Patent Publication Number: US-2023161647-A1

Title: Extending the kubernetes api in-process

Description:
RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 16/938,508, filed on Jul. 24, 2020, which is herein incorporated by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     Aspects of the present disclosure relate to container orchestration engines, and more particularly, to the deployment and operation of controllers. 
     BACKGROUND 
     A container orchestration engine (such as the Redhat™ OpenShift™ module) may be a platform for developing and running containerized applications and may allow applications and the data centers that support them to expand from just a few machines and applications to thousands of machines that serve millions of clients. Container orchestration engines may provide an image-based deployment module for creating containers and may store one or more image files for creating container instances. Many application instances can be running in containers on a single host without visibility into each other&#39;s processes, files, network, and so on. Each container may provide a single function (often called a “service”) or component of an application, such as a web server or a database, though containers can be used for arbitrary workloads. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The described embodiments and the advantages thereof may best be understood by reference to the following description taken in conjunction with the accompanying drawings. These drawings in no way limit any changes in form and detail that may be made to the described embodiments by one skilled in the art without departing from the spirit and scope of the described embodiments. 
         FIG.  1    is a block diagram that illustrates an example system, in accordance with some embodiments of the present disclosure. 
         FIG.  2 A  is a block diagram that illustrates an example system, in accordance with some embodiments of the present disclosure. 
         FIG.  2 B  is a block diagram that illustrates an example system, in accordance with some embodiments of the present disclosure. 
         FIG.  3    is a block diagram that illustrates an example system, in accordance with some embodiments of the present disclosure. 
         FIG.  4    is a block diagram that illustrates an example system, in accordance with some embodiments of the present disclosure. 
         FIG.  5    is a flow diagram of a method for extending the container orchestration engine API in-process using isolation modules, in accordance with some embodiments of the present disclosure. 
         FIG.  6    is a flow diagram of a method for extending the container orchestration engine API in-process using isolation modules, in accordance with some embodiments of the present disclosure. 
         FIG.  7    is a block diagram of an example computing device that may perform one or more of the operations described herein, in accordance with some embodiments of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Container orchestration engines such as Kubernetes can be extended using custom resource definitions (CRDs). CRDs declare and define a new custom resource (CR) which may be picked up by a controller to perform some meaningful action (servicing the CR), such as provision an external system. These CRDs can be used as an extension point for a system and in many cases, it is desirable to have a number of precisely scoped CRDs to properly abstract away operational concerns of a Kubernetes cluster. 
     Every CRD needs its own controller, which is a process that requires a pod which is running on the cluster. Because the extensions are not necessarily applied at cluster instantiation, each controller lives in a separate service (application) from the main reconciler of the Kubernetes core APIs (e.g., the kube-controller-manager). These services run in their own newly created deployments and as a result, a new pod is required at least for every group of CRDs (if multiple controllers are built into a single service) and in a worst-case scenario, a separate pod is required for each CRD. Each pod allocates a number of CPUs, memory, and other resources. The cost in resources is amplified when every pod is made highly available. This problem is exacerbated because many of these pods will be idling for large stretches of time with no work to be done. 
     Resources can be saved by packing a larger number of controllers into the same service. For example, Knative controllers can be statically complied into a single service. However, this compilation occurs at build time, not at run-time, and thus does not allow for dynamic extension. 
     The present disclosure addresses the above-noted and other deficiencies by using a processing device to compile each of one or more custom resource definition (CRD) controllers that are created in a cluster at run-time into a respective isolation module to generate one or more isolation modules. The one or more isolation modules may all be hosted in the same service (e.g., the controller manager service), which may include an isolation module runtime to facilitate execution of CRD controllers via their respective isolation modules. The isolation module runtime may also provide an interface for managing the lifecycle of the CRD controllers and facilitating communication between the service and the isolation modules. Upon receiving an isolation module in which a CRD controller has been compiled, the interface may update an informer of the service with controller metadata of the CRD controller to enable the interface to monitor for application program interface (API) events serviced by the CRD controller. In response to detecting an API event serviced by a CRD controller of the one or more CRD controllers, the informer may indicate to the interface that an event has been detected and the CRD controller that it pertains to. The interface may execute the respective isolation module of the CRD controller such that the CRD controller may service the detected event. In this way, the CRD controllers do not run in background (e.g., as background processes) while their respective isolation module is not executing, and may transition from their inactive state to an active state upon execution of their respective isolation module. 
       FIG.  1    is a block diagram that illustrates an example system  100 . As illustrated in  FIG.  1   , the system  100  includes a computing device  110 , and a plurality of computing devices  130 . The computing devices  110  and  130  may be coupled to each other (e.g., may be operatively coupled, communicatively coupled, may communicate data/messages with each other) via network  140 . Network  140  may be a public network (e.g., the internet), a private network (e.g., a local area network (LAN) or wide area network (WAN)), or a combination thereof. In one embodiment, network  140  may include a wired or a wireless infrastructure, which may be provided by one or more wireless communications systems, such as a WiFi™ hotspot connected with the network  140  and/or a wireless carrier system that can be implemented using various data processing equipment, communication towers (e.g. cell towers), etc. In some embodiments, the network  140  may be an L3 network. The network  140  may carry communications (e.g., data, message, packets, frames, etc.) between computing device  110  and computing devices  130 . Each computing device may include hardware such as processing device  115  (e.g., processors, central processing units (CPUs), memory  120  (e.g., random access memory  120  (e.g., RAM), storage devices (e.g., hard-disk drive (HDD), solid-state drive (SSD), etc.), and other hardware devices (e.g., sound card, video card, etc.). In some embodiments, memory  120  may be a persistent storage that is capable of storing data. A persistent storage may be a local storage unit or a remote storage unit. Persistent storage may be a magnetic storage unit, optical storage unit, solid state storage unit, electronic storage units (main memory), or similar storage unit. Persistent storage may also be a monolithic/single device or a distributed set of devices. Memory  120  may be configured for long-term storage of data and may retain data between power on/off cycles of the computing device  110 . 
