What Data Structure Does PostgreSQL Use?


Larry Thompson

PostgreSQL, also known as Postgres, is a powerful open-source relational database management system (RDBMS) that utilizes a variety of data structures to efficiently store and manage data. In this article, we will explore the primary data structure used by PostgreSQL and how it contributes to the performance and functionality of this popular database.

The B-Tree Data Structure

At the core of PostgreSQL’s storage mechanism lies the B-Tree data structure. B-Trees are balanced search trees that efficiently organize and index data for fast retrieval. They are particularly well-suited for handling large amounts of data and provide excellent performance for various types of queries.

B-Trees in PostgreSQL serve multiple purposes. They are used to implement indexes, which improve query performance by enabling quick lookup and sorting of data. Additionally, B-Trees are utilized in the storage layer to efficiently manage disk space and facilitate sequential access to records.

B-Tree Indexes

One of the key features of PostgreSQL is its support for different types of indexes. The default index type used by PostgreSQL is the B-Tree index. This type of index allows efficient searching, insertion, deletion, and sorting operations on indexed columns.

B-Tree indexes in PostgreSQL use a variant called “multi-versioning” or MVCC (Multi-Version Concurrency Control). This means that each modification to a row creates a new version rather than overwriting the existing one. The B-Tree index structure handles these versions effectively, allowing concurrent read and write operations without sacrificing consistency or performance.

Storage Layer

In addition to its role in indexing, the B-Tree data structure plays an essential role in managing disk space within PostgreSQL’s storage layer. PostgreSQL organizes data into pages that are typically 8KB in size. Each page can contain multiple rows or tuples.

The B-Tree structure allows PostgreSQL to efficiently manage these pages and perform sequential access to records. It enables fast range scans, which are crucial for executing queries that involve sorting or filtering large datasets.

Furthermore, PostgreSQL implements a feature known as “heap-only tuples” to optimize storage. When a row is updated or deleted, the old version is not immediately removed from the disk.

Instead, PostgreSQL marks it as “dead” and reuses the space when new rows are inserted. The B-Tree structure helps manage these dead rows efficiently.


PostgreSQL utilizes the B-Tree data structure extensively to deliver exceptional performance and functionality. Whether it’s indexing for efficient query execution or managing disk space effectively, the B-Tree structure plays a vital role in making PostgreSQL a reliable and powerful relational database management system.

By leveraging the capabilities of the B-Tree data structure, PostgreSQL ensures that developers can build robust applications that can handle large amounts of data while maintaining excellent performance.

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