Databases Reference
In-Depth Information
That said, many applications could compromise on availability and that is a possible trade-off
choice they can make.
Partition Tolerance
Parallel processing and scaling out are proven methods and are being adopted as the model for
scalability and higher performance as opposed to scaling up and building massive super computers.
The past few years have shown that building giant monolithic computational contraptions is
expensive and impractical in most cases. Adding a number of commodity hardware units in a cluster
and making them work together is a more cost-, algorithm-, and resource-effective and effi cient
solution. The emergence of cloud computing is a testimony to this fact.
Read the note titled “Vertical Scaling Challenges and Fallacies of Distributed Computing” to
understand some of trade-offs associated with the two alternative scaling strategies.
Because scaling out is the chosen path, partitioning and occasional faults in a cluster are a given.
The third pillar of CAP rests on partition tolerance or fault-tolerance. In other words, partition
tolerance measures the ability of a system to continue to service in the event a few of its cluster
members become unavailable.
VERTICAL SCALING CHALLENGES AND FALLACIES OF
DISTRIBUTED COMPUTING
The traditional choice has been in favor of consistency and so system architects
have in the past shied away from scaling out and gone in favor of scaling up.
Scaling up or vertical scaling involves larger and more powerful machines.
Involving larger and more powerful machines works in many cases but is often
characterized by the following:
Vendor lock-in
— Not everyone makes large and powerful machines and those
who do often rely on proprietary technologies for delivering the power and
effi ciency that you desire. This means there is a possibility of vendor lock-in.
Vendor lock-in in itself is not bad, at least not as much as it is often projected.
Many applications over the years have successfully been built and run on
proprietary technology. Nevertheless, it does restrict your choices and is less
fl exible than its open counterparts.
Higher costs
— Powerful machines usually cost a lot more than the price of
commodity hardware.
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Data growth perimeter
— Powerful and large machines work well until the
data grows to fi ll it. At that point, there is no choice but to move to a yet
larger machine or to scale out. The largest of machines has a limit to the
amount of data it can hold and the amount of processing it can carry out
successfully. (In real life a team of people is better than a superhero!)
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