Picture this: You’re translating a text, and your terminology database is just sitting there like a brick, taking its sweet time to find matches. Meanwhile, your coffee gets cold, your deadline gets closer, and you start questioning your life choices.That was us. Until we got tired of waiting and rewrote search from the ground up. The result? A 27x speed increase.
Why Was It Slow in the First Place?
Most translation tools search for terms the same way a toddler looks for their favorite toy—by picking up every single item in the room and checking if it’s the one. Sure, it works, but if you have 10,000+ terms, it’s like watching a sloth do calculus.
At first, we thought, “Maybe we just need a bigger hammer?” But throwing more processing power at the problem wasn’t the answer. We needed something smarter.
Enter the Trie
A trie (pronounced “try,” as in “try not to cry when your search takes 10 seconds”) is a data structure that doesn’t waste time checking every word separately. Instead, it builds a tree-like map of all possible terms, allowing us to follow a direct path to a match instead of blindly rummaging through everything like a raccoon in a trash can.
Let’s take an example. Say our database has the following terms:
- Head
- Head of Aerodynamics
- Head of Engineering
- Head Start
A normal search system might check every single term one by one. But a trie? It’s built different.
Instead of storing these words separately, it structures them into a branching tree, like this:
Now, if we’re searching for “Head of Aerodynamics,” we don’t need to compare it against all 10,000 terms. We start at “Head” and follow the existing branches. No detours, no wasted time. Just smooth, instant results.
What About Languages Without Spaces?
For languages like Japanese and Chinese, things get even trickier. Words don’t have spaces, so your average search tool just panics and starts chopping characters randomly, often with hilariously bad results.
Imagine searching for 機械工学 (Mechanical Engineering) and the system tries matching 機械 (Machine) + 工学 (Engineering) separately, or worse, just 機 (Machine) + 械工 (Nonsense) + 学 (Study). Not helpful.
Instead, we adjusted the trie to work character by character, ensuring precise matches without false starts or accidental word splits. Now, instead of flailing around, the search smoothly follows each character, locking onto exact terms without breaking a sweat.
The End Result
A 107-word sentence means 5,778 potential term combinations, each needing to be checked against 10,210 defined terms—adding up to a staggering 58,993,380 individual checks. That’s a whole lot of work for a search algorithm, but instead of grinding to a halt, our system now blazes through it.
We cut search time from 0.55 seconds to just 0.02 seconds. That’s a 27x speed boost, without missing a single match. No more waiting, no more frustration—just instant results, exactly when you need them.