Cluster-level Logging in Kubernetes with Fluentd (2022)

Logs are crucial to help you understand what is happening inside your Kubernetes cluster. Even though most applications have some kind of native logging mechanism out of the box, in the distributed and containerized environment (like Kubernetes), users will be better off with the centralized logging solution. That’s because they need to collect logs from multiple applications with different log formats and send them to some logging backend for subsequent storage, processing, and analysis. Kubernetes provides all the basic resources needed to implement such functionality.

In this tutorial, we explore Kubernetes logging architecture and demonstrate how to collect application and system logs using Fluentd. We also look into some details of the Fluentd configuration language to teach you how to configure log sources, match rules, and output destinations for your custom logging solution. Let’s get started!

Docker containers in Kubernetes write logs to standard output (stdout) and standard (stderr) error streams. Docker redirects these streams to a logging driver configured in Kubernetes to write to a file in JSON format. Kubernetes then exposes log files to users via kubectl logs command. Users can also get logs from a previous instantiation of a container setting the --previous flag of this command to true. That way they can get container logs if the container crashed and was restarted.

However, if a pod is deleted from the node forever, all corresponding containers and their logs are also deleted. The same happens when the node dies. In this case, users are no longer able to access application logs. To avoid this situation, container logs should have a separate shipper, storage, and lifecycle that are independent of pods and nodes. Kubernetes does not provide a native storage solution for log data, but you can easily integrate your preferred logging shipper into the Kubernetes cluster using Kubernetes API and controllers.

In essence, Kubernetes architecture facilitates a number of ways to manage application logs. Several common approaches to consider are:

  • using a logging sidecar container running inside an app’s pod.
  • using a node-level logging agent that runs on every node.
  • push logs directly from within an application to some backend.

Let’s briefly discuss the details of the first and the second approach.

Let’s assume you have an application container producing some logs and outputting them to stdout , stderr , and/or a log file. In this case, you can create one or more sidecar containers inside the application pod. The sidecars will be watching for the log file/s and an app’s container stdout/stderr and will stream log data to their own stdout and stderr streams. Optionally, a sidecar container can also pass the retrieved logs to a node-level logging agent for subsequent processing and storage. This approach has a number of benefits described in this great article from the official documentation. Let’s summarize them:

  • With sidecar containers, you can separate several log streams from your app container. This is handy when your app container produces logs with different log formats. Mixing different log formats would deteriorate manageability of your logging pipeline.
  • Sidecar containers can read logs from those parts of your application that lack support for writing to stdout or stderr .
  • Because sidecar containers use stdout and stderr , you can use built-in logging tools like kubectl logs .
  • Sidecar containers can be used to rotate log files which cannot be rotated by the application itself.

At the same time, however, sidecar containers for logging have certain limitations:

  • Writing logs to a file and then streaming them to stdout can significantly increase disk usage. If your application writes to a single file, it’s better to set /dev/stdout as the destination instead of implementing the streaming sidecar container approach.
  • If you want to ship logs from multiple applications, you have to design a sidecar(s) for each of them.

In this approach, you deploy a node-level logging agent on each node of your cluster. This agent is usually a container with access to log files of all application containers running on that node. Production clusters normally have more than one nodes spun up. If this is your case, you’ll need to deploy a logging agent on each node.

The easiest way to do this in Kubernetes is to create a special type of deployment called DaemonSet. The DaemonSet controller will ensure that for every node running in your cluster you have a copy of the logging agent pod. The DaemonSet controller will also periodically check the count of nodes in the cluster and spin up/down a logging agent when the node count changes. DaemonSet structure is particularly suitable for logging solutions because you create only one logging agent per node and do not need to change the applications running on the node. The limitation of this approach, however, is that node-level logging only works for applications’ standard output and standard error streams.

(Video) Kubernetes Cluster log analysis 2: How to deploy Fluentd on Kubernetes Cluster

Cluster-level Logging in Kubernetes with Fluentd (1)

Using node-level logging agents is the preferred approach in Kubernetes because it allows centralizing logs from multiple applications via installation of a single logging agent per each node. We now discuss how to implement this approach using Fluentd deployed as a DaemonSet in your Kubernetes cluster.

