http://jpetazzo.github.io/2013/10/08/docker-containers-metrics/ .debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/prometheus.md)] --- class: extra-details ## Application metrics - Arbitrary metrics related to your application and business - System performance: request latency, error rate... - Volume information: number of rows in database, message queue size... - Business data: inventory, items sold, revenue... .debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/prometheus.md)] --- class: extra-details ## Detecting scrape targets - Prometheus can leverage Kubernetes service discovery (with proper configuration) - Services or pods can be annotated with: - `prometheus.io/scrape: true` to enable scraping - `prometheus.io/port: 9090` to indicate the port number - `prometheus.io/path: /metrics` to indicate the URI (`/metrics` by default) - Prometheus will detect and scrape these (without needing a restart or reload) .debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/prometheus.md)] --- ## Querying labels - What if we want to get metrics for containers belonging to a pod tagged `worker`? - The cAdvisor exporter does not give us Kubernetes labels - Kubernetes labels are exposed through another exporter - We can see Kubernetes labels through metrics `kube_pod_labels` (each container appears as a time series with constant value of `1`) - Prometheus *kind of* supports "joins" between time series - But only if the names of the tags match exactly .debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/prometheus.md)] --- ## Unfortunately ... - The cAdvisor exporter uses tag `pod_name` for the name of a pod - The Kubernetes service endpoints exporter uses tag `pod` instead - See [this blog post](https://www.robustperception.io/exposing-the-software-version-to-prometheus) or [this other one](https://www.weave.works/blog/aggregating-pod-resource-cpu-memory-usage-arbitrary-labels-prometheus/) to see how to perform "joins" - Alas, Prometheus cannot "join" time series with different labels (see [Prometheus issue #2204](https://github.com/prometheus/prometheus/issues/2204) for the rationale) - There is a workaround involving relabeling, but it's "not cheap" - see [this comment](https://github.com/prometheus/prometheus/issues/2204#issuecomment-261515520) for an overview - or [this blog post](https://5pi.de/2017/11/09/use-prometheus-vector-matching-to-get-kubernetes-utilization-across-any-pod-label/) for a complete description of the process .debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/prometheus.md)] --- ## In practice - Grafana is a beautiful (and useful) frontend to display all kinds of graphs - Not everyone needs to know Prometheus, PromQL, Grafana, etc. - But in a team, it is valuable to have at least one person who know them - That person can set up queries and dashboards for the rest of the team - It's a little bit like knowing how to optimize SQL queries, Dockerfiles... Don't panic if you don't know these tools! ...But make sure at least one person in your team is on it 💯 ??? :EN:- Collecting metrics with Prometheus :FR:- Collecter des métriques avec Prometheus .debug[[k8s/prometheus.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/prometheus.md)] --- class: pic .interstitial[![Image separating from the next module](https://gallant-turing-d0d520.netlify.com/containers/train-of-containers-2.jpg)] --- name: toc-resource-limits class: title Resource Limits .nav[ [Previous section](#toc-collecting-metrics-with-prometheus) | [Back to table of contents](#toc-module-11) | [Next section](#toc-defining-min-max-and-default-resources) ] .debug[(automatically generated title slide)] --- # Resource Limits - We can attach resource indications to our pods (or rather: to the *containers* in our pods) - We can specify *limits* and/or *requests* - We can specify quantities of CPU and/or memory .debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## CPU vs memory - CPU is a *compressible resource* (it can be preempted immediately without adverse effect) - Memory is an *incompressible resource* (it needs to be swapped out to be reclaimed; and this is costly) - As a result, exceeding limits will have different consequences for CPU and memory .debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Exceeding CPU limits - CPU can be reclaimed instantaneously (in fact, it is preempted hundreds of times per second, at each context switch) - If a container uses too much CPU, it can be throttled (it will be scheduled less often) - The processes in that container will run slower (or rather: they will not run faster) .debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Exceeding memory limits - Memory needs to be swapped out before being reclaimed - "Swapping" means writing memory pages to disk, which is very slow - On a classic system, a process that swaps can get 1000x slower (because disk I/O is 1000x slower than memory I/O) - Exceeding the memory limit (even by a small amount) can reduce performance *a lot* - Kubernetes *does not support swap* (more on that later!) - Exceeding the memory limit will cause the container to be killed .debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Limits vs requests - Limits are "hard limits" (they can't be exceeded) - a container exceeding its memory limit is killed - a container exceeding its CPU limit is throttled - Requests are used for scheduling purposes - a container using *less* than what it requested will never be killed or throttled - the scheduler uses the requested sizes to determine placement - the resources requested by all pods on a node will never exceed the node size .debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Pod quality of service Each pod is assigned a QoS class (visible in `status.qosClass`). - If limits = requests: - as long as the container uses less than the limit, it won't be affected - if all containers in a pod have *(limits=requests)*, QoS is considered "Guaranteed" - If requests < limits: - as long as the container uses less than the request, it won't be affected - otherwise, it might be killed/evicted if the node gets overloaded - if at least one container has *(requests<limits)*, QoS is considered "Burstable" - If a pod doesn't have any request nor limit, QoS is considered "BestEffort" .debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Quality of service impact - When a node is overloaded, BestEffort pods are killed first - Then, Burstable pods that exceed their requests - Burstable and Guaranteed pods below their requests are never killed (except if their node fails) - If we only use Guaranteed pods, no pod should ever be killed (as long as they stay within their limits) (Pod QoS is also explained in [this page](https://kubernetes.io/docs/tasks/configure-pod-container/quality-service-pod/) of the Kubernetes documentation and in [this blog post](https://medium.com/google-cloud/quality-of-service-class-qos-in-kubernetes-bb76a89eb2c6).) .debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Where is my swap? - The semantics of memory and swap limits on Linux cgroups are complex - In particular, it's not possible to disable swap for a cgroup (the closest option is to [reduce "swappiness"](https://unix.stackexchange.com/questions/77939/turning-off-swapping-for-only-one-process-with-cgroups)) - The architects of Kubernetes wanted to ensure that Guaranteed pods never swap - The only solution was to disable swap entirely .debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Alternative point of view - Swap enables paging¹ of anonymous² memory - Even when swap is disabled, Linux will still page memory for: - executables, libraries - mapped files - Disabling swap *will reduce performance and available resources* - For a good time, read [kubernetes/kubernetes#53533](https://github.com/kubernetes/kubernetes/issues/53533) - Also read this [excellent blog post about swap](https://jvns.ca/blog/2017/02/17/mystery-swap/) ¹Paging: reading/writing memory pages from/to disk to reclaim physical memory ²Anonymous memory: memory that is not backed by files or blocks .debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Enabling swap anyway - If you don't care that pods are swapping, you can enable swap - You will need to add the flag `--fail-swap-on=false` to kubelet (otherwise, it won't start!) .debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Specifying resources - Resource requests are expressed at the *container* level - CPU is expressed in "virtual CPUs" (corresponding to the virtual CPUs offered by some cloud providers) - CPU can be expressed with a decimal value, or even a "milli" suffix (so 100m = 0.1) - Memory is expressed in bytes - Memory can be expressed with k, M, G, T, ki, Mi, Gi, Ti suffixes (corresponding to 10^3, 10^6, 10^9, 10^12, 2^10, 2^20, 2^30, 2^40) .debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Specifying resources in practice This is what the spec of a Pod with resources will look like: ```yaml containers: - name: httpenv image: jpetazzo/httpenv resources: limits: memory: "100Mi" cpu: "100m" requests: memory: "100Mi" cpu: "10m" ``` This set of resources makes sure that this service won't be killed (as long as it stays below 100 MB of RAM), but allows its CPU usage to be throttled if necessary. .debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Default values - If we specify a limit without a request: the request is set to the limit - If we specify a request without a limit: there will be no limit (which means that the limit will be the size of the node) - If we don't specify anything: the request is zero and the limit is the size of the node *Unless there are default values defined for our namespace!* .debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## We need default resource values - If we do not set resource values at all: - the limit is "the size of the node" - the request is zero - This is generally *not* what we want - a container without a limit can use up all the resources of a node - if the request is zero, the scheduler can't make a smart placement decision - To address this, we can set default values for resources - This is done with a LimitRange object .debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/resource-limits.md)] --- class: pic .interstitial[![Image separating from the next module](https://gallant-turing-d0d520.netlify.com/containers/two-containers-on-a-truck.jpg)] --- name: toc-defining-min-max-and-default-resources class: title Defining min, max, and default resources .nav[ [Previous section](#toc-resource-limits) | [Back to table of contents](#toc-module-11) | [Next section](#toc-namespace-quotas) ] .debug[(automatically generated title slide)] --- # Defining min, max, and default resources - We can create LimitRange objects to indicate any combination of: - min and/or max resources allowed per pod - default resource *limits* - default resource *requests* - maximal burst ratio (*limit/request*) - LimitRange objects are namespaced - They apply to their namespace only .debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## LimitRange example ```yaml apiVersion: v1 kind: LimitRange metadata: name: my-very-detailed-limitrange spec: limits: - type: Container min: cpu: "100m" max: cpu: "2000m" memory: "1Gi" default: cpu: "500m" memory: "250Mi" defaultRequest: cpu: "500m" ``` .debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Example explanation The YAML on the previous slide shows an example LimitRange object specifying very detailed limits on CPU usage, and providing defaults on RAM usage. Note the `type: Container` line: in the future, it might also be possible to specify limits per Pod, but it's not [officially documented yet](https://github.com/kubernetes/website/issues/9585). .debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## LimitRange details - LimitRange restrictions are enforced only when a Pod is created (they don't apply retroactively) - They don't prevent creation of e.g. an invalid Deployment or DaemonSet (but the pods will not be created as long as the LimitRange is in effect) - If there are multiple LimitRange restrictions, they all apply together (which means that it's possible to specify conflicting LimitRanges,
