update

README.md

@@ -1,46 +1,54 @@
-# 力扣题库（完整版）
-
-> 最后更新日期： **2023.12.09**
->
-> 使用脚本前请务必仔细完整阅读本 `README.md` 文件
-
-### 仓库介绍
-
-使用 Python 脚本分批将力扣（`leetcode.com` 和 `leetcode-cn.com`）上面的题目保存下来，方便没有网的时候进行学习。（仅包含可以在网页上直接打开的公开题目，不包含 VIP 题目）
-
-
-
-### 仓库目录结构
-
-国外版力扣题库，在仓库 `leetcode/problem` 文件夹下；国内版力扣题库，在仓库  `leetcode-cn/problem (Chinese)` 和 `leetcode-cn/problem (English)` 文件夹下。（部分题目只有中文版，无对应英文版）。
-
-
-
-### 特别注意！
-
-#### 版权相关
-
-**所有版权都为 LeetCode (及力扣中国) 官方所有，此处仅供学习使用，不要他用。也请大家不要滥用，不要侵犯力扣平台的合法权益。**
-
-**感谢 LeetCode 平台为我们提供大量的算法题目进行练习与提升。如果大家经济条件允许，请大家多多支持力扣，例如冲冲会员等。**
-
-力扣题库的权益归属力扣，使用力扣题库，需要遵循力扣使用条例，若您不同意此条例，请立即关闭当前网页，不要继续使用本题库。
-
-力扣（LeetCode）• 使用条例： https://leetcode-cn.com/terms-c/
-
-LeetCode Terms of Service： https://leetcode.com/terms/
-
-
-
-#### 脚本原作者
-
-Python脚本是在网上教程的基础上进行二改得到的，原版地址：https://blog.csdn.net/weixin_37267014/article/details/81429057
-
-
-
-#### 其他
-
-由于脚本运行时会向力扣网站发出大量请求，所以请大家不要随便尝试此脚本，以免影响力扣网站正常运行。
-
-因为使用此脚本所造成的一系列问题，责任由您自己承担，作者不承担相应责任。
-
+# 力扣题库（完整版）
+
+> 最后更新日期： **2024.12.20**
+>
+> 使用脚本前请务必仔细完整阅读本 `README.md` 文件
+
+### 仓库介绍
+
+使用 Python 脚本分批将力扣（`leetcode.com` 和 `leetcode-cn.com`）上面的题目保存下来，方便没有网的时候进行学习。（仅包含可以在网页上直接打开的公开题目，不包含 VIP 题目）
+
+
+
+### 仓库目录结构
+
+国外版力扣题库，在仓库 `leetcode/problem` 文件夹下；国内版力扣题库，在仓库  `leetcode-cn/problem (Chinese)` 和 `leetcode-cn/problem (English)` 文件夹下。（部分题目只有中文版，无对应英文版）。
+
+
+
+### 安装依赖
+
+```bash
+pip install -r requirements.txt
+```
+
+
+
+### 特别注意！
+
+#### 版权相关
+
+**所有版权都为 LeetCode (及力扣中国) 官方所有，此处仅供学习使用，不要他用。也请大家不要滥用，不要侵犯力扣平台的合法权益。**
+
+**感谢 LeetCode 平台为我们提供大量的算法题目进行练习与提升。如果大家经济条件允许，请大家多多支持力扣，例如充充会员等。**
+
+力扣题库的权益归属力扣，使用力扣题库，需要遵循力扣使用条例，若您不同意此条例，请立即关闭当前网页，不要继续使用本题库。
+
+力扣（LeetCode）• 使用条例： https://leetcode-cn.com/terms-c/
+
+LeetCode Terms of Service： https://leetcode.com/terms/
+
+
+
+#### 脚本原作者
+
+Python脚本是在网上教程的基础上进行二改得到的，原版地址：https://blog.csdn.net/weixin_37267014/article/details/81429057
+
+
+
+#### 其他
+
+由于脚本运行时会向力扣网站发出大量请求，所以请大家不要随便尝试此脚本，以免影响力扣网站正常运行。
+
+因为使用此脚本所造成的一系列问题，责任由您自己承担，作者不承担相应责任。
+

leetcode-cn.py

@@ -8,9 +8,15 @@ import requests
 from requests.exceptions import RequestException
 from bs4 import BeautifulSoup
 
+import urllib3
+urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
+
 def get_proble_set(url):
     try:
-        response = requests.get(url)
+        # response = requests.get(url)
+        response = requests.get(url, headers = {
+            'User-Agent': "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36"
+        }, verify=False)
         if response.status_code == 200:
             return response.text
         return None
@@ -34,9 +40,9 @@ def parse_proble_set(problemSet):
         continue
 
 def construct_url(problemTitle):
-    url = "https://leetcode.cn/problems/"+ problemTitle + "/"
+    url = "https://leetcode.cn/problems/" + problemTitle + "/"
     # print(url)
-    get_proble_content(url,problemTitle)
+    get_proble_content(url, problemTitle)
 
 def save_problem(title,content, editorType = ""):
     #content = bytes(content,encoding = 'utf8')
@@ -118,7 +124,11 @@ def saveJSON(data, filename):
 
 def main():
     url = "https://leetcode.cn/api/problems/all/"
-    html = json.loads(get_proble_set(url))
+    jsonContent = get_proble_set(url)
+    if jsonContent == None:
+        print('列表请求失败！')
+        return
+    html = json.loads(jsonContent)
     saveJSON(html, "origin-data.json")
 
     # html = json.load(open("origin-data.json", 'r', encoding='utf-8'))

leetcode-cn/originData/[no content]binary-tree-nodes.json

@@ -0,0 +1,56 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3259",
+            "questionFrontendId": "100177",
+            "categoryTitle": "Database",
+            "boundTopicId": 2653971,
+            "title": "Binary Tree Nodes",
+            "titleSlug": "binary-tree-nodes",
+            "content": null,
+            "translatedTitle": null,
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Medium",
+            "likes": 0,
+            "dislikes": 0,
+            "isLiked": null,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": null,
+            "topicTags": [],
+            "companyTagStats": null,
+            "codeSnippets": null,
+            "stats": "{\"totalAccepted\": \"23\", \"totalSubmission\": \"30\", \"totalAcceptedRaw\": 23, \"totalSubmissionRaw\": 30, \"acRate\": \"76.7%\"}",
+            "hints": [],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\":{\"Tree\":[\"N\",\"P\"]},\"rows\":{\"Tree\":[[1,2],[3,2],[6,8],[9,8],[2,5],[8,5],[5,null]]}}",
+            "metaData": "{\"mysql\":[\"Create table If Not Exists Tree (N int,P int)\"],\"mssql\":[\"Create table Tree (N int,P int)\"],\"oraclesql\":[\"Create table Tree (N int,P int)\"],\"database\":true,\"name\":\"binary_tree_nodes\",\"pythondata\":[\"Tree = pd.DataFrame([], columns=['N', 'P']).astype({'N':'Int64', 'P':'Int64'})\"],\"postgresql\":[\"Create table If Not Exists Tree (N int,P int)\\n\"],\"database_schema\":{\"Tree\":{\"N\":\"INT\",\"P\":\"INT\"}}}",
+            "judgerAvailable": true,
+            "judgeType": "large",
+            "mysqlSchemas": [
+                "Create table If Not Exists Tree (N int,P int)",
+                "Truncate table Tree",
+                "insert into Tree (N, P) values ('1', '2')",
+                "insert into Tree (N, P) values ('3', '2')",
+                "insert into Tree (N, P) values ('6', '8')",
+                "insert into Tree (N, P) values ('9', '8')",
+                "insert into Tree (N, P) values ('2', '5')",
+                "insert into Tree (N, P) values ('8', '5')",
+                "insert into Tree (N, P) values ('5', 'None')"
+            ],
+            "enableRunCode": true,
+            "envInfo": "{\"mysql\":[\"MySQL\",\"<p>\\u7248\\u672c\\uff1a<code>MySQL 8.0<\\/code><\\/p>\"],\"mssql\":[\"MS SQL Server\",\"<p>mssql server 2019.<\\/p>\"],\"oraclesql\":[\"Oracle\",\"<p>Oracle Sql 11.2.<\\/p>\"],\"pythondata\":[\"Pandas\",\"<p>Python 3.10 with Pandas 2.0.2 and NumPy 1.25.0<\\/p>\"],\"postgresql\":[\"PostgreSQL\",\"<p>PostgreSQL 16<\\/p>\"]}",
+            "book": null,
+            "isSubscribed": false,
+            "isDailyQuestion": false,
+            "dailyRecordStatus": null,
+            "editorType": "CKEDITOR",
+            "ugcQuestionId": null,
+            "style": "LEETCODE",
+            "exampleTestcases": "{\"headers\":{\"Tree\":[\"N\",\"P\"]},\"rows\":{\"Tree\":[[1,2],[3,2],[6,8],[9,8],[2,5],[8,5],[5,null]]}}",
+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]bitwise-user-permissions-analysis.json

@@ -0,0 +1,60 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3512",
+            "questionFrontendId": "3204",
+            "categoryTitle": "Database",
+            "boundTopicId": 2827445,
+            "title": "Bitwise User Permissions Analysis",
+            "titleSlug": "bitwise-user-permissions-analysis",
+            "content": null,
+            "translatedTitle": "按位用户权限分析",
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Medium",
+            "likes": 0,
+            "dislikes": 0,
+            "isLiked": null,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": null,
+            "topicTags": [
+                {
+                    "name": "Database",
+                    "slug": "database",
+                    "translatedName": "数据库",
+                    "__typename": "TopicTagNode"
+                }
+            ],
+            "companyTagStats": null,
+            "codeSnippets": null,
+            "stats": "{\"totalAccepted\": \"63\", \"totalSubmission\": \"66\", \"totalAcceptedRaw\": 63, \"totalSubmissionRaw\": 66, \"acRate\": \"95.5%\"}",
+            "hints": [],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\":{\"user_permissions\":[\"user_id\",\"permissions\"]},\"rows\":{\"user_permissions\":[[1,5],[2,12],[3,7],[4,3]]}}",
+            "metaData": "{\"mysql\":[\"Create table if not exists user_permissions(user_id int, permissions int)\"],\"mssql\":[\"Create table user_permissions(user_id int, permissions int)\"],\"oraclesql\":[\"Create table user_permissions(user_id NUMBER, permissions NUMBER)\"],\"database\":true,\"name\":\"analyze_permissions\",\"postgresql\":[\"CREATE TABLE IF NOT EXISTS user_permissions (\\n    user_id int,\\n    permissions int\\n);\\n\"],\"pythondata\":[\"user_permissions = pd.DataFrame({\\n    'user_id': pd.Series(dtype='int'),\\n    'permissions': pd.Series(dtype='int')\\n})\"],\"database_schema\":{\"user_permissions\":{\"user_id\":\"INT\",\"permissions\":\"INT\"}}}",
+            "judgerAvailable": true,
+            "judgeType": "large",
+            "mysqlSchemas": [
+                "Create table if not exists user_permissions(user_id int, permissions int)",
+                "Truncate table user_permissions",
+                "insert into user_permissions (user_id, permissions) values ('1', '5')",
+                "insert into user_permissions (user_id, permissions) values ('2', '12')",
+                "insert into user_permissions (user_id, permissions) values ('3', '7')",
+                "insert into user_permissions (user_id, permissions) values ('4', '3')"
+            ],
+            "enableRunCode": true,
+            "envInfo": "{\"mysql\":[\"MySQL\",\"<p>\\u7248\\u672c\\uff1a<code>MySQL 8.0<\\/code><\\/p>\"],\"mssql\":[\"MS SQL Server\",\"<p>mssql server 2019.<\\/p>\"],\"oraclesql\":[\"Oracle\",\"<p>Oracle Sql 11.2.<\\/p>\"],\"pythondata\":[\"Pandas\",\"<p>Python 3.10 with Pandas 2.0.2 and NumPy 1.25.0<\\/p>\"],\"postgresql\":[\"PostgreSQL\",\"<p>PostgreSQL 16<\\/p>\"]}",
+            "book": null,
+            "isSubscribed": false,
+            "isDailyQuestion": false,
+            "dailyRecordStatus": null,
+            "editorType": "CKEDITOR",
+            "ugcQuestionId": null,
+            "style": "LEETCODE",
+            "exampleTestcases": "{\"headers\":{\"user_permissions\":[\"user_id\",\"permissions\"]},\"rows\":{\"user_permissions\":[[1,5],[2,12],[3,7],[4,3]]}}",
+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]books-with-null-ratings.json

@@ -0,0 +1,62 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3695",
+            "questionFrontendId": "3358",
+            "categoryTitle": "Database",
+            "boundTopicId": 2992013,
+            "title": "Books with NULL Ratings",
+            "titleSlug": "books-with-null-ratings",
+            "content": null,
+            "translatedTitle": "评分为 NULL 的图书",
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Easy",
+            "likes": 0,
+            "dislikes": 0,
+            "isLiked": null,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": null,
+            "topicTags": [
+                {
+                    "name": "Database",
+                    "slug": "database",
+                    "translatedName": "数据库",
+                    "__typename": "TopicTagNode"
+                }
+            ],
+            "companyTagStats": null,
+            "codeSnippets": null,
+            "stats": "{\"totalAccepted\": \"85\", \"totalSubmission\": \"89\", \"totalAcceptedRaw\": 85, \"totalSubmissionRaw\": 89, \"acRate\": \"95.5%\"}",
+            "hints": [],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\":{\"books\":[\"book_id\",\"title\",\"author\",\"published_year\",\"rating\"]},\"rows\":{\"books\":[[1,\"The Great Gatsby\",\"F. Scott\",1925,4.5],[2,\"To Kill a Mockingbird\",\"Harper Lee\",1960,null],[3,\"Pride and Prejudice\",\"Jane Austen\",1813,4.8],[4,\"The Catcher in the Rye\",\"J.D. Salinger\",1951,null],[5,\"Animal Farm\",\"George Orwell\",1945,4.2],[6,\"Lord of the Flies\",\"William Golding\",1954,null]]}}",
+            "metaData": "{\"mysql\":[\"Create table books (\\n    book_id int,\\n    title varchar(255),\\n    author varchar(100),\\n    published_year int,\\n    rating decimal(3,1)\\n)\"],\"mssql\":[\"Create table books (\\n    book_id int,\\n    title varchar(255),\\n    author varchar(100),\\n    published_year int,\\n    rating decimal(3,1)\\n)\"],\"oraclesql\":[\"Create table books (\\n    book_id number,\\n    title varchar2(255),\\n    author varchar2(100),\\n    published_year number,\\n    rating number(3,1)\\n)\"],\"database\":true,\"name\":\"find_unrated_books\",\"postgresql\":[\"CREATE TABLE books (\\n    book_id INTEGER,\\n    title VARCHAR(255),\\n    author VARCHAR(100),\\n    published_year INTEGER,\\n    rating NUMERIC(3, 1)\\n);\\n\"],\"pythondata\":[\"books = pd.DataFrame({ \\\"book_id\\\": pd.Series(dtype=\\\"int\\\"), \\\"title\\\": pd.Series(dtype=\\\"str\\\"), \\\"author\\\": pd.Series(dtype=\\\"str\\\"), \\\"published_year\\\": pd.Series(dtype=\\\"int\\\"), \\\"rating\\\": pd.Series(dtype=\\\"float\\\") })\\n\"],\"database_schema\":{\"books\":{\"book_id\":\"INT\",\"title\":\"VARCHAR(255)\",\"author\":\"VARCHAR(100)\",\"published_year\":\"INT\",\"rating\":\"DECIMAL(3, 1)\"}}}",
+            "judgerAvailable": true,
+            "judgeType": "large",
+            "mysqlSchemas": [
+                "Create table books (\n    book_id int,\n    title varchar(255),\n    author varchar(100),\n    published_year int,\n    rating decimal(3,1)\n)",
+                "Truncate table books",
+                "insert into books (book_id, title, author, published_year, rating) values ('1', 'The Great Gatsby', 'F. Scott', '1925', '4.5')",
+                "insert into books (book_id, title, author, published_year, rating) values ('2', 'To Kill a Mockingbird', 'Harper Lee', '1960', NULL)",
+                "insert into books (book_id, title, author, published_year, rating) values ('3', 'Pride and Prejudice', 'Jane Austen', '1813', '4.8')",
+                "insert into books (book_id, title, author, published_year, rating) values ('4', 'The Catcher in the Rye', 'J.D. Salinger', '1951', NULL)",
+                "insert into books (book_id, title, author, published_year, rating) values ('5', 'Animal Farm', 'George Orwell', '1945', '4.2')",
+                "insert into books (book_id, title, author, published_year, rating) values ('6', 'Lord of the Flies', 'William Golding', '1954', NULL)"
+            ],
+            "enableRunCode": true,
+            "envInfo": "{\"mysql\":[\"MySQL\",\"<p>\\u7248\\u672c\\uff1a<code>MySQL 8.0<\\/code><\\/p>\"],\"mssql\":[\"MS SQL Server\",\"<p>mssql server 2019.<\\/p>\"],\"oraclesql\":[\"Oracle\",\"<p>Oracle Sql 11.2.<\\/p>\"],\"pythondata\":[\"Pandas\",\"<p>Python 3.10 with Pandas 2.2.2 and NumPy 1.26.4<\\/p>\"],\"postgresql\":[\"PostgreSQL\",\"<p>PostgreSQL 16<\\/p>\"]}",
+            "book": null,
+            "isSubscribed": false,
+            "isDailyQuestion": false,
+            "dailyRecordStatus": null,
+            "editorType": "CKEDITOR",
+            "ugcQuestionId": null,
+            "style": "LEETCODE",
+            "exampleTestcases": "{\"headers\":{\"books\":[\"book_id\",\"title\",\"author\",\"published_year\",\"rating\"]},\"rows\":{\"books\":[[1,\"The Great Gatsby\",\"F. Scott\",1925,4.5],[2,\"To Kill a Mockingbird\",\"Harper Lee\",1960,null],[3,\"Pride and Prejudice\",\"Jane Austen\",1813,4.8],[4,\"The Catcher in the Rye\",\"J.D. Salinger\",1951,null],[5,\"Animal Farm\",\"George Orwell\",1945,4.2],[6,\"Lord of the Flies\",\"William Golding\",1954,null]]}}",
+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]calculate-compressed-mean.json

@@ -0,0 +1,60 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3282",
+            "questionFrontendId": "2985",
+            "categoryTitle": "Database",
+            "boundTopicId": 2586418,
+            "title": "Calculate Compressed Mean",
+            "titleSlug": "calculate-compressed-mean",
+            "content": null,
+            "translatedTitle": "计算订单平均商品数量",
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Easy",
+            "likes": 0,
+            "dislikes": 0,
+            "isLiked": null,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": null,
+            "topicTags": [
+                {
+                    "name": "Database",
+                    "slug": "database",
+                    "translatedName": "数据库",
+                    "__typename": "TopicTagNode"
+                }
+            ],
+            "companyTagStats": null,
+            "codeSnippets": null,
+            "stats": "{\"totalAccepted\": \"49\", \"totalSubmission\": \"56\", \"totalAcceptedRaw\": 49, \"totalSubmissionRaw\": 56, \"acRate\": \"87.5%\"}",
+            "hints": [],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\":{\"Orders\":[\"order_id\",\"item_count\",\"order_occurrences\"]},\"rows\":{\"Orders\":[[10,1,500],[11,2,1000],[12,3,800],[13,4,1000]]}}",
+            "metaData": "{\"mysql\":[\"Create Table if Not Exists Orders ( order_id int, item_count int, order_occurrences int)\"],\"mssql\":[\"Create Table Orders ( order_id int, item_count int, order_occurrences int)\"],\"oraclesql\":[\"Create Table Orders ( order_id int, item_count int, order_occurrences int)\"],\"database\":true,\"languages\":[\"mysql\",\"mssql\",\"oraclesql\"],\"database_schema\":{\"Orders\":{\"order_id\":\"INT\",\"item_count\":\"INT\",\"order_occurrences\":\"INT\"}}}",
+            "judgerAvailable": true,
+            "judgeType": "large",
+            "mysqlSchemas": [
+                "Create Table if Not Exists Orders ( order_id int, item_count int, order_occurrences int)",
+                "Truncate table Orders",
+                "insert into Orders (order_id, item_count, order_occurrences) values ('10', '1', '500')",
+                "insert into Orders (order_id, item_count, order_occurrences) values ('11', '2', '1000')",
+                "insert into Orders (order_id, item_count, order_occurrences) values ('12', '3', '800')",
+                "insert into Orders (order_id, item_count, order_occurrences) values ('13', '4', '1000')"
+            ],
+            "enableRunCode": true,
+            "envInfo": "{\"mysql\":[\"MySQL\",\"<p>\\u7248\\u672c\\uff1a<code>MySQL 8.0<\\/code><\\/p>\"],\"mssql\":[\"MS SQL Server\",\"<p>mssql server 2019.<\\/p>\"],\"oraclesql\":[\"Oracle\",\"<p>Oracle Sql 11.2.<\\/p>\"]}",
+            "book": null,
+            "isSubscribed": false,
+            "isDailyQuestion": false,
+            "dailyRecordStatus": null,
+            "editorType": "CKEDITOR",
+            "ugcQuestionId": null,
+            "style": "LEETCODE",
+            "exampleTestcases": "{\"headers\":{\"Orders\":[\"order_id\",\"item_count\",\"order_occurrences\"]},\"rows\":{\"Orders\":[[10,1,500],[11,2,1000],[12,3,800],[13,4,1000]]}}",
+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]calculate-parking-fees-and-duration.json

@@ -0,0 +1,62 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3473",
+            "questionFrontendId": "3166",
+            "categoryTitle": "Database",
+            "boundTopicId": 2791590,
+            "title": "Calculate Parking Fees and Duration",
+            "titleSlug": "calculate-parking-fees-and-duration",
+            "content": null,
+            "translatedTitle": "计算停车费与时长",
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Medium",
+            "likes": 0,
+            "dislikes": 0,
+            "isLiked": null,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": null,
+            "topicTags": [
+                {
+                    "name": "Database",
+                    "slug": "database",
+                    "translatedName": "数据库",
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+                }
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+}

leetcode-cn/originData/[no content]calculate-product-final-price.json

@@ -0,0 +1,64 @@
+{
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+            "questionFrontendId": "3293",
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+            "title": "Calculate Product Final Price",
+            "titleSlug": "calculate-product-final-price",
+            "content": null,
+            "translatedTitle": "计算产品最终价格",
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+                "insert into Products (product_id, category, price) values ('2', 'Clothing', '50')",
+                "insert into Products (product_id, category, price) values ('3', 'Electronics', '1200')",
+                "insert into Products (product_id, category, price) values ('4', 'Home', '500')",
+                "Truncate table Discounts",
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+}

leetcode-cn/originData/[no content]calculate-trapping-rain-water.json

@@ -0,0 +1,61 @@
+{
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+            "title": "Calculate Trapping Rain Water",
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leetcode-cn/originData/[no content]ceo-subordinate-hierarchy.json

