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When your Home Lab turns into a Home DC

June 4, 2015 by frankdenneman

A little bit over a year ago I decide to update my lab and build two servers. My old lab had plenty of compute power, however they were lacking bandwidth, 3 Gbit/s SATA and 1 Gb network bandwidth. I turned to one of the masters of building a home lab, Erik Bussink, and we thought that the following configuration was sufficient to handle my needs.
Overview

Component	Type	Cost
CPU	Intel Xeon E5 1650 v2	540 EUR
CPU Cooler	Noctua NH-U9DX i4	67 EUR
Motherboard	SuperMicro X9SRH-7TF	482 EUR
Memory	Kingston ValueRAM KVR16R11D4/16HA	569 EUR
SSD	Intel DC 3700 100GB	203 EUR
Power Supply	Corsair RM550	90 EUR
Case	Fractal Design Define R4	95 EUR
Price per Server	(without disks)	1843 EUR

The systems are great, but really quickly I started to hit some limitations. Limitations that I have addressed in the last year, and that are interesting enough to share.
Adding a third host
As FVP is a scale out clustered platform, having two hosts to test with simply just don’t cut it. For big scale out testing I use nested ESXi but to do simple tests I just needed one more host. The challenged I faced was the dilemma of investing in “old” tech or going with new hardware. Intel updated their Xeon line to version 3, the Intel Xeon v3 has more cache (from 12MB to 15MB) more memory bandwidth, increased max memory support up to 768GB and uses DDR4 memory. (Intel ark comparison)
New shiny hardware might be better, but the main goal is to expand my cluster and one of the things I believe in is a uniform host configuration within a cluster. Time is a precious resource and the last thing I want to spend time on is to troubleshoot behavior that is caused by using non-uniform hardware. You might win some time by having a little bit more cache, more memory bandwidth but once you need to troubleshoot weird behavior you lose a lot more.
A dilemma is not a dilemma if you go back and forth between the options, thus I researched if it was possible. The most predominant one is the change in CPU microarchitecture. The v3 is part of the new Intel Haswell microarchitecture. The Xeon v2 is build upon the Ivy Bridge. That means that the cluster has to run in EVC mode Ivy Bridge. The EVC dialog box of the cluster indicates that Haswell chips are supported in this EVC mode, thus DRS functionality remains available if I go for the Haswell chip

The Haswell chip uses DDR4 memory and that means different memory timings and different memory bandwidth. FVP can use memory as a storage I/O acceleration resource and a lot of testing will be done with memory. That means that applications can behave differently when FVP decides to replicate fault tolerant writes to the DDR4 host or vice versa. In itself it’s a very interesting test, thus again another dilemma is faced. However, these tests are quite unique and I rather have uniform performance across the cluster and avoid any troubleshooting behavior due to hardware disparity.
Due to the difference in memory type, a new Motherboard is required too. That meant that I have to find a motherboard that contains the same chipsets and network configuration. The SuperMicro X9SRH-7TF rocks. Onboard 10 GbE is excellent. Some other users in the community have reported overheating problems, Erik Bussink was hit hard by the overheat problem and bought another board just to get rid of weird errors caused by the overheating. That by itself made me wonder if I would buy another X9SRH-7TF or go for a new Supermicro board and buy a separate Intel X540-T2 dual port 10GbE NIC to get the same connectivity levels.
After weighing the pros and cons I decided to go for the uniform cluster configuration. Primarily because testing and understanding software behavior is hard enough. Second-guessing whether behavior is caused by the hardware disparity is a time sucking beast and even worse, it typically kills a lot of joy in your work. Contrary to popular belief, prices of older hardware does not decline forever, due to availability of newer hardware and remaining stock, prices go up. The third host was almost 500 Euro’s, more expensive than the previous price I had to pay.
Networking
Networking is interesting as I changed a lot during the last year. The hosts are now equipped with an Intel PRO 1000 PT dual ports with the 82571 chip. Contrary to my initial post these are supported by vSphere 5.5.
However network behavior is a large part of understanding scale out architectures, thus more NIC ports are needed. An additional HP NC365T Quad-port Ethernet Server Adapter was placed in each server. The HP NIC is based on the Intel 82580 chipset but is a lot cheaper than buying Intel branded cards. Each host has one NIC dedicated to IPMI, 2 10GbE ports and 6 1GbE. In hindsight, I would rather go for two Quad NIC cards as it allows me to setup different network configurations without having to tear them down each time.
With the introduction of the third host I had to buy a 10GbE switch. The two host were directly connected to each other, however this configuration is not possible with three hosts. Thus I had to look for a nice cheap 10GbE switch that doesn’t break the bank and is quiet. Most 10GbE switches are made for the data center where noise isn’t really a big issue. My home lab is located in my home office, spending most of my day with something that sounds like a jet plane is not my idea of fun. The NETGEAR ProSafe Plus XS708E 8-port 10-Gigabit fit most of my needs. 8 ports for less than 900 euro’s, it’s kind of a steal compared to the alternatives. However I wasn’t really impressed by the noise levels (and spending 900 euro’s but that’s a different story). Again my main go-to-guy for all hardware related questions Erik Bussink provided the solution, the Noctua NF-A4-x10 FLX coolers. Designed to fit into 1U boxes they were perfect.

