Monitoring and Observability
Monitoring strawberries
Building observability for indoor farming
According to the United Nations, 2.5 billion more people will be living in cities by 2050. This trend has caused indoor farming to draw a lot of attention and effort in recent years, in an attempt to scale the production of highly nutritious, healthy food inside cities.
Over the past 3 years, Agricool has recycled 20 industrial containers into farms that grow strawberries, herbs and salads, in the very heart of cities, and without any pesticide. These urban farms are currently operated in Paris and Dubaï.
Operating a fleet of indoor farms presents a diverse set of observability challenges. At the most traditional end of the observability spectrum, engineers rely on devops tools to operate computers, microservices, and an IoT infrastructure embedded inside the farms. On the other hand, living organisms like strawberry plants draw their own observability requirements, such as disease detection, physiological measurements, nutrient absorption, water analysis, or exposition rate to pollinating bumblebees.
The purpose of this talk is to highlight observability challenges and best practices that are specific to indoor farming, and to illustrate them through the learnings that were made at Agricool when building observability pipelines.
Deployment of microservices to automate indoor farming environments.
How to build an operational model for indoor farming based on telemetry, alerting and event stores, by using widely adopted devops/observability tools like Docker, Prometheus, InfluxDB, Grafana and Redash.
Discover agronomic SLAs to drive design decisions for observability. eg. outages in irrigation systems that last too long can cause irreparable damage to strawberry plants, or a too great exposure to pollinating bumblebees can damage flowers and reduce yield.
Understand that indoor farming requires to apply observability to a wide-ranging set of technical aspects such as microservices, hardware, sensors, actuators, hydraulic circuits, lighting systems, electrical cabinets, climate regulations, plant health or water quality.
Build observability within an R&D context in which scientists are continuously figuring out how to use time series to make breakthroughs about plant physiology.
Align employees with different technical backgrounds (agronomists, engineers, technical operators) around a shared set of observability best practices.
Enable domain experts to craft their own visualizations and alert rules.
Weitere Infos
| Format | devroom |
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Weitere Sessions
| 02.02.20 |
Introduction and welcome to the monitoring and observability devroom
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| 02.02.20 |
Distributed tracing is a tool that belongs to every developer's tool belt, but what it actually can do remains a mystery to most developers. In this slideless talk, we will introduce you to the world of distributed tracing by developing a cloud native application from scratch and applying all important distributed tracing concepts in practice, at first by hand and then by using existing libraries to automate our work. You will learn not only what distributed tracing is, but how it works, what it ...
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| 02.02.20 |
This talk presents current capabilities of Grafana to integrate metrics, logs and traces and shows how to setup both Grafana and application code to be able to correlate all 3 in Grafana. It assumes some familiarity with Grafana to follow the How To steps but should be suitable for beginner users. Afterwards it shows future direction of Grafana in context of "Experiences", for even more seamless experience when correlating data from multiple data sources.
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| 02.02.20 |
Jaeger and Opentracing provide ready to use tracing services for distributed systems and are becoming widely used de-facto standard because of their ease of use. Making use of these libraries, Ceph, can reach to a much-improved monitoring state, supporting visibility to its background distributed processes. This would, in turn, add up to the way Ceph is being debugged, “making Ceph more transparent” in identifying abnormalities. In this session, the audience will get to learn about using ...
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| 02.02.20 |
Society would end if all ModBus stopped working overnight. Good thing it has zero security built in. Still, it's useful to get data out of industrial systems, be they a datacenter or a power plant.
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| 02.02.20 |
The cardinality of monitoring data we are collecting today continues to rise, in no small part due to the ephemeral nature of containers and compute platforms like Kubernetes. Querying a flat dataset comprised of an increasing number of metrics requires searching through millions and in some cases billions of metrics to select a subset to display or alert on. The ability to use wildcards or regex within the tag name and values of these metrics and traces are becoming less of a nice-to-have ...
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| 02.02.20 |
Representing distributions in a metrics-based monitoring system is both important and hard. Doing it right unlocks many powerful use cases that would otherwise require expensive event processing. Prometheus offers the somewhat weirdly named Histogram and Summary metric types for distributions. How have they become what they are today with all their weal and woe? To help understand the present, let's shed light on the past. Studying this piece of Prometheus's history will also allow a glimpse of ...
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