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介紹：

各位，在 it 運營中，監(jiān)視服務(wù)器指標(biāo)（例如 cpu/內(nèi)存和磁盤或文件系統(tǒng)的利用率）是一項非常通用的任務(wù)，但如果任何指標(biāo)被觸發(fā)為關(guān)鍵指標(biāo)，則需要專門人員通過以下方式執(zhí)行一些基本故障排除：登錄服務(wù)器并找出使用的最初原因，如果該人收到多個相同的警報，導(dǎo)致無聊且根本沒有生產(chǎn)力，則他必須多次執(zhí)行該操作。因此，作為一種解決方法，可以開發(fā)一個系統(tǒng)，一旦觸發(fā)警報，該系統(tǒng)就會做出反應(yīng)，并通過執(zhí)行一些基本的故障排除命令來對這些實例采取行動。只是總結(jié)問題陳述和期望 -

問題陳述：

開發(fā)一個能夠滿足低于預(yù)期的系統(tǒng) -

每個 ec2 實例都應(yīng)該由 cloudwatch 監(jiān)控。
一旦觸發(fā)警報，就必須有一些東西可以登錄到受影響的 ec2 實例并執(zhí)行一些基本的故障排除命令。
然后，創(chuàng)建一個 jira 問題來記錄該事件，并在評論部分添加命令的輸出。
然后，發(fā)送一封自動電子郵件，其中提供所有警報詳細信息和 jira 問題詳細信息。

架構(gòu)圖：

使用 EventBridge 和 Lambda 進行自動故障排除和 ITSM 系統(tǒng)

先決條件：

ec2 實例
cloudwatch 警報
eventbridge 規(guī)則
lambda 函數(shù)
jira 賬戶
簡單的通知服務(wù)

實施步驟：

a. cloudwatch 代理安裝和配置設(shè)置：
打開 systems manager 控制臺并單擊“文檔”
搜索“aws-configureawspackage”文檔并通過提供所需的詳細信息來執(zhí)行。
包名稱 = amazoncloudwatchagent
安裝后，需要根據(jù)配置文件配置 cloudwatch 代理。為此，請執(zhí)行 amazoncloudwatch-manageagent 文檔。另外，請確保 json cloudwatch 配置文件存儲在 ssm 參數(shù)中。
一旦您看到指標(biāo)正在向 cloudwatch 控制臺報告，請為 cpu 和內(nèi)存利用率等創(chuàng)建警報。
b.設(shè)置eventbridge規(guī)則：
為了跟蹤警報狀態(tài)的變化，這里，我們稍微定制了模式來跟蹤警報狀態(tài)從 ok 到 alarm 的變化，而不是反向變化。然后，將此規(guī)則添加到 lambda 函數(shù)作為觸發(fā)器。

{
  "source": ["aws.cloudwatch"],
  "detail-type": ["cloudwatch alarm state change"],
  "detail": {
    "state": {
      "value": ["alarm"]
    },
    "previousstate": {
      "value": ["ok"]
    }
  }
}

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c.創(chuàng)建 lambda 函數(shù)以在 jira 中發(fā)送電子郵件和記錄事件： 此 lambda 函數(shù)是為由 eventbridge 規(guī)則觸發(fā)的多個活動創(chuàng)建的，并作為使用 aws sdk(boto3) 添加的目標(biāo) sns 主題。一旦觸發(fā) eventbridge 規(guī)則，就會將 json 事件內(nèi)容發(fā)送到 lambda，該函數(shù)通過該函數(shù)捕獲多個詳細信息以不同的方式進行處理。到目前為止，我們已經(jīng)研究了兩種類型的警報 - i。 cpu 利用率和 ii.內(nèi)存利用率。一旦這兩個警報中的任何一個被觸發(fā)并且警報狀態(tài)從 ok 更改為 alarm，就會觸發(fā) eventbridge，這也會觸發(fā) lambda 函數(shù)來執(zhí)行表單代碼中提到的那些任務(wù)。