preventing any Pod from being created) - If a LimitRange specifies a `max` for a resource but no `default`,
that `max` value becomes the `default` limit too .debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/resource-limits.md)] --- class: pic .interstitial[![Image separating from the next module](https://gallant-turing-d0d520.netlify.com/containers/wall-of-containers.jpeg)] --- name: toc-namespace-quotas class: title Namespace quotas .nav[ [Previous section](#toc-defining-min-max-and-default-resources) | [Back to table of contents](#toc-module-11) | [Next section](#toc-limiting-resources-in-practice) ] .debug[(automatically generated title slide)] --- # Namespace quotas - We can also set quotas per namespace - Quotas apply to the total usage in a namespace (e.g. total CPU limits of all pods in a given namespace) - Quotas can apply to resource limits and/or requests (like the CPU and memory limits that we saw earlier) - Quotas can also apply to other resources: - "extended" resources (like GPUs) - storage size - number of objects (number of pods, services...) .debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Creating a quota for a namespace - Quotas are enforced by creating a ResourceQuota object - ResourceQuota objects are namespaced, and apply to their namespace only - We can have multiple ResourceQuota objects in the same namespace - The most restrictive values are used .debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Limiting total CPU/memory usage - The following YAML specifies an upper bound for *limits* and *requests*: ```yaml apiVersion: v1 kind: ResourceQuota metadata: name: a-little-bit-of-compute spec: hard: requests.cpu: "10" requests.memory: 10Gi limits.cpu: "20" limits.memory: 20Gi ``` These quotas will apply to the namespace where the ResourceQuota is created. .debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Limiting number of objects - The following YAML specifies how many objects of specific types can be created: ```yaml apiVersion: v1 kind: ResourceQuota metadata: name: quota-for-objects spec: hard: pods: 100 services: 10 secrets: 10 configmaps: 10 persistentvolumeclaims: 20 services.nodeports: 0 services.loadbalancers: 0 count/roles.rbac.authorization.k8s.io: 10 ``` (The `count/` syntax allows limiting arbitrary objects, including CRDs.) .debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## YAML vs CLI - Quotas can be created with a YAML definition - ...Or with the `kubectl create quota` command - Example: ```bash kubectl create quota my-resource-quota --hard=pods=300,limits.memory=300Gi ``` - With both YAML and CLI form, the values are always under the `hard` section (there is no `soft` quota) .debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Viewing current usage When a ResourceQuota is created, we can see how much of it is used: ``` kubectl describe resourcequota my-resource-quota Name: my-resource-quota Namespace: default Resource Used Hard -------- ---- ---- pods 12 100 services 1 5 services.loadbalancers 0 0 services.nodeports 0 0 ``` .debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Advanced quotas and PriorityClass - Since Kubernetes 1.12, it is possible to create PriorityClass objects - Pods can be assigned a PriorityClass - Quotas can be linked to a PriorityClass - This allows us to reserve resources for pods within a namespace - For more details, check [this documentation page](https://kubernetes.io/docs/concepts/policy/resource-quotas/#resource-quota-per-priorityclass) .debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/resource-limits.md)] --- class: pic .interstitial[![Image separating from the next module](https://gallant-turing-d0d520.netlify.com/containers/Container-Ship-Freighter-Navigation-Elbe-Romance-1782991.jpg)] --- name: toc-limiting-resources-in-practice class: title Limiting resources in practice .nav[ [Previous section](#toc-namespace-quotas) | [Back to table of contents](#toc-module-11) | [Next section](#toc-checking-pod-and-node-resource-usage) ] .debug[(automatically generated title slide)] --- # Limiting resources in practice - We have at least three mechanisms: - requests and limits per Pod - LimitRange per namespace - ResourceQuota per namespace - Let's see a simple recommendation to get started with resource limits .debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Set a LimitRange - In each namespace, create a LimitRange object - Set a small default CPU request and CPU limit (e.g. "100m") - Set a default memory request and limit depending on your most common workload - for Java, Ruby: start with "1G" - for Go, Python, PHP, Node: start with "250M" - Set upper bounds slightly below your expected node size (80-90% of your node size, with at least a 500M memory buffer) .debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Set a ResourceQuota - In each namespace, create a ResourceQuota object - Set generous CPU and memory limits (e.g. half the cluster size if the cluster hosts multiple apps) - Set generous objects limits - these limits should not be here to constrain your users - they should catch a runaway process creating many resources - example: a custom controller creating many pods .debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Observe, refine, iterate - Observe the resource usage of your pods (we will see how in the next chapter) - Adjust individual pod limits - If you see trends: adjust the LimitRange (rather than adjusting every individual set of pod limits) - Observe the resource usage of your namespaces (with `kubectl describe resourcequota ...`) - Rinse and repeat regularly .debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/resource-limits.md)] --- ## Additional resources - [A Practical Guide to Setting Kubernetes Requests and Limits](http://blog.kubecost.com/blog/requests-and-limits/) - explains what requests and limits are - provides guidelines to set requests and limits - gives PromQL expressions to compute good values
(our app needs to be running for a while) - [Kube Resource Report](https://github.com/hjacobs/kube-resource-report/) - generates web reports on resource usage - [static demo](https://hjacobs.github.io/kube-resource-report/sample-report/output/index.html) | [live demo](https://kube-resource-report.demo.j-serv.de/applications.html) ??? :EN:- Setting compute resource limits :EN:- Defining default policies for resource usage :EN:- Managing cluster allocation and quotas :EN:- Resource management in practice :FR:- Allouer et limiter les ressources des conteneurs :FR:- Définir des ressources par défaut :FR:- Gérer les quotas de ressources au niveau du cluster :FR:- Conseils pratiques .debug[[k8s/resource-limits.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/resource-limits.md)] --- class: pic .interstitial[![Image separating from the next module](https://gallant-turing-d0d520.netlify.com/containers/ShippingContainerSFBay.jpg)] --- name: toc-checking-pod-and-node-resource-usage class: title Checking pod and node resource usage .nav[ [Previous section](#toc-limiting-resources-in-practice) | [Back to table of contents](#toc-module-11) | [Next section](#toc-cluster-sizing) ] .debug[(automatically generated title slide)] --- # Checking pod and node resource usage - Since Kubernetes 1.8, metrics are collected by the [resource metrics pipeline](https://kubernetes.io/docs/tasks/debug-application-cluster/resource-metrics-pipeline/) - The resource metrics pipeline is: - optional (Kubernetes can function without it) - necessary for some features (like the Horizontal Pod Autoscaler) - exposed through the Kubernetes API using the [aggregation layer](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/apiserver-aggregation/) - usually implemented by the "metrics server" .debug[[k8s/metrics-server.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/metrics-server.md)] --- ## How to know if the metrics server is running? - The easiest way to know is to run `kubectl top` .exercise[ - Check if the core metrics pipeline is available: ```bash kubectl top nodes ``` ] If it shows our nodes and their CPU and memory load, we're good! .debug[[k8s/metrics-server.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/metrics-server.md)] --- ## Installing metrics server - The metrics server doesn't have any particular requirements (it doesn't need persistence, as it doesn't *store* metrics) - It has its own repository, [kubernetes-incubator/metrics-server](https://github.com/kubernetes-incubator/metrics-server) - The repository comes with [YAML files for deployment](https://github.com/kubernetes-incubator/metrics-server/tree/master/deploy/1.8%2B) - These files may not work on some clusters (e.g. if your node names are not in DNS) - The container.training repository has a [metrics-server.yaml](https://github.com/jpetazzo/container.training/blob/master/k8s/metrics-server.yaml#L90) file to help with that (we can `kubectl apply -f` that file if needed) .debug[[k8s/metrics-server.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/metrics-server.md)] --- ## Showing container resource usage - Once the metrics server is running, we can check container resource usage .exercise[ - Show resource usage across all containers: ```bash kubectl top pods --containers --all-namespaces ``` ] - We can also use selectors (`-l app=...`) .debug[[k8s/metrics-server.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/metrics-server.md)] --- ## Other tools - kube-capacity is a great CLI tool to view resources (https://github.com/robscott/kube-capacity) - It can show resource and limits, and compare them with usage - It can show utilization per node, or per pod - kube-resource-report can generate HTML reports (https://github.com/hjacobs/kube-resource-report) ??? :EN:- The *core metrics pipeline* :FR:- Le *core metrics pipeline* .debug[[k8s/metrics-server.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/metrics-server.md)] --- class: pic .interstitial[![Image separating from the next module](https://gallant-turing-d0d520.netlify.com/containers/aerial-view-of-containers.jpg)] --- name: toc-cluster-sizing class: title Cluster sizing .nav[ [Previous section](#toc-checking-pod-and-node-resource-usage) | [Back to table of contents](#toc-module-11) | [Next section](#toc-the-horizontal-pod-autoscaler) ] .debug[(automatically generated title slide)] --- # Cluster sizing - What happens when the cluster gets full? - How can we scale up the cluster? - Can we do it automatically? - What are other methods to address capacity planning? .debug[[k8s/cluster-sizing.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/cluster-sizing.md)] --- ## When are we out of resources? - kubelet monitors node resources: - memory - node disk usage (typically the root filesystem of the node) - image disk usage (where container images and RW layers are stored) - For each resource, we can provide two thresholds: - a hard threshold (if it's met, it provokes immediate action) - a soft threshold (provokes action only after a grace period) - Resource thresholds and grace periods are configurable (by passing kubelet command-line flags) .debug[[k8s/cluster-sizing.