Metrics on Digi Hunch

Kubernetes Operator

Thu, 07 Apr 2022 09:39:00 -0400

Kubernetes has a number of tools to automate the deployment of a single workload. In previous posts, we had covered Helm and Kustomize. What are left unresolved is how to maintain the status of workload after deployment is completed. In this post, I will give an introduction to Kubernetes Operator. Compared with Helm (templating approach) and Kustomize (patching approach), Kubernetes Operator follows the operator pattern. Operators are usually provided by the developer of the application.

Operator Pattern

In Kubernetes, we know that a controller takes care of routine tasks to ensure that desired state expressed by Kubernetes resource types matches the current state. One example is that the Deployment controller ensures the number of pods running matches the amount specified in the replica field. Controller is the key to ensure that resources can be managed by declarative manifests for Kubernetes resources.

Kubernetes makes use of controller pattern throughout its own design. One of its key component, Controller Manager, is a collection of many controllers. Each controller is in charge of a control loop, responsible for listening the object it manages. Another component, Kube-scheduler, is also a special type of Controller. The kube-scheduler monitors unscheduled Pod and health of nodes and determines the best Node to schedule the new Pod to. Then it writes the decision to etcd store for kubelet to execute.

This controller pattern is fairly successful in what it does and we can extend the use of it. Beyond the built-in resource types, we can create our own custom resource definitions (CRDs), and create controllers that watches for the manifest that declares custom resources (CRs). The controller ensures that the resource status matches their specifications. This is also known as reconciliation, which is implemented as a control loop. Operator pattern can be illustrated in the diagram below:

Operator Pattern

Technically, there is no difference between a controller and an operator. What makes an Operator (used to install workload) different than a native Kubernetes controller, are two things. First, an Operator usually needs CRDs because the built-in resource types are insufficient. Second, the operator reflects the domain knowledge to keep the target workload running. For example, stateful workloads such as database needs their operational steps executed in certain orders.

On Operator Pattern, CNCF published a whitepaper with a deeper review. This white paper is the best reference for a good understanding of the Operator Pattern.

Custom Resource Definition

The built-in controllers work with built-in objects (pre-defined APIs). Custom operators usually need their own APIs to function. To extend Kubernetes API, we define the schema of these APIs in the form of CRDs (custom resource definitions) using OpenAPIv3 standard. Then, we can declare Custom Resources (CRs) in compliance with the schema. The OpenAPIv3 schema in the CRD resource tells validating web hook (admission control) how to validate the schema when we send an CR in to API server.

When we work with third-party operators, they usually provide CRDs along with the operator implementation. For example, in my operator example project, we have a minimalist CRD WordPress with one property: sqlRootPassword and we can declare a CR as in this example. For a more realistic use case, we can take a look at Kiali CRD. In the next section, we’ll use it along with Kiali operator to install Kiali.

Operator Usage

Like Artifact Hub to Helm, OperatorHub is a public registry of most used Kubernetes Operators. In this section, we will take an example of using Operators. We will install Kiali as an add-on to Istio using Kiali CR and operator, which also depends on Prometheus to be installed using Prometheus Operator first. Note that the Kiali installation outlined in this section is not the the quick-start install manifests from Istio’s sample directory. For Kiali on production system we have to customize the installation.

Suppose we have installed Istio, we can then install Prometheus operator using Helm. The Prometheus operator will install Prometheus. Then we use Helm again to install Kiali operator. The Kiali operator will watch for creation of Kiali CRD, to deploy services:

$ helm install -f prometheus-values.yaml --namespace istio-system --repo https://prometheus-community.github.io/helm-charts --version 13.6.0 istio-prometheus prometheus --insecure-skip-tls-verify
$ helm install -f kiali-operator-values.yaml --namespace kiali-operator --repo https://kiali.org/helm-charts --version 1.45.0 kiali-op kiali-operator --create-namespace
$ kubectl apply -f kiali-cr.yaml

I include example content for each file in the commands above on Github gist (prometheus-values.yalm, kiali-operator-values.yaml and kiali-cr.yaml). For more options for installing Kiali, refer to their documentation.