But as you can see the design of the Netgear is a bit weird. The coolers are positioned at the far end of the PCB with all the heatsinks. When the switch is properly loaded, the thing emits a lot of heat. Regardless of what type of internal fan is used. To avoid heat buildup in the switch I used simple physics, but I will come to that later.
Now having three hosts with seven 1 GbE connections, two storage systems eating up 3 ports and an uplink to the rest of the network I needed a proper switch. Lessons learned in that area, research thoroughly before pressing the buy button. I started of with buying an HP 1810-24G v2 switch. Silent, 24 ports, VLAN support. Awesome! No not awesome because it couldn’t route VLANs. And to the observant reader, 25 ports required, 24 ports offered. A VCDX’esque-like constraint. To work around the 24 ports limitation I changed my network design and wrote some scripts to build and tear down different network configurations. Not optimal, but dealing with home labs is almost like the real world.
While testing network behavior and hitting the VMkernel network stack routing problem I decided it was time to upgrade my network with some proper equipment. I asked around in the community and a lot where using the Cisco SG300 series switch. Craig Kilborn on twitter blogged about his HP v1910 24G and told me that it was quite noisy. A Noctua hack might do the trick, but I actually wanted some more ports than 24. Erik pointed out the Cisco SG500-28-K9-G5 switches that are stackable and fanless. Perfect! I could finally use all the NICs in my servers and have room for some expansion.

Time for a new rack
So from this point on I have three 19” sized switches, the IKEA lack hack table was nice, but these babies deserved better. The third host didn’t fit the table therefor new furniture had to be bought anyways. After spending countless of hours looking at 19” racks I came across a 6U Patch case.

This case had a lockable glass door (kids) and removable side panels, perfect for my little physics experiment. Just place the case in an upright position, remove the side panels and let the heat escape from the top. The fans will suck in “cold” air from the bottom. The dimensions of the patch case were perfect as it fitted exactly in my setup. The case is an Alfaco 19-6406. But with this networking equipment I’m feeling that my home lab is slowly turning into a #HomeDC.

With all this compute and network power I wanted to see what you can do when you have enterprise grade flash devices. I’m already using the Intel DC S3700 SSD’s and I’m very impressed by their consistent high performance. However Intel has released the Intel SSD DC p3700 PCIe card that use NVMe. I turned to Intel and they were so generous of loaning me three of these beasts for a couple of months. The results are extremely impressive, soon I will post some cool test results, but imagine seeing more than 500.000 IOPS in your homeDC on a daily basis.
Management server
To keep the power bill as low as possible, all three hosts are shutdown after testing, but I would like to have the basic management VMs running. In order to do this, I used a Mac Mini. William wrote extensively about how to install ESXi on a Mac, if you are interested I would recommend to check out his work: http://www.virtuallyghetto.com/apple. Unfortunately 16Gb is quite limited when you are running three windows VMs with SQL DB’s, therefor I might expand my management cluster by adding another Mac Mini. Time to find me some additional sponsors. 🙂

