This article walks you through using Nocalhost to seamlessly connect your local development machine to a remote Kubernetes cluster, allowing you to use your favourite IDE to develop and debug Apache APISIX ingress controller. Giving you the ability to comfortably develop and debug your remote apps with your existing skills.
Now close the development window and end development mode.
Right-click the apisix-ingress-controller in the cluster inspector
Select End DevMode
Nocalhost will make apisix-ingress-controller end development mode, and reset the apisix-ingress-controller Pod to its original version. Enable the port-forwarding and check the result after ending development mode.
All code changes in development mode will only take effect in the development container.
After exiting the development mode, Nocalhost will reset the remote container to its original state (before the code is modified). In this way, after exiting the development mode, the modification of the code will not cause any changes or impact on the original environment.
Debugging an application is not easy, and debugging an application in the Kubernetes cluster is even more difficult. Nocalhost is here to help by providing the same debugging experience you're used in the IDE when debugging in the remote Kubernetes cluster.
Add the debug.sh file with the following shell scripts in your source code directory.
#! /bin/sh# Only add this if you are in ChinaexportGOPROXY=https://goproxy.cn# The debug scriptsdlv --headless --log --listen=:9009 --api-version=2 --accept-multiclient debug main.go -- ingress --config-path conf/config-default.yaml
The Delve listening port needs to be the same as remoteDebugPort in the configuration.
Now set a breakpoint on the healthz function. Hover over just to the left of the line number and click on the red dot. Once it’s set, visit http://127.0.0.1:8080/healthz in your local browser, GoLand should pop to the foreground. Click the play button to close the request and the progress should continue loading.
In addition, as I enable the dev.hotReload, so every time you make the code change, Nocalhost will automatically re-run the scripts in debug.sh. This is very useful when you make the code change and debug again.
Today, we’ve learned how to use Nocalhost to develop and debug the APISIX ingress controller in Kubernetes. Now, instead of waiting for slow local development processes, we can iterate quickly with an instant feedback loop and a productive cloud-native development environment.
How to debug microservices in Kubernetes with proxy, sidecar or service mesh?
This article explains three patterns/tools for debugging microservices in Kubernetes and the changes brought by the introduction of Istio for debugging microservices.
Kubernetes is arguably the best environment for running microservices so far, but the experience of debugging microservices in a Kubernetes environment may not be as user-friendly. This article will show you how to debug microservices in Kubernetes, introduce common tools, and explain how the introduction of Istio impacts debugging microservices.
Debugging microservices is vastly different from traditional monolithic applications#
The debugging of microservices has been a long-standing problem for software developers. This challenge does not exist in traditional monolithic applications because developers can leverage the debugger in IDEs to add breakpoints, modify environment variables, single-step execution, etc. for their applications, all of which provide great help in software debugging. With the popularity of Kubernetes, the debugging of microservices becomes a thorny issue, where the following issues are more complicated than the debugging of traditional monolithic applications.
A microservice often depends on multiple other microservices, some shared volumes across multiple microservices, and authorizations based on service accounts. When debugging a microservice, how do you deploy other dependent services to quickly build a latest set of staging environments?
When microservices are running on a developer’s local computer, there is usually no direct access to the services in a Kubernetes cluster. How can you debug microservices deployed in a Kubernetes cluster as if they were local services?
Usually, it takes a long process to update the code and build it into an image before pushing it to the cluster. How do you speed up the development cycle? Let’s look at the tools that address those challenges.
The main solutions for debugging microservices in Kubernetes are:
Proxy: by building a VPN, deploying a proxy in the Kubernetes cluster, and adding local debug endpoints to make the services in Kubernetes directly accessible to local applications, your architecture will look like [ local service ] <-> [ proxy ] <-> [ app in Kubernetes ].
Sidecar: Inject a sidecar into the pod of the microservice to be debugged to intercept all traffic to and from the service, so that the service can be tracked and monitored, and the service can also be debugged in this sidecar.
Service Mesh: To get an overall picture of the application, inject sidecars into all microservices so that you can get a dashboard that monitors global status.
Here are three typical open source projects that implement the above solutions, each of which can help you debug microservices from a different perspective. You can apply them at different stages of software development and they can be said to be complementary to each other.
Proxy – debugging microservices with Telepresence#
Telepresence is essentially a local proxy that proxies data volumes, environment variables, and networks in a Kubernetes cluster locally. The following diagram shows the main usage scenarios for Telepresence.
Users need to manually execute the telepresence command locally, which will automatically deploy the agent to Kubernetes. Once the agent has been deployed,
Local services will have complete access to other services in the Kubernetes cluster, environment variables, Secret, ConfigMap, etc.
Services in the cluster also have direct access to the locally exposed endpoints.
However, this approach requires users to run multiple commands while debugging locally, and in some network environments it may not be possible to establish a VPN connection to the Kubernetes cluster.
Nocalhost is a Kubernetes-based cloud development environment. To use it, you just need to install a plugin in your IDE – VS Code to extend Kubernetes and shorten the development feedback cycle. The development environment can be isolated by creating different namespaces for different users and using ServiceAccount when binding to different user corners. Nocalhost also provides a web console and API for administrators to manage different development environments.
As long as you have a Kubernetes cluster and have admin rights to the cluster, you can refer to the Nocalhost documentation to quickly start trying it out. To use the Nocalhost plugin in VS Code, you need to configure the Kubernetes cluster in the plugin first.
Select the Kubeconfig file you just exported or copy and paste the contents of the file directly into the configuration.
