Mesos vs Kubernetes

Pengaturcaraan

1. Gambaran keseluruhan

Dalam tutorial ini, kita akan memahami keperluan asas untuk sistem orkestrasi kontena.

Kami akan menilai ciri sistem yang diinginkan. Dari itu, kami akan cuba membandingkan dua sistem orkestrasi kontena yang paling popular yang digunakan hari ini, Apache Mesos dan Kubernetes.

2. Orkestrasi Kontena

Sebelum kita mula membandingkan Mesos dan Kubernetes, mari luangkan masa untuk memahami apa bekas itu dan mengapa kita memerlukan orkestrasi kontena.

2.1. Bekas

A bekas ialah unit standard perisian yang kod pakej dan semua kebergantungan yang diperlukan .

Oleh itu, ia menyediakan kebebasan platform dan kesederhanaan operasi. Docker adalah salah satu platform kontena yang paling popular digunakan.

Docker memanfaatkan ciri kernel Linux seperti Kumpulan Kumpulan dan ruang nama untuk memberikan pengasingan proses yang berbeza. Oleh itu, beberapa bekas boleh berjalan secara bebas dan selamat.

Agak remeh untuk membuat gambar docker, yang kita perlukan hanyalah Dockerfile:

FROM openjdk:8-jdk-alpine VOLUME /tmp COPY target/hello-world-0.0.1-SNAPSHOT.jar app.jar ENTRYPOINT ["java","-jar","/app.jar"] EXPOSE 9001

Oleh itu, beberapa baris ini cukup baik untuk membuat gambar Docker dari aplikasi Spring Boot menggunakan Docker CLI:

docker build -t hello_world .

2.2. Orkestrasi Kontena

Oleh itu, kami telah melihat bagaimana kontena dapat membuat penyebaran aplikasi dipercayai dan berulang. Tetapi mengapa kita memerlukan orkestrasi kontena?

Sekarang, walaupun kami mempunyai beberapa kontena untuk diurus, kami baik-baik saja dengan Docker CLI. Kita juga dapat mengautomasikan beberapa tugas mudah. Tetapi apa yang berlaku semasa kita menguruskan beratus-ratus kontena?

Sebagai contoh, fikirkan seni bina dengan beberapa perkhidmatan mikro, semuanya dengan keperluan skalabiliti dan ketersediaan yang berbeza.

Akibatnya, keadaan dapat dengan cepat terkawal, dan di situlah keuntungan dari sistem orkestrasi kontena. A sistem bekas mengarang musik merawat kelompok mesin dengan aplikasi multi-bekas sebagai entiti penempatan tunggal . Ini menyediakan automasi dari penyebaran awal, penjadualan, kemas kini ke ciri lain seperti pemantauan, penskalaan, dan failover.

3. Gambaran Ringkas Mesos

Apache Mesos adalah pengurus kluster sumber terbuka yang dikembangkan pada asalnya di UC Berkeley . Ini menyediakan aplikasi dengan API untuk pengurusan sumber dan penjadualan di seluruh kluster. Mesos memberi kita fleksibiliti untuk menjalankan beban kerja berkontena dan bukan kontena dengan cara diedarkan.

3.1. Senibina

Seni bina Mesos terdiri daripada Mesos Master, Mesos Agent, dan Kerangka Aplikasi:

Mari kita fahami komponen seni bina di sini:

  • Kerangka kerja : Ini adalah aplikasi sebenar yang memerlukan pelaksanaan tugas atau beban kerja yang diedarkan . Contoh biasa ialah Hadoop atau Badai. Kerangka kerja di Mesos terdiri daripada dua komponen utama:
    • Penjadual : Ini bertanggungjawab untuk mendaftar dengan Master Node sehingga tuan dapat mula menawarkan sumber
    • Pelaksana : Ini adalah proses yang dilancarkan pada nod ejen untuk menjalankan tugas kerangka kerja
  • Ejen Mesos : Ini bertanggungjawab untuk menjalankan tugas . Setiap ejen menerbitkan sumber yang ada seperti CPU dan memori kepada master. Setelah menerima tugas dari tuan, mereka memperuntukkan sumber yang diperlukan kepada pelaksana kerangka.
  • Mesos Master : Ini bertanggungjawab untuk menjadualkan tugas yang diterima dari Kerangka Kerja pada salah satu nod ejen yang ada. Tuan membuat tawaran sumber kepada Kerangka Kerja. Penjadual Framework dapat memilih untuk menjalankan tugas pada sumber yang ada.