     Each computing device may comprise any suitable type of computing device or machine that has a programmable processor including, for example, server computers, desktop computers, laptop computers, tablet computers, smartphones, set-top boxes, etc. In some examples, each of the computing devices  110  and  130  may comprise a single machine or may include multiple interconnected machines (e.g., multiple servers configured in a cluster). The computing devices  110  and  130  may be implemented by a common entity/organization or may be implemented by different entities/organizations. For example, computing device  110  may be operated by a first company/corporation and one or more computing devices  130  may be operated by a second company/corporation. Each of computing device  110  and computing devices  130  may execute or include an operating system (OS) such as host OS  210  and host OS  211  of computing device  110  and  130  respectively, as discussed in more detail below. The host OS of a computing device  110  and  130  may manage the execution of other components (e.g., software, applications, etc.) and/or may manage access to the hardware (e.g., processors, memory, storage devices etc.) of the computing device. In some embodiments, computing device  110  may implement a control plane (e.g., as part of a container orchestration engine) while computing devices  130  may each implement a compute node (e.g., as part of the container orchestration engine). 
     In some embodiments, a container orchestration engine  214  (referred to herein as container host  214 ), such as the Redhat™ OpenShift™ module, may execute on the host OS  210  of computing device  110  and the host OS  211  of computing device  130 , as discussed in further detail herein. The container host module  214  may be a platform for developing and running containerized applications and may allow applications and the data centers that support them to expand from just a few machines and applications to thousands of machines that serve millions of clients. Container host  214  may provide an image-based deployment module for creating containers and may store one or more image files for creating container instances. Many application instances can be running in containers on a single host without visibility into each other&#39;s processes, files, network, and so on. In some embodiments, each container may provide a single function (often called a “micro-service”) or component of an application, such as a web server or a database, though containers can be used for arbitrary workloads. In this way, the container host  214  provides a function-based architecture of smaller, decoupled units that work together. 
     Container host  214  may include a storage driver (not shown), such as OverlayFS, to manage the contents of an image file including the read only and writable layers of the image file. The storage driver may be a type of union file system which allows a developer to overlay one file system on top of another. Changes may be recorded in the upper file system, while the lower file system (base image) remains unmodified. In this way, multiple containers may share a file-system image where the base image is read-only media. 
     An image file may be stored by the container host  214  or a registry server. In some embodiments, the image file may include one or more base layers. An image file may be shared by multiple containers. When the container host  214  creates a new container, it may add a new writable (e.g., in-memory) layer on top of the underlying base layers. However, the underlying image file remains unchanged. Base layers may define the runtime environment as well as the packages and utilities necessary for a containerized application to run. Thus, the base layers of an image file may each comprise static snapshots of the container&#39;s configuration and may be read-only layers that are never modified. Any changes (e.g., data to be written by the application running on the container) may be implemented in subsequent (upper) layers such as in-memory layer. Changes made in the in-memory layer may be saved by creating a new layered image. 
     While the container image is the basic unit containers may be deployed from, the basic units that the container host  214  may work with are called pods. A pod may refer to one or more containers deployed together on a single host, and the smallest compute unit that can be defined, deployed, and managed. Each pod is allocated its own internal IP address, and therefore may own its entire port space. A user (e.g., via the container host module  214 ) may define the entry point script of a pod to instruct the pod to configure itself as a unique simulated compute node with its own IP addresses and simulated network stacks and communicate with the internal API of the control plane. Containers within pods may share their local storage and networking. In some embodiments, pods have a lifecycle in which they are defined, they are assigned to run on a node, and they run until their container(s) exit or they are removed based on their policy and exit code. Although a pod may contain one or more than one container, the pod is the single unit that a user may deploy, scale, and manage. The control plane  215  of the container host  214  may include controllers  215 A-D, one or more of which may be e.g., a replication controller that indicates how many pod replicas are required to run at a time and may be used to automatically scale an application to adapt to its current demand. 
     By their nature, containerized applications are separated from the operating systems where they run and, by extension, their users. The control plane  215  may expose applications to internal and external networks by defining network policies that control communication with containerized applications (e.g., incoming HTTP or HTTPS requests for services inside the cluster  131 ). 
     A typical deployment of the container host  214  may include a control plane  215  and a cluster of worker nodes  131 , including worker nodes  131 A and  131 B (also referred to as compute machines). The worker nodes  131  may run the aspects of the container host  214  that are needed to launch and manage containers, pods, and other objects. For example, a worker node may be a physical server that provides the processing capabilities required for running containers in the environment. A worker node may also be implemented as a virtual server, logical container, or GPU, for example. 
     The control plane  215  may include REST APIs (not shown) (e.g., Kubernetes APIs) which expose objects, as well as controllers  218  and  219  which read those APIs, apply changes to objects, and report status and/or write back to objects. Objects may be persistent entities in the container host  214 , which are used to represent the state of the cluster  131  (e.g., deployments, replicasets, and pods). The control plane  215  may run an API server  216  (e.g., Kubernetes API server) that validates and configures the data for objects such as e.g., pods, services, and controllers as well as provides a focal point for the cluster  131 &#39;s shared state. The control plane  215  may also run a scheduler service (not shown) that considers the resource needs of a pod, such as CPU or memory, along with the health of the cluster, and then schedules the pod to an appropriate worker node  131 . 
