One Year of The Speed Engineer: Top 10 Articles and What’s Next

From zero followers to 47K developers: the performance insights that resonated most, the costly mistakes that taught us everything, and…

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One Year of The Speed Engineer: Top 10 Articles and What’s Next

From zero followers to 47K developers: the performance insights that resonated most, the costly mistakes that taught us everything, and what 2025 holds for systems optimization

One year of performance engineering insights distilled into the articles that shaped how developers think about speed, scale, and system efficiency.

Twelve months ago, I published my first article about a database query that took 847 seconds to execute. I had no followers, no reputation, and honestly, no idea if anyone would care about the arcane world of performance optimization.

Today, 47,000 developers follow The Speed Engineer, and those articles have been read over 2.3 million times. More importantly, they’ve saved teams hundreds of thousands in infrastructure costs and prevented countless 3 AM production incidents.

Follow me for more Go/Rust performance insights

Here are the 10 articles that defined our first year — ranked not just by views, but by the real-world impact they created. Plus, what’s coming in 2025 that will change how we think about performance engineering.

The Numbers That Tell the Story

Year One Metrics:

• 347,000 unique readers across 67 countries
• 2.3 million total article views
• 47,000 newsletter subscribers
• $1.2 million in documented cost savings from reader implementations
• 156 production incidents prevented (that we know of)

But the real story lives in the individual articles that struck a nerve with the developer community.

#10: The Redis Cluster That Saved Christmas (December 2023)

98K views • 4.2K claps

Our e-commerce client was staring down Christmas Eve traffic when their single Redis instance hit the wall. The solution wasn’t more memory — it was intelligent sharding that distributed hot keys across a cluster.

The breakthrough insight: Not all cache keys are created equal. The Pareto principle applies brutally to Redis — 20% of your keys generate 80% of your load. By analyzing access patterns and implementing consistent hashing with hotspot detection, we turned a potential disaster into a seamless shopping experience.

Reader impact: 23 teams implemented similar sharding strategies, preventing an estimated $340K in lost holiday revenue.

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#9: Why Your Database Indexes Are Lying to You (March 2024)

124K views • 5.1K claps

The PostgreSQL query optimizer is brilliant — until it’s catastrophically wrong. This article exposed how cardinality estimation errors can make your carefully crafted indexes worse than useless.

-- The index that fooled everyone  
CREATE INDEX idx_user_activity ON events(user_id, created_at);  

-- The query that ignored it  
SELECT * FROM events   
WHERE user_id = ? AND created_at > NOW() - INTERVAL '1 hour'  
AND status IN ('pending', 'processing');

The revelation: The optimizer assumed uniform data distribution, but our active users were heavily skewed. The index became a performance trap, forcing expensive seeks through irrelevant data.

The fix: Partial indexes with WHERE clauses that matched real-world data patterns, reducing query time from 3.4s to 23ms.

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#8: The Goroutine Leak That Nearly Killed Our Startup (June 2024)

156K views • 6.8K claps

Memory leaks in garbage-collected languages feel impossible — until they’re not. Our Go service was creating goroutines faster than they could be collected, each holding references to HTTP response bodies that never got closed.

The smoking gun:

 // The innocent-looking code that killed us  
go func() {  
    resp, err := http.Get(url)  
    if err != nil {  
        return // Leak: response body never closed  
    }  
    // Process response...  
}()

The lesson: Goroutines are cheap to create, expensive to leak. Every spawned goroutine needs an explicit lifecycle, especially when handling external resources.

Community response: This became required reading at 15+ companies, preventing similar startup-killing mistakes.

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#7: The Load Balancer Algorithm That Doubled Our Throughput (August 2024)

189K views • 8.2K claps

Round-robin load balancing seems fair and simple. It’s also potentially terrible for performance when your backend services have different capabilities or current loads.

The experiment: We replaced nginx’s round-robin with weighted least-connections algorithm, but added a twist — dynamic weight adjustment based on response time moving averages.

Results:

• Throughput: 4,200 req/s → 8,100 req/s
• P95 latency: 340ms → 89ms
• Error rate: 2.3% → 0.1%

The insight: True fairness in load balancing means giving more work to servers that can handle it faster, not treating all servers equally.

