Lecture Summary

Overview of Today's Agenda

• Focus on tasks with checkboxes today.
• Tasks without checkboxes might be covered tomorrow.
• AWS account limits are affecting ASG (Auto Scaling Group) implementation.
• Slides are available at the end of the 100ex sts.com project.
• Topics to be covered: Horizontal and vertical scaling, indexing in databases, Rust vs Node.js.

Main Concepts

Auto Scaling Groups (ASG)

• Discussion on ASG likely postponed to tomorrow due to AWS limits.
• Definition: A group of servers that automatically adjusts capacity to maintain steady, predictable performance.
• Used for horizontal scaling on AWS.

Scaling in Databases

• Discussed indexing, normalization, and sharding.
• Indexing covered today, normalization and sharding tomorrow.

Rust vs Node.js

• Rust is multi-threaded, fast, and memory-safe.
• Node.js is single-threaded, less scalable compared to Java, Go, Rust.
• Node.js has a cluster module for emulating multi-threading.

Detailed Discussion

Types of Scaling

• Vertical Scaling: Increasing the size of individual servers.

  • Example: Upgrading AWS EC2 instances (e.g., from c5xlarge to c5.4xlarge).
  • Limitations: Node.js can utilize only one CPU due to its single-threaded nature.

• Horizontal Scaling: Adding more servers to distribute the load.

  • Supported by ASG in AWS.

Programming Languages and Multi-threading

• Node.js:

  • Single-threaded, uses cluster module to emulate threading.
  • Not suitable for CPU-intensive tasks due to single-threading.

• Rust, Go, Java:

  • Support multi-threading, allowing parallel execution across multiple cores.
  • Code must be specifically written for parallel execution.

Practical Implementation

• Demonstrated how Node.js can use the cluster module to emulate multi-threading.
• Showcased Rust's capability to handle multiple threads naturally.
• Discussed the limitations of using Node.js for performance-intensive applications.

Capacity Estimation and Scaling

• Capacity Estimation: Crucial for understanding load and scaling requirements.

  • Infrastructure is often scaled based on estimates of requests per second.

• Auto Scaling:

  • Based on load metrics like CPU utilization and requests per second.
  • Implemented using AWS Auto Scaling Groups.

• Special Use Cases:

  • Real-time applications like chat or games using WebSockets.
  • Applications like video transcoding requiring extended compute time.
  • Strategy: Use hot pools or queues to handle sustained loads.

Conclusion

• Discussed implementing horizontal scaling using AWS Auto Scaling Groups.
• Practical considerations regarding scaling in real-time applications and responsive loads.

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Note: Future tasks include an in-depth exploration of ASG and implementing scaling strategies.