Today, I want to talk about one of my favorite actions as a technology professional: “trashing” something. No, don’t immediately think of kicking a physical server or DROP TABLE-ing a database. For me, trashing usually means re-evaluating the value I’ve assigned to a concept, an architecture, or sometimes even an approach, and shedding unnecessary burdens.
One of the most important lessons I’ve learned in my career is that not every shiny new technology solves every problem, and sometimes the simplest solution is the best. This week, I encountered a similar situation again and sent a “data collection” approach that I’ve habitually used for years into my mind’s trash bin. This isn’t new for me; I’ve been in this industry since 2006, and this trashing process is a continuous evolution.
The Concept of the Trash Bin: How Does a Technology Fall Out of Favor for Me?
When I say “trash a technology,” I’m not talking about completely ignoring it. Rather, I’m talking about changing its priority for me, its application scenario, and even the mental capacity I allocate to it. I perform this action when I realize that something I once considered “indispensable” has, over time, only created extra overhead.
For example, while working on a manufacturing ERP, I had considered a complex event-sourcing architecture for real-time reporting. In theory, it looked great: every operation would be recorded as an event, dashboards would update instantly. However, what the operators and managers on the floor really wanted was to see accurate numbers at the end of the day and simply answer the question “how much did we produce?” In this case, the extra maintenance, debugging, and learning curve required by that complex architecture far outweighed the “real-time” advantage it provided. I threw that architecture into my mind’s trash bin and reverted to a simpler batch processing approach.
The Traps of Shiny Innovation and My Lesson
Eagerly jumping on every new thing in the industry is, unfortunately, a common pitfall. There have been times when I thought, “This time it will be different,” and placed an immature tool at the heart of a large project. Once, I tried a new SD-WAN solution for more dynamic routing between different ISPs at company exits. The marketing materials promised wonders. However, it caused more problems than the BGP-based solution I had used before, which I managed with simple scripts I wrote myself. Especially MTU/MSS mismatches, DNS resolution issues, and instability in VPN tunnels really bothered me.
This experience once again showed me how valuable the “if it ain’t broke, don’t fix it” rule is, especially on the network side. A new technology, instead of solving an existing problem, often brings new and unforeseen problems with it. After a while, I shelved that SD-WAN solution, saying “this isn’t for us,” which in a sense, meant I trashed it.
Real World, Real Costs: A Moment of Decision
The cost of continuing to use a technology isn’t just license fees or server costs. One of the biggest costs is the human effort you spend understanding it, maintaining it, troubleshooting it, and training your team. This can accumulate over time into a massive debt. In the spam blocker application I developed for Android, I was initially inclined to use too many third-party libraries. I was adding a library for every small feature.
After a while, I realized that each of these libraries brought its own dependencies, its own security vulnerabilities, and its own update cycles. The application size swelled, build times lengthened, and some libraries even clashed with Play Store policies. On April 28, while trying to publish an update, I experienced a two-week metadata reject simply because one library needed a new permission. That’s when I decided I needed to trash this approach. By rewriting most features with my own simple code, I both took control and got rid of unnecessary complexity.
The Filter of 20 Years of Experience: Minimalism and Pragmatism
After twenty years, my approach to new technology has radically changed. My first question is no longer, “How great is this?” but “Does this solve the specific problem I have in the simplest, most reliable, and most sustainable way?” Anything that doesn’t pass this filter is potentially a candidate for my mind’s trash bin. For example, for the financial calculators I run on my own VPS, I tried complex caching strategies for years. Redis, Memcached, even custom in-memory caches… Eventually, I realized that optimizing database queries and using Nginx’s simple disk cache provided more than enough performance for my needs in most cases. More complex solutions only brought more monitoring and maintenance costs.
# Nginx simple disk cache example
http {
proxy_cache_path /var/cache/nginx levels=1:2 keys_zone=my_cache:10m inactive=60m;
server {
location /api/data {
proxy_cache my_cache;
proxy_cache_valid 200 302 10m;
proxy_cache_valid 404 1m;
proxy_cache_revalidate on;
proxy_cache_min_uses 1;
proxy_cache_use_stale error timeout updating http_500 http_503;
add_header X-Cache-Status $upstream_cache_status;
proxy_pass http://backend_server;
}
}
}A simple Nginx configuration like the one above often works better than the larger, more complex systems we chase as “solutions.” This also summarizes the philosophy behind the “trashing” action: getting rid of excess, simplifying, and keeping what truly works.
What I trashed this week wasn’t actually a technology, but the fallacy of “more is better.” I put a stop to the operational burden caused by chasing more detail than necessary in data collection and analysis. After 20 years, I see very clearly: the complexity of a system is often directly proportional to its fragility.
What about you? What technology, approach, or idea have you “trashed” in your career? What led you to make that decision, and what did it teach you? I’m eagerly awaiting your comments.