Monitor Kubernetes in Rancher using InfluxData

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The document discusses monitoring Kubernetes in Rancher using InfluxData. It provides an introduction to Rancher and how it can deploy and manage Kubernetes clusters. It then discusses the time series data problem and how InfluxData solutions like Telegraf, InfluxDB, Chronograf and Kapacitor can collect, store and analyze the high volume time series data generated by containers. It highlights key features of Rancher and InfluxData and demonstrates how their tools can be used together to monitor Kubernetes workloads.

Ingeniería

© 2017 InfluxData. All rights reserved.1 © 2017 InfluxData. All rights reserved.1
An introduction to Rancher and how to handle time series data in container orchestration with InfluxData
12 SEP 2017
Monitoring Kubernetes in Rancher

© 2017 InfluxData. All rights reserved.2 © 2017 InfluxData. All rights reserved.2
Our Speakers Today
William Jimenez
Solution Architect
Gunnar Aasen
Partner Engineering

© 2017 InfluxData. All rights reserved.3 © 2017 InfluxData. All rights reserved.3
Agenda
¨Introduction to Rancher
¨Getting started with K8s on Rancher
¨Understanding the time-series problem
¨Introduction to InfluxData
¨Demo
¨Q & A

Rancher Highlights
• Unified approach to running containers on-prem or in the cloud
• Powerful REST API for automating the end-to-end container
lifecycle
• Enterprise controls such as RBAC and audit logging with
integration into common authentication providers such as AD,
SAML, LDAP.
• Automated Kubernetes deployments and upgrades
• Integrations with storage technologies such as EBS/NFS/NetApp,
etc.

© 2017 InfluxData. All rights reserved.8 © 2017 InfluxData. All rights reserved.8
What makes time-series unique
¨Needs fast ingestion of data
¨High & low precision data have different retention policy
¨Software agent or DB must be able to compute
¨Unique & changing time query
Containers generating huge data load
¨Regular vs. irregular
¨Containers themselves + components within
Understanding the time series problem

© 2017 InfluxData. All rights reserved.9 © 2017 InfluxData. All rights reserved.9
Time to metrics… graphs… value…
¨Primary developers of InfluxDB
¨Created the TICK stack: platform for time series
¨Telegraf and Chronograf fully open source
¨InfluxDB and Kapacitor open core
¨Single nodes scale to 1 million points ingested per second
¨Enterprise clustered and high availability solution
¨InfluxCloud managed platform
¨Rolling out more Rancher
Introduction

© 2017 InfluxData. All rights reserved.10 © 2017 InfluxData. All rights reserved.10
Platform Components
Telegraf
¨Collection of metrics and events
¨Many input plugins, deployed to worker nodes
InfluxDB
¨High performance time-series database
Chronograf
¨Graphing UI for InfluxDB & Kapacitor
Kapacitor
¨Metrics processing and alerting engine

© 2017 InfluxData. All rights reserved.11 © 2017 InfluxData. All rights reserved.11
Demo

© 2017 InfluxData. All rights reserved.12 © 2017 InfluxData. All rights reserved.12
Learn More
URL or other notes on where to find further info.
URL or other notes on where to find further info.
https://www.influxdata.com/blog/kubernetes-monitoring-and-autoscaling-wi
th-telegraf-and-kapacitor/
http://rancher.com/kubernetes/
https://github.com/rancher/rancher#installation

© 2017 InfluxData. All rights reserved.13 © 2017 InfluxData. All rights reserved.13
Q & A?

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Many developers and DevOps engineers have become aware of using their observability data to gain greater insights into their infrastructure systems. InfluxDB is the purpose-built time series database used to collect metrics and gain observability into apps, servers, containers, and networks. Developers use InfluxDB to improve the quality and efficiency of their CI/CD pipelines. Start using InfluxDB to aggregate infrastructure and application performance monitoring metrics to enable better anomaly detection, root-cause analysis, and alerting. This session will demonstrate how to record metrics, logs, and traces with one library — OpenTelemetry — and store them in one open source time series database — InfluxDB. Zoe will demonstrate how easy it is to set up the OpenTelemetry Operator for Kubernetes and to store and analyze your data in InfluxDB.

Gain Better Observability with OpenTelemetry and InfluxDB

InfluxData

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

How a Heat Treating Plant Ensures Tight Process Control and Exceptional Quali...

