[zh] Delete logging-stackdriver.md
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---
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title: 使用 Stackdriver 生成日志
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content_type: concept
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---
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<!--
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reviewers:
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- piosz
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- x13n
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title: Logging Using Stackdriver
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content_type: concept
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-->
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<!-- overview -->
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<!--
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Before reading this page, it's highly recommended to familiarize yourself
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with the [overview of logging in Kubernetes](/docs/concepts/cluster-administration/logging).
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-->
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在阅读这篇文档之前，强烈建议你先熟悉一下 [Kubernetes 日志概况](/zh/docs/concepts/cluster-administration/logging)
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<!--
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By default, Stackdriver logging collects only your container's standard output and
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standard error streams. To collect any logs your application writes to a file (for example),
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see the [sidecar approach](/docs/concepts/cluster-administration/logging#sidecar-container-with-a-logging-agent)
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in the Kubernetes logging overview.
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-->
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{{< note >}}
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默认情况下，Stackdriver 日志机制仅收集容器的标准输出和标准错误流。
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如果要收集你的应用程序写入一个文件（例如）的任何日志，请参见 Kubernetes 日志概述中的 [sidecar 方式](/zh/docs/concepts/cluster-administration/logging#sidecar-container-with-a-logging-agent)
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{{< /note >}}
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<!-- body -->
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<!--
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## Deploying
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-->
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## 部署 {#deploying}
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<!--
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To ingest logs, you must deploy the Stackdriver Logging agent to each node in your cluster.
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The agent is a configured `fluentd` instance, where the configuration is stored in a `ConfigMap`
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and the instances are managed using a Kubernetes `DaemonSet`. The actual deployment of the
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`ConfigMap` and `DaemonSet` for your cluster depends on your individual cluster setup.
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-->
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为了接收日志，你必须将 Stackdriver 日志代理部署到集群中的每个节点。
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此代理是一个已配置的 `fluentd`，其配置存在一个 `ConfigMap` 中，且实例使用 Kubernetes 的 `DaemonSet` 进行管理。
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`ConfigMap` 和 `DaemonSet` 的实际部署，取决你的集群设置。
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<!--
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### Deploying to a new cluster
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-->
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### 部署到一个新的集群
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#### Google Kubernetes Engine
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<!--
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Stackdriver is the default logging solution for clusters deployed on Google Kubernetes Engine.
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Stackdriver Logging is deployed to a new cluster by default unless you explicitly opt-out.
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-->
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对于部署在 Google Kubernetes Engine 上的集群，Stackdriver 是默认的日志解决方案。
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Stackdriver 日志机制会默认部署到你的新集群上，除非你明确地不选择。
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<!--
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#### Other platforms
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-->
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#### 其他平台
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<!--
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To deploy Stackdriver Logging on a *new* cluster that you're
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creating using `kube-up.sh`, do the following:
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-->
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为了将 Stackdriver 日志机制部署到你正在使用 `kube-up.sh` 创建的*新*集群上，执行如下操作：
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<!--
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1. Set the `KUBE_LOGGING_DESTINATION` environment variable to `gcp`.
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1. **If not running on GCE**, include the `beta.kubernetes.io/fluentd-ds-ready=true`
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in the `KUBE_NODE_LABELS` variable.
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-->
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1. 设置环境变量 `KUBE_LOGGING_DESTINATION` 为 `gcp`。
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1. **如果不是跑在 GCE 上**，在 `KUBE_NODE_LABELS` 变量中包含 `beta.kubernetes.io/fluentd-ds-ready=true`。
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<!--
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Once your cluster has started, each node should be running the Stackdriver Logging agent.
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The `DaemonSet` and `ConfigMap` are configured as addons. If you're not using `kube-up.sh`,
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consider starting a cluster without a pre-configured logging solution and then deploying
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Stackdriver Logging agents to the running cluster.
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-->
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集群启动后，每个节点都应该运行 Stackdriver 日志代理。
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`DaemonSet` 和 `ConfigMap` 作为附加组件进行配置。
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如果你不是使用 `kube-up.sh`，可以考虑不使用预先配置的日志方案启动集群，然后部署 Stackdriver 日志代理到正在运行的集群。
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<!--
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The Stackdriver logging daemon has known issues on platforms other
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than Google Kubernetes Engine. Proceed at your own risk.
