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基于k8s上loggie/vector/openobserve日志收集

2023年 8月 21日 159.6k 0

在上次的日志收集组件变化中简单的介绍了新方案，通常要么基于K8s收集容器的标准输出，要么收集文件。我们尝试使用最新的方式进行配置日志收集的组合进行测试，如下：

image-20230811224414090.png

但是，在开始之前，我们需要部署kafka,zookeeper和kowl

1.kafka

修改kafka的ip地址

version: "2"
services:
  zookeeper:
    container_name: zookeeper    
    image: uhub.service.ucloud.cn/marksugar-k8s/zookeeper:latest
    container_name: zookeeper
    restart: always
    ports:
      - '2182:2181'
    environment:
      - ALLOW_ANONYMOUS_LOGIN=yes
    logging:
      driver: "json-file"
      options:
        max-size: "100M"
    mem_limit: 2048m      
  kafka:
    hostname: 172.16.100.151
    image: uhub.service.ucloud.cn/marksugar-k8s/kafka:2.8.1
    container_name: kafka
    user: root
    restart: always
    ports:
      - '9092:9092'
    volumes:
      - "/data/log/kafka:/bitnami/kafka"  # chmod 777 -R /data/kafka
    environment:
      - KAFKA_BROKER_ID=1
      - KAFKA_LISTENERS=PLAINTEXT://:9092
      - KAFKA_ADVERTISED_LISTENERS=PLAINTEXT://172.16.100.151:9092
      - KAFKA_ZOOKEEPER_CONNECT=zookeeper:2181
      - ALLOW_PLAINTEXT_LISTENER=yes
    depends_on:
      - zookeeper
    logging:
      driver: "json-file"
      options:
        max-size: "100M"
    mem_limit: 2048m      
  kowl:
    container_name: kowl
    # network_mode: host
    restart: always  
    # image: quay.io/cloudhut/kowl:v1.5.0
    image: uhub.service.ucloud.cn/marksugar-k8s/kowl:v1.5.0
    restart: on-failure
    hostname: kowl
    ports:
    - "8081:8080"
    environment:
      KAFKA_BROKERS: 172.16.100.151:9092
    volumes:
    - /etc/localtime:/etc/localtime:ro  # 时区2
    depends_on:
      - kafka
    logging:
      driver: "json-file"
      options:
        max-size: "100M"
    mem_limit: 2048m

2.loggie

接着参考官网helm-chart下载，而后解压，配置loggie的用例

VERSION=v1.4.0
helm pull https://github.com/loggie-io/installation/releases/download/$VERSION/loggie-$VERSION.tgz && tar xvzf loggie-$VERSION.tgz

根据官网的配置示例进行修改，而后得到一个如下的latest.yaml，我们关键需要定义资源配额，加速后镜像地址，外部挂载容器的实际目录

image: uhub.service.ucloud.cn/marksugar-k8s/loggie:v1.4.0

resources:
  limits:
    cpu: 2
    memory: 2Gi
  requests:
    cpu: 100m
    memory: 100Mi

extraArgs: {}
  # log.level: debug
  # log.jsonFormat: true

extraVolumeMounts:
  - mountPath: /var/log/pods
    name: podlogs
  - mountPath: /var/lib/docker/containers
    name: dockercontainers
  - mountPath: /var/lib/kubelet/pods
    name: kubelet

extraVolumes:
  - hostPath:
      path: /var/log/pods
      type: DirectoryOrCreate
    name: podlogs
  - hostPath:
      # path: /var/lib/docker/containers
      path: /data/containerd # containerd的实际目录
      type: DirectoryOrCreate
    name: dockercontainers
  - hostPath:
      path: /var/lib/kubelet/pods
      type: DirectoryOrCreate
    name: kubelet

extraEnvs: {}
timezone: Asia/Shanghai

## ref: https://kubernetes.io/docs/concepts/configuration/assign-pod-node/
nodeSelector: {}

## Affinity for pod assignment
## ref: https://kubernetes.io/docs/concepts/configuration/assign-pod-node/#affinity-and-anti-affinity
affinity: {}
# podAntiAffinity:
#   requiredDuringSchedulingIgnoredDuringExecution:
#   - labelSelector:
#       matchExpressions:
#       - key: app
#         operator: In
#         values:
#         - loggie
#     topologyKey: "kubernetes.io/hostname"

## Tolerations for pod assignment
## ref: https://kubernetes.io/docs/concepts/configuration/taint-and-toleration/
tolerations: []
# - effect: NoExecute
#   operator: Exists
# - effect: NoSchedule
#   operator: Exists

updateStrategy:
  type: RollingUpdate

## Agent mode, ignored when aggregator.enabled is true
config:
  loggie:
    reload:
      enabled: true
      period: 10s
    monitor:
      logger:
        period: 30s
        enabled: true
      listeners:
        filesource:
          period: 10s
        filewatcher:
          period: 5m
        reload:
          period: 10s
        sink:
          period: 10s
        queue:
          period: 10s
        pipeline:
          period: 10s