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/cluster-sizing.md)] --- ## What happens then? - If disk usage is too high: - kubelet will try to remove terminated pods - then, it will try to *evict* pods - If memory usage is too high: - it will try to evict pods - The node is marked as "under pressure" - This temporarily prevents new pods from being scheduled on the node .debug[[k8s/cluster-sizing.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/cluster-sizing.md)] --- ## Which pods get evicted? - kubelet looks at the pods' QoS and PriorityClass - First, pods with BestEffort QoS are considered - Then, pods with Burstable QoS exceeding their *requests* (but only if the exceeding resource is the one that is low on the node) - Finally, pods with Guaranteed QoS, and Burstable pods within their requests - Within each group, pods are sorted by PriorityClass - If there are pods with the same PriorityClass, they are sorted by usage excess (i.e. the pods whose usage exceeds their requests the most are evicted first) .debug[[k8s/cluster-sizing.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/cluster-sizing.md)] --- class: extra-details ## Eviction of Guaranteed pods - *Normally*, pods with Guaranteed QoS should not be evicted - A chunk of resources is reserved for node processes (like kubelet) - It is expected that these processes won't use more than this reservation - If they do use more resources anyway, all bets are off! - If this happens, kubelet must evict Guaranteed pods to preserve node stability (or Burstable pods that are still within their requested usage) .debug[[k8s/cluster-sizing.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/cluster-sizing.md)] --- ## What happens to evicted pods? - The pod is terminated - It is marked as `Failed` at the API level - If the pod was created by a controller, the controller will recreate it - The pod will be recreated on another node, *if there are resources available!* - For more details about the eviction process, see: - [this documentation page](https://kubernetes.io/docs/tasks/administer-cluster/out-of-resource/) about resource pressure and pod eviction, - [this other documentation page](https://kubernetes.io/docs/concepts/configuration/pod-priority-preemption/) about pod priority and preemption. .debug[[k8s/cluster-sizing.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/cluster-sizing.md)] --- ## What if there are no resources available? - Sometimes, a pod cannot be scheduled anywhere: - all the nodes are under pressure, - or the pod requests more resources than are available - The pod then remains in `Pending` state until the situation improves .debug[[k8s/cluster-sizing.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/cluster-sizing.md)] --- ## Cluster scaling - One way to improve the situation is to add new nodes - This can be done automatically with the [Cluster Autoscaler](https://github.com/kubernetes/autoscaler/tree/master/cluster-autoscaler) - The autoscaler will automatically scale up: - if there are pods that failed to be scheduled - The autoscaler will automatically scale down: - if nodes have a low utilization for an extended period of time .debug[[k8s/cluster-sizing.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/cluster-sizing.md)] --- ## Restrictions, gotchas ... - The Cluster Autoscaler only supports a few cloud infrastructures (see [here](https://github.com/kubernetes/autoscaler/tree/master/cluster-autoscaler/cloudprovider) for a list) - The Cluster Autoscaler cannot scale down nodes that have pods using: - local storage - affinity/anti-affinity rules preventing them from being rescheduled - a restrictive PodDisruptionBudget .debug[[k8s/cluster-sizing.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/cluster-sizing.md)] --- ## Other way to do capacity planning - "Running Kubernetes without nodes" - Systems like [Virtual Kubelet](https://virtual-kubelet.io/) or [Kiyot](https://static.elotl.co/docs/latest/kiyot/kiyot.html) can run pods using on-demand resources - Virtual Kubelet can leverage e.g. ACI or Fargate to run pods - Kiyot runs pods in ad-hoc EC2 instances (1 instance per pod) - Economic advantage (no wasted capacity) - Security advantage (stronger isolation between pods) Check [this blog post](http://jpetazzo.github.io/2019/02/13/running-kubernetes-without-nodes-with-kiyot/) for more details. ??? :EN:- What happens when the cluster is at, or over, capacity :EN:- Cluster sizing and scaling :FR:- Ce qui se passe quand il n'y a plus assez de ressources :FR:- Dimensionner et redimensionner ses clusters .debug[[k8s/cluster-sizing.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/cluster-sizing.md)] --- class: pic .interstitial[![Image separating from the next module](https://gallant-turing-d0d520.netlify.com/containers/blue-containers.jpg)] --- name: toc-the-horizontal-pod-autoscaler class: title The Horizontal Pod Autoscaler .nav[ [Previous section](#toc-cluster-sizing) | [Back to table of contents](#toc-module-11) | [Next section](#toc-extending-the-kubernetes-api) ] .debug[(automatically generated title slide)] --- # The Horizontal Pod Autoscaler - What is the Horizontal Pod Autoscaler, or HPA? - It is a controller that can perform *horizontal* scaling automatically - Horizontal scaling = changing the number of replicas (adding/removing pods) - Vertical scaling = changing the size of individual replicas (increasing/reducing CPU and RAM per pod) - Cluster scaling = changing the size of the cluster (adding/removing nodes) .debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/horizontal-pod-autoscaler.md)] --- ## Principle of operation - Each HPA resource (or "policy") specifies: - which object to monitor and scale (e.g. a Deployment, ReplicaSet...) - min/max scaling ranges (the max is a safety limit!) - a target resource usage (e.g. the default is CPU=80%) - The HPA continuously monitors the CPU usage for the related object - It computes how many pods should be running: `TargetNumOfPods = ceil(sum(CurrentPodsCPUUtilization) / Target)` - It scales the related object up/down to this target number of pods .debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/horizontal-pod-autoscaler.md)] --- ## Pre-requirements - The metrics server needs to be running (i.e. we need to be able to see pod metrics with `kubectl top pods`) - The pods that we want to autoscale need to have resource requests (because the target CPU% is not absolute, but relative to the request) - The latter actually makes a lot of sense: - if a Pod doesn't have a CPU request, it might be using 10% of CPU... - ...but only because there is no CPU time available! - this makes sure that we won't add pods to nodes that are already resource-starved .debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/horizontal-pod-autoscaler.md)] --- ## Testing the HPA - We will start a CPU-intensive web service - We will send some traffic to that service - We will create an HPA policy - The HPA will automatically scale up the service for us .debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/horizontal-pod-autoscaler.md)] --- ## A CPU-intensive web service - Let's use `jpetazzo/busyhttp` (it is a web server that will use 1s of CPU for each HTTP request) .exercise[ - Deploy the web server: ```bash kubectl create deployment busyhttp --image=jpetazzo/busyhttp ``` - Expose it with a ClusterIP service: ```bash kubectl expose deployment busyhttp --port=80 ``` - Get the ClusterIP allocated to the service: ```bash kubectl get svc busyhttp ``` ] .debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/horizontal-pod-autoscaler.md)] --- ## Monitor what's going on - Let's start a bunch of commands to watch what is happening .exercise[ - Monitor pod CPU usage: ```bash watch kubectl top pods -l app=busyhttp ``` - Monitor service latency: ```bash httping http://`$CLUSTERIP`/ ``` - Monitor cluster events: ```bash kubectl get events -w ``` ] .debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/horizontal-pod-autoscaler.md)] --- ## Send traffic to the service - We will use `ab` (Apache Bench) to send traffic .exercise[ - Send a lot of requests to the service, with a concurrency level of 3: ```bash ab -c 3 -n 100000 http://`$CLUSTERIP`/ ``` ] The latency (reported by `httping`) should increase above 3s. The CPU utilization should increase to 100%. (The server is single-threaded and won't go above 100%.) .debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/horizontal-pod-autoscaler.md)] --- ## Create an HPA policy - There is a helper command to do that for us: `kubectl autoscale` .exercise[ - Create the HPA policy for the `busyhttp` deployment: ```bash kubectl autoscale deployment busyhttp --max=10 ``` ] By default, it will assume a target of 80% CPU usage. This can also be set with `--cpu-percent=`. -- *The autoscaler doesn't seem to work. Why?* .debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/horizontal-pod-autoscaler.md)] --- ## What did we miss? - The events stream gives us a hint, but to be honest, it's not very clear: `missing request for cpu` - We forgot to specify a resource request for our Deployment! - The HPA target is not an absolute CPU% - It is relative to the CPU requested by the pod .debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/horizontal-pod-autoscaler.md)] --- ## Adding a CPU request - Let's edit the deployment and add a CPU request - Since our server can use up to 1 core, let's request 1 core .exercise[ - Edit the Deployment definition: ```bash kubectl edit deployment busyhttp ``` - In the `containers` list, add the following block: ```yaml resources: requests: cpu: "1" ``` ] .debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/horizontal-pod-autoscaler.md)] --- ## Results - After saving and quitting, a rolling update happens (if `ab` or `httping` exits, make sure to restart it) - It will take a minute or two for the HPA to kick in: - the HPA runs every 30 seconds by default - it needs to gather metrics from the metrics server first - If we scale further up (or down), the HPA will react after a few minutes: - it won't scale up if it already scaled in the last 3 minutes - it won't scale down if it already scaled in the last 5 minutes .debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/horizontal-pod-autoscaler.md)] --- ## What about other metrics? - The HPA in API group `autoscaling/v1` only supports CPU scaling - The HPA in API group `autoscaling/v2beta2` supports metrics from various API groups: - metrics.k8s.io, aka metrics server (per-Pod CPU and RAM) - custom.metrics.k8s.io, custom metrics per Pod - external.metrics.k8s.io, external metrics (not associated to Pods) - Kubernetes doesn't implement any of these API groups - Using these metrics requires [registering additional APIs](https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/#support-for-metrics-apis) - The metrics provided by metrics server are standard; everything else is custom - For more details, see [this great blog post](https://medium.com/uptime-99/kubernetes-hpa-autoscaling-with-custom-and-external-metrics-da7f41ff7846) or [this talk](https://www.youtube.com/watch?v=gSiGFH4ZnS8) .debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/horizontal-pod-autoscaler.md)] --- ## Cleanup - Since `busyhttp` uses CPU cycles, let's stop it before moving on .exercise[ - Delete the `busyhttp` Deployment: ```bash kubectl delete deployment busyhttp ``` ] ??? :EN:- Auto-scaling resources :FR:- *Auto-scaling* (dimensionnement automatique) des ressources .debug[[k8s/horizontal-pod-autoscaler.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/horizontal-pod-autoscaler.md)] --- class: pic .interstitial[![Image separating from the next module](https://gallant-turing-d0d520.netlify.com/containers/chinook-helicopter-container.jpg)] --- name: toc-extending-the-kubernetes-api class: title Extending the Kubernetes API .nav[ [Previous section](#toc-the-horizontal-pod-autoscaler) | [Back to table of contents](#toc-module-12) | [Next section](#toc-operators) ] .debug[(automatically generated title slide)] --- # Extending the Kubernetes API There are multiple ways to extend the Kubernetes API. We are going to cover: - Custom Resource Definitions (CRDs) - Admission Webhooks - The Aggregation Layer .debug[[k8s/extending-api.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/extending-api.md)] --- ## Revisiting the API server - The Kubernetes API server is a central point of the control plane (everything connects to it: controller manager, scheduler, kubelets) - Almost everything in Kubernetes is materialized by a resource - Resources have a type (or "kind") (similar to strongly typed languages) - We can see existing types with `kubectl api-resources` - We can list resources of a given type with `kubectl get
