# Supercharged PostgreSQL Tips: Less Boring, More Powerful

Most PostgreSQL optimization guides feel like laundry lists of settings and indexes. But **real performance gains often come from clever ideas, not just the usual tricks**. Let’s unpack a few of those ideas in ways that actually make sense for you.

These techniques go beyond “add an index and pray,” and they *really* help in situations where the database planner isn’t doing exactly what you want.

---

## 🎯 1. Stop Wasting Time Scanning Tables When You Don’t Have To

Imagine a table of users with a `plan` column that is **only allowed** to be `'free'` or `'pro'` because of a **check constraint**:

```sql
CREATE TABLE users (
  id SERIAL PRIMARY KEY,
  username TEXT NOT NULL,
  plan TEXT NOT NULL CHECK (plan IN ('free','pro'))
);
```

Now someone runs this:

```sql
SELECT * FROM users WHERE plan = 'Pro';
```

That returns *no rows* but PostgreSQL still scans every row! Why? Because the planner doesn’t automatically assume your constraint means some values are impossible.

### 🧠 Clever Fix: Enable Constraint-Based Planning

By turning on:

```sql
SET constraint_exclusion = on;
```

PostgreSQL figures out up front that `'Pro'` (capital “P”) can’t match the check constraint. It returns the result instantly, no table scan!

**Why this matters:** In reporting environments where analysts craft queries by hand, mistakes like casing can cause huge performance hits. Constraint exclusion can stop that.

---

## 📉 2. Make Indexes Smaller & Faster with Function-Based Indexes

Let’s say you have a sale table:

```sql
CREATE TABLE sale (
  id SERIAL PRIMARY KEY,
  sold_at timestamptz NOT NULL,
  charged int NOT NULL
);
```

And analysts run queries to sum sales per day. Without an index, PostgreSQL must scan all 10M row; slow!

The common solution is:  
👉 `CREATE INDEX ON sale(sold_at);`

That helps: query time drops but the index is **huge**, and PostgreSQL still considers time even though you only care about dates.

### 🧠 Better Solution: Index Only What You *Need*

Instead, index just the **date part**:

```sql
CREATE INDEX ON sale ((date_trunc('day', sold_at)));
```

This makes the index much smaller and faster, because PostgreSQL only needs to index dates, not full timestamps.

**Why this matters:**

* Smaller index = less disk usage
    
* Faster scans = quicker aggregations
    
* Better performance without huge overhead
    

💡 This is called a *function-based index*: a powerful tool junior developers often overlook.

---

## 🧪 3. Avoid Human Errors with Virtual Generated Columns

Function-based indexes work great *if* programmers always use the exact same expression in queries. That rarely happens!

### 🧠 Safeguard with Virtual Generated Columns

PostgreSQL 18 lets you define a column that **computes itself**:

```sql
ALTER TABLE sale
  ADD sold_date DATE GENERATED ALWAYS AS (date_trunc('day', sold_at));
```

Now every row stores a date value that matches the index expression exactly. Later queries like this use the index automatically:

```sql
SELECT sold_date, SUM(charged)
FROM sale
WHERE sold_date BETWEEN '2025-01-01' AND '2025-01-31'
GROUP BY sold_date;
```

No mistakes. No confusing expressions. PostgreSQL *just uses the index*.

**Why this matters:**

* Less manual SQL discipline
    
* Faster by design
    
* Cleaner schemas
    

---

## 🔐 4. Enforce Uniqueness with Less Overhead Using Hash Indexes

Suppose you have millions of URLs and want to ensure you never process the same URL twice.

A naive way is to use a unique B-Tree index:

```python
CREATE UNIQUE INDEX urls_unique ON urls(url);
```

That works but B-Tree indexes can get big and slow if your values are long strings.

### 🧠 A Better Fit: Unique Hash Index

Hash indexes store a fingerprint of the value instead of the full string. For long or complex text values (like URLs), this can be:

* *much smaller*
    
* slightly faster for equality checks
    

Hash indexes aren’t used everywhere, but **when you only need uniqueness, they can be perfect**.

---

## 🔍 5. Think Like the Planner: Help PostgreSQL Know What You **Really** Care About

PostgreSQL’s optimizer makes choices based on statistics it has about data. Sometimes these stats are outdated, especially if:

* the table gets updated frequently
    
* auto-ANALYZE doesn’t kick in quickly enough
    

### 🧠 Tip: Fine-Tune Auto-ANALYZE

By lowering thresholds for a specific table, PostgreSQL refreshes statistics faster so the planner stops guessing and starts knowing.

This won’t magically speed every query but in high-write environments it can prevent bad plans from becoming permanent performance problems.

---

## 🧠 Final Thoughts for Junior Developers

Here’s what to take away:

✅ Don’t just index everything blindly  
👉 Know why an index helps *specific queries*  
👉 Smaller indexes often outperform bigger ones  
👉 Planner hints like `constraint_exclusion` can eliminate needless work

These “unconventional” ideas are unconventional **because most developers don’t think about them** but they can make dramatic differences in real workloads.

---

## 🎓 Quick Cheat Sheet

| Optimisation | What It Does | When to Use It |
| --- | --- | --- |
| **constraint\_exclusion** | Prevents pointless scans on impossible predicates | When queries include impossible lookups |
| **Function-based index** | Indexes only part of a value | When you aggregate on computed values |
| **Virtual generated column** | Locks in correct expressions | When team SQL varies |
| **Hash unique index** | Smaller unique enforcement | When values are long/complex |

---

If you want to explore this in depth, try using `EXPLAIN ANALYZE` on your queries. It’s the best way to *understand what PostgreSQL is actually doing* before and after your changes.

Source: [https://hakibenita.com/postgresql-unconventional-optimizations](https://hakibenita.com/postgresql-unconventional-optimizations)