We chose Fluentd because it’s a very popular log collection agent with broad support for various data sources and outputs such as application logs (e.g., Apache, Python), network protocols (e.g., HTTP, TCP, Syslog), cloud APIs (e.g AWS Cloud Watch, AWS SQS) and more. Fluentd also supports a variety of output destinations including:

  • Log management backends (Elasticsearch, Splunk)
  • Big data stores (Hadoop DFS)
  • Data archiving (Files, AWS S3)
  • PubSub queues (Kafka, RabbitMQ)
  • Data warehouses (BigQuery, AWS RedShift)
  • Monitoring systems (Datadog)
  • Notification systems (email, Slack, etc.)

In this tutorial, we’ll focus on one of the most popular log management backends — Elasticsearch, which offers great full-text search, log aggregation, analysis, and visualization functionality. The Fluentd community has developed a number of pre-set Docker images with the Fluentd configuration for various log backends including Elasticsearch.

We used the DaemonSet and the Docker image from the fluentd-kubernetes-daemonset GitHub repository. There you can also find Docker images and templates for other log outputs supported by Fluentd such as Loggly, Kafka, Kinesis, and more. Using the repository is the simplest way to get you started if you don’t know much about Fluentd configuration.

To complete examples used below, you’ll need the following prerequisites:

  • A running Kubernetes cluster. See Supergiant documentation for more information about deploying a Kubernetes cluster with Supergiant. As an alternative, you can install a single-node Kubernetes cluster on a local system using Minikube.
  • A kubectl command line tool installed and configured to communicate with the cluster. See how to install kubectl here.

Fluentd will be collecting logs both from user applications and cluster components such as kube-apiserver and kube-scheduler, so we need to grant it some permissions.

The first thing we need to do is to create an identity for the future Fluentd DaemonSet . Let’s create a new ServiceAccount in the kube-system namespace where Fluentd should be deployed:

apiVersion: v1
kind: ServiceAccount
metadata:
name: fluentd
namespace: kube-system

Next, let’s grant Fluentd permissions to read, list, and watch pods and namespaces in your Kubernetes cluster. The manifest for the ClusterRole should look something like this:

apiVersion: rbac.authorization.k8s.io/v1beta1
kind: ClusterRole
metadata:
name: fluentd
namespace: kube-system
rules:
- apiGroups:
- ""
resources:
- pods
- namespaces
verbs:
- get
- list
- watch

Finally, we need to bind the Fluentd ServiceAccount to these permissions using the ClusterRoleBinding resource:

(Video) Fluentd on Kubernetes: Log collection explained

kind: ClusterRoleBinding
apiVersion: rbac.authorization.k8s.io/v1beta1
metadata:
name: fluentd
roleRef:
kind: ClusterRole
name: fluentd
apiGroup: rbac.authorization.k8s.io
subjects:
- kind: ServiceAccount
name: fluentd
namespace: kube-system

Let’s save these manifests in the rbac.yml separating them by the ---delimiter and create all resources in bulk:

kubectl create -f rbac.ymlserviceaccount “fluentd” created
clusterrole.rbac.authorization.k8s.io “fluentd” created
clusterrolebinding.rbac.authorization.k8s.io “fluentd” created

Fluentd repository contains a working example of the Fluentd DaemonSet, which we can use with some tweaks.

apiVersion: extensions/v1beta1
kind: DaemonSet
metadata:
name: fluentd
namespace: kube-system
labels:
k8s-app: fluentd-logging
version: v1
kubernetes.io/cluster-service: "true"
spec:
template:
metadata:
labels:
k8s-app: fluentd-logging
version: v1
kubernetes.io/cluster-service: "true"
spec:
serviceAccount: fluentd
serviceAccountName: fluentd
tolerations:
- key: node-role.kubernetes.io/master
effect: NoSchedule
containers:
- name: fluentd
image: fluent/fluentd-kubernetes-daemonset:elasticsearch
env:
- name: FLUENT_ELASTICSEARCH_HOST
value: "f505e785.qb0x.com"
- name: FLUENT_ELASTICSEARCH_PORT
value: "30216"
- name: FLUENT_ELASTICSEARCH_SCHEME
value: "https"
- name: FLUENT_UID
value: "0"
# X-Pack Authentication
# =====================
- name: FLUENT_ELASTICSEARCH_USER
value: "abf54990f0a286dc5d76"
- name: FLUENT_ELASTICSEARCH_PASSWORD
value: "75c4bd6f7b"
resources:
limits:
memory: 200Mi
requests:
cpu: 100m
memory: 200Mi
volumeMounts:
- name: varlog
mountPath: /var/log
- name: varlibdockercontainers
mountPath: /var/lib/docker/containers
readOnly: true
terminationGracePeriodSeconds: 30
volumes:
- name: varlog
hostPath:
path: /var/log
- name: varlibdockercontainers
hostPath:
path: /var/lib/docker/containers

