Buffer Header Queues in PostgreSQL

In PostgreSQL, buffer headers are data structures that represent a single page of data in the shared buffer cache. Each buffer header has information about the state of the page it represents, such as whether it is dirty (has been modified since it was read into the cache), the location of the page on disk, etc.

Buffer headers are organized into a number of different queues within the buffer manager. These queues help the buffer manager keep track of the state of each buffer header and determine which pages need to be written to disk. Some of the key buffer header queues in PostgreSQL include:

  • FreeList: A queue of unused buffer headers that are available for use by the buffer manager.
  • LRU (Least Recently Used) list: A queue of buffer headers for pages that are not currently in use. The buffer manager uses this list to determine which pages to evict from the cache when it needs to make room for new pages.
  • Dirty list: A queue of buffer headers for pages that have been modified and need to be written to disk. The database writer process is responsible for writing these pages to disk.
  • Pinned list: A queue of buffer headers for pages that are currently being used by a transaction and cannot be evicted from the cache.

By organizing buffer headers into these different queues, the buffer manager in PostgreSQL can efficiently manage the shared buffer cache and ensure that data is properly protected and persisted to disk.

How Buffer Header Queues influence PostgreSQL performance?

The buffer header queues in PostgreSQL play a critical role in the performance of the database. By managing the state of buffer headers and the pages they represent, the buffer manager can optimize the use of the shared buffer cache to reduce I/O operations and improve overall performance.

For example:

  • The LRU list helps the buffer manager determine which pages to evict from the cache when it needs to make room for new pages. By evicting pages that have not been used recently, the buffer manager can ensure that the cache is filled with the most frequently used pages, reducing the need for I/O operations to retrieve data from disk.
  • The dirty list ensures that modified pages are written to disk in a timely manner, protecting the data from being lost in the event of a system crash. This helps reduce the risk of data loss and ensures that the database remains in a consistent state even in the event of a failure.
  • The pinned list helps ensure that pages that are currently being used by transactions are not evicted from the cache. This helps reduce the need for I/O operations to retrieve the same page multiple times and can improve the performance of transactions that access the same data frequently.

Overall, the efficient management of buffer headers and the pages they represent by the buffer manager can have a significant impact on the performance of a PostgreSQL database.

Queries to monitor PostgreSQL buffer header queues

To monitor the buffer header queues in PostgreSQL, you can use a number of system views and functions that provide information about the state of the buffer cache and the buffer headers. Here are a few examples of queries that can be used to monitor the buffer header queues:

  1. To view the number of pages in each buffer header queue:

2. To view the current size of the shared buffer cache:

3. To view the current number of pages in the shared buffer cache:

4. To view information about individual buffer headers, including the state of the page and the table it belongs to:

By running these queries periodically and analyzing the results, you can monitor the state of the buffer header queues and gain insight into the performance of your PostgreSQL database.

About Shiv Iyer 422 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.