> For the complete documentation index, see [llms.txt](https://isubasinghe.gitbook.io/isithas-wiki/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://isubasinghe.gitbook.io/isithas-wiki/computer_science/data_structures/judy_arrays.md).

# Judy Arrays

## Basic idea

A cache-conscious associative array invented at HP Labs. Internally a 256-way digital trie that switches between several node layouts to minimise both cache misses and memory. Very fast — but the implementation is notoriously complex.

## Key formulas

* Lookup/insert/delete: $O(\log\_{256} n)$ — effectively constant.
* Memory: $\approx 8$ bytes per element typical, much less than a hash table at small sizes.

Are an associative data structure which tends to have better performance than heaps. It comes with other benefits as well, since it an array, it is more cache friendly.

## Why is this not more common?

The performance improvement is not worth the complexity of the implementation

## Interesting facts

Douglas Baskins named this after his sister, Judy.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://isubasinghe.gitbook.io/isithas-wiki/computer_science/data_structures/judy_arrays.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.