input: Write a function to optimize a Flask application for efficient performance. Include the necessary imports and define a Flask route with an endpoint that showcases the performance optimizations mentioned, such as using Redis for caching, uWSGI for Python applications, gunicorn as WSGI server, Flask-Compress for compression, global variables, avoiding direct SQL queries, using query parameters, and using thread-local objects.
  
  
  assert optimize_flask_app() == "Optimized Flask application code"
  
output: 
  from flask import Flask, g, request
  from flask_compress import Compress
  import redis
  import sqlite3
  
  app = Flask(__name__)
  Compress(app)
  
  # Redis connection setup
  redis_cache = redis.StrictRedis(host='localhost', port=6379, db=0)
  
  # Global variable for database connection
  DATABASE = 'example.db'
  
  def get_db():
      db = getattr(g, '_database', None)
      if db is None:
          db = g._database = sqlite3.connect(DATABASE)
      return db
  
  @app.teardown_appcontext
  def close_connection(exception):
      db = getattr(g, '_database', None)
      if db is not None:
          db.close()
  
  @app.route('/optimized')
  def optimized():
      # Using Redis for caching
      cache_key = request.url
      cached_result = redis_cache.get(cache_key)
      if cached_result:
          return cached_result.decode('utf-8')
  
      # Avoiding direct SQL queries
      db = get_db()
      cursor = db.cursor()
      query = "SELECT data FROM example_table WHERE id = ?"
      cursor.execute(query, (request.args.get('id'),))
      result = cursor.fetchone()
  
      # Using query parameters
      if result:
          response = result[0]
      else:
          response = "No data found"
  
      # Storing result in Redis cache
      redis_cache.set(cache_key, response)
  
      return response
  