搭建亿级时间线数据的监控系统，我有绝招！

[[inputs.kafka_consumer]]
## Kafka brokers.
## 这里设置Kafka brokers的IP地址
brokers = ["192.168.100.10:9092"]
​
## Topics to consume.
topics = ["telegraf"]
......
## Data format to consume.
## Each data format has its own unique set of configuration options, read
## more about them here:
## https://github.com/influxdata/telegraf/blob/master/docs/DATA_FORMATS_INPUT.md
## 这里设置数据格式，其他格式参考 DATA_FORMAT_INPUT.md，Logfmt为日志格式。
data_format = "Logfmt"

# Configuration for sending metrics to InfluxDB
[[outputs.influxdb]]
## The full HTTP or UDP URL for your InfluxDB instance.
##
## Multiple URLs can be specified for a single cluster, only ONE of the
## urls will be written to each interval.
# urls = ["unix:///var/run/influxdb.sock"]
# urls = ["udp://127.0.0.1:8089"]
## 这里设置华为云GaussDB(for Influx)的配置的ELB IP地址或者节点内网IP地址
urls=[“https://rwuser:password@IP:8635”]
​
## The target database for metrics; will be created as needed.
## For UDP url endpoint database needs to be configured on server side.
## 设置数据库名称（默认telegraf）
database = "telegraf"
...
...
## HTTP Basic Auth
## 这里设置GaussDB(for Influx)数据库用户名和密码,实际密码替换password
username = "rwuser"
password = "password"
​
## HTTP User-Agent
# user_agent = "telegraf"
​
## UDP payload size is the maximum packet size to send.
# udp_payload = "512B"
​
## Optional TLS Config for use on HTTP connections.
# tls_ca = "/etc/telegraf/ca.pem"
# tls_cert = "/etc/telegraf/cert.pem"
# tls_key = "/etc/telegraf/key.pem"
## Use TLS but skip chain & host verification
## 这里设置跳过ssl验证
# insecure_skip_verify = true

pip install influxdb
# Debian Or Ubuntu系统上使用如下命令
sudo apt-get install python-influxdb

# Python
from influxdb import InfluxDBClient
​
## 使用GaussDB(for Influx)的ELB IP或者实例节点的IP替换填写host，用户密码填写password，
## 并指定连接数据库database。
client = InfluxDBClient(host='localhost', port=8635, username='rwuser', password='password', database='telegraf', ssl=True, verify_ssl=False)

json_body = [ 
  {   
      "measurement": "my_tab",
      "tags": {
          "host": "server01",
          "region": "us-west"
      },  
      "time": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.localtime()),
      "fields": {
          "used_cpu": cpu_used_rate,
          "used_mem": mem_used_rate
      }   
  }]
client.write_points(json_body)    

## 这里的IP可以是云上服务器的弹性IP，也可以是本地自建服务器的Ip地址。
http://IP:3000

FROM autogen cpu Where cpu = cpu-total
SELECT field (usage_user) mean ()
GROUP BY time (1m) fill (0)