在我的学士学位论文中，我尝试使用http连接将机器数据（在本例中是用python脚本发送的历史数据）发送到kafka。我使用的是运行在windows系统docker中的合流平台。
我尝试使用python脚本将数据发送到rest代理。起初，我得到了关于我能够解析的数据类型的错误响应。

import pandas as pd
import csv, os, json, requests, time, datetime, copy, sys

if len(sys.argv) > 1:
    bgrfc_value = str(sys.argv[1])
else:
    print("No arguments for bgrfc given, defaulting to 'false'")
    bgrfc_value = 'false'

if len(sys.argv) > 2:
    filePath = str(sys.argv[2])
else:
    filePath = "path"

if len(sys.argv) > 3:
    batchSize = int(float(str(sys.argv[3])))
else:
    batchSize = 10

# Build skeleton JSON

basejson = {"message": {"meta" : "", "data": ""}}

# metajson = [{'meta_key' : 'sender', 'meta_value': 'OPCR'},

# {'meta_key' : 'receiver', 'meta_value': 'CAT'},

# {'meta_key' : 'message_type', 'meta_value': 'MA1SEK'},

# {'meta_key' : 'bgrfc', 'meta_value': bgrfc_value}]

# basejson['message']['meta'] = metajson

url = "http://127.0.0.1:8082/"
headers = {'Content-Type':'application/json','Accept':'application/json'}

def assign_timestamps(batch):
    newtimestamps = []
    oldtimestamps = []

    # Batch timestamps to list, add 10 newly generated timestamps to a list
    for item in batch['tag_tsp'].values.tolist():
        newtimestamps.append(datetime.datetime.now())
        oldtimestamps.append(datetime.datetime.strptime(str(item), "%Y%m%d%H%M%S.%f"))

    # Sort old timestamps without sorting the original array to preserve variance
    temp = copy.deepcopy(oldtimestamps)
    temp.sort()
    mrtimestamp = temp[0]

    # Replicate variance of old timestamps into the new timestamps
    for x in range(batchSize):
        diff = mrtimestamp - oldtimestamps[x]
        newtimestamps[x] = newtimestamps[x] - diff
        newtimestamps[x] = newtimestamps[x].strftime("%Y%m%d%H%M%S.%f")[:-3]

    # Switch old timestamps with new timestamps
    batch['tag_tsp'] = newtimestamps
    return batch

# Build and send JSON, wait for a sec

def build_json(batch):
    assign_timestamps(batch)
    batchlist = []
    for index, row in batch.iterrows():
        batchlist.append(row.to_dict())

    basejson['message']['data'] = batchlist
    print(basejson)
    req = requests.post(url, json = json.loads(json.dumps(basejson)), headers = headers)
    print(req.status_code)
    time.sleep(1)

while(True):
    df = pd.read_csv(filePath, sep=";", parse_dates=[2], decimal=",", usecols = ['SENSOR_ID', 'KEP_UTC_TIME', 'VALUE'], dtype={'SENSOR_ID': object})
    df = df[::-1]
    df.rename(columns={'SENSOR_ID' : 'ext_id', 'KEP_UTC_TIME' : 'tag_tsp', 'VALUE' : 'tag_value_int'}, inplace=True)

    # Fill list with batches of 10 rows from the df
    list_df = [df[ i:i + batchSize] for i in range(0, df.shape[0], batchSize)]

    for batch in list_df:
        build_json(batch)

脚本发送数据，但作为响应，我得到状态代码500。