Cosmonic’s Post

We're closely following the progress of WebAssembly, including WebAssembly System Interface (WASI). There's a new experimental workload in .NET 8 for WASI that extends the capabilities of Wasm towards the Cloud. https://lnkd.in/eSzEStqw #WebAssembly

In our new blog post, CEO Matt Butcher explains "The History and Evolution of #WebAssembly in #Kubernetes" and how the most exciting application of Wasm is on cloud and edge. To do that well, Wasm needs to run INSIDE of K8s – read about it here: https://bit.ly/4dAzkQm

🛑 Serverless is a trap! With all the hype going around the #serverless (and myself writing countless serverless applications) I will tell you that it is a trap! And here is why. This is a series of posts talking about issues with #serverless. ❗️ Problem #1: communication performance issues. Serverless often means lots of separate components (think even smaller than microservices, nanoservices if you will) talking with each other through HTTP protocol. Usually, all the communication is handled within the vendor's cloud, therefore network communication overhead is not extreme (instead of 200 ms latency, you get 20 ms latency). However, it is almost impossible not to create chains of serverless components calling each other and resulting in those latencies adding up. Eventually, you end up wondering why this single HTTP request to my backend takes a whole 1 second. For example, in the context of AWS - when creating a serverless API (API Gateway + Lambda function) there is an inherent 50-100 ms integration latency that is always there. Further, serverless functions are limited in CPU / RAM / Network throughput. The less CPU / RAM, the less network throughput your function gets. It's funny, how a single HTTP request on your machine taking 1 second to complete might take 30 seconds on a low-end function to complete. Therefore, you end up paying for a massive serverless function which you don't need just to ensure you get a similar HTTP requests experience like on your machine or any VM actually. Serverless services throttle. You now need to think about what to do with all these failed payloads - ignore them or set up a dead-letter queue and reprocess them. Serverless services might duplicate your requests. It's just the nature of a such massive parallel distributed service. And now you need to think again about how to handle these duplications so some financial app doesn't send money twice. ❗️ TL;DR Handling serverless components communication in a serverless environment is a challenge. It requires a great deal for developers to handle edge cases and performance issues. All the time spent on dealing with serverless is the time not spent on developing your application. Therefore, don't go serverless.

Just finished my latest blog post. This time I write about Amazon Graviton and show how to migrate three different workloads, two Lambda function based and one ECS Fargate, to the ARM based CPU. Graviton has the best cost vs performance metrics available. Doing migration to Graviton can reduce your cloud cost and lower your carbon footprint. Check it out!! #aws #awscommunity #awscommunitybuilders #awsambassadors #serverless #sigmatechnologycloud https://lnkd.in/d9kaTFt8

For anyone involved in large-scale data processing, particularly those considering Apache Flink or Google Cloud Dataflow. The article gives guideline on how to setup a fair benchmark consideration as well as potential optimization that can be done on the pipeline: Real-world Benchmark: It offers a practical, real-world benchmark study conducted by Yahoo, providing valuable insights into the cost and performance of these platforms under specific use cases. This can help organizations make informed decisions about their technology stack. Fair Comparison Methodology: The blog post outlines the methodology used to ensure a fair comparison between Flink and Dataflow, emphasizing the importance of controlling variables and establishing a consistent testing environment. This level of rigor adds credibility to the findings. Cost Optimization Focus: It focuses on cost-effectiveness, a critical factor for businesses. The post helps readers understand how to minimize costs on both platforms.

For anyone involved in large-scale data processing, particularly those considering Apache Flink or Google Cloud Dataflow. The article gives guideline on how to setup a fair benchmark consideration as well as potential optimization that can be done on the pipeline: Real-world Benchmark: It offers a practical, real-world benchmark study conducted by Yahoo, providing valuable insights into the cost and performance of these platforms under specific use cases. This can help organizations make informed decisions about their technology stack. Fair Comparison Methodology: The blog post outlines the methodology used to ensure a fair comparison between Flink and Dataflow, emphasizing the importance of controlling variables and establishing a consistent testing environment. This level of rigor adds credibility to the findings. Cost Optimization Focus: It focuses on cost-effectiveness, a critical factor for businesses. The post helps readers understand how to minimize costs on both platforms.

For anyone involved in large-scale data processing, particularly those considering Apache Flink or Google Cloud Dataflow. The article gives guideline on how to setup a fair benchmark consideration as well as potential optimization that can be done on the pipeline: Real-world Benchmark: It offers a practical, real-world benchmark study conducted by Yahoo, providing valuable insights into the cost and performance of these platforms under specific use cases. This can help organizations make informed decisions about their technology stack. Fair Comparison Methodology: The blog post outlines the methodology used to ensure a fair comparison between Flink and Dataflow, emphasizing the importance of controlling variables and establishing a consistent testing environment. This level of rigor adds credibility to the findings. Cost Optimization Focus: It focuses on cost-effectiveness, a critical factor for businesses. The post helps readers understand how to minimize costs on both platforms.

For anyone involved in large-scale data processing, particularly those considering Apache Flink or Google Cloud Dataflow. The article gives guideline on how to setup a fair benchmark consideration as well as potential optimization that can be done on the pipeline: Real-world Benchmark: It offers a practical, real-world benchmark study conducted by Yahoo, providing valuable insights into the cost and performance of these platforms under specific use cases. This can help organizations make informed decisions about their technology stack. Fair Comparison Methodology: The blog post outlines the methodology used to ensure a fair comparison between Flink and Dataflow, emphasizing the importance of controlling variables and establishing a consistent testing environment. This level of rigor adds credibility to the findings. Cost Optimization Focus: It focuses on cost-effectiveness, a critical factor for businesses. The post helps readers understand how to minimize costs on both platforms.

For anyone involved in large-scale data processing, particularly those considering Apache Flink or Google Cloud Dataflow. The article gives guideline on how to setup a fair benchmark consideration as well as potential optimization that can be done on the pipeline: Real-world Benchmark: It offers a practical, real-world benchmark study conducted by Yahoo, providing valuable insights into the cost and performance of these platforms under specific use cases. This can help organizations make informed decisions about their technology stack. Fair Comparison Methodology: The blog post outlines the methodology used to ensure a fair comparison between Flink and Dataflow, emphasizing the importance of controlling variables and establishing a consistent testing environment. This level of rigor adds credibility to the findings. Cost Optimization Focus: It focuses on cost-effectiveness, a critical factor for businesses. The post helps readers understand how to minimize costs on both platforms.

🚀 Excited to share my latest article on optimizing local application performance! 🚀 In this practical guide, I dive into using Google Cloud Profiler to enhance app performance. Check it out and discover how you can supercharge your tech stack! 🔗 https://lnkd.in/dyrDZxi4