@@ -0,0 +1,64 @@
+{
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+            "title": "CEO Subordinate Hierarchy",
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+            "translatedTitle": "首席执行官下属层级",
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leetcode-cn/originData/[no content]class-performance.json

@@ -0,0 +1,62 @@
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leetcode-cn/originData/[no content]classifying-triangles-by-lengths.json

@@ -0,0 +1,53 @@
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+            "title": "Classifying Triangles by Lengths",
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leetcode-cn/originData/[no content]consecutive-available-seats-ii.json

@@ -0,0 +1,61 @@
+{
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+            "questionId": "3448",
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+            "title": "Consecutive Available Seats II",
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+                "insert into Cinema (seat_id, free) values ('2', '0')",
+                "insert into Cinema (seat_id, free) values ('3', '1')",
+                "insert into Cinema (seat_id, free) values ('4', '1')",
+                "insert into Cinema (seat_id, free) values ('5', '1')"
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+}

leetcode-cn/originData/[no content]customer-purchasing-behavior-analysis.json

@@ -0,0 +1,66 @@
+{
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+            "title": "Customer Purchasing Behavior Analysis",
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+            "content": null,
+            "translatedTitle": "客户购买行为分析",
+            "translatedContent": null,
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+                "insert into Transactions (transaction_id, customer_id, product_id, transaction_date, amount) values ('1', '101', '1', '2023-01-01', '100.0')",
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+                "Truncate table Products",
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+                "insert into Products (product_id, category, price) values ('3', 'C', '200.0')"
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+}

leetcode-cn/originData/[no content]employee-task-duration-and-concurrent-tasks.json

@@ -0,0 +1,63 @@
+{
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+            "questionId": "3465",
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+            "title": "Employee Task Duration and Concurrent Tasks",
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+}

leetcode-cn/originData/[no content]employees-project-allocation.json

@@ -0,0 +1,59 @@
+{
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+            "title": "Employees Project Allocation",
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+                "Truncate table Project",
+                "insert into Project (project_id, employee_id, workload) values ('1', '1', '45')",
+                "insert into Project (project_id, employee_id, workload) values ('1', '2', '90')",
+                "insert into Project (project_id, employee_id, workload) values ('2', '3', '12')",
+                "insert into Project (project_id, employee_id, workload) values ('2', '4', '68')",
+                "Truncate table Employees",
+                "insert into Employees (employee_id, name, team) values ('1', 'Khaled', 'A')",
+                "insert into Employees (employee_id, name, team) values ('2', 'Ali', 'B')",
+                "insert into Employees (employee_id, name, team) values ('3', 'John', 'B')",
+                "insert into Employees (employee_id, name, team) values ('4', 'Doe', 'A')"
+            ],
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+            "editorType": "CKEDITOR",
+            "ugcQuestionId": null,
+            "style": "LEETCODE",
+            "exampleTestcases": "{\"headers\":{\"Project\":[\"project_id\",\"employee_id\",\"workload\"],\"Employees\":[\"employee_id\",\"name\",\"team\"]},\"rows\":{\"Project\":[[1,1,45],[1,2,90],[2,3,12],[2,4,68]],\"Employees\":[[1,\"Khaled\",\"A\"],[2,\"Ali\",\"B\"],[3,\"John\",\"B\"],[4,\"Doe\",\"A\"]]}}",
+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]find-all-unique-email-domains.json

@@ -0,0 +1,55 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3365",
+            "questionFrontendId": "100260",
+            "categoryTitle": "Database",
+            "boundTopicId": 2653967,
+            "title": "Find All Unique Email Domains",
+            "titleSlug": "find-all-unique-email-domains",
+            "content": null,
+            "translatedTitle": null,
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Easy",
+            "likes": 1,
+            "dislikes": 0,
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+            "codeSnippets": null,
+            "stats": "{\"totalAccepted\": \"40\", \"totalSubmission\": \"60\", \"totalAcceptedRaw\": 40, \"totalSubmissionRaw\": 60, \"acRate\": \"66.7%\"}",
+            "hints": [],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\":{\"Emails\":[\"id\",\"email\"]},\"rows\":{\"Emails\":[[336,\"hwkiy@test.edu\"],[489,\"adcmaf@outlook.com\"],[449,\"vrzmwyum@yahoo.com\"],[95,\"tof@test.edu\"],[320,\"jxhbagkpm@example.org\"],[411,\"zxcf@outlook.com\"]]}}",
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+            "judgeType": "large",
+            "mysqlSchemas": [
+                "Create table If Not Exists Emails (id int, email varchar(255))",
+                "Truncate table Emails",
+                "insert into Emails (id, email) values ('336', 'hwkiy@test.edu')",
+                "insert into Emails (id, email) values ('489', 'adcmaf@outlook.com')",
+                "insert into Emails (id, email) values ('449', 'vrzmwyum@yahoo.com')",
+                "insert into Emails (id, email) values ('95', 'tof@test.edu')",
+                "insert into Emails (id, email) values ('320', 'jxhbagkpm@example.org')",
+                "insert into Emails (id, email) values ('411', 'zxcf@outlook.com')"
+            ],
+            "enableRunCode": true,
+            "envInfo": "{\"mysql\":[\"MySQL\",\"<p>\\u7248\\u672c\\uff1a<code>MySQL 8.0<\\/code><\\/p>\"],\"mssql\":[\"MS SQL Server\",\"<p>mssql server 2019.<\\/p>\"],\"oraclesql\":[\"Oracle\",\"<p>Oracle Sql 11.2.<\\/p>\"],\"pythondata\":[\"Pandas\",\"<p>Python 3.10 with Pandas 2.0.2 and NumPy 1.25.0<\\/p>\"],\"postgresql\":[\"PostgreSQL\",\"<p>PostgreSQL 16<\\/p>\"]}",
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+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]find-bursty-behavior.json

@@ -0,0 +1,62 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3400",
+            "questionFrontendId": "3089",
+            "categoryTitle": "Database",
+            "boundTopicId": 2704231,
+            "title": "Find Bursty Behavior",
+            "titleSlug": "find-bursty-behavior",
+            "content": null,
+            "translatedTitle": "查找突发行为",
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Medium",
+            "likes": 0,
+            "dislikes": 0,
+            "isLiked": null,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": null,
+            "topicTags": [
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+                    "name": "Database",
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+            "sampleTestCase": "{\"headers\": {\"Posts\": [\"post_id\", \"user_id\", \"post_date\"]}, \"rows\": {\"Posts\": [[1, 1, \"2024-02-27\"], [2, 5, \"2024-02-06\"], [3, 3, \"2024-02-25\"], [4, 3, \"2024-02-14\"], [5, 3, \"2024-02-06\"], [6, 2, \"2024-02-25\"]]}}",
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+            "judgerAvailable": true,
+            "judgeType": "large",
+            "mysqlSchemas": [
+                "Create table If Not Exists Posts (post_id int, user_id int,  post_date date)",
+                "Truncate table Posts",
+                "insert into Posts (post_id, user_id, post_date) values ('1', '1', '2024-02-27')",
+                "insert into Posts (post_id, user_id, post_date) values ('2', '5', '2024-02-06')",
+                "insert into Posts (post_id, user_id, post_date) values ('3', '3', '2024-02-25')",
+                "insert into Posts (post_id, user_id, post_date) values ('4', '3', '2024-02-14')",
+                "insert into Posts (post_id, user_id, post_date) values ('5', '3', '2024-02-06')",
+                "insert into Posts (post_id, user_id, post_date) values ('6', '2', '2024-02-25')"
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+            "enableRunCode": true,
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+        }
+    }
+}

leetcode-cn/originData/[no content]find-candidates-for-data-scientist-position-ii.json

@@ -0,0 +1,76 @@
+{
+    "data": {
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+            "questionId": "3589",
+            "questionFrontendId": "3278",
+            "categoryTitle": "Database",
+            "boundTopicId": 2900887,
+            "title": "Find Candidates for Data Scientist Position II",
+            "titleSlug": "find-candidates-for-data-scientist-position-ii",
+            "content": null,
+            "translatedTitle": "寻找数据科学家职位的候选人 II",
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Medium",
+            "likes": 0,
+            "dislikes": 0,
+            "isLiked": null,
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+            "contributors": [],
+            "langToValidPlayground": null,
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+            "hints": [],
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+                "Create table if not exists Projects( project_id int, skill varchar(50), importance int)",
+                "Truncate table Candidates",
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+                "insert into Candidates (candidate_id, skill, proficiency) values ('101', 'Tableau', '3')",
+                "insert into Candidates (candidate_id, skill, proficiency) values ('101', 'PostgreSQL', '4')",
+                "insert into Candidates (candidate_id, skill, proficiency) values ('101', 'TensorFlow', '2')",
+                "insert into Candidates (candidate_id, skill, proficiency) values ('102', 'Python', '4')",
+                "insert into Candidates (candidate_id, skill, proficiency) values ('102', 'Tableau', '5')",
+                "insert into Candidates (candidate_id, skill, proficiency) values ('102', 'PostgreSQL', '4')",
+                "insert into Candidates (candidate_id, skill, proficiency) values ('102', 'R', '4')",
+                "insert into Candidates (candidate_id, skill, proficiency) values ('103', 'Python', '3')",
+                "insert into Candidates (candidate_id, skill, proficiency) values ('103', 'Tableau', '5')",
+                "insert into Candidates (candidate_id, skill, proficiency) values ('103', 'PostgreSQL', '5')",
+                "insert into Candidates (candidate_id, skill, proficiency) values ('103', 'Spark', '4')",
+                "Truncate table Projects",
+                "insert into Projects (project_id, skill, importance) values ('501', 'Python', '4')",
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+                "insert into Projects (project_id, skill, importance) values ('501', 'PostgreSQL', '5')",
+                "insert into Projects (project_id, skill, importance) values ('502', 'Python', '3')",
+                "insert into Projects (project_id, skill, importance) values ('502', 'Tableau', '4')",
+                "insert into Projects (project_id, skill, importance) values ('502', 'R', '2')"
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+            "enableRunCode": true,
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+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]find-candidates-for-data-scientist-position.json

@@ -0,0 +1,61 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3256",
+            "questionFrontendId": "100174",
+            "categoryTitle": "Database",
+            "boundTopicId": 2653973,
+            "title": "Find Candidates for Data Scientist Position",
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+            "content": null,
+            "translatedTitle": null,
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Easy",
+            "likes": 1,
+            "dislikes": 0,
+            "isLiked": null,
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+            "contributors": [],
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+            "hints": [],
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+                "insert into Candidates (candidate_id, skill) values ('123', 'PostgreSQL')",
+                "insert into Candidates (candidate_id, skill) values ('234', 'PowerBI')",
+                "insert into Candidates (candidate_id, skill) values ('234', 'SQL Server')",
+                "insert into Candidates (candidate_id, skill) values ('147', 'Python')",
+                "insert into Candidates (candidate_id, skill) values ('147', 'Tableau')",
+                "insert into Candidates (candidate_id, skill) values ('147', 'Java')",
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+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]find-cities-in-each-state-ii.json

@@ -0,0 +1,71 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3650",
+            "questionFrontendId": "3328",
+            "categoryTitle": "Database",
+            "boundTopicId": 2958682,
+            "title": "Find Cities in Each State II",
+            "titleSlug": "find-cities-in-each-state-ii",
+            "content": null,
+            "translatedTitle": "查找每个州的城市 II",
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Medium",
+            "likes": 0,
+            "dislikes": 0,
+            "isLiked": null,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": null,
+            "topicTags": [
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+                    "name": "Database",
+                    "slug": "database",
+                    "translatedName": "数据库",
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+            "stats": "{\"totalAccepted\": \"58\", \"totalSubmission\": \"81\", \"totalAcceptedRaw\": 58, \"totalSubmissionRaw\": 81, \"acRate\": \"71.6%\"}",
+            "hints": [],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\":{\"cities\":[\"state\",\"city\"]},\"rows\":{\"cities\":[[\"New York\",\"New York City\"],[\"New York\",\"Newark\"],[\"New York\",\"Buffalo\"],[\"New York\",\"Rochester\"],[\"California\",\"San Francisco\"],[\"California\",\"Sacramento\"],[\"California\",\"San Diego\"],[\"California\",\"Los Angeles\"],[\"Texas\",\"Tyler\"],[\"Texas\",\"Temple\"],[\"Texas\",\"Taylor\"],[\"Texas\",\"Dallas\"],[\"Pennsylvania\",\"Philadelphia\"],[\"Pennsylvania\",\"Pittsburgh\"],[\"Pennsylvania\",\"Pottstown\"]]}}",
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+                "Create table if not exists cities( state varchar(100),city varchar(100))",
+                "Truncate table cities",
+                "insert into cities (state, city) values ('New York', 'New York City')",
+                "insert into cities (state, city) values ('New York', 'Newark')",
+                "insert into cities (state, city) values ('New York', 'Buffalo')",
+                "insert into cities (state, city) values ('New York', 'Rochester')",
+                "insert into cities (state, city) values ('California', 'San Francisco')",
+                "insert into cities (state, city) values ('California', 'Sacramento')",
+                "insert into cities (state, city) values ('California', 'San Diego')",
+                "insert into cities (state, city) values ('California', 'Los Angeles')",
+                "insert into cities (state, city) values ('Texas', 'Tyler')",
+                "insert into cities (state, city) values ('Texas', 'Temple')",
+                "insert into cities (state, city) values ('Texas', 'Taylor')",
+                "insert into cities (state, city) values ('Texas', 'Dallas')",
+                "insert into cities (state, city) values ('Pennsylvania', 'Philadelphia')",
+                "insert into cities (state, city) values ('Pennsylvania', 'Pittsburgh')",
+                "insert into cities (state, city) values ('Pennsylvania', 'Pottstown')"
+            ],
+            "enableRunCode": true,
+            "envInfo": "{\"mysql\":[\"MySQL\",\"<p>\\u7248\\u672c\\uff1a<code>MySQL 8.0<\\/code><\\/p>\"],\"mssql\":[\"MS SQL Server\",\"<p>mssql server 2019.<\\/p>\"],\"oraclesql\":[\"Oracle\",\"<p>Oracle Sql 11.2.<\\/p>\"],\"pythondata\":[\"Pandas\",\"<p>Python 3.10 with Pandas 2.0.2 and NumPy 1.25.0<\\/p>\"],\"postgresql\":[\"PostgreSQL\",\"<p>PostgreSQL 16<\\/p>\"]}",
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+            "isDailyQuestion": false,
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+            "editorType": "CKEDITOR",
+            "ugcQuestionId": null,
+            "style": "LEETCODE",
+            "exampleTestcases": "{\"headers\":{\"cities\":[\"state\",\"city\"]},\"rows\":{\"cities\":[[\"New York\",\"New York City\"],[\"New York\",\"Newark\"],[\"New York\",\"Buffalo\"],[\"New York\",\"Rochester\"],[\"California\",\"San Francisco\"],[\"California\",\"Sacramento\"],[\"California\",\"San Diego\"],[\"California\",\"Los Angeles\"],[\"Texas\",\"Tyler\"],[\"Texas\",\"Temple\"],[\"Texas\",\"Taylor\"],[\"Texas\",\"Dallas\"],[\"Pennsylvania\",\"Philadelphia\"],[\"Pennsylvania\",\"Pittsburgh\"],[\"Pennsylvania\",\"Pottstown\"]]}}",
+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]find-cities-in-each-state.json

@@ -0,0 +1,58 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3505",
+            "questionFrontendId": "3198",
+            "categoryTitle": "Database",
+            "boundTopicId": 2821350,
+            "title": "Find Cities in Each State",
+            "titleSlug": "find-cities-in-each-state",
+            "content": null,
+            "translatedTitle": null,
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Easy",
+            "likes": 0,
+            "dislikes": 0,
+            "isLiked": null,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": null,
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+            "codeSnippets": null,
+            "stats": "{\"totalAccepted\": \"2\", \"totalSubmission\": \"3\", \"totalAcceptedRaw\": 2, \"totalSubmissionRaw\": 3, \"acRate\": \"66.7%\"}",
+            "hints": [],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\":{\"cities\":[\"state\",\"city\"]},\"rows\":{\"cities\":[[\"California\",\"Los Angeles\"],[\"California\",\"San Francisco\"],[\"California\",\"San Diego\"],[\"Texas\",\"Houston\"],[\"Texas\",\"Austin\"],[\"Texas\",\"Dallas\"],[\"New York\",\"New York City\"],[\"New York\",\"Buffalo\"],[\"New York\",\"Rochester\"]]}}",
+            "metaData": "{\"mysql\":[\"Create table if not exists cities( state varchar(100),city varchar(100))\"],\"mssql\":[\"Create table  cities( state varchar(100),city varchar(100))\"],\"oraclesql\":[\"Create table cities( state varchar2(100),city varchar2(100))\"],\"database\":true,\"name\":\"find_cities\",\"postgresql\":[\"CREATE TABLE IF NOT EXISTS cities (\\n    state VARCHAR(100),\\n    city VARCHAR(100)\\n);\"],\"pythondata\":[\"cities = pd.DataFrame(columns=['state', 'city'])\"],\"database_schema\":{\"cities\":{\"state\":\"VARCHAR(100)\",\"city\":\"VARCHAR(100)\"}}}",
+            "judgerAvailable": true,
+            "judgeType": "large",
+            "mysqlSchemas": [
+                "Create table if not exists cities( state varchar(100),city varchar(100))",
+                "Truncate table cities",
+                "insert into cities (state, city) values ('California', 'Los Angeles')",
+                "insert into cities (state, city) values ('California', 'San Francisco')",
+                "insert into cities (state, city) values ('California', 'San Diego')",
+                "insert into cities (state, city) values ('Texas', 'Houston')",
+                "insert into cities (state, city) values ('Texas', 'Austin')",
+                "insert into cities (state, city) values ('Texas', 'Dallas')",
+                "insert into cities (state, city) values ('New York', 'New York City')",
+                "insert into cities (state, city) values ('New York', 'Buffalo')",
+                "insert into cities (state, city) values ('New York', 'Rochester')"
+            ],
+            "enableRunCode": true,
+            "envInfo": "{\"mysql\":[\"MySQL\",\"<p>\\u7248\\u672c\\uff1a<code>MySQL 8.0<\\/code><\\/p>\"],\"mssql\":[\"MS SQL Server\",\"<p>mssql server 2019.<\\/p>\"],\"oraclesql\":[\"Oracle\",\"<p>Oracle Sql 11.2.<\\/p>\"],\"pythondata\":[\"Pandas\",\"<p>Python 3.10 with Pandas 2.0.2 and NumPy 1.25.0<\\/p>\"],\"postgresql\":[\"PostgreSQL\",\"<p>PostgreSQL 16<\\/p>\"]}",
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+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]find-expensive-cities.json

@@ -0,0 +1,67 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3284",
+            "questionFrontendId": "2987",
+            "categoryTitle": "Database",
+            "boundTopicId": 2586420,
+            "title": "Find Expensive Cities",
+            "titleSlug": "find-expensive-cities",
+            "content": null,
+            "translatedTitle": "寻找房价最贵的城市",
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Easy",
+            "likes": 0,
+            "dislikes": 0,
+            "isLiked": null,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": null,
+            "topicTags": [
+                {
+                    "name": "Database",
+                    "slug": "database",
+                    "translatedName": "数据库",
+                    "__typename": "TopicTagNode"
+                }
+            ],
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+            "codeSnippets": null,
+            "stats": "{\"totalAccepted\": \"43\", \"totalSubmission\": \"46\", \"totalAcceptedRaw\": 43, \"totalSubmissionRaw\": 46, \"acRate\": \"93.5%\"}",
+            "hints": [],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\":{\"Listings\":[\"listing_id\",\"city\",\"price\"]},\"rows\":{\"Listings\":[[113,\"LosAngeles\",7560386],[136,\"SanFrancisco\",2380268],[92,\"Chicago\",9833209],[60,\"Chicago\",5147582],[8,\"Chicago\",5274441],[79,\"SanFrancisco\",8372065],[37,\"Chicago\",7939595],[53,\"LosAngeles\",4965123],[178,\"SanFrancisco\",999207],[51,\"NewYork\",5951718],[121,\"NewYork\",2893760]]}}",
+            "metaData": "{\"mysql\":[\"Create Table if Not Exists Listings (listing_id int, city varchar(50), price int)\"],\"mssql\":[\"Create Table Listings (listing_id int, city varchar(50), price int)\"],\"oraclesql\":[\"Create Table  Listings (listing_id int, city varchar(50), price int)\"],\"database\":true,\"languages\":[\"mysql\",\"mssql\",\"oraclesql\"],\"database_schema\":{\"Listings\":{\"listing_id\":\"INT\",\"city\":\"VARCHAR(50)\",\"price\":\"INT\"}}}",
+            "judgerAvailable": true,
+            "judgeType": "large",
+            "mysqlSchemas": [
+                "Create Table if Not Exists Listings (listing_id int, city varchar(50), price int)",
+                "Truncate table Listings",
+                "insert into Listings (listing_id, city, price) values ('113', 'LosAngeles', '7560386')",
+                "insert into Listings (listing_id, city, price) values ('136', 'SanFrancisco', '2380268')",
+                "insert into Listings (listing_id, city, price) values ('92', 'Chicago', '9833209')",
+                "insert into Listings (listing_id, city, price) values ('60', 'Chicago', '5147582')",
+                "insert into Listings (listing_id, city, price) values ('8', 'Chicago', '5274441')",
+                "insert into Listings (listing_id, city, price) values ('79', 'SanFrancisco', '8372065')",
+                "insert into Listings (listing_id, city, price) values ('37', 'Chicago', '7939595')",
+                "insert into Listings (listing_id, city, price) values ('53', 'LosAngeles', '4965123')",
+                "insert into Listings (listing_id, city, price) values ('178', 'SanFrancisco', '999207')",
+                "insert into Listings (listing_id, city, price) values ('51', 'NewYork', '5951718')",
+                "insert into Listings (listing_id, city, price) values ('121', 'NewYork', '2893760')"
+            ],
+            "enableRunCode": true,
+            "envInfo": "{\"mysql\":[\"MySQL\",\"<p>\\u7248\\u672c\\uff1a<code>MySQL 8.0<\\/code><\\/p>\"],\"mssql\":[\"MS SQL Server\",\"<p>mssql server 2019.<\\/p>\"],\"oraclesql\":[\"Oracle\",\"<p>Oracle Sql 11.2.<\\/p>\"]}",
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+            "style": "LEETCODE",
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+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]find-longest-calls.json

