如何将 Kubernetes 日志发送到 AWS CloudWatch? [英] How to Send Kubernetes Logs to AWS CloudWatch?

问题描述

在 docker 中设置 AWS CloudWatch Logs 驱动程序是通过 log-driver=awslogs 和 log-opt 完成的，例如 -

Setting an AWS CloudWatch Logs driver in docker is done with log-driver=awslogs and log-opt, for example -

#!/bin/bash

docker run 
    --log-driver=awslogs 
    --log-opt awslogs-region=eu-central-1 
    --log-opt awslogs-group=whatever-group 
    --log-opt awslogs-stream=whatever-stream 
    --log-opt awslogs-create-group=true 
    wernight/funbox 
        fortune

我的问题

我想在 Kubernetes 集群中使用 AWS CloudWatch 日志，其中每个 pod 包含几个 Docker 容器.每个部署都有一个单独的日志组，每个容器都有一个单独的流.我找不到通过 Kubernetes create/apply 将日志参数发送到 docker 容器的方法.

My Problem

I would like to use AWS CloudWatch logs in a Kubernetes cluster, where each pod contains a few Docker containers. Each deployment would have a separate Log Group, and each container would have a separate stream. I could not find a way to send the logging parameters to the docker containers via Kubernetes create / apply.

如何将 log-driver 和 log-opt 参数发送到 Pod/部署中的 Docker 容器?

How can I send the log-driver and log-opt parameters to a Docker container in a pod / deployment?

• 为每台机器上的 Docker 守护进程设置相关参数.这是可能的，但这样同一台机器上的所有容器将共享同一个流 - 因此与我的情况无关.
• 用于 kubectl apply 的 RTFM
• 阅读kops
中的相关 README
• 阅读Kubernetes 日志架构

• Setting relevant parameters for the Docker daemon on each machine. It's possible, but this way all the containers on the same machine would share the same stream - therefore irrelevant for my case.
• RTFM for kubectl apply
• Reading the relevant README in kops
• Read Kubernetes Logging Architecture

推荐答案

据我所知，与 Docker 日志驱动程序相比，Kubernetes 更喜欢集群级别的日志记录.

From what I understand, Kubernetes prefer Cluster-level logging to Docker logging driver.

我们可以使用 fluentd 来收集、转换容器日志并将其推送到 CloudWatch Logs.

We could use fluentd to collect, transform, and push container logs to CloudWatch Logs.

您只需要创建一个带有 ConfigMap 和 Secret 的 fluentd DaemonSet.文件可以在 Github 中找到.它已经过 Kubernetes v1.7.5 测试.

All you need is to create a fluentd DaemonSet with ConfigMap and Secret. Files can be found in Github. It has been tested with Kubernetes v1.7.5.

以下是一些解释.

使用 DaemonSet，fluentd 从主机文件夹 /var/lib/docker/containers 收集每个容器日志.

With DaemonSet, fluentd collect every container logs from the host folder /var/lib/docker/containers.

fluent-plugin-kubernetes_metadata_filter 插件从 Kubernetes API 服务器加载 pod 的元数据.

fluent-plugin-kubernetes_metadata_filter plugin load the pod's metadata from Kubernetes API server.

日志记录应该是这样的.

The log record would be like this.

{
    "log": "INFO: 2017/10/02 06:44:13.214543 Discovered remote MAC 62:a1:3d:f6:eb:65 at 62:a1:3d:f6:eb:65(kube-235)
",
    "stream": "stderr",
    "docker": {
        "container_id": "5b15e87886a7ca5f7ebc73a15aa9091c9c0f880ee2974515749e16710367462c"
    },
    "kubernetes": {
        "container_name": "weave",
        "namespace_name": "kube-system",
        "pod_name": "weave-net-4n4kc",
        "pod_id": "ac4bdfc1-9dc0-11e7-8b62-005056b549b6",
        "labels": {
            "controller-revision-hash": "2720543195",
            "name": "weave-net",
            "pod-template-generation": "1"
        },
        "host": "kube-234",
        "master_url": "https://10.96.0.1:443/api"
    }
}

使用 Fluentd 制作一些标签 record_transformer 过滤插件.

Make some tags with Fluentd record_transformer filter plugin.

{
    "log": "...",
    "stream": "stderr",
    "docker": {
        ...
    },
    "kubernetes": {
        ...
    },
    "pod_name": "weave-net-4n4kc",
    "container_name": "weave"
}

出

fluent-plugin-cloudwatch-logs 插件发送到 AWS CloudWatch Logs.

Out

fluent-plugin-cloudwatch-logs plugin send to AWS CloudWatch Logs.

通过log_group_name_key和log_stream_name_key配置，日志组和流名称可以是记录的任何字段.

With log_group_name_key and log_stream_name_key configuration, log group and stream name can be any field of the record.

<match kubernetes.**>
  @type cloudwatch_logs
  log_group_name_key pod_name
  log_stream_name_key container_name
  auto_create_stream true
  put_log_events_retry_limit 20
</match>

这篇关于如何将 Kubernetes 日志发送到 AWS CloudWatch?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！