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-->
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{{< warning >}}
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除了 Google Kubernetes Engine，Stackdriver 日志守护进程在其他的平台有已知的问题。
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请自行承担风险。
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{{< /warning >}}
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<!--
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### Deploying to an existing cluster
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-->
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### 部署到一个已知集群
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<!--
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1. Apply a label on each node, if not already present.
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-->
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1. 在每个节点上打标签（如果尚未存在）
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<!--
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The Stackdriver Logging agent deployment uses node labels to determine to which nodes
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it should be allocated. These labels were introduced to distinguish nodes with the
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Kubernetes version 1.6 or higher. If the cluster was created with Stackdriver Logging
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configured and node has version 1.5.X or lower, it will have fluentd as static pod. Node
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cannot have more than one instance of fluentd, therefore only apply labels to the nodes
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that don't have fluentd pod allocated already. You can ensure that your node is labelled
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properly by running `kubectl describe` as follows:
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-->
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Stackdriver 日志代理部署使用节点标签来确定应该将其分配到给哪些节点。
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引入这些标签是为了区分 Kubernetes 1.6 或更高版本的节点。
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如果集群是在配置了 Stackdriver 日志机制的情况下创建的，并且节点的版本为 1.5.X 或更低版本，则它将使用 fluentd 用作静态容器。
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节点最多只能有一个 fluentd 实例，因此只能将标签打在未分配过 fluentd pod 的节点上。
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你可以通过运行 `kubectl describe` 来确保你的节点被正确标记，如下所示：
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```
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kubectl describe node $NODE_NAME
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```
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<!--
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The output should be similar to this:
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-->
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输出应类似于如下内容：
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```
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Name: NODE_NAME
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Role:
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Labels: beta.kubernetes.io/fluentd-ds-ready=true
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...
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```
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<!--
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Ensure that the output contains the label `beta.kubernetes.io/fluentd-ds-ready=true`. If it
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is not present, you can add it using the `kubectl label` command as follows:
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-->
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确保输出内容包含 `beta.kubernetes.io/fluentd-ds-ready=true` 标签。
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如果不存在，则可以使用 `kubectl label` 命令添加，如下所示：
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```
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kubectl label node $NODE_NAME beta.kubernetes.io/fluentd-ds-ready=true
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```
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<!--
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If a node fails and has to be recreated, you must re-apply the label to
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the recreated node. To make this easier, you can use Kubelet's command-line parameter
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for applying node labels in your node startup script.
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-->
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{{< note >}}
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如果节点发生故障并且必须重新创建，则必须将标签重新打在重新创建了的节点。
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为了让此操作更便捷，你可以在节点启动脚本中使用 Kubelet 的命令行参数给节点添加标签。
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{{< /note >}}
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<!--
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1. Deploy a `ConfigMap` with the logging agent configuration by running the following command:
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-->
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2. 通过运行以下命令，部署一个带有日志代理配置的 `ConfigMap`：
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```
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kubectl apply -f https://k8s.io/examples/debug/fluentd-gcp-configmap.yaml
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```
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<!--
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The command creates the `ConfigMap` in the `default` namespace. You can download the file
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manually and change it before creating the `ConfigMap` object.
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-->
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该命令在 `default` 命名空间中创建 `ConfigMap`。你可以在创建 `ConfigMap` 对象之前手动下载文件并进行更改。
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<!--
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1. Deploy the logging agent `DaemonSet` by running the following command:
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-->
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3. 通过运行以下命令，部署日志代理的 `DaemonSet`：
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```
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kubectl apply -f https://k8s.io/examples/debug/fluentd-gcp-ds.yaml
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```
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<!--
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You can download and edit this file before using it as well.
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-->
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你也可以在使用前下载和编辑此文件。
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<!--
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## Verifying your Logging Agent Deployment
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-->
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## 验证日志代理部署
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<!--
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After Stackdriver `DaemonSet` is deployed, you can discover logging agent deployment status
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by running the following command:
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-->
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部署 Stackdriver `DaemonSet` 之后，你可以通过运行以下命令来查看日志代理的部署状态：
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```shell
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kubectl get ds --all-namespaces
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```
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<!--
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If you have 3 nodes in the cluster, the output should looks similar to this:
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-->
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如果你的集群中有 3 个节点，则输出应类似于如下：
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```
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NAMESPACE NAME DESIRED CURRENT READY NODE-SELECTOR AGE
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...
