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Category: Hardware

AMD Processor Recommendations for SQL Server

Glenn Berry has some thoughts on AMD’s EPYC line of processors:

Over the years, I have written many articles about the fine art of processor selection for SQL Server. This is an important topic, because it has a direct relationship to your SQL Server license costs. It also affects your performance and scalability. As new processor families are introduced, I do the required analytical work and update my recommendations. In this post, I will list my recommended AMD Processors for SQL Server.

I’m just happy that the answer isn’t a null set anymore.

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Installing .NET Core on a Raspberry Pi 4

Hasan Savran continues a series on Microsoft + Pi:

I have been writing about Azure IOT Hub and Raspberry Pi 4. So far, I bought a Raspberry Pi 4. I registered it as Azure IOT Edge device. Now, I am ready to write some code in Raspberry Pi. In this post, I will show you how install .NET Core 3.1 to Raspberry Pi so we can write some code to generate some data and push this data to Azure IOT Hub.

     First, you need to go to the .NET Core homepage to get the latest version’s url. Following page lists all .NET Core version, 3.1 was the latest when I was writing this blog. Pick the latest one from this list.

Another route might be to install Docker on your Pi.

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Choosing the Right Azure VM Type for SQL Server

Glenn Berry walks us through Azure virtual machine classes and picks out good ones for running SQL Server:

For high performance OLTP SQL Server workloads, the memory optimized type of Azure VMs is usually the best choice. According to Microsoft, “Memory optimized VM sizes offer a high memory-to-CPU ratio that are great for relational database servers.” This gives you lower core counts, with more memory, which is usually what you want for SQL Server, to minimize your license costs and still have good performance.

You can go even further down this path with Constrained vCPU capable VM sizes, where you can constrain the VM vCPU count (to one half or one quarter of the original VM size) to reduce the cost of SQL Server licensing, while maintaining the same memory, storage, and I/O bandwidth as a non-constrained VM. These constrained Azure VMs have a suffix in the name that indicates the number of active vCPUs in the VM.

Click through for the comparison.

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Persistent Memory for SQL Server on Linux

The SQL Server team shows how you can configure persistent memory for SQL Server on Linux:

With the release of SQL Server 2019 on Linux, Microsoft introduced persistent memory (PMEM) support on Linux. This is an exciting development, as previous versions of SQL Server on Linux didn’t support PMEM. Let’s look at how to configure the PMEM for SQL Server on Linux.

SQL Server 2016 introduced support for non-volatile DIMMs and an optimization called Tail of the Log Caching on NVDIMM. These leveraged Windows Server direct access to a persistent memory device in DAX mode to reduce the number of operations needed to harden a log buffer to persistent storage.

SQL Server 2019 extends the support for PMEM devices to Linux, providing full enlightenment of data and transaction logs placed on PMEM. Enlightenment is a way to access the storage device using efficient user-space memcpy() operations. Rather than going through the file system and storage stack, SQL Server leverages DAX support on Linux to place data directly into the device. This helps to reduce latency.

Click through for the configuration steps.

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On Quantum Supremacy

John Cook has some thoughts on Google’s quantum supremacy announcement:

Google announced today that it has demonstrated “quantum supremacy,” i.e. that they have solved a problem on a quantum computer that could not be solved on a classical computer. Google says

Our machine performed the target computation in 200 seconds, and from measurements in our experiment we determined that it would take the world’s fastest supercomputer 10,000 years to produce a similar output.

IBM disputes this claim. They don’t dispute that Google has computed something with a quantum computer that would take a lot of conventional computing power, only that it “would take the world’s fastest supercomputer 10,000 years” to solve. IBM says it would take 2.5 days.

If you want to jump the gun but also stay on the Microsoft stack, the Q# programming language is open-source and you can run a simulator on your machine. Manning also has a Q# book in the works.