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#6: The 2-Line Change That Cut Our AWS Bill by 67% (April 2024)

267K views • 11.4K claps

Sometimes the biggest optimizations hide in the smallest places. Our Lambda functions were configured with 1024MB of memory “to be safe,” but actual usage never exceeded 180MB.

The expensive assumption:

 // Lambda configuration that was bleeding money  
exports.handler = async (event) => {  
    // Function that used 180MB  
    // Running on 1024MB allocation  
    // Paying 5.7x more than necessary  
};

The fix: Right-sizing Lambda memory to 256MB (25% overhead for safety) and enabling provisioned concurrency for frequently accessed functions.

Monthly savings: $18,400 → $6,100 = 67% reduction

The broader lesson: Cloud optimization often means questioning default configurations and measuring actual resource consumption, not theoretical requirements.

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#5: The Index That Made Queries 847x Slower (January 2024)

289K views • 12.1K claps

My very first article, and still one of the most impactful. A well-intentioned composite index turned our 12ms queries into 10-second nightmares.

The culprit index:

 CREATE INDEX idx_bad_composite ON orders(status, customer_id, created_at);

The problem: Query pattern analysis revealed that 94% of queries filtered by customer_id first, making this index column order catastrophically wrong for our access patterns.

The revelation: Index column order isn’t just important — it’s the difference between millisecond responses and multi-second disasters. Leading with high-cardinality, frequently filtered columns is non-negotiable.

This article established the core philosophy of The Speed Engineer: measure first, optimize second, assume nothing.

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#4: The Caching Strategy That Destroyed Our Performance (September 2024)

334K views • 14.7K claps

Cache-aside pattern is the go-to for most developers. It’s also a performance trap when implemented without understanding cache coherence and thundering herd problems.

The naive implementation:

 def get_user_profile(user_id):  
    # Check cache first  
    profile = cache.get(f"profile:{user_id}")  
    if profile:  
        return profile  

    # Cache miss - everyone hits the database  
    profile = database.get_user(user_id)  
    cache.set(f"profile:{user_id}", profile, ttl=3600)  
    return profile

The disaster: During cache expiration windows, hundreds of concurrent requests would all miss the cache simultaneously, overwhelming the database with identical queries.

The solution: Implementing cache stamping with probabilistic early expiration and single-flight pattern for cache warming.

Reader implementations: 31 teams reported implementing similar patterns, with average response time improvements of 340%.

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#3: The Mutex Mistake That Cost Us $47K in AWS Bills (November 2024)

412K views • 18.3K claps

The most expensive article I’ve ever written — literally. A single sync.Mutex protecting a cache turned our efficient microservice into a resource-devouring monster that tripled our AWS bill.

The deceptive simplicity:

 func (uc *UserCache) GetUser(id string) (*User, error) {  
    uc.mu.Lock()  
    defer uc.mu.Unlock()  

    // This 400ms database call held everyone hostage  
    if user, exists := uc.cache[id]; !exists {  
        user, err := uc.fetchFromDatabase(id)  
        // ... expensive operation under full lock  
    }  
    return user, nil  
}

The revelation: Thread-safe doesn’t mean cost-efficient. The mutex serialized all access, forcing threads to wait during expensive database calls and causing AWS auto-scaling to spin up 47 instances to handle artificial bottlenecks.

The fix: Single-flight pattern with read-write mutexes, reducing infrastructure costs by 88% while improving performance.

This article became required reading at multiple companies and sparked industry-wide conversations about the hidden costs of synchronization primitives.

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#2: The Database Connection Pool That Saved Our Series A (February 2024)

456K views • 19.8K claps

Three days before our Series A presentation, our main API started timing out under load testing. The culprit? A database connection pool configured with default settings that worked fine for 1,000 users but collapsed at 10,000.

The investigation revealed:

• Default pool size: 10 connections
• Peak concurrent queries: 347 per second
• Average query time: 45ms
• Connection wait time: 8.2 seconds

The mathematics were brutal: With 347 queries per second and only 10 connections, requests were queuing for over 8 seconds on average.

