[Tracing] Dynamic tracing in Linux using uprobe and kprobe

This post is based on the following articles to trace again by myself. Through this process, I can get a deeper impression of the user-space tracing using uprobe.
https://opensource.com/article/17/7/dynamic-tracing-linux-user-and-kernel-space
http://www.brendangregg.com/perf.html

[Darknet][Python] Support using Numpy array as image input instead of image path in Darknet Python API

When I dealt with Darknet's detect function in Python, I found that there is only one way for accepting an image file path as the argument, which is convenient for inferencing images from files.  But, it is not very well for images from video files or cameras.
Due to this reason, I dig out some solutions already on the Internet, and modify as follows:

[Spring Boot] My first Spring Boot Project for demo

Recently, the term "Microservices" is very hot in the cloud service/architecture and it gets my attention. So, I want to learn more about what it is. Furthermore, I also notice Spring Cloud is a very popular microservice framework based on Spring Boot and it provides tools for developers to quickly build some of the common patterns in distributed systems (e.g. configuration management, service discovery, circuit breakers, intelligent routing, micro-proxy, control bus, one-time tokens, global locks, leadership election, distributed sessions, cluster state)

[Dockerfile] Some of the little skills used in Dockerfile

I collect some of the little skills used in my Dockerfiles and also keep in the record for my reference.

[IJava] A Jupyter kernel for executing Java code

As we know that Jupyter’s is a Python-based solution, and it provides a lot of convenience for interactivity with Python code. But, I just wonder if Jupyter can use Java as its kernel, and it can. I find that it allows to integrate additional "kernels" with Java capabilities and someone has done it -IJava: https://github.com/SpencerPark/IJava

[Kubernetes] The simple introduction of VPA

VPA stands for Vertical Pod Autoscaling, which frees you from having to think about what values to specify for a container's CPU and memory requests. The autoscaler can recommend values for CPU and memory requests, or it can automatically update values for CPU and memory requests.

Before using VPA, we need to install Metrics Server first as follows:

[Kubernetes] Call Kubernetes APIs

If you want to use Bash Shell  to call Kubernetes APIs, Here is the way to get the token and use it as follows:

Prepare variables

#APISERVER="https://10.10.0.100:6443"
$ APISERVER=$(kubectl config view | grep server | cut -f 2- -d ":" | tr -d " ")
$ TOKEN=$(kubectl describe secret $(kubectl get secrets | grep default | cut -f1 -d ' ') | \
    grep -E '^token' | cut -f2 -d':' | tr -d '\t')

Call APIs

$ curl $APISERVER/api --header "Authorization: Bearer ${TOKEN//[[:space:]]/}" --insecure
{
  "kind": "APIVersions",
  "versions": [
    "v1"
  ],
  "serverAddressByClientCIDRs": [
    {
      "clientCIDR": "0.0.0.0/0",
      "serverAddress": "192.168.0.100:6443"
    }
  ]
}

[Docker] Troubleshooting to docker private registry

I create a private docker registry as follows and sometimes it cannot reply the http request.

$ sudo docker run -p 7443:7443 --restart=always \
  -v /raid/registry2:/var/lib/registry \
  -e REGISTRY_HTTP_ADDR=0.0.0.0:7443 \
  --name registry registry:2

$ curl -v http://192.168.10.10:7443/v2/_catalog
*   Trying 192.168.10.10...
* TCP_NODELAY set
* Connected to 192.168.10.10 (192.168.10.10) port 7443 (#0)
> GET /v2/_catalog HTTP/1.1
> Host: 192.168.0.109:7443
> User-Agent: curl/7.58.0
> Accept: */*
...(hang)...