lambda 先決條件：
我們需要導(dǎo)入以下模塊才能使代碼正常工作 -

>> 操作系統(tǒng)
>> 系統(tǒng)
>> json
>> boto3
>> 時間
>> 請求

注意： 從上面的模塊中，除了“requests”模塊之外，其余的都默認在 lambda 底層基礎(chǔ)設(shè)施中下載。 lambda 不支持直接導(dǎo)入“requests”模塊。因此，首先，通過執(zhí)行以下命令將請求模塊安裝在本地計算機（筆記本電腦）的文件夾中 -

pip3 install requests -t <directory path> --no-user
</directory>

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_之后，這將被下載到您執(zhí)行上述命令的文件夾或您想要存儲模塊源代碼的文件夾中，這里我希望 lambda 代碼正在您的本地計算機中準(zhǔn)備。如果是，則使用 module.txt 創(chuàng)建整個 lambda 源代碼的 zip 文件。之后，將 zip 文件上傳到 lambda 函數(shù)。

所以，我們在這里執(zhí)行以下兩個場景 -

1. cpu 利用率 - 如果觸發(fā) cpu 利用率警報，則 lambda 函數(shù)需要獲取實例并登錄到該實例并執(zhí)行前 5 個高消耗進程。然后，它將創(chuàng)建一個 jira 問題并在評論部分添加流程詳細信息。同時，它將發(fā)送一封電子郵件，其中包含警報詳細信息和 jira 問題詳細信息以及流程輸出。

2.內(nèi)存利用率 - 與上面相同的方法

現(xiàn)在，讓我重新構(gòu)建 lambda 應(yīng)該執(zhí)行的任務(wù)細節(jié) -

登錄實例
執(zhí)行基本故障排除步驟。
創(chuàng)建 jira 問題
向收件人發(fā)送包含所有詳細信息的電子郵件

場景 1：當(dāng)警報狀態(tài)從 ok 更改為 alarm 時

第一組（定義cpu和內(nèi)存函數(shù)）：

################# importing required modules ################
############################################################
import json
import boto3
import time
import os
import sys
sys.path.append('./python')   ## this will add requests module along with all dependencies into this script
import requests
from requests.auth import httpbasicauth

################## calling aws services ###################
###########################################################
ssm = boto3.client('ssm')
sns_client = boto3.client('sns')
ec2 = boto3.client('ec2')

################## defining blank variable ################
###########################################################
cpu_process_op = ''
mem_process_op = ''
issueid = ''
issuekey = ''
issuelink = ''

################# function for cpu utilization ################
###############################################################
def cpu_utilization(instanceid, metric_name, previous_state, current_state):
    global cpu_process_op
    if previous_state == 'ok' and current_state == 'alarm':
        command = 'ps -eo user,pid,ppid,cmd,%mem,%cpu --sort=-%cpu | head -5'
        print(f'impacted instance id is : {instanceid}, metric name: {metric_name}')
        # start a session
        print(f'starting session to {instanceid}')
        response = ssm.send_command(instanceids = [instanceid], documentname="aws-runshellscript", parameters={'commands': [command]})
        command_id = response['command']['commandid']
        print(f'command id: {command_id}')
        # retrieve the command output
        time.sleep(4)
        output = ssm.get_command_invocation(commandid=command_id, instanceid=instanceid)
        print('please find below output -\n', output['standardoutputcontent'])
        cpu_process_op = output['standardoutputcontent']
    else:
        print('none')

