Database Performance Benchmark 2025 Review - What Actually Works

Here's what nobody tells you about database benchmarking: it's designed to waste your time and cloud budget. I spent three months running every benchmark tool I could find, and 90% of them broke before producing useful results.

The Shit That Actually Matters

TPC-C is useless for web apps. Period. I ran it for a week straight and learned nothing about why my e-commerce app was slow. TPC-C simulates some old warehouse system from the 90s - who the hell knows why we still use it. Unless you're literally managing warehouses, ignore it.

Cloud provider benchmarks are lies. AWS RDS MySQL gets 50% slower when you add real network latency. Azure PostgreSQL performance drops by 30% when other tenants wake up. That "99.99% availability" rating? I've seen three outages in two months.

MongoDB's performance claims assume you never do JOINs. Once you start using $lookup aggregations (their pathetic JOIN equivalent), performance falls off a cliff. That 2,000 ops/sec marketing bullshit? Try maybe 200 if you're lucky.

What Actually Broke During Testing

PostgreSQL 17 memory leaks: The new parallel query processing has a memory leak when you run more than 50 concurrent connections. Took down my test server twice before I figured out the connection limit workaround.

MySQL 9.0 optimizer regression: Their "improved" optimizer sometimes picks table scans over perfectly good indexes. Error message: "Using temporary; Using filesort" - the kiss of death for performance.

MongoDB 8.0 time series disaster: Time series collections lock up completely if you insert data out of chronological order. Got `WriteConflictException` errors until I gave up and used InfluxDB instead.

The Real Performance Numbers (From My Laptop, Not a Marketing Department)

Tested on 16GB RAM, NVMe SSD, i7-12700H. Your mileage will vary:

• PostgreSQL 17: Fastest for complex queries with JOINs. Handles 200 concurrent connections before choking. Memory usage grows linearly - plan accordingly.

• MySQL 9.0: Best connection handling (500+ concurrent). Query performance varies wildly based on optimizer mood. InnoDB buffer pool tuning is still black magic.

• MongoDB 8.0: Fastest simple inserts, but aggregation pipelines are slower than SQL JOINs. Sharding is a nightmare - avoid unless you hate yourself.

Why Benchmarks Don't Match Reality

Network latency kills everything. That 2ms query becomes 50ms when your app server is in a different AZ. Nobody mentions this in benchmarks because it makes their numbers look like shit.

Cache warmup takes forever. First-run performance is 10x slower than steady-state. Most benchmarks ignore this completely. PostgreSQL and MySQL need like 10-15 minutes to actually warm up their caches, but benchmark vendors pretend this doesn't exist.

Maintenance operations fuck everything up. VACUUM, ANALYZE, index maintenance - your "consistent" performance disappears every time PostgreSQL decides to clean house.

Real talk: benchmark tools spend more time installing dependencies and failing to connect than actually testing databases.

What I Learned After Blowing Nearly Two Grand on Cloud Benchmarks

I spent three months and way too much money testing every database benchmark I could find.

Here's what I wish someone had told me before I started:

Docker benchmarks lie about performance. Every containerized benchmark I ran showed performance 20-30% slower than bare metal.

The overlay2 filesystem adds latency to every disk operation. Docker's networking stack adds another 5-10% overhead.

If you're benchmarking in containers, your production performance will be different.

Cloud networks are inconsistent as hell. I ran the same benchmark on AWS RDS at different times of day and got results that varied by 40%.

US business hours = slower performance because everyone else is using AWS too. My "consistent" benchmark results:

• 3 AM EST: 1,200 tpmC
• 2 PM EST: 850 tpm

C

• Same hardware, same queries, different time

The Warmup Period Nobody Mentions

Cold start performance is bullshit. Every database performs terribly when it starts up because:

• Buffer pools are empty (no data in memory)
• Query planner has no statistics
• Indexes aren't cached
• Connection pools haven't been established

Real example: PostgreSQL 17 took like 10-15 minutes to reach steady-state after a restart.

Initial query times: around 2.3 seconds.

After warmup: maybe 180ms.

Benchmarks that don't account for this are useless.

The fix: Run your workload for at least 30 minutes before recording any results.

Why Synthetic Data Benchmarks Are Garbage

Real data is messy, synthetic data is perfect. I generated 1M user records with perfect data distribution and got amazing benchmark results.

Then I loaded actual user data from our production system:

• 40% of users had missing profile data (NULL values everywhere)
• Email addresses clustered around common domains (gmail.com, yahoo.com)
• Creation timestamps bunched around marketing campaigns
• Geographic data heavily skewed toward major cities

Result: Query performance dropped by like 60% (maybe more) because the query planner's statistics were completely fucked.

The painful truth: If you can't benchmark with production-like data, your benchmarks are fiction.

Cloud Provider Marketing vs Reality

AWS Aurora "4x faster" claim: I tested Aurora My

SQL against regular RDS MySQL.

Aurora was 15% faster, not 4x. The 4x number comes from:

• Highly optimized test configurations customers can't replicate
• Specific query patterns that hit Aurora's caching sweet spots
• Dedicated hardware no customer actually gets

Google AlloyDB "100x faster analytics": True for specific BI queries, but only if:

• Your queries exactly match their optimized patterns
• You don't need transactions during analytics (they lock each other)
• You're willing to pay 3x more than regular Cloud SQL

Database-Specific Gotchas That Kill Performance

PostgreSQL 17 connection leak bug: If you have more than 50 concurrent connections doing parallel queries, memory usage grows infinitely until the system crashes.