` .debug[[k8s/extending-api.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/extending-api.md)] --- ## Creating new types - We can create new types with Custom Resource Definitions (CRDs) - CRDs are created dynamically (without recompiling or restarting the API server) - CRDs themselves are resources: - we can create a new type with `kubectl create` and some YAML - we can see all our custom types with `kubectl get crds` - After we create a CRD, the new type works just like built-in types .debug[[k8s/extending-api.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/extending-api.md)] --- ## A very simple CRD The YAML below describes a very simple CRD representing different kinds of coffee: ```yaml apiVersion: apiextensions.k8s.io/v1alpha1 kind: CustomResourceDefinition metadata: name: coffees.container.training spec: group: container.training version: v1alpha1 scope: Namespaced names: plural: coffees singular: coffee kind: Coffee shortNames: - cof ``` .debug[[k8s/extending-api.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/extending-api.md)] --- ## Creating a CRD - Let's create the Custom Resource Definition for our Coffee resource .exercise[ - Load the CRD: ```bash kubectl apply -f ~/container.training/k8s/coffee-1.yaml ``` - Confirm that it shows up: ```bash kubectl get crds ``` ] .debug[[k8s/extending-api.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/extending-api.md)] --- ## Creating custom resources The YAML below defines a resource using the CRD that we just created: ```yaml kind: Coffee apiVersion: container.training/v1alpha1 metadata: name: arabica spec: taste: strong ``` .exercise[ - Create a few types of coffee beans: ```bash kubectl apply -f ~/container.training/k8s/coffees.yaml ``` ] .debug[[k8s/extending-api.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/extending-api.md)] --- ## Viewing custom resources - By default, `kubectl get` only shows name and age of custom resources .exercise[ - View the coffee beans that we just created: ```bash kubectl get coffees ``` ] - We can improve that, but it's outside the scope of this section! .debug[[k8s/extending-api.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/extending-api.md)] --- ## What can we do with CRDs? There are many possibilities! - *Operators* encapsulate complex sets of resources (e.g.: a PostgreSQL replicated cluster; an etcd cluster...
see [awesome operators](https://github.com/operator-framework/awesome-operators) and [OperatorHub](https://operatorhub.io/) to find more) - Custom use-cases like [gitkube](https://gitkube.sh/) - creates a new custom type, `Remote`, exposing a git+ssh server - deploy by pushing YAML or Helm charts to that remote - Replacing built-in types with CRDs (see [this lightning talk by Tim Hockin](https://www.youtube.com/watch?v=ji0FWzFwNhA)) .debug[[k8s/extending-api.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/extending-api.md)] --- ## Little details - By default, CRDs are not *validated* (we can put anything we want in the `spec`) - When creating a CRD, we can pass an OpenAPI v3 schema (BETA!) (which will then be used to validate resources) - Generally, when creating a CRD, we also want to run a *controller* (otherwise nothing will happen when we create resources of that type) - The controller will typically *watch* our custom resources (and take action when they are created/updated) * Examples: [YAML to install the gitkube CRD](https://storage.googleapis.com/gitkube/gitkube-setup-stable.yaml), [YAML to install a redis operator CRD](https://github.com/amaizfinance/redis-operator/blob/master/deploy/crds/k8s_v1alpha1_redis_crd.yaml) * .debug[[k8s/extending-api.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/extending-api.md)] --- ## (Ab)using the API server - If we need to store something "safely" (as in: in etcd), we can use CRDs - This gives us primitives to read/write/list objects (and optionally validate them) - The Kubernetes API server can run on its own (without the scheduler, controller manager, and kubelets) - By loading CRDs, we can have it manage totally different objects (unrelated to containers, clusters, etc.) .debug[[k8s/extending-api.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/extending-api.md)] --- ## Service catalog - *Service catalog* is another extension mechanism - It's not extending the Kubernetes API strictly speaking (but it still provides new features!) - It doesn't create new types; it uses: - ClusterServiceBroker - ClusterServiceClass - ClusterServicePlan - ServiceInstance - ServiceBinding - It uses the Open service broker API .debug[[k8s/extending-api.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/extending-api.md)] --- ## Admission controllers - Admission controllers are another way to extend the Kubernetes API - Instead of creating new types, admission controllers can transform or vet API requests - The diagram on the next slide shows the path of an API request (courtesy of Banzai Cloud) .debug[[k8s/extending-api.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/extending-api.md)] --- class: pic ![API request lifecycle](images/api-request-lifecycle.png) .debug[[k8s/extending-api.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/extending-api.md)] --- ## Types of admission controllers - *Validating* admission controllers can accept/reject the API call - *Mutating* admission controllers can modify the API request payload - Both types can also trigger additional actions (e.g. automatically create a Namespace if it doesn't exist) - There are a number of built-in admission controllers (see [documentation](https://kubernetes.io/docs/reference/access-authn-authz/admission-controllers/#what-does-each-admission-controller-do) for a list) - We can also dynamically define and register our own .debug[[k8s/extending-api.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/extending-api.md)] --- class: extra-details ## Some built-in admission controllers - ServiceAccount: automatically adds a ServiceAccount to Pods that don't explicitly specify one - LimitRanger: applies resource constraints specified by LimitRange objects when Pods are created - NamespaceAutoProvision: automatically creates namespaces when an object is created in a non-existent namespace *Note: #1 and #2 are enabled by default; #3 is not.* .debug[[k8s/extending-api.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/extending-api.md)] --- ## Admission Webhooks - We can setup *admission webhooks* to extend the behavior of the API server - The API server will submit incoming API requests to these webhooks - These webhooks can be *validating* or *mutating* - Webhooks can be set up dynamically (without restarting the API server) - To setup a dynamic admission webhook, we create a special resource: a `ValidatingWebhookConfiguration` or a `MutatingWebhookConfiguration` - These resources are created and managed like other resources (i.e. `kubectl create`, `kubectl get`...) .debug[[k8s/extending-api.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/extending-api.md)] --- ## Webhook Configuration - A ValidatingWebhookConfiguration or MutatingWebhookConfiguration contains: - the address of the webhook - the authentication information to use with the webhook - a list of rules - The rules indicate for which objects and actions the webhook is triggered (to avoid e.g. triggering webhooks when setting up webhooks) .debug[[k8s/extending-api.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/extending-api.md)] --- ## The aggregation layer - We can delegate entire parts of the Kubernetes API to external servers - This is done by creating APIService resources (check them with `kubectl get apiservices`!) - The APIService resource maps a type (kind) and version to an external service - All requests concerning that type are sent (proxied) to the external service - This allows to have resources like CRDs, but that aren't stored in etcd - Example: `metrics-server` (storing live metrics in etcd would be extremely inefficient) - Requires significantly more work than CRDs! .debug[[k8s/extending-api.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/extending-api.md)] --- ## Documentation - [Custom Resource Definitions: when to use them](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/) - [Custom Resources Definitions: how to use them](https://kubernetes.io/docs/tasks/access-kubernetes-api/custom-resources/custom-resource-definitions/) - [Service Catalog](https://kubernetes.io/docs/concepts/extend-kubernetes/service-catalog/) - [Built-in Admission Controllers](https://kubernetes.io/docs/reference/access-authn-authz/admission-controllers/) - [Dynamic Admission Controllers](https://kubernetes.io/docs/reference/access-authn-authz/extensible-admission-controllers/) - [Aggregation Layer](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/apiserver-aggregation/) ??? :EN:- Extending the Kubernetes API :EN:- Custom Resource Definitions (CRDs) :EN:- The aggregation layer :EN:- Admission control and webhooks :FR:- Comment étendre l'API Kubernetes :FR:- Les CRDs *(Custom Resource Definitions)* :FR:- Extension via *aggregation layer*, *admission control*, *webhooks* .debug[[k8s/extending-api.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/extending-api.md)] --- class: pic .interstitial[![Image separating from the next module](https://gallant-turing-d0d520.netlify.com/containers/container-cranes.jpg)] --- name: toc-operators class: title Operators .nav[ [Previous section](#toc-extending-the-kubernetes-api) | [Back to table of contents](#toc-module-12) | [Next section](#toc-designing-an-operator) ] .debug[(automatically generated title slide)] --- # Operators - Operators are one of the many ways to extend Kubernetes - We will define operators - We will see how they work - We will install a specific operator (for ElasticSearch) - We will use it to provision an ElasticSearch cluster .debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators.md)] --- ## What are operators? *An operator represents **human operational knowledge in software,**