################# function for memory utilization ################
############################################################### 
def mem_utilization(instanceid, metric_name, previous_state, current_state):
    global mem_process_op
    if previous_state == 'ok' and current_state == 'alarm':
        command = 'ps -eo user,pid,ppid,cmd,%mem,%cpu --sort=-%mem | head -5'
        print(f'impacted instance id is : {instanceid}, metric name: {metric_name}')
        # start a session
        print(f'starting session to {instanceid}')
        response = ssm.send_command(instanceids = [instanceid], documentname="aws-runshellscript", parameters={'commands': [command]})
        command_id = response['command']['commandid']
        print(f'command id: {command_id}')
        # retrieve the command output
        time.sleep(4)
        output = ssm.get_command_invocation(commandid=command_id, instanceid=instanceid)
        print('please find below output -\n', output['standardoutputcontent'])
        mem_process_op = output['standardoutputcontent']
    else:
        print('none')

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第二組（創(chuàng)建 jira 問題）：

################## create jira issue ################
#####################################################
def create_issues(instanceid, metric_name, account, timestamp, region, current_state, previous_state, cpu_process_op, mem_process_op, metric_val):
    ## create issue ##
    url ='https://<your-user-name>.atlassian.net//rest/api/2/issue'
    username = os.environ['username']
    api_token = os.environ['token']
    project = 'anirbanspace'
    issue_type = 'incident'
    assignee = os.environ['username']
    summ_metric  = '%cpu utilization' if 'cpu' in metric_name else '%memory utilization' if 'mem' in metric_name else '%filesystem utilization' if metric_name == 'disk_used_percent' else none
    metric_val = metric_val
    summary = f'client | {account} | {instanceid} | {summ_metric} | metric value: {metric_val}'
    description = f'client: company\naccount: {account}\nregion: {region}\ninstanceid = {instanceid}\ntimestamp = {timestamp}\ncurrent state: {current_state}\nprevious state = {previous_state}\nmetric value = {metric_val}'

    issue_data = {
        "fields": {
            "project": {
                "key": "scrum"
            },
            "summary": summary,
            "description": description,
            "issuetype": {
                "name": issue_type
            },
            "assignee": {
                "name": assignee
            }
        }
    }
    data = json.dumps(issue_data)
    headers = {
        "accept": "application/json",
        "content-type": "application/json"
    }
    auth = httpbasicauth(username, api_token)
    response = requests.post(url, headers=headers, auth=auth, data=data)
    global issueid
    global issuekey
    global issuelink
    issueid = response.json().get('id')
    issuekey = response.json().get('key')
    issuelink = response.json().get('self')

    ################ add comment to above created jira issue ###################
    output = cpu_process_op if metric_name == 'cpuutilization' else mem_process_op if metric_name == 'mem_used_percent' else none
    comment_api_url = f"{url}/{issuekey}/comment"
    add_comment = requests.post(comment_api_url, headers=headers, auth=auth, data=json.dumps({"body": output}))

    ## check the response
    if response.status_code == 201:
        print("issue created successfully. issue key:", response.json().get('key'))
    else:
        print(f"failed to create issue. status code: {response.status_code}, response: {response.text}")
</your-user-name>

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第三組（發(fā)送電子郵件）：

################## send an email ################
#################################################
def send_email(instanceid, metric_name, account, region, timestamp, current_state, current_reason, previous_state, previous_reason, cpu_process_op, mem_process_op, metric_val, issueid, issuekey, issuelink):
    ### define a dictionary of custom input ###
    metric_list = {'mem_used_percent': 'memory', 'disk_used_percent': 'disk', 'cpuutilization': 'cpu'}

