Fixing Leaderboard Issues Exposing User Data And Slow Loading Times

Jul 8, 2025 by gitftunila 68 views

You Can See All Users From the Leaderboard and Leaderboard Loading is Slow

Introduction

This article addresses the issue of user data exposure and slow loading times on the Heartbound leaderboard. The current implementation of the leaderboard API endpoint allows access to the full user information for all users, which raises privacy concerns. Additionally, the large amount of data being transferred causes significant delays in loading the leaderboard, impacting user experience. This article will delve into the problem, its implications, and provide a detailed solution to rectify these issues.

The main issue is that when calling the endpoint GET https://heartbound-d00f1b55e2ad.herokuapp.com/api/users/leaderboard, the response includes complete information for every user. This response contains sensitive data such as user IDs, usernames, avatars, display names, pronouns, about sections, banner colors, banner URLs, roles, credits, levels, experience points, message counts, voice statistics, equipped items, badges, and daily streaks. This extensive data exposure is not only unnecessary for displaying the leaderboard but also poses a significant security risk. Furthermore, the massive data payload results in slow loading times, making the leaderboard cumbersome to use.

Problem Details

The primary issue stems from the API's response structure. Currently, the endpoint returns a comprehensive list of all users with all their associated data, as shown in the example provided:

[
 {
 "id": "316657253065293824",
 "username": ".sevlar",
 "avatar": "https://cdn.discordapp.com/attachments/1390851076072411241/1391747512087609364/B48D1F45-A555-42C1-A83F-2A40B0963D4A.png?ex=686d0560&is=686bb3e0&hm=46b4ecb982937c916beabfca920acfdc33461c594074571e538eeac7e41ce463&",
 "displayName": "",
 "pronouns": "",
 "about": "‎",
 "bannerColor": "#ED1C27",
 "bannerUrl": "https://cdn.discordapp.com/attachments/1390851076072411241/1391748004079210576/iu.png?ex=686d05d5&is=686bb455&hm=089cb6d3a49f451eaa57388e1d7b03931244d3026d9192ec2d5a9c637999a5b1&",
 "roles": [
 "USER"
 ],
 "credits": 42765,
 "level": 10,
 "experience": 925,
 "xpForNextLevel": 1100,
 "messageCount": 288,
 "messagesToday": 0,
 "messagesThisWeek": 27,
 "messagesThisTwoWeeks": 288,
 "voiceRank": 19,
 "voiceTimeMinutesToday": 233,
 "voiceTimeMinutesThisWeek": 233,
 "voiceTimeMinutesThisTwoWeeks": 1363,
 "voiceTimeMinutesTotal": 1363,
 "equippedUserColorId": "505666a6-30d3-44d9-854e-cb81103407a6",
 "equippedListingId": null,
 "equippedAccentId": null,
 "equippedBadgeId": "b32f538b-4725-4e8e-8fad-58dc3177579d",
 "badgeUrl": "https://res.cloudinary.com/drywja3ta/image/upload/v1751507062/bjf3tijcnbweoensvih1.png",
 "badgeName": "vesper",
 "nameplateColor": "#F8BBD0",
 "dailyStreak": 3,
 "lastDailyClaim": "2025-07-07T10:48:01.26463"
 },
 {
 // ......all other 1000+ users entries etc
 }
]

This extensive data payload, which can be over 59,000 lines long, includes information that is not relevant to the leaderboard display. The leaderboard typically only needs user IDs, names, experience points, credits, voice time, and levels. The inclusion of other data points such as avatars, pronouns, banner URLs, roles, message counts, and equipped items is unnecessary and contributes to the bloat. The more data included, the longer it takes to process, transmit, and render, resulting in slow loading times. In practical terms, users have reported loading times of around 2 seconds, which is a noticeable delay that detracts from the overall user experience.

