Postgres the strict priest, MySQL the trickster rogue and why their feud still decides your 3 a.m. pager duty.
You never forget the night your database betrays you. For me, it was MySQL at 2:13 a.m., silently chopping off usernames longer than 50 characters like it was pruning a bonsai tree. No errors, no warnings just gone. Meanwhile, Slack was blowing up:
PM: Can we just restore from backup?
Me: Backup hasn’t run since yesterday.
PM: Can you fix it in prod?
Me: [logs intensify]
By sunrise we’d rolled back, re-imported CSVs, and sworn never to trust “silent truncation” again. That night cost me 14 hours, two angry customers, and a permanent twitch whenever I hear the phrase “VARCHAR(50).”
Every dev has one of these stories. Maybe it was Postgres deadlocking half your tables, maybe it was MySQL guessing your schema into oblivion. Either way, the scars explain why this flame war still rages after 20+ years.
And here’s the thing this article isn’t some marketing fluff comparing synthetic benchmarks nobody actually cares about. This is the real stuff:
- The mistakes that ruined weekends,
- The side-by-side receipts (queries, EXPLAIN plans, error messages),
- And the 2025 decision matrix you can screenshot and throw in your team’s Notion before your next migration meeting.
Because in 2025, this isn’t just a legacy debate. AI pipelines, DBaaS platforms, and cloud bills have made the choice between Postgres and MySQL more relevant than ever.
TLDR
- Postgres → strict schemas, JSONB, pgvector, AI-ready.
- MySQL → fast reads, WordPress/Magento dominance, PlanetScale scaling magic.
- Both → guaranteed to ruin at least one of your weekends.
- Bonus → I built a decision matrix + cheat sheet at the end, so you don’t repeat my 3 a.m. rollback.
The night my db betrayed me
It was supposed to be a routine migration. Copy the schema, import the rows, flip the switch, grab tacos. Easy. Except databases can smell confidence.
We were moving from an aging MySQL 5.7 instance into a managed service. Nothing exotic just a few user tables, some JSON blobs, and columns that had seen too many schema changes. I kicked off the migration, watched the logs scroll, everything looked fine.
Then QA pings: “Why are half the usernames cut off?” I check. Sure enough, MySQL had happily chopped anything past 50 characters in VARCHAR(50) with zero warnings.
CREATE TABLE users (
id INT PRIMARY KEY AUTO_INCREMENT,
username VARCHAR(50),
email VARCHAR(255)
);
In MySQL, that means: “Don’t worry boss, I’ll just trim it.”
In Postgres, it explodes immediately: ERROR: value too long for type character varying(50).
That difference silent truncation vs loud failure isn’t a benchmark stat. It’s the reason three devs spent the night combing backups while a PM asked if we could “just regex it back.”
By sunrise we’d restored from CSVs like it was 2009. The migration cost us ~14 hours, angry customers, and one junior dev swearing off databases forever.
And that’s the real heart of the Postgres vs MySQL fight. It’s not about microseconds. It’s about which database ruins your weekend less.
Why devs still fight this war
Back in the early 2000s, MySQL was everywhere. If you spun up a LAMP stack (Linux, Apache, MySQL, PHP), you were basically running the internet blogs, forums, shops, you name it. MySQL was fast, easy, and free. Nobody cared about perfect schemas; they just wanted their WordPress site online.
Postgres took the opposite path. Born in academia at Berkeley in the ’80s, it cared about correctness, not convenience. Real transactions, strict schemas, serious relational theory. If MySQL was the fun older cousin sneaking you into LAN parties, Postgres was the professor grading you with a red pen.
That split shaped the culture:
- MySQL folks: “It’s simple, fast, gets the job done.”
- Postgres folks: “We actually enforce constraints. Enjoy your silent data loss.”
Twenty years later, the flame wars still light up Reddit and Hacker News. Someone posts a benchmark, half the comments yell “use Postgres,” the other half yell “nobody needs window functions to run a blog.”
The console war analogy fits:
- MySQL is the plug-and-play console cheap, fast, and limited.
- Postgres is the custom PC build takes more effort, but you get control and raw power.
And even though both engines have borrowed features from each other (MySQL adding InnoDB, Postgres getting faster every release), devs still cling to identity. It’s not just tech it’s which one ruined fewer weekends.
Speed vs sanity
If databases were RPG classes, MySQL would be the rogue: fast, scrappy, great at quick hits. Postgres is the wizard: slower to start, but once you unlock the spellbook, it wipes the floor.
For years, MySQL had the bragging rights: “It’s faster.” And for simple reads, it was. That’s why WordPress installs felt snappy in 2006. But speed came at a cost. The second you needed analytics-style queries, MySQL often tapped out.
Example: top 10 players by kill/death ratio over the last 30 days.