Then select the service you need to test and select the corresponding Dev Container. VS Code will automatically open a new code window.
Here is an example of the bookinfo sample provided by Istio. You can open the cloned code in your local IDE and click the hammer next to the code file to enter development mode. Selecting the corresponding DevContainer and Nocalhost will automatically inject a development container sidecar into the pod and automatically enter the container in the terminal, as shown in the following figure.
In development mode, the code is modified locally without rebuilding the image, and the remote development environment takes effect in real time, which can greatly accelerate the development speed. At the same time, Nocalhost also provides a server for managing the development environment and user rights, as shown in the following figure.
Service Mesh – debugging microservices with Istio#
The above method of using proxy and sidecar can only debug one service at a time. You’ll need a mesh to get the global status of the application, such as the metrics of the service obtained, and debug the performance of the service by understanding the dependency and invocation process of the service through distributed tracing. These observability features need to be implemented by injecting sidecar uniformly for all services. And, when your services are in the process of migrating from VMs to Kubernetes, using Istio can bring VMs and Kubernetes into a single network plane (as shown below), making it easy for developers to debug and do incremental migrations.
Of course, these benefits do not come without a “cost.” With the introduction of Istio, your Kubernetes services will need to adhere to the Istio naming convention and you’ll need to know how to debug microservices using the Istioctl command line and logging.
Use the istioctl analyze command to debug the deployment of microservices in your cluster, and you can use YAML files to examine the deployment of resources in a namespace or across your cluster.
Use istioctl proxy-config secret to ensure that the secret of a pod in a service mesh is loaded correctly and is valid.
In the process of microservicing applications and migrating from virtual machines to Kubernetes, developers need to make a lot of changes in their mindset and habits. By building a VPN between local and Kubernetes via proxy, developers can easily debug services in Kubernetes as if they were local services. By injecting a sidecar into the pod, you can achieve real-time debugging and speed up the development process. Finally, the Istio service mesh truly enables global observability, and you can also use tools like Tetrate Service Bridge to manage heterogeneous platforms, helping you gradually move from monolithic applications to microservices.
With the rapid development of business, the organizational structure of the technical department has been continuously expanded and adjusted horizontally and vertically. At the same time, the enterprise's production materials: application systems, have also become larger and larger. In order to adapt the application system to the adjustment of the enterprise organizational structure and sort out the boundaries of the organizational structure for application rights and responsibilities, most organizations will choose to use the "microservice" architecture to split the application system horizontally, so that the maintenance boundary of the application system is adapted The power and responsibility boundary of the organizational structure.
Generally speaking, the larger the organizational structure, the more detailed the application system will be, and the number of "microservices" will also increase. In the practice of splitting "microservices", it is easy to mark the authority and responsibility boundaries of the organizational structure into the splitting granularity of "microservices", which may lead to too fine granularity of "microservices". The problem of further sharp increase in number. In the end, the calling relationship between "microservices" is like cross-departmental collaboration, and it has become more and more complex. The problem is particularly prominent when you want to add new requirements.
While "microservices" bring convenience, it also brings additional challenges for developers: how to quickly start a complete development environment? The development requirements depend on how other colleagues can coordinate and debug? How to quickly debug these microservices?
For managers, it also brings a series of challenges: how to manage the development environment of developers? How to let new colleagues quickly develop?
Imagine what difficulties would you encounter when developing a cloud-native application consisting of 200 "microservices"?
In the era of monolithic applications, it is extremely friendly to developers. Developers use the local machine to run the application, modify the code to take effect in real time, and visit Localhost through a browser to view the code effect in real time.
Monolithic applications are different from "microservices" applications. Monolithic applications are organized as "ALL-IN-ONE". All calling relationships are limited to their own classes and functions, and the hardware requirements of applications are generally not too high.
The development of "microservice" applications is quite different. Due to the mutual dependence, when a certain function or microservice needs to be developed, all dependent services have to be activated. With the increase in the number of microservices, more and more local resources are needed to develop applications, which ultimately leads to the local inability to meet the configuration requirements of development.
Cloud Native liberates deployment and operation, What about development?#
The popularity of cloud native and Kubernetes further shields the complexity of "microservice" applications, which is mainly reflected in the deployment and operation and maintenance stages.
In order to solve the problem of environmental consistency in the development, testing, and production stages of microservice applications, modern microservice application development will package each component into a Docker image and deploy it in the form of workload. Using continuous integration and continuous deployment in the DevOps pipeline, combined with Kubernetes probes, HPA, and application self-healing capabilities, completely liberated the deployment and operation and maintenance of microservice applications.
But we overlooked a key node: the development phase.
After microservice applications are packaged with Kubernetes workloads, the problem of rapid application startup in the development process is solved. Developers only need to install a single-node Kubernetes cluster locally, such as Minikube, Kind, etc., to quickly start microservice applications.
But for developers, the original monolithic application development experience no longer exists. Since the application is difficult to run outside of the Docker container, each code modification needs to go through the following steps:
Execute docker build to build the image
Execute docker tag to tag the image
Execute docker push to push the image to the warehouse
Modify the image version of the Kubernetes workload
Wait for the mirror pull to end
Wait for the Pod to rebuild
View the modified code effect
This directly slows down the cyclic feedback process of development, and each modification requires several minutes or even ten minutes of waiting time.
Currently, Tencent Cloud CODING DevOps (nearly 200 microservices) is using Nocalhost for development. Practice has verified that Nocalhost can greatly improve development efficiency and shorten feedback loops.