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L1 cache, or Level 1 cache, is the smallest and fastest cache in the CPU memory hierarchy. Cloud Foundry and Kubernetes are frequently mentioned together because both help organizations deploy and run applications in the cloud. Modern cloud platforms rely heavily on both containerization and virtualization to run applications efficiently. A query that performs well on a small dataset can become painfully One server may be handling complex requests that consume significant CPU resources, while another processes lightweight requests with minimal effort.

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• Stateless services are easier to scale horizontally because any instance can handle any request.
• When a design is ready to become software, you can hand it off to Claude Code, which continues from your existing work instead of starting over from a screenshot.
• I am providing code and resources in this repository to you under an open source license.
• L1 cache, or Level 1 cache, is the smallest and fastest cache in the CPU memory hierarchy.

Partition Tolerance is continued serving when “network failures” happen. One individual server that is part of the overall distributed system is called Node. So, we will keep 25 servers with a load balancer in front of them to handle our requests.

• ACID is a set of properties of relational database transactions.
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• In this setup, you replicate the data into multiple servers instead of one.

Vertical vs Horizontal Scaling

This reduces complexity on the client side, since clients only talk to a single endpoint. It handles routing to the right service, rate limiting, authentication, logging, and sometimes response shaping. It improves both reliability and performance, since a single server’s failure does not bring down the entire system. Load balancing spreads incoming traffic across multiple servers so no single server gets overloaded.

It uses multiple hash functions to set bits in a bit array when items are inserted. A Bloom filter is a space efficient data structure that quickly answers “might this item be in the set? LSM Trees batch writes in memory and periodically flush them to disk, which makes writes very fast but reads more complex. B Trees are balanced trees that keep data sorted and let you find ranges efficiently, common in relational databases.

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