Handling Complex Data – Choosing the Right Component for Your Data Structure 

Managing complex datasets is a common challenge in modern applications. Whether you’re dealing with self-referencing relationships, hierarchical structures, or heterogeneous data, selecting the right component can significantly impact performance, user experience, and maintainability. 

In this blog post, I will break down the differences between three key components that can help you visualize and interact with complex data while using App Builder: 

• TreeGrid 

• Tree Component 

• Hierarchical Grid (HGrid) 

Each of these components is optimized for different data structures. By understanding their capabilities, you can decide which best fits your application’s needs.

Understanding Data Structures & the Right Component to Use 

Data structures can generally be categorized into three main types: 

1. Self-Referencing (Hierarchical) Data – Entities that reference themselves, for example, data coming in from an employee table that includes the employee’s manager or supervisor business key. A manager or supervisor is also an employee. So, the employee record arrives with a self-referencing relationship between an employee and their supervisor. 

2. Homogeneous Parent-Child Data – Data where each parent record has multiple child records of the same type. A good example would be a category hierarchy in an e-commerce database. 

Category hierarchy using a tree structure

Electronics (ID: 1)
├── Laptops (ID: 2)
│ ├── Gaming Laptops (ID: 4)
│ ├── Ultrabooks (ID: 5)
├── Smartphones (ID: 3)

Schema: 

category_id	category_name	parent_category_id
1	Electronics	NULL
2	Laptops	1
3	Smartphones	1
4	Gaming Laptops	2
5	Ultrabooks	2

• “Electronics” is the top-level category. 

• “Laptops” and “Smartphones” are subcategories of “Electronics.” 

• “Gaming Laptops” and “Ultrabooks” are subcategories of “Laptops.” 

This is a homogeneous relationship because both parent and child belong to the same entity type (categories). 

However, it’s true that all homogeneous parent-child relationships are self-referencing, but not all self-referencing structures are hierarchical. If the relationship forms a tree-like hierarchy, it’s typically a homogeneous parent-child relationship. If the relationship simply links instances, it’s self-referencing but not necessarily hierarchical. 

Now, let’s have a look at the last data structure type: 

3. Heterogeneous Data – Each level in the hierarchy has a different structure, such as customers with orders and order details. 

By understanding the structure of your data, whether it’s self-referencing, homogeneous, or has nothing in common, you can make an informed decision about which component best fits your application’s needs. 

With that said and before we go into exploring the different components and the data they can consume, below you can find a short comparison table of each data structure type for your convenience. 

Comparison Table 

Aspect	Homogeneous Data (Same structure)	Self-Referencing Data (Same type links to itself)	Heterogeneous Data (Different structures at each level)
Definition	A parent-child relationship where both entities have the same structure.	An entity contains a reference to another instance of itself.	A hierarchical relationship where each level has a different structure.
Data Structure	Tree or Graph (e.g., category trees, org charts).	Linked List, Trees, Graphs (e.g., singly linked list, hierarchical organization).	Relational Database with Foreign Keys (e.g., Customers → Orders → Order Details).
Hierarchy Type	Each entity in the hierarchy has the same attributes and structure.	Links within the same entity type, may or may not form a hierarchy.	Each level has different entity types and attributes.
Database Example	Employee-Manager structure (employees table referencing itself).	Linked List (each node has a next reference).	Customers → Orders → Order Details (different structures per level).
Code Example	A tree of categories where subcategories are also categories.	A linked list where nodes point to other nodes.	A database with customers, orders, and order_details tables.
Use Case	Organization charts, nested categories, comment threads.	Linked lists, graph structures, recursion-based operations.	E-commerce orders, invoices, sales reports.

Grid Types & What Are They Best For 

TreeGrid: Best for Self-Referencing Data

If your dataset follows a structured, hierarchical format where records consistently relate to one another, the TreeGrid is the ideal choice. 

A TreeGrid works well for an employee management system, where each employee may have subordinates reporting to them in a self-referencing structure. 

Advantages: 

• Automatically handles self-referencing relationships 

• Comes with built-in features like sorting, filtering, and editing 

• Scales well for large datasets 

Use Case: 

• Employee hierarchies 

• Organizational structures 

• Folder/file management 

Tree Component: Ideal for Irregular Datasets

If different levels in your hierarchy contain different data types, the Tree component provides the necessary flexibility. Unlike the TreeGrid, which expects a uniform structure, the Tree allows full customization of nodes and content. 

A Tree is useful for visualizing product categories where the first level represents high-level categories, the second level represents individual products, and the third level contains purchase records. 

Advantages: 

• Fully customizable structure and appearance 

• Works well for irregular datasets where levels don’t share the same format 

• Supports deep nesting without performance trade-offs 

Use Case: 

• Product categories and subcategories 

• Navigation menus 

• Content taxonomies 

 

Hierarchical Grid: Best for Parent-Child Data Relationships

If your dataset follows a strict parent-child relationship where each parent entity contains multiple child records of the same type, the Hierarchical Grid (HGrid) is your best choice. 

A Hierarchical Grid is perfect for customer order management, where each customer has multiple orders, and each order contains multiple order details. 

Advantages: 

• Supports deeply nested data structures 

• Allows dynamic expansion of child rows 

• Provides built-in features like filtering, sorting, and editing 

Use Case: 

• Customers → Orders → Order Details 

• Projects → Tasks → Subtasks 

• Departments → Employees → Roles 

Choosing the Right Component 

To summarize, selecting the right component depends on the structure of your data: 

Data Structure	Best Component	Benefits
Self-referencing hierarchy	TreeGrid	Handles recursive data structures efficiently.
Parent-child	Hierarchical Grid	Displays structured relationships with expandable levels.
Heterogeneous hierarchy	Tree Component	Provides flexibility when different levels have different structures.

 

Balancing Built-in Functionality with Configurability

Built-in features like sorting, filtering, and editing can greatly simplify development, but they come at the cost of flexibility. When choosing a component: 

• Prioritize out-of-the-box functionality when working with structured, self-referencing, or parent-child data. 

• Choose flexibility when your data doesn’t follow a strict schema and requires custom interactions. 

By understanding the structure of your data, whether it’s self-referencing, homogeneous, or entirely heterogeneous, you can make an informed decision about which component best fits your application’s needs. 

In the demo-focused session below, you can see how to handle hierarchical data with App Builder.

Wrap Up 

Handling complex datasets effectively is a key part of application development. Whether you’re working with deeply nested structures, diverse data formats, or hierarchical relationships, selecting the right component— Flat Grid, TreeGrid, Tree, or Hierarchical Grid—can significantly impact usability and performance. 

Need help implementing these components? Reach out to us via Discord, GitHub, or Forums. And if you haven’t explored how App Builder can streamline your workflow, try it now for free! It’s designed to make working with hierarchical data faster, easier, and more efficient. 

Meanwhile, you can check out our previous webinar recordings on YouTube.