@@ -0,0 +1,73 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3432",
+            "questionFrontendId": "3124",
+            "categoryTitle": "Database",
+            "boundTopicId": 2750525,
+            "title": "Find Longest Calls",
+            "titleSlug": "find-longest-calls",
+            "content": null,
+            "translatedTitle": "查找最长的电话",
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Medium",
+            "likes": 0,
+            "dislikes": 0,
+            "isLiked": null,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": null,
+            "topicTags": [
+                {
+                    "name": "Database",
+                    "slug": "database",
+                    "translatedName": "数据库",
+                    "__typename": "TopicTagNode"
+                }
+            ],
+            "companyTagStats": null,
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+            "stats": "{\"totalAccepted\": \"50\", \"totalSubmission\": \"83\", \"totalAcceptedRaw\": 50, \"totalSubmissionRaw\": 83, \"acRate\": \"60.2%\"}",
+            "hints": [],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\":{\"Contacts\":[\"id\",\"first_name\",\"last_name\"],\"Calls\":[\"contact_id\",\"type\",\"duration\"]},\"rows\":{\"Contacts\":[[1,\"John\",\"Doe\"],[2,\"Jane\",\"Smith\"],[3,\"Alice\",\"Johnson\"],[4,\"Michael\",\"Brown\"],[5,\"Emily\",\"Davis\"]],\"Calls\":[[1,\"incoming\",120],[1,\"outgoing\",180],[2,\"incoming\",300],[2,\"outgoing\",240],[3,\"incoming\",150],[3,\"outgoing\",360],[4,\"incoming\",420],[4,\"outgoing\",200],[5,\"incoming\",180],[5,\"outgoing\",280]]}}",
+            "metaData": "{\"mysql\":[\"Create table if Not Exists Contacts(id int, first_name varchar(20), last_name varchar(20))\",\"Create table if Not Exists Calls(contact_id int, type ENUM('incoming', 'outgoing'), duration int)\"],\"mssql\":[\"Create table Contacts(id int, first_name varchar(20), last_name varchar(20))\",\"Create table Calls(contact_id int, type varchar(20) NOT NULL CHECK (type in ('incoming' , 'outgoing')), duration int)\"],\"oraclesql\":[\"Create table Contacts(id int, first_name varchar(20), last_name varchar(20))\",\"Create table Calls(contact_id int, type varchar(20) NOT NULL CHECK (type in ('incoming' , 'outgoing')), duration int)\"],\"database\":true,\"name\":\"find_longest_calls\",\"pythondata\":[\"Contacts = pd.DataFrame([], columns=['id', 'first_name', 'last_name']).astype({'id':'Int64', 'first_name':'object', 'last_name':'object'})\\n\",\"Calls = pd.DataFrame([], columns=['contact_id', 'type', 'duration']).astype({'contact_id': 'Int64', 'type': 'category', 'duration': 'Int64'})\\n\"],\"postgresql\":[\"CREATE TABLE Contacts (\\n    id SERIAL PRIMARY KEY,\\n    first_name VARCHAR(20),\\n    last_name VARCHAR(20)\\n);\\n\",\"CREATE TABLE Calls (\\n    contact_id INT,\\n    type VARCHAR(20) NOT NULL CHECK (type IN ('incoming', 'outgoing')),\\n    duration INT\\n);\\n\"],\"manual\":true,\"database_schema\":{\"Contacts\":{\"id\":\"INT\",\"first_name\":\"VARCHAR(20)\",\"last_name\":\"VARCHAR(20)\"},\"Calls\":{\"contact_id\":\"INT\",\"type\":\"ENUM('incoming', 'outgoing')\",\"duration\":\"INT\"}}}",
+            "judgerAvailable": true,
+            "judgeType": "large",
+            "mysqlSchemas": [
+                "Create table if Not Exists Contacts(id int, first_name varchar(20), last_name varchar(20))",
+                "Create table if Not Exists Calls(contact_id int, type ENUM('incoming', 'outgoing'), duration int)",
+                "Truncate table Contacts",
+                "insert into Contacts (id, first_name, last_name) values ('1', 'John', 'Doe')",
+                "insert into Contacts (id, first_name, last_name) values ('2', 'Jane', 'Smith')",
+                "insert into Contacts (id, first_name, last_name) values ('3', 'Alice', 'Johnson')",
+                "insert into Contacts (id, first_name, last_name) values ('4', 'Michael', 'Brown')",
+                "insert into Contacts (id, first_name, last_name) values ('5', 'Emily', 'Davis')",
+                "Truncate table Calls",
+                "insert into Calls (contact_id, type, duration) values ('1', 'incoming', '120')",
+                "insert into Calls (contact_id, type, duration) values ('1', 'outgoing', '180')",
+                "insert into Calls (contact_id, type, duration) values ('2', 'incoming', '300')",
+                "insert into Calls (contact_id, type, duration) values ('2', 'outgoing', '240')",
+                "insert into Calls (contact_id, type, duration) values ('3', 'incoming', '150')",
+                "insert into Calls (contact_id, type, duration) values ('3', 'outgoing', '360')",
+                "insert into Calls (contact_id, type, duration) values ('4', 'incoming', '420')",
+                "insert into Calls (contact_id, type, duration) values ('4', 'outgoing', '200')",
+                "insert into Calls (contact_id, type, duration) values ('5', 'incoming', '180')",
+                "insert into Calls (contact_id, type, duration) values ('5', 'outgoing', '280')"
+            ],
+            "enableRunCode": true,
+            "envInfo": "{\"mysql\":[\"MySQL\",\"<p>\\u7248\\u672c\\uff1a<code>MySQL 8.0<\\/code><\\/p>\"],\"mssql\":[\"MS SQL Server\",\"<p>mssql server 2019.<\\/p>\"],\"oraclesql\":[\"Oracle\",\"<p>Oracle Sql 11.2.<\\/p>\"],\"pythondata\":[\"Pandas\",\"<p>Python 3.10 with Pandas 2.0.2 and NumPy 1.25.0<\\/p>\"],\"postgresql\":[\"PostgreSQL\",\"<p>PostgreSQL 16<\\/p>\"]}",
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+            "isDailyQuestion": false,
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+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]find-overlapping-shifts-ii.json

@@ -0,0 +1,62 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3585",
+            "questionFrontendId": "3268",
+            "categoryTitle": "Database",
+            "boundTopicId": 2895162,
+            "title": "Find Overlapping Shifts II",
+            "titleSlug": "find-overlapping-shifts-ii",
+            "content": null,
+            "translatedTitle": "查找重叠的班次 II",
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Hard",
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+            "isLiked": null,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": null,
+            "topicTags": [
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+                    "name": "Database",
+                    "slug": "database",
+                    "translatedName": "数据库",
+                    "__typename": "TopicTagNode"
+                }
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+            "stats": "{\"totalAccepted\": \"90\", \"totalSubmission\": \"127\", \"totalAcceptedRaw\": 90, \"totalSubmissionRaw\": 127, \"acRate\": \"70.9%\"}",
+            "hints": [],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\": {\"EmployeeShifts\":[\"employee_id\",\"start_time\",\"end_time\"]},\"rows\":{\"EmployeeShifts\":[[1,\"2023-10-01 09:00:00\",\"2023-10-01 17:00:00\"],[1,\"2023-10-01 15:00:00\",\"2023-10-01 23:00:00\"],[1,\"2023-10-01 16:00:00\",\"2023-10-02 00:00:00\"],[2,\"2023-10-01 09:00:00\",\"2023-10-01 17:00:00\"],[2,\"2023-10-01 11:00:00\",\"2023-10-01 19:00:00\"],[3,\"2023-10-01 09:00:00\",\"2023-10-01 17:00:00\"]]}}",
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+                "insert into EmployeeShifts (employee_id, start_time, end_time) values ('1', '2023-10-01 09:00:00', '2023-10-01 17:00:00')",
+                "insert into EmployeeShifts (employee_id, start_time, end_time) values ('1', '2023-10-01 15:00:00', '2023-10-01 23:00:00')",
+                "insert into EmployeeShifts (employee_id, start_time, end_time) values ('1', '2023-10-01 16:00:00', '2023-10-02 00:00:00')",
+                "insert into EmployeeShifts (employee_id, start_time, end_time) values ('2', '2023-10-01 09:00:00', '2023-10-01 17:00:00')",
+                "insert into EmployeeShifts (employee_id, start_time, end_time) values ('2', '2023-10-01 11:00:00', '2023-10-01 19:00:00')",
+                "insert into EmployeeShifts (employee_id, start_time, end_time) values ('3', '2023-10-01 09:00:00', '2023-10-01 17:00:00')"
+            ],
+            "enableRunCode": true,
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+            "exampleTestcases": "{\"headers\": {\"EmployeeShifts\":[\"employee_id\",\"start_time\",\"end_time\"]},\"rows\":{\"EmployeeShifts\":[[1,\"2023-10-01 09:00:00\",\"2023-10-01 17:00:00\"],[1,\"2023-10-01 15:00:00\",\"2023-10-01 23:00:00\"],[1,\"2023-10-01 16:00:00\",\"2023-10-02 00:00:00\"],[2,\"2023-10-01 09:00:00\",\"2023-10-01 17:00:00\"],[2,\"2023-10-01 11:00:00\",\"2023-10-01 19:00:00\"],[3,\"2023-10-01 09:00:00\",\"2023-10-01 17:00:00\"]]}}",
+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]find-overlapping-shifts.json

@@ -0,0 +1,59 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3574",
+            "questionFrontendId": "3262",
+            "categoryTitle": "Database",
+            "boundTopicId": 2887671,
+            "title": "Find Overlapping Shifts",
+            "titleSlug": "find-overlapping-shifts",
+            "content": null,
+            "translatedTitle": "查找重叠的班次",
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Medium",
+            "likes": 0,
+            "dislikes": 0,
+            "isLiked": null,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": null,
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+                "insert into EmployeeShifts (employee_id, start_time, end_time) values ('1', '08:00:00', '12:00:00')",
+                "insert into EmployeeShifts (employee_id, start_time, end_time) values ('1', '11:00:00', '15:00:00')",
+                "insert into EmployeeShifts (employee_id, start_time, end_time) values ('1', '14:00:00', '18:00:00')",
+                "insert into EmployeeShifts (employee_id, start_time, end_time) values ('2', '09:00:00', '17:00:00')",
+                "insert into EmployeeShifts (employee_id, start_time, end_time) values ('2', '16:00:00', '20:00:00')",
+                "insert into EmployeeShifts (employee_id, start_time, end_time) values ('3', '10:00:00', '12:00:00')",
+                "insert into EmployeeShifts (employee_id, start_time, end_time) values ('3', '13:00:00', '15:00:00')",
+                "insert into EmployeeShifts (employee_id, start_time, end_time) values ('3', '16:00:00', '18:00:00')",
+                "insert into EmployeeShifts (employee_id, start_time, end_time) values ('4', '08:00:00', '10:00:00')",
+                "insert into EmployeeShifts (employee_id, start_time, end_time) values ('4', '09:00:00', '11:00:00')"
+            ],
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+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]find-peak-calling-hours-for-each-city.json

@@ -0,0 +1,62 @@
+{
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+        "question": {
+            "questionId": "3281",
+            "questionFrontendId": "2984",
+            "categoryTitle": "Database",
+            "boundTopicId": 2586419,
+            "title": "Find Peak Calling Hours for Each City",
+            "titleSlug": "find-peak-calling-hours-for-each-city",
+            "content": null,
+            "translatedTitle": "找到每座城市的高峰通话时间",
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Medium",
+            "likes": 0,
+            "dislikes": 0,
+            "isLiked": null,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": null,
+            "topicTags": [
+                {
+                    "name": "Database",
+                    "slug": "database",
+                    "translatedName": "数据库",
+                    "__typename": "TopicTagNode"
+                }
+            ],
+            "companyTagStats": null,
+            "codeSnippets": null,
+            "stats": "{\"totalAccepted\": \"37\", \"totalSubmission\": \"54\", \"totalAcceptedRaw\": 37, \"totalSubmissionRaw\": 54, \"acRate\": \"68.5%\"}",
+            "hints": [],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\":{\"Calls\":[\"caller_id\",\"recipient_id\",\"call_time\",\"city\"]},\"rows\":{\"Calls\":[[8,4,\"2021-08-24 22:46:07\",\"Houston\"],[4,8,\"2021-08-24 22:57:13\",\"Houston\"],[5,1,\"2021-08-11 21:28:44\",\"Houston\"],[8,3,\"2021-08-17 22:04:15\",\"Houston\"],[11,3,\"2021-08-17 13:07:00\",\"New York\"],[8,11,\"2021-08-17 14:22:22\",\"New York\"]]}}",
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+            "judgerAvailable": true,
+            "judgeType": "large",
+            "mysqlSchemas": [
+                "Create table If Not Exists Calls (caller_id int, recipient_id int, call_time datetime, city varchar(40))",
+                "Truncate table Calls",
+                "insert into Calls (caller_id, recipient_id, call_time, city) values ('8', '4', '2021-08-24 22:46:07', 'Houston')",
+                "insert into Calls (caller_id, recipient_id, call_time, city) values ('4', '8', '2021-08-24 22:57:13', 'Houston')",
+                "insert into Calls (caller_id, recipient_id, call_time, city) values ('5', '1', '2021-08-11 21:28:44', 'Houston')",
+                "insert into Calls (caller_id, recipient_id, call_time, city) values ('8', '3', '2021-08-17 22:04:15', 'Houston')",
+                "insert into Calls (caller_id, recipient_id, call_time, city) values ('11', '3', '2021-08-17 13:07:00', 'New York')",
+                "insert into Calls (caller_id, recipient_id, call_time, city) values ('8', '11', '2021-08-17 14:22:22', 'New York')"
+            ],
+            "enableRunCode": true,
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+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]find-third-transaction.json

@@ -0,0 +1,65 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3283",
+            "questionFrontendId": "2986",
+            "categoryTitle": "Database",
+            "boundTopicId": 2586391,
+            "title": "Find Third Transaction",
+            "titleSlug": "find-third-transaction",
+            "content": null,
+            "translatedTitle": "找到第三笔交易",
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Medium",
+            "likes": 0,
+            "dislikes": 0,
+            "isLiked": null,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": null,
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+                {
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+                    "slug": "database",
+                    "translatedName": "数据库",
+                    "__typename": "TopicTagNode"
+                }
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+            "stats": "{\"totalAccepted\": \"32\", \"totalSubmission\": \"47\", \"totalAcceptedRaw\": 32, \"totalSubmissionRaw\": 47, \"acRate\": \"68.1%\"}",
+            "hints": [],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\":{\"Transactions\":[\"user_id\",\"spend\",\"transaction_date\"]},\"rows\":{\"Transactions\":[[1,65.56,\"2023-11-18 13:49:42\"],[1,96.0,\"2023-11-30 02:47:26\"],[1,7.44,\"2023-11-02 12:15:23\"],[1,49.78,\"2023-11-12 00:13:46\"],[2,40.89,\"2023-11-21 04:39:15\"],[2,100.44,\"2023-11-20 07:39:34\"],[3,37.33,\"2023-11-03 06:22:02\"],[3,13.89,\"2023-11-11 16:00:14\"],[3,7.0,\"2023-11-29 22:32:36\"]]}}",
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+            "judgeType": "large",
+            "mysqlSchemas": [
+                "Create Table if Not Exists Transactions (user_id int, spend decimal(5,2), transaction_date datetime) ",
+                "Truncate table Transactions",
+                "insert into Transactions (user_id, spend, transaction_date) values ('1', '65.56', '2023-11-18 13:49:42')",
+                "insert into Transactions (user_id, spend, transaction_date) values ('1', '96.0', '2023-11-30 02:47:26')",
+                "insert into Transactions (user_id, spend, transaction_date) values ('1', '7.44', '2023-11-02 12:15:23')",
+                "insert into Transactions (user_id, spend, transaction_date) values ('1', '49.78', '2023-11-12 00:13:46')",
+                "insert into Transactions (user_id, spend, transaction_date) values ('2', '40.89', '2023-11-21 04:39:15')",
+                "insert into Transactions (user_id, spend, transaction_date) values ('2', '100.44', '2023-11-20 07:39:34')",
+                "insert into Transactions (user_id, spend, transaction_date) values ('3', '37.33', '2023-11-03 06:22:02')",
+                "insert into Transactions (user_id, spend, transaction_date) values ('3', '13.89', '2023-11-11 16:00:14')",
+                "insert into Transactions (user_id, spend, transaction_date) values ('3', '7.0', '2023-11-29 22:32:36')"
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+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]find-top-performing-driver.json

@@ -0,0 +1,72 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3624",
+            "questionFrontendId": "3308",
+            "categoryTitle": "Database",
+            "boundTopicId": 2935114,
+            "title": "Find Top Performing Driver",
+            "titleSlug": "find-top-performing-driver",
+            "content": null,
+            "translatedTitle": null,
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Medium",
+            "likes": 0,
+            "dislikes": 0,
+            "isLiked": null,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": null,
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+                    "name": "Database",
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+                    "translatedName": "数据库",
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+            ],
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+            "stats": "{\"totalAccepted\": \"40\", \"totalSubmission\": \"66\", \"totalAcceptedRaw\": 40, \"totalSubmissionRaw\": 66, \"acRate\": \"60.6%\"}",
+            "hints": [],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\":{\"Drivers\":[\"driver_id\",\"name\",\"age\",\"experience\",\"accidents\"],\"Vehicles\":[\"vehicle_id\",\"driver_id\",\"model\",\"fuel_type\",\"mileage\"],\"Trips\":[\"trip_id\",\"vehicle_id\",\"distance\",\"duration\",\"rating\"]},\"rows\":{\"Drivers\":[[1,\"Alice\",34,10,1],[2,\"Bob\",45,20,3],[3,\"Charlie\",28,5,0]],\"Vehicles\":[[100,1,\"Sedan\",\"Gasoline\",20000],[101,2,\"SUV\",\"Electric\",30000],[102,3,\"Coupe\",\"Gasoline\",15000]],\"Trips\":[[201,100,50,30,5],[202,100,30,20,4],[203,101,100,60,4],[204,101,80,50,5],[205,102,40,30,5],[206,102,60,40,5]]}}",
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+            "judgerAvailable": true,
+            "judgeType": "large",
+            "mysqlSchemas": [
+                "CREATE TABLE If not exists Drivers (\n    driver_id INT ,\n    name VARCHAR(100),\n    age INT,\n    experience INT,\n    accidents INT\n)",
+                "CREATE TABLE If not exists Vehicles (\n    vehicle_id INT ,\n    driver_id INT,\n    model VARCHAR(100),\n    fuel_type VARCHAR(50),\n    mileage INT)",
+                "CREATE TABLE  If not exists Trips (\n    trip_id INT ,\n    vehicle_id INT,\n    distance INT,\n    duration INT,\n    rating INT\n)",
+                "Truncate table Drivers",
+                "insert into Drivers (driver_id, name, age, experience, accidents) values ('1', 'Alice', '34', '10', '1')",
+                "insert into Drivers (driver_id, name, age, experience, accidents) values ('2', 'Bob', '45', '20', '3')",
+                "insert into Drivers (driver_id, name, age, experience, accidents) values ('3', 'Charlie', '28', '5', '0')",
+                "Truncate table Vehicles",
+                "insert into Vehicles (vehicle_id, driver_id, model, fuel_type, mileage) values ('100', '1', 'Sedan', 'Gasoline', '20000')",
+                "insert into Vehicles (vehicle_id, driver_id, model, fuel_type, mileage) values ('101', '2', 'SUV', 'Electric', '30000')",
+                "insert into Vehicles (vehicle_id, driver_id, model, fuel_type, mileage) values ('102', '3', 'Coupe', 'Gasoline', '15000')",
+                "Truncate table Trips",
+                "insert into Trips (trip_id, vehicle_id, distance, duration, rating) values ('201', '100', '50', '30', '5')",
+                "insert into Trips (trip_id, vehicle_id, distance, duration, rating) values ('202', '100', '30', '20', '4')",
+                "insert into Trips (trip_id, vehicle_id, distance, duration, rating) values ('203', '101', '100', '60', '4')",
+                "insert into Trips (trip_id, vehicle_id, distance, duration, rating) values ('204', '101', '80', '50', '5')",
+                "insert into Trips (trip_id, vehicle_id, distance, duration, rating) values ('205', '102', '40', '30', '5')",
+                "insert into Trips (trip_id, vehicle_id, distance, duration, rating) values ('206', '102', '60', '40', '5')"
+            ],
+            "enableRunCode": true,
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+            "style": "LEETCODE",
+            "exampleTestcases": "{\"headers\":{\"Drivers\":[\"driver_id\",\"name\",\"age\",\"experience\",\"accidents\"],\"Vehicles\":[\"vehicle_id\",\"driver_id\",\"model\",\"fuel_type\",\"mileage\"],\"Trips\":[\"trip_id\",\"vehicle_id\",\"distance\",\"duration\",\"rating\"]},\"rows\":{\"Drivers\":[[1,\"Alice\",34,10,1],[2,\"Bob\",45,20,3],[3,\"Charlie\",28,5,0]],\"Vehicles\":[[100,1,\"Sedan\",\"Gasoline\",20000],[101,2,\"SUV\",\"Electric\",30000],[102,3,\"Coupe\",\"Gasoline\",15000]],\"Trips\":[[201,100,50,30,5],[202,100,30,20,4],[203,101,100,60,4],[204,101,80,50,5],[205,102,40,30,5],[206,102,60,40,5]]}}",
+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]find-top-scoring-students-ii.json