3.2. Maraton

Seperti yang baru kita lihat, Mesos cukup fleksibel dan memungkinkan kerangka kerja menjadwalkan dan melaksanakan tugas melalui API yang ditentukan dengan baik. Walau bagaimanapun, tidak mudah untuk melaksanakan primitif ini secara langsung, terutama ketika kita ingin menjadwalkan aplikasi khusus. Contohnya, aplikasi orkestrasi yang dikemas sebagai bekas.

Di sinilah kerangka kerja seperti Marathon dapat membantu kita. Marathon adalah rangka orkestrasi kontena yang berjalan di Mesos . Dalam hal ini, Marathon bertindak sebagai kerangka untuk kelompok Mesos. Marathon memberikan beberapa faedah yang biasanya kami harapkan dari platform orkestrasi seperti penemuan perkhidmatan, pengimbangan beban, metrik, dan API pengurusan kontena.

Marathon menganggap perkhidmatan yang sudah lama berjalan sebagai aplikasi dan contoh aplikasi sebagai tugas. Senario khas boleh mempunyai banyak aplikasi dengan kebergantungan membentuk apa yang disebut Kumpulan Aplikasi

3.3. Contohnya

Oleh itu, mari kita lihat bagaimana kita dapat menggunakan Marathon untuk menyebarkan gambar Docker ringkas yang kita buat sebelumnya. Perhatikan bahawa memasang kluster Mesos tidak banyak terlibat dan oleh itu kita dapat menggunakan penyelesaian yang lebih mudah seperti Mesos Mini. Mesos Mini membolehkan kita memunculkan kelompok Mesos tempatan di persekitaran Docker. Ia merangkumi Mesos Master, Mesos Agen tunggal, dan Marathon.

Setelah Mesos bergabung dengan Marathon dan berjalan, kami dapat menggunakan wadah kami sebagai perkhidmatan aplikasi yang sudah lama berjalan. Yang kita perlukan definisi aplikasi JSON kecil:

#hello-marathon.json { "id": "marathon-demo-application", "cpus": 1, "mem": 128, "disk": 0, "instances": 1, "container": { "type": "DOCKER", "docker": { "image": "hello_world:latest", "portMappings": [ { "containerPort": 9001, "hostPort": 0 } ] } }, "networks": [ { "mode": "host" } ] }

Mari kita fahami apa sebenarnya yang berlaku di sini:

  • Kami telah memberikan id untuk permohonan kami
  • Kemudian, kami menentukan keperluan sumber untuk aplikasi kami
  • Kami juga menentukan berapa banyak contoh yang ingin kami jalankan
  • Kemudian, kami telah memberikan butiran bekas untuk melancarkan aplikasi dari
  • Finally, we've defined the network mode for us to be able to access the application

We can launch this application using the REST APIs provided by Marathon:

curl -X POST \ //localhost:8080/v2/apps \ -d @hello-marathon.json \ -H "Content-type: application/json"

4. Brief Overview of Kubernetes

Kubernetes is an open-source container orchestration system initially developed by Google. It's now part of Cloud Native Computing Foundation (CNCF). It provides a platform for automating deployment, scaling, and operations of application container across a cluster of hosts.

4.1. Architecture

Kubernetes architecture consists of a Kubernetes Master and Kubernetes Nodes:

Let's go through the major parts of this high-level architecture:

  • Kubernetes Master: The master is responsible for maintaining the desired state of the cluster. It manages all nodes in the cluster. As we can see, the master is a collection of three processes:
    • kube-apiserver: This is the service that manages the entire cluster, including processing REST operations, validating and updating Kubernetes objects, performing authentication and authorization
    • kube-controller-manager: This is the daemon that embeds the core control loop shipped with Kubernetes, making the necessary changes to match the current state to the desired state of the cluster
    • kube-scheduler: This service watches for unscheduled pods and binds them to nodes depending upon requested resources and other constraints
  • Kubernetes Nodes: The nodes in a Kubernetes cluster are the machines that run our containers. Each node contains the necessary services to run the containers:
    • kubelet: This is the primary node agent which ensures that the containers described in PodSpecs provided by kube-apiserver are running and healthy
    • kube-proxy: This is the network proxy running on each node and performs simple TCP, UDP, SCTP stream forwarding or round-robin forwarding across a set of backends
    • container runtime: This is the runtime where container inside the pods are run, there are several possible container runtimes for Kubernetes including the most widely used, Docker runtime

4.2. Kubernetes Objects

In the last section, we saw several Kubernetes objects which are persistent entities in the Kubernetes system. They reflect the state of the cluster at any point in time.

