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Data preferences

Ever wondered whether one should approach problem X with data structure Y or Z? This article covers a variety of topics related to these dilemmas.


This article makes references to "[something]-time" operations. This terminology comes from algorithm analysis' Big O Notation.

Long-story short, it describes the worst-case scenario of runtime length. In laymen's terms:

"As the size of a problem domain increases, the runtime length of the algorithm..."

  • Constant-time, O(1): "...does not increase."

  • Logarithmic-time, O(log n): "...increases at a slow rate."

  • Linear-time, O(n): "...increases at the same rate."

  • Etc.

Imagine if one had to process 3 million data points within a single frame. It would be impossible to craft the feature with a linear-time algorithm since the sheer size of the data would increase the runtime far beyond the time allotted. In comparison, using a constant-time algorithm could handle the operation without issue.

By and large, developers want to avoid engaging in linear-time operations as much as possible. But, if one keeps the scale of a linear-time operation small, and if one does not need to perform the operation often, then it may be acceptable. Balancing these requirements and choosing the right algorithm / data structure for the job is part of what makes programmers' skills valuable.

Array vs. Dictionary vs. Object

Godot stores all variables in the scripting API in the Variant class. Variants can store Variant-compatible data structures such as Array and Dictionary as well as Objects.

Godot implements Array as a Vector<Variant>. The engine stores the Array contents in a contiguous section of memory, i.e. they are in a row adjacent to each other.


For those unfamiliar with C++, a Vector is the name of the array object in traditional C++ libraries. It is a "templated" type, meaning that its records can only contain a particular type (denoted by angled brackets). So, for example, a PackedStringArray would be something like a Vector<String>.

Contiguous memory stores imply the following operation performance:

  • Iterate: Fastest. Great for loops.

    • Op: All it does is increment a counter to get to the next record.

  • Insert, Erase, Move: Position-dependent. Generally slow.

    • Op: Adding/removing/moving content involves moving the adjacent records over (to make room / fill space).

    • Fast add/remove from the end.

    • Slow add/remove from an arbitrary position.

    • Slowest add/remove from the front.

    • If doing many inserts/removals from the front, then...

      1. invert the array.

      2. do a loop which executes the Array changes at the end.

      3. re-invert the array.

      This makes only 2 copies of the array (still constant time, but slow) versus copying roughly 1/2 of the array, on average, N times (linear time).

  • Get, Set: Fastest by position. E.g. can request 0th, 2nd, 10th record, etc. but cannot specify which record you want.

    • Op: 1 addition operation from array start position up to desired index.

  • Find: Slowest. Identifies the index/position of a value.

    • Op: Must iterate through array and compare values until one finds a match.

      • Performance is also dependent on whether one needs an exhaustive search.

    • If kept ordered, custom search operations can bring it to logarithmic ti