@@ -0,0 +1,77 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3503",
+            "questionFrontendId": "3188",
+            "categoryTitle": "Database",
+            "boundTopicId": 2814944,
+            "title": "Find Top Scoring Students II",
+            "titleSlug": "find-top-scoring-students-ii",
+            "content": null,
+            "translatedTitle": "查找得分最高的学生 II",
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Hard",
+            "likes": 0,
+            "dislikes": 0,
+            "isLiked": null,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": null,
+            "topicTags": [],
+            "companyTagStats": null,
+            "codeSnippets": null,
+            "stats": "{\"totalAccepted\": \"57\", \"totalSubmission\": \"106\", \"totalAcceptedRaw\": 57, \"totalSubmissionRaw\": 106, \"acRate\": \"53.8%\"}",
+            "hints": [],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\":{\"students\":[\"student_id\",\"name\",\"major\"],\"courses\":[\"course_id\",\"name\",\"credits\",\"major\",\"mandatory\"],\"enrollments\":[\"student_id\",\"course_id\",\"semester\",\"grade\",\"GPA\"]},\"rows\":{\"students\":[[1,\"Alice\",\"Computer Science\"],[2,\"Bob\",\"Computer Science\"],[3,\"Charlie\",\"Mathematics\"],[4,\"David\",\"Mathematics\"]],\"courses\":[[101,\"Algorithms\",3,\"Computer Science\",\"Yes\"],[102,\"Data Structures\",3,\"Computer Science\",\"Yes\"],[103,\"Calculus\",4,\"Mathematics\",\"Yes\"],[104,\"Linear Algebra\",4,\"Mathematics\",\"Yes\"],[105,\"Machine Learning\",3,\"Computer Science\",\"No\"],[106,\"Probability\",3,\"Mathematics\",\"No\"],[107,\"Operating Systems\",3,\"Computer Science\",\"No\"],[108,\"Statistics\",3,\"Mathematics\",\"No\"]],\"enrollments\":[[1,101,\"Fall 2023\",\"A\",4.0],[1,102,\"Spring 2023\",\"A\",4.0],[1,105,\"Spring 2023\",\"A\",4.0],[1,107,\"Fall 2023\",\"B\",3.5],[2,101,\"Fall 2023\",\"A\",4.0],[2,102,\"Spring 2023\",\"B\",3.0],[3,103,\"Fall 2023\",\"A\",4.0],[3,104,\"Spring 2023\",\"A\",4.0],[3,106,\"Spring 2023\",\"A\",4.0],[3,108,\"Fall 2023\",\"B\",3.5],[4,103,\"Fall 2023\",\"B\",3.0],[4,104,\"Spring 2023\",\"B\",3.0]]}}",
+            "metaData": "{\"mysql\":[\"CREATE TABLE if not exists students (\\n    student_id INT ,\\n    name VARCHAR(255),\\n    major VARCHAR(255)\\n)\",\"CREATE TABLE if not exists courses (\\n    course_id INT ,\\n    name VARCHAR(255),\\n    credits INT,\\n    major VARCHAR(255),\\n    mandatory ENUM('yes', 'no') DEFAULT 'no'\\n)\",\"CREATE TABLE  if not exists enrollments (\\n    student_id INT,\\n    course_id INT,\\n    semester VARCHAR(255),\\n    grade VARCHAR(10),\\nGPA decimal(3,2)\\n\\n);\"],\"mssql\":[\"CREATE TABLE students (\\n    student_id INT ,\\n    name VARCHAR(255),\\n    major VARCHAR(255)\\n)\",\"CREATE TABLE courses (\\n    course_id INT PRIMARY KEY,\\n    name VARCHAR(255),\\n    credits INT,\\n    major VARCHAR(255),\\n    mandatory VARCHAR(3) CHECK (mandatory IN ('yes', 'no')) DEFAULT 'no'\\n)\",\"CREATE TABLE enrollments (\\n    student_id INT,\\n    course_id INT,\\n    semester VARCHAR(255),\\n    grade VARCHAR(1),\\n    GPA DECIMAL(3,2)\\n)\"],\"oraclesql\":[\"CREATE TABLE students (\\n    student_id NUMBER ,\\n    name VARCHAR2(255),\\n    major VARCHAR2(255)\\n)\",\"CREATE TABLE courses (\\n    course_id NUMBER ,\\n    name VARCHAR2(255),\\n    credits NUMBER,\\n    major VARCHAR2(255),\\n    mandatory VARCHAR2(3) CHECK (mandatory IN ('Yes', 'No')) \\n)\",\"CREATE TABLE enrollments (\\n    student_id NUMBER,\\n    course_id NUMBER,\\n    semester VARCHAR2(255),\\n    grade VARCHAR2(1),\\n    GPA Number (3,2)\\n)\"],\"database\":true,\"name\":\"find_top_scoring_students\",\"pythondata\":[\"students = pd.DataFrame([], columns=['student_id', 'name', 'major']).astype({\\n    'student_id': 'Int64',  # Nullable integer type for student_id\\n    'name': 'string',       # String type for names\\n    'major': 'string'       # String type for majors\\n})\",\"courses = pd.DataFrame([], columns=['course_id', 'name', 'credits', 'major', 'mandatory']).astype({'course_id': 'Int64', 'name': 'string', 'credits': 'Int64', 'major': 'string', 'mandatory': 'string'})\\n\\n # pd.CategoricalDtype(categories=['yes', 'no'])\\n\",\"enrollments = pd.DataFrame([], columns=['student_id', 'course_id', 'semester', 'grade', 'GPA']).astype({'student_id': 'Int64', 'course_id': 'Int64', 'semester': 'string', 'grade': 'string', 'GPA': 'float'})\\n\"],\"postgresql\":[\"CREATE TABLE IF NOT EXISTS students (\\n    student_id SERIAL,\\n    name VARCHAR(255),\\n    major VARCHAR(255)\\n);\",\"CREATE TABLE courses (\\n    course_id INTEGER,  -- or SERIAL if you want auto-increment\\n    name VARCHAR(255),\\n    credits INTEGER,    -- or NUMERIC if you need decimal places\\n    major VARCHAR(255),\\n    mandatory VARCHAR(3) CHECK (mandatory IN ('Yes', 'No'))\\n);\\n\",\"CREATE TABLE IF NOT EXISTS enrollments (\\n    student_id INT,\\n    course_id INT,\\n    semester VARCHAR(255),\\n    grade VARCHAR(10),\\n    GPA NUMERIC(3,2)\\n);\\n\"],\"database_schema\":{\"students\":{\"student_id\":\"INT\",\"name\":\"VARCHAR(255)\",\"major\":\"VARCHAR(255)\"},\"courses\":{\"course_id\":\"INT\",\"name\":\"VARCHAR(255)\",\"credits\":\"INT\",\"major\":\"VARCHAR(255)\",\"mandatory\":\"ENUM('yes', 'no')\"},\"enrollments\":{\"student_id\":\"INT\",\"course_id\":\"INT\",\"semester\":\"VARCHAR(255)\",\"grade\":\"VARCHAR(10)\",\"GPA\":\"DECIMAL(3, 2)\"}}}",
+            "judgerAvailable": true,
+            "judgeType": "large",
+            "mysqlSchemas": [
+                "CREATE TABLE if not exists students (\n    student_id INT ,\n    name VARCHAR(255),\n    major VARCHAR(255)\n)",
+                "CREATE TABLE if not exists courses (\n    course_id INT ,\n    name VARCHAR(255),\n    credits INT,\n    major VARCHAR(255),\n    mandatory ENUM('yes', 'no') DEFAULT 'no'\n)",
+                "CREATE TABLE  if not exists enrollments (\n    student_id INT,\n    course_id INT,\n    semester VARCHAR(255),\n    grade VARCHAR(10),\nGPA decimal(3,2)\n\n);",
+                "Truncate table students",
+                "insert into students (student_id, name, major) values ('1', 'Alice', 'Computer Science')",
+                "insert into students (student_id, name, major) values ('2', 'Bob', 'Computer Science')",
+                "insert into students (student_id, name, major) values ('3', 'Charlie', 'Mathematics')",
+                "insert into students (student_id, name, major) values ('4', 'David', 'Mathematics')",
+                "Truncate table courses",
+                "insert into courses (course_id, name, credits, major, mandatory) values ('101', 'Algorithms', '3', 'Computer Science', 'Yes')",
+                "insert into courses (course_id, name, credits, major, mandatory) values ('102', 'Data Structures', '3', 'Computer Science', 'Yes')",
+                "insert into courses (course_id, name, credits, major, mandatory) values ('103', 'Calculus', '4', 'Mathematics', 'Yes')",
+                "insert into courses (course_id, name, credits, major, mandatory) values ('104', 'Linear Algebra', '4', 'Mathematics', 'Yes')",
+                "insert into courses (course_id, name, credits, major, mandatory) values ('105', 'Machine Learning', '3', 'Computer Science', 'No')",
+                "insert into courses (course_id, name, credits, major, mandatory) values ('106', 'Probability', '3', 'Mathematics', 'No')",
+                "insert into courses (course_id, name, credits, major, mandatory) values ('107', 'Operating Systems', '3', 'Computer Science', 'No')",
+                "insert into courses (course_id, name, credits, major, mandatory) values ('108', 'Statistics', '3', 'Mathematics', 'No')",
+                "Truncate table enrollments",
+                "insert into enrollments (student_id, course_id, semester, grade, GPA) values ('1', '101', 'Fall 2023', 'A', '4.0')",
+                "insert into enrollments (student_id, course_id, semester, grade, GPA) values ('1', '102', 'Spring 2023', 'A', '4.0')",
+                "insert into enrollments (student_id, course_id, semester, grade, GPA) values ('1', '105', 'Spring 2023', 'A', '4.0')",
+                "insert into enrollments (student_id, course_id, semester, grade, GPA) values ('1', '107', 'Fall 2023', 'B', '3.5')",
+                "insert into enrollments (student_id, course_id, semester, grade, GPA) values ('2', '101', 'Fall 2023', 'A', '4.0')",
+                "insert into enrollments (student_id, course_id, semester, grade, GPA) values ('2', '102', 'Spring 2023', 'B', '3.0')",
+                "insert into enrollments (student_id, course_id, semester, grade, GPA) values ('3', '103', 'Fall 2023', 'A', '4.0')",
+                "insert into enrollments (student_id, course_id, semester, grade, GPA) values ('3', '104', 'Spring 2023', 'A', '4.0')",
+                "insert into enrollments (student_id, course_id, semester, grade, GPA) values ('3', '106', 'Spring 2023', 'A', '4.0')",
+                "insert into enrollments (student_id, course_id, semester, grade, GPA) values ('3', '108', 'Fall 2023', 'B', '3.5')",
+                "insert into enrollments (student_id, course_id, semester, grade, GPA) values ('4', '103', 'Fall 2023', 'B', '3.0')",
+                "insert into enrollments (student_id, course_id, semester, grade, GPA) values ('4', '104', 'Spring 2023', 'B', '3.0')"
+            ],
+            "enableRunCode": true,
+            "envInfo": "{\"mysql\":[\"MySQL\",\"<p>\\u7248\\u672c\\uff1a<code>MySQL 8.0<\\/code><\\/p>\"],\"mssql\":[\"MS SQL Server\",\"<p>mssql server 2019.<\\/p>\"],\"oraclesql\":[\"Oracle\",\"<p>Oracle Sql 11.2.<\\/p>\"],\"pythondata\":[\"Pandas\",\"<p>Python 3.10 with Pandas 2.0.2 and NumPy 1.25.0<\\/p>\"],\"postgresql\":[\"PostgreSQL\",\"<p>PostgreSQL 16<\\/p>\"]}",
+            "book": null,
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+            "isDailyQuestion": false,
+            "dailyRecordStatus": null,
+            "editorType": "CKEDITOR",
+            "ugcQuestionId": null,
+            "style": "LEETCODE",
+            "exampleTestcases": "{\"headers\":{\"students\":[\"student_id\",\"name\",\"major\"],\"courses\":[\"course_id\",\"name\",\"credits\",\"major\",\"mandatory\"],\"enrollments\":[\"student_id\",\"course_id\",\"semester\",\"grade\",\"GPA\"]},\"rows\":{\"students\":[[1,\"Alice\",\"Computer Science\"],[2,\"Bob\",\"Computer Science\"],[3,\"Charlie\",\"Mathematics\"],[4,\"David\",\"Mathematics\"]],\"courses\":[[101,\"Algorithms\",3,\"Computer Science\",\"Yes\"],[102,\"Data Structures\",3,\"Computer Science\",\"Yes\"],[103,\"Calculus\",4,\"Mathematics\",\"Yes\"],[104,\"Linear Algebra\",4,\"Mathematics\",\"Yes\"],[105,\"Machine Learning\",3,\"Computer Science\",\"No\"],[106,\"Probability\",3,\"Mathematics\",\"No\"],[107,\"Operating Systems\",3,\"Computer Science\",\"No\"],[108,\"Statistics\",3,\"Mathematics\",\"No\"]],\"enrollments\":[[1,101,\"Fall 2023\",\"A\",4.0],[1,102,\"Spring 2023\",\"A\",4.0],[1,105,\"Spring 2023\",\"A\",4.0],[1,107,\"Fall 2023\",\"B\",3.5],[2,101,\"Fall 2023\",\"A\",4.0],[2,102,\"Spring 2023\",\"B\",3.0],[3,103,\"Fall 2023\",\"A\",4.0],[3,104,\"Spring 2023\",\"A\",4.0],[3,106,\"Spring 2023\",\"A\",4.0],[3,108,\"Fall 2023\",\"B\",3.5],[4,103,\"Fall 2023\",\"B\",3.0],[4,104,\"Spring 2023\",\"B\",3.0]]}}",
+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]find-top-scoring-students.json

@@ -0,0 +1,76 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3488",
+            "questionFrontendId": "3182",
+            "categoryTitle": "Database",
+            "boundTopicId": 2805903,
+            "title": "Find Top Scoring Students",
+            "titleSlug": "find-top-scoring-students",
+            "content": null,
+            "translatedTitle": "查找得分最高的学生",
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Medium",
+            "likes": 0,
+            "dislikes": 0,
+            "isLiked": null,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": null,
+            "topicTags": [
+                {
+                    "name": "Database",
+                    "slug": "database",
+                    "translatedName": "数据库",
+                    "__typename": "TopicTagNode"
+                }
+            ],
+            "companyTagStats": null,
+            "codeSnippets": null,
+            "stats": "{\"totalAccepted\": \"66\", \"totalSubmission\": \"131\", \"totalAcceptedRaw\": 66, \"totalSubmissionRaw\": 131, \"acRate\": \"50.4%\"}",
+            "hints": [],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\":{\"students\":[\"student_id\",\"name\",\"major\"],\"courses\":[\"course_id\",\"name\",\"credits\",\"major\"],\"enrollments\":[\"student_id\",\"course_id\",\"semester\",\"grade\"]},\"rows\":{\"students\":[[1,\"Alice\",\"Computer Science\"],[2,\"Bob\",\"Computer Science\"],[3,\"Charlie\",\"Mathematics\"],[4,\"David\",\"Mathematics\"]],\"courses\":[[101,\"Algorithms\",3,\"Computer Science\"],[102,\"Data Structures\",3,\"Computer Science\"],[103,\"Calculus\",4,\"Mathematics\"],[104,\"Linear Algebra\",4,\"Mathematics\"]],\"enrollments\":[[1,101,\"Fall 2023\",\"A\"],[1,102,\"Fall 2023\",\"A\"],[2,101,\"Fall 2023\",\"B\"],[2,102,\"Fall 2023\",\"A\"],[3,103,\"Fall 2023\",\"A\"],[3,104,\"Fall 2023\",\"A\"],[4,103,\"Fall 2023\",\"A\"],[4,104,\"Fall 2023\",\"B\"]]}}",
+            "metaData": "{\"mysql\":[\"CREATE TABLE if not exists students (\\n    student_id INT ,\\n    name VARCHAR(255),\\n    major VARCHAR(255)\\n)\",\"CREATE TABLE  if not exists courses (\\n    course_id INT ,\\n    name VARCHAR(255),\\n    credits INT,\\n    major VARCHAR(255)\\n)\\n\",\"CREATE TABLE  if not exists enrollments (\\n    student_id INT,\\n    course_id INT,\\n    semester VARCHAR(255),\\n    grade VARCHAR(10)\\n\\n);\"],\"mssql\":[\"CREATE TABLE students (\\n    student_id INT ,\\n    name VARCHAR(255),\\n    major VARCHAR(255)\\n)\",\"CREATE TABLE  courses (\\n    course_id INT ,\\n    name VARCHAR(255),\\n    credits INT,\\n    major VARCHAR(255)\\n)\\n\",\"CREATE TABLE  enrollments (\\n    student_id INT,\\n    course_id INT,\\n    semester VARCHAR(255),\\n    grade VARCHAR(10)\\n\\n);\"],\"oraclesql\":[\"CREATE TABLE students (\\n    student_id NUMBER ,\\n    name VARCHAR2(255),\\n    major VARCHAR2(255)\\n)\",\"CREATE TABLE courses (\\n    course_id NUMBER,\\n    name VARCHAR2(255),\\n    credits NUMBER,\\n    major VARCHAR2(255)\\n)\",\"CREATE TABLE enrollments (\\n    student_id NUMBER,\\n    course_id NUMBER,\\n    semester VARCHAR2(255),\\n    grade VARCHAR2(10) \\n    \\n)\"],\"database\":true,\"name\":\"find_top_scoring_students\",\"pythondata\":[\"enrollments = pd.DataFrame([], columns=['student_id', 'course_id', 'semester', 'grade']).astype({\\n    'student_id': 'Int64',  # Nullable integer type\\n    'course_id': 'Int64',   # Nullable integer type\\n    'semester': 'object',   # Object type for arbitrary strings\\n    'grade': 'object'       # Object type for arbitrary strings\\n})\",\"students = pd.DataFrame([], columns=['student_id', 'name', 'major']).astype({\\n    'student_id': 'Int64',  # Nullable integer type\\n    'name': 'object',       # Object type for arbitrary strings, equivalent to VARCHAR\\n    'major': 'object'       # Object type for arbitrary strings, equivalent to VARCHAR\\n})\",\"courses = pd.DataFrame([], columns=['course_id', 'name', 'credits', 'major']).astype({\\n    'course_id': 'Int64',  # Nullable integer type\\n    'name': 'object',      # Object type for arbitrary strings, equivalent to VARCHAR\\n    'credits': 'Int64',    # Nullable integer type\\n    'major': 'object'      # Object type for arbitrary strings, equivalent to VARCHAR\\n})\"],\"postgresql\":[\"CREATE TABLE IF NOT EXISTS students (\\n    student_id INT,\\n    name VARCHAR(255),\\n    major VARCHAR(255)\\n);\\n\",\"CREATE TABLE IF NOT EXISTS courses (\\n    course_id INT,\\n    name VARCHAR(255),\\n    credits INT,\\n    major VARCHAR(255)\\n);\\n\",\"CREATE TABLE IF NOT EXISTS enrollments (\\n    student_id INT,\\n    course_id INT,\\n    semester VARCHAR(255),\\n    grade VARCHAR(10)\\n);\\n\"],\"database_schema\":{\"students\":{\"student_id\":\"INT\",\"name\":\"VARCHAR(255)\",\"major\":\"VARCHAR(255)\"},\"courses\":{\"course_id\":\"INT\",\"name\":\"VARCHAR(255)\",\"credits\":\"INT\",\"major\":\"VARCHAR(255)\"},\"enrollments\":{\"student_id\":\"INT\",\"course_id\":\"INT\",\"semester\":\"VARCHAR(255)\",\"grade\":\"VARCHAR(10)\"}}}",
+            "judgerAvailable": true,
+            "judgeType": "large",
+            "mysqlSchemas": [
+                "CREATE TABLE if not exists students (\n    student_id INT ,\n    name VARCHAR(255),\n    major VARCHAR(255)\n)",
+                "CREATE TABLE  if not exists courses (\n    course_id INT ,\n    name VARCHAR(255),\n    credits INT,\n    major VARCHAR(255)\n)\n",
+                "CREATE TABLE  if not exists enrollments (\n    student_id INT,\n    course_id INT,\n    semester VARCHAR(255),\n    grade VARCHAR(10)\n\n);",
+                "Truncate table students",
+                "insert into students (student_id, name, major) values ('1', 'Alice', 'Computer Science')",
+                "insert into students (student_id, name, major) values ('2', 'Bob', 'Computer Science')",
+                "insert into students (student_id, name, major) values ('3', 'Charlie', 'Mathematics')",
+                "insert into students (student_id, name, major) values ('4', 'David', 'Mathematics')",
+                "Truncate table courses",
+                "insert into courses (course_id, name, credits, major) values ('101', 'Algorithms', '3', 'Computer Science')",
+                "insert into courses (course_id, name, credits, major) values ('102', 'Data Structures', '3', 'Computer Science')",
+                "insert into courses (course_id, name, credits, major) values ('103', 'Calculus', '4', 'Mathematics')",
+                "insert into courses (course_id, name, credits, major) values ('104', 'Linear Algebra', '4', 'Mathematics')",
+                "Truncate table enrollments",
+                "insert into enrollments (student_id, course_id, semester, grade) values ('1', '101', 'Fall 2023', 'A')",
+                "insert into enrollments (student_id, course_id, semester, grade) values ('1', '102', 'Fall 2023', 'A')",
+                "insert into enrollments (student_id, course_id, semester, grade) values ('2', '101', 'Fall 2023', 'B')",
+                "insert into enrollments (student_id, course_id, semester, grade) values ('2', '102', 'Fall 2023', 'A')",
+                "insert into enrollments (student_id, course_id, semester, grade) values ('3', '103', 'Fall 2023', 'A')",
+                "insert into enrollments (student_id, course_id, semester, grade) values ('3', '104', 'Fall 2023', 'A')",
+                "insert into enrollments (student_id, course_id, semester, grade) values ('4', '103', 'Fall 2023', 'A')",
+                "insert into enrollments (student_id, course_id, semester, grade) values ('4', '104', 'Fall 2023', 'B')"
+            ],
+            "enableRunCode": true,
+            "envInfo": "{\"mysql\":[\"MySQL\",\"<p>\\u7248\\u672c\\uff1a<code>MySQL 8.0<\\/code><\\/p>\"],\"mssql\":[\"MS SQL Server\",\"<p>mssql server 2019.<\\/p>\"],\"oraclesql\":[\"Oracle\",\"<p>Oracle Sql 11.2.<\\/p>\"],\"pythondata\":[\"Pandas\",\"<p>Python 3.10 with Pandas 2.0.2 and NumPy 1.25.0<\\/p>\"],\"postgresql\":[\"PostgreSQL\",\"<p>PostgreSQL 16<\\/p>\"]}",
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+            "editorType": "CKEDITOR",
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+            "style": "LEETCODE",
+            "exampleTestcases": "{\"headers\":{\"students\":[\"student_id\",\"name\",\"major\"],\"courses\":[\"course_id\",\"name\",\"credits\",\"major\"],\"enrollments\":[\"student_id\",\"course_id\",\"semester\",\"grade\"]},\"rows\":{\"students\":[[1,\"Alice\",\"Computer Science\"],[2,\"Bob\",\"Computer Science\"],[3,\"Charlie\",\"Mathematics\"],[4,\"David\",\"Mathematics\"]],\"courses\":[[101,\"Algorithms\",3,\"Computer Science\"],[102,\"Data Structures\",3,\"Computer Science\"],[103,\"Calculus\",4,\"Mathematics\"],[104,\"Linear Algebra\",4,\"Mathematics\"]],\"enrollments\":[[1,101,\"Fall 2023\",\"A\"],[1,102,\"Fall 2023\",\"A\"],[2,101,\"Fall 2023\",\"B\"],[2,102,\"Fall 2023\",\"A\"],[3,103,\"Fall 2023\",\"A\"],[3,104,\"Fall 2023\",\"A\"],[4,103,\"Fall 2023\",\"A\"],[4,104,\"Fall 2023\",\"B\"]]}}",
+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]find-trending-hashtags-ii.json

