A rolling/sliding window implementation for Google-golang
var p = rolling.NewPointPolicy(rolling.NewWindow(5))
for x := 0; x < 5; x = x + 1 {
p.Append(x)
}
p.Reduce(func(w Window) float64 {
fmt.Println(w) // [ [0] [1] [2] [3] [4] ]
return 0
})
w.Append(5)
p.Reduce(func(w Window) float64 {
fmt.Println(w) // [ [5] [1] [2] [3] [4] ]
return 0
})
w.Append(6)
p.Reduce(func(w Window) float64 {
fmt.Println(w) // [ [5] [6] [2] [3] [4] ]
return 0
})The above creates a window that always contains 5 data points and then fills it with the values 0 - 4. When the next value is appended it will overwrite the first value. The window continuously overwrites the oldest value with the latest to preserve the specified value count. This type of window is useful for collecting data that have a known interval on which they are capture or for tracking data where time is not a factor.
var p = rolling.NewTimeWindow(rolling.NewWindow(3000), time.Millisecond)
var start = time.Now()
for range time.Tick(time.Millisecond) {
if time.Since(start) > 3*time.Second {
break
}
p.Append(1)
}The above creates a time window that contains 3,000 buckets where each bucket
contains, at most, 1ms of recorded data. The subsequent loop populates each
bucket with exactly one measure (the value 1) and stops when the window is full.
As time progresses, the oldest values will be removed such that if the above
code performed a time.Sleep(3*time.Second) then the window would be empty
again.
The choice of bucket size depends on the frequency with which data are expected to be recorded. On each increment of time equal to the given duration the window will expire one bucket and purge the collected values. The smaller the bucket duration then the less data are lost when a bucket expires.
This type of bucket is most useful for collecting real-time values such as request rates, error rates, and latencies of operations.
Each window exposes a Reduce(func(w Window) float64) float64 method that can
be used to aggregate the data stored within. The method takes in a function
that can compute the contents of the Window into a single value. For
convenience, this package provides some common reductions:
fmt.Println(p.Reduce(rolling.Count))
fmt.Println(p.Reduce(rolling.Avg))
fmt.Println(p.Reduce(rolling.Min))
fmt.Println(p.Reduce(rolling.Max))
fmt.Println(p.Reduce(rolling.Sum))
fmt.Println(p.Reduce(rolling.Percentile(99.9)))
fmt.Println(p.Reduce(rolling.FastPercentile(99.9)))The Count, Avg, Min, Max, and Sum each perform their expected
computation. The Percentile aggregator first takes the target percentile and
returns an aggregating function that works identically to the Sum, et al.
For cases of very large datasets, the FastPercentile can be used as a
replacement for the standard percentile calculation. This alternative version
uses the p-squared algorithm for estimating the percentile by processing
only one value at a time, in any order. The results are quite accurate but can
vary from the actual percentile by a small amount. It's a tradeoff of accuracy
for speed when calculating percentiles from large data sets. For more on the
p-squared algorithm see: http://www.cs.wustl.edu/~jain/papers/ftp/psqr.pdf.
Any function that matches the form of func(rolling.Window)float64 may be given
to the Reduce method of any window policy. The Window type is a named
version of [][]float64. Calling len(window) will return the number of
buckets. Each bucket is, itself, a slice of floats where len(bucket) is the
number of values measured within that bucket. Most aggregate will take the form
of:
func MyAggregate(w rolling.Window) float64 {
for _, bucket := range w {
for _, value := range bucket {
// aggregate something
}
}
}Pull requests, issues and comments welcome. For pull requests:
- Add tests for new features and bug fixes
- Follow the existing style
- Separate unrelated changes into multiple pull requests
See the existing issues for things to start contributing.
For bigger changes, make sure you start a discussion first by creating an issue and explaining the intended change.
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