寻求适用于CSV文件监控、解析、JSON转换及Camunda BPM流程触发的开源ETL/数据处理软件方案

Hey George, let’s break down the best open-source tools and approaches to meet your requirements—monitoring CSV folders, parsing rows to JSON, and triggering Camunda BPM processes via API. Here are my top recommendations:

1. Apache NiFi #

NiFi is a perfect fit for this use case thanks to its native support for file system monitoring and low-code data transformation. Here’s how to set it up:

• Use the ListenDirectory processor to monitor your target folder, configure it to filter only .csv files.
• Add a ConvertRecord processor: set the record reader to CSVReader (define your CSV schema if needed) and the record writer to JSONRecordWriter to convert each CSV row into a JSON object.
• Use the InvokeHTTP processor to call Camunda’s process start API. You can use the UpdateAttribute processor first to extract specific fields from the JSON, or directly construct the API request body to map CSV columns as Camunda process variables (follow Camunda’s REST API format where variables are structured like {"variables": {"column1": {"value": "data", "type": "String"}}}).
• Bonus: NiFi’s visual flow editor makes debugging and modifying your pipeline a breeze, no heavy coding required.

2. Apache Airflow #

If you prefer a code-first approach or need to integrate with other scheduled tasks, Airflow is a great choice. While it doesn’t have real-time file monitoring out of the box, you can implement it with minimal code:

• Use the FileSystemSensor to watch your folder for new CSV files (adjust the poke_interval for near-real-time checks).
• Add a PythonOperator to handle the CSV parsing and Camunda API calls. Here’s a quick code snippet for the operator:

import csv
import json
import requests
from airflow.operators.python import PythonOperator

def process_csv_trigger_camunda(file_path):
    camunda_api_endpoint = "http://your-camunda-instance/engine-rest/process-definition/key/your-process-key/start"
    with open(file_path, "r") as csv_file:
        reader = csv.DictReader(csv_file)
        for row in reader:
            # Map CSV rows to Camunda's variable format
            process_variables = {
                col: {"value": val, "type": "String"} for col, val in row.items()
            }
            response = requests.post(
                camunda_api_endpoint,
                json={"variables": process_variables}
            )
            response.raise_for_status()  # Handle errors as needed

• Bonus: Airflow integrates seamlessly with other data tools, so you can easily add data validation or logging steps later.

3. MuleSoft Community Edition (Mule CE) #

Mule CE is a lightweight integration platform ideal for quick, visual pipeline building:

• Use the File Connector to monitor your target folder and pick up new CSV files.
• Add a CSV to JSON Transformer to convert each row into a JSON payload.
• Use the HTTP Connector to call Camunda’s process start API, mapping the JSON fields directly to Camunda process variables via the visual mapper.
• Bonus: The drag-and-drop interface lets you build your pipeline in minutes, and it supports error handling (like retries for failed API calls) out of the box.

4. Custom Python Script with Watchdog #

If you want a lightweight, fully customizable solution without heavy ETL tools, a Python script with the watchdog library works perfectly:

• Use watchdog to listen for file creation events in your target folder.
• Parse the CSV with Python’s built-in csv module, convert each row to a dictionary, then map it to Camunda’s variable format.
• Call Camunda’s API using the requests library. Here’s a minimal working framework:

from watchdog.observers import Observer
from watchdog.events import FileSystemEventHandler
import csv
import json
import requests
import time

class CSVMonitorHandler(FileSystemEventHandler):
    def on_created(self, event):
        if not event.is_directory and event.src_path.endswith(".csv"):
            time.sleep(1)  # Wait for file to finish writing
            self.process_csv(event.src_path)

    def process_csv(self, file_path):
        camunda_url = "http://your-camunda-instance/engine-rest/process-definition/key/your-process-key/start"
        with open(file_path, "r") as f:
            reader = csv.DictReader(f)
            for row_num, row in enumerate(reader, 1):
                try:
                    payload = {
                        "variables": {k: {"value": v, "type": "String"} for k, v in row.items()}
                    }
                    response = requests.post(camunda_url, json=payload)
                    response.raise_for_status()
                    print(f"Successfully triggered process for row {row_num}")
                except Exception as e:
                    print(f"Failed to process row {row_num}: {str(e)}")

if __name__ == "__main__":
    event_handler = CSVMonitorHandler()
    observer = Observer()
    observer.schedule(event_handler, path="/path/to/your/csv/folder", recursive=False)
    observer.start()
    try:
        while True:
            time.sleep(1)
    except KeyboardInterrupt:
        observer.stop()
    observer.join()

• Bonus: This approach is super flexible—you can add custom validation, logging, or error handling logic with just a few lines of code.

• Real-time needs: NiFi, Mule CE, and the Python+Watchdog script offer real-time monitoring. Airflow uses polling, which can be near-real-time but not instant.
• Ease of use: NiFi and Mule CE are best if you prefer visual, low-code tools. Airflow and Python scripts are better for users comfortable with coding.
• Scalability: If you anticipate growing data volumes or adding more processing steps later, NiFi or Airflow will scale better than a custom script.

内容的提问来源于stack exchange，提问作者George