When people focusing more on the security of containers, distroless based images are frequently used to reduce the attack surface. In these images, the package manager, the non-dependent modules or libraries, even the shells are stripped off, only the app and its required dependencies are kept. For the statically linked executable, produced by golang for example, we can even use “scratch” as the base.

The potential exploit of vulnerability is therefore greatly reduced. But, on the other hand, it is difficult to troubleshoot the application if even the shell is not available, leaving only the logs from the app.

In…

I am tracking some EOD job status to check if it starts late than the scheduled time, or overruns. Created as a Prometheus client program with Golang, the data are collected as a Prometheus gauge metric to be displayed in the Grafana dashboard using the table format.

Worked and tested perfectly on my laptop.

However, I had the typical problem that we commonly encountered. Yes, it worked on my laptop ;) It doesn’t work when I deployed into Kubernetes running as a container!

After some troubleshooting, I realized it's the default timezone in the container that causes the issue.

Verify

Launch…

I have a request to perform some Google API search for some keywords and save the result (the URLs) into text files. It's a good chance to use Go’s concurrency pattern to assembly the pipeline, therefore, parallelizing the IO and CPU.

Google Search API

Given a keyword, say a category, the following function calls Google search API using go-resty, then picks up the respective Links from the result using the gjson library.

func SearchGoogle(category string, count int) ([]SearchResult, error) {
 results := []SearchResult{}
 // ... skip some initial assignment
 for i := 0; i < pages; i++ {
  resp, err := client.R()…

I need to mine some business metrics from a DB2 database and present it in the Grafana dashboard. A Prometheus scraping target is to be developed.

I published a Medium paper about 3 years ago to run DB2 queries in Golang by using the DB2 ODBC/CLI driver. Following that, let's create a container image as a Prometheus scraping target and run it in Kubernetes.

The app as a scraping target

Some Golang code excerpts to describe how the data collection works,

The DB and metric struct

type MetricConfig struct {
 Name      string `yaml:"name"`
 Desc      string `yaml:"desc"`
 Sql       string `yaml:"sql"`
 Frequency string `yaml:"frequency"`
}type DBMetricsConfig struct…

I need to run some monitoring in a restricted environment. It is restricted in the sense of first its air-gapped, no internet connection is there. Secondly, the environment is kind of locked, no extra dependency modules can be installed.

The lightweight Kubernetes distribution, K3s, with its self-contained dependencies serves the requirement perfectly. This paper will show how I set up the Prometheus in the above-mentioned restricted environment.

Install K3s in the airgap server

The airgap setup for K3s is well documented. The following is a quick command list of the packages and scripts that need to be downloaded on an internet-facing server.

curl -LO https://github.com/k3s-io/k3s/releases/download/v1.21.1%2Bk3s1/k3s-airgap-images-amd64.tar.gzcurl…

Recently I need to test some J2EE integration work on IBM MQ. Since it is for testing purposes I run it as a container instead of the full-blown VM.

Preparing a custom QMSC file

Other than the default MQ settings, I need to use my custom queue definition and security controls. In the MQ container, this is achieved by defining a custom MQSC file and put it into the specific /etc/mqm directory.

Create the following file 80-my-test.mqsc

define listener(LISTENER) trptype(tcp) control(qmgr) port(1414) replace
start listener(LISTENER)define authinfo(my.authinfo) authtype(idpwos) chckclnt(reqdadm) chcklocl(optional) adoptctx(yes) replace
alter qmgr connauth(my.authinfo)
refresh security(*) type(connauth)def chl(SYSTEM.ADMIN.SVRCONN) chltype(SVRCONN) replaceset chlauth('*') type(addressmap)…

Starting from OpenShift 4.6, user workload monitoring is formally supported by introducing a second Prometheus operator instance in a new namespace called openshift-user-workload-monitoring. This paper demonstrates how a user workload can be monitored and the alerts can be created.

Turn on user workload monitoring

Create or update the following configMap in openshift-monitoring

apiVersion: v1
kind: ConfigMap
metadata:
  name: cluster-monitoring-config
  namespace: openshift-monitoring
data:
  config.yaml: |
    enableUserWorkload: true

With the enableUserWorkload key set as true, the 2nd Prometheus operator will be installed, which will create a Prometheus and Thanos Ruler as shown as below,

oc -n openshift-user-workload-monitoring get pods
NAME…

Let's explore how we can integrate an OpenID Connect (OIDC) implementation, keycloak, as an identity provider for OpenShift, other than the common one such as HTTPasswd, LDAP.

Setup Keycloak on OpenShift

Install the Keycloak operator from the OperatorHub, create a keycloak instance in the namespace of keycloak. We can access the admin web interface once the pods are running. Get the admin user and its password from the corresponding Secret object in the namespace.

Create our own realm, myrealm, first.

Secondly, create the client named idp-4-ocp. In the settings tab, select the “Access Type” as “confidential”. Set the “Validation Redirection URIs” as “https://*” for…