Another significant issue is the exposure of sensitive user information. By providing complete user profiles through the leaderboard API, the system inadvertently exposes data that should ideally be kept private. This includes personally identifiable information (PII) and other potentially sensitive details that could be misused if accessed maliciously. This is a critical security concern that needs immediate attention to protect user privacy and maintain data integrity. The risk is compounded by the fact that this information is accessible through a simple API call, making it vulnerable to unauthorized access and data breaches.

The initial symptom of this problem was observed in the network tab, where the excessive data transfer was evident. While measures have been taken to mitigate the visual display of this issue, the underlying problem persists. The fact that the issue was initially visible in the network tab underscores the magnitude of the data being transferred. The current workaround, which involves cutting off the display, is merely a superficial fix and does not address the core problem. The performance bottleneck and the security vulnerability remain, necessitating a more comprehensive solution.

Proposed Solutions

To address the issues of sensitive data exposure and slow loading times, a two-pronged approach is recommended. This involves modifying the API response structure and limiting the number of users fetched.

1. Create a Leaderboard-Specific DTO

The first part of the solution involves creating a new Data Transfer Object (DTO) specifically designed for the leaderboard. This LeaderboardDTO should contain only the essential information required for displaying the leaderboard, such as:

id: User identifier.
username: User's display name or username.
experience: User's experience points.
credits: User's in-game credits.
voiceTime: User's voice chat time.
level: User's current level.

By using a DTO, the API response will only include these necessary fields, excluding sensitive and irrelevant data such as avatar URLs, pronouns, banner details, roles, and message counts. This significantly reduces the payload size and minimizes the exposure of private user information. The implementation of a DTO also aligns with best practices for API design, ensuring that the API response is tailored to the specific needs of the client application.

Using a DTO offers several benefits. First, it enhances security by limiting the data exposed through the API. Second, it improves performance by reducing the amount of data that needs to be transferred over the network. Third, it simplifies data handling on the client-side, as the application only needs to process the fields that are relevant to the leaderboard display. This approach also makes the codebase more maintainable, as changes to user profiles will not necessarily impact the leaderboard API response.

The LeaderboardDTO can be implemented as a simple class or record in the backend code, depending on the programming language and framework being used. The key is to ensure that the API endpoint maps the full user data to this DTO before sending the response. This mapping process allows the backend to selectively include the required fields, effectively filtering out the unnecessary data. For example, in a Java-based backend, this could involve creating a LeaderboardDTO class with the specified fields and using a library like ModelMapper to transform User entities into LeaderboardDTO instances.

2. Limit the Number of Users Fetched

The second part of the solution focuses on limiting the number of users fetched from the database. Since the leaderboard typically displays only the top 100 users, there is no need to retrieve data for every registered user. The API should be modified to fetch only the top 100 users based on their leaderboard ranking criteria (e.g., experience points, credits, or a combination of factors). This significantly reduces the amount of data processed and transferred, leading to faster loading times.

The implementation of this limit should ideally be done on the backend to ensure data security. While it is possible to implement a limit on the frontend using query parameters (e.g., ?limit=100), this approach is less secure. A malicious user could bypass the frontend filter by removing the limit parameter from the URL, gaining access to the full user list. Therefore, the filtering should be enforced on the backend to prevent such exploits. This can be achieved by modifying the database query to include a LIMIT clause or by implementing pagination to fetch data in smaller chunks.

By limiting the number of users fetched, the API response size can be drastically reduced. Instead of sending data for thousands of users, the API will only send data for the top 100. This not only reduces the network bandwidth usage but also decreases the processing time on both the server and the client. The result is a significantly faster leaderboard loading time and a better user experience. This approach also reduces the strain on the database, as fewer records need to be retrieved and processed.

To implement this, the backend API should include a mechanism to limit the number of results returned. This could be a hardcoded limit or a configurable parameter that can be adjusted as needed. The best practice is to implement a configurable limit that can be adjusted without requiring code changes. This allows for flexibility in case the number of displayed leaderboard entries needs to be increased in the future. Additionally, proper indexing on the database fields used for ranking can further optimize the query performance.

