The document discusses monitoring Kubernetes clusters using Prometheus. It describes the various sources of metrics in Kubernetes including metrics from nodes, containers, the Kubernetes API, etcd, and derived metrics. It also covers the new Kubernetes metrics server, how metrics are used for scheduling and autoscaling via the horizontal pod autoscaler, and how metrics can be aggregated at different levels in the Kubernetes hierarchy.
KubeCon Prometheus Salon -- Kubernetes metrics deep diveBob Cotton
Kubernetes generates a wealth of metrics. Some explicitly within the Kubernetes API server, the Kublet, and cAdvisor or implicitly by observing events such as the kube-state-metrics project. A subset of these metrics are used within Kubernetes itself to make scheduling decisions, however other metrics can be used to determine the overall health of the system or for capacity planning purposes.
Kubernetes exposes metrics from several places, some available internally, others through add-on projects. In this session you will learn about:
- Node level metrics, as exposed from the node_exporter
- Kublet metrics
- API server metrics
- etcd metrics
- cAdvisor metrics
- Metrics exposed from kube-state-metrics
Join this session to learn about how these metrics are calculated, their use within Kubernetes scheduling decisions and application in monitoring, alerting and capacity planning. This session will also cover the new metrics implementation/proposals that are to replace the cAdvisor metrics in Kubernetes 1.8.
20180503 kube con eu kubernetes metrics deep diveBob Cotton
Kubernetes generates a wealth of metrics. Some explicitly within the Kubernetes API server, the Kublet, and cAdvisor or implicitly by observing events such as the kube-state-metrics project. A subset of these metrics are used within Kubernetes itself to make scheduling decisions, however, other metrics can be used to determine the overall health of the system or for capacity planning purposes.
Kubernetes exposes metrics from several places, some available internally, others through add-on projects. In this session you will learn about:
- Node level metrics, as exposed from the node_exporter
- Kublet metrics
- API server metrics
- etcd metrics
- cAdvisor metrics
- Metrics exposed from kube-state-metrics
Deploying Kafka Streams Applications with Docker and Kubernetesconfluent
(Gwen Shapira + Matthias J. Sax, Confluent) Kafka Summit SF 2018
Kafka Streams, Apache Kafka’s stream processing library, allows developers to build sophisticated stateful stream processing applications which you can deploy in an environment of your choice. Kafka Streams is not only scalable, but fully elastic allowing for dynamic scale-in and scale-out as the library handles state migration transparently in the background. By running Kafka Streams applications on Kubernetes, you will be able to use Kubernetes powerful control plane to standardize and simplify the application management—from deployment to dynamic scaling.
In this technical deep dive, we’ll explain the internals of dynamic scaling and state migration in Kafka Streams. We’ll then show, with a live demo, how a Kafka Streams application can run in a Docker container on Kubernetes and the dynamic scaling of an application running in Kubernetes.
At a blistering pace and for a variety of reasons, companies are migrating their on-premise database infrastructures to cloud-based solutions - to save costs on hardware, tame the impact of disaster recovery, or even to improve security. Zalando is not an exception: more than two years ago we migrated our first production services to AWS.
In addition to the fully managed database services like RDS and Aurora, Amazon offers a wide spectra of EC2 Instances with different types of performance and price. Without a lot of experience in running cloud databases it’s not easy to make the right choice, and as a result you will either have pure database performance or will overpay for over-provisioned resources.
In this talk I will compare different ways of running PostgreSQL on AWS, explain why we decided to run most of our databases on EC2 Instances instead of RDS, how we chose EC2 Instance types and EBS Volumes, which AWS CloudWatch metrics MUST be monitored (and why), and what problems we hit plus how to avoid them.
Flink Forward Berlin 2017: Ruben Casado Tejedor - Flink-Kudu connector: an op...Flink Forward
Kappa Architecture is a software architecture pattern that makes use of an immutable, append only log. All the processing of the event will be performed in the input streams and persisted as real-time views. Apache Flink is very well suited to be the processing engine because it provides support for event-time semantics, stateful exactly-once processing, and achieves high throughput and low latency at the same time. Apache Kudu Kudu is a storage system good at both ingesting streaming data and good at analyzing it using ad-hoc queries (e.g. interactive SQL based) and full-scan processes (e.g Spark/Flink). So Kudu is a good fit to store the real-time views in a Kappa Architecture. We have developed and open-sourced a connector to integrate Apache Kudu and Apache Flink. It allows reading/writing data from/to Kudu using the DataSet and DataStream Flink's APIs. The connector has been submitted to the Apache Bahir project and is already available from maven central repository.
