java-spring-api VS elasticsearch-mapper-attachments

We start with an example app that will generate logs. We will use a Java API from a previous post on deploying with Helm. For the complete code for this section take a look at this branch here. We see that we have a logback.xml where we specify to write logs to a file. (See here te relevant part)

We will add OAS3 documentation to a Java Spring API from a previous post on Kubernetes Helm. The completed code for this post is found on github here.

We will start off with a Spring Boot API from this previous post on deployments with helm. We will extend this application with a database connection and use testcontainers in the integration tests. The complete code for this post can be on github here.

In this post we will take the Kubernetes deployment we build in several previous posts on Kubernetes and turn it into a Helm package. The final project code can be found on github here.

We are going to reuse an application that we made in an earlier post (found here). It’s just a basic setup with a greetings endpoint. The code that is used is found on github here.

Now we have the architecture in place. We only need to add a Prometheus Rule for our application that triggers an alert. We build from the Spring Boot application that we deployed in the cluster in an earlier post. The configuration of this rule is done as another CRD and is found on this branch on github.

The full code can be found on this branch. Here are the steps I took.

We start off with the example application from an earlier post. The complete autoscaling code can be found on github.

If you just can’t wait to dive into the code. The complete configuration can be found on github.

With our library in place we can remove the pipeline config out of our Jenkins file and refer to it as follows: (or checkout the code here)

Elasticsearch - www.elastic.co/

The increasing complexity of modern systems led to the rise of AIOps (Artificial Intelligence for IT Operations) and observability practices. AIOps leveraged machine learning algorithms to automate problem detection, analysis, and resolution. Observability focused on gaining insights into system behaviour through metrics, logs, and traces. As a result, tools like Prometheus, Grafana, and ELK stack (Elasticsearch, Logstash, Kibana) gained popularity.

My only experience with NetFlow collection is on my home firewall/router running pfSense Community Edition, which is free to download and can be installed on a wide assortment of X86 hardware. I installed the Softflowd package, which exports NetFlow data to a dedicated Elasticsearch/Logstash/Kibana (ELK) server on my LAN. I believe Security Onion and ElastiFlow also can be NetFlow collectors.

Elasticsearch, Logstash, and Kibana (ELK) Stack: An open source suite of tools for log management and analysis, providing real-time insights into security events.

Recently I have taken an interest in big data. https://neo4j.com/ , https://cassandra.apache.org/ , https://clickhouse.com/, https://www.elastic.co/ - are all databases I have experience with. Neo4j and Cassandra only as a hobby, but Clickhouse I have used in production, and Elasticsearch I have used for some 7 years now.

Buy an enterprise-class, wired router like the Negate 2100 ($349 USD), which runs pfSense, and configure the Deco AXE5400 device(s) to operate in Access Point Mode. Then install the Softflowd package through the pfSense web UI. Softflowd will collect and export NetFlow data to a NefFlow collector, which is the separate computer/VM/container referred to above, running software like Security Onion, ElastiFlow, or Elasticsearch/Logstash/Kibana (ELK).

If you’re unaware, elastic search is some like enterprise level search shit. They just put it in a theme. https://www.elastic.co

systemctl status kibana ● kibana.service - Kibana Loaded: loaded (/etc/systemd/system/kibana.service; enabled; vendor preset: enabled) Active: active (running) since Tue 2023-03-28 09:40:05 UTC; 33min ago Docs: https://www.elastic.co Main PID: 3168 (node) Tasks: 11 (limit: 9432) Memory: 303.3M CPU: 35.190s CGroup: /system.slice/kibana.service └─3168 /usr/share/kibana/bin/../node/bin/node /usr/share/kibana/bin/../src/cli/dist --logging.dest=/var/log/kibana/kibana.log --pid.file=/run/kibana/kibana.pid "--deprecation.skip_deprecated_settings[0]=logging.dest" Mar 28 09:40:05 wazuh systemd[1]: Started Kibana.