Build a blameless culture or join one ⚡
Build a blameless culture or join one ⚡
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Build a blameless culture or join one ⚡
Mistakes are inevitable; bugs will hit production; and outages are bound to happen, but how we respond to these errors and events significantly impacts our team's success and overall well-being.
Blameless culture is all about prioritizing learning and improvement over finger-pointing and punishment. It helps in
building a safe space to share and discuss errors openly
building a culture where things are not put under the rug
building a collaborative environment for better decision-making
building a culture of continuous improvement
If you see people playing the blame game at your organization, change it. Sometimes these changes need to happen bottom-up. It does not matter if you are a senior engineer, a tech lead, or a manager, be the change agent in your organization. and try to build a blameless culture and let others see its impact.
Few things you can do to build a blameless culture:
shift focus - from "Who made the mistake?" to "What went wrong?"
encourage transparency - reward teams who openly share their challenges
encourage experimentation and learning from failures
create and share RCAs of events in an open channel
Blameless culture is not about ensuring that mistakes never happen. It's about creating an environment where we learn from setbacks and become a better team.
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📹 Video I posted this week
This week I posted Why thread pools even exist? and how to implement them?
Threads are an excellent way to speed up your execution by leveraging multiple CPU cores of your machine. But spinning up a large number of threads can be catastrophic and that is where thread pools kick in.
In this video, we go in-depth into what thread pools are, why we need them, some real-world use cases where they fit, and more importantly a quick prototype for implementing them from scratch.
🧠 Research paper I read this week
This week I spent reading Cache-Efficient Top-k Aggregation over High Cardinality Large Datasets
The problem is non-trivial especially when the dataset is massive with a high cardinality. The two key principles they are operating to find top-k on are
use efficient data structures to keep intermediate results in cache, and
Identify likely top groups early and only compute exact results for them and prune the rest.
This paper is definitely a must-read if you find working on massive datasets interesting.
You can download this and other papers I recommend from my papershelf.
📰 Articles I read this week
I read a few engineering blogs almost every single day, and here are the three articles I would recommend you to read.
Thank you so much for reading this edition of the newsletter 🔮 If you found it interesting, you will also love my courses
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