KubeCon Prometheus Salon -- Kubernetes metrics deep diveBob Cotton
Kubernetes generates a wealth of metrics. Some explicitly within the Kubernetes API server, the Kublet, and cAdvisor or implicitly by observing events such as the kube-state-metrics project. A subset of these metrics are used within Kubernetes itself to make scheduling decisions, however other metrics can be used to determine the overall health of the system or for capacity planning purposes.
Kubernetes exposes metrics from several places, some available internally, others through add-on projects. In this session you will learn about:
- Node level metrics, as exposed from the node_exporter
- Kublet metrics
- API server metrics
- etcd metrics
- cAdvisor metrics
- Metrics exposed from kube-state-metrics
Join this session to learn about how these metrics are calculated, their use within Kubernetes scheduling decisions and application in monitoring, alerting and capacity planning. This session will also cover the new metrics implementation/proposals that are to replace the cAdvisor metrics in Kubernetes 1.8.
20180503 kube con eu kubernetes metrics deep diveBob Cotton
Kubernetes generates a wealth of metrics. Some explicitly within the Kubernetes API server, the Kublet, and cAdvisor or implicitly by observing events such as the kube-state-metrics project. A subset of these metrics are used within Kubernetes itself to make scheduling decisions, however, other metrics can be used to determine the overall health of the system or for capacity planning purposes.
Kubernetes exposes metrics from several places, some available internally, others through add-on projects. In this session you will learn about:
- Node level metrics, as exposed from the node_exporter
- Kublet metrics
- API server metrics
- etcd metrics
- cAdvisor metrics
- Metrics exposed from kube-state-metrics
Deploying Kafka Streams Applications with Docker and Kubernetesconfluent
(Gwen Shapira + Matthias J. Sax, Confluent) Kafka Summit SF 2018
Kafka Streams, Apache Kafka’s stream processing library, allows developers to build sophisticated stateful stream processing applications which you can deploy in an environment of your choice. Kafka Streams is not only scalable, but fully elastic allowing for dynamic scale-in and scale-out as the library handles state migration transparently in the background. By running Kafka Streams applications on Kubernetes, you will be able to use Kubernetes powerful control plane to standardize and simplify the application management—from deployment to dynamic scaling.
In this technical deep dive, we’ll explain the internals of dynamic scaling and state migration in Kafka Streams. We’ll then show, with a live demo, how a Kafka Streams application can run in a Docker container on Kubernetes and the dynamic scaling of an application running in Kubernetes.
At a blistering pace and for a variety of reasons, companies are migrating their on-premise database infrastructures to cloud-based solutions - to save costs on hardware, tame the impact of disaster recovery, or even to improve security. Zalando is not an exception: more than two years ago we migrated our first production services to AWS.
In addition to the fully managed database services like RDS and Aurora, Amazon offers a wide spectra of EC2 Instances with different types of performance and price. Without a lot of experience in running cloud databases it’s not easy to make the right choice, and as a result you will either have pure database performance or will overpay for over-provisioned resources.
In this talk I will compare different ways of running PostgreSQL on AWS, explain why we decided to run most of our databases on EC2 Instances instead of RDS, how we chose EC2 Instance types and EBS Volumes, which AWS CloudWatch metrics MUST be monitored (and why), and what problems we hit plus how to avoid them.
Flink Forward Berlin 2017: Ruben Casado Tejedor - Flink-Kudu connector: an op...Flink Forward
Kappa Architecture is a software architecture pattern that makes use of an immutable, append only log. All the processing of the event will be performed in the input streams and persisted as real-time views. Apache Flink is very well suited to be the processing engine because it provides support for event-time semantics, stateful exactly-once processing, and achieves high throughput and low latency at the same time. Apache Kudu Kudu is a storage system good at both ingesting streaming data and good at analyzing it using ad-hoc queries (e.g. interactive SQL based) and full-scan processes (e.g Spark/Flink). So Kudu is a good fit to store the real-time views in a Kappa Architecture. We have developed and open-sourced a connector to integrate Apache Kudu and Apache Flink. It allows reading/writing data from/to Kudu using the DataSet and DataStream Flink's APIs. The connector has been submitted to the Apache Bahir project and is already available from maven central repository.
Managing multiple event types in a single topic with Schema Registry | Bill B...HostedbyConfluent
With Apache Kafka, it's typical to place different events in their own topic. But different event types can be related. Consider customer interactions with an online retailer. The customer searches through the site and clicks on various items before deciding on a final purchase. But businesses can gain insight by processing these events in sequence. Using the event type per topic leaves a lot of work for developers. Is there a better way?
