PostgreSQL vs MySQL vs MongoDB vs Cassandra - Which Database Will Ruin Your Weekend Less?

Four databases, four different ways to ruin your weekend. Here's what failed and how to avoid the same mistakes.

PostgreSQL 17.6 (current stable as of August 2025) is solid, but the parallel query optimizer can consume significant memory. Complex queries with parallel workers can spike memory usage beyond what you'd expect. Common memory tuning for production systems:

-- Memory tuning for parallel queries
SET work_mem = '256MB';  -- default 4MB often insufficient
SET shared_buffers = '4GB';  -- default 128MB too small for production
ALTER SYSTEM SET max_parallel_workers_per_gather = 2;  -- default 4 can spike memory

PostgreSQL memory tuning guide explains why defaults don't work in production.

MySQL 8.4.6 (LTS from July 2025) is solid, but Oracle's Innovation track (9.0/9.1) is their experimental playground. The new optimizer hints are decent, but anything past 8.4 feels like beta software. Enterprise Firewall in 8.4 can add 200-500ms latency:

-- Check if firewall is murdering performance  
SHOW VARIABLES LIKE 'mysql_firewall_mode';
SELECT * FROM INFORMATION_SCHEMA.MYSQL_FIREWALL_WHITELIST;

-- Turn it off if queries are timing out
SET GLOBAL mysql_firewall_mode = OFF;

Stick with MySQL 8.4.6 LTS unless you enjoy 3am debugging sessions with Innovation track weirdness.

MongoDB 8.0.9 (latest stable as of May 2025) delivers substantial performance improvements - 32% faster reads, 59% faster updates, and 200%+ better time-series performance vs 7.0. But the WiredTiger cache management still requires careful tuning:

// Dangerous - can OOM your server
db.adminCommand({setParameter: 1, wiredTigerCacheSizeGB: 30})  // on 32GB server

// Safe approach - leave room for OS
db.adminCommand({setParameter: 1, wiredTigerCacheSizeGB: 20})  // 60-70% max

MongoDB will consume all available RAM if you let it. WiredTiger memory management explains the defaults.

Cassandra 5.0.5 (released August 2025) finally has working SAI indexes. After 10 years of "model your data for your queries" bullshit, you can run multi-column queries without dying inside. But Java 17 broke existing heap configs:

## Old Java 8 config (will break)
-Xms8G -Xmx8G -XX:NewRatio=3

## Java 17 config that actually works  
-Xms16G -Xmx16G --add-exports java.base/jdk.internal.misc=ALL-UNNAMED

Cassandra Java 17 migration guide covers the heap tuning nightmare.

What Versions to Actually Use in Production

PostgreSQL 17.6: Stable and reliable for production use. Version 18 is still in beta (18 Beta 3 as of August 2025) - the memory usage patterns in parallel queries need more testing. Stick with 17.x for production. Security updates for 17.x guaranteed until November 2029.

MySQL 8.4.6: The LTS release recommended for production. Oracle's Innovation track (9.0/9.1) introduces new features rapidly but may have stability issues. MySQL LTS lifecycle means 8.4 gets patches until 2032.

MongoDB 8.0.9: Latest stable release with improved bulk write performance. Watch for balancer behavior during peak traffic - chunk migrations can impact performance. The sharding improvements make it worth upgrading from 7.0.x if you can schedule maintenance windows.

Cassandra 5.0.5: Released August 2025, includes working SAI indexes. Major enterprises are beginning 5.0.x migrations. If you're on 4.1.x, thorough testing is essential before upgrading. Cassandra 5.0 migration guide covers upgrade considerations.

The Real Problems Nobody Talks About

Here's what actually breaks in the field, not some textbook explanation:

PostgreSQL: Connection limits can kill busy sites. The error "FATAL: sorry, too many clients already" isn't helpful when traffic spikes. Default 100 concurrent connections, each using 4-8MB RAM. At 500 connections you're using 4GB just for connection overhead. Common fixes:

## Check connection usage
SELECT count(*) FROM pg_stat_activity;
## Kill runaway connections
SELECT pg_terminate_backend(pid) FROM pg_stat_activity WHERE query_start < now() - interval '5 minutes';

PostgreSQL connection pooling with pgbouncer prevents these issues.

