What is LOAD BALANCING? ⚖️

Summary

The video script discusses consistent hashing, a technique crucial for scalable systems. It explains how hashing distributes requests evenly across servers, ensuring load balancing. The script highlights the problem of server addition, which can disrupt the distribution, and introduces consistent hashing as a solution to minimize the impact of such changes, maintaining system efficiency and cache utility.

Takeaways

• 🔑 Consistent hashing is crucial for systems that need to scale to handle large volumes of requests efficiently.
• 🖥️ A server's role is to process requests and send responses, which is essential for user satisfaction and business success.
• 📈 When scaling up, load balancing becomes important to distribute the incoming requests evenly across multiple servers.
• 🔄 Consistent hashing helps in distributing requests uniformly across servers without overloading any single server.
• 🆔 The request ID, which is often generated randomly, is used to determine which server should handle a particular request by hashing.
• 💡 If a server is added or removed, standard hashing can cause significant changes in the distribution of requests, leading to inefficiencies.
• 🔄 Consistent hashing minimizes the impact of adding or removing servers by ensuring only a small subset of keys is remapped to different servers.
• 📊 The 'pie chart' analogy illustrates how consistent hashing allows for a smooth transition when servers are added or removed, maintaining balance.
• 🗃️ Caching user-specific information on the server that handles their requests can improve performance, but standard hashing can disrupt this by changing which server a user is directed to.
• 🔑 Consistent hashing is designed to maintain a minimal change in the cache even when the system scales, preserving the efficiency of the system.
• 🛠️ Understanding consistent hashing is vital for developers building scalable systems, as it addresses the challenges of traditional hashing methods in a distributed environment.

Q & A

What is consistent hashing?

Consistent hashing is a technique used in distributed systems to distribute a large number of keys across a set of servers in a way that minimizes the number of keys that need to be remapped when servers are added or removed.

Why is consistent hashing important in system design?

Consistent hashing is important in system design because it allows systems to scale effectively by evenly distributing load across multiple servers and minimizing reorganization when the number of servers changes.

What is the basic concept of hashing objects in the context of consistent hashing?

In the context of consistent hashing, hashing objects refers to the process of applying a hash function to a request ID or other data to determine which server should handle the request, ensuring an even distribution of load.

What is a server in the context of this script?

In this script, a server is a computer that runs a program and serves requests from clients, such as mobile devices, by processing the requests and sending back responses.

How does a server handle requests from clients?

A server handles requests from clients by receiving the requests, processing them using an algorithm, and then sending back the appropriate response, such as an image with a facial recognition result.

What is load balancing and why is it necessary?

Load balancing is the process of distributing incoming network traffic across multiple servers to ensure no single server becomes overwhelmed. It is necessary to maintain system performance and reliability as demand increases.

How does consistent hashing help with load balancing?

Consistent hashing helps with load balancing by assigning requests to servers in a way that minimizes the redistribution of requests when servers are added or removed, thus maintaining an even load across all servers.

What happens when a new server is added to a system using standard hashing?

When a new server is added to a system using standard hashing, a significant number of requests may need to be reassigned to different servers, causing a large shift in the load distribution and potentially invalidating cached data.

Why is it problematic to have a large number of changes in the assignment of requests to servers?

A large number of changes in the assignment of requests to servers can be problematic because it can lead to a significant amount of data in the cache becoming obsolete, requiring reprocessing and negatively impacting performance.

How does consistent hashing minimize the impact of adding a new server?

Consistent hashing minimizes the impact of adding a new server by ensuring that only a small subset of keys (requests) need to be reassigned, maintaining the overall balance of the load and reducing the need to update the cache.

What is the significance of the 'pie chart' analogy used in the script?

The 'pie chart' analogy in the script is used to visually represent the distribution of requests among servers. It helps to illustrate how consistent hashing allows for minimal changes in the distribution when servers are added or removed.