Deep Dive into PostgreSQL’s Data Pages and Data Rows: Understanding Internal Storage and Retrieval Mechanisms

Internally, PostgreSQL organizes data into data pages and data rows for efficient storage and retrieval. Let’s dive into the details of how these components work in PostgreSQL:

1. Data Pages

  – Storage: Data pages are fixed-size blocks on disk, typically configured to be 8 KB in size. They are used to store data rows and other metadata.

  – Allocation: PostgreSQL manages data pages through a storage management system. When a table is created, PostgreSQL allocates data pages to store its data rows. As the table grows and more rows are inserted, additional data pages are allocated dynamically to accommodate the new data.

  – Indexing: PostgreSQL uses various types of indexes (e.g., B-tree, Hash, GiST, GIN) to speed up data retrieval. These indexes are also stored in data pages alongside data rows.

2. Data Rows

  – Structure: Data rows represent individual records or entries within a table. Each data row consists of fixed-length and variable-length fields.

    – Fixed-length fields: These fields store data types with a fixed size, such as integers or fixed-length strings.

    – Variable-length fields: These fields store data types with variable sizes, such as variable-length strings or arrays. Variable-length fields include a pointer within the data row, which references the actual data stored elsewhere.

  – Storage: Data rows are stored within data pages. When a data row is inserted, it is placed in an available space within a data page. If there isn’t enough space, PostgreSQL may perform a process called “page split,” where it creates a new data page and redistributes some of the rows.

  – Visibility: PostgreSQL uses a multi-version concurrency control (MVCC) mechanism to handle concurrent transactions. Each data row has transaction-related metadata, including a transaction ID (XID) and a visibility marker, to determine its visibility to other transactions.

To optimize data retrieval, PostgreSQL employs various techniques like caching, query planning, and indexing. Caching involves keeping frequently accessed data pages and indexes in memory for faster access. Query planning involves analyzing the query and selecting the most efficient execution plan. Indexes provide a way to quickly locate data based on certain criteria, improving query performance.

Overall, PostgreSQL’s storage and retrieval mechanisms, including data pages and data rows, are designed to balance efficient data organization, concurrency control, and query performance.

About Shiv Iyer 455 Articles
Open Source Database Systems Engineer with a deep understanding of Optimizer Internals, Performance Engineering, Scalability and Data SRE. Shiv currently is the Founder, Investor, Board Member and CEO of multiple Database Systems Infrastructure Operations companies in the Transaction Processing Computing and ColumnStores ecosystem. He is also a frequent speaker in open source software conferences globally.