There are several parts in this configuration to pay attention to:

  • The DaemonSet uses fluent/fluentd-kubernetes-daemonset:elasticsearch Docker image specifically configured with the Elasticsearch as the Fluentd output.
  • You should provide some environmental variables in order to connect to your Elasticsearch cluster. These are your Elasticsearch host, port, and credentials (username, password). You can connect to either Elasticsearch deployed in the Kubernetes cluster or remote Elasticsearch cluster as in this example (we used a Qbox-hosted Elasticsearch cluster)
  • Fluentd needs root permission to read logs in /var/log and write pos_file to /var/log. To avoid permission error, set FLUENT_UID environment variable to 0 in your DaemonSet manifest

Let’s save the manifest in the fluentd-elasticsearch.yml and create the DaemonSet:

kubectl create -f fluentd-elasticsearch.yml

If you are running a single-node cluster with Minikube as we did, the DaemonSet will create one Fluentd pod in the kube-system namespace. You can find its name using kubectl get pods --namespace=kube-system and use kubectl logs <fluentd-pod-name> to see its logs:

Almost immediately, Fluentd will connect to Elasticsearch using the provided host and credentials:

2018–09–11 09:20:05 +0000 [info]: Connection opened to Elasticsearch cluster => {:host=>”f505e785.qb0x.com”, :port=>30216, :scheme=>”https”, :user=>”abf54990f0a286dc5d76", :password=>”obfuscated”}

To see the logs collected by Fluentd, let’s log into the Kibana dashboard. Under the Management -> Index Patterns -> Create New Index Pattern , you’ll find a new Logstash index generated by the Fluentd DaemonSet. Under the hood, Fluentd uses Logstash as the intermediary log shipper to pass logs to Elasticsearch. After configuring a new index pattern, you’ll be able to access your app logs under the Discover tab (see the image below).

Cluster-level Logging in Kubernetes with Fluentd (2)

Here, you’ll see a number of logs generated by your Kubernetes applications and Kubernetes system components. A common log document created by Fluentd will contain a log message, the name of the stream that generated the log, and Kubernetes-specific information such as the namespace, the Docker container ID, pod ID, and labels (see the example below).

(Video) How Fluentd simplifies collecting and consuming logs | Fluentd simply explained

log:INFO: == Kubernetes addon reconcile completed at 2018-09-11T09:31:39+0000 ==
stream:stdout docker.container_id:9b596c9195003246af0f71406f05ab4d339601dadc213048202992739fe9267e
kubernetes.container_name:kube-addon-manager
kubernetes.namespace_name:kube-system
kubernetes.pod_name:kube-addon-manager-minikube
kubernetes.pod_id:f6d8ff9d-8a6e-11e8-9e55-0800270c281a kubernetes.labels.component:kube-addon-manager
kubernetes.labels.version:v8.6

In the previous example, we used a pre-set Fluentd configuration for Elasticsearch, so we did not have to go into details of the Fluentd configuration syntax. If you wish to know more about how to configure Fluentd sources, output destinations, filters, and more, please consult the official Fluentd documentation.

Just to help you get a basic idea of the Fluentd configuration syntax, we’ll show you how to configure some log sources, outputs, and match rules and to mount a custom Fluentd ConfigMap to your Fluentd DaemonSet.

