Before reading this page, it’s highly recommended to familiarize yourself with the overview of logging in Kubernetes.
Note: By default, Stackdriver logging collects only your container’s standard output and standard error streams. To collect any logs your application writes to a file (for example), see the sidecar approach in the Kubernetes logging overview.
To ingest logs, you must deploy the Stackdriver Logging agent to each node in your cluster.
The agent is a configured
fluentd instance, where the configuration is stored in a
and the instances are managed using a Kubernetes
DaemonSet. The actual deployment of the
DaemonSet for your cluster depends on your individual cluster setup.
Stackdriver is the default logging solution for clusters deployed on Google Kubernetes Engine. Stackdriver Logging is deployed to a new cluster by default unless you explicitly opt-out.
To deploy Stackdriver Logging on a new cluster that you’re
kube-up.sh, do the following:
KUBE_LOGGING_DESTINATIONenvironment variable to
Once your cluster has started, each node should be running the Stackdriver Logging agent.
ConfigMap are configured as addons. If you’re not using
consider starting a cluster without a pre-configured logging solution and then deploying
Stackdriver Logging agents to the running cluster.
Warning: The Stackdriver logging daemon has known issues on platforms other than Google Kubernetes Engine. Proceed at your own risk.
Apply a label on each node, if not already present.
The Stackdriver Logging agent deployment uses node labels to determine to which nodes
it should be allocated. These labels were introduced to distinguish nodes with the
Kubernetes version 1.6 or higher. If the cluster was created with Stackdriver Logging
configured and node has version 1.5.X or lower, it will have fluentd as static pod. Node
cannot have more than one instance of fluentd, therefore only apply labels to the nodes
that don’t have fluentd pod allocated already. You can ensure that your node is labelled
properly by running
kubectl describe as follows:
kubectl describe node $NODE_NAME
The output should be similar to this:
Name: NODE_NAME Role: Labels: beta.kubernetes.io/fluentd-ds-ready=true ...
Ensure that the output contains the label
beta.kubernetes.io/fluentd-ds-ready=true. If it
is not present, you can add it using the
kubectl label command as follows:
kubectl label node $NODE_NAME beta.kubernetes.io/fluentd-ds-ready=true
Note: If a node fails and has to be recreated, you must re-apply the label to the recreated node. To make this easier, you can use Kubelet’s command-line parameter for applying node labels in your node startup script.
ConfigMap with the logging agent configuration by running the following command:
kubectl create -f https://k8s.io/examples/debug/fluentd-gcp-configmap.yaml
The command creates the
ConfigMap in the
default namespace. You can download the file
manually and change it before creating the
Deploy the logging agent
DaemonSet by running the following command:
kubectl create -f https://k8s.io/examples/debug/fluentd-gcp-ds.yaml
You can download and edit this file before using it as well.
DaemonSet is deployed, you can discover logging agent deployment status
by running the following command:
kubectl get ds --all-namespaces
If you have 3 nodes in the cluster, the output should looks similar to this:
NAMESPACE NAME DESIRED CURRENT READY NODE-SELECTOR AGE ... default fluentd-gcp-v2.0 3 3 3 beta.kubernetes.io/fluentd-ds-ready=true 5m ...
To understand how logging with Stackdriver works, consider the following synthetic log generator pod specification counter-pod.yaml:
This pod specification has one container that runs a bash script that writes out the value of a counter and the date once per second, and runs indefinitely. Let’s create this pod in the default namespace.
kubectl create -f https://k8s.io/examples/debug/counter-pod.yaml
You can observe the running pod:
$ kubectl get pods NAME READY STATUS RESTARTS AGE counter 1/1 Running 0 5m
For a short period of time you can observe the ‘Pending’ pod status, because the kubelet
has to download the container image first. When the pod status changes to
you can use the
kubectl logs command to view the output of this counter pod.
$ kubectl logs counter 0: Mon Jan 1 00:00:00 UTC 2001 1: Mon Jan 1 00:00:01 UTC 2001 2: Mon Jan 1 00:00:02 UTC 2001 ...
As described in the logging overview, this command fetches log entries from the container log file. If the container is killed and then restarted by Kubernetes, you can still access logs from the previous container. However, if the pod is evicted from the node, log files are lost. Let’s demonstrate this by deleting the currently running counter container:
$ kubectl delete pod counter pod "counter" deleted
and then recreating it:
$ kubectl create -f https://k8s.io/examples/debug/counter-pod.yaml pod "counter" created
After some time, you can access logs from the counter pod again:
$ kubectl logs counter 0: Mon Jan 1 00:01:00 UTC 2001 1: Mon Jan 1 00:01:01 UTC 2001 2: Mon Jan 1 00:01:02 UTC 2001 ...