    ### conditions ###
    if previous_state == 'ok' and current_state == 'alarm' and metric_name in list(metric_list.keys()):
        metric_msg = metric_list[metric_name]
        output = cpu_process_op if metric_name == 'cpuutilization' else mem_process_op if metric_name == 'mem_used_percent' else none
        print('this is output', output)
        email_body = f"hi team, \n\nplease be informed that {metric_msg} utilization is high for the instanceid {instanceid}. please find below more information \n\nalarm details:\nmetricname = {metric_name}, \naccount = {account}, \ntimestamp = {timestamp}, \nregion = {region}, \ninstanceid = {instanceid}, \ncurrentstate = {current_state}, \nreason = {current_reason}, \nmetricvalue = {metric_val}, \nthreshold = 80.00 \n\nprocessoutput: \n{output}\nincident deatils:\nissueid = {issueid}, \nissuekey = {issuekey}, \nlink = {issuelink}\n\nregards,\nanirban das,\nglobal cloud operations team"
        res = sns_client.publish(
            topicarn = os.environ['snsarn'],
            subject = f'high {metric_msg} utilization alert : {instanceid}',
            message = str(email_body)
            )
        print('mail has been sent') if res else print('email not sent')
    else:
        email_body = str(0)

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第四組（調(diào)用 lambda 處理函數(shù)）：

################## lambda handler function ################
###########################################################
def lambda_handler(event, context):
    instanceid = event['detail']['configuration']['metrics'][0]['metricstat']['metric']['dimensions']['instanceid']
    metric_name = event['detail']['configuration']['metrics'][0]['metricstat']['metric']['name']
    account = event['account']
    timestamp = event['time']
    region = event['region']
    current_state = event['detail']['state']['value']
    current_reason = event['detail']['state']['reason']
    previous_state = event['detail']['previousstate']['value']
    previous_reason = event['detail']['previousstate']['reason']
    metric_val = json.loads(event['detail']['state']['reasondata'])['evaluateddatapoints'][0]['value']
    ##### function calling #####
    if metric_name == 'cpuutilization':
        cpu_utilization(instanceid, metric_name, previous_state, current_state)
        create_issues(instanceid, metric_name, account, timestamp, region, current_state, previous_state, cpu_process_op, mem_process_op, metric_val)
        send_email(instanceid, metric_name, account, region, timestamp, current_state, current_reason, previous_state, previous_reason, cpu_process_op, mem_process_op, metric_val, issueid, issuekey, issuelink)
    elif metric_name == 'mem_used_percent':
        mem_utilization(instanceid, metric_name, previous_state, current_state)
        create_issues(instanceid, metric_name, account, timestamp, region, current_state, previous_state, cpu_process_op, mem_process_op, metric_val)
        send_email(instanceid, metric_name, account, region, timestamp, current_state, current_reason, previous_state, previous_reason, cpu_process_op, mem_process_op, metric_val, issueid, issuekey, issuelink)
    else:
        none

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報警郵件截圖：

使用 EventBridge 和 Lambda 進行自動故障排除和 ITSM 系統(tǒng)

注意：在理想情況下，閾值是 80%，但為了測試我將其更改為 10%。請看原因。

警報 jira 問題：

使用 EventBridge 和 Lambda 進行自動故障排除和 ITSM 系統(tǒng)

場景 2：當(dāng)警報狀態(tài)從“正常”更改為“數(shù)據(jù)不足”時

在這種情況下，如果未捕獲任何服務(wù)器 cpu 或內(nèi)存利用率指標(biāo)數(shù)據(jù)，則警報狀態(tài)將從 ok 更改為 insufficient_data。可以通過兩種方式實現(xiàn)此狀態(tài) - a.) 如果服務(wù)器處于停止?fàn)顟B(tài) b.) 如果 cloudwatch 代理未運行或進入死亡狀態(tài)。
因此，根據(jù)下面的腳本，您將能夠看到，當(dāng) cpu 或內(nèi)存利用率警報狀態(tài)獲取的數(shù)據(jù)不足時，lambda 將首先檢查實例是否處于運行狀態(tài)。如果實例處于運行狀態(tài)，那么它將登錄并檢查 cloudwatch 代理狀態(tài)。發(fā)布后，它將創(chuàng)建一個 jira 問題并在 jira 問題的評論部分發(fā)布代理狀態(tài)。之后，它將發(fā)送一封包含警報詳細信息和代理狀態(tài)的電子郵件。