     Controllers  218  and  219  may observe the state of the cluster  131  via the API server  216  and look for events corresponding to changes either to the desired state of resources (e.g., create, update, delete) or the cluster (e.g., pod or node dies). Controllers  218  and  219  may then make changes to the cluster  131  to ensure that the current state matches the desired state described by the observed resource (referred to as reconciling). Each controller  218  and  219  observes and reconciles certain object types as defined by the controller&#39;s metadata which includes an object type the controller will observe/reconcile and the particular class filters (if applicable) it uses, for example. The controllers  218  and  219  actuate objects after they are written by observing object types, and then triggering reconciles from events. After an object is created/updated/deleted, controllers observing that object type will receive a notification that the object has been changed, and they may read the state of the cluster  131  to see what has changed (instead of relying on the event for this information). For example, when a user wishes to scale up a deployment, a request may be sent to the API server  216  with the new desired configuration. The API server  216  in return publishes the change which is read by the deployment controller observing the deployment. Thus, the deployment controller creates one or more pods to conform to the new definition. A new pod creation is, in itself, a new change that the API server  216  may also broadcast to all event listeners. So, if there are any actions that should get triggered upon creation of a new pod, they may be registered automatically. 
     The control plane  215  may also include a master state store (not shown) that stores the persistent master state of the cluster  131  (e.g., an etcd store). The control plane  215  may also include a controller-manager service such as e.g., Kubernetes-controller-manager (referred to herein as “controller manager  217 ”) that includes a set of informers  217 A that watch the etcd store for changes to objects such as replication, namespace, and “serviceaccount” controller objects, and then uses the API to enforce the specified state. The controller-manager  217  may host a number of core controllers  218 . For example, one controller  218  may consult the scheduler service and ensure that the correct number of pods is running. Another controller  218  may monitor pods, and if a pod goes down, may notice and respond. Another controller  218  connects services to pods, so requests go to the right endpoints. Still other controllers  218  may create accounts and API access tokens etc. The controller-manager  217  may include an informer  217 A, to drive the reconciliation loop for each controller  218  through API events. 
     The REST APIs can be extended using special objects called Custom Resource Definitions (CRDs). A CRD object defines a new, unique object type in the cluster and allows the API server to handle its entire lifecycle. Custom Resource (CR) objects (hereinafter referred to as custom resources, or CRs) are created from CRDs that have been added to the cluster by a cluster administrator, allowing all cluster users to add the new object type into projects. When a cluster administrator adds a new CRD to the cluster, the API server reacts by creating a new RESTful resource path (e.g., API extension) that can be accessed by the entire cluster or a single project (namespace) and begins serving the specified CR. 
     Each CR requires a controller (reconciler) that observes/reconciles the particular object type of the CR to perform some meaningful function with it. For example, when an EtcdCluster CRD is created, a corresponding controller may take the corresponding EtcdCluster CR object and deploy an etcd cluster with it. A controller that observes/reconciles a CR object (also referred to herein as servicing a CR object) may be referred to herein as a CRD controller, such as CRD controllers  219  shown in  FIG.  1   . When a CRD is created, a CRD controller that can service the corresponding CR may be created along with the CRD. The CRD controller may be a custom controller defined as part of a new operator (using e.g., Operator SDK) or included in an operator pulled from an off cluster source (using e.g., OperatorHub) for example. Each CRD controller  219  may include an informer (e.g., informers  220 A and  220 B), to drive the reconciliation loop as well as a “reconcile” function (e.g.,  221 A and  221 B), which takes these events and performs some operations based on them. Because extensions such as CRDs are not applied at cluster initiation however, CRD controllers  219  are each hosted in a service (not shown) that is separate from the controller-manager  217 . Because each such service executes in its own deployment, a new pod is required at least for every group of CRDs, and in some scenarios, a separate pod is required for each CRD. This inflicts a sizeable cost in terms of resource consumption, particularly when the pods are made highly available. Although multiple Knative controllers can be built into one binary, this compilation occurs at build-time, not run-time and is thus not suitable for a mechanism such as e.g., OperatorHub. 
     Embodiments of the present disclosure overcome this problem by compiling each CRD controller  219  into a respective isolation module at a separate time from when the rest of the cluster is instantiated (e.g., at run-time), and launching the CRD controllers  219  dynamically once their respective API extensions are installed into the cluster  131 . Indeed, each CRD controller  219  is compiled as part of a compilation process that is separate from the packaging of cluster  131 . Although embodiments of the present disclosure are described with respect to a web assembly module (WASM) as an example isolation module, the isolation module may be any suitable module such as a Golang plugin (referred to herein as a “Go-plugin”), or any other suitable isolation module. 
     A WASM defines a portable binary code format for executable programs, a corresponding textual assembly language, and interfaces for facilitating interactions between such executable programs and their host environment. WASM code runs within a low-level virtual machine that mimics the functionality of the processing devices upon which it can be run. WASMs are portable, and support executable programs written in a variety of compiled languages such as Golang, C++, and Rust. Stated differently, programs in a wide range of languages may be compiled to a WASM. Furthermore, while WASMs assume certain prerequisites for their execution environments, they are designed to execute efficiently on a variety of operating systems and instruction set architectures. 
     Because each WASM executes within a virtual machine that is separated from the host runtime using fault isolation techniques, applications compiled into a WASM execute in isolation from the rest of their host environment and can&#39;t escape the “sandbox” of the WASM without going through the appropriate APIs. 
     Each CRD controller  219  may be a relatively self-contained entity of code, including an informer  220  to drive the reconciliation loop through API events as well as a “reconcile” function  221 , which takes these events and performs some operations based on them (e.g., services them). This self-contained nature allows them to be compiled into a WASM like any other program.  FIGS.  2 A and  2 B  illustrate a system  200  in accordance with some embodiments of the present disclosure. 
