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5 Critical Things to Check if You Want to Optimize the Performance of an Existing System

Gaurav Sharma

• 4 min read
5 Critical Things to Check if You Want to Optimize the Performance of an Existing System

Performance issues are not always about writing better code or throwing more hardware at the problem. Often, the bottlenecks lie hidden in plain sight — inside your database queries, system logs, data flows, or architectural decisions. If you're looking to optimize the performance of an existing system, not a new build, here's a comprehensive guide packed with practical insights, real-world scenarios, and tools you can actually use.

Let’s go beyond generic tips. Let’s get specific.

🔍 1. Deep-Dive into Database Indexing and Query Profiling

The Problem:
Many performance issues stem from inefficient database access — missing indexes, slow joins, or unbounded data scans.

What to Check:

  • Are the right columns indexed, especially those used in WHERE, JOIN, and ORDER BY clauses?
  • Are there any unused or duplicate indexes bloating your DB?
  • Are queries scanning entire tables unnecessarily?

Real-World Example:
In one audit, a customer order report was taking 14 seconds to load. A quick profiling showed a missing index on order_status. Once added, the same query took under 0.5 seconds.

Tools to Use:

  • SQL Server: Database Engine Tuning Advisor, Query Store, Actual Execution Plans
  • PostgreSQL: EXPLAIN ANALYZE, pg_stat_statements
  • MySQL: EXPLAIN, Slow Query Log

Action Tip:
Involve a DBA or senior backend engineer to review slow-running queries and recommend index strategies. Always test changes in staging.

🔁 2. Check Stored Procedure Call Frequency & Spikes

The Problem:
Some stored procedures (SPs) are fine in isolation — but when called excessively, they become a bottleneck.

What to Check:

  • Which SPs are getting hit most frequently?
  • Are any SPs being hit every few seconds without real need?
  • Can calls be batched or cached instead of repeated?

Real-World Example:
In one system, a GetUserSessionDetails SP was getting hit every 10 seconds per active session, even when the data wasn’t changing. Rewriting the logic to cache the data for 2 minutes reduced DB CPU usage by 35%.

Tools to Use:

  • SQL Profiler (for SQL Server)
  • Application logs integrated with DB call metrics
  • Custom instrumentation using Datadog/New Relic/AppDynamics

Action Tip:
Correlate SP hit frequency with business workflows. If there’s no user-visible reason for the frequency, it likely needs optimization.

📦 3. Analyze How Much Data the Application Fetches in One Go

The Problem:
Over-fetching data — especially in APIs or service calls — puts pressure on DB, network, memory, and UI rendering.

What to Check:

  • Are APIs sending back entire objects when only a few fields are needed?
  • Is pagination implemented and respected?
  • Are SELECT * queries still lurking in production?

Real-World Example:
A mobile app page was loading slowly. Investigation showed the backend API was returning 300 KB of data per user, including unused audit fields and lookup objects. Post optimization: trimmed to 40 KB, reducing load time by 60%.

Tools to Use:

  • Fiddler/Postman/Charles Proxy – inspect payload sizes
  • Application telemetry – measure response size vs. actual render usage

Action Tip:
Audit key APIs or data calls for payload bloat. Use lightweight DTOs or serializers that exclude unnecessary fields.

📈 4. Use Profilers to Map Real-Time Bottlenecks

The Problem:
Without profiling tools, you're guessing. You need to know which part of your system is taking the most time or consuming the most memory.

What to Check:

  • Are there memory leaks causing latency buildup?
  • Which function calls or code paths are taking the longest?
  • Are there garbage collection delays or thread locks?

Real-World Example:
In a .NET Core app, GC spikes were happening every 60 seconds. Memory profiler revealed a static list holding onto thousands of objects unnecessarily. Fixing it improved response stability.

Tools to Use:

  • DotTrace, VisualVM, PerfView, YourKit, Dynatrace
  • Chrome/Firefox dev tools for frontend profiling
  • Flame graphs for visualizing hotspots

Action Tip:
Make profiling a scheduled part of sprint retrospectives. Even a 30-minute profiling session can uncover hidden gains.

🔒 5. Recheck Your Caching Strategy & Cache Hit Rate

The Problem:
You may be caching, but poorly. Or caching the wrong things. Or worse, your cache invalidation logic is broken.

What to Check:

  • Are the most expensive operations being cached?
  • Is cache getting refreshed too frequently?
  • Is cache shared across all app servers correctly?

Real-World Example:
An e-commerce system was caching product details, but the cache expired every 60 seconds. For high-traffic items, this led to 100s of hits per minute. Post-change: extended cache TTL to 5 minutes, with manual invalidation on product update.

Tools to Use:

  • Redis/Memcached stats dashboards
  • Datadog/New Relic caching metrics
  • Custom log hooks on cache miss/fill events

Action Tip:
Log your cache hit ratio. Anything below 85–90% for hot data is a red flag.

⚙️ Bonus Check: Are You Overloading a Single Resource?

Sometimes, an application uses a single config file, shared logging path, or a tight loop that locks a file/db/table — which becomes a contention point.

Real-World Example:
A logging library was configured to write to a single shared file on a mounted drive. During peak usage, I/O contention spiked latency across the system. Switching to async logging with file rotation fixed it.

Tooling Tip:

  • Review file usage, threading contention via profilers
  • Use APMs to correlate latency spikes to resource usage

📌 Final Thoughts: Performance Optimization Is System Thinking

Performance isn’t about one magical fix. It’s about system awareness:

  • How often are things getting called?
  • Where’s the real load coming from?
  • Are the infra and software layers talking clearly?

It’s about visibility. Patterns. Pressure points. And asking the right questions.

The best engineers don’t guess where the slowness is.
They trace it.
They measure it.
They fix it — systematically.

💬 What You Can Do Today

✅ Pick a known slow screen or feature and ask: How many DB calls does it make? How much data does it fetch?

✅ Sit with a DBA or use profilers to analyze SPs with high hit count.

✅ Include profiling logs and slow-query tracking in your next retrospective.

✅ Audit your caching strategy — especially cache expiry patterns and hit/miss ratios.

✅ Bookmark this blog or share with a teammate working on performance. It might save weeks of blind debugging.

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