MySQL:
SELECT player_id, SUM(kills)/SUM(deaths) AS kd_ratio
FROM matches
WHERE match_date > NOW() - INTERVAL 30 DAY
GROUP BY player_id
ORDER BY kd_ratio DESC
LIMIT 10;
It works until you want rolling averages or windowed stats. Historically, you either hacked around it or shoved logic into the app layer.
Postgres:
SELECT player_id,
AVG(kills::numeric / NULLIF(deaths,0))
OVER (PARTITION BY player_id) AS kd_ratio
FROM matches
WHERE match_date > NOW() - INTERVAL '30 days'
ORDER BY kd_ratio DESC
LIMIT 10;
Clean, safe, and no app-layer spaghetti. Postgres enforces type casting and avoids silent divide-by-zero chaos.
TLDR:
- MySQL → blazing fast for simple reads, but fragile with complexity.
- Postgres → slower baseline, but designed for heavy queries, JSONB, CTEs, and analytics.
Speed feels great until you’re up at 3 a.m. explaining to your PM why the report is wrong because you had to “approximate” in MySQL.
Schema hell
Nothing haunts a dev like schema migrations. Add a column? Fine. Rename in prod? Say goodbye to your weekend.
MySQL made this worse with its “relaxed” attitude. In the old MyISAM days you didn’t even get foreign keys. Even with InnoDB, constraints often felt optional.
ALTER TABLE orders ADD COLUMN customer_id INT;
-- no constraint, no complaint.
Postgres, on the other hand, plays strict parent:
ALTER TABLE orders ADD COLUMN customer_id INT REFERENCES customers(id);
-- ERROR if data doesn’t match.
That strictness saves you. In MySQL, garbage data sneaks in and months later your joins are a swamp. In Postgres, it fails immediately and forces you to fix it.
Same story with JSON. MySQL bolted it on; it works, but clunky. Postgres’s JSONB turned into a legit feature indexable, queryable, and production-grade.
Reddit sums it up best:
Dev A: “MySQL let me insert an empty string into a NOT NULL field. I hate it here.”
Dev B: “Postgres would’ve smacked you with an error. That’s love.”
I’ve lived this. Friday migration on MySQL looked fine. Monday morning? Half the foreign keys didn’t apply. In Postgres, the migration would’ve failed loudly, not three days later.
Strictness is annoying in the moment but it saves you from the silent disasters MySQL is infamous for.
Scaling lies
Every DB vendor swears their engine “scales effortlessly.” Reality check: scaling a relational database feels more like holding a Jenga tower while your traffic doubles.
MySQL had replication early. Great for reads, useless for writes. Sharding? Possible, but duct-taped. PlanetScale’s Vitess has made it less painful, but it’s still not magic.
Postgres was worse for a long time. Before version 10, logical replication was a nightmare. Tools like Slony-I and pgpool gave DBAs night sweats. Now it’s better: logical replication, hot standbys, extensions like Citus for horizontal scaling. But the pain never fully goes away.
Then came DBaaS. Aurora, Supabase, PlanetScale, Neon. Each promises “infinite scale.” Translation: reads are easy, writes still bottleneck, and your AWS bill scales faster than your app.
I once watched a Postgres RDS replica fall 45 minutes behind during Black Friday. Customers saw “In stock” banners for items long gone. AWS support told us to “scale vertically.” Cool, thanks.
Scaling truth:
- Denormalize schemas.
- Add caching (hi Redis).
- Or jump to distributed systems (CockroachDB, Yugabyte).
Relational DBs don’t scale effortlessly. They scale expensively.
Side-by-side receipts
Talk is cheap. Queries are receipts. Here are the classics that separate Postgres from MySQL.
1. Silent truncation
MySQL:
CREATE TABLE users (
id INT PRIMARY KEY AUTO_INCREMENT,
username VARCHAR(50)
);
INSERT INTO users (username)
VALUES ('this_is_a_username_longer_than_fifty_characters');
-- MySQL: silently trims it.
Postgres:
CREATE TABLE users (
id SERIAL PRIMARY KEY,
username VARCHAR(50)
);
-- Same insert → ERROR: value too long for type character varying(50)
MySQL shrugs. Postgres slaps you.
2. JSON
MySQL:
INSERT INTO events (payload)
VALUES ('{"user":"42","action":"jump"}');
SELECT payload->"$.action" FROM events;
Postgres:
INSERT INTO events (payload)
VALUES ('{"user":"42","action":"jump"}'::jsonb);
SELECT payload->>'action' FROM events;
-- plus: GIN indexes for speed
JSON in MySQL works; in Postgres, it’s a first-class citizen.
3. Query planner
MySQL:
EXPLAIN SELECT FROM orders WHERE customer_id = 42;
-- often: full table scan unless indexed manually
Postgres:
EXPLAIN SELECT FROM orders WHERE customer_id = 42;
-- auto-uses btree index if available
Postgres’s planner usually “just works.”
TLDR:
- MySQL = permissive, fast, but risky.
