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System Design Basics for Backend Developers · Lesson

Indexing and Query Optimization

Understand how database indexes work, when to use them, and how to optimize queries for fast reads without crippling writes.

Indexing and Query Optimization is a free System Design Basics for Backend Developers lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the System Design Basics for Backend Developers learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

Why Indexes Matter

Without an index, finding a row means scanning every row — a full table scan. As tables grow into millions of rows, this becomes painfully slow.

An index is a separate data structure that lets the database jump straight to matching rows.

The B-Tree Index

Most relational indexes use a B-tree: a balanced tree that keeps keys sorted. Lookups, range scans, and ordering all become logarithmic instead of linear.

  • Fast equality lookups (WHERE id = 5)
  • Fast range queries (WHERE age > 30)
  • Supports ORDER BY without re-sorting

Creating an Index

You create an index on the column(s) you frequently filter or sort by.

Here we index the email column so login lookups are instant.

CREATE INDEX idx_users_email
  ON users (email);

SELECT * FROM users
WHERE email = 'a@example.com';

Composite Indexes

A composite index covers multiple columns. Column order matters: the index helps queries that filter on a left-prefix of the columns.

An index on (country, city) helps WHERE country = ? and WHERE country = ? AND city = ?, but not WHERE city = ? alone.

CREATE INDEX idx_loc
  ON customers (country, city);

Covering Indexes

If an index contains every column a query needs, the database answers from the index alone and never touches the table. This is a covering index.

It is one of the most powerful read optimizations available.

The Write Cost

Indexes are not free. Every INSERT, UPDATE, or DELETE must also update each affected index.

  • More indexes = slower writes
  • More indexes = more storage

Index for the queries you actually run, not speculatively.

Reading EXPLAIN

Use EXPLAIN (or EXPLAIN ANALYZE) to see the query plan. Look for Index Scan (good) versus Seq Scan (full table scan).

EXPLAIN ANALYZE
SELECT * FROM orders
WHERE customer_id = 42;

Selectivity

An index helps most when the column is highly selective — it filters down to a tiny fraction of rows. Indexing a boolean is_active with a 50/50 split is nearly useless; the planner may ignore it.

Avoiding Index-Defeating Queries

Wrapping an indexed column in a function or doing a leading wildcard defeats the index.

  • WHERE LOWER(email) = ? — index on email unused
  • WHERE name LIKE '%son' — leading wildcard, no index

Store data in the form you query, or use a functional index.

-- Defeats the index:
SELECT * FROM users WHERE LOWER(email) = 'a@x.com';
-- Better: store email already lowercased

Indexing and Sharding Together

In a sharded system each shard maintains its own indexes. A query that includes the shard key hits one shard and one index; a query without it must fan out to every shard. Design indexes and shard keys together.

A Practical Workflow

Optimize iteratively: find slow queries from logs, run EXPLAIN, add a targeted (often composite or covering) index, re-measure, and drop indexes that are never used.

Quick Check

Test your understanding of indexing.

Recap

You learned how to make reads fast with indexes:

  • B-tree indexes power equality, range, and ordering
  • Composite indexes follow the left-prefix rule
  • Covering indexes answer queries without touching the table
  • Indexes cost write speed and storage — index deliberately
  • Use EXPLAIN and watch for index-defeating patterns

Frequently asked questions

Is the “Indexing and Query Optimization” lesson free?

Yes — the full text of “Indexing and Query Optimization” is free to read here on the web, and the System Design Basics for Backend Developers course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the System Design Basics for Backend Developers course, upgrade to CoddyKit PRO.

What will I learn in “Indexing and Query Optimization”?

Understand how database indexes work, when to use them, and how to optimize queries for fast reads without crippling writes. You practise System Design Basics for Backend Developers with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start System Design Basics for Backend Developers?

No prior experience is required. System Design Basics for Backend Developers on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Indexing and Query Optimization” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this System Design Basics for Backend Developers lesson?

Yes. Every System Design Basics for Backend Developers lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

All lessons in this course

  1. SQL vs. NoSQL Databases
  2. Sharding and Data Replication
  3. Data Consistency Models
  4. Indexing and Query Optimization
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