The core ideas of RYW consistency, as implemented in various NoSQL systems, are:

Let N = the number of copies of each record distributed across nodes of a parallel system.

Let W = the number of nodes that must successfully acknowledge a write for it to be successfully committed. By definition, W <= N.

Let R = the number of nodes that must send back the same value of a unit of data for it to be accepted as read by the system. By definition, R <= N.

The greater N-R and N-W are, the more node or network failures you can typically tolerate without blocking work.

As long as R + W > N, you are assured of RYW consistency.

Example: Let N = 3, W = 2, and R = 2. Suppose you write a record successfully to at least two nodes out of three. Further suppose that you then poll all three of the nodes. Then the only way you can get two values that agree with each other is if at least one of them — and hence both — return the value that was correctly and successfully written to at least two nodes in the first place.

In a conventional parallel DBMS, N = R = W, which is to say N-R = N-W = 0. Thus, a single hardware failure causes data operations to fail too. For some applications — e.g., highly parallel OLTP web apps — that kind of fragility is deemed unacceptable.

On the other hand, if W< N, it is possible to construct edge cases in which two or more consecutive failures cause incorrect data values to actually be returned. So you want to clean up any discrepancies quickly and bring the system back to a consistent state. That is where the idea of eventual consistency comes in, although you definitely can — and in some famous NoSQL implementations actually do — have eventual consistency in a system that is not RYW consistent.

Much technology goes into eventual consistency, as well as into the data distribution and polling in the first place. And in tunable systems, the choices of N, R, and W — perhaps on a “table” by “table” basis — can get pretty interesting. I’m ducking all those subjects for now, however, not least because of how much I still have to learn about them.

One point I will note, however, is this — RYW consistency and table joins make for awkward companions. If you want to join two tables, each of them distributed across some kind of parallel cluster, there are only two possibilities:

• In most cases, the data you need to join is co-located on the same nodes.
• You’re going to have an awful lot of network traffic.

In an R = W = N scenario, co-location may be realistic. But when R < N and W < N, a join can return incorrect results even when both of the tables being joined would have been read correctly.

In our example above, we had N = 3 and R = W = 2. Single-table RYW consistency was ensured. But suppose you join two records, each of which had been written correctly to 2 out of 3 nodes — but with only 1 node being correct about both records. Then only that 1 node out of 3 will return a correct value for the join, and badness will ensue.

Any architecture I can think of to circumvent that problem results in — you guessed it — an awful lot of network traffic.

via RYW Read-Your-Writes consistency explained | DBMS2 — DataBase Management System Services.

Here is a challenge that takes you away from those repetitive boring type of queries that you write over and over again, several times a day. All of us, the database people, are familiar with thinking in set based manner as well as row by row style. Here is something that is very interesting where you might need to process records in a ‘three-line-at-a-time’ fashion.

For the purpose of this challenge, imagine that you are working for a bank which just decided to scan all the banking documents. Assume that they have an old fashioned scanner which scans the documents and produces a text file with the customer number. So far so good. Well, not really! Unfortunately the scanner produces a graphical representation of the customer number using three lines of symbols: space, unerscores and pipe characters. Here is an example of the output produced by the scanner.

Here are the rules to keep in mind while reading and recognizing the output generated by the scanner.

• Each digit is represented using 9 cells (3×3)
• Only spaces, underscores and pipe characters are used
• The number of digits in each account number may vary.
• The Scanner is not 100% reliable and it might produce some digits that are invalid

The Challenge

Your job is to read the output produced by the scanner and identify the the customer number represented by each image. Remember that the scanner is not very reliable and it might produce invalid digit representations. For each digit that is not valid, set the value to ‘X’

TSQL Challenge 21 – Reading, recognizing and processing graphical representation of digits. – TSQL Challenges.

When I was a college professor I used to tell my students that “there are no stupid questions”, but that was 25 years ago, back when entry into the computer room was closely guarded.  Back then, Geekdom had “barriers to entry“, and only those people who had studied for years could gain entry into the multi-million dollar mainframe and become a “Data Processing” professional.

Today, you don’t need no stinkin High School diploma to elevate yourself to the title of “IT professional”, and the dolts among us need to be constantly reminded that 30 years ago they would be selling shoes at K-Mart:

http://www.dba-oracle.com/t_narcissistic_personality_disorders.htm

We have seen many types of DBAs Database Administrators whose characteristic potential determines who they are. Those technocrats are perfect fabulous, and wise. They protect a company from ruining.

Anyway, there are many “common sense” parallels to people with personality disorders, and I’m dealing with a computer professional who has a a textbook case of narcissistic personality disorder (NPD).  Other terms I hear used to describe NPD victims include “prick”, “assh*le”, and “twit”, but I’ve learned that there is a lot that a savvy computer guy can do to get along with the computer narcissist, and get them to cooperate.

Because I work exclusively with computer professionals, I’m exposed to all sorts of personality disorders, a regular DSM-V of weird behavior, but narcissism is right up there at the top of the heap.

Disclaimer: This article is only intended as a light-hearted look at the DBA profession, and it is based solely on my unscientific observations of DBA personality types. It is my hope that you will see yourself in some of these DBA personality types and that these descriptions will remind you of a fellow DBA.

Also see my fun  Computer Motivational Posters and other humor articles.

Some Oracle concepts are best expressed in a visual manner.