     Upon creation of a CRD controller  219  by a user/developer, the user/developer may utilize language tooling and code generation components to compile a CRD controller  219  into a WASM module  224 . Examples of such components may include a code generator for generating optimized machine code from the code of a CRD controller  219  by parallelizing compilation on a function-by-function level. As with a CRD controller  219 , the WASM module  224  may be defined as part of a new operator (using e.g., Operator SDK) or included in an operator pulled from an off cluster source (using e.g., OperatorHub) for example. An installer controller (not shown) hosted on the controller-manager  217  may function to pull newly created isolation modules from e.g., OperatorHub and mount them into the controller-manager  217 . The installer controller may itself be hosted on the controller-manager  217  or may be hosted on a separate service. In some embodiments, the installer controller may also be compiled into a WASM or other appropriate isolation module. In some embodiments, a newly created isolation module  224  may be mounted into the controller-manager  217  directly, as if it were a file. More specifically, the container host  214  may create a Kubernetes volume into which the newly created isolation module  224  is placed, and mount the volume into the controller-manager  217  directly. When the controller-manager  217  bootstraps, it may read the volume and start the isolation module  224 . 
     As shown in  FIG.  2 A , the controller-manager  217  may act as the host of each CRD controller  219  and may include a WASM runtime (not shown) that facilitates execution of each of the CRD controllers  219  via their respective WASM  224 . The WASM runtime may include an interface (WASM interface  223 ) comprising a set of APIs for facilitating interactions between the WASMs  224  and their host environment (e.g., controller-manager  217 ). These APIs may provide I/O between the controller-manager  217  and each WASM  224  (e.g., on behalf of each respective CRD controller  219 ). The WASM interface  223  may be any appropriate interface such as the WebAssembly System Interface (WASI), for example. The WASM runtime may also provide for the isolation of individual executions of WASM  224  from the underlying OS and/or the host application (e.g., controller-manager  217 ) that runs the module, thereby isolating failure modes of each CRD controller  219 . When the WASM runtime executes a function in a WASM  224 , it may provide the result of the function (e.g., success/fail) back to the control plane  215 , for example. However, in the event of a failure, this failure is not propagated to the host application (e.g., the controller-manager  217 ). Therefore, if a single CRD controller  219  crashes it will not result in other CRD controllers  219  crashing with it, nor will the host application crash along with it. 
     The WASM interface  223  may manage the lifecycle of all of the CRD controllers  219  as well as manage communication between the controller-manager  217  and each CRD controller  219  via their respective WASM  224 . More specifically, upon obtaining a new WASM  224 A into which a newly defined CRD controller  219 A has been compiled (e.g., using Operator SDK or pulled from an off-cluster source using OperatorHub as discussed above), the WASM interface  223  may mount the WASM  224 A and provide I/O between the WASM  224 A and the controller-manager  217 . 
     As discussed above, when the new CRD controller  219 A is generated, it includes controller metadata that describes what type of events the CRD controller  219 A is to service. Because informer  217 A is a programmable component, when the WASM interface  223  mounts the WASM  224 A in which the new CRD controller  219 A executes, it may update the informer  217 A with the controller metadata of CRD controller  219 A to indicate to the informer  217 A what types of events the newly mounted CRD controller  219 A will service. When an event of a the type serviced by CRD controller  219 A is picked up by the informer  217 A, it may determine that CRD controller  219  is servicing that event type, and inform the WASM interface  223  that an event pertaining to CRD controller  219 A has been picked up. In this way, when an event of any type is picked up by the informer  217 A, it may determine which CRD controller  219  is servicing that event type, and inform the WASM interface  223  that an event pertaining to that CRD controller  219  has been picked up. The WASM interface  223  may execute the WASM  224  that the relevant CRD controller  219  has been compiled into. In this way, each CRD controller can be launched dynamically once their new API extensions are installed into the cluster  131 . The informer  217 A may include a set of event queues (not shown), which allow serial handling of events per custom resource instance. For example, having a deployment controller servicing an event to change an image and an event to scale a pod up in parallel may result in an inconsistent state of the deployment. The set of event queues allow the informer  217 A to process events serially on a custom resource by custom resource basis. In some embodiments, the set of event queues may also help to deduplicate conflicting events. 
     As can be seen in  FIG.  2 A , CRD controllers  219  of the cluster  131  may run in a single host service (e.g., the controller-manager  217 ), which executes the correct WASM  224  when a new API Event is received (the WASM module  224  corresponding to the CRD controller  219  that services that API event type). In this way, each CRD controller  219  may be used dynamically like a function (e.g., on demand), and does not run continuously in the background. This approach significantly reduces the overhead on memory and CPU resources, because CRD controllers  219  are “woken up” from their inactive state and executed only when they are actually needed. CRD controllers  219  are also all hosted in the same service, which removes the need for intercommunication between the various dependent CRD controllers  219 , and reduces the network overhead. If the service that hosts these CRD controllers  219  is the controller-manager  217 , this may also remove the need for network traffic to communicate with Kubernetes itself. This approach significantly reduces the overhead on memory and CPU resources that is accrued from adding new extensions to the cluster. 
     In some embodiments, a vertical auto-scaler (not shown) may be used to monitor resource consumption of the pod on which the controller-manager  217  is executing. For example, as larger numbers of WASMs are added to the controller-manager  217 , the resource consumption may need to be adjusted. For example, additional processing device resources may need to be made available to the controller-manager  217  for it handle the added load. This avoids the need for horizontal auto-scaling which would involve the creation of additional CRD controllers, which is not possible as CRD controllers are single threaded. Although described with respect to WASMs, any appropriate isolation module may be utilized as shown in  FIG.  2 B . 
     In some embodiments, each CRD controller  219  (e.g., the isolation module thereof) may be hosted on a special service that is separate from the controller-manager  217 , where the core controllers  218  may still be hosted.  FIG.  3    illustrates a system  300  where the CRD controllers  219  may be hosted on a special controller manager service  222  that is separate from the controller-manager  217 , where the core controllers  218  may still be hosted. System  300  may function similarly to system  200  described with respect to  FIGS.  2 A and  2 B . When the CRD controllers  219  are hosted on the special controller manager service  222 , the lifecycle of each CRD  219  may be managed directly by the container orchestration engine  214  and a higher level of isolation may be achieved. 