完整代碼：

################# Importing Required Modules ################
############################################################
import json
import boto3
import time
import os
import sys
sys.path.append('./python')   ## This will add requests module along with all dependencies into this script
import requests
from requests.auth import HTTPBasicAuth

################## Calling AWS Services ###################
###########################################################
ssm = boto3.client('ssm')
sns_client = boto3.client('sns')
ec2 = boto3.client('ec2')

################## Defining Blank Variable ################
###########################################################
cpu_process_op = ''
mem_process_op = ''
issueid = ''
issuekey = ''
issuelink = ''

################# Function for CPU Utilization ################
###############################################################
def cpu_utilization(instanceid, metric_name, previous_state, current_state):
    global cpu_process_op
    if previous_state == 'OK' and current_state == 'INSUFFICIENT_DATA':
        ec2_status = ec2.describe_instance_status(InstanceIds=[instanceid,])['InstanceStatuses'][0]['InstanceState']['Name']
        if ec2_status == 'running':
            command = 'systemctl status amazon-cloudwatch-agent;sleep 3;systemctl restart amazon-cloudwatch-agent'
            print(f'Impacted Instance ID is : {instanceid}, Metric Name: {metric_name}')
            # Start a session
            print(f'Starting session to {instanceid}')
            response = ssm.send_command(InstanceIds = [instanceid], DocumentName="AWS-RunShellScript", Parameters={'commands': [command]})
            command_id = response['Command']['CommandId']
            print(f'Command ID: {command_id}')
            # Retrieve the command output
            time.sleep(4)
            output = ssm.get_command_invocation(CommandId=command_id, InstanceId=instanceid)
            print('Please find below output -\n', output['StandardOutputContent'])
            cpu_process_op = output['StandardOutputContent']
        else:
            cpu_process_op = f'Instance current status is {ec2_status}. Not able to reach out!!'
            print(f'Instance current status is {ec2_status}. Not able to reach out!!')
    else:
        print('None')

################# Function for Memory Utilization ################
############################################################### 
def mem_utilization(instanceid, metric_name, previous_state, current_state):
    global mem_process_op
    if previous_state == 'OK' and current_state == 'INSUFFICIENT_DATA':
        ec2_status = ec2.describe_instance_status(InstanceIds=[instanceid,])['InstanceStatuses'][0]['InstanceState']['Name']
        if ec2_status == 'running':
            command = 'systemctl status amazon-cloudwatch-agent'
            print(f'Impacted Instance ID is : {instanceid}, Metric Name: {metric_name}')
            # Start a session
            print(f'Starting session to {instanceid}')
            response = ssm.send_command(InstanceIds = [instanceid], DocumentName="AWS-RunShellScript", Parameters={'commands': [command]})
            command_id = response['Command']['CommandId']
            print(f'Command ID: {command_id}')
            # Retrieve the command output
            time.sleep(4)
            output = ssm.get_command_invocation(CommandId=command_id, InstanceId=instanceid)
            print('Please find below output -\n', output['StandardOutputContent'])
            mem_process_op = output['StandardOutputContent']
            print(mem_process_op)
        else:
            mem_process_op = f'Instance current status is {ec2_status}. Not able to reach out!!'
            print(f'Instance current status is {ec2_status}. Not able to reach out!!')     
    else:
        print('None')

################## Create JIRA Issue ################
#####################################################
def create_issues(instanceid, metric_name, account, timestamp, region, current_state, previous_state, cpu_process_op, mem_process_op, metric_val):
    ## Create Issue ##
    url ='https://<your-user-name>.atlassian.net//rest/api/2/issue'
    username = os.environ['username']
    api_token = os.environ['token']
    project = 'AnirbanSpace'
    issue_type = 'Incident'
    assignee = os.environ['username']
    summ_metric  = '%CPU Utilization' if 'CPU' in metric_name else '%Memory Utilization' if 'mem' in metric_name else '%Filesystem Utilization' if metric_name == 'disk_used_percent' else None
    metric_val = metric_val
    summary = f'Client | {account} | {instanceid} | {summ_metric} | Metric Value: {metric_val}'
    description = f'Client: Company\nAccount: {account}\nRegion: {region}\nInstanceID = {instanceid}\nTimestamp = {timestamp}\nCurrent State: {current_state}\nPrevious State = {previous_state}\nMetric Value = {metric_val}'

