By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData Collective
  • Analytics
    AnalyticsShow More
    data analytics in sports industry
    Here’s How Data Analytics In Sports Is Changing The Game
    6 Min Read
    data analytics on nursing career
    Advances in Data Analytics Are Rapidly Transforming Nursing
    8 Min Read
    data analytics reveals the benefits of MBA
    Data Analytics Technology Proves Benefits of an MBA
    9 Min Read
    data-driven image seo
    Data Analytics Helps Marketers Substantially Boost Image SEO
    8 Min Read
    construction analytics
    5 Benefits of Analytics to Manage Commercial Construction
    5 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-23 SmartData Collective. All Rights Reserved.
Reading: The Problem with the Relational Database (Part 1 ) –The Deployment Model
Share
Notification Show More
Latest News
data analytics in sports industry
Here’s How Data Analytics In Sports Is Changing The Game
Big Data
data analytics on nursing career
Advances in Data Analytics Are Rapidly Transforming Nursing
Analytics
data analytics reveals the benefits of MBA
Data Analytics Technology Proves Benefits of an MBA
Analytics
anti-spoofing tips
Anti-Spoofing is Crucial for Data-Driven Businesses
Security
ai in software development
3 AI-Based Strategies to Develop Software in Uncertain Times
Software
Aa
SmartData Collective
Aa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Big Data > Data Warehousing > The Problem with the Relational Database (Part 1 ) –The Deployment Model
Data Warehousing

The Problem with the Relational Database (Part 1 ) –The Deployment Model

TonyBain
Last updated: 2009/05/24 at 7:36 AM
TonyBain
6 Min Read
SHARE

This is the first detailed post in a series I am doing focusing on the issues that exist today with the Relational Database.  This first post is on the deployment model.  It could be argued that this isn’t directly related to the “relational database” but rather is an implementation model problem.  I disagree with this as many characteristics of the relational database lead to the deployment model described (we will explore in later posts).

For most of my career I have been involved with the enterprise and the databases in this environment.  Over the years I have seen the volume of databases increase dramatically in line with an increase of data centric applications.  This has led to even medium sized organizations often having dozens of physical database servers.   Enterprise organizations often have hundreds of database servers, occasionally thousands of them.  The volume does vary heavily by database platform however, SQL Server typically suffering the most sprawl out of all the mainstream enterprise relational database platforms.

Deployment

Problems happen when DBA’s try to co-locate independant databases on a single server.  The problems are due to the dynamic nature of databases in terms…

More Read

What is Data Pipeline A detailed explaination

What is Data Pipeline? A Detailed Explanation

Understanding ETL Tools as a Data-Centric Organization
Differentiating Between Data Lakes and Data Warehouses
How Will The Cloud Impact Data Warehousing Technologies?
Big Data Is More Prevalent in Daily Life Than You Might Think

This is the first detailed post in a series I am doing focusing on the issues that exist today with the Relational Database.  This first post is on the deployment model.  It could be argued that this isn’t directly related to the “relational database” but rather is an implementation model problem.  I disagree with this as many characteristics of the relational database lead to the deployment model described (we will explore in later posts).

For most of my career I have been involved with the enterprise and the databases in this environment.  Over the years I have seen the volume of databases increase dramatically in line with an increase of data centric applications.  This has led to even medium sized organizations often having dozens of physical database servers.   Enterprise organizations often have hundreds of database servers, occasionally thousands of them.  The volume does vary heavily by database platform however, SQL Server typically suffering the most sprawl out of all the mainstream enterprise relational database platforms.

Deployment

Problems happen when DBA’s try to co-locate independant databases on a single server.  The problems are due to the dynamic nature of databases in terms of data volume and dynamic nature of query load.  This dynamic nature makes managing capacity a complicated and time consuming task.  When relational databases share resources you risk a small number of intensive database queries causing concurrent impact to a wider group of other queries.  Because of this, typically small numbers of databases share the same servers.  On average for SQL Server around a 10:1 database to server ratio is seen in the enterprise.

The brokenness of this model is pretty easy to spot.  Firstly, resource inefficiency and ineffective distribution is a clear problem.  While I am generalizing somewhat, an organization with 100 database servers often could have 70% of those servers vastly underutilized, 20% of those servers effectively used and 10% of those servers highly over utilized with users suffering from poor performance, “bottlenecks”, as a result.

Utilization 

With this deployment model it isn’t possible to take the unused “resources” (CPU, Memory, I/O bandwidth) from elsewhere in the organization and re-apply it to where needed (even with downtime, let alone in real time).  Instead new infrastructure investment is made to continually add new resource capacity for the bottlenecked databases.

A relational database is capped by the limits of the server on which it currently sits.  A DBA monitors the server trying to keep current query demands as optimal as possible to avoid premature bottlenecking, and continually planning to stay one step ahead of database requirements growth.  This is a costly process and one often not helped by the unpredictability of the relational database (which we will discuss later).  Multiply this need across the hundreds of servers described and you can imagine it is a significant contributor of the cost of ownership.

When you reach the limits possible on a single server many database platforms have few practical options available for further scalability (such as distributed scalability for reasons again we will address in a later post in this series).  Too often organizations with multi-million $ servers are being forced to split workloads, move real time operations to batch operations, replicate data for offline processing purposes and mandate specific times when users can run particular intensive functions.  Again, all this manual fiddling becomes a management nightmare and significant overhead when you multiple it out.

This issue in isolation can potentially be addressed through technologies such as virtualization.  While virtualization is yet to make major impact on the way in which production databases are deployed in the enterprise, this may change in the future.  However as we delve further into the problems associated with the relational database, we will see this is not the only issue that we face taking this technology forward.

Related articles by Zemanta
  • Is the Relational Database Doomed? (readwriteweb.com)


Link to original post

TonyBain May 24, 2009
Share this Article
Facebook Twitter Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

data analytics in sports industry
Here’s How Data Analytics In Sports Is Changing The Game
Big Data
data analytics on nursing career
Advances in Data Analytics Are Rapidly Transforming Nursing
Analytics
data analytics reveals the benefits of MBA
Data Analytics Technology Proves Benefits of an MBA
Analytics
anti-spoofing tips
Anti-Spoofing is Crucial for Data-Driven Businesses
Security

Stay Connected

1.2k Followers Like
33.7k Followers Follow
222 Followers Pin

You Might also Like

What is Data Pipeline A detailed explaination
Big Data

What is Data Pipeline? A Detailed Explanation

8 Min Read
etl for data-driven businesses
Big Data

Understanding ETL Tools as a Data-Centric Organization

8 Min Read
data lake vs data warehouse
Data Lake

Differentiating Between Data Lakes and Data Warehouses

7 Min Read
moving to the cloud
Big DataCloud ComputingData WarehousingExclusive

How Will The Cloud Impact Data Warehousing Technologies?

6 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

AI chatbots
AI Chatbots Can Help Retailers Convert Live Broadcast Viewers into Sales!
Chatbots
ai is improving the safety of cars
From Bolts to Bots: How AI Is Fortifying the Automotive Industry
Artificial Intelligence

Quick Link

  • About
  • Contact
  • Privacy
Follow US

© 2008-23 SmartData Collective. All Rights Reserved.

Removed from reading list

Undo
Go to mobile version
Welcome Back!

Sign in to your account

Lost your password?