MySQL: Binary logs can fill disk space and crash MySQL. The error "ERROR 3 (HY000): Error writing file '/var/lib/mysql/mysql-bin.000142' (errno: 28 - No space left on device)" indicates disk space issues. Monitor binlog growth:

## Check binlog size before it kills you
SHOW BINARY LOGS;
## Purge old logs (CAREFUL!)
PURGE BINARY LOGS BEFORE DATE_SUB(NOW(), INTERVAL 3 DAY);

MySQL binlog management prevents these 3am disasters.

MongoDB: Oplog size limits can break replica set syncing during heavy operations. Default 5% disk space might only represent hours of operations on busy systems.

// Check oplog status before disaster strikes
db.oplog.rs.find().sort({$natural: -1}).limit(1)
db.oplog.rs.find().sort({$natural: 1}).limit(1)
// Resize BEFORE you're fucked
db.adminCommand({replSetResizeOplog: 1, size: 50000}) // 50GB

MongoDB oplog sizing should be 3-5 days minimum.

Cassandra: Delete-heavy workloads accumulate tombstones that can slow reads. The gc_grace_seconds default of 10 days may be too long for high-delete scenarios.

## Check tombstone warnings
grep \"Read [0-9]* live rows and [0-9]* tombstone\" /var/log/cassandra/system.log
## Force cleanup (nuclear option)
nodetool compact keyspace table

Tuning gc_grace_seconds to shorter values (like 3600 for 1 hour) can help, but requires careful testing. Docker on macOS can introduce clock skew issues that affect Cassandra's gossip protocol.

Which Database Will Fuck You First

SQL vs NoSQL isn't just about schemas - it's about failure modes. PostgreSQL fails predictably. MongoDB fails creatively. Cassandra fails in ways that require a PhD to understand.

Based on DB-Engines popularity trends, here's what the data shows:

PostgreSQL is eating Oracle's lunch in enterprise. Every startup that can't afford Oracle licensing goes with Postgres. Stack Overflow Developer Survey 2025 shows it's the second most loved database after SQLite.

MySQL powers half the internet because it's predictable. Facebook, Twitter, YouTube all scaled with MySQL first. MySQL user adoption is driven by "it just works" mentality - boring technology that keeps running.

MongoDB is popular because developers can JSON.stringify() and call it data modeling. MongoDB community stats show millions of downloads, but production deployments are way lower - everyone tries it, not everyone keeps it.

Cassandra is what Netflix, Apple, and Uber use when MySQL sharding becomes impossible. Cassandra users are companies that actually need global scale, not startups who think they do.

Performance Reality - No Marketing Bullshit

PostgreSQL maxes out around 50k QPS on a single node. After that you need read replicas, Citus sharding, or just switch to something else.

MySQL can hit 100k simple queries per second, but complex JOINs kill performance. MySQL at scale requires careful query optimization.

MongoDB scales horizontally but sharding is a pain in the ass. MongoDB sharding means dealing with shard keys, balancer storms, and chunk migrations that slow everything down.

Cassandra scales infinitely in theory, horribly in practice. Cassandra operations requires dedicated platform teams - not something your PHP developer can manage.

Those version-specific gotchas are just the appetizer. The real nightmare starts when you scale beyond toy applications and discover what each database actually does under load. This isn't marketing benchmark bullshit - this is the stuff that makes you wake up at 3am to a flooded Slack channel.

The Performance Numbers That Actually Matter

Marketing benchmarks are lies. Here's what breaks in production when you're debugging at 3am:

PostgreSQL 17.6 is genuinely faster for analytics. I upgraded a client's reporting database from 16.10 to 17.6 and saw 40% faster aggregations on their user analytics. But the parallel query improvements can spike memory usage during complex operations and OOM kill your server. You need at least 32GB RAM for serious parallel analytics work.