    issue_data = {
        "fields": {
            "project": {
                "key": "SCRUM"
            },
            "summary": summary,
            "description": description,
            "issuetype": {
                "name": issue_type
            },
            "assignee": {
                "name": assignee
            }
        }
    }
    data = json.dumps(issue_data)
    headers = {
        "Accept": "application/json",
        "Content-Type": "application/json"
    }
    auth = HTTPBasicAuth(username, api_token)
    response = requests.post(url, headers=headers, auth=auth, data=data)
    global issueid
    global issuekey
    global issuelink
    issueid = response.json().get('id')
    issuekey = response.json().get('key')
    issuelink = response.json().get('self')

    ################ Add Comment To Above Created JIRA Issue ###################
    output = cpu_process_op if metric_name == 'CPUUtilization' else mem_process_op if metric_name == 'mem_used_percent' else None
    comment_api_url = f"{url}/{issuekey}/comment"
    add_comment = requests.post(comment_api_url, headers=headers, auth=auth, data=json.dumps({"body": output}))

    ## Check the response
    if response.status_code == 201:
        print("Issue created successfully. Issue key:", response.json().get('key'))
    else:
        print(f"Failed to create issue. Status code: {response.status_code}, Response: {response.text}")

################## Send An Email ################
#################################################
def send_email(instanceid, metric_name, account, region, timestamp, current_state, current_reason, previous_state, previous_reason, cpu_process_op, mem_process_op, metric_val, issueid, issuekey, issuelink):
    ### Define a dictionary of custom input ###
    metric_list = {'mem_used_percent': 'Memory', 'disk_used_percent': 'Disk', 'CPUUtilization': 'CPU'}

    ### Conditions ###
    if previous_state == 'OK' and current_state == 'INSUFFICIENT_DATA' and metric_name in list(metric_list.keys()):
        metric_msg = metric_list[metric_name]
        output = cpu_process_op if metric_name == 'CPUUtilization' else mem_process_op if metric_name == 'mem_used_percent' else None
        email_body = f"Hi Team, \n\nPlease be informed that {metric_msg} utilization alarm state has been changed to {current_state} for the instanceid {instanceid}. Please find below more information \n\nAlarm Details:\nMetricName = {metric_name}, \n Account = {account}, \nTimestamp = {timestamp}, \nRegion = {region},  \nInstanceID = {instanceid}, \nCurrentState = {current_state}, \nReason = {current_reason}, \nMetricValue = {metric_val}, \nThreshold = 80.00  \n\nProcessOutput = \n{output}\nIncident Deatils:\nIssueID = {issueid}, \nIssueKey = {issuekey}, \nLink = {issuelink}\n\nRegards,\nAnirban Das,\nGlobal Cloud Operations Team"
        res = sns_client.publish(
            TopicArn = os.environ['snsarn'],
            Subject = f'Insufficient {metric_msg} Utilization Alarm : {instanceid}',
            Message = str(email_body)
        )
        print('Mail has been sent') if res else print('Email not sent')
    else:
        email_body = str(0)