Fortunately, there is. Schema Registry now supports having multiple event types in the same topic. By placing various event types in a single topic, you can now handle different related events in-order. In this presentation, I'll introduce Schema Registry then we'll dive into how it handles multiple event types in a single topic, including examples.
You will learn how and when to apply the multiple event types per topic pattern. Additionally, you'll learn how schema references work in Schema Registry.
Kafka on Kubernetes—From Evaluation to Production at Intuit confluent
(Shrinand Javadekar, Intuit Inc.) Kafka Summit SF 2018
Kubernetes is fast becoming the platform of choice for running distributed, containerized applications in the cloud. It has great features for availability, scalability, monitoring, ease of deployment, a rich set of tools and an extremely fast-growing ecosystem that is making it ever more useful. However, running stateful applications such as Kafka on Kubernetes is not a common practice today. At Intuit, we took an experimentation and data-driven approach for evaluating Kafka on Kubernetes in AWS.
In this talk, we will provide details of our functional and non-functional requirements, the experimental configuration and the details of the evaluation. The evaluation process included functional tests for producing/consuming messages, network isolation tests, cross-region tests as well as performance and stress tests. We will focus on the problems we ran into and how we addressed them. This talk will demonstrate a Kubernetes cluster running Kafka along with the details of how each component is configured. Specifically, we will cover the Kafka and ZooKeeper StatefulSets, the ConfigMaps used for storing the server.properties used by all brokers, the service objects for enabling access to the brokers, securing the data and, last but not least, integration with Splunk and Wavefront for logging and monitoring respectively.
Kubernetes @ Squarespace (SRE Portland Meetup October 2017)Kevin Lynch
In this presentation I talk about our motivation to converting our microservices to run on Kubernetes. I discuss many of the technical challenges we encountered along the way, including networking issues, Java issues, monitoring and alerting, and managing all of our resources!
PGConf APAC 2018 - Managing replication clusters with repmgr, Barman and PgBo...PGConf APAC
Speaker: Ian Barwick
PostgreSQL and reliability go hand-in-hand - but your data is only truly safe with a solid and trusted backup system in place, and no matter how good your application is, it's useless if it can't talk to your database.
In this talk we'll demonstrate how to set up a reliable replication
cluster using open source tools closely associated with the PostgreSQL project. The talk will cover following areas:
- how to set up and manage a replication cluster with `repmgr`
- how to set up and manage reliable backups with `Barman`
- how to manage failover and application connections with `repmgr` and `PgBouncer`
Ian Barwick has worked for 2ndQuadrant since 2014, and as well as making various contributions to PostgreSQL itself, is lead `repmgr` developer. He lives in Tokyo, Japan.
Now that you have your apps running on K8s, wondering how to get the response time that you need ? Tuning applications to get the performance that you need can be challenging. When you have to tune a number of microservices in Kubernetes to fix a response time or a throughput issue, it can get really overwhelming. This talk looks at some common performance issues and ways to solve them and more importantly the tools that can help you.
We will also be specifically looking at Kruize that helps to not only right size your containers but also optimize the runtimes.
( https://github.com/kruize/kruize )
Kafka’s New Control Plane: The Quorum Controller | Colin McCabe, ConfluentHostedbyConfluent
Currently, Apache Kafka® uses Apache ZooKeeper™ to store its metadata. Data such as the location of partitions and the configuration of topics are stored outside of Kafka itself, in a separate ZooKeeper cluster. In 2019, we outlined a plan to break this dependency and bring metadata management into Kafka itself through a dynamic service that runs inside the Kafka Cluster. We call this the Quorum Controller.
In this talk, we’ll look at how the Quorum Controller works and how it integrates with other parts of the next-generation Kafka architecture, such as the Raft quorum and snapshotting mechanism. We’ll also explain how the Quorum Controller will simplify operations, improve security, and enhance scalability and performance.
Finally, we’ll look at some of the practicalities, such as how to monitor and run the Quorum Controller yourself. We’ll talk about some of the performance gains we’ve seen, and our plans for the future.
A detailed overview of Kapacitor, InfluxDB’s native data processing engine. How to install, configure and build custom TICKscripts enable alerting and anomaly detection
(Nina Hanzlikova, Zalando) Kafka Summit SF 2018
My team at Zalando fell in love with KStreams and their programming model straight out of the gate. However, as a small team of developers, building out and supporting our infrastructure while still trying to deliver solutions for our business has not always resulted in a smooth journey.
