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937. Reorder Data in Log Files

LeetCode article · C++ solution
Website made by wuisabel-gif · Original C++ code by keineahnung2345
greedyC++Markdown
937

I like to read this solution as a small machine: keep the useful information, throw away the noise. For 937. Reorder Data in Log Files, the solution in this repository is mainly a greedy solution.

Guide

What?

The first job is to translate the English prompt into state, transition, and stopping conditions. Instead of trying to be clever immediately, read the code as a sequence of questions:

  • What state are we keeping?
  • How do we move from one state to the next?
  • When do we know the answer is already determined?

For this file, the main tools are: greedy.

Guide

When?

This is the kind of solution you want when the problem has structure hiding inside a messy-looking input. The accepted code reduces that pressure by storing exactly the information that remains useful later.

The important function names to track are string_split, reorderLogFiles, indices.

Guide

Why?

The code is doing bookkeeping so your brain does not have to keep the entire search space open at once.

  • Sorting is used to make local choices comparable, so the later scan does not have to rediscover order.
  • The two-dimensional vector is the memory of the solution: grid state, DP state, or adjacency shape.
  • Substring checks are convenient but not free, so they are part of the real complexity story.
  • The final return is not magic; it is the invariant after the loops or recursion have finished doing their accounting.

Guide

How?

Walk through the solution in this order:

  1. Initialize the memory or helper structure.
  2. Process candidates in the order the invariant expects.
  3. Update the answer only when the current state is valid.
  4. Return the value that represents the fully processed input.

The most important competitive-programming habit here is to trust the invariant. Once the invariant is right, the loops become much less scary.

Guide

Complexity

  • Time: O(n) to O(n log n), depending on the dominant loop or data structure operation
  • Space: O(n) in the usual case for auxiliary containers or recursion

Guide

C++ Solution

Your submission

The accepted solution

solution.cpp
01//Runtime: 12 ms, faster than 88.74% of C++ online submissions for Reorder Data in Log Files.
02//Memory Usage: 13.8 MB, less than 47.06% of C++ online submissions for Reorder Data in Log Files.
03
04class Solution {
05public:
06    std::vector<std::string> string_split(std::string str, std::string delimiter){
07        size_t pos = 0;
08        std::string token;
09        std::vector<std::string> result;
10        while ((pos = str.find(delimiter)) != std::string::npos) {
11            token = str.substr(0, pos);
12            result.push_back(token);
13            str.erase(0, pos + delimiter.length());
14        }
15        result.push_back(str);
16        return result;
17    }
18    
19    vector<string> reorderLogFiles(vector<string>& logs) {
20        vector<vector<string>> split_logs(logs.size());
21        vector<string> ans(logs.size());
22        
23        for(int i = 0; i < logs.size(); i++){
24            split_logs[i] = string_split(logs[i], " ");
25        }
26        
27        for(int i = logs.size() - 1, j = logs.size() - 1; i >= 0; i--){
28            //[0]: convert from string to char
29            if(isdigit(split_logs[i][1][0])){
30                ans[j--] = logs[i];
31                logs.erase(logs.begin() + i);
32                split_logs.erase(split_logs.begin() + i);
33            }
34        }
35        
36        vector<int> indices(split_logs.size());
37        iota(indices.begin(), indices.end(), 0);
38        
39        //move identifier to the end
40        for(int i = 0; i < split_logs.size(); i++){
41            split_logs[i].push_back(split_logs[i][0]);
42            split_logs[i].erase(split_logs[i].begin());
43        }
44        
45        sort(indices.begin(), indices.end(), 
46            [&split_logs](const int i, const int j){
47                return split_logs[i] < split_logs[j];
48            });
49        
50        for(int i = 0; i < indices.size(); i++){
51            ans[i] = logs[indices[i]];
52        }
53        
54        return ans;
55    }
56};

Cost

Complexity

Time
O(n) to O(n log n), depending on the dominant loop or data structure operation
Dominated by the main traversal, recursion, or data-structure operations in the code.
Space
O(n) in the usual case for auxiliary containers or recursion
Auxiliary state plus the answer structure where the problem requires one.