Question

我正在构建一个用户可以回答问题的Web应用程序。我试图运行一个查询，将这些答案插入到表格中，但用户只能回复同一个问题。在我的情况下，查询必须检查question_id＆amp;的重复项。 reply_user。（reply_id已被定义为主键）。

例如，当我在表格中有答案时：question_id = 1＆amp; reply_user = John，John无法在question_id 1上回复。但是另一个用户当然可以。

我目前正在运行这个：

INSERT INTO replies (question_id, reply_user, reply_content, reply_anwer)
  VALUES (:questionid, :replyuser, :replycontent, :replyanswer)
  SELECT question_id, reply_user FROM replies WHERE NOT EXISTS (
    SELECT question_id FROM replies
      WHERE question_id = question_id AND reply_user = reply_user
  )

我尝试了不存在但我无法找到解决方案。

感谢您的帮助

Answer 1

您需要"""Import.""" import tensorflow as tf import cv2 sess = tf.InteractiveSession() x = tf.placeholder(tf.float32, shape=[512, 512]) y_ = tf.placeholder(tf.float32, shape=[1, 1]) def get_image_from_file(file_name): """Function get_image_from_file.""" return cv2.resize(cv2.imread(file_name, 0), (512, 512), interpolation=cv2.INTER_CUBIC) def weight_variable(shape): """Foo.""" initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial) def bias_variable(shape): """Foo.""" initial = tf.constant(0.1, shape=shape) return tf.Variable(initial) def conv2d(x, w): """Foo.""" return tf.nn.conv2d(x, w, strides=[1, 1, 1, 1], padding='SAME') def max_pool_2x2(x): """Max pool 2x2.""" return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') if __name__ == "__main__": # 1st layer w_conv1 = weight_variable([5, 5, 1, 16]) b_conv1 = bias_variable([16]) x_image = tf.reshape(x, [-1, 512, 512, 1]) h_conv1 = tf.nn.relu(conv2d(x_image, w_conv1) + b_conv1) h_pool1 = max_pool_2x2(h_conv1) # 2nd layer w_conv2 = weight_variable([5, 5, 16, 32]) b_conv2 = bias_variable([32]) h_conv2 = tf.nn.relu(conv2d(h_pool1, w_conv2) + b_conv2) h_pool2 = max_pool_2x2(h_conv2) # connection layer w_fc1 = weight_variable([32 * 32 * 64, 128]) b_fc1 = bias_variable([128]) h_pool2_flat = tf.reshape(h_pool2, [-1, 32 * 32 * 64]) h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1) + b_fc1) # zapobieganie przeuczeniu keep_prob = tf.placeholder(tf.float32) h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) # output layer w_fc2 = weight_variable([128, 1]) b_fc2 = bias_variable([1]) y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, w_fc2) + b_fc2) cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv)) train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) sess.run(tf.initialize_all_variables()) for i in range(24): img = get_image_from_file("./MOJE/koty_nauka/kot" + str(i + 1) + ".jpg") out = y_conv.eval(feed_dict={ x: img, y_: [[1]], keep_prob: 1.0}) print("----") print(out) 2提交：

unique

然后使用此查询：

ALTER TABLE `replies` ADD UNIQUE (`question_id`,`reply_user`);

如果INSERT INTO replies (question_id, reply_user, reply_content, reply_anwer) VALUES (:questionid, :replyuser, :replycontent, :replyanswer)，question_id存在查询，则不运行其他运行。

Answer 2

在两个键上创建唯一的索引

ALTER TABLE replies ADD UNIQUE `preventDoubleAnswer` (`question_id`, `reply_user`) COMMENT '';

并将数据插入：

INSERT IGNORE ...

INSERT IGNORE仅在满足唯一键时才会插入新数据。注意：IGNORE param被省略，它会抛出一个MYSQL错误

SQL：检查2列中的重复项

2 个答案: