Kapacitor Stream Processing

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Kapacitor is the brains of the TICK Stack. Nathaniel will cover the stream processing capabilities of Kapacitor, how to process data before it gets stored in InfluxDB and after it is stored, best practices around anomaly detection and machine learning. In addition, Nathaniel will discuss how to configure the clustered version of Kapacitor.

Internet

Nathaniel Cook, Software Developer - Team Lead
June 14 2018
Kapacitor

© 2018 InfluxData. All rights reserved.2
Overview
• Introduction to Kapacitor
• Installation Process
• Explore some use cases

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Introduction to Kapacitor
Take action on your data
Runs as separate process

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Installation

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Installation Steps
$ wget https://dl.influxdata.com/kapacitor/releases/kapacitor_1.5.0_amd64.deb
$ sudo dpkg -i kapacitor_1.5.0_amd64.deb
$ kapacitord
$ kapacitor define mytask -tick path/to/script.tick
https://docs.influxdata.com/kapacitor/latest/introduction/getting_started/

© 2018 InfluxData. All rights reserved.6
Flow Programing

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Flow Programing
Source Window Mean
● Data is processed at each node
● Data flows along edges
Result

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Use Cases

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Use Cases
• Preprocess ETL
• Postprocess ETL
• Alert on Relative Change
• Hot Cache
• Autoscale Services
• Forecast Time Series
Follow along with TICKscript using:
https://github.com/nathanielc/influxdays

© 2018 InfluxData. All rights reserved.10
Preprocess ETL

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Preprocess ETL
InfluxDBKapacitorTelegraf

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Postprocess ETL

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Postprocess ETL
InfluxDB KapacitorTelegraf

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Alert on Relative Change

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Alert on Relative Change
Previous Current
Relative
Change
Difference
Trigger Alert

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Hot Cache

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Hot Cache
Kapacitor Client

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Autoscale Services

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Autoscaling
Kapacitor
Nodes
Cluster
Manager

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Forecast

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Forecast
Historical
Predicted
Forecast Trigger Alert
Current

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Bring it All Together

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Bring it All Together
Pick Any Combination of the use cases:
• Predictive Autoscaling
• Preprocess to clean data before forecasting
• Alert on relative change from predicted values
• Alert on autoscale events
• Hot cache autoscale metrics

© 2018 InfluxData. All rights reserved.24
Questions?
Twitter: @nathanielvcook
Github: nathanielc

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American Metal Processing Company ("AMP") is the US' largest commercial rotary heat treat facility with customers in the automotive, construction, military, and agriculture industries. They use their atmosphere-protected rotary retort furnaces to provide their clients with three primary hardening services: neutral hardening (quench and temper), carburizing, and carbonitriding. This furnace style ensures consistent, uniform heat treatment process vs. traditional batch-or-belt-style furnaces; excels at processing high volumes of smaller parts with tight tolerances; and improves the strength and toughness of plain carbon steels. Discover why AMP’s use of Telegraf, InfluxDB, Node-RED, and Grafana allows them to gain 24/7 insights into their plant operations and metallurgical results. Learn how they use time-stamped data to gain accurate metrics about their consumables usage, furnace profiles, and machine status. Join this webinar as Grant Pinkos dives into: American Metal Processing's approach to heat treating in a digitized environment through connected systems Their approach to collecting and measuring sensor data to enable predictive maintenance and improve product quality Why they need a time series database for managing and analyzing vast amounts of time-stamped data

