Database Comparison: AI-Optimized Technical Reference

Production Reality Assessment

Database	PostgreSQL 17	MySQL 8.4.6	MongoDB 8.0	Cassandra 5.0
Release Date	September 26, 2024	July 22, 2025	October 2, 2024	September 5, 2024
Data Loss Risk	Low (requires backup configuration)	Very Low (stable)	Low (requires write concern configuration)	Medium (eventual consistency)
Learning Time	3 months dangerous, 2 years proficient	2 weeks functional, 6 months productive	1 week euphoric, 6 months problematic	6 months basic, 2 years operational
Error Message Quality	Helpful	Clear	Improving	Cryptic
Documentation Quality	Excellent but dense	Excellent and practical	Decent with examples	Assumes distributed systems expertise

Critical Configuration Requirements

PostgreSQL 17

Version-Specific Changes:

• Fixed autovacuum launcher deadlock from version 16
• Streaming I/O improvements require re-benchmarking random_page_cost settings
• B-tree bulk loading only helps during CREATE INDEX operations
• MERGE statement improvements available

Failure Points:

• Connection pooling: Default 100 connections, each uses 10MB RAM
• Query planner makes unpredictable decisions affecting performance
• Schema migrations with ADD COLUMN DEFAULT rewrite entire table
• Autovacuum can cause unexpected performance issues

Critical Settings:

-- Connection management
max_connections = 200  -- Adjust based on workload
shared_buffers = 25% of RAM
work_mem = RAM / max_connections / 4

-- For SSDs (critical for performance)
random_page_cost = 1.1  -- Default 4.0 is for spinning disks

-- Monitoring queries
SELECT count(*) FROM pg_stat_activity WHERE state = 'active';
SELECT query FROM pg_stat_activity WHERE waiting = true;

MySQL 8.4.6

Version-Specific Changes:

• Fixed utf8 vs utf8mb4 confusion (legacy apps still affected)
• Improved performance schema overhead
• Connection compression for high-latency scenarios
• Default authentication changed to caching_sha2_password

Failure Points:

• Replication is single-threaded by default
• sql_mode='' accepts invalid dates like '2023-02-30'
• Docker deployments break with older drivers due to auth changes

Critical Settings:

-- Essential for data integrity
sql_mode = 'STRICT_TRANS_TABLES,ERROR_FOR_DIVISION_BY_ZERO,NO_AUTO_CREATE_USER,NO_ENGINE_SUBSTITUTION'

-- For file uploads
max_allowed_packet = 128M
wait_timeout = 600

-- Emergency debugging
SHOW PROCESSLIST;
SHOW ENGINE INNODB STATUS\G;

MongoDB 8.0

Version-Specific Changes:

• 32% throughput improvement on YCSB benchmarks
• Queryable encryption with range queries functional
• Performance gains lost when using $lookup operations

Failure Points:

• Multi-document transactions perform 10x slower than SQL equivalents
• Default write concern in older versions was "fire and forget"
• Memory usage explodes with large lookup results

Critical Settings:

// Essential write concern
{w: 'majority'}

// Monitoring commands
db.currentOp()
db.collection.explain("executionStats").find({query})
rs.status()

Cassandra 5.0

Version-Specific Changes:

• Unified compaction strategy reduces operational complexity
• Many operational pain points fixed

Failure Points:

• Clock drift between nodes causes random failures
• Tombstone accumulation degrades read performance
• Requires full-time operations expertise

Critical Settings:

# Mandatory monitoring
nodetool status
nodetool tpstats
nodetool compactionstats

# Emergency repair (3+ day operation)
nodetool repair -pr

Resource Requirements

Financial Costs

Requirement	PostgreSQL	MySQL	MongoDB	Cassandra
Software License	$0	$0	$0 (SSPL restrictions)	$0
Operations Complexity	Medium	Low	Medium	Very High
Expert Hiring Cost	High	Medium	High	Extreme
Operational Overhead	Medium	Low	Medium	Critical

Time Investments

• PostgreSQL: 3-6 months for production competency
• MySQL: 2-6 weeks for functional deployment
• MongoDB: 6 months to understand operational reality
• Cassandra: 6+ months minimum, requires dedicated expert

Decision Matrix

Use PostgreSQL When:

• Complex queries and joins required
• ACID guarantees essential
• Advanced SQL features needed (window functions, CTEs)
• Team can invest in learning curve

Breaking Points:

• Write scaling beyond single node
• Connection count > 1000 concurrent
• Tables > 100GB without partitioning

Use MySQL When:

• Rapid deployment required
• Team expertise limited
• Proven stability over features
• Legacy system compatibility

Breaking Points:

• Complex analytical queries
• Advanced data types required
• Replication lag in high-write scenarios

Use MongoDB When:

• Document-based data model fits naturally
• Rapid prototyping essential
• Schema flexibility critical in early stages

Breaking Points:

• Complex relationships across collections
• Strong consistency requirements
• Performance-critical join operations

Use Cassandra When:

• Linear write scaling essential
• Multi-datacenter deployment required
• Eventual consistency acceptable
• Dedicated operations team available

Breaking Points:

• Complex queries across partitions
• Strong consistency requirements
• Limited operations expertise

Common Failure Scenarios

Connection Issues

PostgreSQL: Connection exhaustion at 100 concurrent connections
MySQL: "MySQL server has gone away" from timeout or packet size
MongoDB: Replica set failover connection storms
Cassandra: Clock drift causing coordinator rejections

Performance Degradation

PostgreSQL: Query planner switching to sequential scans
MySQL: Replication lag in high-write scenarios
MongoDB: $lookup operations with large result sets
Cassandra: Tombstone accumulation in delete-heavy workloads

Data Integrity

PostgreSQL: Autovacuum not running due to configuration errors
MySQL: utf8 charset truncating emoji characters
MongoDB: Write concern not configured for durability
Cassandra: Network partitions causing inconsistent reads

Migration Complexity Assessment

Time Requirements

• SQL to SQL: 3-6 months for schema translation
• SQL to NoSQL: 6-12 months for application rewrite
• NoSQL to SQL: 3-9 months for schema design
• Distributed to Single-Node: 1-3 months for data consolidation

Breaking Changes

• PostgreSQL: Sequence handling, query planner differences
• MySQL: Date validation, auto-increment behavior
• MongoDB: Transaction model, aggregation pipeline requirements
• Cassandra: Consistency model, query pattern restrictions

Operational Readiness Checklist

Before Production Deployment

1. Backup Strategy: Automated, tested, documented recovery procedures
2. Monitoring: Query performance, connection counts, resource utilization
3. Scaling Plan: Read replicas, connection pooling, sharding strategy
4. Disaster Recovery: RTO/RPO requirements, failover procedures
5. Team Training: 24/7 on-call expertise, troubleshooting procedures

Performance Baseline Requirements

• Connection pooling: Mandatory for >50 concurrent users
• Index strategy: Document all queries before schema design
• Resource monitoring: CPU, memory, disk I/O, network latency
• Query analysis: Slow query logging and regular review

Critical Warning Indicators

Immediate Action Required

• PostgreSQL: VACUUM FULL running during business hours
• MySQL: Replication lag >5 seconds in production
• MongoDB: Memory usage >80% with frequent page faults
• Cassandra: Node down for >10 minutes without repair

Architecture Limitations

• Single Database: >1TB without partitioning strategy
• Microservices: >5 databases without data consolidation plan
• NoSQL: Complex reporting requirements without analytics layer
• Distributed: <3 dedicated operations staff for 24/7 coverage