@@ -0,0 +1,63 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3409",
+            "questionFrontendId": "3103",
+            "categoryTitle": "Database",
+            "boundTopicId": 2719939,
+            "title": "Find Trending Hashtags II ",
+            "titleSlug": "find-trending-hashtags-ii",
+            "content": null,
+            "translatedTitle": "查找热门话题标签 II",
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Hard",
+            "likes": 0,
+            "dislikes": 0,
+            "isLiked": null,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": null,
+            "topicTags": [
+                {
+                    "name": "Database",
+                    "slug": "database",
+                    "translatedName": "数据库",
+                    "__typename": "TopicTagNode"
+                }
+            ],
+            "companyTagStats": null,
+            "codeSnippets": null,
+            "stats": "{\"totalAccepted\": \"10\", \"totalSubmission\": \"60\", \"totalAcceptedRaw\": 10, \"totalSubmissionRaw\": 60, \"acRate\": \"16.7%\"}",
+            "hints": [],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\":{\"Tweets\":[\"user_id\",\"tweet_id\",\"tweet\",\"tweet_date\"]},\"rows\":{\"Tweets\":[[135,13,\"Enjoying a great start to the day. #HappyDay #MorningVibes\",\"2024-02-01\"],[136,14,\"Another #HappyDay with good vibes! #FeelGood\",\"2024-02-03\"],[137,15,\"Productivity peaks! #WorkLife #ProductiveDay\",\"2024-02-04\"],[138,16,\"Exploring new tech frontiers. #TechLife #Innovation\",\"2024-02-04\"],[139,17,\"Gratitude for today's moments. #HappyDay #Thankful\",\"2024-02-05\"],[140,18,\"Innovation drives us. #TechLife #FutureTech\",\"2024-02-07\"],[141,19,\"Connecting with nature's serenity. #Nature #Peaceful\",\"2024-02-09\"]]}}",
+            "metaData": "{\"mysql\":[\"Create table If Not Exists Tweets (user_id int, tweet_id int,  tweet_date date, tweet varchar(100))\"],\"mssql\":[\"Create table Tweets (user_id int, tweet_id int,  tweet_date date, tweet varchar(100))\"],\"oraclesql\":[\"Create table Tweets (user_id int, tweet_id int,  tweet_date date, tweet varchar(100))\",\"ALTER SESSION SET nls_date_format='YYYY-MM-DD'\"],\"database\":true,\"name\":\"find_trending_hashtags\",\"postgresql\":[\"CREATE TABLE Tweets (\\n    user_id INT,\\n    tweet_id INT,\\n    tweet_date DATE,\\n    tweet VARCHAR(100)\\n);\\n\"],\"pythondata\":[\"Tweets = pd.DataFrame([], columns=['user_id', 'tweet_id', 'tweet_date', 'tweet']).astype({'user_id':'Int64', 'tweet_id':'Int64', 'tweet_date':'datetime64[ns]', 'tweet':'object'})\\n\"],\"database_schema\":{\"Tweets\":{\"user_id\":\"INT\",\"tweet_id\":\"INT\",\"tweet_date\":\"DATE\",\"tweet\":\"VARCHAR(100)\"}}}",
+            "judgerAvailable": true,
+            "judgeType": "large",
+            "mysqlSchemas": [
+                "Create table If Not Exists Tweets (user_id int, tweet_id int,  tweet_date date, tweet varchar(100))",
+                "Truncate table Tweets",
+                "insert into Tweets (user_id, tweet_id, tweet, tweet_date) values ('135', '13', 'Enjoying a great start to the day. #HappyDay #MorningVibes', '2024-02-01')",
+                "insert into Tweets (user_id, tweet_id, tweet, tweet_date) values ('136', '14', 'Another #HappyDay with good vibes! #FeelGood', '2024-02-03')",
+                "insert into Tweets (user_id, tweet_id, tweet, tweet_date) values ('137', '15', 'Productivity peaks! #WorkLife #ProductiveDay', '2024-02-04')",
+                "insert into Tweets (user_id, tweet_id, tweet, tweet_date) values ('138', '16', 'Exploring new tech frontiers. #TechLife #Innovation', '2024-02-04')",
+                "insert into Tweets (user_id, tweet_id, tweet, tweet_date) values ('139', '17', 'Gratitude for today's moments. #HappyDay #Thankful', '2024-02-05')",
+                "insert into Tweets (user_id, tweet_id, tweet, tweet_date) values ('140', '18', 'Innovation drives us. #TechLife #FutureTech', '2024-02-07')",
+                "insert into Tweets (user_id, tweet_id, tweet, tweet_date) values ('141', '19', 'Connecting with nature's serenity. #Nature #Peaceful', '2024-02-09')"
+            ],
+            "enableRunCode": true,
+            "envInfo": "{\"mysql\":[\"MySQL\",\"<p>\\u7248\\u672c\\uff1a<code>MySQL 8.0<\\/code><\\/p>\"],\"mssql\":[\"MS SQL Server\",\"<p>mssql server 2019.<\\/p>\"],\"oraclesql\":[\"Oracle\",\"<p>Oracle Sql 11.2.<\\/p>\"],\"pythondata\":[\"Pandas\",\"<p>Python 3.10 with Pandas 2.0.2 and NumPy 1.25.0<\\/p>\"],\"postgresql\":[\"PostgreSQL\",\"<p>PostgreSQL 16<\\/p>\"]}",
+            "book": null,
+            "isSubscribed": false,
+            "isDailyQuestion": false,
+            "dailyRecordStatus": null,
+            "editorType": "CKEDITOR",
+            "ugcQuestionId": null,
+            "style": "LEETCODE",
+            "exampleTestcases": "{\"headers\":{\"Tweets\":[\"user_id\",\"tweet_id\",\"tweet\",\"tweet_date\"]},\"rows\":{\"Tweets\":[[135,13,\"Enjoying a great start to the day. #HappyDay #MorningVibes\",\"2024-02-01\"],[136,14,\"Another #HappyDay with good vibes! #FeelGood\",\"2024-02-03\"],[137,15,\"Productivity peaks! #WorkLife #ProductiveDay\",\"2024-02-04\"],[138,16,\"Exploring new tech frontiers. #TechLife #Innovation\",\"2024-02-04\"],[139,17,\"Gratitude for today's moments. #HappyDay #Thankful\",\"2024-02-05\"],[140,18,\"Innovation drives us. #TechLife #FutureTech\",\"2024-02-07\"],[141,19,\"Connecting with nature's serenity. #Nature #Peaceful\",\"2024-02-09\"]]}}",
+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]find-trending-hashtags.json

@@ -0,0 +1,56 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3392",
+            "questionFrontendId": "3087",
+            "categoryTitle": "Database",
+            "boundTopicId": 2693470,
+            "title": "Find Trending Hashtags",
+            "titleSlug": "find-trending-hashtags",
+            "content": null,
+            "translatedTitle": "查找热门话题标签",
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Medium",
+            "likes": 0,
+            "dislikes": 0,
+            "isLiked": null,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": null,
+            "topicTags": [],
+            "companyTagStats": null,
+            "codeSnippets": null,
+            "stats": "{\"totalAccepted\": \"31\", \"totalSubmission\": \"63\", \"totalAcceptedRaw\": 31, \"totalSubmissionRaw\": 63, \"acRate\": \"49.2%\"}",
+            "hints": [],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\":{\"Tweets\":[\"user_id\",\"tweet_id\",\"tweet\",\"tweet_date\"]},\"rows\":{\"Tweets\":[[135,13,\"Enjoying a great start to the day. #HappyDay\",\"2024-02-01\"],[136,14,\"Another #HappyDay with good \",\"2024-02-03\"],[137,15,\"Productivity peaks! #WorkLife\",\"2024-02-04\"],[138,16,\"Exploring new tech frontiers. #TechLife\",\"2024-02-04\"],[139,17,\"Gratitude for today's moments. #HappyDay\",\"2024-02-05\"],[140,18,\"Innovation drives us. #TechLife\",\"2024-02-07\"],[141,19,\"Connecting with nature's serenity. #Nature\",\"2024-02-09\"]]}}",
+            "metaData": "{\"mysql\":[\"Create table If Not Exists Tweets (user_id int, tweet_id int,  tweet_date date, tweet varchar(100))\"],\"mssql\":[\"Create table Tweets (user_id int, tweet_id int,  tweet_date date, tweet varchar(100))\"],\"oraclesql\":[\"Create table Tweets (user_id int, tweet_id int,  tweet_date date, tweet varchar(100))\",\"ALTER SESSION SET nls_date_format='YYYY-MM-DD'\"],\"database\":true,\"name\":\"find_trending_hashtags\",\"postgresql\":[\"CREATE TABLE Tweets (\\n    user_id INT,\\n    tweet_id INT,\\n    tweet_date DATE,\\n    tweet VARCHAR(100)\\n);\",\"SET DateStyle = 'ISO, MDY';\"],\"pythondata\":[\"Tweets = pd.DataFrame([], columns=['user_id', 'tweet_id', 'tweet_date', 'tweet']).astype({'user_id':'Int64', 'tweet_id':'Int64', 'tweet_date':'datetime64[ns]', 'tweet':'object'})\\n\"],\"database_schema\":{\"Tweets\":{\"user_id\":\"INT\",\"tweet_id\":\"INT\",\"tweet_date\":\"DATE\",\"tweet\":\"VARCHAR(100)\"}}}",
+            "judgerAvailable": true,
+            "judgeType": "large",
+            "mysqlSchemas": [
+                "Create table If Not Exists Tweets (user_id int, tweet_id int,  tweet_date date, tweet varchar(100))",
+                "Truncate table Tweets",
+                "insert into Tweets (user_id, tweet_id, tweet, tweet_date) values ('135', '13', 'Enjoying a great start to the day. #HappyDay', '2024-02-01')",
+                "insert into Tweets (user_id, tweet_id, tweet, tweet_date) values ('136', '14', 'Another #HappyDay with good ', '2024-02-03')",
+                "insert into Tweets (user_id, tweet_id, tweet, tweet_date) values ('137', '15', 'Productivity peaks! #WorkLife', '2024-02-04')",
+                "insert into Tweets (user_id, tweet_id, tweet, tweet_date) values ('138', '16', 'Exploring new tech frontiers. #TechLife', '2024-02-04')",
+                "insert into Tweets (user_id, tweet_id, tweet, tweet_date) values ('139', '17', 'Gratitude for today's moments. #HappyDay', '2024-02-05')",
+                "insert into Tweets (user_id, tweet_id, tweet, tweet_date) values ('140', '18', 'Innovation drives us. #TechLife', '2024-02-07')",
+                "insert into Tweets (user_id, tweet_id, tweet, tweet_date) values ('141', '19', 'Connecting with nature's serenity. #Nature', '2024-02-09')"
+            ],
+            "enableRunCode": true,
+            "envInfo": "{\"mysql\":[\"MySQL\",\"<p>\\u7248\\u672c\\uff1a<code>MySQL 8.0<\\/code><\\/p>\"],\"mssql\":[\"MS SQL Server\",\"<p>mssql server 2019.<\\/p>\"],\"oraclesql\":[\"Oracle\",\"<p>Oracle Sql 11.2.<\\/p>\"],\"pythondata\":[\"Pandas\",\"<p>Python 3.10 with Pandas 2.0.2 and NumPy 1.25.0<\\/p>\"],\"postgresql\":[\"PostgreSQL\",\"<p>PostgreSQL 16<\\/p>\"]}",
+            "book": null,
+            "isSubscribed": false,
+            "isDailyQuestion": false,
+            "dailyRecordStatus": null,
+            "editorType": "CKEDITOR",
+            "ugcQuestionId": null,
+            "style": "LEETCODE",
+            "exampleTestcases": "{\"headers\":{\"Tweets\":[\"user_id\",\"tweet_id\",\"tweet\",\"tweet_date\"]},\"rows\":{\"Tweets\":[[135,13,\"Enjoying a great start to the day. #HappyDay\",\"2024-02-01\"],[136,14,\"Another #HappyDay with good \",\"2024-02-03\"],[137,15,\"Productivity peaks! #WorkLife\",\"2024-02-04\"],[138,16,\"Exploring new tech frontiers. #TechLife\",\"2024-02-04\"],[139,17,\"Gratitude for today's moments. #HappyDay\",\"2024-02-05\"],[140,18,\"Innovation drives us. #TechLife\",\"2024-02-07\"],[141,19,\"Connecting with nature's serenity. #Nature\",\"2024-02-09\"]]}}",
+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]first-letter-capitalization.json

@@ -0,0 +1,53 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3703",
+            "questionFrontendId": "3368",
+            "categoryTitle": "Database",
+            "boundTopicId": 2999592,
+            "title": "First Letter Capitalization",
+            "titleSlug": "first-letter-capitalization",
+            "content": null,
+            "translatedTitle": "首字母大写",
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Hard",
+            "likes": 0,
+            "dislikes": 0,
+            "isLiked": null,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": null,
+            "topicTags": [],
+            "companyTagStats": null,
+            "codeSnippets": null,
+            "stats": "{\"totalAccepted\": \"32\", \"totalSubmission\": \"34\", \"totalAcceptedRaw\": 32, \"totalSubmissionRaw\": 34, \"acRate\": \"94.1%\"}",
+            "hints": [],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\":{\"user_content\":[\"content_id\",\"content_text\"]},\"rows\":{\"user_content\":[[1,\"hello world of SQL\"],[2,\"the QUICK brown fox\"],[3,\"data science AND machine learning\"],[4,\"TOP rated programming BOOKS\"]]}}",
+            "metaData": "{\"mysql\":[\"CREATE TABLE If not exists user_content (\\n    content_id INT,\\n    content_text VARCHAR(255)\\n)\"],\"mssql\":[\"CREATE TABLE user_content (\\n    content_id INT,\\n    content_text VARCHAR(255)\\n)\"],\"oraclesql\":[\"CREATE TABLE user_content (\\n    content_id NUMBER,\\n    content_text VARCHAR2(255)\\n)\"],\"database\":true,\"name\":\"process_text\",\"postgresql\":[\"CREATE TABLE IF NOT EXISTS user_content (\\n    content_id SERIAL PRIMARY KEY,\\n    content_text VARCHAR(255)\\n);\\n\"],\"pythondata\":[\"user_content = pd.DataFrame({\\n    'content_id': pd.Series(dtype='int'),\\n    'content_text': pd.Series(dtype='str')\\n})\"],\"database_schema\":{\"user_content\":{\"content_id\":\"INT\",\"content_text\":\"VARCHAR(255)\"}}}",
+            "judgerAvailable": true,
+            "judgeType": "large",
+            "mysqlSchemas": [
+                "CREATE TABLE If not exists user_content (\n    content_id INT,\n    content_text VARCHAR(255)\n)",
+                "Truncate table user_content",
+                "insert into user_content (content_id, content_text) values ('1', 'hello world of SQL')",
+                "insert into user_content (content_id, content_text) values ('2', 'the QUICK brown fox')",
+                "insert into user_content (content_id, content_text) values ('3', 'data science AND machine learning')",
+                "insert into user_content (content_id, content_text) values ('4', 'TOP rated programming BOOKS')"
+            ],
+            "enableRunCode": true,
+            "envInfo": "{\"mysql\":[\"MySQL\",\"<p>\\u7248\\u672c\\uff1a<code>MySQL 8.0<\\/code><\\/p>\"],\"mssql\":[\"MS SQL Server\",\"<p>mssql server 2019.<\\/p>\"],\"oraclesql\":[\"Oracle\",\"<p>Oracle Sql 11.2.<\\/p>\"],\"pythondata\":[\"Pandas\",\"<p>Python 3.10 with Pandas 2.2.2 and NumPy 1.26.4<\\/p>\"],\"postgresql\":[\"PostgreSQL\",\"<p>PostgreSQL 16<\\/p>\"]}",
+            "book": null,
+            "isSubscribed": false,
+            "isDailyQuestion": false,
+            "dailyRecordStatus": null,
+            "editorType": "CKEDITOR",
+            "ugcQuestionId": null,
+            "style": "LEETCODE",
+            "exampleTestcases": "{\"headers\":{\"user_content\":[\"content_id\",\"content_text\"]},\"rows\":{\"user_content\":[[1,\"hello world of SQL\"],[2,\"the QUICK brown fox\"],[3,\"data science AND machine learning\"],[4,\"TOP rated programming BOOKS\"]]}}",
+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]friday-purchase-iii.json

@@ -0,0 +1,74 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3424",
+            "questionFrontendId": "3118",
+            "categoryTitle": "Database",
+            "boundTopicId": 2740389,
+            "title": "Friday Purchase III ",
+            "titleSlug": "friday-purchase-iii",
+            "content": null,
+            "translatedTitle": null,
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Medium",
+            "likes": 0,
+            "dislikes": 0,
+            "isLiked": null,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": null,
+            "topicTags": [
+                {
+                    "name": "Database",
+                    "slug": "database",
+                    "translatedName": "数据库",
+                    "__typename": "TopicTagNode"
+                }
+            ],
+            "companyTagStats": null,
+            "codeSnippets": null,
+            "stats": "{\"totalAccepted\": \"33\", \"totalSubmission\": \"58\", \"totalAcceptedRaw\": 33, \"totalSubmissionRaw\": 58, \"acRate\": \"56.9%\"}",
+            "hints": [],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\":{\"Purchases\":[\"user_id\",\"purchase_date\",\"amount_spend\"],\"Users\":[\"user_id\",\"membership\"]},\"rows\":{\"Purchases\":[[11,\"2023-11-03\",1126],[15,\"2023-11-10\",7473],[17,\"2023-11-17\",2414],[12,\"2023-11-24\",9692],[8,\"2023-11-24\",5117],[1,\"2023-11-24\",5241],[10,\"2023-11-22\",8266],[13,\"2023-11-21\",12000]],\"Users\":[[11,\"Premium\"],[15,\"VIP\"],[17,\"Standard\"],[12,\"VIP\"],[8,\"Premium\"],[1,\"VIP\"],[10,\"Standard\"],[13,\"Premium\"]]}}",
+            "metaData": "{\"mysql\":[\"Create Table if Not Exists Purchases( user_id int, purchase_date date, amount_spend int)\",\"Create Table if Not Exists Users (user_id int, membership enum('Standard', 'Premium', 'VIP'))\"],\"mssql\":[\"Create Table Purchases( user_id int, purchase_date date, amount_spend int)\",\"Create Table  Users (user_id int, membership varchar(30) NOT NULL CHECK ( membership in ('Standard', 'Premium', 'VIP')))\"],\"oraclesql\":[\"Create Table  Purchases( user_id int, purchase_date date, amount_spend int)\",\"ALTER SESSION SET nls_date_format='YYYY-MM-DD'\",\"Create Table  Users (user_id int, membership varchar(30) NOT NULL CHECK ( membership in ('Standard', 'Premium', 'VIP')))\"],\"database\":true,\"name\":\"friday_purchases\",\"postgresql\":[\"CREATE TABLE Purchases (\\n    user_id int,\\n    purchase_date date,\\n    amount_spend int\\n);\",\"SET datestyle = 'ISO, MDY'; \\n\",\"CREATE TABLE Users (\\n    user_id int,\\n    membership varchar(30) NOT NULL CHECK (membership IN ('Standard', 'Premium', 'VIP'))\\n);\"],\"pythondata\":[\"Purchases = pd.DataFrame([], columns=['user_id', 'purchase_date', 'amount_spend']).astype({'user_id':'Int64', 'purchase_date':'datetime64[ns]', 'amount_spend':'Int64'})\\n\",\"Users = pd.DataFrame([], columns=['user_id', 'membership']).astype({\\n    'user_id': 'Int64',\\n    'membership': pd.CategoricalDtype(categories=['Standard', 'Premium', 'VIP'])\\n})\"],\"database_schema\":{\"Purchases\":{\"user_id\":\"INT\",\"purchase_date\":\"DATE\",\"amount_spend\":\"INT\"},\"Users\":{\"user_id\":\"INT\",\"membership\":\"ENUM('Standard', 'Premium', 'VIP')\"}}}",
+            "judgerAvailable": true,
+            "judgeType": "large",
+            "mysqlSchemas": [
+                "Create Table if Not Exists Purchases( user_id int, purchase_date date, amount_spend int)",
+                "Create Table if Not Exists Users (user_id int, membership enum('Standard', 'Premium', 'VIP'))",
+                "Truncate table Purchases",
+                "insert into Purchases (user_id, purchase_date, amount_spend) values ('11', '2023-11-03', '1126')",
+                "insert into Purchases (user_id, purchase_date, amount_spend) values ('15', '2023-11-10', '7473')",
+                "insert into Purchases (user_id, purchase_date, amount_spend) values ('17', '2023-11-17', '2414')",
+                "insert into Purchases (user_id, purchase_date, amount_spend) values ('12', '2023-11-24', '9692')",
+                "insert into Purchases (user_id, purchase_date, amount_spend) values ('8', '2023-11-24', '5117')",
+                "insert into Purchases (user_id, purchase_date, amount_spend) values ('1', '2023-11-24', '5241')",
+                "insert into Purchases (user_id, purchase_date, amount_spend) values ('10', '2023-11-22', '8266')",
+                "insert into Purchases (user_id, purchase_date, amount_spend) values ('13', '2023-11-21', '12000')",
+                "Truncate table Users",
+                "insert into Users (user_id, membership) values ('11', 'Premium')",
+                "insert into Users (user_id, membership) values ('15', 'VIP')",
+                "insert into Users (user_id, membership) values ('17', 'Standard')",
+                "insert into Users (user_id, membership) values ('12', 'VIP')",
+                "insert into Users (user_id, membership) values ('8', 'Premium')",
+                "insert into Users (user_id, membership) values ('1', 'VIP')",
+                "insert into Users (user_id, membership) values ('10', 'Standard')",
+                "insert into Users (user_id, membership) values ('13', 'Premium')"
+            ],
+            "enableRunCode": true,
+            "envInfo": "{\"mysql\":[\"MySQL\",\"<p>\\u7248\\u672c\\uff1a<code>MySQL 8.0<\\/code><\\/p>\"],\"mssql\":[\"MS SQL Server\",\"<p>mssql server 2019.<\\/p>\"],\"oraclesql\":[\"Oracle\",\"<p>Oracle Sql 11.2.<\\/p>\"],\"pythondata\":[\"Pandas\",\"<p>Python 3.10 with Pandas 2.0.2 and NumPy 1.25.0<\\/p>\"],\"postgresql\":[\"PostgreSQL\",\"<p>PostgreSQL 16<\\/p>\"]}",
+            "book": null,
+            "isSubscribed": false,
+            "isDailyQuestion": false,
+            "dailyRecordStatus": null,
+            "editorType": "CKEDITOR",
+            "ugcQuestionId": null,
+            "style": "LEETCODE",
+            "exampleTestcases": "{\"headers\":{\"Purchases\":[\"user_id\",\"purchase_date\",\"amount_spend\"],\"Users\":[\"user_id\",\"membership\"]},\"rows\":{\"Purchases\":[[11,\"2023-11-03\",1126],[15,\"2023-11-10\",7473],[17,\"2023-11-17\",2414],[12,\"2023-11-24\",9692],[8,\"2023-11-24\",5117],[1,\"2023-11-24\",5241],[10,\"2023-11-22\",8266],[13,\"2023-11-21\",12000]],\"Users\":[[11,\"Premium\"],[15,\"VIP\"],[17,\"Standard\"],[12,\"VIP\"],[8,\"Premium\"],[1,\"VIP\"],[10,\"Standard\"],[13,\"Premium\"]]}}",
+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]friday-purchases-i.json