In general, The Fluentd configuration file can include the following directives:

  1. Source directives define the input sources (e.g Docker, Ruby on Rails).
  2. Match directives define the output destinations.
  3. Filter directives determine the event processing pipelines.
  4. System directives set system-wide configuration.
  5. Label directives group the output and filters for internal routing.
  6. @include directives include other files.

Let’s take a look at common Fluentd configuration options for Kubernetes. You can find a full example of the Kubernetes configuration in the kubernetes.conf file from the official GitHub repository.

<match **>
@type stdout
</match>
<match fluent.**>
@type null
</match>
<match docker>
@type file
path /var/log/fluent/docker.log
time_slice_format %Y%m%d
time_slice_wait 10m
time_format %Y%m%dT%H%M%S%z
compress gzip
utc
</match>
<source>
@type tail
@id in_tail_container_logs
path /var/log/containers/*.log
pos_file /var/log/fluentd-containers.log.pos
tag kubernetes.*
read_from_head true
<% if is_v1 %>
<parse>
@type json
time_format %Y-%m-%dT%H:%M:%S.%NZ
</parse>
<% else %>
format json
time_format %Y-%m-%dT%H:%M:%S.%NZ
<% end %>
</source>

The first three blocks of the configuration above are match directives. These directives filter logs by name or provider and specify the output destination for them using the @type variable. For example, the first match directive selects all logs using ** glob pattern and sends them to the Fluentd stdout making them accessible via kubectl logs <fluentd-pod> command.

As in the second match directive, we can use the output @type null to ignore certain logs. In this case, we exclude internal Fluentd logs. Finally, in the third match directive, we filter Docker logs and write them to /var/log/fluent/docker.log . Inside the directive’s body, we can also set file compression type, log format, and other useful options.

The final block of the configuration above includes the source directive. This directive tells Fluentd where to look for logs. In our example, we tell Fluentd that containers in the cluster log to /var/log/containers/*.log . We set @type to tail , so Fluentd can tail these logs and retrieve messages for each line of the log file. Finally, we specify a position file that Fluentd uses to bookmark its place within the logs.

You can experiment with these configuration options configuring Fluentd to send various log types to any output destination you prefer. For example, to send all logs that match fluent pattern to the file /var/log/my-fluentd.log you can use the following match directive:

<match fluent.**>
@type file
path /var/log/my-fluentd.log
time_slice_format %Y%m%d
time_slice_wait 10m
time_format %Y%m%dT%H%M%S%z
compress gzip
utc
</match>

For a full list of supported output destination, please consult the official Fluentd documentation.

The Fluentd Docker image we used in the first part of this tutorial ships with the default Fluentd configuration stored in the /fluentd/etc/ directory. To change the default configuration, you need to mount your custom Fluentd configuration for Kubernetes using the ConfigMap volume.

(Video) Application Logging in Azure Kubernetes Service (AKS) with Fluentd

You can save the custom configuration we created above or your own config in the kubernetes.conf and create the ConfigMap with the following command:

kubectl create configmap fluentd-conf --from-file=kubernetes.conf --namespace=kube-system

Note: Fluentd ConfigMap should be saved in the kube-system namespace where your Fluentd DaemonSet will be deployed.

Once the ConfigMap is created, let’s modify our Fluentd DaemonSet manifest to include the ConfigMap .

apiVersion: extensions/v1beta1
kind: DaemonSet
metadata:
name: fluentd
namespace: kube-system
labels:
k8s-app: fluentd-logging
version: v1
kubernetes.io/cluster-service: "true"
spec:
template:
metadata:
labels:
k8s-app: fluentd-logging
version: v1
kubernetes.io/cluster-service: "true"
spec:
serviceAccount: fluentd
serviceAccountName: fluentd
tolerations:
- key: node-role.kubernetes.io/master
effect: NoSchedule
containers:
- name: fluentd
image: fluent/fluentd:v1.2-debian
resources:
limits:
memory: 200Mi
requests:
cpu: 100m
memory: 200Mi
volumeMounts:
- name: config-volume
mountPath: /fluentd/etc/kubernetes.conf
subPath: kubernetes.conf
- name: varlog
mountPath: /var/log
- name: varlibdockercontainers
mountPath: /var/lib/docker/containers
readOnly: true
terminationGracePeriodSeconds: 30
volumes:
- name: config-volume
configMap:
name: fluentd-conf
- name: varlog
hostPath:
path: /var/log
- name: varlibdockercontainers
hostPath:
path: /var/lib/docker/containers

As you see, we created a new ConfigMap volume with our custom Fluentd configuration and mounted it at /fluentd/etc/kubernetes.conf path in the container. Before creating this DaemonSet, please ensure that the old one is deleted.