I use this example to install Kiali and it includes two Operators, the Prometheus Operator and the Kiali Operator. The Prometheus Operator is one of the first ever written Kubernetes Operator. As soon as the operator is deployed, it starts to deploy the operator service. For the Kiali operator, we need to deploy Kiali CR after the Kiali Operator has been deployed. Both are valid patterns.

Operator Development

Operator is powerful. However, authoring an Operator is not a trivial effort. One usually start with a framework. A framework creates a body of boiler plate code that has the pattern implemented and allows developers to enrich the functions following the pattern. The white paper introduced three frameworks:

CNCF Operator Framework – aims at Operator Developers with an SDK, a scaffolding tool and a test harness. It currently supports three project types: Golang, Helm and Ansible. CNCF Operator framework consists of SDK and OLM.
Kopf (Kubernetes Operator Pythonic Framework) – an easy-to-use framework in Python that abstracts away most of the low-level Kubernetes API communications hassle.
kubebuilder – helps build a Manager similar to the native kube-controller-manager. For difference with OperatorSDK, read here.
Metacontroller: lightweight Kubernetes Controller as a Service

In CNCF Operator Framework, the Operator SDK supports development using Ansible, Helm and Golang. The author of this post makes a general comparison as follows:

Type	Best use case	Underlying technology	Amt of Effort
Helm	Stateless workload	Helm Charts	Med
Ansible	Stateless workload	Ansible Roles and Playbooks	Med
Golang	Stateful workload	Code developed in Golang	High

The aforementioned Kiali operator is an example of Operator developed in Ansible. The prometheus operator, is developed in Golang as the workload can be stateful depending on configuration. One needs to know how to develop operator in Golang in order to tackle the most complicated situations. This is requires some serious development effort. The documentation with a quick start section is available here. Even that is not very straightforward. RedHat, the maintainer of the CNCF Operator framework has a good blog post on how to develop an Operator in Golang.

The example requires some development knowledge to go through. On my MacOS (Intel) I have to configure the following prerequisites:

Install gcc, using command: xcode-select –install
Install the right version of golang. You can find the version here. The MacOS has a version of golang installed already so I had to install version 1.17 and link to it: brew install go@1.17 && brew link –force go@1.17
Install operator-sdk with home brew: brew install operator-sdk
When you run “operator-sdk version”, ensure the result shows a golang version that matches your installation.
If you need to push docker image, also connect to docker registry by running: docker login

Then we can create our working directory, initialize the repository and create boilerplate code (scaffolding) with these commands:

$ mkdir wordpress-operator && cd wordpress-operator
$ operator-sdk init --domain digihunch.com --repo github.com/digihunch/wordpress-operator
$ operator-sdk create api --group wordpress --version v1 --kind WordPress --resource --controller

With the repo initialized, we can go to the section “Defining the API” and “Implementing the Controller”. The blog post does not cover every code editing needed to bring up wordpress. You are supposed to go to the author’s repository to fit the changes into your own repo. The author’s repo has a few more controllers such as common.go and mysql.go.

At the end of the lab, you should be able to run the controller and bring up wordpress. I used my own repository for this lab and have made the code changes for this lap in a couple commits. To test locally with the code:

$ git clone git@github.com:digihunch/wordpress-operator.git
$ cd wordpress-operator
$ make install run

Then we can validate wordpress install from a new terminal as the instruction shows:

$ kubectl create -f config/samples/wordpress_v1_wordpress.yaml
$ minikube service wordpress --url

For Developers that requires more details, RedHat has an eBook for Kubernetes Operators, in supplement to the documentation. As DevOps professional, I’m mainly concerned with understanding how Operator works and using Operators correctly.

Too many Tools?

Now we seem to have too many choice of tools when it comes to deploying workload on Kubernetes. Kustomize and Helm can deploy simple workloads. Operator can deploy stateful workloads, as well as keep the workload status in check. Further, we have FluxCD and ArgoCD based on GitOps workflow.