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Delft University is the oldest and largest technical university in the Netherlands with 25,000+ students. Since 1999, they have had a team of students (undergraduate and graduate) designing, building, and racing cars, as part of the Formula Student worldwide competition. The competition has grown to include teams from 1K+ universities in 20+ countries. Students are responsible for all aspects of car manufacturing (research, construction, testing, developing, marketing, management, and fundraising). Delft University's team includes 90 students across disciplines. Discover how Delft University's team uses Marple and InfluxDB to collect telemetry and sensor metrics while they develop, test, and race their electrics cars. They collect sensor data about their EV's control systems using a time series platform. During races, they are collecting IoT data about their batteries, accelerometer, gyroscope, tires, etc. The engineers are able to share important car stats during races which help the drivers tweak their driving decisions — all with the goal of winning. After races, the entire team are able to analyze data in Marple to understand what to do better next time. By using Marple + InfluxDB, their team are able to collect, share and analyze high frequency car data used to make their car faster at competitions. Join this webinar as Robbin Baauw and Nero Vanbiervliet dive into: Marple's approach to empowering engineers to organize, analyze, and visualize their data Delft University's collaborative methodology to building and racing their Formula-style race car How InfluxDB is crucial to their collaborative engineering and racing process

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InfluxData is excited to announce the general availability of InfluxDB Cloud's new storage engine! It is a cloud-native, real-time, columnar database optimized for time series data. InfluxDB's rebuilt core was coded in Rust and sits on top of Apache Arrow and DataFusion. InfluxData's team picked Apache Parquet as the persistent format. In this webinar, Paul Dix and Balaji Palani will demonstrate key product features including the removal of cardinality limits! They will dive into: The next phase of the InfluxDB platform How using Apache Arrow's ecosystem has improved InfluxDB's performance and scalability Key features of InfluxDB Cloud's new core — including SQL native support

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balena.io helps companies develop, deploy, update, and manage IoT devices. By using Linux containers and other cloud technologies, balena enables teams to quickly and easily build fleets of connected devices. Developers are able to use containers with the language of choice and pull IoT sensor data from 70+ different single board computers into balenaCloud. Discover how to use balena.io to automate your InfluxDB deployments at the edge! During this one-hour session, experts from balena and InfluxData will demonstrate how to build and deploy your own air quality IoT solution. You will learn: The fundamentals of IoT sensor deployment and management using balena. How to use a time series platform to collect and visualize metrics from edge devices. Tips and tricks to using balenaCloud to automate InfluxDB deployments and Telegraf configurations. How to use InfluxDB's Edge Data Replication feature to collect sensor data and push it to InfluxDB Cloud for analysis. No coding experience required, just a curiosity to start your own IoT adventure.

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RudderStack — the creators of the leading open source Customer Data Platform (CDP) — needed a scalable way to collect and store metrics related to customer events and processing times (down to the nanosecond). They provide their clients with data pipelines that simplify data collection from applications, websites, and SaaS platforms. RudderStack's solution enables clients to stream customer data in real time — they quickly deploy flexible data pipelines that send the data to the customer's entire stack without engineering headaches. Customers are able to stream data from any tool using their 16+ SDK's, and they are able to transform the data in-transit using JavaScript or Python. How does RudderStack use a time series platform to provide their customers with real-time analytics? Join this webinar as Ryan McCrary dives into: RudderStack's approach to streamlining data pipelines with their 180+ out-of-the-box integrations Their data architecture including Kapacitor for alerting and Grafana for customized dashboards Why using InfluxDB was crucial for them for fast data collection and providing single-sources of truths for their customers

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Kapacitor Stream Processing

1. Nathaniel Cook, Software Developer - Team Lead June 14 2018 Kapacitor

5. © 2018 InfluxData. All rights reserved.5 Installation Steps $ wget https://dl.influxdata.com/kapacitor/releases/kapacitor_1.5.0_amd64.deb $ sudo dpkg -i kapacitor_1.5.0_amd64.deb $ kapacitord $ kapacitor define mytask -tick path/to/script.tick https://docs.influxdata.com/kapacitor/latest/introduction/getting_started/

9. © 2018 InfluxData. All rights reserved.9 Use Cases • Preprocess ETL • Postprocess ETL • Alert on Relative Change • Hot Cache • Autoscale Services • Forecast Time Series Follow along with TICKscript using: https://github.com/nathanielc/influxdays

23. © 2018 InfluxData. All rights reserved.23 Bring it All Together Pick Any Combination of the use cases: • Predictive Autoscaling • Preprocess to clean data before forecasting • Alert on relative change from predicted values • Alert on autoscale events • Hot cache autoscale metrics

25. Thank You

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