Ballooning, Queue Depths and other back pressure features revisited

May 20, 2015 by frankdenneman

Recently I’ve been involved in a couple conversations about ballooning, QoS and queue depths. Remarks like ballooning is bad, increase the queue depths and use QoS are just the sound bits that spark the conversation. What I learn from these conversations is that it seems we might have lost track of the original intention of these features.
Hypervisor resource management
Features such as ballooning and queue depths are invented to solve the gap between resource demand and resource availability. When a system experiences a state where resource demand exceeds the resources it controls the system has a problem. This is especially true in systems such as a hypervisor where you cannot control the demand directly. A guest operating system or an application in a virtual machine can demand a lot of resources. The resource management schedulers inside the hypervisor are tasked to fulfilling the demand of that particular machine while at the same time satisfy the resource demand of other virtual machines. Typically the guest OS resource schedulers are not integrated with the hypervisor resource schedulers, this can lead to a situation in which the administrator typically resorts to taking draconian measures. Measures such as disabling ballooning, increasing the queue-depth to become the digital equivalent of the Mariana trench.
Sometimes it is taken for granted, but resource management inside the hypervisor is actually a though challenge to solve. Much research is done on solving this problem; a lot of research papers trying to find an answer to this challenge are published on a monthly basis. Let’s step back and take a look from an engineer perspective (or should I say developer?) and see what the problem is and how to solve it in the most elegant way. It’s an architect job to understand that this functionality is not a replacement for a proper design. Let’s start by understanding the problem.
Load-shedding or back pressure
When dealing with the situation where resource demand exceeds resource availability you can do two things. Well if you don’t do anything, it’s likely to encounter a system failure that can affect a lot more than only that particular resource or virtual machines. Overall you don’t design for system failure, you want to avoid it and to do so you can either drop the load or apply some form of back pressure. I think we all agree that dropping load, sometimes referred to as load-shedding is not the most elegant way of dealing with temporary overload, that’s why a lot of effort is going into back pressure features.
A back pressure feature that everyone is familiar with is the memory balloon driver. Guest OS memory schedulers deal with used and free memory in such a way that this is transparent to the hypervisor. When the hypervisor is running out of physical machine memory it needs to figure out a way to retrieve memory. By using the balloon driver, the hypervisor asks the guest OS memory scheduler to provide a list of pages that it doesn’t use or doesn’t deem as important. After getting the info, the hypervisor proceeds to free up the physical memory pages to be able to satisfy incoming memory requests. Instead of dropping the new incoming workload it applies a back pressure system in the most elegant way. I don’t know why people are still talking about ballooning as bad. The feature is awesome, it’s the architect / sys admin job to come up with a plan to avoid back pressure in the system. Again the back pressure feature is not substitute for proper design and management.
But the most misunderstood back pressure feature could be queue-depths. Sometimes I hear people refer to queue depths as a performance enhancement. And this is not true. It just allows you to temporarily deal with I/O overload. The best way to have a clear understanding of queue depths is to use the bathroom sink analogy.
The drain is the equivalent of the data path leading to the storage array, the sink itself is the queue sitting on top of the data path / drain. The faucet represents the virtual machine workloads. Typically you open up the faucet to a level that allows the drain to cope with the flow of water. Same applies to virtual machine workloads and the underlying storage system. You run an x amount of workload that is suitable for your storage system. The moment you open up the faucet more your sink will fill up and at one point your sink will overflow. Thus you have to do some back pressure mechanism. In the bathroom sink world this typically is done by flowing the water back into the second sink. In the I/O scheduler world this typically resolves in a queue full statement. This typically bogs down the performance of the virtual machine so much that many admins/architect resolve by increasing the queue depth. Because this allows them to avoid the queue full state (temporarily) But in essence you just replaced your bathroom sink by a bigger sink, or something when people go overboard the increase the queue depth to the digital equivalent of a full size bathtub. This bathtub impacts a lot of other workloads as many workload now end up at the top of the queue instead of the deeper part, waiting their turn to go through the drain to the storage system. Result: latency increases in all applications due to improper designed systems. And remember when the bathtub overflows you typically have a bigger mess to deal with.
Back pressure features are not a substitute for proper design, therefor think about implementing a bigger drain of even better multiple drains. More bandwidth or just more data paths to the same storage system lead to a short delay of seeing the same back pressure problem again, it just occurs on a different level. Typically when a storage controller fills up its cache, it sends a queue full to all the connected systems, so the problem has now evolved from a system wide problem to a cluster wide problem. This is one of the big reasons why scale out storage systems are a great fit in the virtual datacenter. You create drains to different sewer system typically in a more plan-able manner. If you are looking for more information about this topic, I published a short series on the challenge of traditional storage architectures in virtual datacenters.
Quality of Service faces the same predicament. QoS is a great feature dealing with temporary overload, but again, it is not a substitute for a proper design. Back pressure features are great, it allows the system to deal with resource contentions while avoiding system failures. These features are unmissable in the dynamic virtual datacenter of today. When detecting that these features are activated on a frequent basis, one must review the virtual datacenter architecture, the current workloads and future workloads.
I think overall it all boils down to understand the workload in your system and have an accurate view of the capabilities of your systems. Proper monitoring and analytics tools are therefore indispensable. Not only for daily operations but also for architects dealing with maintaining a proper service level for their current workloads while architecting an environment that can deal with unknown future workloads.

