如何修复"Kibana服务器尚未准备好"?使用AKS时出现错误 [英] How to fix "Kibana server is not ready yet" error when using AKS

问题描述

我正在Azure Kubernetes服务中设置ELK服务.但我只会看到此错误:

I'm setting up ELK services in Azure Kubernetes Service. But I only see this error:

"Kibana服务器尚未准备就绪"

"Kibana server is not ready yet"

我正在使用Helm安装稳定/弹性堆栈的发行版，而AKS中没有任何更改(所有设置均为默认设置).

I'm using Helm to install the stable/elastic-stack release without any changes (default for everything) in AKS.

helm install --name elk stable/elastic-stack

我还添加了一个入口控制器，以将Kibana服务器公开. 但是，我只看到"Kibana服务器尚未准备就绪"错误.

And I also added an ingress controller to expose the Kibana server to public. However, I only see "Kibana server is not ready yet" error.

我已经检查了Kibana Pod和ElasticSearch是否正在运行.作为Kubernetes中的新手，我不知道如何在Kibana实例中查找错误日志.谁可以帮我这个事?如果您能指出我缺少的步骤，也将不胜感激.

I've checked the Kibana pod is running, as well as the ElasticSearch. As a newbie in Kubernetes, I have no idea about how to find the error log in Kibana instance. Can anyone help me on this? It is also appreciated if you can indicate what step I am missing.

推荐答案

很可能您没有在Kibana部署中使用原始变量更改ELASTICSEARCH_URL环境变量的值，因为它附带了舵图.因此，您必须替换 Elasticsearch URL在Kibana配置中包含实际服务地址.

Most probably you didn't change the value for ELASTICSEARCH_URL environment variable in Kibana deployment with your original one, as it was shipped with default values from Elastic-stack Helm chart. Therefore, you have to replace Elasticsearch URL with actual service address inside Kibana configuration.

您可以通过两种方式进行操作:

You can do it in a two ways:

• 更新头盔图表中的值:

• Update the value within Helm Chart:

helm upgrade -f new-values.yml {release name} {package name or path}

Elastic-stack舵图的默认values.yaml可以找到文档中获取详细信息也可能有用.

The default values.yaml for Elastic-stack Helm chart can be found here. Also might be useful to get more details in the official Helm documentation.

• 替换相关的ELASTICSEARCH_URL环境变量 Kibana部署:

• Replace ELASTICSEARCH_URL environment variable in the related to Kibana deployment:

kubectl edit deployment elk-kibana

kubectl delete pod <elk-kibana-Pod-name>

等待，直到Kubernetes成功终止旧版本并启动新的Kibana Pod.

Wait until Kubernetes successfully terminates the old and spin up a new Kibana Pod.

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