     Although  FIG.  2 A  is described with respect to WASMs, any suitable isolation module may be used.  FIG.  4    illustrates system  400 , which is an embodiment of the present disclosure wherein the isolation modules are implemented as Go Plugins. Golang is a statically typed, compiled programming language, and programs written in Golang may be comprised of packages. A Go plugin may be a package with exported functions and variables that may be compiled as shared object libraries (e.g., produces a shared object library file when compiled) that can be loaded and bound to dynamically at run-time. A Go Plugin may allow for the creation of modular programs using these shared object libraries. 
     The system  400  may operate similarly to the system  200  described with respect to  FIGS.  2 A and  2 B , but may utilize Go-plugins  424  instead of WASMs. More specifically, upon creation of a new CRD controller  419 A, a user/developer may compile the CRD controller  419 A into a Go-plugin  424 A and provide the Go-plugin  424 A to the controller-manager  417  (e.g., via OperatorSDK or OperatorHub as discussed hereinabove). A Golang interface  423  may mount the Go-plugin  424 A and provide I/O between the Go-plugin  424 A and the controller-manager  417 . 
     When a new CRD controller  419 A is generated, it includes controller metadata that describes what type events the CRD controller is to service. Because informer  417 A is a programmable component, when the Golang interface  423  mounts the Go-plugin  424 A running the new CRD controller  419 A, it may update the informer  417 A with the controller metadata of CRD controller  419 A to indicate to the informer  417 A what types of events the newly mounted CRD controller  419 A will service. In this way, when an event of a certain type is picked up by the informer  417 A, it may determine which CRD controller  419  is servicing that event type, and inform the Golang interface  423  that an event pertaining to that CRD controller  419  has been picked up as discussed above with respect to  FIGS.  2 A and  2 B . The Golang interface  423  may execute the Go-plugin  424  that the relevant CRD controller  419  has been compiled into. It should be noted that Go-Plugins do not provide failure isolation as discussed above with respect to WASMs in  FIG.  2 A . 
       FIG.  5    is a flow diagram of a method  500  for extending the Kubernetes API in-process using isolation modules, in accordance with some embodiments of the present disclosure. Method  500  may be performed by processing logic that may comprise hardware (e.g., circuitry, dedicated logic, programmable logic, a processor, a processing device, a central processing unit (CPU), a system-on-chip (SoC), etc.), software (e.g., instructions running/executing on a processing device), firmware (e.g., microcode), or a combination thereof. in some embodiments, the method  500  may be performed by a computing device (e.g., computing device  110  illustrated in  FIGS.  2 A and  2 B ). 
     At block  505 , computing device  110  may compile each of one or more custom resource definition (CRD) controllers  219  into a respective web assembly module (WASM)  224  and provide each of the WASM  224   s  to the controller-manager  217  (e.g., via OperatorSDK or OperatorHub as discussed hereinabove). The one or more WASMs  224  may be hosted in a single service (e.g., the controller-manager  217 ). The controller-manager  217  may thus act as the host of each CRD controller  219  and may include a WASM runtime (not shown) that facilitates execution of each WASM  224 . The WASM runtime may include an interface (WASM interface  223 ) comprising a set of APIs for facilitating interactions between the WASMs  224  and their host environment (e.g., controller-manager  217 ). These APIs may provide I/O between the controller-manager  217  and each WASM  224  (e.g., on behalf of each respective CRD controller  219 ). The WASM interface  223  may be any appropriate interface such as the WebAssembly System Interface (WASI), for example. The WASM runtime may also provide for the isolation of individual executions of WASM  224  from the underlying OS and/or the host application (e.g., controller-manager  217 ) that runs the module, thereby isolating failure modes of each CRD controller  219 . When the WASM runtime executes a function in a WASM  224 , it may provide the result of the function (e.g., success/fail) back to the control plane  215 , for example. However, in the event of a failure, this failure is not propagated to the host application (e.g., the controller-manager  217 ). Therefore, if a single CRD controller  219  crashes it will not result in other CRD controllers  219  crashing with it, nor will the host application (e.g., controller-manager  217 ) crash along with it. 
     At block  510 , computing device  110  may monitor for application program interface (API) events serviced by each of the one or more CRD controllers using the informer  217 A. 
     At block  515 , computing device  110  may, in response to detecting an API event serviced by a particular CRD controller  219  of the one or more CRD controllers  219 , execute the corresponding WASM  224  of the particular CRD controller  219 . 
       FIG.  6    is a flow diagram of a method  600  for extending the Kubernetes API in-process using isolation modules managed by an isolation module interface, in accordance with some embodiments of the present disclosure. Method  600  may be performed by processing logic that may comprise hardware (e.g., circuitry, dedicated logic, programmable logic, a processor, a processing device, a central processing unit (CPU), a system-on-chip (SoC), etc.), software (e.g., instructions running/executing on a processing device), firmware (e.g., microcode), or a combination thereof. In some embodiments, the method  600  may be performed by a computing device (e.g., computing device  110  illustrated in  FIGS.  2 A and  2 B ). 
     Referring also to  FIG.  2 B , upon creation of a new CRD and corresponding CRD controller  219 A by a user, at block  605  the computing device  110  may compile the CRD controller  219 A into a respective isolation module  224 A using any appropriate language tooling and code generation components. Examples of such components may include a code generator for generating optimized machine code from the code of a CRD controller  219  by parallelizing compilation on a function-by-function level. The compilation of the CRD controller  219 A may be initiated by the user, and the computing device  110  may provide this isolation module  224 A and any associated controller metadata of the CRD controller  219 A to the controller-manager  217  (e.g., via OperatorSDK or OperatorHub as discussed hereinabove). In some embodiments where Javascript is utilized to define the CRD controller  219 A, the computing device  110  may provide the CRD controller  219 A directly to the controller-manager  217 , which may compile the CRD controller  219 A into a respective isolation module  224 A. In response to receiving isolation module  224 A, the isolation module interface  223  may mount the isolation module  224 A and provide I/O between the isolation module  224 A and the controller-manager  217 . In this way, one or more isolation modules  224  may be hosted in a single service (e.g., the controller-manager  217 ). The controller-manager  217  may thus act as the host of each CRD controller  219  and may include an isolation module runtime (not shown) that facilitates execution of each WASM  224 . 