VMware Tools is out of date on this virtual machine while summary states Current

May 5, 2015 by frankdenneman

For some apparent reason all my virtual machines show an alert that the VMware tools is out of date. While the summary states that its running the current version

When trying to upgrade the VMware tools, all options are grayed out:

It appears to be a cosmetic error but I stil wanted to know why it shows the alert on my virtual machines.
As it turns out I created my templates on my management cluster host (lab) and that host runs a newer version of vSphere 5.5 (2068190)

My workload cluster host run a slightly older version of vSphere 5.5 and it uses a different version of VMtools.

The VMtools version list helps you to identify which version of VMtools installed in your virtual machine belongs to which ESXi version: https://packages.vmware.com/tools/versions
Hope this clarifies this weird behavior of the UI for some.

Hammer, MagicBands and challenging status quo

April 23, 2015 by frankdenneman

One of my favorite business books is the famous book of Michael Hammer “Reengineering the Corporation”. The theme is the book is to take a hard look at business processes and radically change these old and existing processes.
Hammer states that typically companies sped up their processes by implementing a newer iteration of existing technology. Many processes are dated before the advent of the computers and just by automating the process it can only optimizes performance marginally. Embedding computers in the archaic processes cannot address their fundamental performance challenges.
By understanding what the process is trying to achieve one can break away from the existing design principles of the process. One great example is one of my favorite technologies is the Disney’s MagicBand and the way it’s used to radically change processes.

Disney’s MagicBand
The MagicBand is what is commonly referred to as a wearable. Inside the bracelet are a RFID chip and a radio. The parks have long range and short-range scanners along with sensors to interact with the MagicBand bracelet. One of the perks of my job is to speak to people who deliver cutting edge technology and as you can imagine I was very thrilled to speak to some of the team members who worked on the MagicBand platform.
In essence the Disney MagicBand replaces every transaction between the customer and cast members or Disney parks and resorts. The MagicBand becomes your key to anything. It allows access to the park, access to the resorts and automatic payment. Its goal is to create a frictionless experience for the customer, increasing the satisfaction, which of course will increase spending.
Instead of tinkering with existing processes Disney overhauled a lot of processes and many more are to follow. A great example is the overhaul of the check-in process and how it’s completely inline with the Hammer doctrine. Instead of buying newer faster desktop computers to speed up the check-in process, customers can go directly to their hotel room, bypassing the check in process all together. Disney’s sends the MagicBand to your home and prior to your visit the resort informs you via email which room is yours for your stay. Just walk up to your room and unlock the door by taping your MagicBand against the sensor on the door.
MagicBands allows Disney to get rid of the ancient turnstiles that is the first port of anxiety of new parents and their strollers, instead of being funnelled into narrow cramped isles the entrance is now in the shape of a inviting V shape form with incredible process speeds. Just hold your MagicBand to the access point and wait to be greeted by an welcoming green glow, from then on its off to your favorite experience.