All things considered, Kubernetes platform facilitates implementation of full logging pipelines by providing such useful abstractions as DaemonSets and ConfigMaps . We saw how to easily implement cluster-level logging using node agents deployed as DaemonSets. Fluentd is one of the best logging solutions for Kubernetes because it ships with excellent Kubernetes plugins and filtering capabilities.

In this tutorial, we demonstrated how Fluentd can easily centralize logs from multiple applications and instantly send them to Elasticsearch or any other output destination. Unlike sidecar containers that should be created for each application running in your cluster, node-level logging with Fluentd requires only one logging agent per node.

In a subsequent tutorial, we’ll continue the discussion of Fluent log solutions focusing on the Fluent Bit, a lightweight alternative to Fluentd suitable for log collection in highly distributed compute environments with high CPU and memory constraints. Stay tuned to our blog to find out more!

Originally published at supergiant.io.

FAQs

How does Fluentd collect logs from Kubernetes? ›

Fluentd is deployed as a daemonset in your Kubernetes cluster and will collect the logs from our various pods. The logs will be processed by Fluentd by adding the context, modifying the structure of the logs and then forwarding it to log storage. The configuration file will be stored in a configmap.

Which function does Fluentd fulfill for application logging in Kubernetes? ›

Fluentd helps you to centralize log information of running applications with Kubernetes metadata and route the information to desired destinations such as ElasticSearch or AWS S3. In this post, I will share how Fluentd works with example Kubernetes and EFK(ElasticSearch/Fluentd/Kibana) stack configuration.

How do I get Kubernetes cluster logs? ›

To get Kubectl pod logs, you can access them by adding the -p flag. Kubectl will then get all of the logs stored for the pod. This includes lines that were emitted by containers that were terminated.

What is cluster level logging? ›

In a cluster, logs should have a separate storage and lifecycle independent of nodes, pods, or containers. This concept is called cluster-level logging. Cluster-level logging architectures require a separate backend to store, analyze, and query logs. Kubernetes does not provide a native storage solution for log data.

Where are logs stored in Fluentd? ›

Look at Logs

For td-agent (rpm/deb), the logs are located at /var/log/td-agent/td-agent. log .

Is Fluentd a log collection tool? ›

Fluentd is a tool that can be used to collect logs from several data sources such as application logs, network protocols. And third-party services. In addition, Fluentd allows you to build a unified logging layer across all supported services and applications.

How does logging work in Kubernetes? ›

In Kubernetes, there are two main levels of logging: Container-level logging – Logs are generated by containers using stdout and stderr , and can be accessed using the logs command in kubectl. Kubernetes has log drivers for each container runtime, and can automatically locate and read these log files.

What is Fluentd in Kubernetes? ›

Fluentd is a popular open-source data collector that we'll set up on our Kubernetes nodes to tail container log files, filter and transform the log data, and deliver it to the Elasticsearch cluster, where it will be indexed and stored.

Can Fluentd collect metrics? ›

Sometimes, the input/filter/output plugin needs to save its internal metrics in memory, influxdb or prometheus format ready in instances. Fluentd has a pluggable system called Metrics that lets a plugin store and reuse its internal state as metrics instances.

What is the best monitoring tool for Kubernetes? ›

9 top open-source tools for monitoring Kubernetes
  1. Kubelet. In a Kubernetes cluster, Kubelet acts as a bridge between the master and the nodes. ...
  2. Container Advisor (cAdvisor) ...
  3. Kube-state-metrics. ...
  4. Kubernetes Dashboard. ...
  5. Prometheus. ...
  6. Jaeger. ...
  7. Kubewatch. ...
  8. Weave Scope.

