通过 API 从 Jira 导出筛选结果

问：

我正在使用 Python 3.11.5，我正在尝试从 Jira 导入信息以进行编辑。具体来说，我有一个 Jira 过滤器，它给了我大约 1000 个结果：

Key Priority    Status  Fix Version/s   Created     Resolution
Key 1   Prio 1  Closed  Version 1   04/Jan/22   Done
Key 2   Prio 2  Done    …       …       Wont’t Do
…
Key 1000    …       …   …       …       …   

有没有办法以更高性能的方式从筛选器调用导入数据？我已经 https://atlassian-python-api.readthedocs.io/jira.html#get-issues-from-jql-search-result-with-all-related-fields 方法

# Export Issues to csv
jira.csv(jql, all_fields=False)

发现。但我不知道我是否必须实施它们。

有没有人对如何以高性能方式导入数据有提示？

我已经尝试过导入数据。但是，使用这种方法，我必须单独导入表的每个日期。总共大约有 6000 个查询，会影响性能。via jira.jql() / jira.issue_field_value()

下面是我如何实现导入 KEY、优先级和状态信息的代码片段：

from atlassian import Jira # To get acces to Jira
import json # Used to convert lists to strings
import pandas as pd # Used to modify table calculations

from datetime import date, datetime

jira = Jira(
    url="https://jira.xxx.de/",
    username='xxx',
    token = "xxx")

def import_raw_data(jql_request):
    # Import defect keys from Jira
    keys = get_issue_key_list(jql_request)
    # Create dataframe for raw data
    df = pd.DataFrame(keys, columns = ["Issue_key"])
    # Create lists for relevant data
    priority = get_jira_value(df["Issue_key"], "priority", df)
    status = get_jira_value(df["Issue_key"], "status", df)
    # Append data to created data frame
    df["Priority"] = pd.DataFrame(priority)
    df["Status"] = pd.DataFrame(status)
    return df

# Import a list of relevant IDs from Jira
def get_issue_key_list(jql_request):
    data = jira.jql(jql_request)
    # Convert into string
    data = json.dumps(data, indent=4)
    # Split string into parts to get all rows with issueKey
    data = data.split(",\n")
    data = [s for s in data if "issueKey\":" in s]
    #print(data,"\n")
    # Split string into parts to get all parts witch contains "TCSS-"
    data = json.dumps(data)
    #print(data,"\n")
    data = data.split("\\\"")
    #print(data,"\n")
    data = [s for s in data if "TCSS-" in s]
    #df = pd.DataFrame(data, columns = ["Issue_key"])
    return data

# Calculate values from Jira (except fix version and release date)
def get_jira_value(issue_key, aspect, df):
    # Import information
    data_list = []
    for i in issue_key:
        raw_data=jira.issue_field_value(i, aspect)
        # Convert into string
        data=json.dumps(raw_data)
        # Split string into parts
        data = data.split(", ")
        # Get all fix versions (none, one ore more then one are possible)
        data = [s for s in data if "name" in s]
        # Remove everything but relevant aspect
        for x in data:
            x = x.replace('"',"")
            x = x.replace("name: ","")
            x = x.replace('}',"")
            value = x
            data_list.append(x)
    return data_list

df = import_raw_data("filter=33501")
print (df)

结果：

    Issue_key Priority  Status
0  TCSS-13779   Medium  Closed
1  TCSS-11464   Medium    Done
2   TCSS-1602   Medium  Closed