When assessing a tool, we should think about the complexity of the workload deployed. If it is a single stateless workload, Kustomize or Helm should be sufficient. If it is not very simple but still stateless, we can consider using Helm charts developed by the community. For multiple workloads, we can build our own top-level chart to combine existing sub-charts created by the community.

Helm is essentially a package manager. It does not follow controller pattern and therefore will not monitor the current status of deployment. Helm has other limitations compared to Operator. For example, as a templating scheme, it reaches limitation when dealing with complex logic, even with the help of its helper functions. It is also hard to reason through the template code when we have to troubleshoot a deployment. Refer to this blog post for the author’s experience with Helm.

If we want our deployment to be fully declarative and continuous, then we will follow the Operator pattern by using a Kubernetes Operator. When we have many workloads of different levels of complexity, we can combine them with GitOps tool. Operator is one of the underlying technologies behind GitOps.

Workload profile	Just Installation	Installation and Maintain Status
Single stateless workload	Helm or Kustomize	Operator (using Ansible or Helm)
Single stateful workload	Helm or Kustomize	Operator (using Golang)
Multiple workloads	Helm (e.g. build parent chart)	GitOps in combination with Operator, Helm and Kustomize

The table above helps refine deployment requirement. It’s not a recommendation, but rather a model of analyzing deployment requirement.

Logging and Monitoring in Kubernetes with PLG stack

Wed, 13 Oct 2021 21:29:00 -0400

We’ve checked out the the actors in PLG stack (Promtail, Loki, Node Exporter, Prometheus, Grafana) and whipped up a quick pipeline on MacOS. Now I’m going a little further to implement the same PLG stack (Prometheus Loki and Grafana) in a Kubernetes cluster. This setup is for demo only, therefore no persistent storage is enabled.

Test Workload

I host a deployment of Flog with three pods running on Minikube. Flog is an open-source emulating log generation behaviour of an application. On the Minikube cluster we start the deployment as below:

minikube start --driver=hyperkit --container-runtime=containerd --memory=12288 --cpus=2
kubectl create ns obsv
kubectl -n obsv create deployment flog --image=mingrammer/flog --replicas=3 -- flog -f rfc3164 -l -d 300ms
kubectl -n obsv get po

The Pods will come up in a heartbeat.

I will use helm to install the objects required for logging and metrics pipelines. There are multiple Helm charts for each components. Some high-level charts (usually with a name suffix of -stack) contain several other resource as sub-charts. They are created as one-stop-shop for multiple components but I found none of them serve my exact purpose. For example, both loki-stack and kube-prometheus-stack include Grafana. But I only need one instance of Grafana. Therefore I stick to the low-level charts.

Log Shipping

Add a helm repo, and install loki and promtail. Note that we need to specify correct loki address when installing Promtail.

helm repo add grafana https://grafana.github.io/helm-charts
helm repo update
helm upgrade --namespace obsv --install loki grafana/loki
helm upgrade --namespace obsv --install promtail grafana/promtail --set "config.lokiAddress=http://loki.obsv.svc.cluster.local:3100/loki/api/v1/push"

When installing Promtail, a DaemonSet is created on the Node. The default configuration applies appropriate configuration and tagging strategy for Kubernetes Pod and Node. So the only customization I specified is Loki address. We can then check logging with Loki. To do so, first expose port 3100 to host, and then use logcli (e.g. on MacOS) to query for logs:

kubectl -n obsv port-forward service/loki 3100:3100
logcli labels
logcli labels pod
logcli query '{pod="flog-775d5fc5c8-p4rlx"}'

Log lines should be pumped to Loki a minute after Loki comes up. The logcli labels command should display the tags. The logcli query command should return the log lines.