MySQL 8.4.6 is solid for production, but Oracle's Innovation track (9.0/9.1) is them fucking around with features nobody asked for. If you want application logic in the database, use PostgreSQL. The Enterprise Firewall in 8.4 can add 200-500ms latency per query if misconfigured. Stick with LTS.

MongoDB 8.0.9 bulk writes are legitimately fast. We migrated an order processing system and batch inserts went from timing out at 60 seconds to completing in 22 seconds. The bulkWrite API improvements include 59% faster updates and proper cross-shard operations. But you'll still get BSONObjectTooLarge: object to insert too large errors when hitting the 16MB document limit during user data imports.

Cassandra 5.0.5 SAI indexes changed everything. Before SAI, you needed separate Elasticsearch for any query that wasn't on the partition key. Now you can run multi-column queries without full table scans. But the memory requirements doubled - budget 64GB minimum per node.

Real Production Failures I've Debugged

Database scaling patterns vary drastically: PostgreSQL and MySQL scale vertically until you hit hardware limits, MongoDB scales horizontally with sharding complexity, and Cassandra scales horizontally but requires distributed systems expertise.

PostgreSQL: VACUUM storms during high write periods. When you see WARNING: oldest xmin is far in the past followed by HINT: Close open transactions soon to avoid wraparound problems, you're fucked. The autovacuum daemon can't keep up, tables bloat to 10x their normal size, and queries that took 50ms now timeout at 30 seconds. VACUUM tuning becomes life-or-death critical.

MySQL: Binary logs silently filling your disk until MySQL crashes with ERROR 3 (HY000): Error writing file '/var/lib/mysql/mysql-bin.000142' (errno: 28 - No space left on device). The query cache in older versions was worse than useless - it created lock contention that made everything slower. MySQL point-in-time recovery becomes impossible when power failures corrupt binlogs.

MongoDB: Sharding key mistakes are unfixable. Pick the wrong shard key and you'll have hot shards forever. MongoDB sharding anti-patterns are everywhere. Also, the 16MB document limit hits at the worst times - during user imports or data migrations.

Cassandra: Tombstone accumulation makes reads unusably slow. Delete-heavy workloads create millions of tombstones that don't get cleaned up fast enough. Cassandra tombstone tuning requires understanding compaction strategies.

What Each Database is Actually Good For

Polyglot persistence sounds smart until you're managing four different databases. Here's when each one actually makes sense:

PostgreSQL: When You Need SQL That Doesn't Suck

Use PostgreSQL when your data has complex relationships and you need real transactions. PostgreSQL ACID compliance actually works, unlike MySQL's half-assed attempts. Good for:

• Financial systems that can't lose money
• Analytics where you need complex JOINs across dozens of tables
• PostGIS geospatial queries for location-based apps
• pgvector AI applications that need embedding search

Don't use PostgreSQL if you need to scale writes beyond 50k QPS on a single node. PostgreSQL scaling limitations hit hard after that. Also avoid if you're on Windows - PostgreSQL on Windows is like running a Ferrari with square wheels.

MySQL: The Boring Choice That Keeps Working

MySQL is what you use when you want to sleep at night. It's not fancy, but it won't randomly corrupt your data. MySQL reliability comes from 25 years of battle testing. Good for:

• Web applications with predictable query patterns
• E-commerce platforms that need 99.9% uptime
• Content management systems with heavy reads
• MySQL replication for read scaling

Don't use MySQL for complex analytics or if you need advanced SQL features. The optimizer gives up on complex queries.

MongoDB: Fast Development, Slow Operations

MongoDB is great until you need to scale it properly. MongoDB operations complexity increases exponentially with cluster size. Good for:

• Rapid prototyping with JSON-heavy applications
• Content management where schema changes frequently
• MongoDB time series for IoT data
• Applications that can live with eventual consistency

Don't use MongoDB if your data has complex relationships or you need strict consistency guarantees. MongoDB ACID limitations bite when you scale. And for fuck's sake, don't run MongoDB on a laptop - the WiredTiger cache will eat all your RAM and make your MacBook sound like a jet engine.