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default fluentd-gcp-v2.0 3 3 3 beta.kubernetes.io/fluentd-ds-ready=true 5m
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...
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```
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<!--
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To understand how logging with Stackdriver works, consider the following
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synthetic log generator pod specification [counter-pod.yaml](/examples/debug/counter-pod.yaml):
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-->
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要了解使用 Stackdriver 进行日志记录的工作方式，请考虑以下具有日志生成的 pod 定义 [counter-pod.yaml](/examples/debug/counter-pod.yaml)：
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{{< codenew file="debug/counter-pod.yaml" >}}
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<!--
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This pod specification has one container that runs a bash script
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that writes out the value of a counter and the datetime once per
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second, and runs indefinitely. Let's create this pod in the default namespace.
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-->
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这个 pod 定义里有一个容器，该容器运行一个 bash 脚本，脚本每秒写一次计数器的值和日期时间，并无限期地运行。
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让我们在默认命名空间中创建此 pod。
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```shell
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kubectl apply -f https://k8s.io/examples/debug/counter-pod.yaml
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```
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<!--
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You can observe the running pod:
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-->
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你可以观察到正在运行的 pod：
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```shell
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kubectl get pods
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```
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```
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NAME READY STATUS RESTARTS AGE
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counter 1/1 Running 0 5m
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```
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<!--
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For a short period of time you can observe the 'Pending' pod status, because the kubelet
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has to download the container image first. When the pod status changes to `Running`
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you can use the `kubectl logs` command to view the output of this counter pod.
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-->
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在短时间内，你可以观察到 "pending" 的 pod 的状态，因为 kubelet 必须先下载容器镜像。
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当 pod 状态变为 `Running` 时，你可以使用 `kubectl logs` 命令查看此 counter pod 的输出。
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```shell
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kubectl logs counter
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```
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```
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0: Mon Jan 1 00:00:00 UTC 2001
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1: Mon Jan 1 00:00:01 UTC 2001
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2: Mon Jan 1 00:00:02 UTC 2001
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...
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```
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<!--
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As described in the logging overview, this command fetches log entries
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from the container log file. If the container is killed and then restarted by
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Kubernetes, you can still access logs from the previous container. However,
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if the pod is evicted from the node, log files are lost. Let's demonstrate this
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by deleting the currently running counter container:
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-->
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正如日志概览所述，此命令从容器日志文件中获取日志项。
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如果该容器被 Kubernetes 杀死然后重新启动，你仍然可以访问前一个容器的日志。
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但是，如果将 Pod 从节点中驱逐，则日志文件会丢失。让我们通过删除当前运行的 counter 容器来演示这一点：
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```shell
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kubectl delete pod counter
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```
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```
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pod "counter" deleted
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```
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<!--
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and then recreating it:
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-->
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然后重建它：
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```shell
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kubectl create -f https://k8s.io/examples/debug/counter-pod.yaml
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```
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```
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pod/counter created
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```
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<!--
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After some time, you can access logs from the counter pod again:
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-->
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一段时间后，你可以再次从 counter pod 访问日志：
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```shell
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kubectl logs counter
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```
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```
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0: Mon Jan 1 00:01:00 UTC 2001
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1: Mon Jan 1 00:01:01 UTC 2001
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2: Mon Jan 1 00:01:02 UTC 2001
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...
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```
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<!--
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As expected, only recent log lines are present. However, for a real-world
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application you will likely want to be able to access logs from all containers,
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especially for the debug purposes. This is exactly when the previously enabled
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Stackdriver Logging can help.
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-->
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如预期的那样，日志中仅出现最近的日志记录。
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但是，对于实际应用程序，你可能希望能够访问所有容器的日志，特别是出于调试的目的。
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这就是先前启用的 Stackdriver 日志机制可以提供帮助的地方。
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<!--
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## Viewing logs
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-->
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## 查看日志
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<!--
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Stackdriver Logging agent attaches metadata to each log entry, for you to use later
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in queries to select only the messages you're interested in: for example,
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the messages from a particular pod.
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-->
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Stackdriver 日志代理为每个日志项关联元数据，供你在后续的查询中只选择感兴趣的消息：
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例如，来自某个特定 Pod 的消息。
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<!--
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The most important pieces of metadata are the resource type and log name.