@@ -0,0 +1,64 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3300",
+            "questionFrontendId": "2993",
+            "categoryTitle": "Database",
+            "boundTopicId": 2591528,
+            "title": "Friday Purchases I",
+            "titleSlug": "friday-purchases-i",
+            "content": null,
+            "translatedTitle": null,
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Medium",
+            "likes": 0,
+            "dislikes": 0,
+            "isLiked": null,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": null,
+            "topicTags": [
+                {
+                    "name": "Database",
+                    "slug": "database",
+                    "translatedName": "数据库",
+                    "__typename": "TopicTagNode"
+                }
+            ],
+            "companyTagStats": null,
+            "codeSnippets": null,
+            "stats": "{\"totalAccepted\": \"24\", \"totalSubmission\": \"26\", \"totalAcceptedRaw\": 24, \"totalSubmissionRaw\": 26, \"acRate\": \"92.3%\"}",
+            "hints": [],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\":{\"Purchases\":[\"user_id\",\"purchase_date\",\"amount_spend\"]},\"rows\":{\"Purchases\":[[11,\"2023-11-07\",1126],[15,\"2023-11-30\",7473],[17,\"2023-11-14\",2414],[12,\"2023-11-24\",9692],[8,\"2023-11-03\",5117],[1,\"2023-11-16\",5241],[10,\"2023-11-12\",8266],[13,\"2023-11-24\",12000]]}}",
+            "metaData": "{\"mysql\":[\"Create Table if Not Exists Purchases( user_id int, purchase_date date, amount_spend int)\"],\"mssql\":[\"Create Table Purchases( user_id int, purchase_date date, amount_spend int)\"],\"oraclesql\":[\"Create Table  Purchases( user_id int, purchase_date date, amount_spend int)\",\"ALTER SESSION SET nls_date_format='YYYY-MM-DD'\"],\"database\":true,\"languages\":[\"mysql\",\"mssql\",\"oraclesql\"],\"database_schema\":{\"Purchases\":{\"user_id\":\"INT\",\"purchase_date\":\"DATE\",\"amount_spend\":\"INT\"}}}",
+            "judgerAvailable": true,
+            "judgeType": "large",
+            "mysqlSchemas": [
+                "Create Table if Not Exists Purchases( user_id int, purchase_date date, amount_spend int)",
+                "Truncate table Purchases",
+                "insert into Purchases (user_id, purchase_date, amount_spend) values ('11', '2023-11-07', '1126')",
+                "insert into Purchases (user_id, purchase_date, amount_spend) values ('15', '2023-11-30', '7473')",
+                "insert into Purchases (user_id, purchase_date, amount_spend) values ('17', '2023-11-14', '2414')",
+                "insert into Purchases (user_id, purchase_date, amount_spend) values ('12', '2023-11-24', '9692')",
+                "insert into Purchases (user_id, purchase_date, amount_spend) values ('8', '2023-11-03', '5117')",
+                "insert into Purchases (user_id, purchase_date, amount_spend) values ('1', '2023-11-16', '5241')",
+                "insert into Purchases (user_id, purchase_date, amount_spend) values ('10', '2023-11-12', '8266')",
+                "insert into Purchases (user_id, purchase_date, amount_spend) values ('13', '2023-11-24', '12000')"
+            ],
+            "enableRunCode": true,
+            "envInfo": "{\"mysql\":[\"MySQL\",\"<p>\\u7248\\u672c\\uff1a<code>MySQL 8.0<\\/code><\\/p>\"],\"mssql\":[\"MS SQL Server\",\"<p>mssql server 2019.<\\/p>\"],\"oraclesql\":[\"Oracle\",\"<p>Oracle Sql 11.2.<\\/p>\"]}",
+            "book": null,
+            "isSubscribed": false,
+            "isDailyQuestion": false,
+            "dailyRecordStatus": null,
+            "editorType": "CKEDITOR",
+            "ugcQuestionId": null,
+            "style": "LEETCODE",
+            "exampleTestcases": "{\"headers\":{\"Purchases\":[\"user_id\",\"purchase_date\",\"amount_spend\"]},\"rows\":{\"Purchases\":[[11,\"2023-11-07\",1126],[15,\"2023-11-30\",7473],[17,\"2023-11-14\",2414],[12,\"2023-11-24\",9692],[8,\"2023-11-03\",5117],[1,\"2023-11-16\",5241],[10,\"2023-11-12\",8266],[13,\"2023-11-24\",12000]]}}",
+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]friday-purchases-ii.json

@@ -0,0 +1,64 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3301",
+            "questionFrontendId": "2994",
+            "categoryTitle": "Database",
+            "boundTopicId": 2591527,
+            "title": "Friday Purchases II ",
+            "titleSlug": "friday-purchases-ii",
+            "content": null,
+            "translatedTitle": null,
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Hard",
+            "likes": 0,
+            "dislikes": 0,
+            "isLiked": null,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": null,
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+                    "name": "Database",
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+            ],
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+            "stats": "{\"totalAccepted\": \"17\", \"totalSubmission\": \"17\", \"totalAcceptedRaw\": 17, \"totalSubmissionRaw\": 17, \"acRate\": \"100.0%\"}",
+            "hints": [],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\":{\"Purchases\":[\"user_id\",\"purchase_date\",\"amount_spend\"]},\"rows\":{\"Purchases\":[[11,\"2023-11-07\",1126],[15,\"2023-11-30\",7473],[17,\"2023-11-14\",2414],[12,\"2023-11-24\",9692],[8,\"2023-11-03\",5117],[1,\"2023-11-16\",5241],[10,\"2023-11-12\",8266],[13,\"2023-11-24\",12000]]}}",
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+            "judgerAvailable": true,
+            "judgeType": "large",
+            "mysqlSchemas": [
+                "Create Table if Not Exists Purchases( user_id int, purchase_date date, amount_spend int)",
+                "Truncate table Purchases",
+                "insert into Purchases (user_id, purchase_date, amount_spend) values ('11', '2023-11-07', '1126')",
+                "insert into Purchases (user_id, purchase_date, amount_spend) values ('15', '2023-11-30', '7473')",
+                "insert into Purchases (user_id, purchase_date, amount_spend) values ('17', '2023-11-14', '2414')",
+                "insert into Purchases (user_id, purchase_date, amount_spend) values ('12', '2023-11-24', '9692')",
+                "insert into Purchases (user_id, purchase_date, amount_spend) values ('8', '2023-11-03', '5117')",
+                "insert into Purchases (user_id, purchase_date, amount_spend) values ('1', '2023-11-16', '5241')",
+                "insert into Purchases (user_id, purchase_date, amount_spend) values ('10', '2023-11-12', '8266')",
+                "insert into Purchases (user_id, purchase_date, amount_spend) values ('13', '2023-11-24', '12000')"
+            ],
+            "enableRunCode": true,
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+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]friends-with-no-mutual-friends.json

@@ -0,0 +1,57 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3358",
+            "questionFrontendId": "100253",
+            "categoryTitle": "Database",
+            "boundTopicId": 2653965,
+            "title": "Friends With No Mutual Friends",
+            "titleSlug": "friends-with-no-mutual-friends",
+            "content": null,
+            "translatedTitle": null,
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Medium",
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+            "dislikes": 0,
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+            "stats": "{\"totalAccepted\": \"17\", \"totalSubmission\": \"39\", \"totalAcceptedRaw\": 17, \"totalSubmissionRaw\": 39, \"acRate\": \"43.6%\"}",
+            "hints": [],
+            "solution": null,
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+            "sampleTestCase": "{\"headers\":{\"Friends\":[\"user_id1\",\"user_id2\"]},\"rows\":{\"Friends\":[[1,2],[2,3],[2,4],[1,5],[6,7],[3,4],[2,5],[8,9]]}}",
+            "metaData": "{\"mysql\":[\"Create Table if Not Exists Friends( user_id1 int, user_id2 int)\"],\"mssql\":[\"Create Table Friends( user_id1 int, user_id2 int)\"],\"oraclesql\":[\"Create Table Friends( user_id1 int, user_id2 int)\"],\"database\":true,\"name\":\"friends_with_no_mutual_friends\",\"pythondata\":[\"Friends = pd.DataFrame([], columns=['user_id1', 'user_id2']).astype({'user_id1':'Int64', 'user_id2':'Int64'})\\n\"],\"postgresql\":[\"CREATE TABLE Friends (\\n    user_id1 int,\\n    user_id2 int\\n);\"],\"database_schema\":{\"Friends\":{\"user_id1\":\"INT\",\"user_id2\":\"INT\"}}}",
+            "judgerAvailable": true,
+            "judgeType": "large",
+            "mysqlSchemas": [
+                "Create Table if Not Exists Friends( user_id1 int, user_id2 int)",
+                "Truncate table Friends",
+                "insert into Friends (user_id1, user_id2) values ('1', '2')",
+                "insert into Friends (user_id1, user_id2) values ('2', '3')",
+                "insert into Friends (user_id1, user_id2) values ('2', '4')",
+                "insert into Friends (user_id1, user_id2) values ('1', '5')",
+                "insert into Friends (user_id1, user_id2) values ('6', '7')",
+                "insert into Friends (user_id1, user_id2) values ('3', '4')",
+                "insert into Friends (user_id1, user_id2) values ('2', '5')",
+                "insert into Friends (user_id1, user_id2) values ('8', '9')"
+            ],
+            "enableRunCode": true,
+            "envInfo": "{\"mysql\":[\"MySQL\",\"<p>\\u7248\\u672c\\uff1a<code>MySQL 8.0<\\/code><\\/p>\"],\"mssql\":[\"MS SQL Server\",\"<p>mssql server 2019.<\\/p>\"],\"oraclesql\":[\"Oracle\",\"<p>Oracle Sql 11.2.<\\/p>\"],\"pythondata\":[\"Pandas\",\"<p>Python 3.10 with Pandas 2.0.2 and NumPy 1.25.0<\\/p>\"],\"postgresql\":[\"PostgreSQL\",\"<p>PostgreSQL 16<\\/p>\"]}",
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+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]invalid-tweets-ii.json

@@ -0,0 +1,72 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3457",
+            "questionFrontendId": "3150",
+            "categoryTitle": "Database",
+            "boundTopicId": 2775397,
+            "title": "Invalid Tweets II",
+            "titleSlug": "invalid-tweets-ii",
+            "content": null,
+            "translatedTitle": "无效的推文 II",
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Easy",
+            "likes": 0,
+            "dislikes": 0,
+            "isLiked": null,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": null,
+            "topicTags": [
+                {
+                    "name": "Database",
+                    "slug": "database",
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+            "hints": [],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\": {\"Tweets\": [\"tweet_id\", \"content\"]}, \"rows\": {\"Tweets\": [[1, \"What an amazing meal @MaxPower @AlexJones @JohnDoe #Learning #Fitness #Love\"], [2, \"Learning something new every day @AnnaWilson #Learning #Foodie\"], [3, \"Never been happier about today's achievements @SaraJohnson @JohnDoe @AnnaWilson #Fashion\"], [4, \"Traveling, exploring, and living my best life @JaneSmith @JohnDoe @ChrisAnderson @AlexJones #WorkLife #Travel\"], [5, \"Work hard, play hard, and cherish every moment @AlexJones #Fashion #Foodie\"], [6, \"Never been happier about today's achievements @ChrisAnderson #Fashion #WorkLife\"], [7, \"So grateful for today's experiences @AnnaWilson @LisaTaylor @ChrisAnderson @MikeBrown #Fashion #HappyDay #WorkLife #Nature\"], [8, \"What an amazing meal @EmilyClark @AlexJones @MikeBrown #Fitness\"], [9, \"Learning something new every day @EmilyClark @AnnaWilson @MaxPower #Travel\"], [10, \"So grateful for today's experiences @ChrisAnderson #Nature\"], [11, \"So grateful for today's experiences @AlexJones #Art #WorkLife\"], [12, \"Learning something new every day @JaneSmith @MikeBrown #Travel\"], [13, \"What an amazing meal @EmilyClark @JohnDoe @LisaTaylor @MaxPower #Foodie #Fitness\"], [14, \"Work hard, play hard, and cherish every moment @LisaTaylor @SaraJohnson @MaxPower @ChrisAnderson #TechLife #Nature #Music\"], [15, \"What a beautiful day it is @EmilyClark @MaxPower @SaraJohnson #Fashion\"], [16, \"What a beautiful day it is @AnnaWilson @JaneSmith #Fashion #Love #TechLife\"]]}}",
+            "metaData": "{\"mysql\":[\"Create table If Not Exists Tweets(tweet_id int, content varchar(500))\"],\"mssql\":[\"Create table Tweets(tweet_id int, content varchar(500))\"],\"oraclesql\":[\"Create table Tweets(tweet_id int, content varchar(500))\"],\"database\":true,\"name\":\"find_invalid_tweets\",\"pythondata\":[\"Tweets = pd.DataFrame([], columns=['tweet_id', 'content']).astype({'tweet_id':'Int64', 'content':'object'})\"],\"postgresql\":[\"Create table If Not Exists Tweets(tweet_id int, content varchar(500))\"],\"database_schema\":{\"Tweets\":{\"tweet_id\":\"INT\",\"content\":\"VARCHAR(500)\"}}}",
+            "judgerAvailable": true,
+            "judgeType": "large",
+            "mysqlSchemas": [
+                "Create table If Not Exists Tweets(tweet_id int, content varchar(500))",
+                "Truncate table Tweets",
+                "insert into Tweets (tweet_id, content) values ('1', 'What an amazing meal @MaxPower @AlexJones @JohnDoe #Learning #Fitness #Love')",
+                "insert into Tweets (tweet_id, content) values ('2', 'Learning something new every day @AnnaWilson #Learning #Foodie')",
+                "insert into Tweets (tweet_id, content) values ('3', 'Never been happier about today's achievements @SaraJohnson @JohnDoe @AnnaWilson #Fashion')",
+                "insert into Tweets (tweet_id, content) values ('4', 'Traveling, exploring, and living my best life @JaneSmith @JohnDoe @ChrisAnderson @AlexJones #WorkLife #Travel')",
+                "insert into Tweets (tweet_id, content) values ('5', 'Work hard, play hard, and cherish every moment @AlexJones #Fashion #Foodie')",
+                "insert into Tweets (tweet_id, content) values ('6', 'Never been happier about today's achievements @ChrisAnderson #Fashion #WorkLife')",
+                "insert into Tweets (tweet_id, content) values ('7', 'So grateful for today's experiences @AnnaWilson @LisaTaylor @ChrisAnderson @MikeBrown #Fashion #HappyDay #WorkLife #Nature')",
+                "insert into Tweets (tweet_id, content) values ('8', 'What an amazing meal @EmilyClark @AlexJones @MikeBrown #Fitness')",
+                "insert into Tweets (tweet_id, content) values ('9', 'Learning something new every day @EmilyClark @AnnaWilson @MaxPower #Travel')",
+                "insert into Tweets (tweet_id, content) values ('10', 'So grateful for today's experiences @ChrisAnderson #Nature')",
+                "insert into Tweets (tweet_id, content) values ('11', 'So grateful for today's experiences @AlexJones #Art #WorkLife')",
+                "insert into Tweets (tweet_id, content) values ('12', 'Learning something new every day @JaneSmith @MikeBrown #Travel')",
+                "insert into Tweets (tweet_id, content) values ('13', 'What an amazing meal @EmilyClark @JohnDoe @LisaTaylor @MaxPower #Foodie #Fitness')",
+                "insert into Tweets (tweet_id, content) values ('14', 'Work hard, play hard, and cherish every moment @LisaTaylor @SaraJohnson @MaxPower @ChrisAnderson #TechLife #Nature #Music')",
+                "insert into Tweets (tweet_id, content) values ('15', 'What a beautiful day it is @EmilyClark @MaxPower @SaraJohnson #Fashion')",
+                "insert into Tweets (tweet_id, content) values ('16', 'What a beautiful day it is @AnnaWilson @JaneSmith #Fashion #Love #TechLife')"
+            ],
+            "enableRunCode": true,
+            "envInfo": "{\"mysql\":[\"MySQL\",\"<p>\\u7248\\u672c\\uff1a<code>MySQL 8.0<\\/code><\\/p>\"],\"mssql\":[\"MS SQL Server\",\"<p>mssql server 2019.<\\/p>\"],\"oraclesql\":[\"Oracle\",\"<p>Oracle Sql 11.2.<\\/p>\"],\"pythondata\":[\"Pandas\",\"<p>Python 3.10 with Pandas 2.0.2 and NumPy 1.25.0<\\/p>\"],\"postgresql\":[\"PostgreSQL\",\"<p>PostgreSQL 16<\\/p>\"]}",
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+            "isDailyQuestion": false,
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+            "editorType": "CKEDITOR",
+            "ugcQuestionId": null,
+            "style": "LEETCODE",
+            "exampleTestcases": "{\"headers\": {\"Tweets\": [\"tweet_id\", \"content\"]}, \"rows\": {\"Tweets\": [[1, \"What an amazing meal @MaxPower @AlexJones @JohnDoe #Learning #Fitness #Love\"], [2, \"Learning something new every day @AnnaWilson #Learning #Foodie\"], [3, \"Never been happier about today's achievements @SaraJohnson @JohnDoe @AnnaWilson #Fashion\"], [4, \"Traveling, exploring, and living my best life @JaneSmith @JohnDoe @ChrisAnderson @AlexJones #WorkLife #Travel\"], [5, \"Work hard, play hard, and cherish every moment @AlexJones #Fashion #Foodie\"], [6, \"Never been happier about today's achievements @ChrisAnderson #Fashion #WorkLife\"], [7, \"So grateful for today's experiences @AnnaWilson @LisaTaylor @ChrisAnderson @MikeBrown #Fashion #HappyDay #WorkLife #Nature\"], [8, \"What an amazing meal @EmilyClark @AlexJones @MikeBrown #Fitness\"], [9, \"Learning something new every day @EmilyClark @AnnaWilson @MaxPower #Travel\"], [10, \"So grateful for today's experiences @ChrisAnderson #Nature\"], [11, \"So grateful for today's experiences @AlexJones #Art #WorkLife\"], [12, \"Learning something new every day @JaneSmith @MikeBrown #Travel\"], [13, \"What an amazing meal @EmilyClark @JohnDoe @LisaTaylor @MaxPower #Foodie #Fitness\"], [14, \"Work hard, play hard, and cherish every moment @LisaTaylor @SaraJohnson @MaxPower @ChrisAnderson #TechLife #Nature #Music\"], [15, \"What a beautiful day it is @EmilyClark @MaxPower @SaraJohnson #Fashion\"], [16, \"What a beautiful day it is @AnnaWilson @JaneSmith #Fashion #Love #TechLife\"]]}}",
+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]loan-types.json

