The Network Data Model

In the 1960s, as organizations began computerizing their data management, two competing paradigms emerged for structuring database systems. IBM's IMS championed the hierarchical model, organizing data as trees with strict parent-child relationships. GE's IDS and the subsequent CODASYL standard developed the network model, allowing more flexible graph structures.

These weren't academic differences—they represented fundamentally different philosophies about how data should be organized and accessed. For nearly two decades, organizations made strategic bets on one paradigm or the other, building massive applications that would run for decades.

Understanding the comparison between these models isn't merely historical interest. It illuminates the nature of data modeling trade-offs that persist today: simplicity versus flexibility, performance versus generality, and the eternal tension between constraint and expressiveness.

The most fundamental difference lies in the underlying graph structures:

Hierarchical Model (Tree Structure):

• Single root: Each database area has exactly one root record type
• Single parent constraint: Every non-root record has exactly one parent
• No cycles: The structure forms a Directed Acyclic Graph (specifically, a tree)
• Implicit relationships: Parent-child links are the only relationships
• Top-down access: Navigation primarily flows from root toward leaves

Network Model (Graph Structure):

• Multiple entry points: Any record type can serve as a starting point
• Multiple parents allowed: A record can be a member of multiple set types
• Cycles permitted: The graph can contain circular paths
• Named relationships: Set types explicitly define and name relationships
• Multi-directional access: Navigate up, down, or across the graph

The most significant practical difference between the models is how they handle various relationship types.

One-to-One (1:1) Relationships:

Both models handle 1:1 relationships adequately:

• Hierarchical: Child segment under parent, with single occurrence
• Network: Set with at most one member per owner occurrence

One-to-Many (1:N) Relationships:

Both models excel at 1:N—this is their natural structure:

• Hierarchical: Parent with multiple children (department → employees)
• Network: Owner with multiple members (department owns employee set)

Many-to-Many (M:N) Relationships:

This is where the models diverge dramatically.

The structural constraints of each model have profound implications for data redundancy and consistency maintenance.

Hierarchical Model Redundancy:

Because each record can have only one parent, data that belongs to multiple contexts must often be duplicated:

Network Model: Reduced Redundancy

The network model's ability to share records across multiple owners eliminates most structural redundancy:

Both models deliver excellent performance for their intended workloads, but their performance profiles differ significantly.

Hierarchical Model Performance Advantages:

1. Simpler Navigation: Only one path exists from root to any record. No ambiguity, no choice overhead.

2. Physical Clustering: Related segments can be stored contiguously on disk (adjacent in a HIDAM or HDAM structure), improving I/O for tree traversals.

3. Predictable Access Patterns: Top-down access aligns with physical storage, yielding consistent performance.

4. Less Pointer Overhead: Each record has only one parent pointer (or implicit adjacency), reducing storage overhead.

Network Model Performance Advantages:

1. Multiple Access Paths: Can reach data from any direction; application chooses optimal path for each query.

2. M:N Traversal Efficiency: Following pointers through intersection records is faster than IMS logical relationships.

3. Bidirectional Navigation: Can traverse from member to owner without searching; owner pointer is explicit.

4. Set Indexing: Some implementations allow indexed access within sets, further optimizing member lookup.

Both models require explicit navigational programming, but the complexity differs based on structural characteristics.

Where Complexity Diverges:

For simple hierarchical queries, both models are manageable. The differences emerge with:

1. M:N Relationships: CODASYL handles naturally; IMS requires complex logical relationship programming

2. Multi-directional Queries: CODASYL navigates freely; IMS requires separate access paths or secondary processing

3. Currency Management: CODASYL's multiple currency types require more careful state management than IMS's simpler parent-child position

4. Schema Evolution: Changing structure in IMS often requires application rewrites; CODASYL set changes can sometimes be isolated

Each model aligns better with certain data patterns and application requirements.

Hierarchical Model Ideal Use Cases:

Network Model Ideal Use Cases:

The competition between hierarchical and network models shaped the database industry for two decades before both were largely superseded by the relational model.

Timeline of Dominance:

Why the Relational Model Won:

Both hierarchical and network models lost market dominance to the relational model for several key reasons:

1. Data Independence: Relational databases separate logical structure from physical storage and from application logic. Programs don't encode navigation paths.

2. Declarative Queries: SQL specifies what data is needed, not how to retrieve it. The optimizer handles access paths.

3. Ad-Hoc Query Capability: Business users can write SQL queries without programmer intervention. Both IMS DL/I and CODASYL DML require programming.

4. Schema Flexibility: Adding an index or changing storage structure doesn't require application changes. Navigational models embedded structure in programs.

5. Vendor Competition: Multiple relational vendors (Oracle, IBM DB2, SQL Server, Informix) drove innovation and lowered costs. IMS was IBM-only; CODASYL implementations fragmented.

The hierarchical and network models represent two valid approaches to structuring databases, each with distinct strengths and limitations shaped by their fundamental architectural decisions.

Module Conclusion:

With this comparison, we complete our exploration of the network data model. We've traveled from fundamental graph structure concepts through CODASYL standardization, set relationship semantics, navigational programming patterns, and finally this comparison with the hierarchical approach.

The network model represented a significant advancement in data modeling capability—freeing designers from the rigid tree structure to represent complex real-world relationships naturally. Its influence persists in modern graph databases that once again embrace explicit relationship modeling and navigational queries, albeit with more user-friendly interfaces.

Understanding these historical models provides perspective on database design trade-offs that remain relevant: simplicity versus expressiveness, performance versus flexibility, and the eternal question of how much control to give programmers versus how much to abstract into the system.