Database Performance Benchmark 2025: AI-Optimized Technical Reference

Critical Configuration Issues

PostgreSQL 17 Production Failures

• Memory leak bug: Parallel query processing with >50 concurrent connections causes infinite memory growth until system crash
• Error: out of memory
• Workaround: Set max_parallel_workers_per_gather = 0 until patched
• Impact: Complete system failure requiring restart

MySQL 9.0 Optimizer Regression

• Issue: New cost-based optimizer ignores indexes, chooses table scans
• Error indicator: Using temporary; Using filesort in EXPLAIN output
• Fix: Add optimizer hints or revert with optimizer_switch='new_index_cost_model=off'
• Performance impact: 60%+ degradation on complex queries

MongoDB 8.0 Time Series Critical Bug

• Failure mode: Time series collections become read-only with out-of-order inserts
• Error: WriteConflictException or silent write failures
• No warning provided: Writes simply stop working
• Alternative: Use InfluxDB for time series data

Performance Reality vs Marketing Claims

Cloud Provider Marketing Debunked

Provider	Claim	Actual Result	Test Conditions
AWS Aurora	"4x faster than MySQL"	15% faster	Same hardware, realistic workload
Google AlloyDB	"100x faster analytics"	True for specific BI queries only	Requires exact query patterns, 3x cost
AWS RDS	99.99% availability	3 outages in 2 months	Production monitoring

Benchmark vs Production Performance Gap

• Network latency: 2ms localhost query becomes 50ms cross-AZ
• Cache warmup time: PostgreSQL/MySQL need 10-15 minutes to reach steady-state
• Performance variation: AWS RDS varies 40% between 3AM and 2PM EST
• Docker overhead: 15-25% performance penalty from overlay2 filesystem and NAT

Resource Requirements and Costs

Cloud Testing Budget Reality

• Minimum viable testing: $500
• Comprehensive evaluation: $1000+
• Hidden costs: Data transfer consumed 30% of budget
• Minimum instance size: t3.medium (smaller instances provide misleading results)

Database Talent Availability

• PostgreSQL: Large talent pool, extensive documentation
• MySQL: Similar availability, Oracle influence creates uncertainty
• MongoDB: Smaller talent pool, higher salaries, rare sharding expertise
• Oracle/SQL Server: Enterprise-only skills, expensive licensing

Benchmark Tool Reality Assessment

Tool	Usefulness	Primary Failure Mode	Setup Time	Recommendation
TPC-C	Marketing only	Simulates 1992 warehouse system	2 days	Avoid (2/10)
YCSB	NoSQL load testing	Java dependency hell, random crashes	4 hours	Use cautiously (6/10)
SysBench	Quick sanity checks	Limited scenarios, MySQL-biased	15 minutes	Recommended (8/10)
pgbench	PostgreSQL testing	PostgreSQL only, basic workloads	5 minutes	Highly recommended (9/10)
HammerDB	Management reports	Crashes during long runs	3+ hours	Avoid for real testing (4/10)

Production Performance Killers

Data Quality Impact

• Synthetic data: Perfect distribution, unrealistic performance
• Production data reality: 40% missing profile data, clustered domains, skewed timestamps
• Performance degradation: 60%+ slower with realistic data patterns

Maintenance Operation Impact

• VACUUM/ANALYZE: Unpredictable performance degradation during maintenance
• Backup operations: Hours of degraded performance during nightly backups
• Connection leaks: too many connections error indicates application issues
• Index maintenance: Scheduled maintenance can halve query performance

Common Query Anti-Patterns

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

Technology Selection Decision Framework

Default Choice for 90% of Applications

1. PostgreSQL 17 - Handles all workloads competently
2. Managed service (AWS RDS, Google Cloud SQL) - Avoid maintenance overhead
3. Read replicas when single instance insufficient
4. Consider sharding only at millions of users (rarely needed)

Specialized Use Cases (10% of applications)

• Time series: InfluxDB or TimescaleDB (not MongoDB)
• Real-time analytics: ClickHouse or DuckDB
• Document storage: PostgreSQL JSON columns until massive scale
• Key-value caching: Redis

Critical Warnings for Production

Connection Pool Configuration

• Single-threaded benchmarks: Useless for production workloads
• Realistic connection pooling: Test with 100 concurrent users, 10 database connections
• PostgreSQL limit: 200 concurrent connections before performance degradation

Network Configuration Issues

• Docker benchmarking: Use --net=host and --privileged (benchmark only, never production)
• Cross-region latency: Factor in realistic network conditions
• AWS peak hours: Performance degrades during US business hours due to resource contention

Effective Performance Testing Strategy

Minimal Viable Benchmark

# PostgreSQL quick test
pgbench -i -s 10 mydb
pgbench -c 10 -j 2 -T 60 mydb

# MySQL quick test
sysbench oltp_read_write --mysql-host=localhost --mysql-user=root --mysql-password=password --mysql-db=test --table-size=100000 --threads=10 --time=60 run

Production Readiness Criteria

1. Use actual database schema - not generic test tables
2. Test 10 most common queries - extract from production logs
3. Simulate realistic concurrent users - not theoretical maximums
4. Measure user-facing metrics - response time over throughput
5. Run for 24+ hours - performance varies throughout day

Ultimate Production Test

Deploy with comprehensive monitoring and fix bottlenecks as they appear. Most performance problems exist in application code, not database configuration.