- Postgres = strict, slower upfront, but safer in the long run.
These aren’t benchmarks. They’re survival lessons.
The 2025 decision matrix (bookmark this)
So which one do you actually pick in 2025? Save this table before your next infra meeting:
This isn’t theory it’s where the industry has moved:
- Postgres = default for new stacks, thanks to pgvector, Citus, Supabase, Neon.
- MySQL = entrenched via WordPress, which still powers 43% of the web.
Keep this table handy. Stick it in your Notion, send it to your PM, or screenshot it for Slack debates. It’s not about benchmarks it’s about which database ruins your weekend less.
Why this debate matters now (AI + DBaaS boom)
You’d think the Postgres vs MySQL flame war would’ve cooled by now. Instead, 2025 has made it louder. Why? Because databases aren’t just row storage anymore they’re part of AI pipelines, cloud bills, and whether your app survives a front-page HN spike.
AI workloads
Postgres isn’t just “academic” anymore. With pgvector, it acts like a vector DB: store embeddings, run similarity queries, power RAG. Supabase even bundles Postgres + AI tooling by default. MySQL? Possible, but you’re duct-taping extra layers on top.
DBaaS explosion
Cloud DBaaS platforms have made Postgres vs MySQL choices unavoidable:
- Aurora (AWS) → managed Postgres/MySQL.
- PlanetScale → MySQL with Vitess scaling.
- Supabase + Neon → Postgres-first, serverless pitch.
All promise “infinite scale.” Reality: reads scale well, writes bottleneck fast, and your invoice scales faster than your app.
Why it matters
If you’re hacking a weekend project, either DB works. But for AI apps, analytics-heavy SaaS, or compliance-sensitive data, the choice isn’t academic it’s the difference between smooth demos or 3 a.m. debugging.
- Pick Postgres → modern features, safer schemas, AI-ready.
- Pick MySQL → legacy stacks, WordPress empires, or when PlanetScale fits.
The stakes got higher. It’s no longer “will my blog load?” It’s “will my AI startup crash mid-demo?”
My hot take
In 2025, Postgres is the default database. Not because it’s trendy, but because it’s strict, feature-rich, and less likely to ruin your weekend. It’s the Git of databases sometimes annoying, but the industry standard.
So here’s the spice:
Choosing MySQL in 2025 is like choosing SVN over Git.
It still works, but if you’re starting fresh, expect raised eyebrows in code review.
To be fair:
- MySQL still powers WordPress, Magento, and huge legacy stacks.
- PlanetScale has done impressive work scaling MySQL horizontally.
- For simple CRUD apps, MySQL is easier to onboard new devs.
But here’s the problem: once you need analytics, JSONB, AI vector search, or serious constraints, MySQL starts to feel like duct tape. Postgres handles those natively.
I admit my bias my scars are from MySQL. That silent truncation still haunts me. Postgres has burned me too (deadlocks, painful migrations), but overall, it’s saved me more than it’s hurt me.
So my recommendation is simple:
- Start with Postgres unless you have a legacy/stack reason to pick MySQL.
- If you pick MySQL, do it eyes open know its quirks, and plan your tooling around them.
Flame me if you want but I’d rather be yelled at in a Medium comment than paged at 3 a.m. because MySQL “helpfully” trimmed my data.
Conclusion
When a database breaks, it’s not just technical it’s personal. You remember the night, the logs, the Slack panic. Postgres and MySQL aren’t just engines; they’re the quiet teammates that either save you or stab you when you least expect it.
This whole debate boils down to trust. Do you want the database that YOLOs through migrations (MySQL), or the one that slams on the brakes and forces you to fix things upfront (Postgres)?
In 2025, the choice matters more than ever. Databases aren’t just for CRUD they’re powering AI pipelines, compliance dashboards, and investor metrics. Pick wrong, and you’re not just debugging your startup’s credibility might be on the line.
My slightly controversial take: the “war” is basically over.
- MySQL will keep powering WordPress, Magento, and scaling experiments with Vitess.
- Postgres has already won the hearts (and wallets) of modern developers. It’s the default in startups, the first-class citizen in AI stacks, and the safe pick when you don’t want surprises.
That doesn’t mean MySQL is obsolete. It just means the lanes are set. If you’re inheriting a legacy empire, stick with MySQL. If you’re building something new, Postgres is your safest bet.
And when not if you’re wrestling with a migration at 2 a.m.? Drop your story in the comments. Because if there’s one universal truth here, it’s this: no database really scales effortlessly, but at least we can share memes about the pain.
Helpful resources
- Postgres docs strict but reliable.
- MySQL docs must-have for legacy stacks.
- pgvector embeddings + AI with Postgres.
- Citus scale Postgres horizontally.
- Vitess how PlanetScale makes MySQL scale.
- Supabase modern Postgres services.
- PlanetScale modern MySQL service.
- HN thread flame wars never die.
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