How to Build a Simple Database from Scratch Using B‑Trees

All application software may rely on databases, which are often the most complex component. Manuals for MySQL, PostgreSQL, and Oracle span thousands of pages, yet creating a very simple database is not difficult.

A Reddit post described the core principles in a few hundred words; the following summarizes those ideas.

1. Store Data as Text

The first step is to write the data to a text file, which becomes the database. For easy reading, the file is divided into fixed‑length records (e.g., 800 bytes each), allowing direct calculation of a record’s offset.

When only a primary key is known, scanning records sequentially is inefficient, so databases typically store data using a B‑tree structure.

2. What Is a B‑Tree?

Understanding B‑trees starts with binary search trees (BSTs), which have three properties:

Each node has at most two child sub‑trees.

The left subtree contains values less than the parent; the right subtree contains greater values.

Finding a target in an n‑node BST requires roughly log₂(n) comparisons.

BSTs are unsuitable for databases because the number of disk reads grows with tree depth; each level may require a separate hard‑disk access, which is costly.

B‑trees improve on BSTs by grouping many values in a single node, reducing the number of levels and disk reads. Their three main characteristics are:

A node can hold multiple keys (e.g., up to four in the illustration).

New levels are added only when a node is full, keeping the tree shallow.

If a node contains a keys, it has a+1 child pointers, maintaining strict ordering.

This structure dramatically cuts disk I/O. For example, a B‑tree with nodes holding 100 keys can store one million records in just three levels, requiring only two disk reads to locate any record.

3. Indexes

Storing data in a B‑tree solves primary‑key lookups, but searching by other fields requires indexes. An index is a separate B‑tree built on a chosen column (e.g., a name field), mapping each key to the record’s location.

This method, known as Indexed Sequential Access Method (ISAM), has multiple implementations (e.g., C‑ISAM, D‑ISAM) that can be used to add indexing to a simple database.

4. Advanced Features

After implementing basic storage and indexing, a functional database can be extended with:

A SQL parser to translate SQL statements into ISAM operations.

Join support for combining tables via foreign keys, with optimization.

Transaction handling with a write‑ahead log to enable rollback on failure.

Backup mechanisms to store database copies.

Remote access over TCP/IP, allowing clients on different machines to interact with the database.

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