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Kafka + Elasticsearch 流式架构应对超大规模日志方案

随着业务发展，越来越多的应用产生的日志数据会越来越多，为了保证系统能够正常采集并分析庞杂的日志数据时， 一般做法是引入 Kafka 的流式架构来解决大量数据异步采集的方案。采集到的日志数据会经过 Kafka 流转， 由相应的数据消费组件将数据从 Kafka 消费存入到 Elasticsearch 中，并通过 Insight 进行可视化展示与分析。

本文将介绍以下两种方案：

  • Fluentbit + Kafka + Logstash + Elasticsearch
  • Fluentbit + Kafka + Vector + Elasticsearch

当我们在日志系统中引入 Kafka 之后，数据流图如下图所示：

logging-kafka

上面两种方案中有共通的地方，不同之处在于消费 Kafka 数据的组件，同时，为了不影响 Insight 数据分析， 我们需要在消费 Kafka 数据并写入到 ES 的数据和原来 Fluentbit 直接写入 ES 的数据的格式一致。

首先我们来看看 Fluentbit 怎么将日志写入 Kafka：

修改 Fluentbit Output 配置

当 Kafka 集群准备就绪之后，我们需要修改 insihgt-system 命名空间下 ConfigMap 的内容， 新增以下三个 Kafka Output 并注释原来三个 Elasticsearch Output：

假设 Kafka Brokers 地址为： insight-kafka.insight-system.svc.cluster.local:9092

    [OUTPUT]
        Name        kafka
        Match_Regex (?:kube|syslog)\.(.*)
        Brokers     insight-kafka.insight-system.svc.cluster.local:9092
        Topics      insight-logs
        format      json
        timestamp_key @timestamp
        rdkafka.batch.size 65536
        rdkafka.compression.level 6
        rdkafka.compression.type lz4
        rdkafka.linger.ms 0
        rdkafka.log.connection.close false
        rdkafka.message.max.bytes 2.097152e+06
        rdkafka.request.required.acks 1
    [OUTPUT]
        Name        kafka
        Match_Regex (?:skoala-gw)\.(.*)
        Brokers     insight-kafka.insight-system.svc.cluster.local:9092
        Topics      insight-gw-skoala
        format      json
        timestamp_key @timestamp
        rdkafka.batch.size 65536
        rdkafka.compression.level 6
        rdkafka.compression.type lz4
        rdkafka.linger.ms 0
        rdkafka.log.connection.close false
        rdkafka.message.max.bytes 2.097152e+06
        rdkafka.request.required.acks 1
    [OUTPUT]
        Name        kafka
        Match_Regex (?:kubeevent)\.(.*)
        Brokers     insight-kafka.insight-system.svc.cluster.local:9092
        Topics      insight-event
        format      json
        timestamp_key @timestamp
        rdkafka.batch.size 65536
        rdkafka.compression.level 6
        rdkafka.compression.type lz4
        rdkafka.linger.ms 0
        rdkafka.log.connection.close false
        rdkafka.message.max.bytes 2.097152e+06
        rdkafka.request.required.acks 1

接下来就是消费 Kafka 数据之后写到 ES 的细微差别。 正如本文开始的描述，本文将介绍 Logstash 与 Vector 作为消费 Kafka 的两种方式。

消费 Kafka 并写入 Elasticsearch

假设 Elasticsearch 的地址为：https://mcamel-common-es-cluster-es-http.mcamel-system:9200

通过 Logstash 消费

如果你对 Logstash 技术栈比较熟悉，你可以继续使用该方式。

当你通过 Helm 部署 Logstash 的时候， 在 logstashPipeline 中增加如下 Pipeline 即可：

replicas: 3
resources:
  requests:
    cpu: 100m
    memory: 1536Mi
  limits:
    cpu: 1000m
    memory: 1536Mi
logstashConfig:
  logstash.yml: |
    http.host: 0.0.0.0
    xpack.monitoring.enabled: false
logstashPipeline:
  insight-event.conf: |
    input {
      kafka {
        add_field => {"kafka_topic" => "insight-event"}
        topics => ["insight-event"]         
        bootstrap_servers => "172.30.120.189:32082" # kafka的ip 和端口
        enable_auto_commit => true
        consumer_threads => 1                       # 对应 partition 的数量
        decorate_events => true
        codec => "plain"
      }
    }

    filter {
      mutate { gsub => [ "message", "@timestamp", "_@timestamp"] }
      json {source => "message"}
      date {
        match => [ "_@timestamp", "UNIX" ]
        remove_field => "_@timestamp"
        remove_tag => "_timestampparsefailure"
      }
      mutate {
        remove_field => ["event", "message"]
      }
    }

    output {
      if [kafka_topic] == "insight-event" {
        elasticsearch {
          hosts => ["https://172.30.120.201:32427"] # elasticsearch 地址
          user => 'elastic'                         # elasticsearch 用户名
          ssl => 'true'
          password => '0OWj4D54GTH3xK06f9Gg01Zk'    # elasticsearch 密码
          ssl_certificate_verification => 'false'
          action => "create"
          index => "insight-es-k8s-event-logs-alias"
          data_stream => "false"
        }
      }
    }
  insight-gw-skoala.conf: |
    input {
      kafka {
        add_field => {"kafka_topic" => "insight-gw-skoala"}
        topics => ["insight-gw-skoala"]         
        bootstrap_servers => "172.30.120.189:32082"
        enable_auto_commit => true
        consumer_threads => 1
        decorate_events => true
        codec => "plain"
      }
    }