Metrics

I use Premetheus with node exporter. In its architecture, Prometheus contain the server, the pushgateway, and alertmanager. The helm chart for Prometheus contains all of those components. It also has a dependency repo for kube-state-metrics. To install:

helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
helm repo add kube-state-metrics https://kubernetes.github.io/kube-state-metrics
helm --namespace=obsv install prometheus prometheus-community/prometheus
kubectl -n obsv get svc

Once installed, a DaemonSet for Prometheus Node Exporter is created. The exporter is already configured by default for Kubernetes monitoring. The Prometheus server is also configured, on port 80 by default. it needs to be forwarded in order to access from Browser:

kubectl --namespace obsv port-forward service/prometheus-server 9100:80

To verify installation of Prometheus, browse to localhost:9100 to examine the metrics.

Visualization

Last but not least, I will configure Grafana. The repo has been added already so we’ll just install the chart:

helm upgrade --namespace obsv --install grafana grafana/grafana
kubectl get secret --namespace obsv grafana -o jsonpath="{.data.admin-password}" | base64 --decode ; echo

The second command retrieves the credential. To access the web portal, we need port forwarding again:

kubectl port-forward --namespace obsv service/grafana 3000:80

To verify installation, browse to http://localhost:3000 and log in as user admin with the password above. Then add two data sources:

Type: Prometheus, URL: http://prometheus-server.obsv.svc.cluster.local:80
Type: Loki, URL: http://loki.obsv.svc.cluster.local:3100

Then we can explore data using both data sources.

PLG stack

Summary

In the last two posts I reviewed the setups for PLG stack in Kubernetes, from a regular environment to k8s cluster. Fluentd, Prometheus are both CNCF projects. The PLG stack seems to be more adopted than EFK but both have their own advantages. Welcome to the PLG vs EFK debate.

Intro to PLG stack -Prometheus, Loki and Grafana

Sun, 03 Oct 2021 12:59:00 -0400

Last month we discussed log shipping with EFK. This week I spent sometime checking out its alternative Loki. Having been exposed to the ELK stack extensively, I am also interested in exploring the counterparts in this new stack, such as Prometheus, Loki and Grafana. So I need to address the issues of shipping both metrics and logs.

Let’s start by clarifying the terms:

Grafana is a visualizer. It supports many backends such as Prometheus, Loki, Elasticsearch, CloudWatch and Azure Monitor. It is the flagship product of Grafana Labs.

Prometheus is a time-series database and alerting platform. To push metrics to Premethus, you can either integrate your application with client library (in their term, instrumenting), or configure an existing exporters for a third party application such as PostgreSQL. Prometheus collects and stores its metrics as time series data ( i.e. metrics information is stored with the timestamp at which it was recorded, alongside optional key-value pairs called labels) and it comes with basic visualization capability. Premetheus is a CNCF project since 2016 and is maintained by Grafana Labs.

Loki is a log aggregation system, also developed by Grafana Labs. Loki does not index the contents of the logs. Instead it groups entries into streams, and indexes a set of labels for each log stream. You can use grafana or logcli to consume the logs. Loki supports clients such as Fluentd, Fluentbit, Logstash and Promtail.

Promtail is a log collection agent built for Loki.

The roles of the components such as Prometheus, Loki, Grafana and Promtail are similar to the ELK stack. Grafana resembles Kibana. Promtail resembles Filebeat. Premethus exporters resemble Metricbeat. Both Premetheus and Loki resemble Elasticsearch in some aspects. Premetheus keeps metrics and Loki persists log streams.

These tools are heavily used in Kubernetes. For a simple start, I’d like to just configure two minimally working pipelines on my MacBook without any containerization. My example setup is to achieve the followings:

Fake up some log lines and ship them to Loki
Ship OS metrics to Prometheus
Display the metrics and logs with Grafana

Here’s the diagram of what we want to implement:

This is simple enough to build a quick and dirty setup

Configure Node Exporter

We can use home brew to install node_exporter on MacOS. For Linux, use the equivalent package management tool:

brew install node_exporter
brew services start node_exporter
curl http://localhost:9100/metrics | grep "node_"

The node_exporter collect metrics and make it available for scrape using port 9100 as shown above.