Let's discuss some of the commonly used Kubernetes objects:

  • Pods: Pod is a basic unit of execution in Kubernetes and can consist of one or more containers, the containers inside a Pod are deployed on the same host
  • Deployment: Deployment is the recommended way to deploy pods in Kubernetes, it provides features like continuously reconciling the current state of pods with the desired state
  • Services: Services in Kubernetes provide an abstract way to expose a group of pods, where the grouping is based on selectors targetting pod labels

There are several other Kubernetes objects which serve the purpose of running containers in a distributed manner effectively.

4.3. Example

So, now we can try to launch our Docker container into the Kubernetes cluster. Kubernetes provides Minikube, a tool that runs single-node Kubernetes cluster on a Virtual Machine. We'd also need kubectl, the Kubernetes Command Line Interface to work with the Kubernetes cluster.

After we've kubectl and Minikube installed, we can deploy our container on the single-node Kubernetes cluster within Minikube. We need to define the basic Kubernetes objects in a YAML file:

# hello-kubernetes.yaml apiVersion: apps/v1 kind: Deployment metadata: name: hello-world spec: replicas: 1 template: metadata: labels: app: hello-world spec: containers: - name: hello-world image: hello-world:latest ports: - containerPort: 9001 --- apiVersion: v1 kind: Service metadata: name: hello-world-service spec: selector: app: hello-world type: LoadBalancer ports: - port: 9001 targetPort: 9001

A detailed analysis of this definition file is not possible here, but let's go through the highlights:

  • We have defined a Deployment with labels in the selector
  • We define the number of replicas we need for this deployment
  • Also, we've provided the container image details as a template for the deployment
  • We've also defined a Service with appropriate selector
  • We've defined the nature of the service as LoadBalancer

Finally, we can deploy the container and create all defined Kubernetes objects through kubectl:

kubectl apply -f yaml/hello-kubernetes.yaml

5. Mesos vs. Kubernetes

Now, we've gone through enough context and also performed basic deployment on both Marathon and Kubernetes. We can attempt to understand where do they stand compared to each other.

Just a caveat though, it's not entirely fair to compare Kubernetes with Mesos directly. Most of the container orchestration features that we seek are provided by one of the Mesos frameworks like Marathon. Hence, to keep things in the right perspective, we'll attempt to compare Kubernetes with Marathon and not directly Mesos.

We'll compare these orchestration systems based on some of the desired properties of such a system.

5.1. Supported Workloads

Mesos is designed to handle diverse types of workloads which can be containerized or even non-containerised. It depends upon the framework we use. As we've seen, it's quite easy to support containerized workloads in Mesos using a framework like Marathon.

Kubernetes, on the other hand, works exclusively with the containerized workload. Most widely, we use it with Docker containers, but it has support for other container runtimes like Rkt. In the future, Kubernetes may support more types of workloads.

5.2. Support for Scalability

Marathon supports scaling through the application definition or the user interface. Autoscaling is also supported in Marathon. We can also scale Application Groups which automatically scales all the dependencies.

As we saw earlier, Pod is the fundamental unit of execution in Kubernetes. Pods can be scaled when managed by Deployment, this is the reason pods are invariably defined as a deployment. The scaling can be manual or automated.

5.3. Handling High Availability

Application instances in Marathon are distributed across Mesos agents providing high availability. Typically a Mesos cluster consists of multiple agents. Additionally, ZooKeeper provides high availability to the Mesos cluster through quorum and leader election.

Similarly, pods in Kubernetes are replicated across multiple nodes providing high availability. Typically a Kubernetes cluster consists of multiple worker nodes. Moreover, the cluster can also have multiple masters. Hence, Kubernetes cluster is capable of providing high availability to containers.

5.4. Service Discovery and Load Balancing

Mesos-DNS can provide service discovery and a basic load balancing for applications. Mesos-DNS generates an SRV record for each Mesos task and translates them to the IP address and port of the machine running the task. For Marathon applications, we can also use Marathon-lb to provide port-based discovery using HAProxy.

Deployment in Kubernetes creates and destroys pods dynamically. Hence, we generally expose pods in Kubernetes through Service, which provides service discovery. Service in Kubernetes acts as a dispatcher to the pods and hence provide load balancing as well.

