3 Reasons 'Hypothetical Indexes' Are the Only Way to Safely Audit Production Postgres

If you’ve ever sat through a three-hour CREATE INDEX CONCURRENTLY on a 2TB table only to realize you indexed the wrong column, you know the specific flavor of soul-crushing regret that only a database administrator can feel. Making guesses about performance on massive production datasets isn't just risky; it’s an expensive waste of engineering time and disk IO.

In a perfect world, we’d all have a staging environment that perfectly mirrors production's scale. In the real world, we have "staging" (a 5GB subset of data from 2019) and a prayer. This is where hypothetical indexes—specifically via the hypopg extension—become a literal lifesaver.

Here are three reasons why they are the only sane way to audit your Postgres indexing strategy.

1. Zero-Byte Storage (The "Infinite Disk" Hack)

A real index on a multi-terabyte table isn't just a metadata entry; it’s a massive chunk of data that needs to live on your NVMe drives. If you want to test three different composite index variations on a 500GB table, you might need an extra 1.5TB of space just for the *experiment*.

Hypothetical indexes take up virtually zero space. They exist only in the connection's backend memory and within the Postgres catalogs during your session. You aren't writing b-tree pages to disk; you're just telling the query planner: "Imagine this existed. What would you do?"

How it looks in practice:

First, you'll need the extension installed (it's available on most managed services like AWS RDS or Aiven).

-- Load the extension
CREATE EXTENSION IF NOT EXISTS hypopg;

-- Let's pretend we have a massive 'orders' table
-- and we want to see if an index on customer_id helps
SELECT * FROM hypopg_create_index('CREATE INDEX ON orders (customer_id)');

The output gives you an index OID and a name like <12345>hypopg_orders_customer_id_idx. You haven't spent a dime on storage, and your cloud bill remains un-triggered.

2. Testing Without the "Index Tax"

Building a real index on a busy production table is a high-stakes game. Even with CONCURRENTLY, you’re looking at increased CPU usage, massive IO spikes, and the dreaded "waiting for table lock" if you happen to have a long-running transaction open.

Hypothetical indexes are created instantly. Because Postgres isn't actually scanning the table to build the index structure, you get the results of your "What if?" scenario in milliseconds.

-- Check if the planner would actually use our fake index
EXPLAIN SELECT * FROM orders WHERE customer_id = 42;

/*
QUERY PLAN
---------------------------------------------------------------------------------------------
 Index Scan using <12345>hypopg_orders_customer_id_idx on orders  (cost=0.04..8.05 rows=1 width=45)
   Index Cond: (customer_id = 42)
*/

If the EXPLAIN output shows a Seq Scan, you know immediately that your proposed index is useless for this query. You just saved yourself hours of build time and potential production lag by failing fast.

3. Safe Auditing of "Partial" and "Expression" Indexes

We’ve all been there: you think a partial index (e.g., WHERE status = 'pending') will solve a performance bottleneck, but the planner decides to ignore it because of your statistics or query structure.

Auditing these complex strategies is where hypopg shines. You can iterate through five different partial index definitions in under a minute to see which one the planner actually bites on.

-- Will the planner use a partial index for 'active' users?
SELECT * FROM hypopg_create_index('CREATE INDEX ON users (last_login) WHERE status = ''active''');

-- Test a query that should trigger it
EXPLAIN SELECT count(*) FROM users WHERE status = 'active' AND last_login > '2023-01-01';

If the planner ignores it, you can simply run SELECT hypopg_reset(); to clear your hypothetical playground and try a different strategy. No DROP INDEX required, no metadata bloat, and zero risk to the running application.

The Big "Gotcha": No EXPLAIN ANALYZE

Before you get too excited, there is one major limitation: You cannot use `EXPLAIN ANALYZE`.

Think about it—EXPLAIN ANALYZE actually executes the query to see how long it takes. Since a hypothetical index doesn't have any actual data, there’s nothing for Postgres to scan. You are strictly testing the Query Planner's intent, not the actual execution speed.

However, in 95% of production tuning cases, knowing *if* the planner will choose an index is the hardest part of the battle.

A Better Way to Audit

Next time you're looking at a slow query on a table that takes a week to re-index, don't just guess and pray.

1. Install `hypopg`.
2. Create the "Fake" index.
3. Run `EXPLAIN`.
4. Compare costs.

It turns out the best way to manage production databases is to lie to them—just a little bit—to see how they react. Your disk space (and your stress levels) will thank you.