Pure Storage

SQL Server 2025 introduces a new compression algorithm called ZSTD (Zstandard), which can enhance database backup performance. Using ZSTD allows for greater control over backup performance, particularly concerning CPU usage and backup duration. I recently conducted some preliminary benchmarks comparing ZSTD at its three compression levels with the existing MS_XPRESS algorithm. The results are compelling and provide additional options for managing database backup performance effectively.

The Test Setup

I set up two types of tests on a 389.37GB database to highlight the performance characteristics of the backup compression algorithms:

In this post, I’ll walk you through a T-SQL script that creates application-consistent snapshots on Pure Storage FlashArray, all from within SQL Server, no external tooling. SQL Server 2025 introduces a powerful new feature: the sp_invoke_external_rest_endpoint stored procedure. This enhancement makes calling REST APIs directly from T-SQL easier than ever. Combining this new capability with Pure Storage’s API allows us to orchestrate snapshot operations seamlessly, with no external tools or scripts required.

In this post, the fifth in our series, I want to illustrate an example of using the T-SQL Snapshot Backup feature in SQL Server 2022 to seed Availability Groups (AGs) with storage-based snapshots. Efficiently seeding an Availability Group is essential for maintaining high availability and ensuring effective disaster recovery. With the introduction of T-SQL Snapshot Backup in SQL Server 2022, snapshots can now be created at the storage layer. This advancement significantly speeds up the initialization of secondary replicas, particularly in environments that handle large databases.

Update (February 2026): This post has been updated to reflect the current best practices for monitoring FlashArray using OpenMetrics. Starting with Purity//FA 6.7.x, FlashArray includes a native OpenMetrics exporter, eliminating the need for a separate exporter service/container. The configuration examples below now demonstrate direct-to-array scraping.

This post introduces you to monitoring your Pure Storage FlashArray using OpenMetrics. It shows you how to get started quickly using Docker Compose to monitor your Pure Storage FlashArray environment.

In this post, the fourth in our series, I want to share an example demonstrating SQL Server 2022’s T-SQL Snapshot Backup feature in a scenario where a database spans multiple storage arrays. If you’re dealing with multi-array environments, you’ll appreciate how this technique freezes database write I/O to take coordinated snapshots across volumes on two FlashArrays. In this post, I’ll walk you through the process, point out some of the script’s key elements, and show you how long the write I/O pause takes.

In this post, the second in our series, I will guide you through using the new T-SQL Snapshot Backup feature in SQL Server 2022 to take a snapshot backup and perform point-in-time database restores using a snapshot backup as the base of the restore. We will explore how to manage storage-level operations, such as cloning snapshots and executing an instantaneous point-in-time restore of a database from the snapshot with minimal impact on your infrastructure. Additionally, I will demonstrate a PowerShell script that utilizes dbatools and the PureStoragePowerShellSDK2 modules to automate the process.

Traditional SQL Server backups can struggle with large databases, resulting in longer backup times and resource contention. T-SQL Snapshot Backup, a new feature in SQL Server 2022, addresses these challenges by allowing storage-based snapshots to be coordinated through T-SQL. This feature delivers faster, more efficient backups, especially for large-scale environments with the most aggressive of recovery objectives.

Anatomy of a Full Backup

Before we start learning about T-SQL Snapshot backup, let’s establish what a backup in SQL Server is. When a backup is initiated in SQL Server, several things occur. Follow along in Figure 1 below; here, you see a database with database files on Disk A and a transaction log file on Disk B.

Welcome back to the fifth installment of our blog series on using the Pure Storage PowerShell SDK2. In this post, we’re diving into a hands-on demonstration of using the Pure Storage FlashArray API to track replication performance between two arrays. This is especially useful for DBAs and storage admins, who must ensure their data replication processes run smoothly and efficiently. A typical scenario here is ensuring a snapshot is entirely replicated between sites before kicking off some other process. Following this demo, you’ll learn how to monitor replication status and verify the completion of snapshot transfers between arrays.

Welcome back to the fourth installment of our blog series on using the Pure Storage PowerShell SDK2. In this post, you’ll learn how to use Purity Tags to classify workloads, giving you the ability to search and manage resources in FlashArray and Cloud Block Store based on the types of workloads you’re running. Using the techniques in this post, combined with those learned in our last post, Using the Pure Storage PowerShellSDK2 - Part 3 - Getting Performance Data from FlashArray you can retrieve information about subsets of objects in your FlashArray or Cloud Block Store across several performance dimensions.

Welcome back to the third installment of our blog series on using the Pure Storage PowerShell SDK2. In this post, we’ll learn how to retrieve performance data from FlashArray and Cloud Block Store. Here, you’ll uncover the intricacies of extracting performance data across several object types, including Volumes and Hosts. We will dig into the object model that exposes crucial performance insights. Moreover, we’ll delve into the realm of performance analysis, addressing common customer questions such as: