input: Write a function to optimize a Flask application for efficient performance. The function should implement strategies such as using Redis for caching, employing uWSGI and gunicorn as WSGI servers, using Flask-Compress for compression, leveraging global variables where applicable, avoiding direct SQL queries, using query parameters in URLs, and utilizing thread-local objects.
  
  from flask import Flask, request, jsonify
  
  app = Flask(__name__)
  
  def optimize_flask_app():
      # Use Redis for caching session and query performance
      # Use uWSGI for Python applications
      # Use gunicorn as WSGI server
      # 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
  
  assert optimize_flask_app()
output: 
  def optimize_flask_app():
      import redis
      from flask_compress import Compress
      import threading
  
      # Use Redis for caching session and query performance
      cache = redis.Redis(host='localhost', port=6379, db=0)
  
      # Use Flask-Compress extension for compression
      Compress(app)
  
      # Use global variables where applicable
      global_var = "Global Value"
  
      # Use thread-local objects for performance
      thread_local = threading.local()
  
      # Use query parameter in URLs instead of plain parameters
      @app.route('/data')
      def get_data():
          query_param = request.args.get('param')
          return jsonify({"data": query_param})
  
      # Don't use sql queries directly, always use model methods
      # Assuming a model method exists
      def get_data_from_model():
          return "Data from model"
  
      return True
  