How can I check Kubernetes cluster status? ›

Check Cluster Health
  1. Run the command kubectl describe cluster . If the status is ready, it means that both the cluster infrastructure and the cluster control plane are ready. ...
  2. If the cluster is not ready, run the following command to determine what is wrong with the cluster infrastructure: kubectl describe wcpcluster.
20 Oct 2021

How do I check logs for Kubernetes service? ›

To do this, you'll have to look at kubelet log. Accessing the logs depends on your Node OS. On some OSes it is a file, such as /var/log/kubelet. log, while other OSes use journalctl to access logs.

What are the 3 types of logging methods? ›

There are three major groups of timber harvest practices; clearcutting, shelterwood and selection systems.

What are 4 types of logging? ›

Types of logs
  • Electrode resistivity devices.
  • Induction logging.
  • Microresistivity logs.
  • Spontaneous (SP) log.
6 Jul 2015

How many logging levels are there? ›

Logging levels explained. The most common logging levels include FATAL, ERROR, WARN, INFO, DEBUG, TRACE, ALL, and OFF. Some of them are important, others less important, while others are meta-considerations.

How do you get fluent logs? ›

You can process Fluentd logs by using <match fluent. **> (Of course, ** captures other logs) in <label @FLUENT_LOG> . If you define <label @FLUENT_LOG> in your configuration, then Fluentd will send its own logs to this label. This is useful for monitoring Fluentd logs.

What is Fluentd and how it works? ›

Fluentd scraps logs from a given set of sources, processes them (converting into a structured data format) and then forwards them to other services like Elasticsearch, object storage etc. Fluentd is especially flexible when it comes to integrations – it works with 300+ log storage and analytic services.

What is the difference between Logstash and Fluentd? ›

FluentD and Logstash are both open source data collectors used for Kubernetes logging. Logstash is centralized while FluentD is decentralized. FluentD offers better performance than Logstash. In fact, FluentD offers many benefits over Logstash.

Why do we need Fluentd? ›

Fluentd allows us to unify data collection and consumption for better use and understanding of data. Although it is written in Ruby, it is most performance-sensitive parts (like object serialization and networking layers) are written in C.

Is Fluentd deprecated? ›

Container Insights Support for FluentD is now in maintenance mode, which means that AWS will not provide any further updates for FluentD and that we are planning to deprecate it in near future.

What is the difference between TD-agent and Fluentd? ›

Should I use td-agent or the Fluentd gem? td-agent prioritizes stability over new features. If you wish to control Fluentd features and updates on your own, using the Fluentd gem is recommended. If you are using Fluentd for the first time or are using it in a large scale environment, using td-agent is recommended.

Does logging level affect performance? ›

Log levels can be set for various parts of the system. In normal operation the log level for all of the subsystems should be set to INFO. DEBUG level can adversely impact performance.

What are the best practices for logging? ›

Writing Best Practices For Application Logs
  • Define which events to log.
  • Include pertinent details.
  • Exclude sensitive information.
  • Use structured logging.
  • Log at the correct level.
  • Additional Information.
4 Apr 2022

How do I get application logs from Kubernetes pod? ›

You can get logs for your application from pods within the Kubernetes cluster.
  1. Open a command-line window.
  2. Locate the pods for your application. Replace the <app-name> placeholder. ...
  3. Fetch logs for your application from a pod. Replace the <pod-name> placeholder with the pod name of the previous step.

What is Fluentd container? ›

Fluentd is an open source data collector, which lets you unify the data collection and consumption for a better use and understanding of data.

Is Fluentd a database? ›

Fluentd is an advanced open-source log collector developed at Treasure Data, Inc (see previous post). Because Fluentd handles logs as semi-structured data streams, the ideal database should have strong support for semi-structured data.

How do I know if fluent bit is running? ›

To test if your Fluent Bit plugin is receiving input from a log file:
  1. Run the following command to append a test log message to your log file: echo "test message" >> /PATH/TO/YOUR/LOG/FILE.
  2. Search New Relic's Logs UI for test message .

What port does Fluentd listen on? ›

If you use this plugin under the multi-process environment, the port will be shared. With this configuration, the three (3) workers share the port 24224 . No need for an additional port. Incoming data will be routed to the workers automatically.

