Progress 4GL

The Heart Of The Internet


Mature Content



The internet has become an expansive repository of information, entertainment, and social interaction. Within this digital ecosystem lies a vast array of material that is designated for mature audiences_content that includes explicit sexual imagery, graphic violence, strong language, or themes that may be psychologically disturbing. Such content is typically categorized under "Mature" or "18+" to signal that it is not suitable for minors.



Why Mature Classification Exists




Legal Compliance: Many jurisdictions have laws regulating the distribution of explicit material. Websites and platforms must adhere to these regulations to avoid legal penalties.


User Protection: Age restrictions help shield younger users from exposure to potentially harmful or distressing content, thereby supporting parental controls and safeguarding children's mental well-being.


Platform Integrity: Content moderation policies ensure that community standards are maintained. By labeling mature content appropriately, platforms reduce the risk of unintentional sharing or accidental viewing.




Managing Mature Content Online




Verification Processes: Platforms may require age verification before granting access to mature sections.


Parental Controls: Families can set up filters that block mature tags or categories on shared devices.


Clear Labeling: Consistent and explicit tagging of mature content allows users to make informed choices.



In summary, the classification of mature content is a vital element in modern digital ecosystems. It protects vulnerable audiences, supports responsible consumption, and helps maintain an orderly and respectful online environment.





5. "I_m not sure that this is a real user." _ Detecting Non_Human Accounts



5.1 Common Indicators of Automated or Bot Accounts



Indicator Description


Rapid account creation Multiple accounts created within minutes/hours.


Uniform usernames Structured patterns (e.g., "user_00001").


Sparse profile info Missing bio, profile picture, location.


High posting frequency 100+ posts per day or bursts of activity.


Identical timestamps Posts scheduled at same minute marks.


Limited interaction diversity Only likes/comments on specific topics.




5.2 Automated Detection Flow (Pseudo_code)




def flag_suspicious_user(user):
score = 0


Creation time heuristic

if user.created_at - last_known_creation > THRESHOLD:
score += 1


Profile completeness check

if not user.profile_picture or not user.bio:
score += 2


Posting frequency

posts_per_day = len(user.posts) / days_since(user.created_at)
if posts_per_day > MAX_POSTS_PER_DAY:
score += 3


Content diversity

topics = set(post.topic for post in user.posts)
if len(topics) < MIN_TOPICS_DIVERSITY:
score += 2


Final decision

return score >= MIN_SCORE_FOR_BLOCKING



Key Parameters:




`MAX_POSTS_PER_DAY`: upper bound on acceptable daily posting volume.


`MIN_TOPICS_DIVERSITY`: minimum number of distinct topics to consider a user_s content diverse.


`MIN_SCORE_FOR_BLOCKING`: cumulative score threshold beyond which the user is flagged.







5. "What If" Scenarios



Scenario Potential Impact Mitigation


User A posts once every hour for 4 days (_96 posts). Exceeds daily posting cap _ blocked. Ensure `MAX_POSTS_PER_DAY` is set conservatively; monitor bursty behavior.


User B posts 10 times in one day, then none for a month. May be allowed if within cap but could be spammer using low-volume strategy. Implement periodic activity checks; require continued engagement to stay active.


User C posts 5 times per day consistently over months (_1500 posts). Within cap but high volume _ flagged. Apply cumulative thresholds or risk scoring beyond daily caps.



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3. Handling Edge Cases



1. High_Volume Legitimate Users



Scenario: Professional photographers, social media influencers, or news outlets may post frequently.


Mitigation:


- Introduce a trusted flag for verified accounts (e.g., business verification).

- Allow higher thresholds for these users while maintaining stricter checks on unverified accounts.



2. New Users with No History



Scenario: Fresh sign_ups may post infrequently initially.


Mitigation:


- Apply a soft limit during the first week (e.g., max 5 posts).

- Once they reach a certain activity level and no infractions occur, relax limits.



3. Spammers with Legitimate Content



Scenario: Users posting legitimate-looking content but in bulk.


Mitigation:


- Monitor posting patterns for frequency spikes.

- Use anomaly detection to flag sudden increases beyond normal user behavior.



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5. Integrating Moderation into the System Architecture



5.1 Data Flow Overview



Step Component Responsibility


User Upload Front_end (React) + API Gateway Capture image file, metadata (title, description), user ID


Storage S3 / Cloud Storage Store original image; generate unique key


Metadata DB DynamoDB / RDS Record entry: `image_id, user_id, title, description, upload_timestamp`


Trigger S3 Event Notification Invoke Lambda `preprocess_and_enqueue`


Lambda Preprocess Lambda Function Retrieve image from S3, run detection pipeline (YOLOv5 + segmentation), save results to S3, publish message to SQS


SQS Queue Managed queue Holds messages `image_id, detection_results, confidence_scores`


Worker Lambdas One or more Lambda instances Consume messages from SQS, evaluate against policy thresholds, write status (`approved`, `rejected`) and reasons to DynamoDB; optionally send email via SES




1.4 Data Flow Diagram (Textual)




Client --> API Gateway / Load Balancer
|
v
Compute Node (YOLOv5 + segmentation)
|
|---> S3 Object Store (Images, Masks)
|
|---> Message Queue (Detection results)
|
v
Worker Lambda (Policy enforcement)
|
|---> DynamoDB (Status, reasons)
|
|---> SES (Email notifications if needed)






2. Failure Modes and Mitigation Strategies



Failure Mode Potential Impact Mitigations / Redundancies


Power Outage Loss of service, data loss if not persisted Uninterruptible Power Supply (UPS) for critical components; backup generators; power redundancy at the rack level; design to allow graceful shutdown.