    discovery:
      enabled: true
      kubernetes:
        # Choose: docker or containerd
        containerRuntime: containerd
        # Collect log files inside the container from the root filesystem of the container, no need to mount the volume
        rootFsCollectionEnabled: false
        # Automatically parse and convert the wrapped container standard output format into the original log content
        parseStdout: false
        # If set to true, it means that the pipeline configuration generated does not contain specific Pod paths and meta information,
        # and these data will be dynamically obtained by the file source, thereby reducing the number of configuration changes and reloads.
        dynamicContainerLog: false
        # Automatically add fields when selector.type is pod in logconfig/clusterlogconfig
        typePodFields:
          logconfig: "${_k8s.logconfig}"
          namespace: "${_k8s.pod.namespace}"
          nodename: "${_k8s.node.name}"
          podname: "${_k8s.pod.name}"
          containername: "${_k8s.pod.container.name}"

    http:
      enabled: true
      port: 9196

## Aggregator mode, by default is disabled
aggregator:
  enabled: false
  replicas: 2
  config:
    loggie:
      reload:
        enabled: true
        period: 10s
      monitor:
        logger:
          period: 30s
          enabled: true
        listeners:
          reload:
            period: 10s
          sink:
            period: 10s
      discovery:
        enabled: true
        kubernetes:
          cluster: aggregator
      http:
        enabled: true
        port: 9196


servicePorts:
  - name: monitor
    port: 9196
    targetPort: 9196
#  - name: gprc
#    port: 6066
#    targetPort: 6066


serviceMonitor:
  enabled: false
  ## Scrape interval. If not set, the Prometheus default scrape interval is used.
  interval: 30s
  relabelings: []
  metricRelabelings: []

而后调试并安装

helm install loggie -f latest.yaml -nloggie --create-namespace --dry-run ./
helm install loggie -f latest.yaml -nloggie --create-namespace ./

默认情况下会以ds的方式进行部署，也就是每个Node节点安装一个。

[root@master-01 ~/loggie-io]# kubectl -n loggie get pod
NAME           READY   STATUS    RESTARTS   AGE
loggie-42rcs   1/1     Running   0          15d
loggie-56sz8   1/1     Running   0          15d
loggie-jnzrc   1/1     Running   0          15d
loggie-k5xqj   1/1     Running   0          15d
loggie-v84wf   1/1     Running   0          14d

2.1 配置收集

在配置收集日志之前，我们先创建一个pod，加入此时有一组pod，他的标签是app: linuxea,在kustomize中表现如下：

commonLabels:
  app: linuxea

而后开始loggie的配置。

在loggie的配置可以大致理解为局部配置和全局配置，如果没有特别的要求，默认的全局配置是够用，倘若不够我们需要局部声明不同的配置信息。

1，此时创建一个sink上游是kafka，ip地址是172.16.100.151:9092，我们输入类型，地址，即将创建的topic的名称

apiVersion: loggie.io/v1beta1
kind: Sink
metadata:
  name: default-kafka
spec:  
  sink: |
    type: kafka
    brokers: ["172.16.100.151:9092"]
    topic: "pod-${fields.environment}-${fields.topic}"

but，如果这是一个加密的，你需要配置如下

apiVersion: loggie.io/v1beta1
kind: Sink
metadata:
  name: default-kafka
spec:  
  sink: |
    type: kafka
    brokers: ["172.16.100.151:9092"]
    topic: "pod-${fields.environment}-${fields.topic}"
    sasl:
      type: scram
      userName: 用户名
      password: 密码
      algorithm: sha256

2，而在LogConfig使用的是标签来关联那些pod的日志将会被收集到，如下

    labelSelector:
      app: linuxea  # 对应deployment的标签

标记有app: linuxea标签的pod均被收集

3，而这些pod的日志的路径paths是pod中标准输出stdout，如果是文件目录这里应该填写对应的地址和正则匹配

4，接着配置一个fields来描述资源，key:value

        fields:
          topic: "java-demo"
          environment: "dev"

而这个自定义的描述被sink中的环境变量所提取，既：

topic: "pod-${fields.environment}-${fields.topic}"

5，在interceptors中我们进行了限流，这意味着每秒最多只能处理

        interceptors: |
          - type: rateLimit
            qps: 90000

6，最后使用sinkRef关联创建的sink： sinkRef: default-kafka

完整的yaml如下：

apiVersion: loggie.io/v1beta1
kind: Sink
metadata:
  name: default-kafka
spec:  
  sink: |
    type: kafka
    brokers: ["172.16.100.151:9092"]
    topic: "pod-${fields.environment}-${fields.topic}"
---
apiVersion: loggie.io/v1beta1
kind: LogConfig
metadata:
  name: java-demo
  namespace: linuxea-dev
spec:
  selector:
    type: pod
    labelSelector:
      app: linuxea  # 对应deployment的标签 
  pipeline:
    sources: |
      - type: file
        name: production-java-demo
        paths:
        - stdout
        ignoreOlder: 12h
        workerCount: 128
        fields:
          topic: "java-demo"
          environment: "dev"
        interceptors: |
          - type: rateLimit
            qps: 90000
          - type: transformer
            actions:
              - action: jsonDecode(body)
    sinkRef: default-kafka
    interceptorRef: default

