From: thepipeline_xyz
Blockchain platforms frequently encounter issues that negatively impact user experience, especially under high demand scenarios [00:00:20]. These issues often lead to a frustrating experience for users and require developers to make excuses for system failures [00:00:30].
Common Problems During High Demand
When a significant number of users attempt to perform actions simultaneously, such as a large-scale NFT mint, blockchain networks can struggle [00:00:04]. Specific issues observed include:
- RPC Failures: The Remote Procedure Call (RPC) mechanism, essential for client-server communication, can fail, preventing user interactions [00:00:23].
- Reverted Transactions: User transactions may fail to complete and be “reverted,” meaning they are cancelled after initiation, leading to confusion and lost effort [00:00:25].
- System Downtime/Excuses: When systems fail to stand up to stress, developers often have to provide public explanations for outages or poor performance, often visible on platforms like Twitter [00:00:27].
Case Study: Solana in Fall 2021
The user experience on Solana in the fall of 2021 was characterized by such issues [00:00:35]. This period saw people from across the globe “spam clicking mint buttons” for NFTs, which the system was not fully anticipated to handle gracefully [00:00:45].
Anticipating and Mitigating Issues
Ideally, blockchain platforms should be able to handle high stress scenarios, like 100,000 concurrent users sending 100 transactions per second, without performance degradation or the need for excuses [00:00:00]. Continuous optimization based on data is crucial to address these technical challenges [00:00:11].
Future Testing Approaches
To proactively identify and resolve potential system design challenges, novel testing methods are being considered:
- AI Agent Simulation: Utilizing AI agents on testnets, especially in combination with high throughput environments, could provide valuable insights [00:00:50].
- Incentivized Stress Testing: AI could potentially be incentivized to intentionally “crash” testnets, mimicking extreme user behavior more effectively than human testers [00:00:58]. This approach aims to push the system to its limits to discover vulnerabilities before they impact live users.