Configure Prometheus

Install Prometheus with homebrew and start the service:

brew install prometheus
brew services start prometheus

If curl to port 9090 with GET returns “Found”, then Prometheus is successfully installed. We also want to configure it so it scrapes node exporter for metrics. Edit the configuration file, in my case, /usr/local/etc/prometheus.yml, by adding the followings:

 - job_name: node
 static_configs:
 - targets: ['localhost:9100']

This tells Prometheus to scrape metrics at specified interval. Restart Prometheus and browse to http://localhost:9090/ for Prometheus UI. Click on Status -> Targets and you can see the node as an export. Click on Graph and Execute a query for example “node_memory_free_bytes” and click on Graph. You should see a plot of the metric value.

We can later configure to display the chart in Grafana.

Generate log lines

We use a tool called flog to generate fake logs:

brew tap mingrammer/flog
brew install flog
flog -f rfc3164 -l -d 300ms -t log -o /tmp/test.log -w

The command above produces log in RFC3164 format to /tmp/test.log, one line every 300ms. The log does not rotate.

Configure Loki

Install Loki with homebrew and start the service. We want to configure Loki before Promtail so it is ready to receive logs. You might as well install LogCLI to interact with Loki.

brew install loki
brew install logcli
brew services start loki

If nc to port 3100 returns success, then Loki is successfully installed.

Configure Promtail

Install promtail using homebrew:

brew install promtail

Note that Promtail is not installed as a homebrew service. Although it can be manually configured as a service, I’d rather stay focused and use command line just for the time being. A copy of configuration file is located in /usr/local/etc/promtail-local-config.yaml but it needs to be modified first with a job to tell it where to scrap the log lines. The configuration looks like this:

server:
 http_listen_port: 9080
 grpc_listen_port: 0

positions:
 filename: /tmp/positions.yaml

clients:
 - url: http://localhost:3100/loki/api/v1/push

scrape_configs:
- job_name: app
 static_configs:
 - targets:
 - localhost
 labels:
 job: applogs
 __path__: /tmp/test.log

Then, use the command as in the documentation to start promtail:

promtail -config.file /usr/local/etc/promtail-local-config.yaml

Port 9080 will be open when Promtail is running. Promtail will push logs to Loki. You can use LogCLI to interact with Loki and see the latest log lines pushed to Loki.

logcli labels job
logcli query '{job="applogs"}'

However, unlike Prometheus, Loki itself does not have any visualization capabilities. We will need Grafana to display the log nicely on the web.

Configure Grafana

We use Grafana to visualize both the logs and metrics. To install Grafana on Mac:

brew install grafana
brew services start grafana

If curl to port 3000 with GET returns “Found”, then Grafana is successfully installed. Browse to localhost:3000, with default credential admin and admin. From the UI, add two data sources. For the first data source, specify Prometheus as type and localhost:9000 as destination. For the second, specify Loki as the destination http://localhost:3100

The steps to see logs in Loki is pretty much the same as on this page of its documentation.

Exploring logs

Exploring metrics data is similar. Click on Explore on the side bar, then select Prometheus from the dropdown as data source. Then execute a query such as “node_memory_free_bytes”.

Explore Metrics

With this quick and dirty configuration, we established a good understanding of what Prometheus, Loki and Grafana do. Next, we will move all these configurations to K8s cluster and understand some specific points of configurations.

Log Shipping in Kubernetes with EFK stack

Sat, 04 Sep 2021 21:50:00 -0400

I first worked on log shipping with ELK stack three years ago. In the context of Kubernetes cluster, log shipping has similar challenges. In this post I will discuss the set up of log shipping with Kubernetes cluster using EFK stack

Logging Architecture

As discussed, if the Kubernetes cluster has a runtime in compliant with CRI (e.g. containerd), then the stdout and stderr of the Pod is stored on the node, in the location /var/log/containers/.

When creating log shipping solution, it is important to use a compliant cluster (e.g. minikube) to ensure what you develop will work across environments.