Can a small team of a couple of developers run their own Kafka infrastructure confidently and still spend most of their time developing code?
In this talk, we will dive into some of the problems we experienced while running Kafka brokers and Kafka Streams applications, as well as the consultations we had with other teams around this matter. We will outline some of the pragmatic decisions we made regarding backups, monitoring and operations to minimize our time spent administering our Kafka brokers and various stream applications.
Event sourcing - what could possibly go wrong ? Devoxx PL 2021Andrzej Ludwikowski
Yet another presentation about Event Sourcing? Yes and no. Event Sourcing is a really great concept. Some could say it’s a Holy Grail of the software architecture. I might agree with that, while remembering that everything comes with a price. This session is a summary of my experience with ES gathered while working on 3 different commercial products. Instead of theoretical aspects, I will focus on possible challenges with ES implementation. What could explode (very often with delayed ignition)? How and where to store events effectively? What are possible schema evolution solutions? How to achieve the highest level of scalability and live with eventual consistency? And many other interesting topics that you might face when experimenting with ES.
Managing Container Clusters in OpenStack Native WayQiming Teng
This is a presentation from the OpenStack Austin Summit. It talks about managing containers in an OpenStack native way where containers are treated as first class citizens.
Introducing Scylla Manager: Cluster Management and Task AutomationScyllaDB
By centralizing cluster administration and automating recurring tasks, Scylla Manager brings greater predictability and control to Scylla-based environments.
In this webinar, you will learn about Scylla Manager’s recurrent repair capabilities, including why recurrent repair is critical for Scylla production cluster administration, and why keeping it manual results in errors and suboptimal performance.
We will present a demo of how to set up and run recurrent and ad-hoc repairs on a Scylla cluster, and give you a sneak peek of the Scylla Manager roadmap, which includes cluster management, rolling upgrades, and integrated monitoring.
Managing multiple event types in a single topic with Schema Registry | Bill B...HostedbyConfluent
With Apache Kafka, it's typical to place different events in their own topic. But different event types can be related. Consider customer interactions with an online retailer. The customer searches through the site and clicks on various items before deciding on a final purchase. But businesses can gain insight by processing these events in sequence. Using the event type per topic leaves a lot of work for developers. Is there a better way?
Fortunately, there is. Schema Registry now supports having multiple event types in the same topic. By placing various event types in a single topic, you can now handle different related events in-order. In this presentation, I'll introduce Schema Registry then we'll dive into how it handles multiple event types in a single topic, including examples.
You will learn how and when to apply the multiple event types per topic pattern. Additionally, you'll learn how schema references work in Schema Registry.
Kafka on Kubernetes—From Evaluation to Production at Intuit confluent
(Shrinand Javadekar, Intuit Inc.) Kafka Summit SF 2018
Kubernetes is fast becoming the platform of choice for running distributed, containerized applications in the cloud. It has great features for availability, scalability, monitoring, ease of deployment, a rich set of tools and an extremely fast-growing ecosystem that is making it ever more useful. However, running stateful applications such as Kafka on Kubernetes is not a common practice today. At Intuit, we took an experimentation and data-driven approach for evaluating Kafka on Kubernetes in AWS.
In this talk, we will provide details of our functional and non-functional requirements, the experimental configuration and the details of the evaluation. The evaluation process included functional tests for producing/consuming messages, network isolation tests, cross-region tests as well as performance and stress tests. We will focus on the problems we ran into and how we addressed them. This talk will demonstrate a Kubernetes cluster running Kafka along with the details of how each component is configured. Specifically, we will cover the Kafka and ZooKeeper StatefulSets, the ConfigMaps used for storing the server.properties used by all brokers, the service objects for enabling access to the brokers, securing the data and, last but not least, integration with Splunk and Wavefront for logging and monitoring respectively.
Kubernetes @ Squarespace (SRE Portland Meetup October 2017)Kevin Lynch
In this presentation I talk about our motivation to converting our microservices to run on Kubernetes. I discuss many of the technical challenges we encountered along the way, including networking issues, Java issues, monitoring and alerting, and managing all of our resources!
PGConf APAC 2018 - Managing replication clusters with repmgr, Barman and PgBo...PGConf APAC
Speaker: Ian Barwick
PostgreSQL and reliability go hand-in-hand - but your data is only truly safe with a solid and trusted backup system in place, and no matter how good your application is, it's useless if it can't talk to your database.