to reliably manage an application. — [CoreOS](https://coreos.com/blog/introducing-operators.html)* Examples: - Deploying and configuring replication with MySQL, PostgreSQL ... - Setting up Elasticsearch, Kafka, RabbitMQ, Zookeeper ... - Reacting to failures when intervention is needed - Scaling up and down these systems .debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators.md)] --- ## What are they made from? - Operators combine two things: - Custom Resource Definitions - controller code watching the corresponding resources and acting upon them - A given operator can define one or multiple CRDs - The controller code (control loop) typically runs within the cluster (running as a Deployment with 1 replica is a common scenario) - But it could also run elsewhere (nothing mandates that the code run on the cluster, as long as it has API access) .debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators.md)] --- ## Why use operators? - Kubernetes gives us Deployments, StatefulSets, Services ... - These mechanisms give us building blocks to deploy applications - They work great for services that are made of *N* identical containers (like stateless ones) - They also work great for some stateful applications like Consul, etcd ... (with the help of highly persistent volumes) - They're not enough for complex services: - where different containers have different roles - where extra steps have to be taken when scaling or replacing containers .debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators.md)] --- ## Use-cases for operators - Systems with primary/secondary replication Examples: MariaDB, MySQL, PostgreSQL, Redis ... - Systems where different groups of nodes have different roles Examples: ElasticSearch, MongoDB ... - Systems with complex dependencies (that are themselves managed with operators) Examples: Flink or Kafka, which both depend on Zookeeper .debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators.md)] --- ## More use-cases - Representing and managing external resources (Example: [AWS S3 Operator](https://operatorhub.io/operator/awss3-operator-registry)) - Managing complex cluster add-ons (Example: [Istio operator](https://operatorhub.io/operator/istio)) - Deploying and managing our applications' lifecycles (more on that later) .debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators.md)] --- ## How operators work - An operator creates one or more CRDs (i.e., it creates new "Kinds" of resources on our cluster) - The operator also runs a *controller* that will watch its resources - Each time we create/update/delete a resource, the controller is notified (we could write our own cheap controller with `kubectl get --watch`) .debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators.md)] --- ## One operator in action - We will install [Elastic Cloud on Kubernetes](https://www.elastic.co/guide/en/cloud-on-k8s/current/k8s-quickstart.html), an ElasticSearch operator - This operator requires PersistentVolumes - We will install Rancher's [local path storage provisioner](https://github.com/rancher/local-path-provisioner) to automatically create these - Then, we will create an ElasticSearch resource - The operator will detect that resource and provision the cluster .debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators.md)] --- ## Installing a Persistent Volume provisioner (This step can be skipped if you already have a dynamic volume provisioner.) - This provisioner creates Persistent Volumes backed by `hostPath` (local directories on our nodes) - It doesn't require anything special ... - ... But losing a node = losing the volumes on that node! .exercise[ - Install the local path storage provisioner: ```bash kubectl apply -f ~/container.training/k8s/local-path-storage.yaml ``` ] .debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators.md)] --- ## Making sure we have a default StorageClass - The ElasticSearch operator will create StatefulSets - These StatefulSets will instantiate PersistentVolumeClaims - These PVCs need to be explicitly associated with a StorageClass - Or we need to tag a StorageClass to be used as the default one .exercise[ - List StorageClasses: ```bash kubectl get storageclasses ``` ] We should see the `local-path` StorageClass. .debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators.md)] --- ## Setting a default StorageClass - This is done by adding an annotation to the StorageClass: `storageclass.kubernetes.io/is-default-class: true` .exercise[ - Tag the StorageClass so that it's the default one: ```bash kubectl annotate storageclass local-path \ storageclass.kubernetes.io/is-default-class=true ``` - Check the result: ```bash kubectl get storageclasses ``` ] Now, the StorageClass should have `(default)` next to its name. .debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators.md)] --- ## Install the ElasticSearch operator - The operator provides: - a few CustomResourceDefinitions - a Namespace for its other resources - a ValidatingWebhookConfiguration for type checking - a StatefulSet for its controller and webhook code - a ServiceAccount, ClusterRole, ClusterRoleBinding for permissions - All these resources are grouped in a convenient YAML file .exercise[ - Install the operator: ```bash kubectl apply -f ~/container.training/k8s/eck-operator.yaml ``` ] .debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators.md)] --- ## Check our new custom resources - Let's see which CRDs were created .exercise[ - List all CRDs: ```bash kubectl get crds ``` ] This operator supports ElasticSearch, but also Kibana and APM. Cool! .debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators.md)] --- ## Create the `eck-demo` namespace - For clarity, we will create everything in a new namespace, `eck-demo` - This namespace is hard-coded in the YAML files that we are going to use - We need to create that namespace .exercise[ - Create the `eck-demo` namespace: ```bash kubectl create namespace eck-demo ``` - Switch to that namespace: ```bash kns eck-demo ``` ] .debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators.md)] --- class: extra-details ## Can we use a different namespace? Yes, but then we need to update all the YAML manifests that we are going to apply in the next slides. The `eck-demo` namespace is hard-coded in these YAML manifests. Why? Because when defining a ClusterRoleBinding that references a ServiceAccount, we have to indicate in which namespace the ServiceAccount is located. .debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators.md)] --- ## Create an ElasticSearch resource - We can now create a resource with `kind: ElasticSearch` - The YAML for that resource will specify all the desired parameters: - how many nodes we want - image to use - add-ons (kibana, cerebro, ...) - whether to use TLS or not - etc. .exercise[ - Create our ElasticSearch cluster: ```bash kubectl apply -f ~/container.training/k8s/eck-elasticsearch.yaml ``` ] .debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators.md)] --- ## Operator in action - Over the next minutes, the operator will create our ES cluster - It will report our cluster status through the CRD .exercise[ - Check the logs of the operator: ```bash stern --namespace=elastic-system operator ``` - Watch the status of the cluster through the CRD: ```bash kubectl get es -w ``` ] .debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators.md)] --- ## Connecting to our cluster - It's not easy to use the ElasticSearch API from the shell - But let's check at least if ElasticSearch is up! .exercise[ - Get the ClusterIP of our ES instance: ```bash kubectl get services ``` - Issue a request with `curl`: ```bash curl http://`CLUSTERIP`:9200 ``` ] We get an authentication error. Our cluster is protected! .debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators.md)] --- ## Obtaining the credentials - The operator creates a user named `elastic` - It generates a random password and stores it in a Secret .exercise[ - Extract the password: ```bash kubectl get secret demo-es-elastic-user \ -o go-template="{{ .data.elastic | base64decode }} " ``` - Use it to connect to the API: ```bash curl -u elastic:`PASSWORD` http://`CLUSTERIP`:9200 ``` ] We should see a JSON payload with the `"You Know, for Search"` tagline. .debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators.md)] --- ## Sending data to the cluster - Let's send some data to our brand new ElasticSearch cluster! - We'll deploy a filebeat DaemonSet to collect node logs .exercise[ - Deploy filebeat: ```bash kubectl apply -f ~/container.training/k8s/eck-filebeat.yaml ``` - Wait until some pods are up: ```bash watch kubectl get pods -l k8s-app=filebeat ``` - Check that a filebeat index was created: ```bash curl -u elastic:`PASSWORD` http://`CLUSTERIP`:9200/_cat/indices ``` ] .debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators.md)] --- ## Deploying an instance of Kibana - Kibana can visualize the logs injected by filebeat - The ECK operator can also manage Kibana - Let's give it a try! .exercise[ - Deploy a Kibana instance: ```bash kubectl apply -f ~/container.training/k8s/eck-kibana.yaml ``` - Wait for it to be ready: ```bash kubectl get kibana -w ``` ] .debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators.md)] --- ## Connecting to Kibana - Kibana is automatically set up to conect to ElasticSearch (this is arranged by the YAML that we're using) - However, it will ask for authentication - It's using the same user/password as ElasticSearch .exercise[ - Get the NodePort allocated to Kibana: ```bash kubectl get services ``` - Connect to it with a web browser - Use the same user/password as before ] .debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators.md)] --- ## Setting up Kibana After the Kibana UI loads, we need to click around a bit .exercise[ - Pick "explore on my own" - Click on Use Elasticsearch data / Connect to your Elasticsearch index" - Enter `filebeat-*` for the index pattern and click "Next step" - Select `@timestamp` as time filter field name - Click on "discover" (the small icon looking like a compass on the left bar) - Play around! ] .debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators.md)] --- ## Scaling up the cluster - At this point, we have only one node - We are going to scale up - But first, we'll deploy Cerebro, an UI for ElasticSearch - This will let us see the state of the cluster, how indexes are sharded, etc. .debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators.md)] --- ## Deploying Cerebro - Cerebro is stateless, so it's fairly easy to deploy (one Deployment + one Service) - However, it needs the address and credentials for ElasticSearch - We prepared yet another manifest for that! .exercise[ - Deploy Cerebro: ```bash kubectl apply -f ~/container.training/k8s/eck-cerebro.yaml ``` - Lookup the NodePort number and connect to it: ```bash kubectl get services ``` ] .debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators.md)] --- ## Scaling up the cluster - We can see on Cerebro that the cluster is "yellow" (because our index is not replicated) - Let's change that! .exercise[ - Edit the ElasticSearch cluster manifest: ```bash kubectl edit es demo ``` - Find the field `count: 1` and change it to 3 - Save and quit ] .debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators.md)] --- ## Deploying our apps with operators - It is very simple to deploy with `kubectl create deployment` / `kubectl expose` - We can unlock more features by writing YAML and using `kubectl apply` - Kustomize or Helm let us deploy in multiple environments (and adjust/tweak parameters in each environment) - We can also use an operator to deploy our application .debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators.md)] --- ## Pros and cons of deploying with operators - The app definition and configuration is persisted in the Kubernetes API - Multiple instances of the app can be manipulated with `kubectl get` - We can add labels, annotations to the app instances - Our controller can execute custom code for any lifecycle event - However, we need to write this controller - We need to be careful about changes (what happens when the resource `spec` is updated?) .debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators.md)] --- ## Operators are not magic - Look at the ElasticSearch resource definition (`~/container.training/k8s/eck-elasticsearch.yaml`) - What should happen if we flip the TLS flag? Twice? - What should happen if we add another group of nodes? - What if we want different images or parameters for the different nodes? *Operators can be very powerful.
