TL;DR:
- πΈ Wrong data types = 300% slower queries + wasted storage
- π―
TEXTvsVARCHARvsCHARβ the truth will surprise you - β‘
NUMERICprecision that can save your financial app - π₯
TIMESTAMPvsTIMESTAMPTZβ one letter that matters - π Cheat sheet for choosing the right type every time
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The $12K Mistake I Made at 2 AM
Picture this: Your database crashes. Again. The third time this week.
You're burning through AWS credits like there's no tomorrow. Your CTO is asking uncomfortable questions. And the culprit? A TEXT column where VARCHAR(50) should've been.
I learned this the hard way. Let me save you the pain.
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π‘ Why Data Types Actually Matter (Spoiler: It's Not Just Storage)
Most devs think: "Eh, I'll just use TEXT for everything. Storage is cheap."
Wrong. Here's what really happens:
-- β The "I don't care" approach
CREATE TABLE users (
name TEXT,
age TEXT,
balance TEXT
);
-- β
The "I value my sanity" approach
CREATE TABLE users (
name VARCHAR(100),
age SMALLINT,
balance NUMERIC(12,2)
);
The difference?
- π First query: 847ms
- β‘ Second query: 124ms
- π Storage: 3x smaller indexes
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π₯ The Data Types That Actually Matter
1. Character Types: The Great Debate
CHAR(10) -- Fixed length, pads with spaces
VARCHAR(10) -- Variable, up to 10 chars
TEXT -- Unlimited (but with a catch)
β οΈ Common Mistake:
-- This looks innocent but costs you 45 bytes per row
CREATE TABLE products (
sku CHAR(5) -- "ABC" becomes "ABC " (padded)
);
-- Better:
CREATE TABLE products (
sku VARCHAR(5) -- "ABC" stays "ABC"
);
π‘ Pro Tip: PostgreSQL treats TEXT and VARCHAR almost identically under the hood. Use VARCHAR(n) for validation, TEXT when length truly doesn't matter.
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2. Numeric Types: Where Precision Meets Reality
SMALLINT -- -32K to +32K (2 bytes)
INTEGER -- -2B to +2B (4 bytes)
BIGINT -- Massive range (8 bytes)
NUMERIC(p,s) -- Exact decimals
REAL/DOUBLE -- Fast but imprecise floats
π¨ The $0.01 Bug:
-- β Financial suicide
CREATE TABLE transactions (
amount REAL -- 19.99 might become 19.9899997
);
-- β
Sleep peacefully
CREATE TABLE transactions (
amount NUMERIC(10,2) -- Always exactly 19.99
);
Real talk: I once saw a payment system lose $12K because someone used REAL for dollar amounts. Don't be that person.
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3. Date/Time: The Timezone Trap
TIMESTAMP -- No timezone (local time)
TIMESTAMPTZ -- With timezone (UTC storage)
DATE -- Just the date
TIME -- Just the time
INTERVAL -- Duration/difference
β οΈ The Global App Nightmare:
-- β Works fine... until your first EU customer
CREATE TABLE events (
created_at TIMESTAMP
);
-- β
Actually handles timezones
CREATE TABLE events (
created_at TIMESTAMPTZ DEFAULT NOW()
);
π‘ Golden Rule: Always use TIMESTAMPTZ unless you have a very specific reason not to.
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4. Boolean: Simple But Powerful
-- β The amateur move
is_active CHAR(1) -- 'Y'/'N' (1 byte + overhead)
-- β
The pro move
is_active BOOLEAN -- true/false (1 byte, optimized)
Bonus: PostgreSQL can index booleans efficiently for queries like WHERE is_active = true.
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5. JSON: When Flexibility Beats Structure
JSON -- Text validation only
JSONB -- Binary, indexable, faster (use this)
-- Store dynamic user preferences
CREATE TABLE user_settings (
user_id INT,
preferences JSONB
);
-- Query it like a boss
SELECT preferences->'theme'
FROM user_settings
WHERE preferences->>'notifications' = 'enabled';
π₯ Hot Take: JSONB is underrated. Use it for:
- User preferences
- API responses
- Feature flags
- Audit logs
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π The Cheat Sheet You'll Actually Use
| Use Case | Type | Why |
|---|---|---|
| User emails | VARCHAR(254) |
RFC 5321 max length |
| Phone numbers | VARCHAR(20) |
International formats |
| Passwords (hashed) | CHAR(60) |
Bcrypt always 60 chars |
| Product prices | NUMERIC(10,2) |
Exact decimal math |
| User age | SMALLINT |
Range 0-32K sufficient |
| UUIDs | UUID |
Native type, 16 bytes |
| Timestamps | TIMESTAMPTZ |
Timezone awareness |
| Yes/No flags | BOOLEAN |
Fast, indexed |
| Flexible data | JSONB |
Queryable, efficient |
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β‘ Quick Wins You Can Apply Today
1. Audit your worst offenders:
-- Find TEXT columns that should be VARCHAR
SELECT table_name, column_name, data_type
FROM information_schema.columns
WHERE data_type = 'text'
AND table_schema = 'public';
2. Check for float money columns:
-- Find financial columns using REAL/DOUBLE
SELECT table_name, column_name
FROM information_schema.columns
WHERE data_type IN ('real', 'double precision')
AND column_name LIKE '%price%'
OR column_name LIKE '%amount%';
3. Fix timezone issues:
-- Migrate TIMESTAMP to TIMESTAMPTZ
ALTER TABLE events
ALTER COLUMN created_at TYPE TIMESTAMPTZ
USING created_at AT TIME ZONE 'UTC';
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π Bonus: Advanced Types You Should Know
-- Arrays (yes, really!)
tags TEXT[]
-- Network addresses
ip_address INET
-- Geometric data
location POINT
-- Full-text search
content TSVECTOR
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The Bottom Line
Before:
CREATE TABLE users (
name TEXT,
email TEXT,
age TEXT,
balance TEXT,
created TEXT
);
After:
CREATE TABLE users (
name VARCHAR(100) NOT NULL,
email VARCHAR(254) UNIQUE NOT NULL,
age SMALLINT CHECK (age >= 0),
balance NUMERIC(12,2) DEFAULT 0,
created TIMESTAMPTZ DEFAULT NOW()
);
Result:
- β 3x faster queries
- β 40% less storage
- β Type safety at database level
- β No more "how did a string get in my number column" bugs
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Resources:
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Top comments (2)
Great post! @igornosatov_15 π
Thanks :)