The Kubernetes document has a section on logging architecture which is a good start point. It outlines several different patterns. Logging at the node level is turned on by default and does not require special configuration, as explained in the section above. The EFK pattern is close to the diagram under using a node logging agent for cluster-level logging.

Node logging agent

The diagram above is stolen from Kubernetes documentation. In EFK stack, the agent is a daemonset running fluentd Pod.

EFK stack

While we can use ELK (Elasticsearch, Logstash, Kibana) stack for log shipping, EFK (Elasticsearch, Fluentd, Kibana) is generally recommended in Kubernetes cluster. We compare the two in the following table:

	ELK	EFK
Development language	All in Java. Logstash and Filebeat both require JVM. Managed as an open-source project by Elastic company	Fluentd in Ruby and does not require JVM to run. Fluentd is a CNCF project built to integrate with Kubernetes.
Typical pattern	Filebeat acts as a lightweight collector to monitor the source log. Logstash as aggregator to receive from filebeat, and push to Elasticsearch	The fluentd Pod can be configured to serve as forwarder and aggregator based on configuration. fluentd-forwarder is deployed as daemonset on node, and ship the result to fluentd-aggregator, which may run in a separate cluster. The fluentd-aggregator pushes processed results to Elasticsearch.
Metrics	use metric beat for data collection	scrape metrics from prometheus server
Event Routing	tag-based	if-then statement

ELK and EFK stacks comparison

The main problems in log shipping are:

column mapping : identify column patterns in each log line and map them to appropriate column in Elasticsearch.
multi-line processing: identify when a logging entry spread across multiple lines and process accordingly.

Suppose we want to congregate the logs from stdout and stderr of PostgreSQL pods. The raw output in /var/log/container on the node, looks like this:

2021-08-28T15:27:46.75370563Z stdout F server stopped
2021-08-28T15:27:46.757173069Z stderr F postgresql-repmgr 15:27:46.75 INFO ==> Starting PostgreSQL in background...
2021-08-28T15:27:46.883126928Z stderr F postgresql-repmgr 15:27:46.88 INFO ==> Registering Primary...
2021-08-28T15:27:47.017164653Z stderr F postgresql-repmgr 15:27:47.01 INFO ==> Loading custom scripts...
2021-08-28T15:27:47.023334611Z stderr F postgresql-repmgr 15:27:47.02 INFO ==> Loading user's custom files from /docker-entrypoint-initdb.d ...
2021-08-28T15:27:47.026169813Z stderr F postgresql-repmgr 15:27:47.02 INFO ==> Starting PostgreSQL in background...
2021-08-28T15:27:47.343607487Z stderr F postgresql-repmgr 15:27:47.34 INFO ==> Stopping PostgreSQL...
2021-08-28T15:27:47.448111425Z stdout F waiting for server to shut down.... done
2021-08-28T15:27:47.448172479Z stdout F server stopped
2021-08-28T15:27:47.453722807Z stderr F postgresql-repmgr 15:27:47.45 INFO ==> ** PostgreSQL with Replication Manager setup finished! **
2021-08-28T15:27:47.453829953Z stdout F
2021-08-28T15:27:47.503516746Z stderr F postgresql-repmgr 15:27:47.50 INFO ==> Starting PostgreSQL in background...
2021-08-28T15:27:47.532558987Z stdout F waiting for server to start....2021-08-28 15:27:47.532 GMT [273] LOG: pgaudit extension initialized
2021-08-28T15:27:47.533307459Z stdout F 2021-08-28 15:27:47.533 GMT [273] LOG: listening on IPv4 address "0.0.0.0", port 5432
2021-08-28T15:27:47.533466407Z stdout F 2021-08-28 15:27:47.533 GMT [273] LOG: listening on IPv6 address "::", port 5432
2021-08-28T15:27:47.537987947Z stdout F 2021-08-28 15:27:47.537 GMT [273] LOG: listening on Unix socket "/tmp/.s.PGSQL.5432"
2021-08-28T15:27:47.547956465Z stdout F 2021-08-28 15:27:47.547 GMT [273] LOG: redirecting log output to logging collector process
2021-08-28T15:27:47.548005463Z stdout F 2021-08-28 15:27:47.547 GMT [273] HINT: Future log output will appear in directory "/opt/bitnami/postgresql/logs".
2021-08-28T15:27:47.551741571Z stdout F 2021-08-28 15:27:47.551 GMT [275] LOG: database system was shut down at 2021-08-28 15:27:47 GMT
2021-08-28T15:27:47.558012894Z stdout F 2021-08-28 15:27:47.557 GMT [273] LOG: database system is ready to accept connections
2021-08-28T15:27:47.618577092Z stdout F done
2021-08-28T15:27:47.618708978Z stdout F server started
2021-08-28T15:27:47.630065958Z stderr F postgresql-repmgr 15:27:47.62 INFO ==> ** Starting repmgrd **
2021-08-28T15:27:47.638116348Z stderr F [2021-08-28 15:27:47] [NOTICE] repmgrd (repmgrd 5.2.1) starting up
2021-08-28T15:27:47.65317627Z stderr F INFO: set_repmgrd_pid(): provided pidfile is /opt/bitnami/repmgr/tmp/repmgr.pid
2021-08-28T15:27:47.653232015Z stderr F [2021-08-28 15:27:47] [NOTICE] starting monitoring of node "orthweb-postgresql-ha-postgresql-0" (ID: 1000)
2021-08-28T15:27:47.681683703Z stderr F [2021-08-28 15:27:47] [NOTICE] monitoring cluster primary "orthweb-postgresql-ha-postgresql-0" (ID: 1000)
2021-08-28T15:28:11.742865958Z stderr F [2021-08-28 15:28:11] [NOTICE] new standby "orthweb-postgresql-ha-postgresql-1" (ID: 1001) has connected

