or
There are two variants of the :or
operator.
Choosing
Input Stack: |
⇨ |
Output Stack: |
This first variant is used for choosing the set of time series to
operate on. It is a binary operator that matches if either of the sub-queries match. For example,
consider the following query:
nf.app,alerttest,:eq,
name,ssCpuUser,:eq,
:or
When matching against the sample data in the table below, the highlighted time series would be
included in the result set:
Name | nf.app | nf.node |
ssCpuUser |
alerttest |
i-0123 |
ssCpuSystem |
alerttest |
i-0123 |
ssCpuUser |
nccp |
i-0abc |
ssCpuSystem |
nccp |
i-0abc |
numRequests |
nccp |
i-0abc |
ssCpuUser |
api |
i-0456 |
Math
Input Stack:ts2: TimeSeriesExpr | ts1: TimeSeriesExpr |
|
⇨ |
Output Stack:(ts1 OR ts2): TimeSeriesExpr | |
|
Compute a new time series where each interval has the value (a OR b)
where a
and b
are the corresponding intervals in the input time series. For example:
Time |
a |
b |
a OR b |
00:01 |
0.0 |
0.0 |
0.0 |
00:01 |
0.0 |
1.0 |
1.0 |
00:02 |
1.0 |
0.0 |
1.0 |
00:03 |
1.0 |
1.0 |
1.0 |
00:04 |
0.5 |
1.7 |
1.0 |
The result will be a signal time series that will
be 1.0
for all intervals where the corresponding values of a
or b
are non-zero.
Example:
Before | After |
![](data:image/png;base64,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) | ![](data:image/png;base64,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) |
minuteOfDay,:time,
:dup,
300,:gt,
:swap,
290,:lt
| minuteOfDay,:time,
:dup,
300,:gt,
:swap,
290,:lt,
:or
|