Cassandra: Scales Forever, Debugs Never

Cassandra handles massive scale but requires PhD-level operations knowledge. Cassandra debugging makes you question your life choices. Good for:

• Global applications that need 99.99% uptime
• Write-heavy workloads that break everything else
• Cassandra time series at petabyte scale
• When MySQL sharding becomes impossible

Don't use Cassandra unless you have dedicated platform engineers. It's not a database you can just "set up" and forget.

The Human Cost of Your Database Choice

Databases don't run themselves. Here's what each one costs in human suffering:

PostgreSQL: You need someone who understands EXPLAIN ANALYZE output and can tune autovacuum. PostgreSQL DBA skills take 2-3 years to develop properly. Budget $120k-180k for a senior PostgreSQL engineer in 2025.

MySQL: Any PHP developer can manage basic MySQL, but MySQL at scale requires understanding replication lag, binlog rotation, and InnoDB tuning. A good MySQL DBA costs $100k-150k annually.

MongoDB: Easy to get started, nightmare to scale. MongoDB operations expertise is rare and expensive. Companies pay $150k-200k for engineers who can properly shard MongoDB clusters.

Cassandra: You need platform engineers, not DBAs. Cassandra operations require understanding of distributed systems, gossip protocols, and JVM tuning. Budget $160k-220k for someone who won't break your cluster.

The Commands That Save Your Ass at 3AM

PostgreSQL - When Everything is Hanging:

-- Find the query blocking everything
SELECT blocked_locks.pid AS blocked_pid,
       blocked_activity.usename AS blocked_user,
       blocking_locks.pid AS blocking_pid,
       blocking_activity.usename AS blocking_user,
       blocked_activity.query AS blocked_statement,
       blocking_activity.query AS blocking_statement
FROM pg_catalog.pg_locks blocked_locks
JOIN pg_catalog.pg_stat_activity blocked_activity ON blocked_activity.pid = blocked_locks.pid
JOIN pg_catalog.pg_locks blocking_locks ON blocking_locks.locktype = blocked_locks.locktype
JOIN pg_catalog.pg_stat_activity blocking_activity ON blocking_activity.pid = blocking_locks.pid
WHERE NOT blocked_locks.granted;

-- Nuclear option: kill the blocking query
SELECT pg_terminate_backend(blocking_pid);

PostgreSQL monitoring queries saved me during a 4-hour table lock disaster.

MySQL - Debugging InnoDB Clusterfuck:

-- Check what's deadlocked
SHOW ENGINE INNODB STATUS\G

-- Find long-running transactions
SELECT trx_id, trx_state, trx_started, trx_mysql_thread_id, trx_query 
FROM information_schema.innodb_trx 
WHERE TIME_TO_SEC(TIMEDIFF(NOW(),trx_started)) > 10;

-- Kill runaway transaction
KILL CONNECTION 12345;

MySQL troubleshooting - that STATUS output is cryptic but tells you everything.

MongoDB - When Sharding Goes Wrong:

// Find operations murdering performance
db.currentOp({"secs_running": {$gte: 5}})

// Check which shard is fucked
sh.status()

// Profile specific database
db.setProfilingLevel(2, {slowms: 100})
db.system.profile.find().sort({ts: -1}).limit(5)

// Emergency: kill runaway operation
db.killOp(operation_id)

MongoDB profiling helped debug a 3-day sharding disaster.

Cassandra - When Compaction is Stuck:

## Check if cluster is healthy or dying
nodetool tpstats

## See what's actually happening
nodetool compactionstats

## Check for tombstone hell
nodetool cfstats keyspace.table | grep "SSTable count\|Tombstones"

## Nuclear option: force major compaction
nodetool compact keyspace table

## Check cluster health
nodetool status

Cassandra monitoring - if pending compactions > 32, you're fucked.