What key capacity metrics should you be monitoring for your Big Data environment? My list includes some key CPU, Memory, File System and
I/O metrics which can give you a good understanding of how well your systems
are performing and whether any potential capacity issues can be identified.
•
Standard CPU
metrics
o utilization,
system/user breakdown
•
Memory
o Usage,
Paging, Swapping
•
User/Process
breakdown – define workloads
•
File System
o Size
o Number of
Files
o Number of
Blocks
o Ratios
o User
breakdown
•
I/O
o Response time
o Read/Writes
o Service times
o Utilization
By capturing the user/process
breakdown on your UNIX/Linux systems, we can start to define workloads and
couple that with the predicted business usage to produce both baseline and
predictive analytical models.
Some of the following key questions
can then be answered:
•
What is the business usage/growth forecast for next 3, 6, 12
months?
•
Will our existing infrastructure be able to cope?
•
If not what will be required?
•
Are any storage devices likely to experience a capacity issue
within the next 3,6,12 months?
•
Are any servers or storage devices experiencing any
performance issues and what is the likely root cause?
This is not an exhaustive list, but it does provide information on the key capacity metrics you should be monitoring for your Big Data environment. In my final blog I'll be looking at CPU breakdown and summarizing.
Jamie Baker
Principal Consultant
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