What is mapping?
Mapping is the process of linking one set of items to another set, so that each item in the first set points to a matching item in the second set. In tech, it often means connecting keys to values (like a dictionary), converting data from one format to another, or relating geographic locations to coordinates.
Let's break it down
- Source: The original thing you have (e.g., a word, an address, a number).
- Target: The thing you want to get to (e.g., a definition, latitude/longitude, a result).
- Rule or Function: The logic that tells you how to go from source to target (e.g., “add 5”, “look up in a table”, “apply a formula”).
- Result: The output you receive after the rule is applied.
Why does it matter?
Mapping lets computers quickly find information, transform data, and make sense of complex relationships. Without mapping, programs would have to search through everything each time, which would be slow and error‑prone. It also helps humans visualize connections, like turning a street address into a point on a map.
Where is it used?
- Programming: Dictionaries, hash tables, and object properties.
- APIs: Converting JSON fields to internal variables.
- Geography: GPS coordinates, GIS layers, and route planning.
- Data analysis: Mapping raw data to categories or labels.
- User interfaces: Linking button clicks to functions.
Good things about it
- Speed: Direct look‑ups are fast.
- Clarity: Makes relationships explicit and easy to understand.
- Reusability: One mapping rule can be applied many times.
- Flexibility: You can change the rule without rewriting the whole system.
Not-so-good things
- Complexity: Large or nested mappings can become hard to maintain.
- Memory use: Storing big mapping tables may consume a lot of RAM.
- Stale data: If the source or target changes and the map isn’t updated, results become incorrect.
- Collision risk: In hash‑based maps, different keys might end up pointing to the same spot, requiring extra handling.