But we need to know exactly the scenarios that they can handle.* ??? :EN:- Kubernetes operators :EN:- Deploying ElasticSearch with ECK :FR:- Les opérateurs :FR:- Déployer ElasticSearch avec ECK .debug[[k8s/operators.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators.md)] --- class: pic .interstitial[![Image separating from the next module](https://gallant-turing-d0d520.netlify.com/containers/container-housing.jpg)] --- name: toc-designing-an-operator class: title Designing an operator .nav[ [Previous section](#toc-operators) | [Back to table of contents](#toc-module-12) | [Next section](#toc-owners-and-dependents) ] .debug[(automatically generated title slide)] --- # Designing an operator - Once we understand CRDs and operators, it's tempting to use them everywhere - Yes, we can do (almost) everything with operators ... - ... But *should we?* - Very often, the answer is **“no!”** - Operators are powerful, but significantly more complex than other solutions .debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators-design.md)] --- ## When should we (not) use operators? - Operators are great if our app needs to react to cluster events (nodes or pods going down, and requiring extensive reconfiguration) - Operators *might* be helpful to encapsulate complexity (manipulate one single custom resource for an entire stack) - Operators are probably overkill if a Helm chart would suffice - That being said, if we really want to write an operator ... Read on! .debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators-design.md)] --- ## What does it take to write an operator? - Writing a quick-and-dirty operator, or a POC/MVP, is easy - Writing a robust operator is hard - We will describe the general idea - We will identify some of the associated challenges - We will list a few tools that can help us .debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators-design.md)] --- ## Top-down vs. bottom-up - Both approaches are possible - Let's see what they entail, and their respective pros and cons .debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators-design.md)] --- ## Top-down approach - Start with high-level design (see next slide) - Pros: - can yield cleaner design that will be more robust - Cons: - must be able to anticipate all the events that might happen - design will be better only to the extent of what we anticipated - hard to anticipate if we don't have production experience .debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators-design.md)] --- ## High-level design - What are we solving? (e.g.: geographic databases backed by PostGIS with Redis caches) - What are our use-cases, stories? (e.g.: adding/resizing caches and read replicas; load balancing queries) - What kind of outage do we want to address? (e.g.: loss of individual node, pod, volume) - What are our *non-features*, the things we don't want to address? (e.g.: loss of datacenter/zone; differentiating between read and write queries;
cache invalidation; upgrading to newer major versions of Redis, PostGIS, PostgreSQL) .debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators-design.md)] --- ## Low-level design - What Custom Resource Definitions do we need? (one, many?) - How will we store configuration information? (part of the CRD spec fields, annotations, other?) - Do we need to store state? If so, where? - state that is small and doesn't change much can be stored via the Kubernetes API
(e.g.: leader information, configuration, credentials) - things that are big and/or change a lot should go elsewhere
(e.g.: metrics, bigger configuration file like GeoIP) .debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators-design.md)] --- class: extra-details ## What can we store via the Kubernetes API? - The API server stores most Kubernetes resources in etcd - Etcd is designed for reliability, not for performance - If our storage needs exceed what etcd can offer, we need to use something else: - either directly - or by extending the API server
(for instance by using the agregation layer, like [metrics server](https://github.com/kubernetes-incubator/metrics-server) does) .debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators-design.md)] --- ## Bottom-up approach - Start with existing Kubernetes resources (Deployment, Stateful Set...) - Run the system in production - Add scripts, automation, to facilitate day-to-day operations - Turn the scripts into an operator - Pros: simpler to get started; reflects actual use-cases - Cons: can result in convoluted designs requiring extensive refactor .debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators-design.md)] --- ## General idea - Our operator will watch its CRDs *and associated resources* - Drawing state diagrams and finite state automata helps a lot - It's OK if some transitions lead to a big catch-all "human intervention" - Over time, we will learn about new failure modes and add to these diagrams - It's OK to start with CRD creation / deletion and prevent any modification (that's the easy POC/MVP we were talking about) - *Presentation* and *validation* will help our users (more on that later) .debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators-design.md)] --- ## Challenges - Reacting to infrastructure disruption can seem hard at first - Kubernetes gives us a lot of primitives to help: - Pods and Persistent Volumes will *eventually* recover - Stateful Sets give us easy ways to "add N copies" of a thing - The real challenges come with configuration changes (i.e., what to do when our users update our CRDs) - Keep in mind that [some] of the [largest] cloud [outages] haven't been caused by [natural catastrophes], or even code bugs, but by configuration changes [some]: https://www.datacenterdynamics.com/news/gcp-outage-mainone-leaked-google-cloudflare-ip-addresses-china-telecom/ [largest]: https://aws.amazon.com/message/41926/ [outages]: https://aws.amazon.com/message/65648/ [natural catastrophes]: https://www.datacenterknowledge.com/amazon/aws-says-it-s-never-seen-whole-data-center-go-down .debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators-design.md)] --- ## Configuration changes - It is helpful to analyze and understand how Kubernetes controllers work: - watch resource for modifications - compare desired state (CRD) and current state - issue actions to converge state - Configuration changes will probably require *another* state diagram or FSA - Again, it's OK to have transitions labeled as "unsupported" (i.e. reject some modifications because we can't execute them) .debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators-design.md)] --- ## Tools - CoreOS / RedHat Operator Framework [GitHub](https://github.com/operator-framework) | [Blog](https://developers.redhat.com/blog/2018/12/18/introduction-to-the-kubernetes-operator-framework/) | [Intro talk](https://www.youtube.com/watch?v=8k_ayO1VRXE) | [Deep dive talk](https://www.youtube.com/watch?v=fu7ecA2rXmc) | [Simple example](https://medium.com/faun/writing-your-first-kubernetes-operator-8f3df4453234) - Zalando Kubernetes Operator Pythonic Framework (KOPF) [GitHub](https://github.com/zalando-incubator/kopf) | [Docs](https://kopf.readthedocs.io/) | [Step-by-step tutorial](https://kopf.readthedocs.io/en/stable/walkthrough/problem/) - Mesosphere Kubernetes Universal Declarative Operator (KUDO) [GitHub](https://github.com/kudobuilder/kudo) | [Blog](https://mesosphere.com/blog/announcing-maestro-a-declarative-no-code-approach-to-kubernetes-day-2-operators/) | [Docs](https://kudo.dev/) | [Zookeeper example](https://github.com/kudobuilder/frameworks/tree/master/repo/stable/zookeeper) .debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators-design.md)] --- ## Validation - By default, a CRD is "free form" (we can put pretty much anything we want in it) - When creating a CRD, we can provide an OpenAPI v3 schema ([Example](https://github.com/amaizfinance/redis-operator/blob/master/deploy/crds/k8s_v1alpha1_redis_crd.yaml#L34)) - The API server will then validate resources created/edited with this schema - If we need a stronger validation, we can use a Validating Admission Webhook: - run an [admission webhook server](https://kubernetes.io/docs/reference/access-authn-authz/extensible-admission-controllers/#write-an-admission-webhook-server) to receive validation requests - register the webhook by creating a [ValidatingWebhookConfiguration](https://kubernetes.io/docs/reference/access-authn-authz/extensible-admission-controllers/#configure-admission-webhooks-on-the-fly) - each time the API server receives a request matching the configuration,
the request is sent to our server for validation .debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators-design.md)] --- ## Presentation - By default, `kubectl get mycustomresource` won't display much information (just the name and age of each resource) - When creating a CRD, we can specify additional columns to print ([Example](https://github.com/amaizfinance/redis-operator/blob/master/deploy/crds/k8s_v1alpha1_redis_crd.yaml#L6), [Docs](https://kubernetes.io/docs/tasks/access-kubernetes-api/custom-resources/custom-resource-definitions/#additional-printer-columns)) - By default, `kubectl describe mycustomresource` will also be generic - `kubectl describe` can show events related to our custom resources (for that, we need to create Event resources, and fill the `involvedObject` field) - For scalable resources, we can define a `scale` sub-resource - This will enable the use of `kubectl scale` and other scaling-related operations .debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators-design.md)] --- ## About scaling - It is possible to use the HPA (Horizontal Pod Autoscaler) with CRDs - But it is not always desirable - The HPA works very well for homogenous, stateless workloads - For other workloads, your mileage may vary - Some systems can scale across multiple dimensions (for instance: increase number of replicas, or number of shards?) - If autoscaling is desired, the operator will have to take complex decisions (example: Zalando's Elasticsearch Operator ([Video](https://www.youtube.com/watch?v=lprE0J0kAq0))) .debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators-design.md)] --- ## Versioning - As our operator evolves over time, we may have to change the CRD (add, remove, change fields) - Like every other resource in Kubernetes, [custom resources are versioned](https://kubernetes.io/docs/tasks/access-kubernetes-api/custom-resources/custom-resource-definition-versioning/ ) - When creating a CRD, we need to specify a *list* of versions - Versions can be marked as `stored` and/or `served` .debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators-design.md)] --- ## Stored version - Exactly one version