Big Data. Big Decisions
InformationWeek
Special Coverage Series

Commentary

George Crump

Will RAID Die In 2012?

The time it takes to rebuild a RAID-protected volume makes it unwieldy with today's high-capacity drives.

RAID has become a staple of the modern day storage system, but as the number and capacity of drives in a storage system continue to increase, questions have risen about the viability of RAID. At issue is the amount of time it takes for a RAID-protected volume to rebuild itself after a drive failure. While in 2011 we saw many predictions of RAID's demise, it continues to be the protection algorithm of choice for most storage systems. Will 2012 be any different?

In Storage Switzerland's recent article What is RAID? we explain that RAID is a protection scheme that allows for volumes to have a drive failure and still be able to provide access to the data on that volume. The problem is that with today's drive technology the speed at which drives can be rebuilt is now measured in double-digit hours if not days. During this time performance can degrade and there is the risk of additional drive failure. If an additional drive fails beyond the RAID algorithms' allowance, then there is a complete data loss and recovery from backup software must begin.

More Insights

Webcasts

More >>

White Papers

More >>

Reports

More >>

There is also the reality that drives are more likely to fail as the capacity per drive increases. As drive capacity increases, so does the bit error rate (BER), which is essentially how much data can be read from a drive before you experience an unrecoverable read error. The BER ratio has stayed relatively the same while drive capacities have skyrocketed. A 2-TB drive is significantly more likely to encounter an error than a 1-TB drive when reading an entire drive, which is what happens during a RAID rebuild.

Given this combination of factors, it is likely that many large storage systems will be in a constant state of rebuild. Clearly the industry is dealing with this reality. We didn't abandon RAID 5 or RAID 6 last year. The most common "solution" has been to just live with the problem. Storage vendors can do this by making sure that there is enough storage controller processing power to provide adequate system performance while the rebuild is occurring. It would not surprise me to see some vendors allocate special standby processors to help with the rebuild process.

Another solution for RAID may be to use flash-based memory for all mission-critical data. While flash modules can fail just like hard drives, the performance of flash makes the rebuild process significantly faster. A rebuild of a flash volume protected by RAID is typically less than 15 minutes in our testing.

Eventually, though, we may just throw RAID out all together and go with an erasure coding algorithm or even more of a mirroring and replication strategy. After all, capacity is now inexpensive, and having a storage system that can automatically maintain x number of copies of data may be the simplest and most practical approach for data that is going to remain on a hard disk. This also gives you greater granularity by being able to set different levels of redundancy for different types or ages of data.

My expectation is that we will see a shift toward flash storage for mission-critical active data where RAID rebuilds will be less time-consuming and space efficiency is more important due to cost. Then we can use more of a replication, redundant copy strategy for older data stored on hard disk.

Track us on Twitter

George Crump is lead analyst of Storage Switzerland, an IT analyst firm focused on the storage and virtualization segments. Find Storage Switzerland's disclosure statement here.



Related Reading




Currently we allow the following HTML tags in comments:

Single tags

These tags can be used alone and don't need an ending tag.

<br> Defines a single line break

<hr> Defines a horizontal line

Matching tags

These require an ending tag - e.g. <i>italic text</i>

<a> Defines an anchor

<b> Defines bold text

<big> Defines big text

<blockquote> Defines a long quotation

<caption> Defines a table caption

<cite> Defines a citation

<code> Defines computer code text

<em> Defines emphasized text

<fieldset> Defines a border around elements in a form

<h1> This is heading 1

<h2> This is heading 2

<h3> This is heading 3

<h4> This is heading 4

<h5> This is heading 5

<h6> This is heading 6

<i> Defines italic text

<p> Defines a paragraph

<pre> Defines preformatted text

<q> Defines a short quotation

<samp> Defines sample computer code text

<small> Defines small text

<span> Defines a section in a document

<s> Defines strikethrough text

<strike> Defines strikethrough text

<strong> Defines strong text

<sub> Defines subscripted text

<sup> Defines superscripted text

<u> Defines underlined text

BYTE encourages readers to engage in spirited, healthy debate, including taking us to task. However, BYTE moderates all comments posted to our site, and reserves the right to modify or remove any content that it determines to be derogatory, offensive, inflammatory, vulgar, irrelevant/off-topic, racist or obvious marketing/SPAM. BYTE further reserves the right to disable the profile of any commenter participating in said activities.

Disqus Tips To upload an avatar photo, first complete your Disqus profile. | View the list of supported HTML tags you can use to style comments. | Please read our commenting policy.

Follow InformationWeek

By The Numbers

What Are Your Primary Concerns About Using Big Data Software?

Base: 417 respondents at organizations using or planning to deploy data analytics, BI or statistical analysis software
Data: InformationWeek 2013 Analytics, Business Intelligence and Information Management Survey of 541 business technology professionals, October 2012

What Do You Think?

What's your attitude about SQL analysis on top of Hadoop?
We want fast, standard SQL analysis capabilities on Hadoop ASAP
Hadoop is for unstructured data; SQL is for relational databases
We'll give SQL on Hadoop a try, but relational DBs will remain the mainstay
Given strong SQL support on Hadoop, we'd nix the data warehouse
We're not interested in Hadoop
No opinion



Related Content

From Our Sponsor

Five Big Data Challenges and How to Overcome Them with Visual Analytics

Five Big Data Challenges and How to Overcome Them with Visual Analytics

Business leaders often need a visual snapshot of data to quickly grasp and use it. This paper identifies five challenges in presenting data and how visual analytics can resolve them. Solutions are suggested to overcome the challenges of: speed, data clarity, data quality, displaying meaningful results, and dealing with outliers.

Game-Changing Analytics: How IT Executives Can Use Analytics to Create Innovation and Business Success

Game-Changing Analytics: How IT Executives Can Use Analytics to Create Innovation and Business Success

Today's competitive advantage requires a deeper understanding of your business, your market and your customers. As an IT executive, you can drive that knowledge transformation. In this white paper, learn how to make decisions as a strategic business leader and three steps to begin an analytics initiative within your enterprise.

Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics

Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics

High-performance data visualization turns sophisticated analyses into meaningful graphics, leading to faster and smarter decision making. In this white paper, learn how visual analytics can transform big data, with additional features such as real-time functionality, mobile compatibility, robust applications for technical groups and accessibility for nontechnical users.

Big Data: Lessons from the Leaders

Big Data: Lessons from the Leaders

Financial performance, competitive advantage, operational efficiency, strategic decision making - every business goal can extract value from big data, and the time for doubt or inaction has long passed. In this Economist Intelligence Unit report, in-depth interviews with data pioneers reveal the link between the effective use of big data and the bottom line among other results.

Decision-Driven Data Management: A Strategy for Better Decisions with Better Data

Decision-Driven Data Management: A Strategy for Better Decisions with Better Data

Which came first, the data or the decision? This white paper makes the case for having a decision in mind, then tailoring big data's volume, variety and velocity to achieve business results such as overcoming customer dissatisfaction or creating well-informed strategies in real time.

Informationweek Reports

Research: The Big Data Management Challenge

Research: The Big Data Management Challenge

The challenge of big data is real, but most organizations don't differentiate 'big data' from traditional data, and nearly 90% of respondents to our survey use conventional databases as the primary means of handling data. We'll help you understand what constitutes big data (it's not just size) and the numerous management challenges it poses.