In this talk we'll demonstrate how to set up a reliable replication
cluster using open source tools closely associated with the PostgreSQL project. The talk will cover following areas:
- how to set up and manage a replication cluster with `repmgr`
- how to set up and manage reliable backups with `Barman`
- how to manage failover and application connections with `repmgr` and `PgBouncer`
Ian Barwick has worked for 2ndQuadrant since 2014, and as well as making various contributions to PostgreSQL itself, is lead `repmgr` developer. He lives in Tokyo, Japan.
Now that you have your apps running on K8s, wondering how to get the response time that you need ? Tuning applications to get the performance that you need can be challenging. When you have to tune a number of microservices in Kubernetes to fix a response time or a throughput issue, it can get really overwhelming. This talk looks at some common performance issues and ways to solve them and more importantly the tools that can help you.
We will also be specifically looking at Kruize that helps to not only right size your containers but also optimize the runtimes.
( https://github.com/kruize/kruize )
Kafka’s New Control Plane: The Quorum Controller | Colin McCabe, ConfluentHostedbyConfluent
Currently, Apache Kafka® uses Apache ZooKeeper™ to store its metadata. Data such as the location of partitions and the configuration of topics are stored outside of Kafka itself, in a separate ZooKeeper cluster. In 2019, we outlined a plan to break this dependency and bring metadata management into Kafka itself through a dynamic service that runs inside the Kafka Cluster. We call this the Quorum Controller.
In this talk, we’ll look at how the Quorum Controller works and how it integrates with other parts of the next-generation Kafka architecture, such as the Raft quorum and snapshotting mechanism. We’ll also explain how the Quorum Controller will simplify operations, improve security, and enhance scalability and performance.
Finally, we’ll look at some of the practicalities, such as how to monitor and run the Quorum Controller yourself. We’ll talk about some of the performance gains we’ve seen, and our plans for the future.
A detailed overview of Kapacitor, InfluxDB’s native data processing engine. How to install, configure and build custom TICKscripts enable alerting and anomaly detection
(Nina Hanzlikova, Zalando) Kafka Summit SF 2018
My team at Zalando fell in love with KStreams and their programming model straight out of the gate. However, as a small team of developers, building out and supporting our infrastructure while still trying to deliver solutions for our business has not always resulted in a smooth journey.
Can a small team of a couple of developers run their own Kafka infrastructure confidently and still spend most of their time developing code?
In this talk, we will dive into some of the problems we experienced while running Kafka brokers and Kafka Streams applications, as well as the consultations we had with other teams around this matter. We will outline some of the pragmatic decisions we made regarding backups, monitoring and operations to minimize our time spent administering our Kafka brokers and various stream applications.
Event sourcing - what could possibly go wrong ? Devoxx PL 2021Andrzej Ludwikowski
Yet another presentation about Event Sourcing? Yes and no. Event Sourcing is a really great concept. Some could say it’s a Holy Grail of the software architecture. I might agree with that, while remembering that everything comes with a price. This session is a summary of my experience with ES gathered while working on 3 different commercial products. Instead of theoretical aspects, I will focus on possible challenges with ES implementation. What could explode (very often with delayed ignition)? How and where to store events effectively? What are possible schema evolution solutions? How to achieve the highest level of scalability and live with eventual consistency? And many other interesting topics that you might face when experimenting with ES.
Managing Container Clusters in OpenStack Native WayQiming Teng
This is a presentation from the OpenStack Austin Summit. It talks about managing containers in an OpenStack native way where containers are treated as first class citizens.
Introducing Scylla Manager: Cluster Management and Task AutomationScyllaDB
By centralizing cluster administration and automating recurring tasks, Scylla Manager brings greater predictability and control to Scylla-based environments.
In this webinar, you will learn about Scylla Manager’s recurrent repair capabilities, including why recurrent repair is critical for Scylla production cluster administration, and why keeping it manual results in errors and suboptimal performance.
We will present a demo of how to set up and run recurrent and ad-hoc repairs on a Scylla cluster, and give you a sneak peek of the Scylla Manager roadmap, which includes cluster management, rolling upgrades, and integrated monitoring.
Kubernetes and Bluemix introduction along with the sample demo application(Color Cluster) on IBM Bluemix Container Service(BCS). Also, some advanced features provided by IBM. Sample code for the repo is here, [Kuberbetes Bluemix Demo](https://github.com/mohan08p/KubernetesMeetup/tree/master/14th%20Oct%202017/ColorDemo)
A brief study on Kubernetes and its componentsRamit Surana
Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions. Using the concepts of "labels" and "pods", it groups the containers which make up an application into logical units for easy management and discovery.