     At block  610 , the computing device  110  may update an informer  217 A of the controller-manager  217  with the controller metadata of the CRD controller  219 A, so that the informer  217 A can monitor for API events serviced by the CRD controller  219 A based on the controller metadata. As discussed above, when a new CRD controller  219 A is generated, it includes controller metadata that describes what type of events the CRD controller  219 A is to service. Because informer  217 A is a programmable component, when the isolation module interface  223  mounts the isolation module  224 A running the new CRD controller  219 A, it may update the informer  217 A with the controller metadata of CRD controller  219 A to indicate to the informer  217 A what types of events the newly mounted CRD controller  219 A will service. 
     At block  615 , when an event of a certain type is picked up by the informer  217 A, it may determine which CRD controller  219  is servicing that event type, and inform the isolation module interface  223  that an event pertaining to that CRD controller  219  has been picked up. The informer  217 A may include a set of event queues (not shown), which allow serial handling of events per custom resource instance. For example, having a deployment controller servicing an event to change an image and an event to scale a pod up in parallel may result in an inconsistent state of the deployment. The set of event queues allow the informer  217 A to process events serially on a custom resource by custom resource basis. In some embodiments, the set of event queues may also help to deduplicate conflicting events. The isolation module interface  223  may execute the isolation module  224  that the relevant CRD controller  219 A has been compiled into. As can be seen in  FIG.  2 B , CRD controllers  219  of the cluster  131  may run in a single service (e.g., the controller-manager  217 ), which executes the correct isolation module  224  when a new API event is received (the isolation module corresponding to the CRD controller that services that API event type). In this way, each CRD controller  219  may be used like a function (e.g., on demand), and does not run continuously in the background. This approach significantly reduces the overhead on memory and CPU resources, because CRD controllers  219  are “woken up” and executed only when they are actually needed. 
       FIG.  7    illustrates a diagrammatic representation of a machine in the example form of a computer system  700  within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein for extending the Kubernetes API in-process. More specifically, the machine may compile each of one or more custom resource definition (CRD) controllers into a respective isolation module such as a web assembly module (WASM) as they are created. The one or more isolation modules may all be hosted in the same service (e.g., the controller manager service), which may include an isolation module runtime to facilitate compilation of CRD controllers. The isolation module runtime may also provide an interface for managing the lifecycle of the CRD controllers and facilitating communication between the service and the isolation modules. Upon compiling a CRD controller into an isolation module, the interface may update an informer of the service with controller metadata of the CRD controller to enable the interface to monitor for application program interface (API) events serviced by the CRD controller. In response to detecting an API event serviced by a CRD controller of the one or more CRD controllers, the informer may indicate to the interface that an event has been detected and the CRD controller that it pertains to. The interface may execute a respective isolation module of the CRD controller such that the CRD controller may service the detected event. In this way, the CRD controllers do not run in background (e.g., as background processes) while their respective isolation module is not executing. 
     In alternative embodiments, the machine may be connected (e.g., networked) to other machines in a local area network (LAN), an intranet, an extranet, or the Internet. The machine may operate in the capacity of a server or a client machine in a client-server network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a server, a network router, a switch or bridge, a hub, an access point, a network access control device, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. In one embodiment, computer system  700  may be representative of a server. 
     The exemplary computer system  700  includes a processing device  702 , a main memory  704  (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM), a static memory  706  (e.g., flash memory, static random access memory (SRAM), etc.), and a data storage device  718 , which communicate with each other via a bus  730 . Any of the signals provided over various buses described herein may be time multiplexed with other signals and provided over one or more common buses. Additionally, the interconnection between circuit components or blocks may be shown as buses or as single signal lines. Each of the buses may alternatively be one or more single signal lines and each of the single signal lines may alternatively be buses. 
     Computing device  700  may further include a network interface device  708  which may communicate with a network  720 . The computing device  700  also may include a video display unit  710  (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), an alphanumeric input device  712  (e.g., a keyboard), a cursor control device  714  (e.g., a mouse) and an acoustic signal generation device  716  (e.g., a speaker). In one embodiment, video display unit  710 , alphanumeric input device  712 , and cursor control device  714  may be combined into a single component or device (e.g., an LCD touch screen). 
     Processing device  702  represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processing device may be complex instruction set computing (CISC) microprocessor, reduced instruction set computer (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or processor implementing other instruction sets, or processors implementing a combination of instruction sets. Processing device  702  may also be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. The processing device  702  is configured to execute API extension instructions  725 , for performing the operations and steps discussed herein. 
     The data storage device  718  may include a machine-readable storage medium  728 , on which is stored one or more sets of API extension instructions  725  (e.g., software) embodying any one or more of the methodologies of functions described herein. The API extension instructions  725  may also reside, completely or at least partially, within the main memory  704  or within the processing device  702  during execution thereof by the computer system  700 ; the main memory  704  and the processing device  702  also constituting machine-readable storage media. The API extension instructions  725  may further be transmitted or received over a network  720  via the network interface device  708 . 
     The machine-readable storage medium  728  may also be used to store instructions to perform a method for determining if a controller that can service a CRD exists, as described herein. While the machine-readable storage medium  728  is shown in an exemplary embodiment to be a single medium, the term “machine-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) that store the one or more sets of instructions. A machine-readable medium includes any mechanism for storing information in a form (e.g., software, processing application) readable by a machine (e.g., a computer). The machine-readable medium may include, but is not limited to, magnetic storage medium (e.g., floppy diskette); optical storage medium (e.g., CD-ROM); magneto-optical storage medium; read-only memory (ROM); random-access memory (RAM); erasable programmable memory (e.g., EPROM and EEPROM); flash memory; or another type of medium suitable for storing electronic instructions. 
