The Three Tiers – Performance Options

This content originally appeared on DEV Community and was authored by davinceleecode

💥💥💥 Presentation Tier (Client/UI Layer)💥💥💥

1. Output Caching

• 🧠 What it does: Stores the generated HTML output of a page or component so it doesn’t have to be recreated for every request.
• 🚀 Benefit: Reduces server processing and response time.
• 💡 Example in ASP.NET:

[OutputCache(Duration = 60, VaryByParam = "none")]
public ActionResult Index()
{
    return View();
}

2. Data Caching

• 🧠 What it does: Stores data (e.g., from a database) temporarily in memory so it can be reused without fetching it again.
• 🚀 Benefit: Reduces database calls, increases speed.
• 💡 Example:

var cachedData = HttpRuntime.Cache["myData"];
if (cachedData == null)
{
    cachedData = GetDataFromDB();
    HttpRuntime.Cache.Insert("myData", cachedData, null, DateTime.Now.AddMinutes(10), TimeSpan.Zero);
}

3. Release Builds

• 🧠 What it does: Compiles the application in Release mode rather than Debug mode.
• 🚀 Benefit: Optimizes code, removes debug symbols, and makes execution faster.
• ⚠️ Always use release builds in production for better performance.

4. Disabling Sessions (If Not Needed)

• 🧠 What it does: Turns off session state when not used in a page.
• 🚀 Benefit: Reduces server memory usage and improves scalability.
• 💡 Example in ASP.NET MVC:

[SessionState(SessionStateBehavior.Disabled)]
public class MyController : Controller
{
    // Controller logic here
}

Summary Table

💥💥💥Application Tier💥💥💥

1. Asynchronous Programming

• 🧠 What it does: Allows tasks like I/O operations (DB/API calls) to run in the background without blocking threads.
• 🚀 Benefit: Improves scalability and responsiveness.
• 💡 Example in C#:

public async Task<IActionResult> GetUserAsync()
{
    var user = await _userService.GetUserDataAsync();
    return View(user);
}

2. Object Pooling

• 🧠 What it does: Reuses instances of expensive objects (e.g., DB connections, HttpClients) instead of creating them repeatedly.
• 🚀 Benefit: Saves memory and CPU.
• 💡 Example:

static readonly HttpClient httpClient = new HttpClient();

3. Caching Business Logic Results

• 🧠 What it does: Store results of logic-heavy calculations or service calls in memory.
• 🚀 Benefit: Avoids repeating expensive logic.
• 💡 Can use: MemoryCache, Redis (for distributed caching)

4. Minimizing Object Creation / Garbage Collection

• 🧠 What it does: Avoid excessive allocations; reuse objects when possible.
• 🚀 Benefit: Reduces memory pressure and GC overhead.
• 💡 Tips:
  • Use structs for small, short-lived value types.
  • Avoid creating unnecessary lists/strings inside loops.

5. Dependency Injection (DI) with Correct Lifetimes

• 🧠 What it does: Manages object lifecycle properly via DI.
• 🚀 Benefit: Prevents memory leaks or redundant instances.
• 💡 Singleton, Scoped, Transient – choose wisely based on service behavior.

6. Bulk Operations / Batching

• 🧠 What it does: Process large data in batches instead of one by one.
• 🚀 Benefit: Reduces the number of database/API round-trips.
• 💡 Example: Instead of saving one record at a time, use SaveRange().

7. Efficient Algorithms and Data Structures

• 🧠 What it does: Use the right logic and collections (e.g., Dictionary over List for lookups).
• 🚀 Benefit: Better performance with large datasets.

8. Parallelism (Only When Safe)

• Use Parallel.ForEach or Task.WhenAll() if tasks are independent and can run in parallel.
• Example:

await Task.WhenAll(ProcessUser(user1), ProcessUser(user2));

Summary Table

💥💥💥Data Tier Performance Options💥💥💥

1. Query Optimization – Improve SQL queries using indexes, avoiding subqueries, using JOINs wisely, etc.
2. Indexing – Add proper indexes (e.g., clustered, non-clustered) to speed up searches and filtering.
3. Connection Pooling – Reuse open database connections to reduce overhead.
4. Caching Query Results – Cache frequently accessed data (in memory, app-side, or with Redis/Memcached).
5. Stored Procedures – Use precompiled SQL logic to reduce execution time and improve consistency.
6. Batch Processing – Insert, update, or delete data in batches rather than row-by-row.
7. Partitioning – Split large tables into smaller partitions (by date, region, etc.) for faster access.
8. Database Sharding – Distribute data across multiple databases to scale horizontally.
9. Use of Read Replicas – Offload read queries to replica servers for better load distribution.
10. Avoiding N+1 Queries – Fetch related data efficiently using JOINs or ORM includes.
11. Use Appropriate Data Types – Prevent unnecessary storage and improve memory efficiency.
12. Connection Lifetime Management – Properly manage and release DB connections to avoid leaks.

This content originally appeared on DEV Community and was authored by davinceleecode

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