The MagicBand platform allows for new experiences as well. What about having a more personalized interaction with cast members? What if Cinderella greets your daughter by name and tells her that she knows she is her favorite princess or that she wishes her a happy birthday? Just mind-blowing and an experience she will never forget.
Range scanners, sensors, WIFI, smart phones apps, user profiles and an insane amount of data crunching make this happen. Customers use the smart phone app to access their schedules and their user profiles. Countless short and long-range sensors scattered across the park pick up the signals of the bracelets. These systems are connected to each other, they collect data, and they use the captured data to optimize the experience of the customers. Data on visitors traffic flow, food orders and waiting times can be used to realign internal resources. Ever heard about Internet of things? This is the poster child of Internet of things.
Stop! Hammer Time
Circling back to the opening statement, using newer iteration of existing technology only provide marginally performance increases. Advances like the MagicBand require new technologies and new ways to operationalize these technologies in datacenters. Not every company is of the same size as Disney, but one thing is certain, most companies face the same challenge Disney has. How to reduce cost, increase efficiency and provide a new experience that makes them unique in a highly competitive market?
A lot of brilliant people are trying to solve these problems by creating technical solutions and it’s up to the IT team to understand if these suit their operation models. How can you reengineer your corporation and create a new service offering while your IT is stuck in the past? Stuck using systems that are designed to work in infrastructures dating back to the early ‘70’s? Where they just found out that the market was bigger than 5 computers? Everything has to align, when the business changes it models, the IT team should not take anything for granted, they too need to aim for quantum leaps of performance.
Markets shift rapidly; IT needs to be able to respond almost in a way that anticipates their needs. Maybe even in a way that it doesn’t feel remarkable at all, high service standards are the norm! Within the realm of virtualized datacenters two technology advancements can create an experience that might provide a ubiquitous experience to the business but provide the magic to the IT team; Scale out storage and object based storage.
Scale out storage and object based storage
Proper Scale out storage systems allows you to operationalize new advancement in storage technology as soon as they are available. They allow virtual datacenters to cater to any performance requirement possible any time. While, and this is very important, without impact current workloads that are using the same platform. Many application vendors move away from a monolithic application architecture, why keep holding on to the relics of the past by using a monolithic storage architecture for performance requirements?
Object based storage, such as VMware VVOLs, allows you to fundamentally change how to provide data services to systems (virtual machines). Instead of creating management constructs and aligning data services to these logical layers (LUNs and datastores), data services can be directly applied to the specific machine. Virtual machines become first class citizens on storage systems, allowing IT teams to cater to requirements that both affect the business as well as the IT team requirements.
Challenge status quo
Hammer stated don’t ask, “How can we do what we do faster?” But ask; Why do we do it the way we do?” In essence, challenge status quo if you want to keep on moving forward in a time that introduces new technologies and application landscapes on a daily basis!

DB Deepdive part 6: Query Plans, Intermediate Results, tempdb and Storage Performance

