Crawler Log Folder: Depth Tracking with a Counter

You start in the main folder of a file system. You are given a list of string operations that were logged by a crawler. Each operation is one of three types: "../" moves to the parent folder (if you are not already at main), "./" stays in the current folder, and "x/" (any other string ending with /) moves into the child folder named x. After performing all the operations, return the minimum number of steps needed to go back to the main folder.

This is LeetCode 1598: Crawler Log Folder, an easy problem that teaches you how to track depth using either a simple counter or a stack. The core idea applies to any problem where you navigate a tree-like structure and need to know how far from the root you are.

Why this problem matters

Path navigation problems appear in many forms: file system utilities, URL resolution, expression evaluation, and bracket matching. They all share the same underlying mechanic: you go deeper into a structure and then come back out. Tracking that depth cleanly is the skill this problem drills.

Crawler Log Folder strips away all the complexity of a real file system. There are no absolute paths, no symbolic links, no edge cases around file names. What remains is the purest version of depth tracking: increment when you go in, decrement when you come out, clamp at zero so you never go above the root. Once this pattern is automatic, you can layer on the complications that problems like "Simplify Path" (LeetCode 71) add.

The key insight

You do not need to simulate an actual folder tree or maintain a stack of directory names. All you need is a single integer: the current depth. Each operation either increases it by one, decreases it by one (but never below zero), or leaves it unchanged.

The algorithm:

1. Initialize depth = 0.
2. For each operation in the log:
   • If it is "../", decrement depth by 1 (but not below 0).
   • If it is "./", do nothing.
   • Otherwise, increment depth by 1.
3. Return depth.

That is the entire solution. The depth counter replaces what would be a stack in a more complex problem. Since we only need the count of folders, not their names, a counter is sufficient.

The solution

def min_operations(logs: list[str]) -> int:
    depth = 0
    for op in logs:
        if op == "../":
            depth = max(0, depth - 1)
        elif op == "./":
            pass
        else:
            depth += 1
    return depth

Let's walk through what each piece does.

We start with depth = 0, representing the main folder. Then we iterate through every log operation. If the operation is "../", we move up one level. The max(0, depth - 1) ensures we never go above the root. If the operation is "./", we skip it entirely because it means "stay here." Any other operation is a child folder name, so we increment depth by one.

After processing all operations, depth tells us exactly how many levels deep we are. That is also the minimum number of "../" operations needed to return to root, which is what the problem asks for.

Visual walkthrough

Let's trace through the example logs = ["d1/", "d2/", "../", "d21/", "./"] step by step. Watch how the depth counter changes with each operation.

Notice that the "../" at step 4 brings us back up from depth 2 to depth 1, and the "./" at step 6 has no effect at all. The final depth of 2 means we need exactly 2 moves to get back to the main folder.

Complexity analysis

Time is O(n) for both approaches, where n is the number of log operations. We process each operation exactly once with constant work per operation.

Space is where the approaches differ. The counter uses O(1) extra space, just a single integer. The stack approach uses O(n) in the worst case, because if every operation is a child folder, the stack grows to length n. For this problem the counter is clearly better, but the stack approach generalizes to harder variants.

The building blocks

1. Depth tracking with a clamped counter

The pattern of using an integer to track nesting depth, clamped so it never goes negative:

depth = 0
for token in tokens:
    if token == "open":
        depth += 1
    elif token == "close":
        depth = max(0, depth - 1)

This exact pattern appears in parentheses validation, HTML tag matching, and any problem where you need to track how deeply nested you are in a structure. The max(0, ...) clamp prevents invalid states. In Crawler Log Folder it prevents going above the root. In parentheses problems it flags unmatched closing brackets.

2. Three-way string classification

The pattern of classifying each input into one of three cases and handling them separately:

for item in items:
    if item == case_a:
        handle_a()
    elif item == case_b:
        handle_b()
    else:
        handle_default()

Many easy string problems follow this structure. You scan through the input, classify each element, and update your state accordingly. The key is recognizing that you do not need a complex parser. Simple equality checks against "../" and "./" cover the special cases, and everything else falls into the default "go deeper" bucket.

Edge cases

Before submitting, think through these scenarios:

• All operations are "../": depth stays at 0 the entire time because max(0, depth - 1) clamps it. Return 0.
• All operations are "./": nothing changes. Return 0.
• All operations go deeper: every operation is a child folder like "a/", "b/", "c/". Depth equals n. Return n.
• Single operation: ["../"] returns 0. ["./"] returns 0. ["x/"] returns 1.
• Deep then fully back up: alternating child folders and "../" operations. Depth oscillates and the final value depends on the exact sequence.
• Already at root and "../" appears: the clamp prevents depth from going to -1. This is the most common bug if you forget the max(0, ...).

From understanding to recall

You have seen how a single integer counter can solve what looks like a file system navigation problem. The logic is minimal: three cases, one counter, one clamp. But writing it quickly under pressure requires that the pattern be automatic.

The details that matter are small. Do you remember to clamp at zero? Do you check "../" before the default case? Do you return depth directly or off by one? These are recall issues, not conceptual ones.

Spaced repetition locks in the pattern. You write the depth counter from memory, first after one day, then three, then a week. After a few rounds, you see "track how deep you are" in a problem statement and the counter template flows out without hesitation.

Related posts

• Simplify Path - The harder variant that requires an actual stack to reconstruct the canonical path, not just track depth
• Valid Parentheses - Another depth tracking problem where a stack validates matching brackets
• Min Stack - Extending the stack data structure with additional tracking, building on the same push/pop mechanics

CodeBricks breaks Crawler Log Folder into its depth tracking and string classification building blocks, then drills them independently with spaced repetition. You type each piece from scratch until the pattern is automatic. When a navigation or nesting problem shows up in your interview, you do not think about it. You just write it.