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The resource type of a container log is `container`, which is named
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`GKE Containers` in the UI (even if the Kubernetes cluster is not on Google Kubernetes Engine).
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The log name is the name of the container, so that if you have a pod with
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two containers, named `container_1` and `container_2` in the spec, their logs
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will have log names `container_1` and `container_2` respectively.
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-->
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元数据最重要的部分是资源类型和日志名称。
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容器日志的资源类型为 `container`，在用户界面中名为 `GKE Containers`（即使 Kubernetes 集群不在 Google Kubernetes Engine 上）。
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日志名称是容器的名称，因此，如果你有一个包含两个容器的 pod，在 spec 中名称定义为 `container_1` 和 `container_2`，则它们的日志的名称分别为 `container_1` 和 `container_2`。
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<!--
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System components have resource type `compute`, which is named
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`GCE VM Instance` in the interface. Log names for system components are fixed.
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For a Google Kubernetes Engine node, every log entry from a system component has one of the following
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log names:
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-->
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系统组件的资源类型为 `compute`，在接口中名为 `GCE VM Instance`。
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系统组件的日志名称是固定的。
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对于 Google Kubernetes Engine 节点，系统组件中的每个日志项都具有以下日志名称之一：
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* docker
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* kubelet
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* kube-proxy
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<!--
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You can learn more about viewing logs on [the dedicated Stackdriver page](https://cloud.google.com/logging/docs/view/logs_viewer).
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-->
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你可以在[Stackdriver 专用页面](https://cloud.google.com/logging/docs/view/overview)
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上了解有关查看日志的更多信息。
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<!--
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One of the possible ways to view logs is using the
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[`gcloud logging`](https://cloud.google.com/logging/docs/api/gcloud-logging)
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command line interface from the [Google Cloud SDK].
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It uses Stackdriver Logging [filtering syntax](https://cloud.google.com/logging/docs/view/advanced_filters)
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to query specific logs. For example, you can run the following command:
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-->
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查看日志的一种可能方法是使用 [Google Cloud SDK](https://cloud.google.com/sdk/)
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中的 [`gcloud logging`](https://cloud.google.com/logging/docs/reference/tools/gcloud-logging)
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命令行接口。
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它使用 Stackdriver 日志机制的
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[过滤语法](https://cloud.google.com/logging/docs/view/advanced_filters)查询特定日志。
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例如，你可以运行以下命令：
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```none
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gcloud beta logging read 'logName="projects/$YOUR_PROJECT_ID/logs/count"' --format json | jq '.[].textPayload'
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```
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```
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...
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"2: Mon Jan 1 00:01:02 UTC 2001\n"
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"1: Mon Jan 1 00:01:01 UTC 2001\n"
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"0: Mon Jan 1 00:01:00 UTC 2001\n"
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...
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"2: Mon Jan 1 00:00:02 UTC 2001\n"
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"1: Mon Jan 1 00:00:01 UTC 2001\n"
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"0: Mon Jan 1 00:00:00 UTC 2001\n"
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```
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<!--
|
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As you can see, it outputs messages for the count container from both
|
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the first and second runs, despite the fact that the kubelet already deleted
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the logs for the first container.
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-->
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如你所见，尽管 kubelet 已经删除了第一个容器的日志，日志中仍会包含 counter
|
||||
容器第一次和第二次运行时输出的消息。
|
||||
|
||||
<!--
|
||||
### Exporting logs
|
||||
-->
|
||||
### 导出日志
|
||||
|
||||
<!--
|
||||
You can export logs to [Google Cloud Storage](https://cloud.google.com/storage/)
|
||||
or to [BigQuery](https://cloud.google.com/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](https://cloud.google.com/logging/docs/export/configure_export_v2).
|
||||
-->
|
||||
你可以将日志导出到 [Google Cloud Storage](https://cloud.google.com/storage/) 或
|
||||
[BigQuery](https://cloud.google.com/bigquery/) 进行进一步的分析。
|
||||
Stackdriver 日志机制提供了接收器（Sink）的概念，你可以在其中指定日志项的存放地。
|
||||
可在 Stackdriver [导出日志页面](https://cloud.google.com/logging/docs/export/configure_export_v2)
|
||||
上获得更多信息。
|
||||
|
||||
<!--
|
||||
## Configuring Stackdriver Logging Agents
|
||||
-->
|
||||
## 配置 Stackdriver 日志代理
|
||||
|
||||
<!--
|
||||
Sometimes the default installation of Stackdriver Logging may not suit your needs, for example:
|
||||
-->
|
||||
有时默认的 Stackdriver 日志机制安装可能无法满足你的需求，例如：
|
||||
|
||||
<!--
|
||||
* You may want to add more resources because default performance doesn't suit your needs.