From this snippet of log, we can see each line in stdout or stderr is appended with a timestamp. There are multi-line log entries but each is still appended with a timestamp. This is just how kubernetes keeps the log file for Pod stdout and stderr. To handle that, we need to first take out the real log line, and then process multi-line.

We will go over the installation of EFK stack and the mechanism to address the two challenges above.

Install Elasticsearch and Kibana

To install Elasticsearch, we use the helm chart provided by the official repository:

helm repo add elastic https://helm.elastic.co

If we run multiple pods on the same hosts, then we need some customized values in order to get the installation to work. The values.yaml file looks like this:

---

antiAffinity: "soft"

esJavaOpts: "-Xmx128m -Xms128m"

# Allocate smaller chunks of memory per pod.
resources:
 requests:
 cpu: "100m"
 memory: "512M"
 limits:
 cpu: "1000m"
 memory: "512M"

Then we can “preview” what values are used for installation, with helm’s template command:

helm template elasticsearch elastic/elasticsearch -f values.yaml

The effect of the antiAffinity property allows multiple Elasticsearch Pod to be scheduled on the same node. This is not required in production with multiple nodes. To install Elasticsearch and Kibana, run:

helm install elasticsearch elastic/elasticsearch -f values.yaml
helm install kibana elastic/kibana

The Kibana service is exposed on port 5601 of the cluster. To access the port on cluster, we need to run port-forward command as below:

kubectl port-forward deployment/kibana-kibana 5601

Install Fluentd

There are different ways to configure Fluentd. For example, in the forwarder-aggregator pattern, a forwarder Pod is a Daemonset on each Kubernetes node. The forwarder pushes to the aggregator, at port 24224. This page has some configuration details. This pattern is similar to filebeat -> logstash pattern in ELK stack.

In our case, we use a simplified pattern, with a fluentd daemonset acting as collector and then forward to Elasticsearch. Similarly, in ELK stack we can use filebeat to push to Elasticsearch without Logstash. The only reason is the entire architecture isn’t as complicated as requiring an aggregator.

