Incident Events Integration with Jira¶

When our applications or systems experience incidents, they usually need to be handled promptly to ensure normal system operation. To better manage and track incident events, we can send these events to Jira to create issues, allowing us to track, analyze, and resolve these problems within Jira. By quickly sending incident events to Jira to create issues, we gain better capabilities for managing and tracking incident events, thereby ensuring smoother system operations. Additionally, this method also helps us better analyze and solve problems, enhancing the stability and reliability of the system.

Configuration¶

Preparation¶

Deploy a DataFlux Func (Automata) to generate an authorization link.
Create a webhook custom notification target (the webhook URL should be the authorization link URL of the deployed DataFlux Func).
Correctly configure the monitors.

Deployment Process¶

Create Webhook Custom Notification Target¶

In the Guance Studio under ["Monitoring / Notification Targets Management"], create a new notification target by selecting webhook custom and filling in the authorization link address of the deployed DataFlux Func as the webhook URL.

Note: In Func, choose the authorization link without parameters.

Create Monitor¶

In the Guance Studio under ["Monitoring / Monitors"], create a new monitor by selecting the metrics you want to observe. After configuring the event notification content, set the alert strategy's notification target to the name of the webhook custom notification target that we just created.

Write Listener Script¶

After configuring the detection rules for the monitor, we need to write a script in the already installed and configured DataFlux Func to fetch new messages and send them to Jira to create issues.

First, we need to import some constants such as jira_server, username, password, project_key, etc.

import json
from jira import JIRA

jira_server = "http://xxxxxx:8080/"
username = "xxxx"
password = "xxxxx"
project_key = "XXXXXXXXX"

After importing the required constants, we need to understand the data structure of the monitoring events so that we can parse and create issues to be sent to Jira.

{
    "Result":100,
    "date":1693034940,
    "df_at_accounts":[

    ],
    "df_at_accounts_nodata":[

    ],
    "df_channels":[
        "chan_968577392a1c4714a464cd2f6ee42a9c"
    ],
    "df_check_range_end":1693034880,
    "df_check_range_start":1693034820,
    "df_date_range":60,
    "df_dimension_tags":"{\"host\":\"share\"}",
    "df_event_id":"event-f20a38aa58b54c6c8d4c9a84e655db1a",
    "df_event_link":"https://console.guance.com/keyevents/monitor?time=1693034040000%2C1693035000000&tags=%7B%22df_event_id%22%3A%22event-f20a38aa58b54c6c8d4c9a84e655db1a%22%7D&w=wksp_968577392a1c4714a464cd2f6ee42a9c",
    "df_event_reason":"\u6ee1\u8db3\u76d1\u63a7\u5668\u4e2d\u6545\u969c\u7684\u8ba4\u5b9a\u6761\u4ef6\uff0c\u4ea7\u751f\u6545\u969c\u4e8b\u4ef6",
    "df_exec_mode":"crontab",
    "df_issue_duration":3840,
    "df_issue_start_time":1693031100,
    "df_label":"[]",
    "df_language":"zh",
    "df_message":">\u7b49\u7ea7\uff1acritical    \n>\u5b9e\u4f8b\uff1ashare    \n>\u5185\u5bb9\uff1a\u78c1\u76d8\u4f7f\u7528\u7387\u4e3a 100.00%    \n>\u5efa\u8bae\uff1a\u767b\u5f55\u534e\u4e3a\u4e91\u63a7\u5236\u53f0\u67e5\u770b RDS \u662f\u5426\u6709\u5f02\u5e38",
    "df_monitor_checker":"custom_metric",
    "df_monitor_checker_event_ref":"13713ac25e993a37d2ca5899e2a7bba6",
    "df_monitor_checker_id":"rul_c439124e218f4c0c9cb114b5d04eeab4",
    "df_monitor_checker_name":"\u5b9e\u4f8b\u540d\u79f0\u4e3a {host} \u78c1\u76d8\u4f7f\u7528\u7387\u8fc7\u9ad8",
    "df_monitor_checker_ref":"aad7deb63b2e58b301f823517fea944d",
    "df_monitor_checker_sub":"check",
    "df_monitor_checker_value":"100",
    "df_monitor_id":"monitor_6774968876c14586bc5afc6d7144f52f",
    "df_monitor_name":"\u9ed8\u8ba4",
    "df_monitor_type":"custom",
    "df_site_name":"\u4e2d\u56fd\u533a1\uff08\u676d\u5dde\uff09",
    "df_source":"monitor",
    "df_status":"critical",
    "df_sub_status":"critical",
    "df_title":"\u5b9e\u4f8b\u540d\u79f0\u4e3a share \u78c1\u76d8\u4f7f\u7528\u7387\u8fc7\u9ad8",
    "df_workspace_name":"observer",
    "df_workspace_uuid":"wksp_968577392a1c4714a464cd2f6ee42a9c",
    "host":"share",
    "timestamp":1693034940,
    "workspace_name":"observer",
    "workspace_uuid":"wksp_968577392a1c4714a464cd2f6ee42a9c"
}

This Json contains the event title df_title, event details df_message, and event status df_status that we need to use. Of course, this Json also includes other related information such as the event creation time, anomaly value, workspace ID, etc., which can be included in the issues we need to generate if necessary.

Once the input data structure is clear, we can write the function to create Jira issues.

@DFF.API('Create_JIRA_Issue_Reply')
def create_jira_issue_reply(**kwargs):

    # Create Jira instance
    jira = JIRA(server=jira_server, basic_auth=(username, password))
    # Get Guance event data
    event = json.dumps(kwargs)
    print(event)
    summary  = kwargs["df_title"]
    description = kwargs["df_message"]

    # Define issue fields
    issue_dict = {
        'project': {'key': project_key},
        'summary': summary,
        'description': description,
        'issuetype': {'name': 'Fault'},  # Change issue type to Task
        'assignee': {'name': 'pacher'},
        'priority': {'name': 'Highest'}
    }

    # Create issue
    issue = jira.create_issue(fields=issue_dict)

    # Print the key of the newly created issue
    print(f"Key of the newly created issue: {issue.key}")

We create a Jira instance using the event details obtained from Guance to form an issue dictionary, which is then sent to Jira. After successful transmission, logs are generated, i.e., the issue.key we created.

Then, we can view the corresponding issue in Jira using the created issue.key.

After writing the script, click Publish to proceed.