image
ELK日志系统大家不会陌生(zipkin + jaeger , prometheus + grafana)解决了大家对于链路对于统计采集的需求,但是真正的对于日志进行存储还是得专业的上,在Istio中官方提供的方案是EFK(Fluentd + Elasticsearch + Kibana)Fluentd 是一个开源的日志收集器，支持多种数据输出并且有一个可插拔架构。 Elasticsearch是一个流行的后端日志记录程序， Kibana 用于查看。
附上:
喵了个咪的博客:w-blog.cn
Istio官方地址:https://preliminary.istio.io/zh
Istio中文文档:https://preliminary.istio.io/zh/docs/
PS : 此处基于当前最新istio版本1.0.3版本进行搭建和演示
一. 准备环境
我们把Fluentd，Elasticsearch 和 Kibana 在一个非生产集合 Services 和 Deployments 在一个新的叫做logging的 Namespace 中。
> vim logging-stack.yaml
# Logging Namespace. All below are a part of this namespace.
apiVersion: v1
kind: Namespace
metadata:
name: logging
---
# Elasticsearch Service
apiVersion: v1
kind: Service
metadata:
name: elasticsearch
namespace: logging
labels:
app: elasticsearch
spec:
ports:
- port: 9200
protocol: TCP
targetPort: db
selector:
app: elasticsearch
---
# Elasticsearch Deployment
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
name: elasticsearch
namespace: logging
labels:
app: elasticsearch
annotations:
sidecar.istio.io/inject: "false"
spec:
template:
metadata:
labels:
app: elasticsearch
spec:
containers:
- image: docker.elastic.co/elasticsearch/elasticsearch-oss:6.1.1
name: elasticsearch
resources:
# need more cpu upon initialization, therefore burstable class
limits:
cpu: 1000m
requests:
cpu: 100m
env:
- name: discovery.type
value: single-node
ports:
- containerPort: 9200
name: db
protocol: TCP
- containerPort: 9300
name: transport
protocol: TCP
volumeMounts:
- name: elasticsearch
mountPath: /data
volumes:
- name: elasticsearch
emptyDir: {}
---
# Fluentd Service
apiVersion: v1
kind: Service
metadata:
name: fluentd-es
namespace: logging
labels:
app: fluentd-es
spec:
ports:
- name: fluentd-tcp
port: 24224
protocol: TCP
targetPort: 24224
- name: fluentd-udp
port: 24224
protocol: UDP
targetPort: 24224
selector:
app: fluentd-es
---
# Fluentd Deployment
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
name: fluentd-es
namespace: logging
labels:
app: fluentd-es
annotations:
sidecar.istio.io/inject: "false"
spec:
template:
metadata:
labels:
app: fluentd-es
spec:
containers:
- name: fluentd-es
image: gcr.io/google-containers/fluentd-elasticsearch:v2.0.1
env:
- name: FLUENTD_ARGS
value: --no-supervisor -q
resources:
limits:
memory: 500Mi
requests:
cpu: 100m
memory: 200Mi
volumeMounts:
- name: config-volume
mountPath: /etc/fluent/config.d
terminationGracePeriodSeconds: 30
volumes:
- name: config-volume
configMap:
name: fluentd-es-config
---
# Fluentd ConfigMap, contains config files.
kind: ConfigMap
apiVersion: v1
data:
forward.input.conf: |-
# Takes the messages sent over TCP
<source>
type forward
</source>
output.conf: |-
<match **>
type elasticsearch
log_level info
include_tag_key true
host elasticsearch
port 9200
logstash_format true
# Set the chunk limits.
buffer_chunk_limit 2M
buffer_queue_limit 8
flush_interval 5s
# Never wait longer than 5 minutes between retries.
max_retry_wait 30
# Disable the limit on the number of retries (retry forever).
disable_retry_limit
# Use multiple threads for processing.
num_threads 2
</match>
metadata:
name: fluentd-es-config
namespace: logging
---
# Kibana Service
apiVersion: v1
kind: Service
metadata:
name: kibana
namespace: logging
labels:
app: kibana
spec:
ports:
- port: 5601
protocol: TCP
targetPort: ui
selector:
app: kibana
---
# Kibana Deployment
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
name: kibana
namespace: logging
labels:
app: kibana
annotations:
sidecar.istio.io/inject: "false"
spec:
template:
metadata:
labels:
app: kibana
spec:
containers:
- name: kibana
image: docker.elastic.co/kibana/kibana-oss:6.1.1
resources:
# need more cpu upon initialization, therefore burstable class
limits:
cpu: 1000m
requests:
cpu: 100m
env:
- name: ELASTICSEARCH_URL
value: http://elasticsearch:9200
ports:
- containerPort: 5601
name: ui
protocol: TCP
---
创建资源
kubectl apply -f logging-stack.yaml
二, 配置Istio
现在有一个正在运行的 Fluentd 守护进程，使用新的日志类型配置 Istio，并将这些日志发送到监听守护进程。
创建一个新的 YAML 文件来保存日志流的配置，Istio 将自动生成并收集。
> vim fluentd-istio.yaml
apiVersion: "config.istio.io/v1alpha2"
kind: logentry
metadata:
name: newlog
namespace: istio-system
spec:
severity: '"info"'
timestamp: request.time
variables:
source: source.labels["app"] | source.workload.name | "unknown"
user: source.user | "unknown"
destination: destination.labels["app"] | destination.workload.name | "unknown"
responseCode: response.code | 0
responseSize: response.size | 0
latency: response.duration | "0ms"
monitored_resource_type: '"UNSPECIFIED"'
---
# fluentd handler 的配置
apiVersion: "config.istio.io/v1alpha2"
kind: fluentd
metadata:
name: handler
namespace: istio-system
spec:
address: "fluentd-es.logging:24224"
---
# 发送 logentry 实例到 fluentd handler 的规则
apiVersion: "config.istio.io/v1alpha2"
kind: rule
metadata:
name: newlogtofluentd
namespace: istio-system
spec:
match: "true" # match for all requests
actions:
- handler: handler.fluentd
instances:
- newlog.logentry
---
PS : 处理程序配置中 address: "fluentd-es.logging:24224" 行指向我们设置的 Fluentd 守护进程示例软件栈。
使其生效
kubectl apply -f fluentd-istio.yaml
三, 查看采集的日志
我们先访问以下我们的示例程序bookinfo,然后老方式通过端口映射访问kibana
kubectl -n logging port-forward $(kubectl -n logging get pod -l app=kibana -o jsonpath='{.items[0].metadata.name}') 5601:5601
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PS : 推荐吧ES和kibana单独部署在集群外部,ES对存储和资源有较高要求
作者：文振熙
链接：https://www.jianshu.com/p/80f1e62917ee
来源：简书
著作权归作者所有。商业转载请联系作者获得授权，非商业转载请注明出处。