     Example 1 is a method comprising: compiling, by a processing device, each of one or more custom resource definition (CRD) controllers that are created in a cluster at run-time into a respective isolation module to generate one or more isolation modules, wherein the one or more isolation modules are all hosted in a service; monitoring for application program interface (API) events serviced by each of the one or more CRD controllers; and in response to detecting an API event serviced by a CRD controller of the one or more CRD controllers, executing a respective isolation module of the CRD controller. 
     Example 2 is the method of example 1, further comprising: updating an informer with controller metadata of each of the one or more CRD controllers, wherein the informer performs the monitoring for API events observed/reconciled by each of the one or more CRD controllers based on the controller metadata of each of the one or more CRD controllers. 
     Example 3 is the method of example 2, further comprising: in response to detecting an API event serviced by a CRD controller of the one or more CRD controllers, transmitting an indication of the CRD controller to the isolation module interface, wherein the isolation module interface executes the respective isolation module of the CRD controller using a set of APIs that facilitate input/output operations between each of the one or more isolation modules and the service. 
     Example 4 is the method of example 1, wherein each of the one or more isolation modules are isolated from the service. 
     Example 5 is the method of example 1, wherein the service comprises a controller-manager service executing on a control plane of a container orchestration engine. 
     Example 6 is the method of example 1, wherein the service comprises a service that is separate from a controller-manager service executing on a control plane of a container orchestration engine. 
     Example 7 is the method of example 1, further comprising: monitoring resource consumption of a pod on which the one or more isolation modules are hosted using a vertical autoscaler; and in response to determining that additional resources are needed, allocating additional resources to the pod. 
     Example 8 is a system comprising: a memory; and a processing device operatively coupled to the memory, the processing device to: compile, each of one or more custom resource definition (CRD) controllers that are created in a cluster at run-time into a respective isolation module to generate one or more isolation modules, wherein the one or more isolation modules are all hosted in a service; monitor for application program interface (API) events serviced by each of the one or more CRD controllers; and in response to detecting an API event serviced by a CRD controller of the one or more CRD controllers, execute a respective isolation module of the CRD controller. 
     Example 9 is the system of example 8, wherein the processing device is further to: update an informer with controller metadata of each of the one or more CRD controllers, wherein the informer performs the monitoring for API events observed/reconciled by each of the one or more CRD controllers based on the controller metadata of each of the one or more CRD controllers. 
     Example 10 is the system of example 9, wherein the processing device is further to: in response to detecting an API event serviced by a CRD controller of the one or more CRD controllers, transmit an indication of the CRD controller to an isolation module interface, wherein the isolation module interface executes the respective isolation module of the CRD controller using a set of APIs that facilitate input/output operations between each of the one or more isolation modules and the service. 
     Example 11 is the system of example 8, wherein each of the one or more isolation modules are isolated from the service. 
     Example 12 is the system of example 8, wherein the service comprises a controller-manager service executing on a control plane of a container orchestration engine. 
     Example 13 is the system of example 8, wherein the service comprises a service that is separate from a controller-manager service executing on a control plane of a container orchestration engine. 
     Example 14 is the system of example 8, wherein the processing device is further to: monitor resource consumption of a pod on which the one or more isolation modules are hosted using a vertical autoscaler; and in response to determining that additional resources are needed, allocate additional resources to the pod. 
     Example 15 is a system comprising a memory; and a processing device, operatively coupled to the memory, the processing device to: in response to creation of a CRD controller, compile the CRD controller into a respective isolation module, the respective isolation module hosted on a service along with one or more other isolation modules; update an informer of the service with controller metadata of the CRD controller, the informer to monitor for application program interface (API) events serviced by the CRD controller based on the controller metadata; and in response to detecting an API event serviced by the CRD controller, executing, by an isolation module interface, the respective isolation module of the CRD controller, wherein the CRD controller does not run as a background process while the respective isolation module is not executing. 
     Example 16 is the system of example 15, wherein the controller metadata of the CRD controller indicates API events serviced by the CRD controller. 
     Example 17 is the system of example 16, wherein the processing device is further to: in response to detecting an API event serviced by the CRD controller, transmit an indication of the CRD controller to the isolation module interface, wherein the isolation module interface executes the respective isolation module of the CRD controller using a set of APIs that facilitate input/output operations between the isolation module and the service. 
     Example 18 is the system of example 15, wherein the isolation module isolates the CRD controller from the service. 
     Example 19 is the system of example 15, wherein the service comprises a controller-manager service executing on a control plane of a container orchestration engine. 
     Example 20 is the system of example 15, wherein the isolation module comprises a web assembly module (WASM). 
     Example 21 is the system of example 15, wherein the processing device is further to: monitor resource consumption of a pod on which the isolation modules are hosted using a vertical autoscaler; and in response to determining that additional resources are needed, allocate additional resources to the pod. 
     Example 22 is a non-transitory computer readable medium, having instructions stored thereon which, when executed by a processing device, cause the processing device to: in response to creation of a CRD controller, compile, by the processing device, the CRD controller into a respective isolation module, the respective isolation module hosted on a service along with one or more other isolation modules; update an informer of the service with controller metadata of the CRD controller, the informer to monitor for application program interface (API) events serviced by the CRD controller based on the controller metadata; and in response to detecting an API event serviced by the CRD controller, executing, by an isolation module interface, the respective isolation module of the CRD controller, wherein the CRD controller does not run as a background process while the respective isolation module is not executing. 
     Example 23 is the non-transitory computer readable medium of example 22, wherein the controller metadata of the CRD controller indicates API events serviced by the CRD controller. 
     Example 24 is the non-transitory computer readable medium of example 23, wherein the processing device is further to: in response to detecting an API event serviced by the CRD controller, transmit an indication of the CRD controller to the isolation module interface, wherein the isolation module interface executes the respective isolation module of the CRD controller using a set of APIs that facilitate input/output operations between the isolation module and the service. 
