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Notes

#

For the last few years, especially after the pandemic forced us to switch to remote work, I’ve been searching for ways to break free from a stressful, sedentary lifestyle.

I’ve been trying to be more consistent. I’ve tried different approaches, but each attempt would start with enthusiasm, only to gradually fade away.

This year something was different. I still don’t know what it was, but I’ve been able to keep making progress. I’ve managed to work out for +130 hours. Almost a 70% increase from last year. Around 20 minutes a day.

These numbers aren’t impressive, but they represent something far more valuable for me: consistency. I’m proud not just of the numbers, but of maintaining a regular routine despite facing significant personal challenges during the second half of the year.

Next year, I’ll try to keep making progress. I’m going to keep making progress. I’m going to keep making progress.

This is the closest I’ll ever publicly get to a year review and a new year resolution. I’ve created this page to keep track of my progress.

#

Investigating challenging problems and bugs is an exciting experience that often leads to remarkable discoveries and personal growth. As a engineer, I’ve always found joy in decoding complex issues and diving deep into the unknown. The thrill of the hunt, the satisfaction of piecing together clues, and the ahá moment when everything falls into place are unique.

One of the most rewarding aspects of problem-solving is the opportunity to learn new things. Every bug, every system failure, and every unexpected behavior is a chance to expand your knowledge and skillset. It’s like being a detective in the digital world, where each case brings its own unique challenges and lessons.

The skills developed through problem-solving and investigation are not only valuable in professional settings but can also be applied to personal life. Embracing the challenge of investigating problems and bugs not only makes us better professionals but also equips us with valuable life skills. It teaches us to be persistent, adaptable, and open to learning.

While the truths we uncover can be difficult or painful, they ultimately lead us to growth and a deeper understanding of ourselves and the world around us.

Indeed, these problem-solving skills can sometimes lead to unexpected and challenging discoveries in our personal relationships. Such as uncovering long-term-perfectly-hidden lies and infidelities.

Slightly inspired by this thread: https://twitter.com/rosapolis/status/1828014752112545924

# Zed screenshot

I’ve never been enthusiastic about the AI revolution.

However, this week, I started using Zed.dev together with their AI assistant integration. I’m surprised by the experience and efficiency gains. I still have reservations, but it can be valuable allies in tackling programming challenges and routine tasks.

For example, a few days ago, I was working on a tool that needed to parse ClickHouse explain output1, an easy task requiring recursively transverse all the properties looking for some specific properties.

With a simple prompt, the model wrote a working version.

Write code to extract all primary key granules from explain_result. An example of explain_result here:

{…}

def extract_primary_key_granules(explain_result):
    def traverse(node):
        granules = []
        if isinstance(node, dict):
            if 'Indexes' in node:
                for index in node['Indexes']:
                    if index.get('Type') == 'PrimaryKey':
                        granules.append({
                            'Keys': index.get('Keys', []),
                            'Initial Granules': index.get('Initial Granules', 0),
                            'Selected Granules': index.get('Selected Granules', 0)
                        })
            for value in node.values():
                granules.extend(traverse(value))
        elif isinstance(node, list):
            for item in node:
                granules.extend(traverse(item))
        return granules

    return traverse(explain_result)

# Example usage:
result = extract_primary_key_granules(explain_result)
for granule in result:
    print(f"Keys: {granule['Keys']}")
    print(f"Initial Granules: {granule['Initial Granules']}")
    print(f"Selected Granules: {granule['Selected Granules']}")
    print()

The task was relatively easy and I had to make changes and improve the initial code. Despite that, this saved me at least an hour of back-and-forth writing the function and running some tests to see if it worked as expected!

I started using Zed AI assistant with ChatGPT integration. A day after they released their own integration that I’m now testing together with Anthropic’s Claude model. I’m not even using Google for simple searches anymore.

Footnotes

  1. This is how a ClickHouse explain output looks like ↩

#

I’ve never invested much time in understanding how operating systems work. It has never been on my interests list, nor have I considered it useful knowledge for my career. What can I say? We all make bad decisions in our lives.

Over the last two years, I have invested a lot of time in understanding how some things work in Linux. Those are the consequences (or benefits?) of working with a complex and low-level system like a database.

This week, I was working on tracking data that was read from the page cache. While debugging some ClickHouse metrics directly collected from the Kernel, I discovered the /proc/thread-self directory1.

/proc/thread-self

When a thread accesses this magic symbolic link, it resolves to the process’s own /proc/self/task/[tid] directory.

So /proc/thread-self points to the proc folder for the current thread. In those folders, you can find a lot of helpful information. In my case, I was interested in /proc/thread-self/io 2, where you have IO statistics.

I was focused on investigating whether the Kernel reported bytes read from S3 inside rchar. I shared more information in this PR in ClickHouse repository. Despite the PR being closed, the examples I shared there still hold valuable insights and a reproducer that can contribute to the collective understanding of the system.

Footnotes

  1. There is also a /proc/self for the current process. This is something I didn’t know either. ↩

  2. https://docs.kernel.org/filesystems/proc.html#proc-pid-io-display-the-io-accounting-fields ↩

#

It’s quite common to find folks sharing papers on minor breakthroughs in areas like NLP, Computer Vision, Machine Learning, Deep Learning, and related fields here.

We’ve carefully gathered a collection of papers focused on databases, distributed systems, and data in general. We’ve explored the latest developments in these areas and gained valuable insights from the many articles in our list.

Some of these publications were suggested to us after we made our list public, adding to the richness of our curated collection.

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