The I.H.S.D.F. Theorem: A Proposed Theorem for the Trade-offs in Horizontally Scalable Systems

Successful software design is all about trade-offs. In the typical (if there is such a thing) distributed system, recognizing the importance of trade-offs within the design of your architecture is integral to the success of your system. Despite this reality, I see time and time again, developers choosing a particular solution based on an ill-placed belief in their solution as a “silver bullet”, or a solution that conquers all, despite the inevitable occurrence of changing requirements. Regardless of the reasons behind this phenomenon, I’d like to outline a few of the methods I use to ensure that I’m making good scalable decisions without losing sight of the trade-offs that accompany them. I’d also like to compile (pun intended) the issues at hand, by formulating a simple theorem that we can use to describe this oft occurring situation.

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Scaling MySQL on a 256-way T5440 server using Solaris ZFS and Java 1.7

How to scale MySQL on a 32 core system with 256 threads? Diagonal scalability in a box. An impressive benchmark that achieved more than 79,000 SQL queries per second on a single 4 RU server! Is this real? If so what is the role of good old horizontal scalability? The goals of the benchmark:

1. Reach a high throughput of SQL queries on a 256-way Sun SPARC Enterprise T5440
2. Do it 21st century style i.e. with MySQL and ZFS , not 20th century style i.e with OraSybInf... and VxFS
3. Do it with minimal tuning i.e as close as possible as out-of-the-box

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Private/Public Cloud

Data centers are reshaping themselves by taking ideas from public cloud providers, such as Amazon and Google. The idea is to make the data center more cost-effective by enabling on-demand utility-based computing rather than dedicated machines. At the same time, it is clear that to make IT operations more effective, it doesn't make sense to run all the applications that are currently hosted in a company's data center in the private cloud. This calls for an integration between private and public cloud. In this post i discuss some of the challenges involved in making that happen: 1. How do we design applications to be cloud-agnostic? 2. How do we enable seamless fail-over to a public cloud? 3. Future-proofing: There are many cases in which we can't make a clear decision as to where our application should be running at the time of writing or developing the application. We would like to be in a position to change the decision as to where our application will be running even after our application has been completely developed.

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Server load balancing architectures, Part 1: Transport-level load balancing

Server farms achieve high scalability and high availability through server load balancing, a technique that makes the server farm appear to clients as a single server. In this two-part article, Gregor Roth explores server load balancing architectures, with a focus on open source solutions. Part 1 covers server load balancing basics and discusses the pros and cons of transport-level server load balancing.

The barrier to entry for many Internet companies is low. Anyone with a good idea can develop a small application, purchase a domain name, and set up a few PC-based servers to handle incoming traffic. The initial investment is small, so the start-up risk is minimal. But a successful low-cost infrastructure can become a serious problem quickly. A single server that handles all the incoming requests may not have the capacity to handle high traffic volumes once the business becomes popular. In such a situations companies often start to scale up: they upgrade the existing infrastructure by buying a larger box with more processors or add more memory to run the applications.

Read the rest of the article on JavaWorld.

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Need help with your Hadoop deployment? This company may help!

A group of top Silicon Valley engineers (ex-Yahoo, Facebook, Google) have come together to launch a new startup called Cloudera. Not yet launched, it intends to help other companies adopt a promising software platform called Hadoop.

Hadoop is an open-source software project (written in Java) designed to let developers write and run applications that process huge amounts of data. While it could potentially improve a wide range of other software, the ecosystem supporting its implementation is still developing. Which is where Cloudera hopes to make a place for itself.

More on Hadoop: It uses the Google-introduced MapReduce systems framework that divides applications into small blocks of work, creating multiple replicas of data blocks that it places on various computer nodes.

It is already in use at large companies like Yahoo.

Read more about Cloudera here.

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SQL Server 2008 Database Performance and Scalability

Microsoft SQL Server 2008 incorporates the tools and technologies that are necessary to implement relational databases, reporting systems, and data warehouses of enterprise scale, and provides optimal performance and responsiveness.
With SQL Server 2008, you can take advantage of the latest hardware technologies while scaling up your servers to support server consolidation. SQL Server 2008 also enables you to scale out your largest data solutions.

This white paper describes the performance and scalability capabilities of Microsoft® SQL Server® 2008 and explains how you can use these capabilities to:
* Optimize performance for any size of database with the tools and features that are available for the database engine, analysis services, reporting services, and integration services.
* Scale up your servers to take full advantage of new hardware capabilities.
* Scale out your database environment to optimize responsiveness and to move your data closer to your users.

Read the entire article about SQL Server 2008 Database Performance and Scalability at MyTestBox.com - web software reviews, news, tips & tricks.

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Paper: Scaling Genome Sequencing - Complete Genomics Technology Overview

Although the problem of scaling human genome sequencing is not exactly about building bigger, faster and more reliable websites it is most interesting in terms of scalability. The paper describes a new technology by the startup company Complete Genomics to sequence the full human genome for the fraction of the cost of earlier possibilities. Complete Genomics is building the world’s largest commercial human genome sequencing center to provide turnkey, outsourced complete human genome sequencing to customers worldwide. By 2010, their data center will contain approximately 60,000 processors with 30 petabytes of storage running their sequencing software on Linux clusters. Do you find this interesting and relevant to HighScalability.com?

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Paper: Scalability Design Patterns

I have introduced pattern languages in my earlier post on The Pattern Bible for Distributed Computing. Achieving highest possible scalability is a complex combination of many factors. This PLoP 2007 paper presents a pattern language that can be used to make a system highly scalable. The Scalability Pattern Language introduced by Kanwardeep Singh Ahluwalia includes patterns to:

• Introduce Scalability
• Optimize Algorithm
• Add Hardware
• Add Parallelism
  • Add Intra-Process Parallelism
  • Add Inter-Porcess Parallelism
  • Add Hybrid Parallelism
• Optimize Decentralization
• Control Shared Resources
• Automate Scalability

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Cloud computing, grid computing, utility computing - list of top providers

You want to have a scalable website. You want a website which can handle traffic spikes (think if you are getting on Digg, Slahsdot, Reddit, Techcrunch or other very popular websites frontpage). Regular hosting companies (especially shared hosting) can offer only so much. The servers usually get crushed under the load in short time. But there is hope. A new breed of hosting companies emerged recently. A new breed which can offer you the scalability you need at a fraction of the cost. Welcome to the world of “cloud computing!” (or “grid computing” or “utility computing”, which are terms for the same thing). Here's a website which compiled a list of cloud computing hosting companies (with short descriptions, prices and customer lists for each of them). Read the entire article about Cloud computing, grid computing, utility computing list at MyTestBox.com - web software reviews, news, tips & tricks.

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Economies of Non-Scale

Scalability forces us to think differently. What worked on a small scale doesn't always work on a large scale -- and costs are no different. If 90% of our application is free of contention, and only 10% is spent on a shared resources, we will need to grow our compute resources by a factor of 100 to scale by a factor of 10! Another important thing to note is that 10x, in this case, is the limit of our ability to scale, even if more resources are added. 1. The cost of non-linearly scalable applications grows exponentially with the demand for more scale. 2. Non-linearly scalable applications have an absolute limit of scalability. According to Amdhal's Law, with 10% contention, the maximum scaling limit is 10. With 40% contention, our maximum scaling limit is 2.5 - no matter how many hardware resources we will throw at the problem. This post discuss in further details how to measure the true cost of non linearly scalable systems and suggest a model for reducing that cost significantly.

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