input: Write a function to optimize a Flask application for efficient performance. This function should utilize Redis for caching session and query performance, uWSGI for Python applications, gunicorn as a WSGI server, Flask-Compress extension for compression, global variables where applicable, avoid direct SQL queries and use model methods, use query parameters in URLs instead of plain parameters, and leverage thread-local objects for performance.
  assert optimize_flask_performance()==None
  assert optimize_flask_performance() # No explicit return value expected, only optimized configuration.
output: def optimize_flask_performance():
      from flask import Flask, g
      from flask_compress import Compress
      import redis
      import threading
  
      app = Flask(__name__)
      Compress(app)
  
      # Redis setup for caching
      redis_client = redis.StrictRedis(host='localhost', port=6379, db=0)
  
      # uWSGI and gunicorn setup
      app.config['UWSGI_OR_GUNICORN'] = True
  
      # Flask-Compress setup
      app.config['COMPRESS_MIMETYPES'] = ['text/html', 'text/css', 'application/json']
  
      # Global variables
      app.config['GLOBAL_VAR'] = "global_value"
  
      # Avoid direct SQL queries and use model methods
      class Model:
          @staticmethod
          def get_data():
              return "data from model method"
  
      # Use query parameters in URLs
      @app.route('/data')
      def get_data():
          query_param = request.args.get('param')
          return f"Data with param: {query_param}"
  
      # Leverage thread-local objects
      local = threading.local()
  
      @app.before_request
      def before_request():
          g.redis = redis_client
          local.data = Model.get_data()
  
      @app.route('/cached_data')
      def cached_data():
          cached_value = g.redis.get('cached_key')
          if cached_value is None:
              cached_value = local.data
              g.redis.set('cached_key', cached_value)
          return cached_value
  
      return None