创建完成

[root@master-01 ~/loggie-io]# kubectl -n loggie get sink
NAME            AGE
default-kafka   15d

[root@master-01 ~/loggie-io]# kubectl -n linuxea-dev get LogConfig 
NAME        POD SELECTOR        AGE
java-demo   {"app":"linuxea"}   15d

日志写入后，到kafka查看的日志格式如下：

{
"fields":{
"containername":"java-demo"
"environment":"dev"
"logconfig":"java-demo"
"namespace":"linuxea-dev"
"nodename":"172.16.100.83"
"podname":"production-java-demo-5cf5b97645-4xh89"
"topic":"java-demo"
}
"body":"2023-08-15T22:10:22.773955049+08:00 stdout F 2023-08-15 22:10:22.773 INFO 7 --- [ main] com.example.demo.DemoApplication : Started DemoApplication in 1.492 seconds (JVM running for ..."
}

image-20230815224624254.png

3.openobserve

我们需要安装openobserve，日志将会被消费到openobserve，安装openobserve在172.16.100.151的节点上

version: "2.2"
services:
  openobserve:
    container_name: openobserve
    restart: always
    image: public.ecr.aws/zinclabs/openobserve:latest
    ports:
      - "5080:5080"
    volumes:
    - /etc/localtime:/etc/localtime:ro  # 时区2
    - /data/openobserve:/data
    environment:
    - ZO_DATA_DIR=/data
    - ZO_ROOT_USER_EMAIL=root@example.com
    - ZO_ROOT_USER_PASSWORD=Complexpass#123
    logging:
      driver: "json-file"
      options:
        max-size: "100M"
    mem_limit: 4096m

接着我们就可以消费kafka后，将日志用vector写入到172.16.100.151上的openobserve了

4.vector

vector作为替代logstash的角色，在此处的作用是消费kafka中的数据此时，我们需要配置在github的vector的releases页面下载安装包，我直接下载的rpm

https://github.com/vectordotdev/vector/releases/download/v0.31.0/vector-0.31.0-1.x86_64.rpm

安装完之后，我们需要创建一个配置文件vector.toml。格式非常简单，如下：

mv /etc/vector/vector.toml /etc/vector/vector.toml-bak
cat > /etc/vector/vector.toml << EOF
[api]
enabled = true
address = "0.0.0.0:8686"


[sources.kafka151]
type = "kafka"
bootstrap_servers = "172.16.100.151:9092"
group_id = "consumer-group-name"
topics = [ "pod-dev-java-demo" ]
[sources.kafka151.decoding]
codec = "json"

[sinks.openobserve]
type = "http"
inputs = [ "kafka151" ]
uri = "http://172.16.100.151:5080/api/pod-dev-java-demo/default/_json"
method = "post"
auth.strategy = "basic"
auth.user = "root@example.com"
auth.password = "Complexpass#123"
compression = "gzip"
encoding.codec = "json"
encoding.timestamp_format = "rfc3339"
healthcheck.enabled = false
EOF

but，如果kafka加密了的话，我们需要添加额外的sasl配置

[sources.kafka151]
type = "kafka"
bootstrap_servers = "172.16.100.151:9092"
group_id = "consumer-group-name"
topics = [ "pod-dev-java-demo" ]
sasl.enabled = true
sasl.mechanism = "SCRAM-SHA-256"
sasl.password = "密码"
sasl.username = "用户名"
[sources.kafka151.decoding]
codec = "json"

对于日志的内容的处理，可以借助https://playground.vrl.dev/

将上述文件替换到/etc/vector/vector.toml而后，启动

systemctl start vector
systemctl enable vector

注意：uri = "http://172.16.100.151:5080/api/pod-dev-java-demo/default/_json",我们可以理解成http://172.16.100.151:5080/api/[group]/[items]/_json，如果在一个项目组的多个项目，我们可以通过这种方式进行归类

回到openobserve查看

image-20230815232619422.png

而后点击explore

image-20230815232643369.png

查看日志回到logs查看

image-20230815232825621.png

5.openobsever搜索

此时我的日志字段如下

{
"fields":{
"podname":"production-java-demo-5cf5b97645-9ws4w"
"topic":"java-demo"
"containername":"java-demo"
"environment":"dev"
"logconfig":"java-demo"
"namespace":"linuxea-dev"
"nodename":"172.16.100.83"
}
"body":"2023-08-15T23:19:33.032689346+08:00 stdout F 2023-08-15 23:19:33.032 INFO 7 --- [ main] com.example.demo.DemoApplication : Started DemoApplication in 1.469 seconds (JVM running for ..."
}

如果我想搜索的内容是body中包含DemoApplication的内容，语法如下

str_match(body, 'DemoApplication')

image-20230815233838453.png

默认情况下，只有msg,meesage,logs才会被全局匹配，对于不是这些字段的，我们需要使用str_match，如果匹配的字段是body的，包含DemoApplication的日志，可以使用如下命令

str_match(body, 'DemoApplication')

现在，一个可以替代传统ELK的日志方案就完成了。

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