What is Fluentd buffer? ›

Fluentd. memory. The memory buffer plugin provides a fast buffer implementation. It uses memory to store buffer chunks.

How do I monitor Kubernetes cluster resources? ›

The most straightforward solution to monitor your Kubernetes cluster is by using a combination of Heapster to collect metrics, InfluxDB to store it in a time series database, and Grafana to present and aggregate the collected information. The Heapster GIT project has the files needed to deploy this design.

What is the biggest disadvantage of Kubernetes? ›

The transition to Kubernetes can become slow, complicated, and challenging to manage. Kubernetes has a steep learning curve. It is recommended to have an expert with a more in-depth knowledge of K8s on your team, and this could be expensive and hard to find.

What is the largest Kubernetes cluster? ›

However, Kubernetes clusters can't grow infinitely large. There are some theoretical upper limits for how big a cluster can be, which are defined by Kubernetes at about 5000 nodes, 150,000 Pods, and 300,000 containers.

How many pods can run in a cluster? ›

More specifically, Kubernetes is designed to accommodate configurations that meet all of the following criteria: No more than 110 pods per node. No more than 5000 nodes. No more than 150000 total pods.

How many pods can a cluster have? ›

By default there can be a maximum of 110 Pods per node, and each node in the cluster has allocated /24 range for its Pods. This results in 256 Pod IPs per node.

What does 0.5 CPU mean in Kubernetes? ›

According to the docs, CPU requests (and limits) are always fractions of available CPU cores on the node that the pod is scheduled on (with a resources. requests. cpu of "1" meaning reserving one CPU core exclusively for one pod). Fractions are allowed, so a CPU request of "0.5" will reserve half a CPU for one pod.

How long are logs stored in Kubernetes? ›

Soon, users will be able to set alerts on specific messages. This is particularly useful for debugging when issues arise. The easy-to-use graphical interface is easier for many users, versus using kubectl. ContainIQ, by default, stores log data for 14-days.

How do I check my Kubernetes cluster capacity? ›

kubectl top pods or kubectl top nodes . This way you will be able to check current usage of pods/nodes. You can also narrow it to namespace . If you will execute kubectl describe node , in output you will be able to see Capacity of that node and how much allocated resources left.

How do I monitor Kubernetes audit logs? ›

Once you've configured your Kubernetes audit policy, use the --audit-policy-file flag to point to the file, and the --audit-log-path to specify the path to the file where the API server should output audit logs. If you don't specify a path, the API server will output logs to stdout .

What is the most common type of logging? ›

Clearcutting. Many large-scale logging companies use the clearcutting method to harvest timber. The clearcutting system removes all the trees in a designated area (typically a woodlot). The reason many logging companies practice clearcutting is simply because it's fast and cheap.

What tools are used in logging? ›

Logging Tools
  • Cant Hooks. Steel Cant Hooks. Aluminum Cant Hooks.
  • Log Peaveys. Steel Log Peaveys.
  • Hookaroons. Steel Hookaroons.
  • Log Arches. Junior Log Arch. Buck Arch Log Arch.
  • Log Carriers. Timber Tongs. 2-Person Log Carrier.

What are the 3 types of logs available through the event viewer? ›

Types of Event Logs

They are Information, Warning, Error, Success Audit (Security Log) and Failure Audit (Security Log).

What is the purpose of logging? ›

What is logging? The purpose of logging is to track error reporting and related data in a centralized way. Logging should be used in big applications and it can be put to use in smaller apps, especially if they provide a crucial function.

What is the main benefit of logging? ›

Provides necessary materials – Logging is a main source of timber which is used for a number of human needs such as providing construction materials, flooring wood, furniture, fuel for industries and homes, sports goods and other kinds of commodities.

How do I choose a log level? ›

When choosing a log level, it's important to know how visible you want the message to be, how big of a problem it is, and what you want the user to do about it. With that in mind, this is the decision tree I follow when choosing a log level: Can you continue execution after this? If no, use the error log level.

