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Time Series Meetup: Virtual Edition | July 2020
1.
July 21, 2020 Welcome
To Time Series Virtual Meetup
2.
2 Agenda ●Introductions ●Our talk today ●Q&A ●Open
Jobs ●Be a speaker
3.
November 10 -
11, 2020 North America Virtual Experience www.influxdays.com/virtual-experience-2020/ Call for Papers is now open! We’re looking for great speakers – submit your speaker application today.
4.
Anomaly Detection with Median
Absolute Deviation Anais Dotis-Georgiou Developer Advocate | InfluxData
5.
Median Absolute Deviation
with Flux for Anomaly Detection & Contributing Custom Flux Packages Getting MAD
6.
© 2020 InfluxData.
All rights reserved.6 Hello! • Developer Advocate • Anais Jackie Dotis on LinkedIn • @AnaisDotis • http://community.influxdata.com/
7.
© 2020 InfluxData.
All rights reserved.7 What is Median Absolute Deviation? • a “deviation from the pack” algorithms • spot containers, virtual machines (VMs), servers, or sensors that are behaving differently from others, you can use the Median Absolute Deviation • reduce incident times and MTTR to uphold SLAs
8.
© 2020 InfluxData.
All rights reserved.8 How Does MAD work?
9.
© 2020 InfluxData.
All rights reserved.9 Numerical Example of MAD
10.
© 2020 InfluxData.
All rights reserved.10 Step One
11.
© 2020 InfluxData.
All rights reserved.11 Step Two
12.
© 2020 InfluxData.
All rights reserved.12 Step Three
13.
© 2020 InfluxData.
All rights reserved.13 Step Four
14.
© 2020 InfluxData.
All rights reserved.14 Step Five
15.
© 2020 InfluxData.
All rights reserved.15 Flux Functions Used • group() • drop() • median() • map() • join()
16.
© 2020 InfluxData.
All rights reserved.16 group() The group() function groups records based on their values for specific columns. It produces tables with new group keys based on provided properties. Specify an empty array of columns to ungroup data or merge all input tables into a single output table. group(columns: ["host", "_measurement"], mode:"by")
17.
© 2020 InfluxData.
All rights reserved.17 drop() The drop() function removes specified columns from a table. Columns are specified either through a list or a predicate function. When a dropped column is part of the group key, it will be removed from the key. If a specified column is not present in a table, it will return an error. drop(columns: ["col1", "col2"])
18.
© 2020 InfluxData.
All rights reserved.18 median() The median() function is a special application of the quantile() function that returns the median _value of an input table or all non-null records in the input table with values that fall within the 0.5 quantile (50th percentile) depending on the method used. median( column: "_value", method: "estimate_tdigest", compression: 0.0 )
19.
© 2020 InfluxData.
All rights reserved.19 map() The map() function applies a function to each record in the input tables. The modified records are assigned to new tables based on the group key of the input table. The output tables are the result of applying the map function to each record of the input tables. When the output record contains a different value for the group key, the record is regrouped into the appropriate table. When the output record drops a column that was part of the group key, that column is removed from the group key. map(fn: (r) => ({ _value: r._value * r._value }))
20.
© 2020 InfluxData.
All rights reserved.20 join() The join() function merges two or more input streams whose values are equal on a set of common columns into a single output stream. Null values are not considered equal when comparing column values. The resulting schema is the union of the input schemas. The resulting group key is the union of the input group keys. join(tables: {key1: table1, key2: table2}, on: ["_time", "_field"], method: "inner")
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© 2020 InfluxData.
All rights reserved.21 Custom Flux Function: Basic Syntax // Basic function definition structure functionName = (functionParameters) => functionOperations // Function definition square = (n) => n * n // Function usage > square(n:3) 9
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© 2020 InfluxData.
All rights reserved.22 Custom Flux Function: pipe-forward data In the example below, the tables parameter is assigned to the <- expression, which represents all data piped-forward into the function. tables is then piped-forward into other operations in the function definition. // Function usage from(bucket: "example-bucket") |> range(start: -1m) |> filter(fn: (r) => r._measurement == "mem" and r._field == "used_percent" ) |> multByX(x:2.0) functionName = (tables=<-) => tables |> functionOperations // Function definition multByX = (tables=<-, x) => tables |> map(fn: (r) => ({ r with _value: r._value * x}))
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© 2020 InfluxData.
All rights reserved.23 Contributing a User Defined Flux Package 1. Write your function 2. Write a test 3. Compile 4. Submit a PR
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© 2020 InfluxData.
All rights reserved.24 mad.flux package anomalydetection import "math" import "experimental" mad = (table=<-, threshold=3.0) => { // MEDiXi = med(x) data = table |> group(columns: ["_time"], mode:"by") med = data |> median(column: "_value") // diff = |Xi - MEDiXi| = math.abs(xi-med(xi)) diff = join(tables: {data: data, med: med}, on: ["_time"], method: "inner") |> map(fn: (r) => ({ r with _value: math.abs(x: r._value_data - r._value_med) })) |> drop(columns: ["_start", "_stop", "_value_med", "_value_data"]) // The constant k is needed to make the estimator consistent for the parameter of interest. k = 1.4826 // MAD = k * MEDi * |Xi - MEDiXi| diff_med = diff |> median(column: "_value") |> map(fn: (r) => ({ r with MAD: k * r._value})) |> filter(fn: (r) => r.MAD > 0.0) output = join(tables: {diff: diff, diff_med: diff_med}, on: ["_time"], method: "inner") |> map(fn: (r) => ({ r with _value: r._value_diff/r._value_diff_med})) |> map(fn: (r) => ({ r with level: if r._value >= threshold then "anomaly" else "normal" })) return output }
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© 2020 InfluxData.
All rights reserved.25 mad_test.flux t_mad = (table=<-) => table |> range(start: 2020-04-27T00:00:00Z, stop: 2020-05-01T00:00:00Z) |> anomalydetection.mad(threshold: 3.0) test t_mad = () => ({input: testing.loadStorage(csv: inData), want: testing.loadMem(csv: outData), fn: t_mad}) package anomalydetection_test import "testing" import "contrib/anaisdg/anomalydetection " inData= "<your annotated csv>" outData="<your annotated csv>"
26.
© 2020 InfluxData.
All rights reserved.26 Compile Flux 1. Install the pkg-config utility: brew install pkg-config 2. Install the pkg-config wrapper utility: go get github.com/influxdata/pkg-config 3. Ensure the GOBIN directory is on your PATH: export PATH=${GOPATH}/bin:${PATH} 4. Navigate to the Flux repository and run the following commands to build Flux: go generate ./libflux/go/libflux go generate ./stdlib go build ./cmd/flux
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