has to be marked as the `stored` version - As the name implies, it is the one that will be stored in etcd - Resources in storage are never converted automatically (we need to read and re-write them ourselves) - Yes, this means that we can have different versions in etcd at any time - Our code needs to handle all the versions that still exist in storage .debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators-design.md)] --- ## Served versions - By default, the Kubernetes API will serve resources "as-is" (using their stored version) - It will assume that all versions are compatible storage-wise (i.e. that the spec and fields are compatible between versions) - We can provide [conversion webhooks](https://kubernetes.io/docs/tasks/access-kubernetes-api/custom-resources/custom-resource-definition-versioning/#webhook-conversion) to "translate" requests (the alternative is to upgrade all stored resources and stop serving old versions) .debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators-design.md)] --- ## Operator reliability - Remember that the operator itself must be resilient (e.g.: the node running it can fail) - Our operator must be able to restart and recover gracefully - Do not store state locally (unless we can reconstruct that state when we restart) - As indicated earlier, we can use the Kubernetes API to store data: - in the custom resources themselves - in other resources' annotations .debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators-design.md)] --- ## Beyond CRDs - CRDs cannot use custom storage (e.g. for time series data) - CRDs cannot support arbitrary subresources (like logs or exec for Pods) - CRDs cannot support protobuf (for faster, more efficient communication) - If we need these things, we can use the [aggregation layer](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/apiserver-aggregation/) instead - The aggregation layer proxies all requests below a specific path to another server (this is used e.g. by the metrics server) - [This documentation page](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/#choosing-a-method-for-adding-custom-resources) compares the features of CRDs and API aggregation ??? :EN:- Guidelines to design our own operators :FR:- Comment concevoir nos propres opérateurs .debug[[k8s/operators-design.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/operators-design.md)] --- class: pic .interstitial[![Image separating from the next module](https://gallant-turing-d0d520.netlify.com/containers/containers-by-the-water.jpg)] --- name: toc-owners-and-dependents class: title Owners and dependents .nav[ [Previous section](#toc-designing-an-operator) | [Back to table of contents](#toc-module-12) | [Next section](#toc-building-our-own-cluster) ] .debug[(automatically generated title slide)] --- # Owners and dependents - Some objects are created by other objects (example: pods created by replica sets, themselves created by deployments) - When an *owner* object is deleted, its *dependents* are deleted (this is the default behavior; it can be changed) - We can delete a dependent directly if we want (but generally, the owner will recreate another right away) - An object can have multiple owners .debug[[k8s/owners-and-dependents.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/owners-and-dependents.md)] --- ## Finding out the owners of an object - The owners are recorded in the field `ownerReferences` in the `metadata` block .exercise[ - Let's create a deployment running `nginx`: ```bash kubectl create deployment yanginx --image=nginx ``` - Scale it to a few replicas: ```bash kubectl scale deployment yanginx --replicas=3 ``` - Once it's up, check the corresponding pods: ```bash kubectl get pods -l app=yanginx -o yaml | head -n 25 ``` ] These pods are owned by a ReplicaSet named yanginx-xxxxxxxxxx. .debug[[k8s/owners-and-dependents.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/owners-and-dependents.md)] --- ## Listing objects with their owners - This is a good opportunity to try the `custom-columns` output! .exercise[ - Show all pods with their owners: ```bash kubectl get pod -o custom-columns=\ NAME:.metadata.name,\ OWNER-KIND:.metadata.ownerReferences[0].kind,\ OWNER-NAME:.metadata.ownerReferences[0].name ``` ] Note: the `custom-columns` option should be one long option (without spaces), so the lines should not be indented (otherwise the indentation will insert spaces). .debug[[k8s/owners-and-dependents.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/owners-and-dependents.md)] --- ## Deletion policy - When deleting an object through the API, three policies are available: - foreground (API call returns after all dependents are deleted) - background (API call returns immediately; dependents are scheduled for deletion) - orphan (the dependents are not deleted) - When deleting an object with `kubectl`, this is selected with `--cascade`: - `--cascade=true` deletes all dependent objects (default) - `--cascade=false` orphans dependent objects .debug[[k8s/owners-and-dependents.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/owners-and-dependents.md)] --- ## What happens when an object is deleted - It is removed from the list of owners of its dependents - If, for one of these dependents, the list of owners becomes empty ... - if the policy is "orphan", the object stays - otherwise, the object is deleted .debug[[k8s/owners-and-dependents.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/owners-and-dependents.md)] --- ## Orphaning pods - We are going to delete the Deployment and Replica Set that we created - ... without deleting the corresponding pods! .exercise[ - Delete the Deployment: ```bash kubectl delete deployment -l app=yanginx --cascade=false ``` - Delete the Replica Set: ```bash kubectl delete replicaset -l app=yanginx --cascade=false ``` - Check that the pods are still here: ```bash kubectl get pods ``` ] .debug[[k8s/owners-and-dependents.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/owners-and-dependents.md)] --- class: extra-details ## When and why would we have orphans? - If we remove an owner and explicitly instruct the API to orphan dependents (like on the previous slide) - If we change the labels on a dependent, so that it's not selected anymore (e.g. change the `app: yanginx` in the pods of the previous example) - If a deployment tool that we're using does these things for us - If there is a serious problem within API machinery or other components (i.e. "this should not happen") .debug[[k8s/owners-and-dependents.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/owners-and-dependents.md)] --- ## Finding orphan objects - We're going to output all pods in JSON format - Then we will use `jq` to keep only the ones *without* an owner - And we will display their name .exercise[ - List all pods that *do not* have an owner: ```bash kubectl get pod -o json | jq -r " .items[] | select(.metadata.ownerReferences|not) | .metadata.name" ``` ] .debug[[k8s/owners-and-dependents.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/owners-and-dependents.md)] --- ## Deleting orphan pods - Now that we can list orphan pods, deleting them is easy .exercise[ - Add `| xargs kubectl delete pod` to the previous command: ```bash kubectl get pod -o json | jq -r " .items[] | select(.metadata.ownerReferences|not) | .metadata.name" | xargs kubectl delete pod ``` ] As always, the [documentation](https://kubernetes.io/docs/concepts/workloads/controllers/garbage-collection/) has useful extra information and pointers. ??? :EN:- Owners and dependents :FR:- Liens de parenté entre les ressources .debug[[k8s/owners-and-dependents.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/owners-and-dependents.md)] --- class: pic .interstitial[![Image separating from the next module](https://gallant-turing-d0d520.netlify.com/containers/distillery-containers.jpg)] --- name: toc-building-our-own-cluster class: title Building our own cluster .nav[ [Previous section](#toc-owners-and-dependents) | [Back to table of contents](#toc-module-13) | [Next section](#toc-adding-nodes-to-the-cluster) ] .debug[(automatically generated title slide)] --- # Building our own cluster - Let's build our own cluster! *Perfection is attained not when there is nothing left to add, but when there is nothing left to take away. (Antoine de Saint-Exupery)* - Our goal is to build a minimal cluster allowing us to: - create a Deployment (with `kubectl create deployment`) - expose it with a Service - connect to that service - "Minimal" here means: - smaller number of components - smaller number of command-line flags - smaller number of configuration files .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Non-goals - For now, we don't care about security - For now, we don't care about scalability - For now, we don't care about high availability - All we care about is *simplicity* .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Our environment - We will use the machine indicated as `dmuc1` (this stands for "Dessine Moi Un Cluster" or "Draw Me A Sheep",
in homage to Saint-Exupery's "The Little Prince") - This machine: - runs Ubuntu LTS - has Kubernetes, Docker, and etcd binaries installed - but nothing is running .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Checking our environment - Let's make sure we have everything we need first .exercise[ - Log into the `dmuc1` machine - Get root: ```bash sudo -i ``` - Check available versions: ```bash etcd -version kube-apiserver --version dockerd --version ``` ] .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## The plan 1. Start API server 2. Interact with it (create Deployment and Service) 3. See what's broken 4. Fix it and go back to step 2 until it works! .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Dealing with multiple processes - We are going to start many processes - Depending on what you're comfortable with, you can: - open multiple windows and multiple SSH connections - use a terminal multiplexer like screen or tmux - put processes in the background with `&`
