Your query takes 10 seconds. You run it again. Still 10 seconds. Your database is scanning every single row looking for what you want. It is like searching for a name in a phone book by reading every page instead of using the alphabetical index.
This is what happens without proper indexing. Queries that should take milliseconds take minutes. Your application feels broken. Your users leave.
Database indexing separates fast systems from slow ones. It is the difference between a system that scales and one that crawls as data grows.
What An Index Actually Does
An index is a lookup table. Instead of scanning every row, the database checks the index first. It finds what it needs instantly.
Think of a library. Without an index, you would read every book to find mentions of “microservices.” With an index, you look up the page number and jump straight there. That is what database indexes do.
Indexes are pointers to data. They are sorted, organised, and optimised for fast searching. They take up extra storage but save massive amounts of query time.
The Problem Nobody Talks About: Write Slowdown
Creating an index improves read performance. But it creates a new problem.
Every time you insert, update, or delete data, the database must also update all the indexes on that table. An insert that takes 1ms now takes 5ms because the database is updating five indexes.
This is the tradeoff. Faster reads. Slower writes. You must choose what matters more for your system.
A read-heavy system like a search engine indexes everything. Fast queries matter. Slow writes are acceptable.
A write-heavy system like a real-time event tracker indexes almost nothing. Fast writes matter. Slower queries are acceptable.
Types Of Indexes: Choosing The Right Tool
Not all indexes are the same. Different problems need different index types.
A B-tree index handles most things. It is fast for equality and range queries. It is the default choice for most databases.
A hash index is faster for equality only. But if you need range queries (greater than, less than), hash indexes fail.
A composite index indexes multiple columns together. A query searching by both user ID and date can use a composite index on (user_id, date).
A partial index indexes only some rows. If you only search recent data, index just recent rows. This saves storage and speeds up inserts.
When Your Index Actually Hurts Performance
Sometimes adding an index makes things slower, not faster.
An index that is never used wastes storage and slows down writes. You created it for a query that does not exist.
Too many indexes means every write operation touches dozens of indexes. Your insert performance collapses.
An index on a column with only a few values (like true/false) is useless. The database scans the index but still has to check most rows.
Real Example: The Slow Report
Imagine a reporting query that runs every morning. It filters by company_id, then date, then product_type. Without indexes, it scans 1 million rows and takes 45 seconds.
Add an index on company_id. Now it narrows to 10,000 rows. Takes 2 seconds.
Add a composite index on (company_id, date). Now it narrows to 100 rows. Takes 50 milliseconds.
Add a covering index that includes product_type. The database never touches the main table. Takes 5 milliseconds.
One query went from 45,000ms to 5ms. That is the power of indexing.
How To Know Which Columns Need Indexes
Look at your slow queries. Most development frameworks tell you which queries are slow.
Find the WHERE clauses. Index those columns. If a query filters by user_id, index user_id.
Find the JOIN conditions. Index those columns. If you join orders and customers on order.customer_id, index that.
Find the ORDER BY clauses. Index those columns. If you sort by created_at, index it.
Ignore the rest. Not every column needs an index.
The Hidden Cost: Storage And Maintenance
Indexes consume storage. A table with 100GB of data might have 30GB of indexes.
Indexes also require maintenance. The database must keep them organized and updated. This costs CPU and memory.
On large tables, rebuilding an index can take hours. On production systems, this means downtime or slow performance.
Plan for index maintenance. Monitor disk usage. Delete indexes that nobody uses.
Monitoring Indexes: Are They Even Working
You created an index. But is it being used?
Most databases track which indexes are accessed. Check the monitoring tools. If an index has zero reads, delete it.
Also, check the write performance. If deleting an unused index makes writes 20% faster, it was worth removing.
Balance is the answer. Have enough indexes for fast reads. Not so many that writers suffer.
The Partial Index Secret That Most Developers Miss
A partial index solves two problems at once. It indexes only rows matching a condition.
Example: You only search orders from the last year. Create a partial index on recent orders. Ignore old orders.
This saves storage. Keeps writing faster. Speeds up queries on recent data.
Most developers do not know that partial indexes exist. They are powerful once you understand them.
Common Mistakes That Kill Database Performance
Over-indexing is mistake number one. Developers create indexes on everything. Writing becomes glacially slow.
Indexing low-cardinality columns is mistake two. A column with only 10 unique values out of 1 million rows is not worth indexing.
Ignoring composite index order is mistake three. Index order matters. (user_id, created_at) is different from (created_at, user_id).
Never monitoring index usage is mistake four. Unused indexes are dead weight.
When To Add An Index
You have a query running 100+ times per day. It takes more than 100 milliseconds. Add an index.
You have a query that filters the same column every time. Index that column.
You have a join condition. Index both sides of the join.
You have a WHERE clause that is always present. Index those columns.
When Not To Add An Index
Your writes are already slow. Do not add more indexes unless absolutely necessary.
Your table is small (fewer than 100,000 rows). Indexes rarely help on small tables.
A column changes frequently. Indexes slow down updates on that column.
You are just guessing. Only index based on actual slow queries.
The Bigger Picture: Indexing Is Foundational
A system with poor indexing will fail at scale. It starts with slow dashboards. Then the APIs timeout. Then users leave.
Good indexing is not optional. It is foundational infrastructure.
Understanding indexes is more important than understanding any programming framework. Frameworks come and go. Slow databases destroy companies.
Spend time understanding how your database indexes work. Learn what queries are slow. Fix them one at a time.
That is how you build systems that do not fall apart.