|
||||
* You may want to introduce additional parsing to extract more metadata from your log messages,
|
||||
like severity or source code reference.
|
||||
* You may want to send logs not only to Stackdriver or send it to Stackdriver only partially.
|
||||
-->
|
||||
* 你可能需要添加更多资源，因为默认的行为表现无法满足你的需求。
|
||||
* 你可能需要引入额外的解析机制以便从日志消息中提取更多元数据，例如严重性或源代码引用。
|
||||
* 你可能想要将日志不仅仅发送到 Stackdriver 或仅将部分日志发送到 Stackdriver。
|
||||
|
||||
<!--
|
||||
In this case you need to be able to change the parameters of `DaemonSet` and `ConfigMap`.
|
||||
-->
|
||||
在这种情况下，你需要更改 `DaemonSet` 和 `ConfigMap` 的参数。
|
||||
|
||||
<!--
|
||||
### Prerequisites
|
||||
-->
|
||||
### 先决条件
|
||||
|
||||
<!--
|
||||
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.
|
||||
-->
|
||||
如果使用的是 GKE，并且集群中启用了 Stackdriver 日志机制，则无法更改其配置，
|
||||
因为它是由 GKE 管理和支持的。
|
||||
但是，你可以禁用默认集成的日志机制并部署自己的。
|
||||
|
||||
<!--
|
||||
You will have to support and maintain a newly deployed configuration
|
||||
yourself: update the image and configuration, adjust the resources and so on.
|
||||
-->
|
||||
{{< note >}}
|
||||
你将需要自己支持和维护新部署的配置了：更新映像和配置、调整资源等等。
|
||||
{{< /note >}}
|
||||
|
||||
<!--
|
||||
To disable the default logging integration, use the following command:
|
||||
-->
|
||||
若要禁用默认的日志记录集成，请使用以下命令：
|
||||
|
||||
```
|
||||
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](#deploying).
|
||||
-->
|
||||
你可以在[部署部分](#deploying)中找到有关如何将 Stackdriver 日志代理安装到
|
||||
正在运行的集群中的说明。
|
||||
|
||||
<!--
|
||||
### Changing `DaemonSet` parameters
|
||||
-->
|
||||
### 更改 `DaemonSet` 参数 {#changing-daemonset-parameters}
|
||||
|
||||
<!--
|
||||
When you have the Stackdriver Logging `DaemonSet` in your cluster, you can modify the
|
||||
`template` field in its spec. The DaemonSet controller manages the pods for you.
|
||||
For example, assume you've 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.
|
||||
-->
|
||||
当集群中有 Stackdriver 日志机制的 `DaemonSet` 时，你只需修改其 spec 中的
|
||||
`template` 字段，DaemonSet 控制器将为你管理 Pod。
|
||||
例如，假设你按照上面的描述已经安装了 Stackdriver 日志机制。
|
||||
现在，你想更改内存限制，来给 fluentd 提供的更多内存，从而安全地处理更多日志。
|
||||
|
||||
<!--
|
||||
Get the spec of `DaemonSet` running in your cluster:
|
||||
-->
|
||||
获取集群中运行的 `DaemonSet` 的 spec：
|
||||
|
||||
```shell
|
||||
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 `DaemonSet` object
|
||||
in the apiserver using the following command:
|
||||
-->
|
||||
然后在 spec 文件中编辑资源需求，并使用以下命令更新 apiserver 中的 `DaemonSet` 对象：
|
||||
|
||||
```shell
|
||||
kubectl replace -f fluentd-gcp-ds.yaml
|
||||
```
|
||||
|
||||
<!--
|
||||
After some time, Stackdriver Logging agent pods will be restarted with the new configuration.
