Databases Reference
In-Depth Information
99% for master node, and 99.9% for power—then the total system availability is the
product of these three numbers: 98.8% (99.9
x
99
x
99.9).
If there are single points of failure such as a master or name node, then NoSQL
systems have the ability to gracefully switch over to use a backup node without a major
service interruption. If a system can quickly recover from a failing component, it's said
to have a property of
automatic failover
. Automatic failover is the general property of
any service to detect a failure and switch to a redundant component.
Failback
is the
process of restoring a system component to its normal operation. Generally, this pro-
cess requires some data synchronization. If your systems are configured with a single
failover, you must use the probability that the failover process doesn't work in combi-
nation with the odds that the failover system fails before failback.
There are other metrics you can use besides the failure metric. If your system has
client request timeout of 30 seconds, you'll want to measure the total percentage of
client requests that fail. In such a case, a better metric might be a factor called
client
yield
, which is the probability of any request returning within a specified time interval.
Other metrics, such as a
harvest
metric, apply when you want to include partial
API
results. Some services, such as federated search engines, may also return partial
results. For example, if you search 10 separate remote systems and one of the sites is
down for your call window of 30 seconds, you'd have a 90% harvest for that specific
call. Harvest is the data available divided by the total data sources.
Finding the best NoSQL service for your application may require comparing the
architecture of two different systems. The actual architecture may be hidden from you
behind a web service interface. In these cases, it might make the most sense to set up a
small pilot project to test the services under a simulated load.
When you set up a pilot project that includes stress testing, a key measurement will
be a frequency distribution chart of read and write response times. These distribu-
tions can give you hints about whether a database service will scale. A key point of this
analysis is that instead of focusing on average or mean response times, you should
look at how long the slowest 5% of your services take to return. In general, a service
with consistent response times will have higher availability than systems that some-
times have a high percentage of slow responses. Let's take a look at an example of this.
8.2.3
Apply your knowledge
Sally is evaluating two NoSQL options for a business unit that's concerned about web
page response times. Web pages are rendered with data from a key-value store. Sally
has narrowed down the field to two key-value store options; we'll call them Service A
and Service B. Sally uses
JMeter
, a popular performance monitoring tool, to create a
chart that has read service response distributions, as shown in figure 8.1.
When Sally looks at the data, she sees that service A has faster mean response
times. But at the 95th percentile level, they're longer than service B. Service B may
have slower average response times, but they're still within the web page load time