Orchestrating Microservices with Kubernetes Weaveworks
- Kubernetes Concepts
- Hands on: Using kubeadm to stand up a Kubernetes cluster
- Hands on: Using kubectl to make changes to running Kubernetes cluster
Business use of Social Media and Impact on Enterprise ArchitectureNUS-ISS
Presented on 7 March 2013 at The Architecture Community of Practice (ACoP) Forum at the Intstitute of Systems Science, National University of Singapore.
Web: www.iss.nus.edu.sg
Twitter:#ISSNUS
Facebook: www.facebook.com/ISS.NUS
WSO2Con US 2015 Kubernetes: a platform for automating deployment, scaling, an...Brian Grant
Kubernetes can run application containers on clusters of physical or virtual machines.
It can also do much more than that.
Kubernetes satisfies a number of common needs of applications running in production, such as co-locating helper processes, mounting storage systems, distributing secrets, application health checking, replicating application instances, horizontal auto-scaling, load balancing, rolling updates, and resource monitoring.
However, even though Kubernetes provides a lot of functionality, there are always new scenarios that would benefit from new features. Ad hoc orchestration that is acceptable initially often requires robust automation at scale. Application-specific workflows can be streamlined to accelerate developer velocity.
This is why Kubernetes was also designed to serve as a platform for building an ecosystem of components and tools to make it easier to deploy, scale, and manage applications. The Kubernetes control plane is built upon the same APIs that are available to developers and users, implementing resilient control loops that continuously drive the current state towards the desired state. This design has enabled Apache Stratos and a number of other Platform as a Service and Continuous Integration and Deployment systems to build atop Kubernetes.
This presentation introduces Kubernetes’s core primitives, shows how some of its better known features are built on them, and introduces some of the new capabilities that are being added.
Deep-dive into Microservice Outer ArchitectureWSO2
To view recording of this webinar please use the below URL:
http://wso2.com/library/webinars/2016/02/deep-dive-into-microservice-outer-architecture/
Microservices architecture (MSA) promotes loosely coupled services as building blocks for software system architecture. It was first adopted by large internet companies like Netflix and now is popular with enterprise architects everywhere.
You may find yourself asking what the main premises of MSA are and whether it replaces SOA. In this webinar Frank and Srinath will
Compare and contrast MSA with SOA and discuss both their pros and cons
Examine what MSA looks like in practice
Answer questions such as where to use databases, how to use security and how to perform service orchestration and integration
Discuss practical challenges
OSMC 2019 | Monitoring Cockpit for Kubernetes Clusters by Ulrike KlusikNETWAYS
Monitoring Kubernetes Clusters with Prometheus is state of the art. The difficulty is to find the significant metrics from the vast amount of available metrics. This talk shows a Monitoring Cockpit defined to get a quick overview of the cluster health and usage. It uses the Standard Metrics available for Kubernetes/OpenShift Clusters and their standard services. The monitoring solution is based on Prometheus, using InfluxDB for central long term storage and Grafana.
Watch this Tech Talk: https://do.co/video_pgupta
An introduction into the world of containers and the orchestration ecosystem, and how Kubernetes can help software developers and cloud infrastructure engineers be more agile, efficient, and productive.
Containers and Kubernetes have changed the infra world for good, bringing agility, efficiency, and more productivity. Still thinking about how to get started with Kubernetes? This talk is designed to give you an introduction into the world of containers and the orchestration ecosystem.
What You'll Learn
- Introduction to containers and microservices
- Introduction to Kubernetes and how it can help
- Essential Kubernetes building blocks (“primitives”) for getting started
About the Presenter
Peeyush Gupta is a cloud enthusiast with 5+ years of experience in developing cloud platforms and helping customers migrate their legacy applications to cloud. He has also been a speaker at multiple meetups and serves the developer community as part of Kubernetes contributor experience group. He is currently working with DigitalOcean as a Senior Developer Advocate.
New to DigitalOcean? Get US $100 in credit when you sign up: https://do.co/deploytoday
To learn more about DigitalOcean: https://www.digitalocean.com/
Follow us on Twitter: https://twitter.com/digitalocean
Like us on Facebook: https://www.facebook.com/DigitalOcean
Follow us on Instagram: https://www.instagram.com/thedigitalocean/
We're hiring: http://do.co/careers
Introduction to Container Storage Interface (CSI)Idan Atias
Among the cool stuff we do at Silk, my colleagues and I develop the Silk CSI Plugin for customers who use our system as the storage layer for their Kubernetes workloads.