We need to create configmap as below:

kind: ConfigMap
apiVersion: v1
metadata:
 name: fluentd-cm
 namespace: default
 labels:
 app.kubernetes.io/component: forwarder
 app.kubernetes.io/instance: fluentd
 app.kubernetes.io/managed-by: Helm
 app.kubernetes.io/name: fluentd
 helm.sh/chart: fluentd-1.3.0
 annotations:
 meta.helm.sh/release-name: fluentd
 meta.helm.sh/release-namespace: default
data:
 fluentd.conf: |
 # Ignore fluentd own events
 <match fluent.**>
 @type null
 </match>

 # HTTP input for the liveness and readiness probes
 <source>
 @type http
 port 9880
 </source>

 # Throw the healthcheck to the standard output instead of forwarding it
 <match fluentd.healthcheck>
 @type null
 </match>

 # Get the logs from the containers running in the node
 <source>
 @type tail
 read_from_head true
 tag kubernetes.*
 path /var/log/containers/orthweb-postgresql-ha-postgresql-**.log
 pos_file /opt/bitnami/fluentd/logs/buffers/fluentd-docker.pos
 <parse>
 @type regexp
 expression ^(?<time>\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}.[^Z]*Z)\s(?<stream>[^\s]+)\s(?<character>[^\s])\s(?<message>.*)$
 </parse>
 </source>
 # enrich with kubernetes metadata
 <filter kubernetes.**>
 @type kubernetes_metadata
 @id filter_kube_metadata
 kubernetes_url "#{ENV['FLUENT_FILTER_KUBERNETES_URL'] || 'https://' + ENV.fetch('KUBERNETES_SERVICE_HOST') + ':' + ENV.fetch('KUBERNETES_SERVICE_PORT') + '/api'}"
 verify_ssl "#{ENV['KUBERNETES_VERIFY_SSL'] || true}"
 ca_file "#{ENV['KUBERNETES_CA_FILE']}"
 skip_labels "#{ENV['FLUENT_KUBERNETES_METADATA_SKIP_LABELS'] || 'false'}"
 skip_container_metadata "#{ENV['FLUENT_KUBERNETES_METADATA_SKIP_CONTAINER_METADATA'] || 'false'}"
 skip_master_url "#{ENV['FLUENT_KUBERNETES_METADATA_SKIP_MASTER_URL'] || 'false'}"
 skip_namespace_metadata "#{ENV['FLUENT_KUBERNETES_METADATA_SKIP_NAMESPACE_METADATA'] || 'false'}"
 </filter>


 <match kubernetes.var.log.containers.orthweb-postgresql-ha-postgresql-**.log>
 @type elasticsearch
 include_tag_key true
 host "elasticsearch-master.default.svc.cluster.local"
 port "9200"
 index_name "postgresql-logs"
 <buffer>
 @type file
 path /opt/bitnami/fluentd/logs/buffers/orthanc-logs.buffer
 flush_thread_count 2
 flush_interval 5s
 </buffer>
 </match>

Then we can create the resource, with helm chart pointing to the config map:

helm install fluentd bitnami/fluentd --set aggregator.enabled=false --set forwarder.configMap=fluentd-cm

We can validate the index creation on Elasticsearch:

kubectl port-forward service/elasticsearch-master 9200
curl -XGET localhost:9200/_cat/indices

From Kibana, we can forward the port as instructed above, and browse to the UI. Once logged on to Kibana, we need to add index pattern first before being able to view the content of index.

How about Fluent Bit

Fluentd has an even more lightweight brother Fluent Bit, also a CNCF project, designed by the same team, for environments with tighter resource restrictions. The technical differences are outlined on this page outlines the technical differences. In terms of use case, Fluentd is a log aggregator and Fluent Bit is simply a forwarder. In terms of ecosystem, Fluentd has a stronger ecosystem whereas Fluent Bit is more seen in IoT devices. Read this post for more comparison.

Summary

EFK stack (Elasticsearch, Fluentd and Kibana) on Kubernetes is a natural choice for ELK users. Fluentd is a CNCF project created for integration with Kubernetes. It is good alternative to enterprise solution such as Splunk. There are lots of plugins supported and articles on configuration. While developing a solution I had to spend time reading the input plugin documentations.