Last time I said that you can set a starting value for the USL’s coefficient of performance and let your modeling software (R, gnuplot, etc) manipulate this as part of the regression to find the best fit. However, there is a subtlety in the USL model that you need to be aware of. Here is a picture of the low-end of the curve:

The graph shows the USL model as the blue curve and linear scalability as the black line. Notice that at concurrencies less than 1, the value of the USL function is actually greater than the linear scalability function. This deserves some thought and explanation, because it can cause problems.

If you think about it, concurrency between one and zero is impossible. In fact, concurrency is not a smooth function, it is a step function. There can be zero requests resident in the system, one …

Last time I said that the USL has a forgotten third coefficient, the coefficient of performance. This is the same thing as the system’s throughput at concurrency=1, or C(1). How do you determine this coefficient? There are at least three ways.

Neil Gunther’s writings, or at least those that I’ve read and remember, say that you should set it equal to your measurement of C(1). Most of his writing discusses a handful of measurements of the system: one at concurrency 1, and at least 4 to 6 at higher concurrencies. I can’t remember a time when he’s discussed taking more than one measurement of throughput at each level of concurrency, so I think the assumption is that you’re going to take a single measurement at various concurrencies (or, in the case of hardware scalability, units of hardware), and …

Read the original article at Why your cloud is speeding for a scalability cliff

Also find Sean Hull’s ramblings on twitter @hullsean. Don’t believe me that you’re headed for the cliff? A startup scales up to no avail Towards the end of 2012 I worked with an internet startup in the online education space. Their web application was not unusual, built in PHP and using Linux, Apache & Mysql [...]

For more articles like these go to Sean Hull's Scalable Startups

Related posts:

1. 3 Ways to Boost Cloud Scalability
2. Cloud Operations …

If you’re familiar with Neil Gunther’s Universal Scalability Law, you may have heard it said that there are two coefficients, variously called alpha and beta or sigma and kappa. There are actually three coefficients, though. See?

No, you don’t see it — but it’s actually there, as a hidden “1″ multiplied by N in the numerator on the right-hand side. When you’re using the USL to model a system’s scalability, you need to use the C(1), the “capacity at one,” as a multiplier. I call this the coefficient of performance. It’s rarely 1; it’s usually thousands.

To illustrate why this matters, consider two systems’ throughput as load increases:

The green line and the blue line are both linearly scalable systems. Add twice the concurrency, get twice the throughput. But the slope of the lines is different. The green system can do three times as much work as the blue system, even though it’s no more …

Here is the typical “Big” data architecture, that covers most components involved in the data pipeline. More or less, we have the same architecture in production in number of places[...]

Read the original article at Why do people leave consulting?

As a long time freelancer, it’s a question that’s intrigued me for some time. I do have some theories… First, definitions… I’m not talking about working for a large consulting firm. Although this role may be called “consultant”, my meaning is consultant as sole proprietor, entrepreneur, gun for hire or lone wolf. 1. Make more [...]

For more articles like these go to Sean Hull's Scalable Startups

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1. Consulting essentials: Getting the business
2. Hiring is …

Read the original article at Anatomy of a Performance Review

A lot of firms come to us with a specific scalability problem. “Our user base is growing rapidly and the website is falling over!” Or they’re selling more widgets, “Our shopping cart is slowing down and we’re seeing users abandon their purchases”. These are real startup growing pains, so what to do?

We like to take a measured approach with these types of challenges, so we thought it would be helpful to run through a hypothetical scenario and see how we work.

Having trouble with scalability? Check out our 5 things toxic to scalability piece. …

Read the original article at Why you should attend Percona Live 2012

What I loved about Percona Live 2011 Last year I was excited to go to Percona Live for the first time in NYC. I arrived just in time to hear Harrison Fisk from Facebook speak about some of the awesome tweaks they’re running with MySQL there. It’s not everyday that you get to hear from [...]

For more articles like these go to Sean Hull's Scalable Startups

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Read the original article at Upcoming for Scalable Startups

Just back from the Labor Day holiday, and ready to dive back in.

I thought this would be a great time to outline some of our upcoming topics so here goes…

1. Why Oracle usability sucks

- a rant about Oracle’s weak points

In the meantime take a peek at our piece on why we wrote the book on Oracle & Open Source. We ruminate on trends in the datacenter and take a stab at Oracle’s future.

2. Why relational databases don’t scale

- Is there any such thing as automatic scalability?
- What blocks scalability?
- Are NoSQL databases magic?

Also one of our articles that went viral – …

Oh I love these things: http://techcrunch.com/2012/08/22/how-big-is-facebooks-data-2-5-billion-pieces-of-content-and-500-terabytes-ingested-every-day/

Every day there are 2.5B content items shares, and 2.7B "Like"s. I care less about GiGo content itself, but metadata, connections, relations are kept transactionally in a relational database. The above 2 use-cases generate 5.2B transactions on the database, and since there are only 86400 seconds a day, we get over 60000 write transactions per second on the database, from these 2 use-cases alone, not to mention all other use-cases, such as new profiles, emails, queries...

And what's the size of new data, on top of all the existing …