With both treemaps and hashmaps, it’s not always easy to identify patterns or clusters within your data because they work on two principles: one focuses on relationships between different objects., In contrast, the other focuses on storing ordered pairs of things.
In the world of web development, there are a lot of terms you might not have heard of. And that’s because hashmaps and treemaps are some of them.
These two data structures are used to store map-like data more efficiently than lists or dictionaries.
They can also be used to represent relationships between objects more concisely than traditional ones.[] notation.
Key Takeaways
- HashMaps provide faster data access and insertion due to their use of hashing, while TreeMaps are slower but maintain a sorted order of keys.
- HashMaps allow for one null key and multiple null values, while TreeMaps doesn’t support null keys but can have multiple null values.
- TreeMaps are more memory-efficient than HashMaps, as they don’t require resizing or rehashing during data insertion or deletion.
Hashmaps vs Treemaps
Hashmaps are a data structure that uses key-value pairs to store and retrieve data quickly, using a hashing function to map each key to a unique index in an array. Treemaps are data structure that holds data in a hierarchical structure organized based on legends. They can be used for various applications, such as indexing and data compression.
A hashmap is a tool that creates ordered pairs for your data. The first element of each team is the key, and the second is the value.
You can then use these ordered pairs to visualize your data more efficiently and accurately than a treemap, which lets you see patterns and clusters but requires more space and processing power.
A treemap is a chart with rectangular cells for the objects in your data. Each cell has an area proportional to the size of the thing.
This way, you can see how many objects are in each row and column and how much space they take up.
Comparison Table
| Parameters of Comparison | Hashmaps | Treemaps |
|---|---|---|
| Meaning | Powerful data storage tool for businesses | Vital map visualization tool for marketers |
| Features | Map consisting of different nodes (trees) representing links between those nodes | The tree-like shape used to represent hierarchical data |
| Consists | Collections of ordered pairs | Set of related images |
| Usage | More detailed analysis of your data or quick exploration of specific areas of interest | It is easy to see relationships between the different objects within your data |
| Keys | Single Null key | Multiple Null keys |
What are Hashmaps?
A hashmap effectively stores diverse objects in an object called the key with its corresponding value.
The key’s purpose is to identify the object, while the value will tell you what it contains.
Creating a hashmap containing all your data is accessible: You must create two objects, one for the keys and another for the values.
Then, you use these two objects for your map visualization. Here’s how:
First, create your crucial thing with all the information about each element (elements could be anything from people to countries).
Next, create your values object as well — this should also have all of your data organized into groups and sorted by their position on an axis. Finally, add these two objects into a MapView, and there you go!
What are Treemaps?
A treemap comprises four axes: sizes, colours, shapes, and values. The values are things like income or population density.
The shapes are rectangles and show the percentage of a particular value in the dataset. Finally, the size of the rectangles shows how much of the dataset there is overall.
Treemaps help you understand how data relates to each other by using colour-coded circles to show different levels of granularity in your data.
You can also use them to explore trends, patterns, clusters, outliers, or comparisons between datasets and find points where they intersect.
Main Differences Between Hashmaps and Treemaps
1) Hashmaps are more efficient and accurate than treemaps.
Treemaps show relationships between objects but can’t precisely identify which thing is at the cluster’s centre. However, Hashmaps can remember exactly which object is at the group’s centre. This means hashmaps can be used to quickly and efficiently find patterns in your data.
2) Treemaps are not scalable.
Treemaps are not scalable because you need to add a new node for each level of your tree (to group objects into clusters). As a result, it isn’t easy to visualize your data in a way that represents how it looks when you store and analyze it. With hashmaps, on the other hand, you only need to add one node for each different level to keep your visualization accurate and efficient.
3) A treemap is always two-dimensional; a hashmap can be three-dimensional . . . or four-dimensional!
A treemap has only one layer. With a hashmap, however, you can create multiple layers so that objects in each level have an additional dimension of space associated with them (to distinguish
4) A hashmap is a compact data structure that can efficiently store data points in a densely populated ordered list. Treemaps are easy to understand and provide a great visualization of nested data—a set of ordered pairs that lets you store your data efficiently and accurately.
5)Hashmaps are used for mapping data points to a specific location. Treemaps are used for creating maps of large areas.
- http://ijeast.com/papers/134-138,Tesma501,IJEAST.pdf
- https://ieeexplore.ieee.org/abstract/document/5565628/
Last Updated : 11 June, 2023
I’ve put so much effort writing this blog post to provide value to you. It’ll be very helpful for me, if you consider sharing it on social media or with your friends/family. SHARING IS ♥️
Sandeep Bhandari holds a Bachelor of Engineering in Computers from Thapar University (2006). He has 20 years of experience in the technology field. He has a keen interest in various technical fields, including database systems, computer networks, and programming. You can read more about him on his bio page.
I can’t help but feel the article has missed out on certain nuances of Hashmaps and Treemaps. The comparison is not entirely accurate, and a more detailed look into their individual features would have been appreciated.
It is clear that Hashmaps and Treemaps have their own unique advantages and disadvantages. The article explains everything in great detail which helps in understanding the concepts fully.
I think the author has done a good job at explaining the complexities of Hashmaps and Treemaps, and how they work. The article is very well-written and easy to understand.
This is a very informative article, it provides all the details one needs to know about the difference between Hashmaps and Treemaps. I particularly enjoyed learning about the main differences between the two, very interesting!
The author fails to effectively explain how Hashmaps and Treemaps are used in practice. A lack of real-world examples and applications leaves the article feeling incomplete.
This article reads like an academic textbook on Hashmaps and Treemaps. A more engaging tone, perhaps with a hint of humor, would have made this an easier and more entertaining read.