Database Indexes Explained: What They Are and Why You Need Them

Learning Objectives

• Define what a database index is and describe its purpose.
• Explain how indexes speed up data retrieval and the trade-offs involved.
• Create, inspect, and drop indexes in SQL with hands-on examples.
• Choose the right index type (single-column, composite, unique, covering, etc.).
• Identify common pitfalls (write overhead, bloat, bad selectivity) and how to avoid them.
• Use Galaxy’s next-gen SQL editor to explore query plans, benchmark performance, and iterate safely.

1. Foundation: What Is a Database Index?

An index is a secondary data structure that the database keeps alongside your table to provide fast lookup paths. Think of it as the index at the back of a textbook: instead of flipping through every page (a full table scan), you jump straight to the topic’s page number.

Most relational databases (PostgreSQL, MySQL, SQL Server, SQLite) default to a B-tree index—an ordered, balanced tree that allows O(log n) searches, inserts, and deletes.

1.1 Visual Analogy

Imagine a phone book:

• The table is the entire phone book sorted by last name.
• An index on last_name is the alphabetical tabs on the side. You flip directly to “S” instead of scanning every page.
• Each tab stores the last name and a pointer to the full entry (row). The pointer lets the database retrieve the row once it knows where to look.

2. Why Use Indexes?

Indexes provide two primary benefits:

1. Speed – Dramatically reduce I/O for read queries by narrowing the search space.
2. Uniqueness enforcement – Unique indexes (e.g., PRIMARY KEY) guarantee data integrity.

However, they come with costs:

• Extra storage (often 10–30% of table size).
• Slower writes (INSERT, UPDATE, DELETE) because the index must be updated.
• Maintenance overhead (vacuuming, re-indexing).

3. Hands-On: Creating and Using Indexes

3.1 Setup: Sample Data

Run the following script in Galaxy’s SQL editor (PostgreSQL syntax):

-- Example dataset: 1M e-commerce orders
CREATE TABLE orders (
id BIGSERIAL PRIMARY KEY,
customer_id INT NOT NULL,
status TEXT NOT NULL,
created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
total_cents INT NOT NULL
);

-- Insert dummy data (1M rows)
INSERT INTO orders (customer_id, status, total_cents)
SELECT (RANDOM()*100000)::INT, 'placed', (RANDOM()*10000)::INT
FROM generate_series(1, 1000000);


You now have a orders table with one million rows and no additional indexes.

3.2 Benchmark Without Index

EXPLAIN ANALYZE
SELECT * FROM orders WHERE customer_id = 42;

Observe the output in Galaxy’s results pane. You should see a Seq Scan (sequential scan) taking hundreds of milliseconds, reading the entire table.

3.3 Create an Index

CREATE INDEX idx_orders_customer_id ON orders(customer_id);

Re-run the query:

EXPLAIN ANALYZE
SELECT * FROM orders WHERE customer_id = 42;

You should now see an Index Scan with a runtime ~10× faster. Galaxy highlights the cost difference side-by-side if you use its Explain Diff feature.

3.4 Composite Index

If your application often filters on status and customer_id, you may create a composite index:

CREATE INDEX idx_orders_customer_status
ON orders(customer_id, status);


Order matters: The index can be used for queries on (customer_id) or (customer_id, status), but not for status alone.

3.5 Covering Index

A covering index stores additional columns so the database can satisfy the query entirely from the index without touching the table (a “index-only scan”).

CREATE INDEX idx_cover_orders
ON orders(customer_id)
INCLUDE (total_cents, created_at);


For reporting queries that only need these columns, I/O drops further.

Exercise 1

Measure performance of this query before and after creating a covering index:

SELECT customer_id, total_cents, created_at
FROM orders
WHERE customer_id = 123
ORDER BY created_at DESC
LIMIT 10;

Document your findings in a Galaxy Notebook cell.

4. How Indexes Work Internally

4.1 B-Tree Mechanics

The B-tree splits keys into ordered pages on disk. Each page stores pointers to child pages or row positions (CTID). The tree stays balanced so that the height is minimal (usually 2–4 levels), keeping lookup time logarithmic.

4.2 Other Index Types

• Hash – Fast equality lookups, but no range scans.
• GIN (Generalized Inverted Index) – Array or full-text search.
• GiST – Geospatial ranges.
• BRIN – Large, naturally ordered tables (time-series), tiny storage footprint.

Choose the type that matches your query patterns.

5. When NOT to Index

• Columns with very low selectivity (e.g., booleans like is_active).
• Small tables that fit in memory—full scans are cheap.
• Write-heavy tables where read latency isn’t critical.
• Frequently updated columns; each update rewrites the index page.

6. Maintenance & Best Practices

6.1 Monitor Usage

In PostgreSQL:

SELECT *
FROM pg_stat_user_indexes
ORDER BY idx_scan DESC LIMIT 10;

Drop indexes with idx_scan = 0 for long periods.

6.2 Vacuum & Reindex

Bloat accumulates from dead rows. Schedule VACUUM or REINDEX CONCURRENTLY.

6.3 Naming Convention

Use idx_

__ to keep things readable.

7. Galaxy Workflow Tips

• Explain Diff – Select two queries and hit ⇧⌘E to compare plans side-by-side.
• AI Copilot – Ask: “Suggest missing indexes for slow_order_query” and Galaxy generates CREATE INDEX statements referencing your schema.
• Collections – Store “Performance Investigations” queries in a shared Collection so teammates can reproduce findings.
• Versioning – Galaxy tracks index DDL history; you can revert a dropped index with a click.

8. Common Misconceptions & Troubleshooting

Misconception 1: “More indexes = faster database.”

Past a point, write overhead outweighs read gains. Index only what you query.

Misconception 2: “The database always uses my index.”

The planner may still choose a sequential scan if statistics say it’s cheaper (e.g., expecting many rows). Use ANALYZE to refresh stats.

Misconception 3: “Order of columns in a composite index doesn’t matter.”

It does. Put the most selective, commonly filtered column first.

Troubleshooting Checklist

1. Run EXPLAIN ANALYZE – Is your index being used?
2. Check pg_stat_all_tables – Are stats up-to-date?
3. Review query predicates – Do they match the index order?
4. Consider SET enable_seqscan = off; temporarily to test impact.

9. Practice Exercises

1. Create a unique index on customer_id, created_at to prevent duplicate orders made at the exact same timestamp.
2. Design a BRIN index for a events(timestamp, payload) table with 100 M rows of time-series data. Measure storage savings.
3. Given a query filtering on LOWER(email), create a function-based index to speed it up.
4. Use Galaxy to benchmark the impact of each index with EXPLAIN ANALYZE and spreadsheet the results.

Key Takeaways

• An index is a lookup structure that accelerates reads at the expense of writes and storage.
• B-tree indexes cover equality and range queries; pick other types for special workloads.
• Create indexes only on columns used in WHERE, JOIN, ORDER BY, or UNIQUE constraints.
• Measure before and after with EXPLAIN ANALYZE—never assume.
• Galaxy’s editor, AI, and collaboration tools shorten the index tuning feedback loop.

Next Steps

• Explore advanced index types (GIN for full-text, GiST for geospatial).
• Read the official docs for your database engine’s indexing nuances.
• Clone Galaxy’s “Indexing Playbook” Collection and try exercises on your own schema.
• Enable Galaxy’s AI “Performance Optimizer” beta to receive proactive index suggestions.