input: Write a function to optimize a Flask application for efficient performance. Include the necessary imports and an example route with performance optimization techniques such as using Redis for caching, employing uWSGI and gunicorn as WSGI servers, applying Flask-Compress for compression, using global variables where applicable, avoiding direct SQL queries, using query parameters in URLs, and employing thread-local objects for performance. If the input is not applicable, return the optimized Flask application skeleton.
  
  from flask import Flask, request, jsonify
  
  app = Flask(__name__)
  
  @app.route('/api/<endpoint>', methods=['GET', 'POST'])
  def process_request(endpoint):
      # Performance optimization
      # 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 process_request('/api/test') is not None
  assert process_request('/api/example').status_code == 200
  assert app.__name__ == "__main__"
  assert app.config['ENV'] == "production"
output: from flask import Flask, request, jsonify
  import redis
  import flask_compress
  
  app = Flask(__name__)
  flask_compress.Compress(app)
  
  # Redis connection setup
  redis_cache = redis.StrictRedis(host='localhost', port=6379, db=0)
  
  # Global variable example
  global_data = {}
  
  @app.route('/api/<endpoint>', methods=['GET', 'POST'])
  def process_request(endpoint):
      # Use Redis for caching
      cached_result = redis_cache.get(endpoint)
      if cached_result:
          return jsonify(eval(cached_result))
  
      # Use query parameters in URLs
      query_params = request.args
  
      # Example of using global variables
      if endpoint in global_data:
          data = global_data[endpoint]
      else:
          # Avoid direct SQL queries, use model methods
          data = fetch_data_from_model(endpoint, query_params)
          global_data[endpoint] = data
  
      # Cache the result in Redis
      redis_cache.set(endpoint, str(data))
  
      return jsonify(data)
  
  def fetch_data_from_model(endpoint, params):
      # Mock model method to fetch data
      return {"endpoint": endpoint, "params": params}