使用Python調度Kubernetes

Kubernetes Scheduling in Python

使用Python調度Kubernetes

The topic of scheduling can be quite daunting. It is a full area of research in Computer Science.

調度這個題目是讓人生畏的，在計算機科學上是一個龐大的研究領域。

At the end of the day scheduling requests is about looking at who is waiting and finding a plan to serve those requests. Generally you want to reduce the time to serve, and use the most appropriate resources to do so.

調度一個請求就是去查看哪些在等待，而後尋找一個計劃去知足這些請求。一般，須要考慮減小服務時間、最大化地利用資源。

Kubernetes is no different. Kubernetes schedules Pods (groups of co-located containers) onto nodes of your cluster. That means that one of the core components of the Kubernetes head node is a scheduler.

Kubernetes沒有什麼不一樣。Kubernetes調度Pods到集羣中的節點上運行。這意味着Kubernetes的核心節點的核心組件是一個調度器。

To demystify further the scheduling process, we can approximate it as Kubernetes needing to look at all the nodes that are in the cluster, filter out the ones that are not a good match to run Pods that are in Pending state, and then pick one.

爲了揭開調度過程的神祕面紗，咱們假設Kubernetes須要在集羣中的全部節點中進行查詢，過濾掉哪些不是很好適合Pods的處於掛起狀態的節點，而後選擇節點來執行。

In more complex terms, the scheduler will run a set of predicates and then run some some priority functions. The end result is a list of ranked nodes that can run the workload (i.e the Pod), the Pod ends up on the node with the highest rank.

在更復雜的狀況下，調度器將運行預測器以運行一些優先功能。根據節點的評分來運行工做負載，Pod將運行與最高評分的節點之上。

This filtering and ranking is nothing new, and can be found in 30 years old batch processing systems.

這些過濾和評分機制不是什麼新鮮的東西，在30年前的批處理系統上就已經在使用。

A Pod with a non-default scheduler

帶非缺省調度器的Pod

Let’s dig deeper and start a Pod that specifies a non-default scheduler. This can be done in a Pod manifest using schedulerName in the Pod Spec. In the gist below we specify a scheduler named foobar. Once you create this Pod, since Kubernetes does not know this scheduler, the Pod will remain in Pending state and never get scheduled until, we somehow assign a node to this Pod.

下面進一步深刻。啓動一個Pod指定爲非缺省調度器。在Pod的manifest文件指定，在Pod Spec中使用schedulerName 屬性。在下面咱們指定了調度器foobar。一旦你建立了這個Pod，由於Kubernetes不知道這個調度器，這個Pod將處於掛起狀態而且永遠不被調度，直到咱們賦給一個節點給這個Pod。

A Kubernetes scheduler in Python

使用Python的Kubernetes調度器

While we can run some very advanced functions to determine the list of available nodes who can serve a Pod request, and then run some equally advanced functions to rank nodes that can serve said Pod request, we can also pick a node at random. Random selection is not as bad as it sounds and in some cases can be a very good compromise since it has very little computational cost.

咱們將運行一些更高級的算法函數去肯定可用的節點，可以對Pod請求提供服務。而後運行一些高級函數對能提供Pod請求服務的節點進行評分，也能夠隨機地選擇一個節點。隨機選擇並無聽起來那麼糟糕，在某些狀況下換時很是好的妥協，由於這種方式的計算成本很小。

To implement a scheduler, we run a watch on the Pods endpoint. Every time there is a change in the set of Pods in the system, we get a notification. When a Pod is in Pending state and has specified our scheduler name, we call our random scheduler.

爲了實現調度器，咱們在 Pods endpoint運行 watch。系統中每一次Pods發生改變，咱們都能獲得通知。當Pod處於掛起狀態而且被指定咱們的調度器名稱時，咱們調用隨機調度器。

To do random scheduling, we just build a list of available nodes in our system and just pick one at random.

實現隨機調度器，咱們只需建立一個系統中可用節點的列表，而且從中隨機地選擇一個。

To schedule the Pod on our random node, we create a Binding object. This is basically a POST that attaches an object which describes the target node.

爲了調度Pod在隨機的節點上，咱們建立一個 Binding 對象。將 POST 關聯到描述目標節點的對象上。

Thanks to Ian Lewis for the tip and a more advanced blog.

感謝Ian Lewis 的建議，更多參閱：advanced blog.

The gist below use the Kubernetes Python client to implement this simple scheduling.

Kubernetes的Python Client實現參考: simple scheduling.

Now, stick this Python script in a container, run it in a Pod and you have a custom scheduler for Kubernetes.

如今Python腳本放到container中，在Pod中運行。恭喜！你如今有了一個自定義的Kubernetes調度器。