Backend Filtering Implementation

Implementing the filtering on the backend is crucial for security and efficiency. By handling the filtering server-side, we ensure that users cannot bypass the limitations and access the full dataset. This approach provides a secure and reliable method to manage the data being exposed. Here’s how to implement the filtering on the backend:

Modify the API endpoint: Update the API endpoint to accept a limit parameter. This parameter will specify the maximum number of users to return. For example: GET https://heartbound-d00f1b55e2ad.herokuapp.com/api/users/leaderboard?limit=100.
Implement the filtering logic: Within the API endpoint's handler function, retrieve the limit parameter from the request. Use this parameter to construct a database query that limits the number of results. Most database systems support a LIMIT clause in SQL queries, which can be used to achieve this.
Secure the endpoint: Ensure that the limit parameter cannot be manipulated to bypass the intended limitations. This can be achieved by setting a maximum allowed value for the limit parameter and validating the input to prevent excessively large requests.
Efficient database queries: Optimize the database query to ensure that the leaderboard data is retrieved efficiently. This includes using appropriate indexes and ensuring that the query only fetches the necessary fields. Consider using techniques like pagination to handle large datasets and improve performance.

Frontend Integration

After implementing the backend changes, the frontend needs to be updated to utilize the new API. Here’s how to integrate the changes on the frontend:

Update API calls: Modify the frontend code to include the limit parameter in the API request. This ensures that the frontend only requests the necessary data from the backend.
Handle pagination: If the backend implements pagination, the frontend needs to handle the pagination logic. This involves making multiple API calls to fetch data in chunks and displaying the data in a user-friendly manner. Techniques like infinite scrolling or load more buttons can be used to improve the user experience.
Display the data: Update the leaderboard display to correctly render the data received from the API. This includes handling cases where the data is not available or when the API returns an error.

By implementing these changes, the frontend will only receive the data it needs, which significantly reduces the amount of data that needs to be processed and rendered. This leads to a faster and more responsive leaderboard.

Step-by-Step Implementation Guide

To effectively implement the proposed solutions, a detailed step-by-step guide is provided below:

Step 1: Define the LeaderboardDTO

Create a new DTO that includes only the necessary fields for the leaderboard:

public class LeaderboardDTO {
 private String id;
 private String username;
 private int experience;
 private int credits;
 private int voiceTime;
 private int level;

 // Getters and setters
}

Step 2: Modify the Backend API

Update the API endpoint to accept a limit parameter.
Implement the filtering logic in the API endpoint handler.
Map the full user data to the LeaderboardDTO before sending the response.

@GetMapping("/leaderboard")
public List<LeaderboardDTO> getLeaderboard(@RequestParam(defaultValue = "100") int limit) {
 List<User> users = userService.getTopUsers(limit);
 return users.stream()
 .map(user -> {
 LeaderboardDTO dto = new LeaderboardDTO();
 dto.setId(user.getId());
 dto.setUsername(user.getUsername());
 dto.setExperience(user.getExperience());
 dto.setCredits(user.getCredits());
 dto.setVoiceTime(user.getVoiceTimeMinutesTotal());
 dto.setLevel(user.getLevel());
 return dto;
 })
 .collect(Collectors.toList());
}

Step 3: Optimize Database Queries

Ensure that the database queries are optimized to retrieve the leaderboard data efficiently. This includes using appropriate indexes and limiting the number of fields fetched.

SELECT id, username, experience, credits, voiceTimeMinutesTotal, level
FROM users
ORDER BY experience DESC
LIMIT 100;

Step 4: Update the Frontend

Modify the frontend code to include the limit parameter in the API request.
Handle the pagination logic if necessary.
Update the leaderboard display to correctly render the data.

fetch('/api/users/leaderboard?limit=100')
 .then(response => response.json())
 .then(data => {
 // Update leaderboard display with the data
 });

Potential Challenges and Mitigation Strategies

While implementing the proposed solutions, several challenges may arise. Understanding these potential issues and having mitigation strategies in place is crucial for a smooth implementation.