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Reviewing the AMD EPYC Line for SQL Server

Glenn Berry takes a look at whether we want to invest in AMD’s latest server-grade processor:

On August 7, 2019, AMD finally unveiled their new 7nm EPYC 7002 Series of server processors, formerly code-named “Rome” at the AMD EPYC Horizon Event in San Francisco. This is the second generation EPYC server processor that uses the same Zen 2 architecture as the AMD Ryzen 3000 Series desktop processors. These new processors are socket compatible with the previous generation AMD EPYC 7001 Series processors, so they will work in existing model servers (with a BIOS update). Despite that, you will need a new model server to be able to use PCIe 4.0 support from the newer processors.

The AMD EPYC 7002 series includes 19 public launch SKUs that have anywhere from 8 to 64 physical cores, plus SMT, for twice the number of logical cores per processor. There are fourteen SKUs that will work in both one-socket and two-socket servers. There are also five less expensive processor SKUs (which have a “P” suffix) that only work in one-socket servers. This processor family has enough compute horsepower, memory bandwidth and capacity, and I/O bandwidth to support large server workloads on a single-socket server.

It certainly looks competitive. And that’s a great thing for consumers, even those who never make the switch, as it will force Intel to up its game.

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AMD and Server CPUs

Glenn Berry has an interesting post on why he’s seriously considering recommending AMD CPUs to people:

AMD claims a 15% Instructions Per Clock (IPC) increase between the desktop Zen+ and Zen 2 generations, and we are likely to see a similar increase between the previous AMD EPYC 7001 “Naples” and the AMD EPYC 7002 series processors.

So far, we don’t know the official base and turbo clock speeds, but there was a recent leak of partial specifications and pricing by a European retailer that listed max boost clock speeds of up to 3.4 GHz. We won’t know the actual single-threaded performance of these processors until they have been released and benchmarked by neutral third-party testers. I am optimistic that they will have higher single-threaded CPU performance than Intel Cascade Lake-SP processors.

I’ve always had a soft spot in my heart for AMD, so I’d love to see them come through with a serious competitor to Intel in the server space, for nostalgic reasons but also to make price more competitive and to make Intel get back on its game.

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Flink’s Network Stack

Nico Kruber dives into the internals of Apache Flink’s network stack:

Flink’s network stack is one of the core components that make up the flink-runtime module and sit at the heart of every Flink job. It connects individual work units (subtasks) from all TaskManagers. This is where your streamed-in data flows through and it is therefore crucial to the performance of your Flink job for both the throughput as well as latency you observe. In contrast to the coordination channels between TaskManagers and JobManagers which are using RPCs via Akka, the network stack between TaskManagers relies on a much lower-level API using Netty.

This blog post is the first in a series of posts about the network stack. In the sections below, we will first have a high-level look at what abstractions are exposed to the stream operators and then go into detail on the physical implementation and various optimisations Flink did. We will briefly present the result of these optimisations and Flink’s trade-off between throughput and latency. Future blog posts in this series will elaborate more on monitoring and metrics, tuning parameters, and common anti-patterns.

There’s a lot in here and it’s worth reading.

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AMD vs Intel CPUs For Data Processing Jobs

Hariharan Iyer and Abhishek Srivastava run some tests against AWS’s new AMD-powered EC2 instances:

Our summary findings from TPCDS benchmarks are as follows:
– TPCDS queries are not as sensitive to local disk performance (and hence to EBS volume sizes)
– r5 (Intel) instances are consistently faster than r5a (AMD) instances. However, the speedup depends on the engine and the speedup for r5 (Intel) is lower for Spark (10%) than for Hive (25%).
– r5 instances are also either cheaper (by about 10% for Hive) or the same cost (for Spark)

At least for Hadoop and Spark work, Intel CPUs are a bit better, but there is some nuance in the story so check it out.

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Analytics Platform System V7 Released

Microsoft has released a new version of their Analytics Platform System:

Microsoft is pleased to announce that the Analytics Platform System (APS) appliance update 7 (AU7) is now generally available. APS is Microsoft’s scale-out Massively Parallel Processing (MPP) system based on SQL Server for data warehouse specific workloads on-premises.

Customers will get significantly improved query performance and enhanced security features with this release. APS AU7 builds on appliance update 6 (APS 2016) release as a foundation. Upgrading to APS appliance update 6 is a prerequisite to upgrade to appliance update 7.

This is useful for the six customers which can afford the licensing for APS.

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