################## Lambda Handler Function ################
###########################################################
def lambda_handler(event, context):
    instanceid = event['detail']['configuration']['metrics'][0]['metricStat']['metric']['dimensions']['InstanceId']
    metric_name = event['detail']['configuration']['metrics'][0]['metricStat']['metric']['name']
    account = event['account']
    timestamp = event['time']
    region = event['region']
    current_state = event['detail']['state']['value']
    current_reason = event['detail']['state']['reason']
    previous_state = event['detail']['previousState']['value']
    previous_reason = event['detail']['previousState']['reason']
    metric_val = 'NA'
    ##### function calling #####
    if metric_name == 'CPUUtilization':
        cpu_utilization(instanceid, metric_name, previous_state, current_state)
        create_issues(instanceid, metric_name, account, timestamp, region, current_state, previous_state, cpu_process_op, mem_process_op, metric_val)
        send_email(instanceid, metric_name, account, region, timestamp, current_state, current_reason, previous_state, previous_reason, cpu_process_op, mem_process_op, metric_val, issueid, issuekey, issuelink)
    elif metric_name == 'mem_used_percent':
        mem_utilization(instanceid, metric_name, previous_state, current_state)
        create_issues(instanceid, metric_name, account, timestamp, region, current_state, previous_state, cpu_process_op, mem_process_op, metric_val)
        send_email(instanceid, metric_name, account, region, timestamp, current_state, current_reason, previous_state, previous_reason, cpu_process_op, mem_process_op, metric_val, issueid, issuekey, issuelink)
    else:
        None
</your-user-name>

關(guān)注：愛掏網(wǎng)

數(shù)據(jù)不足郵件截圖：

使用 EventBridge 和 Lambda 進行自動故障排除和 ITSM 系統(tǒng)

數(shù)據(jù)不足jira問題：

使用 EventBridge 和 Lambda 進行自動故障排除和 ITSM 系統(tǒng)

結(jié)論：

在本文中，我們測試了有關(guān) cpu 和內(nèi)存利用率的場景，但是我們可以在很多指標(biāo)上配置自動事件和自動電子郵件功能，這將減少監(jiān)控和創(chuàng)建事件等方面的大量工作。。該解決方案為我們提供了進一步推進的初步方法，但可以肯定的是，還可以有其他可能性來實現(xiàn)這一目標(biāo)。我相信你們都會理解我們?nèi)绾闻ψ屵@一切產(chǎn)生關(guān)聯(lián)。如果您喜歡這篇文章或有任何其他建議，請點贊和評論，以便我們可以在接下來的文章中補充。 ??

謝謝！！
阿尼班·達斯

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使用 EventBridge 和 Lambda 進行自動故障排除和 ITSM 系統(tǒng)

介紹：

問題陳述：

架構(gòu)圖：

先決條件：

實施步驟：

場景 1：當(dāng)警報狀態(tài)從 ok 更改為 alarm 時

場景 2：當(dāng)警報狀態(tài)從“正常”更改為“數(shù)據(jù)不足”時

結(jié)論：

Android的歷史含代碼

Java集合根據(jù)某個字段去重數(shù)據(jù)

使用React Hooks構(gòu)建CSS的transition和animation

微信小程序地圖map詳解及簡單實例

PHP Echo 輸出語句含代碼

使用第三方 PHP 函數(shù)擴展應(yīng)用程序功能

使用第三方 PHP 函數(shù)管理依賴性

使用 EventBridge 和 Lambda 進行自動故障排除和 ITSM 系統(tǒng)

介紹 ：

問題陳述：

架構(gòu)圖：

先決條件：

實施步驟：

場景 1：當(dāng)警報狀態(tài)從 ok 更改為 alarm 時

場景 2：當(dāng)警報狀態(tài)從“正常”更改為“數(shù)據(jù)不足”時

結(jié)論 ：

Android的歷史含代碼

Java集合根據(jù)某個字段去重數(shù)據(jù)

使用React Hooks構(gòu)建CSS的transition和animation

微信小程序 地圖map詳解及簡單實例

PHP Echo 輸出語句含代碼

使用第三方 PHP 函數(shù)擴展應(yīng)用程序功能

使用第三方 PHP 函數(shù)管理依賴性

介紹：

結(jié)論：

微信小程序地圖map詳解及簡單實例