Dev Release 97

Greetings BlockDAG Community,

Developer Update: In-Depth Load Testing and Development Progress
Today, we undertook a significant milestone in our ongoing journey to refine and optimize the BlockDAG X1 mobile application and blockchain explorer. While our development team continues to work diligently on the upcoming release, which includes major enhancements in user experience, security, and backend efficiency, we parallelly conducted a comprehensive load testing session. This critical step ensures that our systems are not only feature-rich but also robust, scalable, and capable of handling the demands of an expanding user base.
Current Development Focus

1. Enhanced User Experience

• Improved Login Flow: We're refining the login process to make it more intuitive and secure, incorporating multi-factor authentication and streamlining the user interface.
• Better UI: Changing the design inside the application to ensure better user interface.
   

2. Backend Optimization

• Database Schema Redesign: To improve data retrieval speed, we've restructured our database schemas, particularly focusing on the Crocroh database, which handles a significant portion of our application’s load.
• Caching Mechanisms: Implementation of advanced caching strategies to reduce database load and improve response times across all modules.
• Leaderboards and Referral Modules: We’re fine-tuning these modules to handle larger datasets more efficiently, ensuring quick load times and a smoother user experience.
   

3. Blockchain Explorer Development

• UTXO Transactions and Block Listings: We are developing new APIs to handle complex UTXO (Unspent Transaction Output) transactions and blocks, ensuring that users can quickly access detailed transaction and block information.

To validate our development work, a dedicated QA team was aligned to perform rigorous load testing across the entire system. This included both the BlockDAG X1 mobile application and the blockchain explorer. Our goal was to identify performance bottlenecks, establish system thresholds, and ensure that the application can scale effectively as our user base grows.

1. Testing Environment Setup

• Infrastructure: We utilized a distributed testing environment, simulating real-world traffic across multiple regions to mirror our global user base. This setup included several nodes with varying network conditions to test the application’s resilience.
• Tools Used: Industry-standard tools like JMeter and Gatling were employed for generating load and monitoring system performance. These tools allowed us to simulate thousands of concurrent users interacting with the system simultaneously.
  
   

2. Load Testing Algorithms

• Incremental Load Testing: We started with a baseline load, gradually increasing the number of simulated users to identify the exact point at which the system begins to degrade in performance. This approach helped us determine the maximum concurrent user capacity before experiencing latency or failures.
• Stress Testing: Beyond normal load conditions, we pushed the system to its limits to see how it handles unexpected spikes in traffic. This stress testing revealed potential weaknesses in our database query optimization and highlighted areas where additional caching could be beneficial.
• Soak Testing: We ran the application under sustained load for extended periods to observe long-term behavior and potential memory leaks or performance degradation over time.
  
   

Few example algorithms used while testing and it's outcome:
Concurrency Control Algorithm

• Purpose: This algorithm is used to manage the number of concurrent transactions or operations that the system can handle simultaneously. It’s particularly useful for testing the limits of database performance under heavy load.
• Implementation: By simulating a high number of concurrent database transactions, we were able to measure how the system's throughput changes with increasing concurrency. The algorithm dynamically adjusts the number of concurrent users based on real-time performance metrics, ensuring that we can push the system to its limits without causing a complete failure.
• Outcome: We identified optimal concurrency levels for our database operations, enabling us to optimize database connections and query handling for better performance under high load.
   

 Queueing Theory Algorithm

• Purpose: The Queueing Theory Algorithm is used to model the behavior of requests as they are processed by the system, particularly in scenarios where the demand exceeds the system's capacity.
• Implementation: This algorithm was applied to simulate scenarios where requests are queued due to insufficient processing power or slow database queries. By analyzing the length of the queues and the time each request spends in the queue, we were able to determine the system's response under stress.
• Outcome: The application of queueing theory helped us identify bottlenecks in processing times and optimize the system’s resource allocation to reduce wait times and improve overall throughput.
   

3. Load Testing on Crocroh Database

• Query Performance Analysis: A significant portion of our load testing focused on the Crocroh database. We analyzed complex queries, particularly those related to the leaderboard and referral modules, to identify slow-performing queries and optimize them. By indexing key tables and refining our SQL queries, we achieved noticeable improvements in query execution times.
• Read/Write Operations: We tested the database under heavy read/write conditions, mimicking real-world scenarios where users are simultaneously accessing and updating data. This allowed us to fine-tune our database connection pooling and transaction handling strategies.
   

4. Performance Bottlenecks Identified

• Leaderboard Module: Users reported slow loading times on the leaderboard, which was attributed to inefficient query handling under high load. We identified the exact threshold where performance started to degrade and have since implemented optimizations to handle a higher volume of concurrent users.
• API Response Times: We noticed latency in several APIs when subjected to high traffic, particularly those responsible for fetching transaction histories and block details. By optimizing these API endpoints and introducing asynchronous processing where applicable, we’ve reduced response times significantly.
   

The load testing session provided invaluable insights into how our systems perform under various conditions. Here’s what we’ve achieved:

• Threshold Establishment: We now have a clear understanding of the system's breaking points and have documented these for future scaling and infrastructure upgrades.
• Performance Improvements: Several critical areas, particularly the leaderboard and API response times, have been optimized based on the test results.
• Future-Proofing: The data gathered will guide us in making further architectural adjustments, ensuring that the BlockDAG ecosystem can handle increased traffic as we grow.

As we incorporate the learnings from today’s load testing into our development pipeline, we’re confident that the next release will offer a significantly improved user experience. The implementation of these load testing algorithms provided deep insights into the performance characteristics of our system. We identified several critical areas for improvement, particularly in database handling, concurrency management, and system recovery during high load scenarios. These insights will be incorporated into our ongoing development efforts, ensuring that the next release of the BlockDAG X1 mobile application and blockchain explorer is not only feature-rich but also highly optimized for performance and scalability.