Introduction
Indexes are usually the first performance tool engineers reach for. A query is slow, an index is added, and things get faster. This works so well early on that teams often assume that more indexes will always lead to better performance.
In production, that assumption eventually breaks. Teams add indexes, but queries stop getting faster. Sometimes they even get slower. Writes become painful. VACUUM runs longer. CPU and memory usage increase.
This article explains why indexes have diminishing returns in PostgreSQL, what teams typically see in production, and why this slowdown can feel confusing and unexpected.
What an Index Actually Does
An index helps PostgreSQL avoid scanning the entire table. It allows the database to jump directly to the rows it needs.
A real-world analogy: an index is like a book’s table of contents. It helps you find a chapter quickly. But if the book has thousands of similar chapters or if you need to read most of the book anyway, the table of contents stops saving time.
Indexes save work only when they significantly reduce the amount of data PostgreSQL must touch.
When Indexes Work Really Well
Indexes shine when:
Queries filter on highly selective columns
Only a small portion of rows match
Tables are not heavily bloated
Write volume is moderate
In these cases, PostgreSQL does much less work per query, resulting in dramatically improved performance.
Why Indexes Eventually Stop Helping
As systems grow, several things change at once.
First, selectivity drops. Columns that were once unique now match many rows. The index still works, but it points to a large part of the table.
Second, data volume increases. Even indexed lookups require more page reads, more visibility checks, and more CPU.
Third, write traffic increases. Every INSERT, UPDATE, and DELETE must update all related indexes.
At some point, the cost of maintaining indexes rivals or exceeds the benefit they provide.
What Developers Usually See in Production
Teams often report:
Indexes exist, but queries remain slow
Adding new indexes gives little or no improvement
Write-heavy workloads slow down noticeably
VACUUM and autovacuum activity increases
CPU usage rises even for indexed queries
This creates frustration because the usual fix no longer works.
Why the Slowdown Feels Sudden
Index effectiveness declines gradually, but pain appears suddenly.
As tables grow:
Index trees become deeper
More index pages must be read
More heap pages must be checked
Each query gets slightly more expensive. Eventually, the system crosses a threshold where latency spikes and throughput drops. To engineers, it feels like indexes “stopped working overnight.”
The Hidden Cost of Too Many Indexes
Every index has a price.
Costs include:
Indexes that are rarely used still incur these costs.
Real-World Example
A user table starts with three indexes and performs well. Over time, new features are added. Each feature adds an index “just in case.”
Years later, the table has fifteen indexes. Read queries are only slightly faster, but write latency has doubled. VACUUM runs constantly. Engineers blame hardware or cloud limits.
The real issue is index overload.
Advantages and Disadvantages of Index Usage
Advantages (When Used Thoughtfully)
When indexes are added with intent:
Queries remain predictable
Read latency stays low
CPU usage is controlled
VACUUM pressure is manageable
The system scales more gracefully
Indexes remain a powerful optimization tool.
Disadvantages (When Added Indiscriminately)
When indexes are added blindly:
Write performance degrades
Maintenance costs explode
Autovacuum falls behind
CPU and I/O pressure increase
Performance tuning becomes harder
At that point, indexes create more work than they save.
How Teams Should Think About This
Indexes are not free speed boosts. They are trade-offs.
Teams should shift from asking:
“Can we add an index?”
to:
“Is this index still paying for itself at our current scale?”
Index decisions should be revisited as data size and access patterns change.
Simple Mental Checklist
Before adding or keeping an index, ask:
Does this index significantly reduce scanned rows?
Is it used frequently in production?
How much write traffic does this table see?
Does this index increase VACUUM pressure?
Would query or data model changes help more?
These questions prevent index sprawl.
Summary
Indexes in PostgreSQL provide powerful early gains, but their benefits diminish as data volume and write traffic grow. Performance problems feel sudden because small per-query costs accumulate until thresholds are crossed. Teams that treat indexes as evolving trade-offs, not permanent fixes, avoid hidden maintenance costs and keep production systems stable over time.