Oracle RMAN:

This is a BC Oracle training course (c) 2007-2009

The Gung-Ho DBA is generally characterized by a “can-do” attitude. They are always bright and outgoing, and they always seem to be in a hurry. They tend to be impatient (especially with stupid developer questions), and they never “suffer fools gladly.”

http://www.dba-oracle.com/articles.htm#humor1

http://www.rampant-books.com/menu_oracle.htm

http://www.dba-oracle.com/training.htm

http://www.dba-oracle.com/t_visual_guide_oracle_concepts.htm

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What kind of DBA are you.

FAWN: Fast Array of Wimpy Nodes for Sun Oracle, Google, and Facebook:
by mbenedict October 16, 2009 7:12 PM PDT
It’s not just about the raw cost. There’s a finite amount of electricity you can bring to a data center, so at some point the number of queries you can do per kWh becomes very important. The article mentions heat as waste but like electricity, heat itself also becomes a limiting factor in a large data center. There’s only so much cooling capacity available beyond which you get severe diminishing returns.

So a system which promises to be more energy efficient and runs cooler at the same time… that could be a big win.
by symbolset October 17, 2009 8:19 PM PDT
I’ve been a proponent of FAWN for a long time. For ten years the software has provided the redundancy and the scale. FAWN is not the right answer for every problem, but no tool is.

Configuring the right solution for massively parallel problems is a fairly complex geometry. If you approach a large-grain problem from a cents-per-compute-per-second perspective then FAWN is a slam dunk. For fine-grain problems you want to use GPGPU instead. When the problem becomes large enough, custom system boards and esoteric processors enter the solution set.

It’s really only when you don’t know the granularity of the problem, or you need a general solution that solves both ends of the granularity scale and the middle too that Industry Standard architectures are ideal. In these cases a mixed cluster of wimpy nodes combined with GPGPU nodes may be more cost effective.

Oh, and about cooling: The answer to many problems that start “How do you…” is… don’t. As many have shown the correct answer to the cooling problem is not refrigeration, it’s location, location, location. Your servers are rated to 35C (95F) at least, and if the ambient temperature where they are rises above that, you located your servers in the wrong geographic area, which is a different problem. There are lots of places you could put your servers that won’t get that hot in the next decade. Put your servers some place where the ambient temperature never goes out of range, preferably where they have cheap power (I hear Canada is nice). To find the ideal operation for the fans of your datacenter, heat the inlet temperature to 35C. Fire up the equipment and stress test it at maximum capacity. Measure the outlet temperature. Now you have the ideal outlet temperature. Regulate the fan on the exhaust such that the exhaust is consistently that temperature, less a few degrees for safety, and your server components will remain at a consistent temperature (thus preventing swings in temperature which can cause problems). This is not as complicated as you might think. As an added benefit during a “heat wave” stationary inversion the thermodynamics of a hot exhaust plume exiting high above the building plus the related ground-level cool air inlets creates a cooling breeze which diminishes the air conditioning required to cool the humans in the related office spaces when they’re not in the datacenter. Don’t insulate the datacenter part either – that’s swimming upstream. Maintaining a snow load on the roof should not be a design goal. Also, in really intemperate climes filter the exhaust and pass it through the human workspaces (or if you’re really fussy, use a heat exchanger) – the servers are heating air, there’s no sense burning extra energy to heat separate air to keep the humans comfy.

by ckurowic October 18, 2009 11:01 AM PDT
I disagree with your point of recirculating the hot air from the servers to people’s work areas. Some are VERY sensitive to the outgassing that occurs when equipment is new (and even for many months afterward). You have interesting concepts, but I’m afraid you don’t have the engineering background to support it.

by Christopher_Mims October 20, 2009 11:47 AM PDT
Great article – provides a lot of detail that didn’t make it into my own write-up of FAWN for Technology Review. If you’re interested in a slightly different take, though:

http://www.technologyreview.com/computing/22504/?a=f

“We were looking at efficiency at sub-maximum load. We realized the same techniques could serve high loads more efficiently as well,” said David Andersen, the Carnegie Mellon assistant professor of computer science who helped lead the project.
It’s not just academic work. Google, Intel, and NetApp are helping to fund the project, and the researchers are talking to Facebook, too. “We want to understand their challenges,” Andersen said.
Cut the power
These large-scale systems don’t come cheap. Besides the hardware, software, and maintenance costs, there’s power, too–and companies often must pay for energy twice, in effect, because servers’ waste heat means data centers must be cooled down.
by catbutt5 October 16, 2009 11:51 AM PDT
Oh, I needed a good laugh…

“And addressing the brains… Anil Rao is one inventor on a … patent applied for a computer system with numerous independent processor modules that share access to shared resources including storage, networking, and boot-up technology called the BIOS.”

Trying to patent something that’s existed for more than 20 years are you? Good luck with that.

Anil, ever heard of Sun or IBM or companies that sell refrigerator sized (small and large) computers full of little card slots containing memory and processors (even at different frequencies) that share storage, networking and yes, even the BIOS? It’s the same concept.

What’s your act 2? Gonna try to patent the automobile?

by kirkktx October 16, 2009 12:54 PM PDT
“52 queries per joule of energy compared to 346 for a FAWN cluster”

Somewhere I saw that electricity costs exceed hardware costs amortized over the life of the computer. These numbers should certainly attract investors.

http://news.cnet.com/8301-30685_3-10376537-264.html?tag=mncol