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The VertiPaq Engine in DAX

Choosing hardware for VertiPaq

Choosing the right hardware is critical for a solution based on SSAS. Spending more does not always mean having a better machine. This final section in the chapter describes how to choose the right server and, as you will see, the perfect Tabular server is not expensive.

Since the introduction of Analysis Services 2012, we helped several companies adopting the new Tabular model in their solutions. A very common issue was that, when going into production, performance was lower than expected. Worse, sometimes it was lower than in the development environments. Most of the times, the reason for that was incorrect hardware sizing, especially when the server was in a virtualized environment. As you will see, the problem is not the use of a virtual machine by itself, but the technical specs of the underlying hardware. A very complete and detailed hardware-sizing guide for Analysis Services Tabular is available in the whitepaper “Hardware Sizing a Tabular Solution (SQL Server Analysis Services)” (http://msdn.microsoft.com/en-us/library/jj874401.aspx). The goal of this section is to provide a shorter quick guide that will help you understand the issues affecting many data centers when they have to host a Tabular solution. If you use Power Pivot or Power BI Desktop on a personal computer, you might skip details about Non-Uniform Memory Access (NUMA) support, but all the other considerations are equally true for choosing the right hardware.

Can you choose hardware?

The first question is whether you can choose the hardware or not. The problem of using a virtual machine for a Tabular solution is that often the hardware has already been selected and installed, and you can only influence the number of cores and the amount of RAM that are assigned to your server. Unfortunately, these parameters are not so relevant for the performance. If you will have these limited choices, you should collect information about the CPU model and clock of your host server as soon as possible. If you do not have access to this information, ask a small virtual machine running on the same host server and run the Task Manager: in the Performance tab, you will see the CPU model and the clock rate. With this information, you can predict whether the performance will be worse than an average modern laptop. Unfortunately, chances are that you will be in that position, so you have to sharpen your political skills to convince the right people that running Tabular on that server is a bad idea. If you find that your host server is okay, you will only have to avoid the pitfall of running a Virtual Machine on different NUMA nodes (more on that later).

Set hardware priorities

Assuming that you can influence the hardware selection, keep in mind that you have to set priorities in this order:

  1. CPU Clock and Model
  2. Memory Speed
  3. Number of Cores
  4. Memory Size

As you see, disk I/O performance is not in the list, because it is not important at all. There is a condition (paging) where disk I/O affects performance, and we discuss it later in this section. However, you should size the RAM of the system so that you will not have paging at all. Allocate your budget on CPU and memory speed, memory size, and do not waste money on disk I/O bandwidth.

CPU model

The most important factors that affect the speed of code running in the VertiPaq are CPU clock and model. Different CPU models might have a different performance at the same clock rate, so considering the clock alone is not enough. The best practice is to run your own benchmark, measuring the different performance in queries that stress the formula engine. An example of such a query, on a model derived by Adventure Works, is the following:

EVALUATE
ROW (
    "Test", COUNTROWS (
        GENERATE (
            TOPN (
                8000,
                CROSSJOIN (
                    ALL ( Reseller[ResellerKey] ),
                    ALL ( Reseller[GeographyKey] )
                ),
                Reseller[ResellerKey]
            ),
            ADDCOLUMNS (
                SUMMARIZE (
                    Sales,
                    OrderDate[FullDate],
                    Products[ProductKey]
                ),
                "Sales", CALCULATE ( SUM ( Sales[SalesAmount] ) )
            )
        )
    )
)

You can download the sample workbook to test this query on your hardware here: http://www.sqlbi.com/articles/choose-the-right-hardware-for-analysis-services-tabular/. Just open the Excel workbook and run the previous query in DAX Studio, measuring the performance (more on this in Chapter 15).

You can try this query (which is intentionally slow and does not produce any meaningful result) or similar ones. Using a query of a typical workload for your data model is certainly better, because performance might vary on different hardware depending on the memory allocated to materialize intermediate results (the query in the preceding code block has a minimal use of memory).