    filter {
      mutate { gsub => [ "message", "@timestamp", "_@timestamp"] }
      json {source => "message"}
      date {
        match => [ "_@timestamp", "UNIX" ]
        remove_field => "_@timestamp"
        remove_tag => "_timestampparsefailure"
      }
      mutate {
        remove_field => ["event", "message"]
      }
    }

    output {
      if [kafka_topic] == "insight-gw-skoala" {
        elasticsearch {
          hosts => ["https://172.30.120.201:32427"]
          user => 'elastic'
          ssl => 'true'
          password => '0OWj4D54GTH3xK06f9Gg01Zk'
          ssl_certificate_verification => 'false'
          action => "create"
          index => "skoala-gw-alias"
          data_stream => "false"
        }
      }
    }
  insight-logs.conf: |
    input {
      kafka {
        add_field => {"kafka_topic" => "insight-logs"}
        topics => ["insight-logs"]         
        bootstrap_servers => "172.30.120.189:32082"   
        enable_auto_commit => true
        consumer_threads => 1
        decorate_events => true
        codec => "plain"
      }
    }

    filter {
      mutate { gsub => [ "message", "@timestamp", "_@timestamp"] }
      json {source => "message"}
      date {
        match => [ "_@timestamp", "UNIX" ]
        remove_field => "_@timestamp"
        remove_tag => "_timestampparsefailure"
      }
      mutate {
        remove_field => ["event", "message"]
      }
    }

    output {
      if [kafka_topic] == "insight-logs" {
        elasticsearch {
          hosts => ["https://172.30.120.201:32427"]
          user => 'elastic'
          ssl => 'true'
          password => '0OWj4D54GTH3xK06f9Gg01Zk'
          ssl_certificate_verification => 'false'
          action => "create"
          index => "insight-es-k8s-logs-alias"
          data_stream => "false"
        }
      }
    }

通过 Vector 消费

如果你对 Vector 技术栈比较熟悉，你可以继续使用该方式。

当你通过 Helm 部署 Vector 的时候，引用如下规则的 Configmap 配置文件即可：

metadata:
  name: vector
apiVersion: v1
data:
  aggregator.yaml: |
    api:
      enabled: true
      address: '0.0.0.0:8686'
    sources:
      insight_logs_kafka:
        type: kafka
        bootstrap_servers: 'insight-kafka.insight-system.svc.cluster.local:9092'
        group_id: consumer-group-insight
        topics:
          - insight-logs
      insight_event_kafka:
        type: kafka
        bootstrap_servers: 'insight-kafka.insight-system.svc.cluster.local:9092'
        group_id: consumer-group-insight
        topics:
          - insight-event
      insight_gw_skoala_kafka:
        type: kafka
        bootstrap_servers: 'insight-kafka.insight-system.svc.cluster.local:9092'
        group_id: consumer-group-insight
        topics:
          - insight-gw-skoala
    transforms:
      insight_logs_remap:
        type: remap
        inputs:
          - insight_logs_kafka
        source: |2
              . = parse_json!(string!(.message))
              .@timestamp = now()
      insight_event_kafka_remap:
        type: remap
        inputs:
          - insight_event_kafka
          - insight_gw_skoala_kafka
        source: |2
              . = parse_json!(string!(.message))
              .@timestamp = now()
      insight_gw_skoala_kafka_remap:
        type: remap
        inputs:
          - insight_gw_skoala_kafka
        source: |2
              . = parse_json!(string!(.message))
              .@timestamp = now()
    sinks:
      insight_es_logs:
        type: elasticsearch
        inputs:
          - insight_logs_remap
        api_version: auto
        auth:
          strategy: basic
          user: elastic
          password: 8QZJ656ax3TXZqQh205l3Ee0
        bulk:
          index: insight-es-k8s-logs-alias-1418
        endpoints:
          - 'https://mcamel-common-es-cluster-es-http.mcamel-system:9200'
        tls:
          verify_certificate: false
          verify_hostname: false
      insight_es_event:
        type: elasticsearch
        inputs:
          - insight_event_kafka_remap
        api_version: auto
        auth:
          strategy: basic
          user: elastic
          password: 8QZJ656ax3TXZqQh205l3Ee0
        bulk:
          index: insight-es-k8s-event-logs-alias-1418
        endpoints:
          - 'https://mcamel-common-es-cluster-es-http.mcamel-system:9200'
        tls:
          verify_certificate: false
          verify_hostname: false
      insight_es_gw_skoala:
        type: elasticsearch
        inputs:
          - insight_gw_skoala_kafka_remap
        api_version: auto
        auth:
          strategy: basic
          user: elastic
          password: 8QZJ656ax3TXZqQh205l3Ee0
        bulk:
          index: skoala-gw-alias-1418
        endpoints:
          - 'https://mcamel-common-es-cluster-es-http.mcamel-system:9200'
        tls:
          verify_certificate: false
          verify_hostname: false

检查是否正常工作

你可以通过查看 Insight 日志查询界面是否有最新的数据，或者查看原本 Elasticsearch 的索引的数量有没有增长，增长即代表配置成功。

参考

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