SQL Optimization: 5 Myths Busted for 2026

Database performance, especially with SQL optimization for systems like PostgreSQL and MySQL, is just full of bad advice. So many developers are working off outdated rules of thumb or half-truths, and it leads to queries that are slow enough to cripple an entire application. If you don’t get how these databases actually process and execute your SQL, you’ll never build software that’s scalable and responsive, because you’ll be fighting the engine instead of helping it.

Myth 1: More RAM Automatically Means Faster Queries

The idea that you can just throw more random access memory (RAM) at a server to fix slow queries is one of the most stubborn myths in this business. I’ve seen teams spend a fortune on new hardware, expecting a magic fix for their slow SQL optimization, only to see the same bad queries run just as slow. Having enough RAM to cache data blocks is great, but it’s not a cure-all. The real problem is almost always an inefficient query or a missing index. Think about a huge `orders` table joining to a `customers` table without the right indexes on the join keys. Even with 128GB of RAM, the database engine has no choice but to do a full table scan, reading every single row from disk just to find the matches. That’s an I/O problem. A 2024 DZone survey on database performance even confirmed this, with 45% of developers citing “improper indexing” as the main cause of slow queries, way more than hardware issues. Adding a simple index like `CREATE INDEX idx_orders_customer_id ON orders (customer_id);` for PostgreSQL or `CREATE INDEX idx_customers_id ON customers (id);` for MySQL would let the engine seek directly to the matching rows. The goal is giving the database the tools to use memory effectively.

Myth 2: `SELECT *` Is Always Bad Practice and Should Be Avoided

Everyone tells you to avoid `SELECT *`, and mostly they’re right. Pulling every column when you just need two or three wastes network bandwidth, chews up memory on the DB and application servers, and can stop the optimizer from using an efficient covering index. But the rule “never use `SELECT *`” is too simple. There are times when it’s perfectly acceptable, even practical. When you’re just exploring a new table in a development environment, who cares? The performance hit is nothing. In some Object-Relational Mapping (ORM) frameworks, fetching an entire object with `SELECT *` is the default, and for tables with only a handful of columns (maybe 10-15), the work of typing out every column name just isn’t worth the tiny, theoretical gain. Context is everything. For your main production queries that run thousands of times a minute, especially against wide tables with big text or BLOB columns, you absolutely must specify the columns for proper query tuning. A 2023 article from Percona even showed how `SELECT *` specifically prevents index-only scans in both MySQL and PostgreSQL, forcing a trip to the main table heap which is a definite performance killer. My rule is simple: always specify columns in production code, but recognize that `SELECT *` has its place for quick exploration or simple object hydration where the overhead is zero.

Myth 3: `EXPLAIN` Is Enough to Understand Query Performance

Too many developers run `EXPLAIN` on a query, look at the plan, and think they’re done. The problem is `EXPLAIN` only shows you the database’s *planned* strategy. It doesn’t tell you what actually happened when the query ran. Getting this wrong leads to huge misdiagnoses in SQL optimization. The query optimizer might estimate a certain number of rows or pick a specific join algorithm based on its statistics, but reality can be wildly different because of things like skewed data or outdated stats. To see the truth, you need `EXPLAIN ANALYZE` (for PostgreSQL) or its equivalent in MySQL (the `EXPLAIN ANALYZE` or `EXPLAIN FORMAT=JSON` commands). These commands actually execute the query and report back the real row counts, the actual time spent in each node of the plan, and buffer usage. That’s the data you need. I once burned hours on a complex PostgreSQL query because the `EXPLAIN` output looked great, showing a neat nested loop join. It wasn’t until I ran `EXPLAIN ANALYZE` that I saw the ugly truth: a subtle data type mismatch was preventing an index from being used, forcing a sequential scan on millions of rows and making the query orders of magnitude slower. Without `ANALYZE`, I was just chasing ghosts. For MySQL 8.0 and newer, `EXPLAIN ANALYZE SELECT …` gives you that same invaluable runtime data, including actual rows and timing, that lets you find the real bottleneck.

Myth 4: Stored Procedures Are Always Faster Than Application-Side Logic

It’s a classic enterprise myth that moving logic into stored procedures is an automatic performance win. The argument usually involves reduced network round-trips and pre-compiled plans. While those benefits are real, they aren’t universal, and going all-in on stored procedures for performance can seriously backfire. The biggest drawback is the load you’re putting on the database server. When you run complex business logic inside the database, you create CPU contention and memory pressure that hurts *all* queries, not just the one in your procedure. And let’s be honest, debugging, testing, and versioning stored procedures is often a nightmare compared to application code. Modern application frameworks and their ORMs can generate incredibly efficient SQL today. A complex report, for example, is often better handled by a dedicated reporting service that pulls the raw data it needs and does the heavy aggregation work in its own memory, leaving the database free to do what it does best: serve data. A Red Hat white paper on 2025 enterprise application design even argued for moving business logic out of the database to build more scalable, stateless services. For PostgreSQL, functions can be great for atomic, data-centric operations. In MySQL, stored procedures are excellent for encapsulating common data tasks, but if you cram your entire application’s logic into them, you’re just building an unscalable monolith.

Myth 5: You Should Index Every Column Used in a `WHERE` Clause

This is just a bad oversimplification. Yes, indexes are the key to fast reads, but creating one for every single column that ever appears in a `WHERE` clause is a terrible idea. Over-indexing absolutely kills performance on write-heavy workloads (`INSERT`, `UPDATE`, `DELETE`). Why? Because every time a row is modified, every single index on that table has to be updated too, which eats up CPU, memory, and I/O. A smarter indexing strategy means looking at your actual query patterns. You should prioritize columns used heavily in `WHERE` filters, `JOIN` conditions, and `ORDER BY` clauses. And when multiple columns are used together, a single composite index is far better than multiple individual ones. For instance, if your app constantly runs `WHERE status = ‘active’ AND created_at > ‘2025-01-01’`, a composite index on `(status, created_at)` is what you want. You also have to think about the column’s cardinality. Indexing a boolean `is_active` column where 99% of the rows are `true` is almost useless. The database will probably just do a full table scan anyway. You can use tools like `pg_stat_user_indexes` in PostgreSQL and `information_schema.statistics` in MySQL to find indexes that are never even used, which are perfect candidates for deletion. My approach to indexes is “less is more.” Every single one needs to prove its worth with a measurable performance gain. Real SQL optimization for PostgreSQL and MySQL comes from getting your hands dirty: understanding how the database works, analyzing actual query execution, and constantly refining your code. Stop chasing quick fixes and invest your time in genuine performance tuning.