As a creator, there’s a unique joy in seeing people actively using and appreciating the things you create. In my case, it’s a bot that I had developed for Discord servers; a small side-hobby which has expanded over the years.

Journey from a Hobby to a Large-Scale Operation

My bot, named T4, started its journey with library on a humble netbook with only 1GB of RAM. It quickly graduated to a Raspberry Pi 2, with 1GB RAM and slower. While the Raspberry Pi might have been slower, it allowed me to keep T4 online 24/7, a massive improvement.

Seeing T4 grow and being utilized by increasing numbers of people, I had to upgrade to a more capable server on Google Compute Engine. Today, as the bot continues to expand, it operates on an n1-standard-2 server with 8GB of RAM, which, while being more costly, enables the bot to function seamlessly.

Analysis of Resource Usage

Given my penchant for analyzing graphs, I started noticing that T4 had been consuming more than expected RAM. This sparked my curiosity and led me to investigate further.

Below is a graph showing the RAM usage of the entire server.

(The spikes appearing in there are beause I restarted the server two times.)

A graph representing high RAM usage by the bot, T4. The graph shows consistent usage of more than 80% RAM, with sporadic spikes representing bot restarts.
Server RAM percentage usage

As shown in the graph, the server was using more than 80% of the available RAM. Using system resource management tools like htop, I could determine that approximately 5GB of RAM was being consumed by the bot alone. This got me thinking – was there a memory leak, or was the bot just growing to a size where this kind of RAM usage was to be expected?

Debugging and Discovering the Memory Usage

With T4 now a part of over 12,000 servers, debugging seemed like the next logical step. The ability to get process names by hovering over the graphs triggered a thought: could this indeed be made possible to change for each shard in my Discord bot? To my surprise, it was possible, thanks to a Python module named setproctitle.

A closer examination revealed that one particular shard was consuming significantly more RAM than the rest – over 2GB, to be specific. That’s more than six times the 300MB average each shard typically consumes.

A graph of the RAM usage by different shards of the T4 bot over time. One shard stands out, consuming over 2GB of RAM compared to the others, which average around 300MB.
Server RAM GiB usage for shard processes

Interestingly, when I rechecked the stats without the member’s intent factored in, each server was averaging around 300MB. So, the spike had something to do with the members. But what could be causing such a drastic increase in RAM usage on just one shard?

BlueWillow’s Guild: The Culprit

As I delved deeper into the mystery, I stumbled upon BlueWillow’s guild, one of the immense Discord servers, hosting over 2 million members.

Screenshot from the Discord platform, showing the server address for BlueWillow. The server shows more than 100k online users and a total member count of 2.2 million.

My initial reaction was disbelief, but further investigation disclosed the magnitude of BlueWillow’s footprint. According to a Statista report, as of May 2023, BlueWillow was on the list of top most populated Discord servers, ranking 3rd with 1.5 million members.

A bar graph providing data from Statista on the most populated Discord servers globally, measured by user count as of May 2023, with Midjourney, OpenAI, and BlueWillow leading the way.

Indeed, my bot T4, just like BlueWillow, was growing at an exceptional pace, and by May 2023, T4 was listed among the most popular servers in the world.

Understanding the interaction between T4 and the Discord servers, particularly the large ones like BlueWillow, brought to light the fascinating dynamics of server management on such a grand scale. It unveils the interesting challenges and opportunities that arise while managing bots in large servers. I can’t wait to see where this exciting journey takes me next!

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