input: Write a function to optimize a Flask application for efficient performance. The function should include the use of Redis for caching, uWSGI and gunicorn as application and WSGI servers, Flask-Compress for compression, global variables where applicable, model methods in place of direct SQL queries, and query parameters in URLs.
  
  
  from flask import Flask, request, jsonify
  import redis
  
  app = Flask(__name__)
  
  # Example Redis connection
  rds = redis.StrictRedis(host='localhost', port=6379, db=0)
  
  @app.route('/api/<endpoint>', methods=['GET', 'POST'])
  def process_request(endpoint):
      # Example of using Redis for caching
      cached_response = rds.get(endpoint)
      if cached_response:
          return jsonify(cached_response)
  
      # Example of performance optimization
      # Use Flask-Compress extension for compression
      # Use global variables where applicable
      # Don't use SQL queries directly, always use model methods
      # Use query parameter in URLs instead of plain parameters
      # Use thread-local objects for performance
  
      # Your code here
      response_data = {}  # Example response data
      rds.set(endpoint, response_data)
      return jsonify(response_data)
  
  assert process_request('/api/test_endpoint') != None
  assert process_request('/api/cached_endpoint').status_code == 200
  assert process_request('/api/new_endpoint').status_code == 200
  
output: 
  def process_request(endpoint):
      cached_response = rds.get(endpoint)
      if cached_response:
          return jsonify(cached_response)
  
      response_data = {}  # Example response data
      rds.set(endpoint, response_data)
      return jsonify(response_data)
  