Before deep diving into the code and as part of my ramp-up on this subject I prepared some slides that cover some basic and important information on this topic.
These slides start by recapping some basic storage principals in containers and Kubernetes, continues with some more advanced use cases (including an "offline demo" of persisting Redis data on EBS volumes), and ends with a detailed information on the CSI solution itself.
IMHO, reviewing these slides can improve your understanding on this matter and can get you started implementing your own CSI plugin.
The main sources of information I used for preparing these slides are:
* Official CSI docs
* Kubernetes Storage Lingo 101 - Saad Ali, Google
* Container Storage Interface: Present and Future - Jie Yu, Mesosphere, Inc.
Kubermatic How to Migrate 100 Clusters from On-Prem to Google Cloud Without D...Tobias Schneck
Have you ever thought about migrating your Kubernetes clusters to Google Cloud to get your services closer to your customers? Yes? We too! Join us on an interactive journey to discover the main challenges of live migration at scale of etcd's, traffic routing and application workloads from your on-premise platform to GCP. The talk will discuss the current state of the technical concept, known problems and insides of the already proven migration steps for stateless workload.
As part of the journey, we'll see the differences between migrating one or one hundred clusters with productive workloads; What parts can be automated? What steps may need to be manual? Let's see how an automated solution could look like in the future and what steps are missing.
How to Migrate 100 Clusters from On-Prem to Google Cloud Without Downtimeloodse
Have you ever thought about migrating your Kubernetes clusters to Google Cloud to get your services closer to your customers? Yes? Us too! Join us on an interactive journey to discover the main challenges of live migration at scale of etcd’s, traffic routing and application workloads from your on-premise platform to GCP. The talk will discuss the current state of the technical concept, known problems and insides of the already proven migration steps for stateless workloads.
As part of the journey, we'll see
- The differences between migrating one or one hundred clusters with productive workloads
- What parts can be automated?
- What steps may need to be done manually?
No production system is complete without a way to monitor it. In software, we define observability as the ability to understand how our system is performing. This talk dives into capabilities and tools that are recommended for implementing observability when running K8s in production as the main platform today for deploying and maintaining containers with cloud-native solutions.
We start by introducing the concept of observability in the context of distributed systems such as K8s and the difference with monitoring. We continue by reviewing the observability stack in K8s and the main functionalities. Finally, we will review the tools K8s provides for monitoring and logging, and get metrics from applications and infrastructure.
Between the points to be discussed we can highlight:
-Introducing the concept of observability
-Observability stack in K8s
-Tools and apps for implementing Kubernetes observability
-Integrating Prometheus with OpenMetrics
OSDC 2018 | Monitoring Kubernetes at Scale by Monica SarbuNETWAYS
Kubernetes is changing the game in the data centre, but also in the monitoring and troubleshooting landscape. Static tools and vertically scalable TSDBs are no longer fit for the job. Large-scale dynamic infrastructures require scalable dynamic monitoring.
This talk presents how the Elastic Stack collects logs, metrics, and APM traces from the applications running in Kubernetes:
– Collect application logs, metrics and enhance them with Kubernetes metadata
– Collect application metrics from Prometheus endpoints
– Collect Kubernetes metrics
– Collect application performance traces (APM)
– Autodiscover new pods and monitor them based on their type
– Control the monitoring via Kubernetes annotations
– Use Kibana as a single looking glass to visualize the collected data
CloudZone's Meetup at Google offices, 20.08.2018
Covering Google Cloud Platform Kubernetes Engine in Depth, including networking, compute, storage, monitoring & logging
Monitoring Kubernetes with Elasticsearch Services - Ted Jung, Consulting Arch...Amazon Web Services Korea
스폰서 발표 세션 | Monitoring Kubernetes with Elasticsearch Services
Ted Jung, Consulting Architect, Elastic
How you can use Elastic Stack products e.g. Elasticsearch, Beats etc to monitor containers running in Kubernetes.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
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Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Cosmetic shop management system project report.pdfKamal Acharya
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Data file handling has been effectively used in the program.