1. Database Performance

Fetching the top users based on certain criteria may become slow if the database is not properly optimized. The primary mitigation strategy here is to ensure that the database queries are efficient and indexed correctly. This involves creating indexes on the fields used for sorting (e.g., experience points) and ensuring that the queries only fetch the necessary fields. Additionally, techniques like query caching can be used to reduce the load on the database.

If the database performance remains an issue even after optimization, consider using more advanced database techniques such as partitioning or sharding. Partitioning involves dividing a large table into smaller, more manageable pieces, while sharding involves distributing the database across multiple servers. These techniques can significantly improve query performance for large datasets.

2. API Performance

The API endpoint itself may become a bottleneck if it is not implemented efficiently. The mitigation strategy here is to optimize the API code and ensure that it is not performing any unnecessary operations. This includes minimizing the amount of data processing done in the API handler and ensuring that the data mapping (from user entities to LeaderboardDTOs) is done efficiently. Additionally, consider using asynchronous processing or caching to reduce the response time.

Another aspect of API performance is the network bandwidth usage. By reducing the size of the API response (through the use of DTOs and limiting the number of users fetched), the network bandwidth usage can be significantly reduced. This not only improves the performance of the API but also reduces the cost of bandwidth consumption.

3. Data Consistency

Ensuring data consistency between the leaderboard and the user profiles is crucial. If the user data changes frequently, the leaderboard may become out of sync. The mitigation strategy here is to implement a mechanism for updating the leaderboard whenever user data changes. This can be achieved by using event-driven architecture or by scheduling regular updates to the leaderboard data.

Additionally, consider using a caching mechanism to store the leaderboard data. This can reduce the load on the database and improve the performance of the API. However, it is important to ensure that the cache is invalidated whenever the user data changes to prevent stale data from being displayed on the leaderboard.

4. Security Considerations

Limiting the data exposed through the API is a key security measure. However, it is important to consider other security aspects as well. This includes ensuring that the API endpoint is properly authenticated and authorized, and that the data is transmitted over a secure connection (HTTPS). Additionally, consider implementing rate limiting to prevent abuse of the API.

Another security consideration is input validation. Ensure that the API endpoint validates the input parameters (e.g., the limit parameter) to prevent malicious users from injecting invalid values. This can help prevent attacks such as SQL injection and denial-of-service attacks.

5. User Experience

While implementing performance improvements, it is important to consider the user experience. The leaderboard should be easy to use and provide a smooth and responsive experience. This includes ensuring that the leaderboard loads quickly, that the data is displayed clearly, and that the user can easily navigate through the leaderboard.

Additionally, consider providing feedback to the user while the leaderboard is loading. This can be achieved by displaying a loading spinner or a progress bar. This gives the user an indication that the leaderboard is being loaded and prevents them from thinking that the application is frozen.

By addressing these potential challenges and implementing the proposed mitigation strategies, the leaderboard can be significantly improved in terms of performance, security, and user experience.

Conclusion

In conclusion, addressing the issues of sensitive data exposure and slow loading times on the Heartbound leaderboard is crucial for enhancing both user privacy and overall application performance. By implementing a Leaderboard-specific DTO and limiting the number of users fetched, we can significantly reduce the amount of data being transferred and processed. This not only improves loading times but also minimizes the risk of exposing sensitive user information.

The proposed solutions offer a balanced approach that addresses both the security and performance aspects of the problem. Creating a DTO ensures that only the necessary data is exposed, while limiting the number of users fetched reduces the load on the server and the network. These changes, combined with proper backend filtering and frontend integration, will lead to a faster, more secure, and more user-friendly leaderboard.

By following the step-by-step implementation guide and considering the potential challenges and mitigation strategies, the Heartbound team can effectively resolve these issues and provide a better experience for its users. The key is to prioritize security, optimize performance, and ensure data consistency throughout the implementation process. This will not only improve the current state of the leaderboard but also set a strong foundation for future enhancements and scalability.