@@ -0,0 +1,64 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3287",
+            "questionFrontendId": "2990",
+            "categoryTitle": "Database",
+            "boundTopicId": 2586390,
+            "title": "Loan Types",
+            "titleSlug": "loan-types",
+            "content": null,
+            "translatedTitle": "贷款类型",
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Easy",
+            "likes": 0,
+            "dislikes": 0,
+            "isLiked": null,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": null,
+            "topicTags": [
+                {
+                    "name": "Database",
+                    "slug": "database",
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+            "stats": "{\"totalAccepted\": \"50\", \"totalSubmission\": \"82\", \"totalAcceptedRaw\": 50, \"totalSubmissionRaw\": 82, \"acRate\": \"61.0%\"}",
+            "hints": [],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\":{\"Loans\":[\"loan_id\",\"user_id\",\"loan_type\"]},\"rows\":{\"Loans\":[[683,101,\"Mortgage\"],[218,101,\"AutoLoan\"],[802,101,\"Inschool\"],[593,102,\"Mortgage\"],[138,102,\"Refinance\"],[294,102,\"Inschool\"],[308,103,\"Refinance\"],[389,104,\"Mortgage\"]]}}",
+            "metaData": "{\"mysql\":[\"Create Table if not Exists Loans ( loan_id int, user_id int, loan_type varchar(40))\"],\"mssql\":[\"Create Table  Loans (loan_id int, user_id int, loan_type varchar(40))\"],\"oraclesql\":[\"Create Table  Loans ( loan_id int, user_id int, loan_type varchar(40))\"],\"database\":true,\"languages\":[\"mysql\",\"mssql\",\"oraclesql\"],\"database_schema\":{\"Loans\":{\"loan_id\":\"INT\",\"user_id\":\"INT\",\"loan_type\":\"VARCHAR(40)\"}}}",
+            "judgerAvailable": true,
+            "judgeType": "large",
+            "mysqlSchemas": [
+                "Create Table if not Exists Loans ( loan_id int, user_id int, loan_type varchar(40))",
+                "Truncate table Loans",
+                "insert into Loans (loan_id, user_id, loan_type) values ('683', '101', 'Mortgage')",
+                "insert into Loans (loan_id, user_id, loan_type) values ('218', '101', 'AutoLoan')",
+                "insert into Loans (loan_id, user_id, loan_type) values ('802', '101', 'Inschool')",
+                "insert into Loans (loan_id, user_id, loan_type) values ('593', '102', 'Mortgage')",
+                "insert into Loans (loan_id, user_id, loan_type) values ('138', '102', 'Refinance')",
+                "insert into Loans (loan_id, user_id, loan_type) values ('294', '102', 'Inschool')",
+                "insert into Loans (loan_id, user_id, loan_type) values ('308', '103', 'Refinance')",
+                "insert into Loans (loan_id, user_id, loan_type) values ('389', '104', 'Mortgage')"
+            ],
+            "enableRunCode": true,
+            "envInfo": "{\"mysql\":[\"MySQL\",\"<p>\\u7248\\u672c\\uff1a<code>MySQL 8.0<\\/code><\\/p>\"],\"mssql\":[\"MS SQL Server\",\"<p>mssql server 2019.<\\/p>\"],\"oraclesql\":[\"Oracle\",\"<p>Oracle Sql 11.2.<\\/p>\"]}",
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+            "editorType": "CKEDITOR",
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+            "style": "LEETCODE",
+            "exampleTestcases": "{\"headers\":{\"Loans\":[\"loan_id\",\"user_id\",\"loan_type\"]},\"rows\":{\"Loans\":[[683,101,\"Mortgage\"],[218,101,\"AutoLoan\"],[802,101,\"Inschool\"],[593,102,\"Mortgage\"],[138,102,\"Refinance\"],[294,102,\"Inschool\"],[308,103,\"Refinance\"],[389,104,\"Mortgage\"]]}}",
+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]longest-team-pass-streak.json

@@ -0,0 +1,76 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3726",
+            "questionFrontendId": "3390",
+            "categoryTitle": "Database",
+            "boundTopicId": 3021381,
+            "title": "Longest Team Pass Streak",
+            "titleSlug": "longest-team-pass-streak",
+            "content": null,
+            "translatedTitle": null,
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Hard",
+            "likes": 0,
+            "dislikes": 0,
+            "isLiked": null,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": null,
+            "topicTags": [
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+                    "name": "Database",
+                    "slug": "database",
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+            "codeSnippets": null,
+            "stats": "{\"totalAccepted\": \"13\", \"totalSubmission\": \"22\", \"totalAcceptedRaw\": 13, \"totalSubmissionRaw\": 22, \"acRate\": \"59.1%\"}",
+            "hints": [],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\":{\"Teams\":[\"player_id\",\"team_name\"],\"Passes\":[\"pass_from\",\"time_stamp\",\"pass_to\"]},\"rows\":{\"Teams\":[[1,\"Arsenal\"],[2,\"Arsenal\"],[3,\"Arsenal\"],[4,\"Arsenal\"],[5,\"Chelsea\"],[6,\"Chelsea\"],[7,\"Chelsea\"],[8,\"Chelsea\"]],\"Passes\":[[1,\"00:05\",2],[2,\"00:07\",3],[3,\"00:08\",4],[4,\"00:10\",5],[6,\"00:15\",7],[7,\"00:17\",8],[8,\"00:20\",6],[6,\"00:22\",5],[1,\"00:25\",2],[2,\"00:27\",3]]}}",
+            "metaData": "{\"mysql\":[\"CREATE TABLE If not exists Teams (\\n    player_id INT,\\n    team_name VARCHAR(100)\\n)\\n\\n\",\"CREATE TABLE if not exists Passes (\\n    pass_from INT,\\n    time_stamp VARCHAR(5),\\n    pass_to INT\\n)\"],\"mssql\":[\"CREATE TABLE Teams (\\n    player_id INT,\\n    team_name VARCHAR(100)\\n)\\n\",\"\\nCREATE TABLE Passes (\\n    pass_from INT,\\n    time_stamp VARCHAR(5),\\n    pass_to INT\\n)\"],\"oraclesql\":[\"CREATE TABLE Teams (\\n    player_id NUMBER,\\n    team_name VARCHAR2(100)\\n)\\n\\n\",\"CREATE TABLE Passes (\\n    pass_from NUMBER,\\n    time_stamp VARCHAR2(5),\\n    pass_to NUMBER\\n)\"],\"database\":true,\"name\":\"calculate_longest_streaks\",\"pythondata\":[\"Teams = pd.DataFrame(columns=[\\\"player_id\\\", \\\"team_name\\\"]).astype({\\\"player_id\\\": \\\"int\\\", \\\"team_name\\\": \\\"string\\\"})\\n\",\"Passes = pd.DataFrame(columns=[\\\"pass_from\\\", \\\"time_stamp\\\", \\\"pass_to\\\"]).astype({\\\"pass_from\\\": \\\"int\\\", \\\"time_stamp\\\": \\\"string\\\", \\\"pass_to\\\": \\\"int\\\"})\\n\"],\"postgresql\":[\"CREATE TABLE IF NOT EXISTS Teams (\\n    player_id INTEGER,\\n    team_name VARCHAR(100)\\n);\\n\",\"CREATE TABLE IF NOT EXISTS Passes (\\n    pass_from INTEGER,\\n    time_stamp VARCHAR(5),\\n    pass_to INTEGER\\n);\\n\"],\"database_schema\":{\"Teams\":{\"player_id\":\"INT\",\"team_name\":\"VARCHAR(100)\"},\"Passes\":{\"pass_from\":\"INT\",\"time_stamp\":\"VARCHAR(5)\",\"pass_to\":\"INT\"}}}",
+            "judgerAvailable": true,
+            "judgeType": "large",
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+                "CREATE TABLE If not exists Teams (\n    player_id INT,\n    team_name VARCHAR(100)\n)\n\n",
+                "CREATE TABLE if not exists Passes (\n    pass_from INT,\n    time_stamp VARCHAR(5),\n    pass_to INT\n)",
+                "Truncate table Teams",
+                "insert into Teams (player_id, team_name) values ('1', 'Arsenal')",
+                "insert into Teams (player_id, team_name) values ('2', 'Arsenal')",
+                "insert into Teams (player_id, team_name) values ('3', 'Arsenal')",
+                "insert into Teams (player_id, team_name) values ('4', 'Arsenal')",
+                "insert into Teams (player_id, team_name) values ('5', 'Chelsea')",
+                "insert into Teams (player_id, team_name) values ('6', 'Chelsea')",
+                "insert into Teams (player_id, team_name) values ('7', 'Chelsea')",
+                "insert into Teams (player_id, team_name) values ('8', 'Chelsea')",
+                "Truncate table Passes",
+                "insert into Passes (pass_from, time_stamp, pass_to) values ('1', '00:05', '2')",
+                "insert into Passes (pass_from, time_stamp, pass_to) values ('2', '00:07', '3')",
+                "insert into Passes (pass_from, time_stamp, pass_to) values ('3', '00:08', '4')",
+                "insert into Passes (pass_from, time_stamp, pass_to) values ('4', '00:10', '5')",
+                "insert into Passes (pass_from, time_stamp, pass_to) values ('6', '00:15', '7')",
+                "insert into Passes (pass_from, time_stamp, pass_to) values ('7', '00:17', '8')",
+                "insert into Passes (pass_from, time_stamp, pass_to) values ('8', '00:20', '6')",
+                "insert into Passes (pass_from, time_stamp, pass_to) values ('6', '00:22', '5')",
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+        }
+    }
+}

leetcode-cn/originData/[no content]manager-of-the-largest-department.json

@@ -0,0 +1,67 @@
+{
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+            "questionId": "3285",
+            "questionFrontendId": "2988",
+            "categoryTitle": "Database",
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+            "title": "Manager of the Largest Department",
+            "titleSlug": "manager-of-the-largest-department",
+            "content": null,
+            "translatedTitle": "最大部门的经理",
+            "translatedContent": null,
+            "isPaidOnly": true,
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+                "Truncate table Employees",
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+                "insert into Employees (emp_id, emp_name, dep_id, position) values ('8', 'Isabella', '101', 'Manager')",
+                "insert into Employees (emp_id, emp_name, dep_id, position) values ('160', 'Joseph', '100', 'Manager')",
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+                "insert into Employees (emp_id, emp_name, dep_id, position) values ('190', 'Skylar', '100', 'Freelancer')",
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+}

leetcode-cn/originData/[no content]maximize-items.json

@@ -0,0 +1,59 @@
+{
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+        "question": {
+            "questionId": "3257",
+            "questionFrontendId": "100175",
+            "categoryTitle": "Database",
+            "boundTopicId": 2653972,
+            "title": "Maximize Items",
+            "titleSlug": "maximize-items",
+            "content": null,
+            "translatedTitle": null,
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+            "isPaidOnly": true,
+            "difficulty": "Hard",
+            "likes": 0,
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+            "langToValidPlayground": null,
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+                "insert into Inventory (item_id, item_type, item_category, square_footage) values ('1374', 'prime_eligible', 'Watches', '68.0')",
+                "insert into Inventory (item_id, item_type, item_category, square_footage) values ('4245', 'not_prime', 'Art', '26.4')",
+                "insert into Inventory (item_id, item_type, item_category, square_footage) values ('5743', 'prime_eligible', 'Software', '325.0')",
+                "insert into Inventory (item_id, item_type, item_category, square_footage) values ('8543', 'not_prime', 'Clothing', '64.5')",
+                "insert into Inventory (item_id, item_type, item_category, square_footage) values ('2556', 'not_prime', 'Shoes', '15.0')",
+                "insert into Inventory (item_id, item_type, item_category, square_footage) values ('2452', 'prime_eligible', 'Scientific', '85.0')",
+                "insert into Inventory (item_id, item_type, item_category, square_footage) values ('3255', 'not_prime', 'Furniture', '22.6')",
+                "insert into Inventory (item_id, item_type, item_category, square_footage) values ('1672', 'prime_eligible', 'Beauty', '8.5')",
+                "insert into Inventory (item_id, item_type, item_category, square_footage) values ('4256', 'prime_eligible', 'Furniture', '55.5')",
+                "insert into Inventory (item_id, item_type, item_category, square_footage) values ('6325', 'prime_eligible', 'Food', '13.2')"
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+        }
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+}

leetcode-cn/originData/[no content]pizza-toppings-cost-analysis.json

@@ -0,0 +1,53 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3255",
+            "questionFrontendId": "100173",
+            "categoryTitle": "Database",
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+            "title": "Pizza Toppings Cost Analysis",
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+                "insert into Toppings (topping_name, cost) values ('Sausage', '0.7')",
+                "insert into Toppings (topping_name, cost) values ('Chicken', '0.55')",
+                "insert into Toppings (topping_name, cost) values ('Extra Cheese', '0.4')"
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+}

leetcode-cn/originData/[no content]premier-league-table-ranking-ii.json

@@ -0,0 +1,66 @@
+{
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+            "questionId": "3564",
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+            "title": "Premier League Table Ranking II",
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+                "insert into TeamStats (team_id, team_name, matches_played, wins, draws, losses) values ('2', 'Nottingham Forest', '27', '6', '6', '15')",
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+            "exampleTestcases": "{\"headers\": {\"TeamStats\": [\"team_id\", \"team_name\", \"matches_played\", \"wins\", \"draws\", \"losses\"]}, \"rows\": {\"TeamStats\": [[1, \"Chelsea\", 22, 13, 2, 7], [2, \"Nottingham Forest\", 27, 6, 6, 15], [3, \"Liverpool\", 17, 1, 8, 8], [4, \"Aston Villa\", 20, 1, 6, 13], [5, \"Fulham\", 31, 18, 1, 12], [6, \"Burnley\", 26, 6, 9, 11], [7, \"Newcastle United\", 33, 11, 10, 12], [8, \"Sheffield United\", 20, 18, 2, 0], [9, \"Luton Town\", 5, 4, 0, 1], [10, \"Everton\", 14, 2, 6, 6]]}}",
+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]premier-league-table-ranking-iii.json

@@ -0,0 +1,66 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3642",
+            "questionFrontendId": "3322",
+            "categoryTitle": "Database",
+            "boundTopicId": 2949746,
+            "title": "Premier League Table Ranking III",
+            "titleSlug": "premier-league-table-ranking-iii",
+            "content": null,
+            "translatedTitle": "英超积分榜排名 III",
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Medium",
+            "likes": 0,
+            "dislikes": 0,
+            "isLiked": null,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": null,
+            "topicTags": [
+                {
+                    "name": "Database",
+                    "slug": "database",
+                    "translatedName": "数据库",
+                    "__typename": "TopicTagNode"
+                }
+            ],
+            "companyTagStats": null,
+            "codeSnippets": null,
+            "stats": "{\"totalAccepted\": \"84\", \"totalSubmission\": \"104\", \"totalAcceptedRaw\": 84, \"totalSubmissionRaw\": 104, \"acRate\": \"80.8%\"}",
+            "hints": [],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\":{\"SeasonStats\":[\"season_id\",\"team_id\",\"team_name\",\"matches_played\",\"wins\",\"draws\",\"losses\",\"goals_for\",\"goals_against\"]},\"rows\":{\"SeasonStats\":[[2021,1,\"Manchester City\",38,29,6,3,99,26],[2021,2,\"Liverpool\",38,28,8,2,94,26],[2021,3,\"Chelsea\",38,21,11,6,76,33],[2021,4,\"Tottenham\",38,22,5,11,69,40],[2021,5,\"Arsenal\",38,22,3,13,61,48],[2022,1,\"Manchester City\",38,28,5,5,94,33],[2022,2,\"Arsenal\",38,26,6,6,88,43],[2022,3,\"Manchester United\",38,23,6,9,58,43],[2022,4,\"Newcastle\",38,19,14,5,68,33],[2022,5,\"Liverpool\",38,19,10,9,75,47]]}}",
+            "metaData": "{\"mysql\":[\"CREATE TABLE SeasonStats (\\n    season_id INT,\\n    team_id INT,\\n    team_name VARCHAR(255),\\n    matches_played INT,\\n    wins INT,\\n    draws INT,\\n    losses INT,\\n    goals_for INT,\\n    goals_against INT\\n)\"],\"mssql\":[\"CREATE TABLE SeasonStats (\\n    season_id INT,\\n    team_id INT,\\n    team_name NVARCHAR(255),\\n    matches_played INT,\\n    wins INT,\\n    draws INT,\\n    losses INT,\\n    goals_for INT,\\n    goals_against INT\\n)\"],\"oraclesql\":[\"CREATE TABLE SeasonStats (\\n    season_id NUMBER,\\n    team_id NUMBER,\\n    team_name VARCHAR2(255),\\n    matches_played NUMBER,\\n    wins NUMBER,\\n    draws NUMBER,\\n    losses NUMBER,\\n    goals_for NUMBER,\\n    goals_against NUMBER\\n)\"],\"database\":true,\"name\":\"process_team_standings\",\"postgresql\":[\"CREATE TABLE SeasonStats (\\n    season_id INTEGER,\\n    team_id INTEGER,\\n    team_name VARCHAR(255),\\n    matches_played INTEGER,\\n    wins INTEGER,\\n    draws INTEGER,\\n    losses INTEGER,\\n    goals_for INTEGER,\\n    goals_against INTEGER\\n);\\n\"],\"pythondata\":[\"SeasonStats = pd.DataFrame(columns=['season_id', 'team_id', 'team_name', 'matches_played', 'wins', 'draws', 'losses', 'goals_for', 'goals_against'], dtype='int')\\n\"],\"database_schema\":{\"SeasonStats\":{\"season_id\":\"INT\",\"team_id\":\"INT\",\"team_name\":\"VARCHAR(255)\",\"matches_played\":\"INT\",\"wins\":\"INT\",\"draws\":\"INT\",\"losses\":\"INT\",\"goals_for\":\"INT\",\"goals_against\":\"INT\"}}}",
+            "judgerAvailable": true,
+            "judgeType": "large",
+            "mysqlSchemas": [
+                "CREATE TABLE SeasonStats (\n    season_id INT,\n    team_id INT,\n    team_name VARCHAR(255),\n    matches_played INT,\n    wins INT,\n    draws INT,\n    losses INT,\n    goals_for INT,\n    goals_against INT\n)",
+                "Truncate table SeasonStats",
+                "insert into SeasonStats (season_id, team_id, team_name, matches_played, wins, draws, losses, goals_for, goals_against) values ('2021', '1', 'Manchester City', '38', '29', '6', '3', '99', '26')",
+                "insert into SeasonStats (season_id, team_id, team_name, matches_played, wins, draws, losses, goals_for, goals_against) values ('2021', '2', 'Liverpool', '38', '28', '8', '2', '94', '26')",
+                "insert into SeasonStats (season_id, team_id, team_name, matches_played, wins, draws, losses, goals_for, goals_against) values ('2021', '3', 'Chelsea', '38', '21', '11', '6', '76', '33')",
+                "insert into SeasonStats (season_id, team_id, team_name, matches_played, wins, draws, losses, goals_for, goals_against) values ('2021', '4', 'Tottenham', '38', '22', '5', '11', '69', '40')",
+                "insert into SeasonStats (season_id, team_id, team_name, matches_played, wins, draws, losses, goals_for, goals_against) values ('2021', '5', 'Arsenal', '38', '22', '3', '13', '61', '48')",
+                "insert into SeasonStats (season_id, team_id, team_name, matches_played, wins, draws, losses, goals_for, goals_against) values ('2022', '1', 'Manchester City', '38', '28', '5', '5', '94', '33')",
+                "insert into SeasonStats (season_id, team_id, team_name, matches_played, wins, draws, losses, goals_for, goals_against) values ('2022', '2', 'Arsenal', '38', '26', '6', '6', '88', '43')",
+                "insert into SeasonStats (season_id, team_id, team_name, matches_played, wins, draws, losses, goals_for, goals_against) values ('2022', '3', 'Manchester United', '38', '23', '6', '9', '58', '43')",
+                "insert into SeasonStats (season_id, team_id, team_name, matches_played, wins, draws, losses, goals_for, goals_against) values ('2022', '4', 'Newcastle', '38', '19', '14', '5', '68', '33')",
+                "insert into SeasonStats (season_id, team_id, team_name, matches_played, wins, draws, losses, goals_for, goals_against) values ('2022', '5', 'Liverpool', '38', '19', '10', '9', '75', '47')"
+            ],
+            "enableRunCode": true,
+            "envInfo": "{\"mysql\":[\"MySQL\",\"<p>\\u7248\\u672c\\uff1a<code>MySQL 8.0<\\/code><\\/p>\"],\"mssql\":[\"MS SQL Server\",\"<p>mssql server 2019.<\\/p>\"],\"oraclesql\":[\"Oracle\",\"<p>Oracle Sql 11.2.<\\/p>\"],\"pythondata\":[\"Pandas\",\"<p>Python 3.10 with Pandas 2.0.2 and NumPy 1.25.0<\\/p>\"],\"postgresql\":[\"PostgreSQL\",\"<p>PostgreSQL 16<\\/p>\"]}",
+            "book": null,
+            "isSubscribed": false,
+            "isDailyQuestion": false,
+            "dailyRecordStatus": null,
+            "editorType": "CKEDITOR",
+            "ugcQuestionId": null,
+            "style": "LEETCODE",
+            "exampleTestcases": "{\"headers\":{\"SeasonStats\":[\"season_id\",\"team_id\",\"team_name\",\"matches_played\",\"wins\",\"draws\",\"losses\",\"goals_for\",\"goals_against\"]},\"rows\":{\"SeasonStats\":[[2021,1,\"Manchester City\",38,29,6,3,99,26],[2021,2,\"Liverpool\",38,28,8,2,94,26],[2021,3,\"Chelsea\",38,21,11,6,76,33],[2021,4,\"Tottenham\",38,22,5,11,69,40],[2021,5,\"Arsenal\",38,22,3,13,61,48],[2022,1,\"Manchester City\",38,28,5,5,94,33],[2022,2,\"Arsenal\",38,26,6,6,88,43],[2022,3,\"Manchester United\",38,23,6,9,58,43],[2022,4,\"Newcastle\",38,19,14,5,68,33],[2022,5,\"Liverpool\",38,19,10,9,75,47]]}}",
+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]premier-league-table-ranking.json