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2. About Me
● Co-Founder - FreshTracks.io - A CA Accelerator Incubation
● bob@freshtracks.io
● @bob_cotton
3. Agenda
● Short overview of Prometheus
● Sources of metrics
○ Node
○ kubelet and containers
○ Kubernetes API
○ etcd
○ Derived metrics (kube-state-metrics)
● The new K8s metrics server
● Horizontal pod auto-scaler
● K8s cluster hierarchies and metrics aggregation
5. Prometheus - A Short Overview
● Based on monitoring patterns at Google
● Open sourced by SoundCloud in early 2015
● Second project admitted to the Cloud Native Computing Foundation after Kubernetes
● Adoption surge is tracking Kubernetes
○ 63% of teams using Kubernetes use Prometheus
● Metric discovery and collection
● High-performance timeseries database
● Alert definition and generation
6. Prometheus - Unique Features
● Supports “Metrics 2.0” - i.e. metrics contain any number of label/values
● “Pull based” metrics collection
● Service discovery mechanism
● Rich set of “exporters”
● Extremely high-performance TSDB
● PromQL
● Alert Manager
● Easily installable from Helm
● Grafana typically used for visualization
9. Prometheus Exposition Format and Exporters
● The Prometheus exposition format - Text over http
○ Efforts to make it a standard
● Supported by Sysdig and the TICK collector
● Close to 100 exporters for various technologies
● The jmx_exporter can cover any Java/JMX application
● https://prometheus.io/docs/instrumenting/exporters/
Demo
● “Official Exporters”
○ node_exporter
○ jmx_exporter
○ snmp_exporter
○ haproxy_exporter
○ cloudwatch_exporter
○ collectd_exporter
○ mysql_exporter
○ memcached_exporter
11. Host Metrics from the node_exporter
● Standard Host Metrics
○ Load Average
○ CPU
○ Memory
○ Disk
○ Network
○ Many others
● ~1000 Unique series in a typical node
Demo
Node
node_exporter
/metrics
12. Container Metrics from cAdvisor
● cAdvisor is embedded into the kubelet, so we
scrape the kubelet to get container metrics
● These are the so-called “core” metrics
● For each container on the node:
○ CPU Usage (user and system) and time throttled
○ Filesystem read/writes/limits
○ Memory usage and limits
○ Network transmit/receive/dropped
Demo
Node
node_exporter
/metrics
kubelet
cAdvisor
13. Kubernetes Metrics from the K8s API Server
● Metrics about the performance of the K8s API Server
○ Performance of controller work queues
○ Request Rates and Latencies
○ Etcd helper cache work queues and cache performance
○ General process status (File Descriptors/Memory/CPU
Seconds)
○ Golang status (GC/Memory/Threads)
Node
node_exporter
kubelet
cAdvisor
Any other Pod
/metrics
API Server
14. Etcd Metrics from etcd
● Etcd is “master of all truth” within a K8s cluster
○ Leader existence and leader change rate
○ Proposals committed/applied/pending/failed
○ Disk write performance
○ Network and gRPC counters
15. K8s Derived Metrics from kube-state-metrics
● Counts and meta-data about many K8s types
○ Counts of many “nouns”
○ Resource Limits
○ Container states
■ ready/restarts/running/terminated/waiting
○ _labels series just carries labels from Pods
● cronjob
● daemonset
● deployment
● horizontalpodautoscaler
● job
● limitrange
● namespace
● node
● persistentvolumeclaim
● pod
● replicaset
● replicationcontroller
● resourcequota
● service
● statefulset
16. Sources of Metrics in Kubernetes
● Node via the node_exporter
● Container metrics via the kubelet and cAdvisor
● Kubernetes API server
● etcd
● Derived metrics via kube-state-metrics
18. The New “Metrics Server”
● Replaces Heapster
● Standard (versioned and auth) API aggregated into the K8s API Server
● In “beta” in K8s 1.8
● Used by the scheduler and (eventually) the Horizontal Pod Autoscaler
● A stripped-down version of Heapster
● Reports on “core” metrics (CPU/Memory/Network) gathered from cAdvisor
● For internal to K8s use only.
● Pluggable for custom metrics
19.
20. Feeding the Horizontal Pod Autoscaler
● Before the metrics server the HPA utilized Heapster for it’s Core metrics
○ This will be the metrics-server going forward
● API Adapter will bridge to third party monitoring system
○ e.g. Prometheus
22. Core Metrics Aggregation
● K8s clusters form a hierarchy
● We can aggregate the “core” metrics to any level
● This allows for some interesting monitoring
opportunities
○ Using P8s “recording rules” aggregate the core metrics
at every level
○ Insights into all levels of your Kubernetes cluster
● This also applies to any custom application metric
Demo
Namespace
Deployment
Pod
Container