Hardware Failure (CPU/Memory/NIC) Service interruption, degraded performance Hot-swappable modules; spare parts inventory; predictive hardware health monitoring (e.g., SMART for disks, ECC memory error logs); redundancy via dual NICs or link aggregation.


Network Interface Card (NIC) Crash Loss of connectivity Dual NICs with failover; NIC teaming; monitor link status and automatically re-route traffic upon failure detection.


Power Supply Unit (PSU) Failure Partial system shutdown, data loss risk Dual redundant PSUs; PSU health monitoring; power cycle alerts; maintain spare PSUs for rapid replacement.


Kernel Panic / System Crash Unplanned reboot, potential data corruption Use of journaling filesystems (e.g., ext4 with delayed allocation disabled), disabling features like O_DIRECT if not needed, employing crash dump mechanisms, and enabling power-fail safe logging to prevent data loss.


Disk Failure Data loss, service interruption Employ RAID configurations (RAID 1 or 5/6) at the storage level; implement SMART monitoring; plan for hot spares; maintain regular backups; use ECC memory to reduce silent errors.


Hardware Aging / Degradation Increased failure rates over time Implement proactive hardware replacement cycles, monitor error logs (e.g., ECC counts), and maintain an inventory of spare components.



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3. "What If" Scenarios



Scenario A: Unexpected Disk Failure During High_Load Operation



Risk Assessment



Immediate Impact: Loss of data for the affected disk; potential service interruption.


Secondary Impact: Increased load on remaining disks; risk of cascading failures.




Mitigation Plan



Redundant Storage Configuration: Use RAID 5/6 or erasure coding to tolerate single/multiple disk failures without downtime.


Hot Spares: Maintain an active hot spare that can automatically replace the failed disk and rebuild data.


Real_Time Monitoring: Deploy SMART alerts, performance metrics, and error logs to detect early signs of degradation.


Automated Failover: Ensure backup servers or replicas receive traffic immediately if a primary fails.


Scheduled Maintenance Windows: Replace failed disks during low_traffic periods; rebuild should not interrupt service.







3. Service Continuity Plan



3.1 Redundancy Architecture



Component Primary Secondary (Failover) Failover Mechanism


Web Servers Apache Tomcat instances Hot_standby Tomcat in another rack Load balancer health checks; automatic switch


Application Servers Spring Boot microservices Docker Swarm nodes in secondary data center Kubernetes rolling updates & health probes


Database PostgreSQL cluster (primary node) Standby replica via streaming replication Automatic promotion on failure (via Patroni)


Storage Network Attached Storage (NAS) Mirrored NAS cluster Data replication at block level


Load Balancer HAProxy Secondary HAProxy with VRRP Failover via Keepalived


Message Queue RabbitMQ cluster Backup cluster in different site Cluster federation for message replication




3.5 Disaster Recovery Plan





Recovery Point Objective (RPO): 15 minutes of data loss allowed (via continuous replication).


Recovery Time Objective (RTO): < 2 hours to restore services.


Backup Strategy:


- Full backups weekly, incremental daily.

- Off-site tape storage for archival compliance.



Testing Schedule:


- Quarterly DR drills.

- Annual audit of backup integrity.



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4. Executive Summary



This comprehensive design document presents a detailed architecture for a scalable, secure, and maintainable web application that delivers personalized content to users based on their selected interests. The system leverages modern technologies (Node.js/Express, PostgreSQL, Redis) and industry best practices (ORM usage, connection pooling, caching, input validation, authentication, rate limiting). It outlines the full stack of database schemas, server-side logic, API contract, front-end rendering strategies, and deployment workflows.



Key benefits of steroid use:




Scalability: Connection pooling and in-memory caching reduce database load; the architecture supports horizontal scaling via stateless servers.


Security: Input sanitization, parameterized queries, CSRF protection, and rate limiting mitigate common web vulnerabilities.


Maintainability: Clear separation of concerns (models, routes, middleware), modular code structure, and automated testing promote long-term code quality.


User Experience: Fast initial page loads via server-side rendering; dynamic navigation menus reflect user-specific interests seamlessly.



Recommended Next Steps:



Implement the outlined models and seed data in the target database system.


Build route handlers with proper error handling and logging.


Integrate CSRF tokens into all POST forms and enforce rate limiting across routes.


Deploy a staging environment to validate performance and security measures before production rollout.



By following this architecture, the application will robustly handle user-specific navigation, maintain high performance, and remain secure against common web threats.