April 9, 2015 by frankdenneman

Welcome to part 6 of the Database workload characteristics series. Databases are considered to be one of the biggest I/O consumers in the virtual infrastructure. Database operations and database design are a study upon themselves, but I thought it might be interested to take a small peak underneath the surface of database design land. I turned to our resident Database expert Bala Narasimhan, PernixData’s VP of products to provide some insights about the database designs and their I/O preferences.
Previous instalments of the series:
Part 1 – Database Structures
Part 2 – Data pipelines
Part 3 – Ancillary structures for tuning databases
Part 4 – NoSQL platforms
Part 5 – Query Execution Plans
In a previous article I introduced the database query optimizer and described how it works. I then used a TPC-H like query and the SQL Server database to explain how to understand the storage requirements of a query via the query optimizer.
In today’s article we will deep dive into a specific aspect of query execution that severely impacts storage performance; namely intermediate results processing. For today’s discussion I will use the query optimizer within the PostgreSQL database. The reason I do this is because I want to show you that these problems are not database specific. Instead, they are storage performance problems that all databases run into. In the process I hope to make the point that these storage performance problems are best solved at the infrastructure level as opposed to doing proprietary infrastructure tweaks or rewrites within the database.
After a tour of the PostgreSQL optimizer we will go back to SQL Server and talk about a persistent problem regarding intermediate results processing in SQL Server; namely tempdb. We’ll discuss how users have tried to overcome tempdb performance problems to date and introduce a better way.
What are intermediate results?
Databases perform many different operations such as sorts, aggregations and joins. To the extent possible a database will perform these operations in RAM. Many times the data sets are large enough and the amount of RAM available is limited enough that these operations won’t fully fit in RAM. When this happens these operations will be forced to spill to disk. The data sets that are written to and subsequently read from disk as part of executing operations such as sorts, joins and aggregations are called intermediate results.
In today’s article we will use sorting as an example to drive home the point that storage performance is a key requirement for managing intermediate results processing.
The use case
For today’s example we will use a table called BANK that has two columns ACCTNUM and BALANCE. This table tracks the account numbers in a bank and the balance within each account. The table is created as shown below:
Create Table BANK (AcctNum int, Balance int);
The query we are going to analyze is one that computes the number of accounts that have a given balance and then provides this information in ascending order by balance. This query is written in SQL as follows:
Select count(AcctNum), Balance from BANK GROUP BY Balance ORDER BY Balance;
The ORDER BY clause is what will force a sort operation in this query. Specifically we will be sorting on the Balance column. I used the PostgreSQL database to run this query.
I loaded approximately 230 million rows into the BANK table. I made sure that the cardinality of the Balance column is very high. Below I have a screenshot from the PostgreSQL optimizer for this query. Note that the query will do a disk based merge sort and will consume approximately 4 GB of disk space to do this sort. A good chunk of the query execution time was spent in the sort operation.

A disk-based sort, and other database operations that generate intermediate results, is characterized by large writes of intermediate results followed by reads of those results for further processing. IOPS is therefore a key requirement.
What is especially excruciating about the sort operation is that it is a materialization point. What this means is that the query cannot make progress until the sort is finished. You’ve essentially bottlenecked the entire query on the sort and the intermediate results it is processing. There is no better validation of the fact that storage performance is a huge impediment for good query times.
What is tempdb?
tempdb is a system database within SQL Server that is used for a number of reasons including the processing of intermediate results. This means that if we run the query above against SQL Server the sorting operation will spill intermediate results into tempdb as part of processing.
It is no surprise then that tempdb performance is a serious consideration in SQL Server environments. You can read more about tempdb here.
How do users manage storage performance for intermediate results including tempdb?
Over the last couple of years I’ve talked to a number of SQL Server users about tempdb performance. This is a sore point as far as SQL Server performance goes. One thing I’ve seen customers do to remediate the tempdb performance problem is to host tempdb alone in arrays that have fast media, such as flash, in them in the form of either hybrid arrays or All Flash Arrays (AFA). The thought process is that while the ‘fast array’ is too expensive to standardize on, it makes sense to carve out tempdb alone from it. In this manner, customers look at the ‘fast array’ as a performance band aid for tempdb issues.
On the surface this makes sense since an AFA or a hybrid array can provide a performance boost for tempdb. Yet it comes with several challenges. Here are a few:

You now have to manage tempdb separately from all the other datastores for your SQL Server.
You procure the array for tempdb yet you do not leverage any of its data services. You use it as a performance band aid alone. This makes the purchase a lot more expensive than it seems on paper.
For queries that don’t leverage tempdb the array is not useful.
Performance problems in databases are not limited to tempdb. For example, you may be doing full table scans and these don’t benefit from the array.
You cannot leverage innovations in media. You cannot, for example, leverage RAM or PCM or anything else that will come in the future for tempdb.

How can PernixData FVP help?
In my mind PernixData FVP is the ideal solution for storage performance problems related to intermediate results in general and tempdb in particular. Intermediate result processing, including tempdb, shows very good temporal locality and is therefore an ideal candidate for FVP. Below are some other reasons why FVP is ideal for this scenario:

PernixData FVP allows you to use all server side media, flash or RAM, for accelerating tempdb and intermediate results processing.
You don’t need to configure anything separately for this. Instead you operate at the VM level and accelerate your database VM as a result of which every I/O operation is enhanced including intermediate results processing.
As your tempdb requirements change – lets say you need for space for handling it – it’s simply a matter of replacing one flash card with bigger one as far as FVP is concerned. There is no down time and the application is not impacted. This allows you to ride the price/performance curve of flash seamlessly.