Django DRF integration with HDFS using a singleton pattern for resource efficiency and performance

I am trying to integrate Django DRF (Django REST Framework) server with HDFS (Hadoop Distributed File System).
However, I couldn't find any official libraries that provide direct integration between Django and HDFS.

Therefore, I am considering using native Python libraries for HDFS.
However, these libraries typically require creating a client and making calls, which could potentially result in resource wastage and performance degradation.

To address this concern, I have implemented a singleton pattern as shown in the code snippet below.

from django.http import HttpResponse
from hdfs import InsecureClient


class HDFSClient:
    _instance = None

    def __new__(cls, *args, **kwargs):
        if not cls._instance:
            cls._instance = super().__new__(cls)
            cls._instance.client = InsecureClient('http://localhost:9870', user='root')
        return cls._instance

    def read_file(self, file_path):
        with self.client.read(file_path) as reader:
            file_contents = reader.read()
        return file_contents


def read_hdfs_file(request):
    hdfs_client = HDFSClient()
    file_path = '/test.txt'
    file_contents = hdfs_client.read_file(file_path)
    return HttpResponse(file_contents)

I have implemented a singleton pattern to ensure that only one instance of the HDFS client is created and used throughout the Django application.

This approach aims to minimize resource wastage and improve performance.

However, I am unsure if this solution effectively resolves the resource and performance concerns.

I would like to know if there are better alternatives or approaches to address these issues.