InfluxData

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

How Delft University's Engineering Students Make Their EV Formula-Style Race ...

InfluxData

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

Introducing InfluxDB’s New Time Series Database Storage Engine

InfluxData

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.

Start Automating InfluxDB Deployments at the Edge with balena

InfluxData

Learn more about InfluxDB’s new storage engine! The team developed a cloud-native, real-time, columnar database optimized for time series data. We built it all in Rust and it sits on top of Apache Arrow and DataFusion. We chose Apache Parquet as the persistent format, which is an open source columnar data file format. This new storage engine provides InfluxDB Cloud users with new functionality, including the removal of cardinality limits, so developers can bring in massive amounts of time series data at scale. In this webinar, Anais Dotis-Georgiou will dive into: Requirements for rebuilding InfluxDB’s core Key product features and timeline How Apache Arrow’s ecosystem is used to meet those requirements Stick around for a demo and live Q&A

Understanding InfluxDB’s New Storage Engine

InfluxData

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

Streamline and Scale Out Data Pipelines with Kubernetes, Telegraf, and InfluxDB

InfluxData

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“HOSTEL MANAGEMENT SYSTEM” is a software developed for managing various activities in the hostel. For the past few years the number of educational institutions are increasing rapidly. Thereby the number of hostels are also increasing for the accommodation of the students studying in this institution. And hence there is a lot of strain on the person who are running the hostel and software’s are not usually used in this context. This particular project deals with the problems on managing a hostel and avoids the problems which occur when carried manually.

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Monitor Kubernetes in Rancher using InfluxData

1. © 2017 InfluxData. All rights reserved.1 © 2017 InfluxData. All rights reserved.1 An introduction to Rancher and how to handle time series data in container orchestration with InfluxData 12 SEP 2017 Monitoring Kubernetes in Rancher

3. © 2017 InfluxData. All rights reserved.3 © 2017 InfluxData. All rights reserved.3 Agenda Ïntroduction to Rancher ¨Getting started with K8s on Rancher Ünderstanding the time-series problem Ïntroduction to InfluxData ¨Demo ¨Q & A

4. Rancher Labs Introduction

5. Complete Container Management Solution

6. Rancher Highlights • Unified approach to running containers on-prem or in the cloud • Powerful REST API for automating the end-to-end container lifecycle • Enterprise controls such as RBAC and audit logging with integration into common authentication providers such as AD, SAML, LDAP. • Automated Kubernetes deployments and upgrades • Integrations with storage technologies such as EBS/NFS/NetApp, etc.

7. How Rancher Works

8. © 2017 InfluxData. All rights reserved.8 © 2017 InfluxData. All rights reserved.8 What makes time-series unique ¨Needs fast ingestion of data ¨High & low precision data have different retention policy ¨Software agent or DB must be able to compute ¨Unique & changing time query Containers generating huge data load ¨Regular vs. irregular ¨Containers themselves + components within Understanding the time series problem

9. © 2017 InfluxData. All rights reserved.9 © 2017 InfluxData. All rights reserved.9 Time to metrics… graphs… value… ¨Primary developers of InfluxDB ¨Created the TICK stack: platform for time series ¨Telegraf and Chronograf fully open source ÏnfluxDB and Kapacitor open core ¨Single nodes scale to 1 million points ingested per second Ënterprise clustered and high availability solution ÏnfluxCloud managed platform ¨Rolling out more Rancher Introduction

10. © 2017 InfluxData. All rights reserved.10 © 2017 InfluxData. All rights reserved.10 Platform Components Telegraf ¨Collection of metrics and events ¨Many input plugins, deployed to worker nodes InfluxDB ¨High performance time-series database Chronograf ¨Graphing UI for InfluxDB & Kapacitor Kapacitor ¨Metrics processing and alerting engine

12. © 2017 InfluxData. All rights reserved.12 © 2017 InfluxData. All rights reserved.12 Learn More URL or other notes on where to find further info. URL or other notes on where to find further info. https://www.influxdata.com/blog/kubernetes-monitoring-and-autoscaling-wi th-telegraf-and-kapacitor/ http://rancher.com/kubernetes/ https://github.com/rancher/rancher#installation

Monitor Kubernetes in Rancher using InfluxData

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