How to Optimize Your Database for Speed in 2025: 6 Battle-Tested Tricks

Pick the Right Columns Fast

• Primary keys - Already indexed, move on.
• Foreign keys - Speed up those pesky JOINs.
• WHERE regulars - Columns showing up in filters, sorts, or grouping clauses.

A quick rule of thumb: if you query it often, index it. But don’t go wild. Each extra index is another item your database has to update on every INSERT or UPDATE. Think of it like adding more keys to your keychain handy until you can’t fit them in your pocket.

-- Good: only on what we filter by
CREATE INDEX idx_orders_customer_status
ON orders(customer_id, status);

Composite vs Single-Column Indexes

Got a query that always filters on two columns together? One composite index beats two single ones. Less space, more speed.

Pro tip: Run EXPLAIN (MySQL) or EXPLAIN ANALYZE (PostgreSQL) after adding an index. If the query plan still shows a full table scan, something’s off double-check column order and data types.

Start With EXPLAIN

Pop this in front of any SELECT:

EXPLAIN SELECT * FROM users WHERE last_login < NOW() - INTERVAL 30 DAY;

Look for red flags:

• type = ALL - Full table scan. Ouch.
• rows = millions - You’re reading way more data than you need.

SELECT Only What You Need

SELECT * is lazy. Be specific:

-- Lazy
SELECT * FROM products WHERE category = 'shoes';
 
-- Better
SELECT id, name, price FROM products WHERE category = 'shoes';

Less data over the wire, less memory used, happier users.

JOIN Smarter, Not Harder

• Join on indexed columns.
• Avoid sub-queries in loops use JOINs or WITH clauses.
• If you’re joining five tables to show a username, maybe denormalize that username into the main table.

Normalize First

Split data into logical tables. No one wants to store the same address 500 times.

Example:
Split orders and customers. Link with a foreign key. You’ll thank yourself when someone changes their email address.

Denormalize for Speed But Only If You Must

Read-heavy app? Sometimes a little duplication beats a 3-table JOIN. Just document the trade-off and set up triggers or scheduled jobs to keep copies in sync.

Pick the Right Data Types

• Store IP addresses as INET (PostgreSQL) or VARBINARY(16) not VARCHAR(45).
• Use TINYINT for booleans in MySQL saves 3 bytes per row. On a 100-million-row table, that’s almost 300 MB.

In-Memory Caching With Redis or Memcached

Store hot stuff user sessions, top products, config flags.

Real-world snippet:

# Python + Redis
product = redis.get(f"product:{product_id}")
if not product:
    product = db.query("SELECT ... WHERE id = ?", product_id)
    redis.setex(f"product:{product_id}", 300, product)

Five lines, 90 % less database load.

Database-Level Query Cache

MySQL 8 retired the built-in query cache, but PostgreSQL’s shared_buffers still helps. Size it to 25 % of available RAM and watch your cache hit ratio climb.

HTTP Caching Too

Don’t forget CDN and browser cache for API responses. If the data doesn’t change every second, let the CDN serve it.

MySQL Quick Wins

• innodb_buffer_pool_size = 70 % of RAM (on dedicated DB box).
• innodb_log_file_size bump = fewer flushes, more throughput.

PostgreSQL Quick Wins

• shared_buffers = 25 % RAM.
• work_mem = enough for in-memory sorts, but not so high you spawn 1,000 huge processes.

Scale Up vs Scale Out

• Vertical (bigger box) - Fast fix, ceiling hits quick.
• Horizontal (read replicas, sharding) - Long-term play, needs code changes.

Rule of thumb: Measure first. If CPU is at 20 % and queries still crawl, the issue is code, not cores.

Clean the Junk

Archive or delete old logs, temp tables, sessions older than 30 days. One client trimmed 300 GB and cut backup time in half.

Update Stats

• MySQL: ANALYZE TABLE orders;
• PostgreSQL: VACUUM ANALYZE orders;

Fresh stats = smarter query plans. Schedule it in cron or use pgAgent.

Check Indexes for Bloat

Unused indexes waste disk and slow writes. Run:

-- PostgreSQL
SELECT schemaname, tablename, indexname, idx_tup_read, idx_tup_fetch
FROM pg_stat_user_indexes
WHERE idx_tup_fetch = 0;

Drop anything with zero fetches unless it’s enforcing a unique constraint.