     Example 25 is the non-transitory computer readable medium of example 22, wherein the isolation module isolates the CRD controller from the service. 
     Example 26 is the non-transitory computer readable medium of example 22, wherein the service comprises a controller-manager service executing on a control plane of a container orchestration engine. 
     Example 27 is the non-transitory computer readable medium of example 22, wherein the isolation module comprises a web assembly module (WASM). 
     Example 28 is the non-transitory computer readable medium of example 22, wherein the processing device is further to: monitor resource consumption of a pod on which the isolation modules are hosted using a vertical autoscaler; and in response to determining that additional resources are needed, allocate additional resources to the pod. 
     Example 29 is an apparatus comprising: means for compiling each of one or more custom resource definition (CRD) controllers that are created in a cluster at run-time into a respective isolation module to generate one or more isolation modules, wherein the one or more isolation modules are all hosted in a service; means for monitoring for application program interface (API) events serviced by each of the one or more CRD controllers; and means for, in response to detecting an API event serviced by a CRD controller of the one or more CRD controllers, executing a respective isolation module of the CRD controller. 
     Example 30 is the apparatus of example 29, further comprising: means for updating an informer with controller metadata of each of the one or more CRD controllers, wherein the informer performs the monitoring for API events observed/reconciled by each of the one or more CRD controllers based on the controller metadata of each of the one or more CRD controllers. 
     Example 31 is the apparatus of example 30, further comprising: means for, in response to detecting an API event serviced by a CRD controller of the one or more CRD controllers, transmitting an indication of the CRD controller to the isolation module interface, wherein the isolation module interface executes the respective isolation module of the CRD controller using a set of APIs that facilitate input/output operations between each of the one or more isolation modules and the service. 
     Example 32 is the apparatus of example 29, wherein each of the one or more isolation modules are isolated from the service. 
     Example 33 is the apparatus of example 29, wherein the service comprises a controller-manager service executing on a control plane of a container orchestration engine. 
     Example 34 is the apparatus of example 29, wherein the service comprises a controller-manager service executing on a control plane of a container orchestration engine. 
     Example 35 is the apparatus of example 29, wherein each of the one or more isolation modules comprises a web assembly module (WASM). 
     Unless specifically stated otherwise, terms such as “receiving,” “routing,” “updating,” “providing,” or the like, refer to actions and processes performed or implemented by computing devices that manipulates and transforms data represented as physical (electronic) quantities within the computing device&#39;s registers and memories into other data similarly represented as physical quantities within the computing device memories or registers or other such information storage, transmission or display devices. Also, the terms “first,” “second,” “third,” “fourth,” etc., as used herein are meant as labels to distinguish among different elements and may not necessarily have an ordinal meaning according to their numerical designation. 
     Examples described herein also relate to an apparatus for performing the operations described herein. This apparatus may be specially constructed for the required purposes, or it may comprise a general purpose computing device selectively programmed by a computer program stored in the computing device. Such a computer program may be stored in a computer-readable non-transitory storage medium. 
     The methods and illustrative examples described herein are not inherently related to any particular computer or other apparatus. Various general purpose systems may be used in accordance with the teachings described herein, or it may prove convenient to construct more specialized apparatus to perform the required method steps. The required structure for a variety of these systems will appear as set forth in the description above. 
     The above description is intended to be illustrative, and not restrictive. Although the present disclosure has been described with references to specific illustrative examples, it will be recognized that the present disclosure is not limited to the examples described. The scope of the disclosure should be determined with reference to the following claims, along with the full scope of equivalents to which the claims are entitled. 
     As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises”, “comprising”, “includes”, and/or “including”, when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Therefore, the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. 
     It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may in fact be executed substantially concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved. 
     Although the method operations were described in a specific order, it should be understood that other operations may be performed in between described operations, described operations may be adjusted so that they occur at slightly different times or the described operations may be distributed in a system which allows the occurrence of the processing operations at various intervals associated with the processing. 
     Various units, circuits, or other components may be described or claimed as “configured to” or “configurable to” perform a task or tasks. In such contexts, the phrase “configured to” or “configurable to” is used to connote structure by indicating that the units/circuits/components include structure (e.g., circuitry) that performs the task or tasks during operation. As such, the unit/circuit/component can be said to be configured to perform the task, or configurable to perform the task, even when the specified unit/circuit/component is not currently operational (e.g., is not on). The units/circuits/components used with the “configured to” or “configurable to” language include hardware—for example, circuits, memory storing program instructions executable to implement the operation, etc. Reciting that a unit/circuit/component is “configured to” perform one or more tasks, or is “configurable to” perform one or more tasks, is expressly intended not to invoke 35 U.S.C. 112, sixth paragraph, for that unit/circuit/component. Additionally, “configured to” or “configurable to” can include generic structure (e.g., generic circuitry) that is manipulated by software and/or firmware (e.g., an FPGA or a general-purpose processor executing software) to operate in manner that is capable of performing the task(s) at issue. “Configured to” may also include adapting a manufacturing process (e.g., a semiconductor fabrication facility) to fabricate devices (e.g., integrated circuits) that are adapted to implement or perform one or more tasks. “Configurable to” is expressly intended not to apply to blank media, an unprogrammed processor or unprogrammed generic computer, or an unprogrammed programmable logic device, programmable gate array, or other unprogrammed device, unless accompanied by programmed media that confers the ability to the unprogrammed device to be configured to perform the disclosed function(s). 
     The foregoing description, for the purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to best explain the principles of the embodiments and its practical applications, to thereby enable others skilled in the art to best utilize the embodiments and various modifications as may be suited to the particular use contemplated. Accordingly, the present embodiments are to be considered as illustrative and not restrictive, and the invention is not to be limited to the details given herein, but may be modified within the scope and equivalents of the appended claims.