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The general theme here is "less is more". Don't write anticipatory code for potential bottlenecks, you need to measure and identify them first. Start with simpler algorithms. Start with simpler data structures.

Rule 1. You can't tell where a program is going to spend its time. Bottlenecks occur in surprising places, so don't try to second guess and put in a speed hack until you've proven that's where the bottleneck is.

Rule 2. Measure. Don't tune for speed until you've measured, and even then don't unless one part of the code overwhelms the rest.

Rule 3. Fancy algorithms are slow when n is small, and n is usually small. Fancy algorithms have big constants. Until you know that n is frequently going to be big, don't get fancy. (Even if n does get big, use Rule 2 first.)

Rule 4. Fancy algorithms are buggier than simple ones, and they're much harder to implement. Use simple algorithms as well as simple data structures.

Rule 5. Data dominates. If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming.

This last bit of Rule 5 is particularly interesting: "Data structures, not algorithms, are central to programming."

It is so much easier to reason about and build a flow of logic when you've modeled the problem well and gotten the data structure right.