Performance

In this blog post we’re going to explore how SQL Server on Linux responds to external memory pressure. On Windows based SQL Server systems we’ve become accustomed to the OS signaling to SQL Server that there’s a memory shortage. When signaled, SQL Server will kindly start shrinking it’s memory caches, including the buffer pool, to maintain overall system stability and usability. We’ll that story is a little different in SQL Server on Linux…let’s look and see how SQL Server on Linux responds to external memory pressure

I’m very pleased to announce that I will be speaking at PASS Summit 2017!  This is my first time speaking at PASS Summit and I’m very excited to be doing so! What’s more, is I get to help blaze new ground on a emerging technology SQL Server on Linux! My session is “Monitoring Linux Performance for the SQL Server Admin” so if you’re a Windows or SQL Server administrator, this session is for you. We’ll look at some of the internals of SQL Server on Linux and dive into Linux OS internals and show you where to look inside Linux for most important performance data for your SQL Server. I hope to see you there!

With SQL Server on Linux, Microsoft has recognized that they’re opening up their products to a new set of users. People that aren’t used to Windows and it’s tools. In the Linux world we have a set of tools that work with our system performance data and present that to us as text. Specifically, the placeholder for nearly all of the Linux kernel’s performance and configuration data is the /proc virtual file system, [procfs][1]. Inside here you can find everything you need that represents the running state of your system. Processes, memory utilization, and disk performance data all of this is presented as files inside of directories inside /proc.

SQL 2014 Service Pack 2 was recently released by Microsoft and there is a ton of great new features and enhancements in this release.This isn’t just a collection of bug fixes…there’s some serious value in this Service Pack. Check out the full list here. One of the key things added in this Service Pack is an enhancement of the Extended Events for AlwaysOn Availability Group replication.

Why are the new Availability Group Extended Event interesting?

If you’ve used Availability Groups in production systems with high transaction volumes you know that replication latency can impact your availability. If you want to brush up on that check out our blog posts on AG Replication Latency, Monitoring for replication latency, and issues with the DMVs when monitoring. These new extended events add insight at nearly every point inside your Availability Group’s replication. More importantly they also include duration. So using these Extended Events we can pinpoint latency inside our Availability Group replication.

Paradigm Shift!

What do I mean by that? Every once in a while a technology comes along and changes the way things are done, moves the bar…well last week Microsoft released a Channel 9 video on persistent memory using NVDIMMs and DAX on Windows 2016…then combining it with SQL Server! This is one of those technologies that moves the bar! Check it out here.

Why is this important?

Relational databases like SQL Server use a transaction log to ensure the durability of the transactional operations to the database. This is so it can ensure its data is consistent in the event of a system failure. SQL Server uses a logging protocol called write ahead logging (WAL). This means that the data change operations must be written to stable, persistent storage before the process can change data in the database file…so this means our database throughput is at the mercy of the performance of the device the changes (log records) are written to.

In this post we’re going to introduce the basics of CPU scheduling.

In a computer system, only one thing can happen at a time. More specifically, only one task can be on a processor at a point in time. This can expand to several tasks if the system has multiple processors or a processor with multiple cores, which most modern systems have. For example, a four core system can potentially execute four tasks concurrently.

In our final post in our “Load Testing Your Storage Subsystem with Diskspd” series, we’re going to look at output from Diskspd and run some tests and interpret results. In our first post we showed how performance can vary based on access pattern and IO size. In our second post we showed how to design a test to highlight those performance characteristics and in this post we’ll execute those tests and review the results.  First let’s walk through the output from Diskspd, for now don’t focus on the actual results.

In this post we’re going discuss how to implement load testing of your storage subsystem with DiskSpd. We’re going to craft tests to measure bandwidth and latency for specific access patterns and IO sizes. In the last post “Load Testing Your Storage Subsystem with Diskspd”  we looked closely at access patterns and I/O size and discussed the impact each has on key performance attributes.

Diskspd command options

Let’s start with some common command options, don’t get caught up on the syntax. Diskspd’s documentation is fantastic. It’s included with the program download here. Here I’m going to tell you why I set these settings this way, so you can adjust them as needed for your environments.

One of the primary activities I do before bringing SQL Server into production is load testing the storage subsystem. On a new system this is critical because I want to ensure that we’re “getting what we’ve paid for” when it comes to the disk subsystem. All too often there’s a configuration issue, component mismatch, a fundamental misunderstanding of the technology or worse an insufficient disk subsystem…these all can lead to poor disk performance. Even if it’s the simplest test, its imperative to measure performance as it’s significantly harder to make changes to a SQL Server once a database is in production. So do your testing. This is especially an important topic if your disks are not direct attached or in a shared storage environment such as a SAN or VMware data store. Storage networks, controllers, shelves…it gets complicated fast!