5.5 Performing Upgrades and Rollback

Changes to application definitions in Marathon is handled as deployment. Deployment supports start, stop, upgrade, or scale of applications. Marathon also supports rolling starts to deploy newer versions of the applications. However, rolling back is as straight forward and typically requires the deployment of an updated definition.

Deployment in Kubernetes supports upgrade as well as rollback. We can provide the strategy for Deployment to be taken while relacing old pods with new ones. Typical strategies are Recreate or Rolling Update. Deployment's rollout history is maintained by default in Kubernetes, which makes it trivial to roll back to a previous revision.

5.6. Logging and Monitoring

Mesos has a diagnostic utility which scans all the cluster components and makes available data related to health and other metrics. The data can be queried and aggregated through available APIs. Much of this data we can collect using an external tool like Prometheus.

Kubernetes publish detailed information related to different objects as resource metrics or full metrics pipelines. Typical practice is to deploy an external tool like ELK or Prometheus+Grafana on the Kubernetes cluster. Such tools can ingest cluster metrics and present them in a much user-friendly way.

5.7. Storage

Mesos has persistent local volumes for stateful applications. We can only create persistent volumes from the reserved resources. It can also support external storage with some limitations. Mesos has experimental support for Container Storage Interface (CSI), a common set of APIs between storage vendors and container orchestration platform.

Kubernetes offers multiple types of persistent volume for stateful containers. This includes storage like iSCSI, NFS. Moreover, it supports external storage like AWS, GCP as well. The Volume object in Kubernetes supports this concept and comes in a variety of types, including CSI.

5.8. Networking

Container runtime in Mesos offers two types of networking support, IP-per-container, and network-port-mapping. Mesos defines a common interface to specify and retrieve networking information for a container. Marathon applications can define a network in host mode or bridge mode.

Networking in Kubernetes assigns a unique IP to each pod. This negates the need to map container ports to the host port. It further defines how these pods can talk to each other across nodes. This is implemented in Kubernetes by Network Plugins like Cilium, Contiv.

6. When to Use What?

Finally, in comparison, we usually expect a clear verdict! However, it's not entirely fair to declare one technology better than another, regardless. As we've seen, both Kubernetes and Mesos are powerful systems and offers quite competing features.

Performance, however, is quite a crucial aspect. A Kubernetes cluster can scale to 5000-nodes while Marathon on Mesos cluster is known to support up to 10,000 agents. In most practical cases, we'll not be dealing with such large clusters.

Finally, it boils down to the flexibility and types of workloads that we've. If we're starting afresh and we only plan to use containerized workloads, Kubernetes can offer a quicker solution. However, if we've existing workloads, which are a mix of containers and non-containers, Mesos with Marathon can be a better choice.

7. Other Alternatives

Kubernetes and Apache Mesos are quite powerful, but they are not the only systems in this space. There are quite several promising alternatives available to us. While we'll not go into their details, let's quickly list a few of them:

  • Docker Swarm: Docker Swarm is an open-source clustering and scheduling tool for Docker containers. It comes with a command-line utility to manage a cluster of Docker hosts. It's restricted to Docker containers, unlike Kubernetes and Mesos.
  • Nomad: Nomad is a flexible workload orchestrator from HashiCorp to manage any containerized or non-containerised application. Nomad enables declarative infrastructure-as-code for deploying applications like Docker container.
  • OpenShift: OpenShift is a container platform from Red Hat, orchestrated and managed by Kubernetes underneath. OpenShift offers many features on top of what Kubernetes provide like integrated image registry, a source-to-image build, a native networking solution, to name a few.

8. Conclusion

Ringkasnya, dalam tutorial ini, kita membincangkan kontena dan sistem orkestrasi kontena. Kami secara ringkas melalui dua sistem orkestrasi kontena yang paling banyak digunakan, Kubernetes dan Apache Mesos. Kami juga membandingkan sistem ini berdasarkan beberapa ciri. Akhirnya, kami melihat beberapa alternatif lain di ruang ini.

Sebelum menutup, kita harus memahami bahawa tujuan perbandingan tersebut adalah untuk memberikan data dan fakta. Ini sama sekali tidak menyatakan yang lebih baik daripada yang lain, dan itu biasanya bergantung pada kasus penggunaan. Oleh itu, kita mesti menerapkan konteks masalah kita dalam menentukan penyelesaian terbaik untuk kita.