Error message: out of memory.

Workaround: Set `max_parallel_workers_per_gather = 0` until they fix it.

MySQL 9.0 optimizer regression: The new cost-based optimizer sometimes picks table scans over perfectly good indexes.

You'll see `Using temporary; Using filesort` in EXPLAIN output.

Fix: Add optimizer hints or revert to the old optimizer with optimizer_switch='new_index_cost_model=off'.

MongoDB 8.0 sharding nightmare: Time series collections become read-only if you insert data out of chronological order.

No error message, writes just... stop working. Spent 6 hours debugging before finding this in their changelog.

What Actually Predicts Production Performance

Connection pool behavior: Your benchmark needs realistic connection pooling.

Single-threaded benchmarks tell you nothing about how the database handles 100 concurrent users fighting over 10 database connections.

Maintenance operations: Production databases run VACUUM, ANALYZE, backups, and index maintenance.

Your steady-state benchmark performance disappears when PostgreSQL decides to clean house at 2 AM.

Real query patterns: Users write shit queries.

Your benchmark needs queries like:

SELECT * FROM users WHERE LOWER(email) LIKE '%gmail%'  -- kills performance
SELECT COUNT(*) FROM huge_table                        -- table scan nightmare

Network conditions: Test with realistic latency. Your 0.1ms localhost benchmark becomes 50ms when the app server is in us-east-1 and database is in us-west-2.

The only benchmark that matters is the one that mirrors your production environment, data patterns, and user behavior. Everything else is academic masturbation.

Stop Using Benchmarks That Don't Match Your App

After burning through cloud credits and three months of testing, here's my unpopular opinion: most database benchmarks are useless for making real decisions.

TPC-C simulates a 1990s warehouse management system. Your web app doesn't process purchase orders for widgets. It handles user signups, social media posts, and e-commerce transactions. TPC-C tells you nothing about how PostgreSQL handles your specific workload.

YCSB assumes you're building the next Facebook. Most apps have < 1M users and run fine on a single database instance. YCSB benchmarks NoSQL distributed performance that 95% of developers will never need.

Your benchmark should mirror your app's actual queries, not some academic ideal. If your app does mostly SELECT queries with occasional INSERTs, benchmark that. If you're building an analytics dashboard that does complex JOINs, test that workload.

Build Your Own 5-Minute Benchmark (That Actually Matters)

Skip the enterprise benchmark tools. Write a simple script that:

1. Uses your actual database schema - don't benchmark generic tables
2. Runs your 10 most common queries - get these from your logs
3. Simulates realistic concurrent users - not 10,000 users, just enough to stress-test
4. Measures what you care about - response time, not theoretical throughput

Example script for PostgreSQL:

## Test your actual workload
for i in {1..100}; do
  psql -d myapp -c \"SELECT * FROM users WHERE created_at > NOW() - INTERVAL '7 days';\" >/dev/null
done

## Measure response time that matters to users
time psql -d myapp -c \"SELECT COUNT(*) FROM orders WHERE status = 'pending';\"

Run this on your laptop, your staging server, and whatever cloud setup you're considering. Compare the results. Done.

The AI/ML Benchmarking Hype Is Bullshit

"AI-powered query optimization" is marketing speak for "we added more heuristics to the query planner." I tested PostgreSQL 17's improved parallel query processing and Oracle's autonomous database. Results:

• PostgreSQL 17: Query planner improvements helped complex JOINs by maybe 20%. Not revolutionary, just better.
• Oracle Autonomous: Auto-tuning worked for simple queries, failed spectacularly on complex ones. Still needed manual index tuning.

Vector databases are niche as hell. Unless you're building ChatGPT, you don't need vector similarity search. PostgreSQL's pgvector extension is fine for small-scale AI features. MongoDB's vector search is solving a problem most developers don't have.

The Real Benchmark: Can You Hire Someone to Maintain It?

The database choice that matters most: Can you find developers who know how to optimize it?

• PostgreSQL: Tons of experienced developers, excellent documentation, Stack Overflow answers for everything
• MySQL: Similar dev pool, but Oracle's influence makes long-term roadmap uncertain
• MongoDB: Smaller talent pool, more expensive developers, sharding expertise is rare
• Oracle/SQL Server: Enterprise-only skills, expensive licensing, vendor lock-in

Your "benchmark" should include: How much will it cost to hire someone when your current DBA quits?

What I'd Do If I Were Starting Today

For 90% of applications:

1. Start with PostgreSQL 17 - it handles everything competently
2. Use a managed service (AWS RDS, Google Cloud SQL) to avoid maintenance hell
3. Add read replicas when you outgrow a single instance
4. Consider sharding when you hit millions of users (you probably won't)

For the other 10%:

• Time series data? Use InfluxDB or TimescaleDB, not MongoDB
• Real-time analytics? ClickHouse or DuckDB, not "HTAP" marketing bullshit
• Document storage? PostgreSQL JSON columns work fine until massive scale
• Key-value caching? Redis, not a database

Skip the Academic Bullshit, Test What Matters

The best database benchmark is the one that answers your specific question:

• "Can this database handle our Black Friday traffic?"
• "Will query performance degrade as our data grows?"
• "How much will this cost at our projected scale?"

Run your actual queries against your actual data on realistic hardware. Everything else is academic masturbation that wastes time you could spend building features users actually want.

The ultimate benchmark: Deploy to production with good monitoring and see what breaks. Fix the bottlenecks as they appear. Most performance problems are in your application code anyway, not the database.