This content originally appeared on DEV Community and was authored by Ahmed Rakan
State-driven programming is a style where the current state of data drives the computation, rather than relying heavily on conditional statements (if/else) scattered throughout the code. This approach is particularly useful when tracking sequences, patterns, or logical transitions in a dataset.
In this post, we’ll use a practical example: counting connected components in a linked list based on a subset of its values.
Problem
You are given:
-
headof a linked list containing unique integers. - An array
nums, a subset of the linked list values.
Return the number of connected components in nums, where two values are connected if they appear consecutively in the linked list.
Examples
Example 1:
Input: head = [0,1,2,3], nums = [0,1,3]
Output: 2
Explanation: [0, 1] is one component, [3] is another.
Example 2:
Input: head = [0,1,2,3,4], nums = [0,3,1,4]
Output: 2
Explanation: [0, 1] and [3, 4] are the two connected components.
Initial Solution (Imperative)
The standard approach is to:
- Convert
numsto a set for O(1) lookups. - Traverse the linked list.
- Count a component whenever:
- The current node is in
nums, and - The next node is not in
nums(or isNone).
class Solution:
def numComponents(self, head: Optional[ListNode], nums: List[int]) -> int:
s = set(nums)
ans = 0
while head:
if head.val in s and not (head.next and head.next.val in s):
ans +=1
head = head.next
return ans
Trick: A component ends when the current node is in
numsbut the next node is not.
Moving Towards State-Driven Programming
Instead of relying on explicit if statements, we can drive computation using a state table:
- Map each value in
numsto a logical state (1if present,0otherwise). - For each node, compute if it ends a component using only its state and the next node's state.
- Accumulate the result without explicit branching.
Step 1: Create a Logical Hashtable
from collections import defaultdict
table = defaultdict(lambda: 0)
for n in nums:
table[n] = 1
Here, table[val] is 1 if val is in nums, 0 otherwise.
Step 2: Traverse and Accumulate Using State
ans = 0
while head:
state = table[head.val]
ans += state * (not (head.next and table[head.next.val]))
head = head.next
-
state= 1 → current node is part of a component. -
not (head.next and table[head.next.val])→ next node is not part of a component, i.e., component ends here. - Multiply these logical values to count component closures without an
ifstatement.
Full State-Driven Solution
from collections import defaultdict
class Solution:
def numComponents(self, head: Optional[ListNode], nums: List[int]) -> int:
table = defaultdict(lambda: 0)
for n in nums:
table[n] = 1
ans = 0
while head:
state = table[head.val]
ans += state * (not (head.next and table[head.next.val]))
head = head.next
return ans
Diagram: State Propagation
Linked List: 0 -> 1 -> 2 -> 3
Nums: {0, 1, 3}
State Table: {0:1, 1:1, 2:0, 3:1}
Step by Step:
Node 0: state=1, next_state=1 → not counted
Node 1: state=1, next_state=0 → counts as 1 component
Node 2: state=0 → skipped
Node 3: state=1, next_state=None → counts as 1 component
Total Components = 2
Why State-Driven Programming Helps
- Reduces branching → more predictable code execution.
- Encodes logical conditions in a data structure → easier to reason about.
-
Scales to more complex state transitions without additional
ifstatements.
Think of it as treating each node as a finite state machine, where the table drives the transitions and outputs.
Conclusion
State-driven programming is particularly useful when:
- You have logical conditions depending on neighbors or sequence.
- You want to eliminate scattered
ifstatements. - You want to model computation as state transitions.
This linked list connected components problem is a simple yet powerful illustration of the concept.
This content originally appeared on DEV Community and was authored by Ahmed Rakan
Ahmed Rakan | Sciencx (2025-09-16T05:03:27+00:00) Introduction to State-Driven Programming. Retrieved from https://www.scien.cx/2025/09/16/introduction-to-state-driven-programming/
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