Big Data. Big Decisions
InformationWeek
Special Coverage Series

Commentary

George Crump

SSD Options: Tier Vs. Cache

Solid state storage can be used as a cache or an automated tier. Both approaches will make sure that the most active data is on the fastest tier, but you need to know the differences.

I have written a lot recently about the different ways you can implement solid state disk (SSD) in your data center. One of the most popular methods is to use automation, either through automated tiering techniques or through caching. While both technologies make sure that the most active data is on the fastest tier, there are differences in the technologies. What will work best will depend on your data center.

Automated tiering and caching often get confused. While each vendor's technology will vary a bit automated tiering is generally seen to be a more permanent placement of data on a faster tier of storage. It also can be seen as a way to move less active data to a high capacity but more cost effective tier of storage. Caching is often seen as more temporary in nature, accelerating only the most active data and, in most cases, this approach does not move old data to a third tier of storage.

More Insights

Webcasts

More >>

White Papers

More >>

Reports

More >>

The challenge in trying to grasp these two methods is that when used with solid state their use looks similar. In the past, caching was often thought of as a very small area of memory used to accelerate disk access for a very short period of time. Often it held only the most recent minutes of accessed data. Obviously the chances of a cache miss were relatively high, which meant a performance degradation as data was retrieved from mechanical hard disk. This lead to a very narrow deployment model, either a single server or a specific application on that server.

With the falling cost of today's flash-based SSDs, a very large cache can be created and data can reside on cache for a long period of time. This of course reduces the chance of a cache miss. It also means that data can be in cache for hours, even days if the flash memory in the cache is sized large enough. Flash has allowed large caches to be deployed in a much broader fashion and across multiple servers and applications.

A big difference between cache and automated tiering is that the data in cache is always a second copy of the data that is on the hard drive. Automated tiering is an actual move of data from the hard drive. Failure of the cache rarely produces a data loss, just a performance loss since everything would need to be served from mechanical drives until the cache can be replaced.

Since the SSD tier holds potentially the only copy of data in an automated tiering system, the failure of the SSD tier can't be tolerated so these systems have to set the SSD tier in a redundant configuration by using a RAID-like data protection scheme. The overhead of that protection, RAID parity bit calculation for example, may impact performance and of course any RAID algorithm requires extra disk capacity. Having to purchase extra SSD to support a RAID-like function makes an already premium priced technology even more expensive.

In most situations, read performance should be about the same between the two options. Mostly the efficiency of read performance is going to depend on the efficiency and customizability of the caching appliance to promote data. The goal should be to make sure the right data is in cache at the right moment in time. As we discuss in our recent article "Maximizing SSD Investment With Analytics" we believe that this is the largest opportunity for improvement in this technology. Both caching and automated tiering need to become smarter about what they cache and when.

Another area to examine with automated tiering vs. caching is which one can deliver better write performance and can be clear are of distinction between automated tiering and caching. We'll cover this in our next entry.

Follow Storage Switzerland on Twitter

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



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.