Breakdown: Who is Responsible?

The Breakdown: Who is Responsible? ## 1. Database Queries (~60% of Performance Issues) ## 2. Network Calls (~30% of Performance Issues) ## 3. Algorithms / Code Logic (~10% of Performance Issues) ## The "Scale of Latency" (Why Algo loses) ## When does the Algorithm become #1? ## Summary In the vast majority of commercial system designs (web apps, e-commerce, banking, SaaS), the hierarchy of "slowness" is almost always the same. For a typical application, the ranking of importance (and performance bottleneck) looks like this: 1. Database Queries (The bottleneck) 2. Network Calls (The delay) 3. Code Algorithms (The easy part) Here is the breakdown of why, including the estimated impact percentages for a standard web application. In 9 out of 10 system design interviews (and real-world outages), the database is the first thing to break. This is the time it takes for data to travel from Server A to Server B (latency). Unless you are doing AI, Video Processing, or Crypto Mining, your algorithm is rarely the problem. To understand why Algorithms matter so little compared to Network/DB, look at the time scale differences. Conclusion: Optimizing your code to save 5 CPU cycles is useless if your database query takes "11 days" (relative time) to return. There are specific cases where the Algorithm becomes the most important factor (80%+ impact): For a standard company system (like an e-commerce store or social network): Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse - Why: Reading data from a hard drive (Disk I/O) is physically slow. Even with SSDs, searching through millions of rows, joining tables, and waiting for "locks" takes millions of CPU cycles. - The Cost: A bad SQL query can freeze a system for seconds. - System Design Fix: This is why we use Caching (Redis/Memcached). We try desperately to avoid hitting the database. - Why: Even if your code is instant, the speed of light is a limit. If your app has "Microservices" where Service A calls Service B, which calls Service C, you are simply waiting for the data to travel wires. - The Cost: A network call usually takes milliseconds (ms). In CPU time, 1ms is an eternity. - System Design Fix: This is why we use Batching (sending 100 items in 1 request instead of 100 requests) and CDNs. - Why: Modern CPUs can do billions of calculations per second. A loop that iterates 10,000 times to sum a shopping cart takes microseconds (0.000001 seconds). - The Cost: Negligible in comparison to waiting for a database. - System Design Fix: Keep code clean, but don't obsess over micro-optimizations. Focus on readability. - Let's imagine 1 CPU Cycle = 1 Second. - Video Compression/Streaming: (Netflix/YouTube encoding) - Heavy CPU math. - High-Frequency Trading: (Stock markets) - Every nanosecond counts; network is optimized via hardware, so code speed wins. - Encryption/Hashing: (Blockchain/Crypto) - Pure mathematical processing. - AI/ML Inference: Running neural networks is pure matrix multiplication. - Database (60%): Optimize your SQL, add Indexes, use Redis. - Network (30%): Reduce the number of calls (Chatty I/O), keep servers close to users. - Algo (10%): Just avoid obvious mistakes (like nested loops on massive arrays).