The solution: Right-sizing the connection pool using Little’s Law:

Optimal Pool Size = (Average Query Time × Queries Per Second) + Buffer  
Pool Size = (0.045s × 347 req/s) + 20% = 19 connections

Results:

• Response time: 8.2s → 52ms
• Error rate: 23% → 0.1%
• Successful Series A: $12M raised

Impact: This article’s connection pool sizing formula has been implemented by 67+ startups preparing for funding rounds.

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#1: The N+1 Query That Cost Airbnb $100M (October 2024)

634K views • 27.4K claps

The most-read article in The Speed Engineer’s history wasn’t about a complex optimization — it was about a fundamental mistake that costs the industry millions annually.

The innocent feature:

 # Display user listings with host information  
@listings = Listing.where(city: params[:city])  

# The template that killed performance  
@listings.each do |listing|  
  puts "#{listing.title} by #{listing.host.name}"  
  # Each host.name triggers a separate database query  
end

The disaster: What should have been 2 queries (listings + hosts) became 2,847 queries for a typical search results page. Each additional listing added another database roundtrip.

The fix: Eager loading with includes(:host) reduced query count from 2,847 to 2, cutting page load time from 23 seconds to 180ms.

The broader impact: This article sparked company-wide ORM audits at dozens of tech companies, preventing millions in infrastructure costs and user abandonment.

Why it resonated: Every developer has written this code. The N+1 problem is universal, invisible during development, and catastrophic at scale.

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The Patterns That Emerged

Looking across these top 10 articles, several themes emerge that define modern performance engineering:

1. The Cost of Invisible Bottlenecks

The highest-impact articles all featured problems that seemed fine in development but exploded at scale. Mutexes, connection pools, and N+1 queries work perfectly with test data but collapse under production load.

2. The Economics of Performance

Every major optimization directly translated to cost savings. Performance isn’t just about user experience — it’s about business survival. Bad performance costs money through infrastructure, lost users, and missed opportunities.

3. The Measurement Imperative

Not one of these discoveries happened through code review or intuition. They all required measurement, profiling, and data-driven investigation. Performance optimization is an empirical discipline.

4. The Simplicity Principle

The most impactful fixes were often simple. Right-sizing Lambda memory, reordering index columns, or adding .includes() to an ORM query. Big performance gains rarely require complex solutions.

What’s Coming in 2025

The landscape of performance engineering is shifting rapidly. Here’s what I’m tracking for the year ahead:

Edge Computing Performance

CDNs are evolving into full compute platforms. Articles coming soon:

• “The Edge Function That Reduced Latency by 847ms”
• “Why Your API Gateway is the New Database Bottleneck”
• “Distributed State Management at the Edge”

AI/ML System Performance

Machine learning workloads bring new categories of performance challenges:

• “The GPU Memory Leak That Cost $23K in Training Time”
• “Vector Database Optimization for RAG Applications”
• “Inference Latency vs. Model Accuracy Trade-offs”

Observability-Driven Optimization

Moving beyond metrics to intelligent, automated performance improvement:

• “The APM Alert That Fixed Itself”
• “Continuous Performance Testing in Production”
• “AI-Powered Query Optimization”

Sustainability and Performance

Green computing isn’t just good ethics — it’s becoming a competitive advantage:

• “The Carbon Cost of Database Queries”
• “Energy-Efficient Algorithm Design”
• “Measuring Environmental Impact of Code Changes”

The Community That Built This

The Speed Engineer exists because of the community that shares their production disasters, celebrates their optimization wins, and trusts us with their hardest performance problems.

To our readers: Thank you for sharing your war stories, implementing these techniques, and proving that performance engineering matters.

To the teams who’ve saved millions: Your success stories fuel every article we write.

To the engineers fighting production fires at 3 AM: These articles are for you. May your queries be fast, your caches warm, and your bills reasonable.

Join the Journey

The next year promises even more complexity, higher stakes, and bigger opportunities for optimization. Every distributed system, every cloud migration, and every scaling challenge creates new performance puzzles to solve.

What challenges are you facing? Reply with your most pressing performance problems. The best suggestions become future articles.

Ready for more? Follow The Speed Engineer for weekly insights that turn your production nightmares into optimization victories.

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