As expected, only recent log lines are present. However, for a real-world application you will likely want to be able to access logs from all containers, especially for the debug purposes. This is exactly when the previously enabled Stackdriver Logging can help.
Stackdriver Logging agent attaches metadata to each log entry, for you to use later in queries to select only the messages you’re interested in: for example, the messages from a particular pod.
The most important pieces of metadata are the resource type and log name.
The resource type of a container log is
container, which is named
GKE Containers in the UI (even if the Kubernetes cluster is not on Google Kubernetes Engine).
The log name is the name of the container, so that if you have a pod with
two containers, named
container_2 in the spec, their logs
will have log names
System components have resource type
compute, which is named
GCE VM Instance in the interface. Log names for system components are fixed.
For a Google Kubernetes Engine node, every log entry from a system component has one of the following
You can learn more about viewing logs on the dedicated Stackdriver page.
One of the possible ways to view logs is using the
command line interface from the Google Cloud SDK.
It uses Stackdriver Logging filtering syntax
to query specific logs. For example, you can run the following command:
$ gcloud beta logging read 'logName="projects/$YOUR_PROJECT_ID/logs/count"' --format json | jq '..textPayload' ... "2: Mon Jan 1 00:01:02 UTC 2001\n" "1: Mon Jan 1 00:01:01 UTC 2001\n" "0: Mon Jan 1 00:01:00 UTC 2001\n" ... "2: Mon Jan 1 00:00:02 UTC 2001\n" "1: Mon Jan 1 00:00:01 UTC 2001\n" "0: Mon Jan 1 00:00:00 UTC 2001\n"
As you can see, it outputs messages for the count container from both the first and second runs, despite the fact that the kubelet already deleted the logs for the first container.
You can export logs to Google Cloud Storage or to BigQuery to run further analysis. Stackdriver Logging offers the concept of sinks, where you can specify the destination of log entries. More information is available on the Stackdriver Exporting Logs page.
Sometimes the default installation of Stackdriver Logging may not suit your needs, for example:
In this case you need to be able to change the parameters of
If you’re using GKE and Stackdriver Logging is enabled in your cluster, you cannot change its configuration, because it’s managed and supported by GKE. However, you can disable the default integration and deploy your own. Note, that you will have to support and maintain a newly deployed configuration yourself: update the image and configuration, adjust the resources and so on. To disable the default logging integration, use the following command:
gcloud beta container clusters update --logging-service=none CLUSTER
You can find notes on how to then install Stackdriver Logging agents into a running cluster in the Deploying section.
When you have the Stackdriver Logging
DaemonSet in your cluster, you can just modify the
template field in its spec, daemonset controller will update the pods for you. For example,
let’s assume you’ve just installed the Stackdriver Logging as described above. Now you want to
change the memory limit to give fluentd more memory to safely process more logs.
Get the spec of
DaemonSet running in your cluster:
kubectl get ds fluentd-gcp-v2.0 --namespace kube-system -o yaml > fluentd-gcp-ds.yaml
Then edit resource requirements in the spec file and update the
in the apiserver using the following command:
kubectl replace -f fluentd-gcp-ds.yaml
After some time, Stackdriver Logging agent pods will be restarted with the new configuration.
Fluentd configuration is stored in the
ConfigMap object. It is effectively a set of configuration
files that are merged together. You can learn about fluentd configuration on the official
Imagine you want to add a new parsing logic to the configuration, so that fluentd can understand default Python logging format. An appropriate fluentd filter looks similar to this:
<filter reform.**> type parser format /^(?<severity>\w):(?<logger_name>\w):(?<log>.*)/ reserve_data true suppress_parse_error_log true key_name log </filter>
Now you have to put it in the configuration and make Stackdriver Logging agents pick it up.
Get the current version of the Stackdriver Logging
ConfigMap in your cluster
by running the following command:
kubectl get cm fluentd-gcp-config --namespace kube-system -o yaml > fluentd-gcp-configmap.yaml
Then in the value for the key
containers.input.conf insert a new filter right after
source section. Note: Order is important.
ConfigMap in the apiserver is more complicated than updating
DaemonSet. It’s better
ConfigMap to be immutable. Then, in order to update the configuration, you should
ConfigMap with a new name and then change
DaemonSet to point to it
using guide above.
Fluentd is written in Ruby and allows to extend its capabilities using plugins. If you want to use a plugin, which is not included in the default Stackdriver Logging container image, you have to build a custom image. Imagine you want to add Kafka sink for messages from a particular container for additional processing. You can re-use the default container image sources with minor changes:
make build push from this directory. After updating
DaemonSet to pick up the
new image, you can use the plugin you installed in the fluentd configuration.