|
||||
-->
|
||||
一段时间后，Stackdriver 日志代理的 pod 将使用新配置重新启动。
|
||||
|
||||
<!--
|
||||
### Changing fluentd parameters
|
||||
-->
|
||||
### 更改 fluentd 参数
|
||||
|
||||
<!--
|
||||
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 site](https://docs.fluentd.org).
|
||||
-->
|
||||
Fluentd 的配置存在 `ConfigMap` 对象中。
|
||||
它实际上是一组合并在一起的配置文件。
|
||||
你可以在[官方网站](https://docs.fluentd.org)上了解 fluentd 的配置。
|
||||
|
||||
<!--
|
||||
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:
|
||||
-->
|
||||
假设你要向配置添加新的解析逻辑，以便 fluentd 可以理解默认的 Python 日志记录格式。
|
||||
一个合适的 fluentd 过滤器类似如下：
|
||||
|
||||
```
|
||||
<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:
|
||||
-->
|
||||
现在，你需要将其放入配置中，并使 Stackdriver 日志代理感知它。
|
||||
通过运行以下命令，获取集群中当前版本的 Stackdriver 日志机制的 `ConfigMap`：
|
||||
|
||||
```shell
|
||||
kubectl get cm fluentd-gcp-config --namespace kube-system -o yaml > fluentd-gcp-configmap.yaml
|
||||
```
|
||||
|
||||
<!--
|
||||
Then in the value of the key `containers.input.conf` insert a new filter right after
|
||||
the `source` section.
|
||||
-->
|
||||
然后在 `containers.input.conf` 键的值中，在 `source` 部分之后插入一个新的过滤器。
|
||||
|
||||
<!--
|
||||
Order is important.
|
||||
-->
|
||||
|
||||
{{< note >}}
|
||||
顺序很重要。
|
||||
{{< /note >}}
|
||||
|
||||
<!--
|
||||
Updating `ConfigMap` in the apiserver is more complicated than updating `DaemonSet`. It's better
|
||||
to consider `ConfigMap` to be immutable. Then, in order to update the configuration, you should
|
||||
create `ConfigMap` with a new name and then change `DaemonSet` to point to it
|
||||
using [guide above](#changing-daemonset-parameters).
|
||||
-->
|
||||
在 apiserver 中更新 `ConfigMap` 比更新 `DaemonSet` 更复杂。
|
||||
最好考虑 `ConfigMap` 是不可变的。
|
||||
如果是这样，要更新配置，你应该使用新名称创建 `ConfigMap`，然后使用
|
||||
[上面的指南](#changing-daemonset-parameters)将 `DaemonSet` 更改为指向它。
|
||||
|
||||
<!--
|
||||
### Adding fluentd plugins
|
||||
-->
|
||||
### 添加 fluentd 插件
|
||||
|
||||
<!--
|
||||
Fluentd is written in Ruby and allows to extend its capabilities using
|
||||
[plugins](https://www.fluentd.org/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](https://git.k8s.io/contrib/fluentd/fluentd-gcp-image)
|
||||
with minor changes:
|
||||
-->
|
||||
Fluentd 用 Ruby 编写，并允许使用 [plugins](https://www.fluentd.org/plugins) 扩展其功能。
|
||||
如果要使用默认的 Stackdriver 日志机制容器镜像中未包含的插件，则必须构建自定义镜像。
|
||||
假设你要为来自特定容器添加 Kafka 信息接收器，以进行其他处理。
|
||||
你可以复用默认的[容器镜像源](https://git.k8s.io/contrib/fluentd/fluentd-gcp-image)，并仅添加少量更改：
|
||||
|
||||
<!--
|
||||
* Change Makefile to point to your container repository, for example `PREFIX=gcr.io/<your-project-id>`.
|
||||
* Add your dependency to the Gemfile, for example `gem 'fluent-plugin-kafka'`.
|
||||
-->
|
||||
* 将 Makefile 更改为指向你的容器仓库，例如 `PREFIX=gcr.io/<your-project-id>`。
|
||||
* 将你的依赖项添加到 Gemfile 中，例如 `gem 'fluent-plugin-kafka'`。
|
||||
|
||||
<!--
|
||||
Then run `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.
|
||||
-->
|
||||
然后在该目录运行 `make build push`。
|
||||
在更新 `DaemonSet` 以使用新镜像后，你就可以使用在 fluentd 配置中安装的插件了。
|
||||
|
||||
Loading…
Reference in New Issue