4 Backend Practices That Keep Adenty Fast, Stable, and High‑Load Ready 

Performance issues often stay invisible when workloads are small, but once traffic spikes or MarTech integrations grow more complex, bottlenecks surface quickly. Slowdowns ripple across the entire tech ecosystem, hurting user experience, and limiting scalability. So, what does it take to maintain high‑load performance in a modern MarTech environment? 

In this article, we break down four backend practices that strengthen Adenty backend resilience and ensure stable performance under heavy traffic.  

1. Manual model writing instead of automapping  

Automapping may simplify development, but it introduces overhead that becomes costly at scale. For large databases with complex schemas, manual model writing delivers significantly better results. 

Why? Because automapper performance issues stem from: 

• Reflection usage, which can be 5-20× slower than direct property access
• Excessive memory load from intermediary objects and garbage collection pressure
• Deferred LINQ execution, which hides expensive operations 
• Limited control over optimization and profiling

Given Adenty’s growing database, manual model writing provides: 

• Faster, more predictable query execution
• Transparent control over database operations
• Safe and scalable schema evolution 

This shift alone saved 10-15% query time, improving responsiveness for end‑users.

2. Combining Newtonsoft.Json and .NET System.Text.Json

Benchmarks show that System.Text.Json outperforms Newtonsoft.Json under high‑traffic performance conditions. So why use both? 

Because Newtonsoft.Json offers unmatched flexibility for dynamic objects (JObject, JArray, JToken), while System.Text.Json delivers faster serialization and lower memory usage. 

By combining both libraries, Adenty achieves: 

• Faster API response times
• Reduced memory consumption
• Efficient handling of flexible data structures  

This hybrid approach supports backend performance optimization without sacrificing developer convenience. 

3. Regular audits of database indexes and stored procedures 

Indexes act like a table of contents for your database. They guide queries directly to the right location. Without regular audits, redundant or outdated indexes accumulate, slowing down performance and increasing storage costs. 

Adenty continuously optimizes: 

• Database indexing best practices
• Stored procedure optimization
• Query logic and execution plans 

This ensures: 

• Faster data retrieval 
• Lower CPU and I/O usage
• Stable performance as workloads grow

Stored procedures are also reviewed to remove outdated logic and reduce unnecessary overhead. 

4. Caching to reduce API query load 

Adenty provides different features depending on the client’s plan (server‑side cookies, identity, etc.). Without caching, the client’s API would need to validate access on every request — a major bottleneck under high load. 

To prevent this, Adenty uses API performance caching, refreshing cached plans every two hours. This approach: 

• Reduces database strain
• Handles increasing query loads efficiently
• Improves load times and user experience

Caching is a key component of scalable MarTech infrastructure, especially when traffic surges. 

Summing up 

Maintaining performance under high loads requires deliberate engineering choices. While some optimizations may not show visible improvements on small workloads, they become critical at scale. Through these practices, Adenty achieved: 

• 15-20% higher database throughput
• 10-15% faster response times 
• Stable performance regardless of traffic or MarTech complexity

To explore how Adenty supports server‑side visitor tracking, resilient data storage, and seamless MarTech integrations, book an interactive demo and try it for free.