What is the highest logging level? ›

FATAL: The FATAL level designates very severe error events that will presumably lead the application to abort. ALL: The ALL has the lowest possible rank and is intended to turn on all logging. OFF: The OFF has the highest possible rank and is intended to turn off logging.

How do I level up faster on logging? ›

The best and fastest way to level up your logging skills is always to chop down the highest tree you can find. If you pay attention to this while progressing, you will eventually reach a higher logging skill. It is best to increase your weight capacity every time you have the resources to do so.

How do I get logs from Kubernetes deployment? ›

Get Application Logs from the Kubernetes Cluster
  1. Open a command-line window.
  2. Locate the pods for your application. Replace the <app-name> placeholder. Copy kubectl get pods | grep "<app-name>" ...
  3. Fetch logs for your application from a pod. Replace the <pod-name> placeholder with the pod name of the previous step.

How does FluentD send logs to Elasticsearch? ›

Publish logs to Elasticsearch Using Fluentd
  1. Introduction.
  2. Create fluentd configuration.
  3. Mount fluentd configuration - Configmap as volume in the WebLogic container.
  4. Add fluentd container to WebLogic Server pods.
  5. Restart WebLogic Servers.
  6. Create index pattern in Kibana.

What is FluentD in Kubernetes? ›

Fluentd is a popular open-source data collector that we'll set up on our Kubernetes nodes to tail container log files, filter and transform the log data, and deliver it to the Elasticsearch cluster, where it will be indexed and stored.

Can FluentD collect metrics? ›

Sometimes, the input/filter/output plugin needs to save its internal metrics in memory, influxdb or prometheus format ready in instances. Fluentd has a pluggable system called Metrics that lets a plugin store and reuse its internal state as metrics instances.

How do I monitor logs in Kubernetes? ›

The most basic form of logging in Kubernetes is the output generated by individual containers using stdout and stderr. The output for the current running container instance is available to be accessed via the kubectl logs command. The next level up of logging in the Kubernetes world is called node level logging.

How do I manage log in Kubernetes? ›

Kubernetes Logging Best Practices
  1. Decide Whether to Sidecar or Not to Sidecar.
  2. Pick Your Log Analysis Tool – EFK or Dedicated Logging.
  3. Control Access to Logs with RBAC.
  4. Keep Log Formats Consistent.
  5. Set Resource Limits on Log Collection Daemons.
16 Apr 2020

Is Fluentd better than Logstash? ›

FluentD and Logstash are both open source data collectors used for Kubernetes logging. Logstash is centralized while FluentD is decentralized. FluentD offers better performance than Logstash. In fact, FluentD offers many benefits over Logstash.

Is log4j used in Fluentd? ›

There are some log4j appenders for Fluentd.

Should I use Fluentbit or Fluentd? ›

Fluentd was designed to handle heavy throughput — aggregating from multiple inputs, processing data and routing to different outputs. Fluent Bit is not as pluggable and flexible as Fluentd, which can be integrated with a much larger amount of input and output sources.

What is Fluentd used for? ›

What is Fluentd? Fluentd is an open source data collector for building the unified logging layer. Once installed on a server, it runs in the background to collect, parse, transform, analyze and store various types of data.

How do you check pod logs in Kubernetes? ›

Kubectl Logs Command References With Examples. You can view the pods on your cluster using the kubectl get pods command. Add the --namespace <namespace name> flag if your pods are running outside of the default namespace. You can also use labels to filter the results as required by adding <my-label>=<my-value> .

Videos

1. Kubernetes Logs forwarding Using Fluentd to AWS CloudWatch Log groups On AWS EKS Cluster
(Cloud Quick Labs)
2. Introduction to Fluentd: Collect logs and send almost anywhere
(That DevOps Guy)
3. Understanding Logging: Containers & Microservices
(That DevOps Guy)
4. How to configure Fluentbit to collect Logs for our K8S cluster ?
(Is it Observable)
5. Fluentd Daemonset In Kubernetes | GKE | Centralised Logging In K8S | ES Kibana | Tutorial
(Appychip)
6. Fluentd: A Complete Logging Ecosystem for Kubernetes - Masahiro Nakagawa & Yuta Iwama
(CNCF [Cloud Native Computing Foundation])

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