diverse ML Systems at Netflix

The Netflix TechBlog

Occasionally, these use cases involve terabytes of data, so we have to pay attention to performance. The user can choose the most suitable tool for manipulating data, such as Pandas or Polars to use a dataframe API, or one of our internal C++ libraries for various high-performance operations. ETL workflows), as well as downstream (e.g.

Systems 226
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Benchmark (YCSB) numbers for Redis, MongoDB, Couchbase2, Yugabyte and BangDB

High Scalability

An application example is a session store recording recent actions. 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: photo tagging; add a tag is an update, but most operations are to read tags. Conclusion.