(warning: log output might get confusing to read!) .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Starting API server .exercise[ - Try to start the API server: ```bash kube-apiserver # It will fail with "--etcd-servers must be specified" ``` ] Since the API server stores everything in etcd, it cannot start without it. .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Starting etcd .exercise[ - Try to start etcd: ```bash etcd ``` ] Success! Note the last line of output: ``` serving insecure client requests on <IP_ADDRESS>:2379, this is strongly discouraged! ``` *Sure, that's discouraged. But thanks for telling us the address!* .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Starting API server (for real) - Try again, passing the `--etcd-servers` argument - That argument should be a comma-separated list of URLs .exercise[ - Start API server: ```bash kube-apiserver --etcd-servers http://<IP_ADDRESS>:2379 ``` ] Success! .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Interacting with API server - Let's try a few "classic" commands .exercise[ - List nodes: ```bash kubectl get nodes ``` - List services: ```bash kubectl get services ``` ] We should get `No resources found.` and the `kubernetes` service, respectively. Note: the API server automatically created the `kubernetes` service entry. .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- class: extra-details ## What about `kubeconfig`? - We didn't need to create a `kubeconfig` file - By default, the API server is listening on `localhost:8080` (without requiring authentication) - By default, `kubectl` connects to `localhost:8080` (without providing authentication) .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Creating a Deployment - Let's run a web server! .exercise[ - Create a Deployment with NGINX: ```bash kubectl create deployment web --image=nginx ``` ] Success? .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Checking our Deployment status .exercise[ - Look at pods, deployments, etc.: ```bash kubectl get all ``` ] Our Deployment is in bad shape: ``` NAME READY UP-TO-DATE AVAILABLE AGE deployment.apps/web 0/1 0 0 2m26s ``` And, there is no ReplicaSet, and no Pod. .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## What's going on? - We stored the definition of our Deployment in etcd (through the API server) - But there is no *controller* to do the rest of the work - We need to start the *controller manager* .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Starting the controller manager .exercise[ - Try to start the controller manager: ```bash kube-controller-manager ``` ] The final error message is: ``` invalid configuration: no configuration has been provided ``` But the logs include another useful piece of information: ``` Neither --kubeconfig nor --master was specified. Using the inClusterConfig. This might not work. ``` .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Reminder: everyone talks to API server - The controller manager needs to connect to the API server - It *does not* have a convenient `localhost:8080` default - We can pass the connection information in two ways: - `--master` and a host:port combination (easy) - `--kubeconfig` and a `kubeconfig` file - For simplicity, we'll use the first option .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Starting the controller manager (for real) .exercise[ - Start the controller manager: ```bash kube-controller-manager --master http://localhost:8080 ``` ] Success! .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Checking our Deployment status .exercise[ - Check all our resources again: ```bash kubectl get all ``` ] We now have a ReplicaSet. But we still don't have a Pod. .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## What's going on? In the controller manager logs, we should see something like this: ``` E0404 15:46:25.753376 22847 replica_set.go:450] Sync "default/web-5bc9bd5b8d" failed with `No API token found for service account "default"`, retry after the token is automatically created and added to the service account ``` - The service account `default` was automatically added to our Deployment (and to its pods) - The service account `default` exists - But it doesn't have an associated token (the token is a secret; creating it requires signature; therefore a CA) .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Solving the missing token issue There are many ways to solve that issue. We are going to list a few (to get an idea of what's happening behind the scenes). Of course, we don't need to perform *all* the solutions mentioned here. .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Option 1: disable service accounts - Restart the API server with `--disable-admission-plugins=ServiceAccount` - The API server will no longer add a service account automatically - Our pods will be created without a service account .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Option 2: do not mount the (missing) token - Add `automountServiceAccountToken: false` to the Deployment spec *or* - Add `automountServiceAccountToken: false` to the default ServiceAccount - The ReplicaSet controller will no longer create pods referencing the (missing) token .exercise[ - Programmatically change the `default` ServiceAccount: ```bash kubectl patch sa default -p "automountServiceAccountToken: false" ``` ] .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Option 3: set up service accounts properly - This is the most complex option! - Generate a key pair - Pass the private key to the controller manager (to generate and sign tokens) - Pass the public key to the API server (to verify these tokens) .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Continuing without service account token - Once we patch the default service account, the ReplicaSet can create a Pod .exercise[ - Check that we now have a pod: ```bash kubectl get all ``` ] Note: we might have to wait a bit for the ReplicaSet controller to retry. If we're impatient, we can restart the controller manager. .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## What's next? - Our pod exists, but it is in `Pending` state - Remember, we don't have a node so far (`kubectl get nodes` shows an empty list) - We need to: - start a container engine - start kubelet .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Starting a container engine - We're going to use Docker (because it's the default option) .exercise[ - Start the Docker Engine: ```bash dockerd ``` ] Success! Feel free to check that it actually works with e.g.: ```bash docker run alpine echo hello world ``` .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Starting kubelet - If we start kubelet without arguments, it *will* start - But it will not join the cluster! - It will start in *standalone* mode - Just like with the controller manager, we need to tell kubelet where the API server is - Alas, kubelet doesn't have a simple `--master` option - We have to use `--kubeconfig` - We need to write a `kubeconfig` file for kubelet .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Writing a kubeconfig file - We can copy/paste a bunch of YAML - Or we can generate the file with `kubectl` .exercise[ - Create the file `~/.kube/config` with `kubectl`: ```bash kubectl config \ set-cluster localhost --server http://localhost:8080 kubectl config \ set-context localhost --cluster localhost kubectl config \ use-context localhost ``` ] .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Our `~/.kube/config` file The file that we generated looks like the one below. That one has been slightly simplified (removing extraneous fields), but it is still valid. ```yaml apiVersion: v1 kind: Config current-context: localhost contexts: - name: localhost context: cluster: localhost clusters: - name: localhost cluster: server: http://localhost:8080 ``` .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Starting kubelet .exercise[ - Start kubelet with that kubeconfig file: ```bash kubelet --kubeconfig ~/.kube/config ``` ] Success! .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Looking at our 1-node cluster - Let's check that our node registered correctly .exercise[ - List the nodes in our cluster: ```bash kubectl get nodes ``` ] Our node should show up. Its name will be its hostname (it should be `dmuc1`). .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Are we there yet? - Let's check if our pod is running .exercise[ - List all resources: ```bash kubectl get all ``` ] -- Our pod is still `Pending`. 🤔 -- Which is normal: it needs to be *scheduled*. (i.e., something needs to decide which node it should go on.) .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Scheduling our pod - Why do we need a scheduling decision, since we have only one node? - The node might be full, unavailable; the pod might have constraints ... - The easiest way to schedule our pod is to start the scheduler (we could also schedule it manually) .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Starting the scheduler - The scheduler also needs to know how to connect to the API server - Just like for controller manager, we can use `--kubeconfig` or `--master` .exercise[ - Start the scheduler: ```bash kube-scheduler --master http://localhost:8080 ``` ] - Our pod should now start correctly .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- ## Checking the status of our pod - Our pod will go through a short `ContainerCreating` phase - Then it will be `Running` .exercise[ - Check pod status: ```bash kubectl get pods ``` ] Success! .debug[[k8s/dmuc.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/dmuc.md)] --- class: extra-details ## Scheduling a pod manually - We can schedule a pod in `Pending` state by creating a Binding, e.g.: ```bash kubectl create -f- <
``` .debug[[k8s/multinode.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/multinode.md)] --- class: extra-details ## The pod CIDR field is not mandatory - `kubenet` needs the pod CIDR, but other plugins don't need it (e.g. because they allocate addresses in multiple pools, or a single big one) - The pod CIDR field may eventually be deprecated and replaced by an annotation (see [kubernetes/kubernetes#57130](https://github.com/kubernetes/kubernetes/issues/57130)) .debug[[k8s/multinode.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/multinode.md)] --- ## Restarting kubelet wih pod CIDR - We need to stop and restart all our kubelets - We will add the `--network-plugin` and `--pod-cidr` flags - We all have a "cluster number" (let's call that `C`) printed on your VM info card - We will use pod CIDR `10.C.N.0/24` (where `N` is the node number: 1, 2, 3) .exercise[ - Stop all the kubelets (Ctrl-C is fine) - Restart them all, adding `--network-plugin=kubenet --pod-cidr 10.C.N.0/24` ] .debug[[k8s/multinode.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/multinode.md)] --- ## What happens to our pods? - When we stop (or kill) kubelet, the containers keep running - When kubelet starts again, it detects the containers .exercise[ - Check that our pods are still here: ```bash kubectl get pods -o wide ``` ] 🤔 But our pods still use local IP addresses! .debug[[k8s/multinode.md](https://github.com/jpetazzo/container.training/tree/master/slides/k8s/multinode.md)] --- ## Recreating the pods - The IP address of a pod cannot change - kubelet doesn't automatically kill/restart containers with "invalid" addresses


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