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Anais Dotis-Georgiou & Steven Soroka [InfluxData] | Machine Learning with Telegraf Execd Processor Plugin | InfluxDays Virtual Experience NA 2020

Anais Dotis-Georgiou & Steven Soroka [InfluxData] | Machine Learning with Telegraf Execd Processor Plugin | InfluxDays Virtual Experience NA 2020

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Steven Soroka and Anais Dotis-Georgiou
Machine Learning
with Telegraf Execd
Processor Plugin
Introduction and Overview
Telegraf
© 2020 InfluxData. All rights reserved. 3
Introduction to Telegraf
● Active community
● Over 200 plugins
○ 170+ Input
○ 40+ Output
○ 15+ Processors
○ 5+ Aggregators
○ 5+ Serializers
● Single Binary
● 9.2k Stars
● Extendable in any language
© 2020 InfluxData. All rights reserved. 4
Simple example of a Telegraf Configuration
[agent]
interval = "1s"
flush_interval = "10s"
omit_hostname = true
[[inputs.cpu]]
[[outputs.influxdb_v2]]
urls = ["http://127.0.0.1:9999"]
token = "$INFLUX_TOKEN"
organization = "$INFLUX_ORG"
bucket = "brew"
Overview
Execd Processor Plugin
© 2020 InfluxData. All rights reserved. 6
Introduction to the Execd Processor Plugin
● Runs an external program as a separate process, passes
metrics back and forth.
● Your program must read Influx Line Protocol on STDIN and
write metrics in Influx Line Protocol to STDOUT.
● Your program runs continuously in STDIN read loop, so you
only pay startup cost once.
● Can be any language, not just Go
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Anais Dotis-Georgiou & Steven Soroka [InfluxData] | Machine Learning with Telegraf Execd Processor Plugin | InfluxDays Virtual Experience NA 2020

  • 1. Steven Soroka and Anais Dotis-Georgiou Machine Learning with Telegraf Execd Processor Plugin
  • 3. © 2020 InfluxData. All rights reserved. 3 Introduction to Telegraf ● Active community ● Over 200 plugins ○ 170+ Input ○ 40+ Output ○ 15+ Processors ○ 5+ Aggregators ○ 5+ Serializers ● Single Binary ● 9.2k Stars ● Extendable in any language
  • 4. © 2020 InfluxData. All rights reserved. 4 Simple example of a Telegraf Configuration [agent] interval = "1s" flush_interval = "10s" omit_hostname = true [[inputs.cpu]] [[outputs.influxdb_v2]] urls = ["http://127.0.0.1:9999"] token = "$INFLUX_TOKEN" organization = "$INFLUX_ORG" bucket = "brew"
  • 6. © 2020 InfluxData. All rights reserved. 6 Introduction to the Execd Processor Plugin ● Runs an external program as a separate process, passes metrics back and forth. ● Your program must read Influx Line Protocol on STDIN and write metrics in Influx Line Protocol to STDOUT. ● Your program runs continuously in STDIN read loop, so you only pay startup cost once. ● Can be any language, not just Go
  • 7. © 2020 InfluxData. All rights reserved. 7 Execd Processor Plugin Diagram
  • 8. © 2020 InfluxData. All rights reserved. 8 Influx Line Protocol
  • 9. © 2020 InfluxData. All rights reserved. 9 Simple example of Execd Processor Plugin # A simple processor. It reads stdin and writes it to stdout. import sys def main(): for line in sys.stdin: print(line.rstrip()) sys.stdout.flush() if __name__ == '__main__': main() [[processors.execd]] command = ["python", "./processors/forecasting.py"]
  • 10. © 2020 InfluxData. All rights reserved. 10 Context: Monitoring temperatures of two beers (haze_v5 and bv_1). The ideal temperature for the batch of beer is between 18-21. Objectives: ● Forecast temperatures for the next 10 minutes ● Detect anomalous spikes in temperature that may indicate attention is needed or equipment failure. Monitoring a home brew
  • 11. © 2020 InfluxData. All rights reserved. 12 Brewing Setup
  • 12. © 2020 InfluxData. All rights reserved. 13 Seven Segment OCR ssocr -b black -f white -t 85 make_mono remove_isolated crop 30 95 460 220 shear 22 photo.png
  • 13. © 2020 InfluxData. All rights reserved. 14 Execd Processor Plugin for Forecasting and Anomaly Detection Using the Execd Processor Plugin to achieve our goal and keep the beer at a steady temperature.
  • 14. © 2020 InfluxData. All rights reserved. 15 haze_v5
  • 15. © 2020 InfluxData. All rights reserved. 16 bv_1
  • 16. © 2020 InfluxData. All rights reserved. 17 ● Double exponential smoothing employs a level component and a trend component at each period. Double exponential smoothing uses two weights, (also called smoothing parameters), to update the components at each period. ● In other words, an exponentially weighted average of the trend and values of the data together. What is Exponential Smoothing?
  • 17. © 2020 InfluxData. All rights reserved. 18 ● Statsmodels is a Python package that allows users to explore data, estimate statistical models, and perform statistical tests. ● Dataset considerations–small and sparse ● Easy to use ● Statsmodels enables more model configuration options than Flux holtWitners() function ● Using the damped version of Holt helps eliminate false positives. ● Algorithm works with small, irregular, and sparse datasets Algorithm Selection: Holt’s Exponential Smoothing
  • 19. © 2020 InfluxData. All rights reserved. 20 FAQ about the Execd Processor Plugin ● What are the advantages to running the Execd plugin as opposed to a cron job? ● Can I use the Execd Processor Plugin in combination with a Neural Net? ● When shouldn’t I use the Execd Plugin?
  • 21. © 2020 InfluxData. All rights reserved. 22 Thanks!