For example, this query runs in 8.1 seconds on an Intel i7-4770K 3.5 GHz, and in 12.4 seconds on an Intel i7-2860QM 2.5 GHz. These CPUs run a desktop workstation and a notebook, respectively. Do not presume that a server might run faster. Do your test and look at the results, because they are often surprising. If you do not have Excel on the server, you can restore the Power Pivot model on Analysis Services Tabular and run the query on SQL Server Management Studio if you do not have DAX Studio.

In general, Intel Xeon processors used on a server are E5 and E7 series, and it is very common to find clock speed around 2 GHz, even with a very high number of cores available. You should look for a clock speed of 3 GHz or more, whenever possible. Another important factor is the L2 and L3 cache size: the larger, the better. This is especially important for large tables and relationships between tables based on columns that have more than 1 million unique values.

Memory speed

The memory speed is an important factor for VertiPaq. Every operation made by the engine accesses memory at a very high speed. When the RAM bandwidth is the bottleneck, you see CPU usage instead of I/O waits. Unfortunately, we do not have a performance counter that monitors the time spent a waiting the RAM access. In Tabular, this amount of time can be relevant and it is hard to measure.

In general, you should get RAM that has at least 1,600 MHz, but if the hardware platform permits you should select faster RAM (1,833, 2,133, or 2,400 MHz). At the time of this writing (June 2015), 1,833 MHz is a fast standard on a server, whereas it is hard to find 2,133 MHz, and impossible to find 2,400 MHz unless you buy a desktop optimized to play videogames (by the way, did we mention that gaming machines are the top performers for VertiPaq?).

Number of cores

VertiPaq splits execution on multiple threads only when the table involved has multiple segments. Each segment contains 8 million rows by default (1 million on Power Pivot). If you have eight cores, you will not see all of them involved in a query unless you have at least 64 million rows.

For these reasons, scalability over multiple cores is effective only for very large tables. Raising the number of cores will improve performance for a single query only when it hits a large table (200 million of rows or more). In terms of scalability (number of concurrent users), a higher number of cores might not improve performance if users access the same tables (they would contend access to shared RAM). A better way to increase the number of concurrent users is to use more servers in a load balancing configuration.

The best practice is to get the maximum number of cores you can have on a single socket, getting the highest clock rate available. It is not good having two or more sockets on the same server. Analysis Services Tabular does not recognize the NUMA architecture, which splits memory between different sockets. NUMA requires a more expensive intersocket communication whenever a thread running on a socket accesses memory allocated by another socket—you can find more details about NUMA architecture in Hardware Sizing a Tabular Solution (SQL Server Analysis Services) at http://msdn.microsoft.com/en-us/library/jj874401.aspx.

Memory size

You have to store the entire database in memory. You also need RAM to execute process operations (unless you use a separate process server) and to execute queries. Usually optimized queries do not have a high request of RAM, but a single query can materialize temporary tables that could be very large (database tables have a high compression rate, whereas materialization of intermediate tables during a single query generates uncompressed data).

Having enough memory only guarantees that your queries will end returning a result, but increasing available RAM does not produce any performance improvement. Cache used by Tabular does not increase just because of more RAM available. However, a condition of low available memory might affect query performance in a negative way if the server starts paging data. You should simply have enough memory to store all the data of your database and to avoid materialization during query execution.

Disk I/O and paging

You should not allocate budget on storage I/O for Analysis Services Tabular. This is very different from Multidimensional, where random I/O operation on disk occurs very frequently, especially in certain measures. In Tabular, there are no direct storage I/O operations during a query. The only event when this might happen is when you have a low memory condition. However, it is less expensive and more effective to provide more RAM to a server than trying to improve performance by increasing storage I/O throughput when you have a systematic paging caused by low memory available.

Conclusions

You should measure performance before choosing the hardware for SSAS Tabular. It is common to observe a server running twice as slow as a development workstation, even if the server is a very new one. This is because a server designed to be scalable (especially for virtual machines) does not usually perform very well for activities made by a single thread. However, this type of workload is very common in VertiPaq. You will need time and numbers (do your benchmark) to convince your company that a “standard server” could be the weak point of the entire BI solution. Nevertheless, before convincing anybody else, keep in mind that you need to convince yourself. In this chapter, we gave you some insights about the engine. In Chapter 15, you will learn how to measure performance of queries. Take your time and do your tests. We bet they will surprise you.