tpc-c

Notes on tuning postgres for cpu and memory benchmarking

Posted on by gary

Recently I wanted to measure the impact of NUMA placement and Hugepages on the performance of postgres running in a VM on a Nutanix node. To do this I needed to drive postgres to do real transactions but have very little jitter/noise from the filesystem and storage. After reading a lot of blogs I came up with a process and set of postgres.conf tuneables that allowed me to run HammerDB TPROC workload (TPCC-C like) with very low variation around 0.3% variance (standard deviation/mean).

The tunings are not meant to represent best practices – and running repeatedly (without manually vacuuming, or doing a restore – will create problems because I am disabling autovacuum (see this discussion with HammerDB author Steve Shaw here and here)

Results

I have put the benchmark results below – but the main point of this post is to discuss the method which allows me to generate very repeatable postgres benchmark results where I can drive the CPU/Memory to be the limiting bottleneck. The screenshot below shows 5 runs back-to-back. From top to bottom the output shows

  • SQL commits per minute
  • Database VM CPU usage per core
  • Memory bandwidth (from Intel PCM running on the AHV hypervisor host)
  • Database VM IO rates
Multiple benchmark runs with consistent low jitter results
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Generate load on Microsoft SQLserver Windows from HammerDB on Linux

Posted on by gary
HammerDB on Linux driving load to Windows SQL Server

Often it’s nice to be able to drive Windows applications and databases from Linux, especially if you are more comfortable in a Unix environment. This post will show you how to drive a Microsoft SQL Server database running on a Windows server from a remote Linux machine. In this example I am using Ubuntu 22.04, SQLserver 2019, Windows 11 and HammerDB 4.4

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SuperScalin’: How I learned to stop worrying and love SQL Server on Nutanix.

Posted on by gary

TL;DR  It’s pretty easy to get 1M SQL TPM running a TPC-C like workload on a single Nutanix node.  Use 1 vDisk for Log files, and 6 vDisks for data files.  SQL Server  needs enough CPU and RAM to drive it.  I used 16 vCPU’s  and 64G of RAM.

Running database servers on Nutanix is an increasing trend and DBA’s are naturally skeptical about moving their DB’s to new platforms.  I recently had the chance to run some DB benchmarks on a couple of nodes in our lab.  My goal was to achieve 1M SQL transactions per node, and have that be linearly scalable across multiple nodes.

Screen Shot 2014-11-26 at 5.50.58 PM

It turned out to be ridiculously easy to generate decent numbers using SQL Server.  As a Unix and Oracle old-timer it was a shock to me, just how simple it is to throw up a SQL server instance.  In this experiment, I am using Windows Server 2012 and SQL-Server 2012.

For the test DB I provision 1 Disk for the SQL log files, and 6 disks for the data files.  Temp and the other system DB files are left unchanged.  Nothing is tuned or tweaked on the Nutanix side, everything is setup as per standard best practices – no “benchmark specials”.

SQL Server TPCC Scaling

Load is being generated by HammerDB configured to run the OLTP database workload.  I get a little over 1Million SQL transactions per minute (TPM) on a single Nutanix node.  The scaling is more-or-less linear, yielding 4.2 Million TPM  with 4 Nutanix nodes, which fit in a single 2U chassis . Each node is running both the DB itself, and the shared storage using NDFS.  I stopped at 6 nodes, because that’s all I had access to at the time.

The thing that blew me away in this was just how simple it had been.  Prior to using SQL server, I had been trying to set up Oracle to do the same workload.  It was a huge effort that took me back to the 1990’s, configuring kernel parameters by hand – just to stand up the DB.  I’ll come back to Oracle at a later date.

My SQL Server is configured with 16 vCPU’s and 64GB of RAM, so that the SQL server VM itself has as many resources as possible, so as not to be the bottleneck.

I use the following flags on SQL server.  In SQL terminology these are known as traceflags which are set in the SQL console (I used “DBCC trace status” to display the following.  These are fairly standard and are mentioned in our best practice guide.

Screen Shot 2014-11-30 at 8.38.45 PM

One thing I did change from the norm was to set the target recovery time to 240 seconds, rather than let SQL server determine the recovery time dynamically.  I found that in the benchmarking scenario, SQL server would not do any background flushing at all,  and then suddenly would checkpoint a huge amount of data which caused the TPM to fluctuate wildly.  With the recovery time hard coded to 240 seconds, the background page flusher keeps up with the incoming workload, and does not need to issue huge checkpoints.  My guess is that in real (non benchmark conditions) SQL server waits for the incoming work to drop-off and issues the checkpoint at that time.  Since my benchmark never backs off, SQL server eventually has to issue the checkpoint.

Screen Shot 2014-11-26 at 5.39.07 PM