@@ -0,0 +1,61 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3554",
+            "questionFrontendId": "3246",
+            "categoryTitle": "Database",
+            "boundTopicId": 2869459,
+            "title": "Premier League Table Ranking",
+            "titleSlug": "premier-league-table-ranking",
+            "content": null,
+            "translatedTitle": "英超积分榜排名",
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Easy",
+            "likes": 0,
+            "dislikes": 0,
+            "isLiked": null,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": null,
+            "topicTags": [
+                {
+                    "name": "Database",
+                    "slug": "database",
+                    "translatedName": "数据库",
+                    "__typename": "TopicTagNode"
+                }
+            ],
+            "companyTagStats": null,
+            "codeSnippets": null,
+            "stats": "{\"totalAccepted\": \"32\", \"totalSubmission\": \"35\", \"totalAcceptedRaw\": 32, \"totalSubmissionRaw\": 35, \"acRate\": \"91.4%\"}",
+            "hints": [],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\":{\"TeamStats\":[\"team_id\",\"team_name\",\"matches_played\",\"wins\",\"draws\",\"losses\"]},\"rows\":{\"TeamStats\":[[1,\"Manchester City\",10,6,2,2],[2,\"Liverpool\",10,6,2,2],[3,\"Chelsea\",10,5,3,2],[4,\"Arsenal\",10,4,4,2],[5,\"Tottenham\",10,3,5,2]]}}",
+            "metaData": "{\"mysql\":[\"Create table if not exists TeamStats( team_id int, team_name varchar(100),matches_played int, wins int,draws int,losses int)\"],\"mssql\":[\"Create table  TeamStats( team_id int, team_name varchar(100),matches_played int, wins int,draws int,losses int)\"],\"oraclesql\":[\"Create table  TeamStats( team_id NUMBER, team_name varchar2(100),matches_played NUMBER, wins NUMBER,draws NUMBER,losses NUMBER)\"],\"database\":true,\"name\":\"calculate_team_standings\",\"postgresql\":[\"CREATE TABLE IF NOT EXISTS TeamStats (\\n    team_id INT,\\n    team_name VARCHAR(100),\\n    matches_played INT,\\n    wins INT,\\n    draws INT,\\n    losses INT\\n);\\n\"],\"pythondata\":[\"TeamStats = pd.DataFrame(data, columns=[\\\"team_id\\\", \\\"team_name\\\", \\\"matches_played\\\", \\\"wins\\\", \\\"draws\\\", \\\"losses\\\"]).astype({\\\"team_id\\\": \\\"int\\\", \\\"team_name\\\": \\\"string\\\", \\\"matches_played\\\": \\\"int\\\", \\\"wins\\\": \\\"int\\\", \\\"draws\\\": \\\"int\\\", \\\"losses\\\": \\\"int\\\"})\\n\"],\"database_schema\":{\"TeamStats\":{\"team_id\":\"INT\",\"team_name\":\"VARCHAR(100)\",\"matches_played\":\"INT\",\"wins\":\"INT\",\"draws\":\"INT\",\"losses\":\"INT\"}}}",
+            "judgerAvailable": true,
+            "judgeType": "large",
+            "mysqlSchemas": [
+                "Create table if not exists TeamStats( team_id int, team_name varchar(100),matches_played int, wins int,draws int,losses int)",
+                "Truncate table TeamStats",
+                "insert into TeamStats (team_id, team_name, matches_played, wins, draws, losses) values ('1', 'Manchester City', '10', '6', '2', '2')",
+                "insert into TeamStats (team_id, team_name, matches_played, wins, draws, losses) values ('2', 'Liverpool', '10', '6', '2', '2')",
+                "insert into TeamStats (team_id, team_name, matches_played, wins, draws, losses) values ('3', 'Chelsea', '10', '5', '3', '2')",
+                "insert into TeamStats (team_id, team_name, matches_played, wins, draws, losses) values ('4', 'Arsenal', '10', '4', '4', '2')",
+                "insert into TeamStats (team_id, team_name, matches_played, wins, draws, losses) values ('5', 'Tottenham', '10', '3', '5', '2')"
+            ],
+            "enableRunCode": true,
+            "envInfo": "{\"mysql\":[\"MySQL\",\"<p>\\u7248\\u672c\\uff1a<code>MySQL 8.0<\\/code><\\/p>\"],\"mssql\":[\"MS SQL Server\",\"<p>mssql server 2019.<\\/p>\"],\"oraclesql\":[\"Oracle\",\"<p>Oracle Sql 11.2.<\\/p>\"],\"pythondata\":[\"Pandas\",\"<p>Python 3.10 with Pandas 2.0.2 and NumPy 1.25.0<\\/p>\"],\"postgresql\":[\"PostgreSQL\",\"<p>PostgreSQL 16<\\/p>\"]}",
+            "book": null,
+            "isSubscribed": false,
+            "isDailyQuestion": false,
+            "dailyRecordStatus": null,
+            "editorType": "CKEDITOR",
+            "ugcQuestionId": null,
+            "style": "LEETCODE",
+            "exampleTestcases": "{\"headers\":{\"TeamStats\":[\"team_id\",\"team_name\",\"matches_played\",\"wins\",\"draws\",\"losses\"]},\"rows\":{\"TeamStats\":[[1,\"Manchester City\",10,6,2,2],[2,\"Liverpool\",10,6,2,2],[3,\"Chelsea\",10,5,3,2],[4,\"Arsenal\",10,4,4,2],[5,\"Tottenham\",10,3,5,2]]}}",
+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]second-day-verification.json

@@ -0,0 +1,64 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3481",
+            "questionFrontendId": "3172",
+            "categoryTitle": "Database",
+            "boundTopicId": 2798976,
+            "title": "Second Day Verification",
+            "titleSlug": "second-day-verification",
+            "content": null,
+            "translatedTitle": "第二天验证",
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Easy",
+            "likes": 0,
+            "dislikes": 0,
+            "isLiked": null,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": null,
+            "topicTags": [
+                {
+                    "name": "Database",
+                    "slug": "database",
+                    "translatedName": "数据库",
+                    "__typename": "TopicTagNode"
+                }
+            ],
+            "companyTagStats": null,
+            "codeSnippets": null,
+            "stats": "{\"totalAccepted\": \"28\", \"totalSubmission\": \"36\", \"totalAcceptedRaw\": 28, \"totalSubmissionRaw\": 36, \"acRate\": \"77.8%\"}",
+            "hints": [],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\":{\"emails\":[\"email_id\",\"user_id\",\"signup_date\"],\"texts\":[\"text_id\",\"email_id\",\"signup_action\",\"action_date\"]},\"rows\":{\"emails\":[[125,7771,\"2022-06-14 09:30:00\"],[433,1052,\"2022-07-09 08:15:00\"],[234,7005,\"2022-08-20 10:00:00\"]],\"texts\":[[1,125,\"Verified\",\"2022-06-15 08:30:00\"],[2,433,\"Not Verified\",\"2022-07-10 10:45:00\"],[4,234,\"Verified\",\"2022-08-21 09:30:00\"]]}}",
+            "metaData": "{\"mysql\":[\"CREATE TABLE If not exists emails (\\n    email_id INT,\\n    user_id INT,\\n    signup_date DATETIME\\n);\",\"CREATE TABLE If not exists texts (\\n    text_id INT,\\n    email_id INT,\\n    signup_action ENUM('Verified', 'Not Verified'),\\n    action_date DATETIME\\n);\"],\"mssql\":[\"CREATE TABLE emails (\\n    email_id INT,\\n    user_id INT,\\n    signup_date DATETIME\\n);\",\"CREATE TABLE texts (\\n    text_id INT,\\n    email_id INT,\\n    signup_action VARCHAR(12),\\n    action_date DATETIME\\n);\"],\"oraclesql\":[\"CREATE TABLE emails (\\n    email_id NUMBER,\\n    user_id NUMBER,\\n    signup_date DATE\\n)\\n\",\"CREATE TABLE texts (\\n    text_id NUMBER,\\n    email_id NUMBER,\\n    signup_action VARCHAR(32),\\n    action_date DATE\\n)\",\"ALTER SESSION SET nls_date_format='YYYY-MM-DD HH24:MI:SS'\"],\"database\":true,\"name\":\"find_second_day_signups\",\"postgresql\":[\"CREATE TABLE emails (\\n    email_id INTEGER,\\n    user_id INTEGER,\\n    signup_date DATE\\n);\\n\",\"CREATE TABLE texts (\\n    text_id INTEGER,\\n    email_id INTEGER,\\n    signup_action VARCHAR(32),\\n    action_date DATE\\n);\\n\",\"SELECT to_char(action_date, 'YYYY-MM-DD HH24:MI:SS') FROM texts;\\n\"],\"pythondata\":[\"emails = pd.DataFrame([], columns=['email_id', 'user_id', 'signup_date']).astype({\\n    'email_id': 'Int64',  # Nullable integer type\\n    'user_id': 'Int64',   # Nullable integer type\\n    'signup_date': 'datetime64[ns]'  # DateTime type for signup date\\n})\",\"texts = pd.DataFrame([], columns=['text_id', 'email_id', 'signup_action', 'action_date']).astype({\\n    'text_id': 'Int64',             # Nullable integer type for text_id\\n    'email_id': 'Int64',            # Nullable integer type for email_id\\n    'signup_action': 'object',      # Using 'object' to store string values for ENUM\\n    'action_date': 'datetime64[ns]' # DateTime type for action_date\\n})\"],\"database_schema\":{\"emails\":{\"email_id\":\"INT\",\"user_id\":\"INT\",\"signup_date\":\"DATETIME\"},\"texts\":{\"text_id\":\"INT\",\"email_id\":\"INT\",\"signup_action\":\"ENUM('Verified', 'Not Verified')\",\"action_date\":\"DATETIME\"}}}",
+            "judgerAvailable": true,
+            "judgeType": "large",
+            "mysqlSchemas": [
+                "CREATE TABLE If not exists emails (\n    email_id INT,\n    user_id INT,\n    signup_date DATETIME\n);",
+                "CREATE TABLE If not exists texts (\n    text_id INT,\n    email_id INT,\n    signup_action ENUM('Verified', 'Not Verified'),\n    action_date DATETIME\n);",
+                "Truncate table emails",
+                "insert into emails (email_id, user_id, signup_date) values ('125', '7771', '2022-06-14 09:30:00')",
+                "insert into emails (email_id, user_id, signup_date) values ('433', '1052', '2022-07-09 08:15:00')",
+                "insert into emails (email_id, user_id, signup_date) values ('234', '7005', '2022-08-20 10:00:00')",
+                "Truncate table texts",
+                "insert into texts (text_id, email_id, signup_action, action_date) values ('1', '125', 'Verified', '2022-06-15 08:30:00')",
+                "insert into texts (text_id, email_id, signup_action, action_date) values ('2', '433', 'Not Verified', '2022-07-10 10:45:00')",
+                "insert into texts (text_id, email_id, signup_action, action_date) values ('4', '234', 'Verified', '2022-08-21 09:30:00')"
+            ],
+            "enableRunCode": true,
+            "envInfo": "{\"mysql\":[\"MySQL\",\"<p>\\u7248\\u672c\\uff1a<code>MySQL 8.0<\\/code><\\/p>\"],\"mssql\":[\"MS SQL Server\",\"<p>mssql server 2019.<\\/p>\"],\"oraclesql\":[\"Oracle\",\"<p>Oracle Sql 11.2.<\\/p>\"],\"pythondata\":[\"Pandas\",\"<p>Python 3.10 with Pandas 2.0.2 and NumPy 1.25.0<\\/p>\"],\"postgresql\":[\"PostgreSQL\",\"<p>PostgreSQL 16<\\/p>\"]}",
+            "book": null,
+            "isSubscribed": false,
+            "isDailyQuestion": false,
+            "dailyRecordStatus": null,
+            "editorType": "CKEDITOR",
+            "ugcQuestionId": null,
+            "style": "LEETCODE",
+            "exampleTestcases": "{\"headers\":{\"emails\":[\"email_id\",\"user_id\",\"signup_date\"],\"texts\":[\"text_id\",\"email_id\",\"signup_action\",\"action_date\"]},\"rows\":{\"emails\":[[125,7771,\"2022-06-14 09:30:00\"],[433,1052,\"2022-07-09 08:15:00\"],[234,7005,\"2022-08-20 10:00:00\"]],\"texts\":[[1,125,\"Verified\",\"2022-06-15 08:30:00\"],[2,433,\"Not Verified\",\"2022-07-10 10:45:00\"],[4,234,\"Verified\",\"2022-08-21 09:30:00\"]]}}",
+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]second-highest-salary-ii.json

@@ -0,0 +1,66 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3660",
+            "questionFrontendId": "3338",
+            "categoryTitle": "Database",
+            "boundTopicId": 2968919,
+            "title": "Second Highest Salary II",
+            "titleSlug": "second-highest-salary-ii",
+            "content": null,
+            "translatedTitle": "第二高的薪水 II",
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Medium",
+            "likes": 0,
+            "dislikes": 0,
+            "isLiked": null,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": null,
+            "topicTags": [
+                {
+                    "name": "Database",
+                    "slug": "database",
+                    "translatedName": "数据库",
+                    "__typename": "TopicTagNode"
+                }
+            ],
+            "companyTagStats": null,
+            "codeSnippets": null,
+            "stats": "{\"totalAccepted\": \"60\", \"totalSubmission\": \"65\", \"totalAcceptedRaw\": 60, \"totalSubmissionRaw\": 65, \"acRate\": \"92.3%\"}",
+            "hints": [],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\":{\"employees\":[\"emp_id\",\"salary\",\"dept\"]},\"rows\":{\"employees\":[[1,70000,\"Sales\"],[2,80000,\"Sales\"],[3,80000,\"Sales\"],[4,90000,\"Sales\"],[5,55000,\"IT\"],[6,65000,\"IT\"],[7,65000,\"IT\"],[8,50000,\"Marketing\"],[9,55000,\"Marketing\"],[10,55000,\"HR\"]]}}",
+            "metaData": "{\"mysql\":[\"Create table if not exists Employees (\\n    emp_id int,\\n    salary int,\\n    dept varchar(50)\\n)\"],\"mssql\":[\"Create table Employees (\\n    emp_id int,\\n    salary int,\\n    dept varchar(50)\\n)\"],\"oraclesql\":[\"Create table Employees (\\n    emp_id number,\\n    salary number,\\n    dept varchar2(50)\\n)\"],\"database\":true,\"name\":\"find_second_highest_salary\",\"postgresql\":[\"CREATE TABLE IF NOT EXISTS Employees (\\n    emp_id INT,\\n    salary INT,\\n    dept VARCHAR(50)\\n);\\n\"],\"pythondata\":[\"employees = pd.DataFrame({\\n    'emp_id': pd.Series(dtype='int'),\\n    'salary': pd.Series(dtype='int'),\\n    'dept': pd.Series(dtype='string')\\n})\"],\"database_schema\":{\"Employees\":{\"emp_id\":\"INT\",\"salary\":\"INT\",\"dept\":\"VARCHAR(50)\"}}}",
+            "judgerAvailable": true,
+            "judgeType": "large",
+            "mysqlSchemas": [
+                "Create table if not exists Employees (\n    emp_id int,\n    salary int,\n    dept varchar(50)\n)",
+                "Truncate table employees",
+                "insert into employees (emp_id, salary, dept) values ('1', '70000', 'Sales')",
+                "insert into employees (emp_id, salary, dept) values ('2', '80000', 'Sales')",
+                "insert into employees (emp_id, salary, dept) values ('3', '80000', 'Sales')",
+                "insert into employees (emp_id, salary, dept) values ('4', '90000', 'Sales')",
+                "insert into employees (emp_id, salary, dept) values ('5', '55000', 'IT')",
+                "insert into employees (emp_id, salary, dept) values ('6', '65000', 'IT')",
+                "insert into employees (emp_id, salary, dept) values ('7', '65000', 'IT')",
+                "insert into employees (emp_id, salary, dept) values ('8', '50000', 'Marketing')",
+                "insert into employees (emp_id, salary, dept) values ('9', '55000', 'Marketing')",
+                "insert into employees (emp_id, salary, dept) values ('10', '55000', 'HR')"
+            ],
+            "enableRunCode": true,
+            "envInfo": "{\"mysql\":[\"MySQL\",\"<p>\\u7248\\u672c\\uff1a<code>MySQL 8.0<\\/code><\\/p>\"],\"mssql\":[\"MS SQL Server\",\"<p>mssql server 2019.<\\/p>\"],\"oraclesql\":[\"Oracle\",\"<p>Oracle Sql 11.2.<\\/p>\"],\"pythondata\":[\"Pandas\",\"<p>Python 3.10 with Pandas 2.0.2 and NumPy 1.25.0<\\/p>\"],\"postgresql\":[\"PostgreSQL\",\"<p>PostgreSQL 16<\\/p>\"]}",
+            "book": null,
+            "isSubscribed": false,
+            "isDailyQuestion": false,
+            "dailyRecordStatus": null,
+            "editorType": "CKEDITOR",
+            "ugcQuestionId": null,
+            "style": "LEETCODE",
+            "exampleTestcases": "{\"headers\":{\"employees\":[\"emp_id\",\"salary\",\"dept\"]},\"rows\":{\"employees\":[[1,70000,\"Sales\"],[2,80000,\"Sales\"],[3,80000,\"Sales\"],[4,90000,\"Sales\"],[5,55000,\"IT\"],[6,65000,\"IT\"],[7,65000,\"IT\"],[8,50000,\"Marketing\"],[9,55000,\"Marketing\"],[10,55000,\"HR\"]]}}",
+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]snaps-analysis.json

@@ -0,0 +1,62 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3340",
+            "questionFrontendId": "100239",
+            "categoryTitle": "Database",
+            "boundTopicId": 2653966,
+            "title": "Snaps Analysis",
+            "titleSlug": "snaps-analysis",
+            "content": null,
+            "translatedTitle": null,
+            "translatedContent": null,
+            "isPaidOnly": true,
+            "difficulty": "Medium",
+            "likes": 0,
+            "dislikes": 0,
+            "isLiked": null,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": null,
+            "topicTags": [],
+            "companyTagStats": null,
+            "codeSnippets": null,
+            "stats": "{\"totalAccepted\": \"20\", \"totalSubmission\": \"47\", \"totalAcceptedRaw\": 20, \"totalSubmissionRaw\": 47, \"acRate\": \"42.6%\"}",
+            "hints": [],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\":{\"Activities\":[\"activity_id\",\"user_id\",\"activity_type\",\"time_spent\"],\"Age\":[\"user_id\",\"age_bucket\"]},\"rows\":{\"Activities\":[[7274,123,\"open\",4.50],[2425,123,\"send\",3.50],[1413,456,\"send\",5.67],[2536,456,\"open\",3.00],[8564,456,\"send\",8.24],[5235,789,\"send\",6.24],[4251,123,\"open\",1.25],[1435,789,\"open\",5.25]],\"Age\":[[123,\"31-35\"],[789,\"21-25\"],[456,\"26-30\"]]}}",
+            "metaData": "{\"mysql\":[\"Create table if Not Exists Activities(activity_id int, user_id int, activity_type ENUM('send', 'open'), time_spent decimal(5,2))\",\"Create table if not Exists Age( user_id int, age_bucket ENUM('21-25','26-30','31-35'))\"],\"mssql\":[\"Create table Activities (activity_id int, user_id int, activity_type varchar(30)  NOT NULL CHECK (activity_type IN ('send','open')), time_spent decimal(5,2))\",\"Create table  Age( user_id int, age_bucket varchar(30) NOT NULL CHECK ( age_bucket IN ('21-25','26-30','31-35')))\"],\"oraclesql\":[\"Create table Activities (activity_id int, user_id int, activity_type varchar(30)  NOT NULL CHECK (activity_type IN ('send','open')), time_spent decimal(5,2))\",\"Create table  Age( user_id int, age_bucket varchar(30) NOT NULL CHECK ( age_bucket IN ('21-25','26-30','31-35')))\"],\"database\":true,\"name\":\"snap_analysis\",\"pythondata\":[\"Activities = pd.DataFrame([], columns=['activity_id', 'user_id', 'activity_type', 'time_spent']).astype({'activity_id':'Int64', 'user_id':'Int64', 'activity_type':'object', 'time_spent':'Float64'})\\n\",\"Age = pd.DataFrame([], columns=['user_id', 'age_bucket']).astype({'user_id':'Int64', 'age_bucket':'object'})\\n\"],\"postgresql\":[\"CREATE TABLE Activities (\\n    activity_id SERIAL PRIMARY KEY,\\n    user_id INT,\\n    activity_type VARCHAR(30) NOT NULL CHECK (activity_type IN ('send', 'open')),\\n    time_spent DECIMAL(5,2)\\n);\",\"CREATE TABLE Age (\\n    user_id INT,\\n    age_bucket VARCHAR(30) NOT NULL CHECK (age_bucket IN ('21-25', '26-30', '31-35'))\\n);\"],\"database_schema\":{\"Activities\":{\"activity_id\":\"INT\",\"user_id\":\"INT\",\"activity_type\":\"ENUM('send', 'open')\",\"time_spent\":\"DECIMAL(5, 2)\"},\"Age\":{\"user_id\":\"INT\",\"age_bucket\":\"ENUM('21-25', '26-30', '31-35')\"}}}",
+            "judgerAvailable": true,
+            "judgeType": "large",
+            "mysqlSchemas": [
+                "Create table if Not Exists Activities(activity_id int, user_id int, activity_type ENUM('send', 'open'), time_spent decimal(5,2))",
+                "Create table if not Exists Age( user_id int, age_bucket ENUM('21-25','26-30','31-35'))",
+                "Truncate table Activities",
+                "insert into Activities (activity_id, user_id, activity_type, time_spent) values ('7274', '123', 'open', '4.5')",
+                "insert into Activities (activity_id, user_id, activity_type, time_spent) values ('2425', '123', 'send', '3.5')",
+                "insert into Activities (activity_id, user_id, activity_type, time_spent) values ('1413', '456', 'send', '5.67')",
+                "insert into Activities (activity_id, user_id, activity_type, time_spent) values ('2536', '456', 'open', '3.0')",
+                "insert into Activities (activity_id, user_id, activity_type, time_spent) values ('8564', '456', 'send', '8.24')",
+                "insert into Activities (activity_id, user_id, activity_type, time_spent) values ('5235', '789', 'send', '6.24')",
+                "insert into Activities (activity_id, user_id, activity_type, time_spent) values ('4251', '123', 'open', '1.25')",
+                "insert into Activities (activity_id, user_id, activity_type, time_spent) values ('1435', '789', 'open', '5.25')",
+                "Truncate table Age",
+                "insert into Age (user_id, age_bucket) values ('123', '31-35')",
+                "insert into Age (user_id, age_bucket) values ('789', '21-25')",
+                "insert into Age (user_id, age_bucket) values ('456', '26-30')"
+            ],
+            "enableRunCode": true,
+            "envInfo": "{\"mysql\":[\"MySQL\",\"<p>\\u7248\\u672c\\uff1a<code>MySQL 8.0<\\/code><\\/p>\"],\"mssql\":[\"MS SQL Server\",\"<p>mssql server 2019.<\\/p>\"],\"oraclesql\":[\"Oracle\",\"<p>Oracle Sql 11.2.<\\/p>\"],\"pythondata\":[\"Pandas\",\"<p>Python 3.10 with Pandas 2.0.2 and NumPy 1.25.0<\\/p>\"],\"postgresql\":[\"PostgreSQL\",\"<p>PostgreSQL 16<\\/p>\"]}",
+            "book": null,
+            "isSubscribed": false,
+            "isDailyQuestion": false,
+            "dailyRecordStatus": null,
+            "editorType": "CKEDITOR",
+            "ugcQuestionId": null,
+            "style": "LEETCODE",
+            "exampleTestcases": "{\"headers\":{\"Activities\":[\"activity_id\",\"user_id\",\"activity_type\",\"time_spent\"],\"Age\":[\"user_id\",\"age_bucket\"]},\"rows\":{\"Activities\":[[7274,123,\"open\",4.50],[2425,123,\"send\",3.50],[1413,456,\"send\",5.67],[2536,456,\"open\",3.00],[8564,456,\"send\",8.24],[5235,789,\"send\",6.24],[4251,123,\"open\",1.25],[1435,789,\"open\",5.25]],\"Age\":[[123,\"31-35\"],[789,\"21-25\"],[456,\"26-30\"]]}}",
+            "__typename": "QuestionNode"
+        }
+    }
+}