update

README.md

@@ -1,6 +1,6 @@
 # 力扣题库（完整版）
 
-> 最后更新日期： **2024.07.16**
+> 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
 
@@ -16,6 +16,14 @@
 
 
 
+### 安装依赖
+
+```bash
+pip install -r requirements.txt
+```
+
+
+
 ### 特别注意！
 
 #### 版权相关

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-product-final-price.json

@@ -0,0 +1,64 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3609",
+            "questionFrontendId": "3293",
+            "categoryTitle": "Database",
+            "boundTopicId": 2920566,
+            "title": "Calculate Product Final Price",
+            "titleSlug": "calculate-product-final-price",
+            "content": null,
+            "translatedTitle": "计算产品最终价格",
+            "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\": \"21\", \"totalSubmission\": \"24\", \"totalAcceptedRaw\": 21, \"totalSubmissionRaw\": 24, \"acRate\": \"87.5%\"}",
+            "hints": [],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\":{\"Products\":[\"product_id\",\"category\",\"price\"],\"Discounts\":[\"category\",\"discount\"]},\"rows\":{\"Products\":[[1,\"Electronics\",1000],[2,\"Clothing\",50],[3,\"Electronics\",1200],[4,\"Home\",500]],\"Discounts\":[[\"Electronics\",10],[\"Clothing\",20]]}}",
+            "metaData": "{\"mysql\":[\"Create table if not exists Products (product_id int, category varchar(50), price int)\",\"Create table if not exists Discounts(category varchar(50), discount int)\"],\"mssql\":[\"Create table Products (product_id int, category varchar(50), price int)\",\"Create table Discounts(category varchar(50), discount int)\"],\"oraclesql\":[\"Create table Products (product_id Number, category varchar2(50), price Number)\",\"Create table Discounts(category varchar2(50), discount Number)\"],\"database\":true,\"name\":\"calculate_final_prices\",\"postgresql\":[\"CREATE TABLE IF NOT EXISTS Products (\\n    product_id INT,\\n    category VARCHAR(50),\\n    price INT\\n);\\n\",\"CREATE TABLE IF NOT EXISTS Discounts (\\n    category VARCHAR(50),\\n    discount INT\\n);\\n\"],\"pythondata\":[\"Products = pd.DataFrame(columns=['product_id', 'category', 'price'], dtype=int)\\n\",\"Discounts = pd.DataFrame({'category': pd.Series(dtype='str'), 'discount': pd.Series(dtype='int')})\\n\"],\"database_schema\":{\"Products\":{\"product_id\":\"INT\",\"category\":\"VARCHAR(50)\",\"price\":\"INT\"},\"Discounts\":{\"category\":\"VARCHAR(50)\",\"discount\":\"INT\"}}}",
+            "judgerAvailable": true,
+            "judgeType": "large",
+            "mysqlSchemas": [
+                "Create table if not exists Products (product_id int, category varchar(50), price int)",
+                "Create table if not exists Discounts(category varchar(50), discount int)",
+                "Truncate table Products",
+                "insert into Products (product_id, category, price) values ('1', 'Electronics', '1000')",
+                "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",
+                "insert into Discounts (category, discount) values ('Electronics', '10')",
+                "insert into Discounts (category, discount) values ('Clothing', '20')"
+            ],
+            "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\":{\"Products\":[\"product_id\",\"category\",\"price\"],\"Discounts\":[\"category\",\"discount\"]},\"rows\":{\"Products\":[[1,\"Electronics\",1000],[2,\"Clothing\",50],[3,\"Electronics\",1200],[4,\"Home\",500]],\"Discounts\":[[\"Electronics\",10],[\"Clothing\",20]]}}",
+            "__typename": "QuestionNode"
+        }
+    }
+}

leetcode-cn/originData/[no content]ceo-subordinate-hierarchy.json

@@ -0,0 +1,64 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3549",
+            "questionFrontendId": "3236",
+            "categoryTitle": "Database",
+            "boundTopicId": 2861886,
+            "title": "CEO Subordinate Hierarchy",
+            "titleSlug": "ceo-subordinate-hierarchy",
+            "content": null,
+            "translatedTitle": "首席执行官下属层级",
+            "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\": \"45\", \"totalSubmission\": \"57\", \"totalAcceptedRaw\": 45, \"totalSubmissionRaw\": 57, \"acRate\": \"78.9%\"}",
+            "hints": [],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\":{\"Employees\":[\"employee_id\",\"employee_name\",\"manager_id\",\"salary\"]},\"rows\":{\"Employees\":[[1,\"Alice\",null,150000],[2,\"Bob\",1,120000],[3,\"Charlie\",1,110000],[4,\"David\",2,105000],[5,\"Eve\",2,100000],[6,\"Frank\",3,95000],[7,\"Grace\",3,98000],[8,\"Helen\",5,90000]]}}",
+            "metaData": "{\"mysql\":[\"Create table if not exists employees(employee_id int, employee_name varchar(100), manager_id int, salary int)\"],\"mssql\":[\"Create table employees(employee_id int, employee_name varchar(100), manager_id int, salary int)\"],\"oraclesql\":[\"Create table employees(employee_id NUMBER, employee_name varchar2(100), manager_id NUMBER, salary NUMBER)\"],\"database\":true,\"name\":\"find_subordinates\",\"manual\":false,\"postgresql\":[\"CREATE TABLE IF NOT EXISTS employees (\\n    employee_id INT,\\n    employee_name VARCHAR(100),\\n    manager_id INT,\\n    salary INT\\n);\\n\"],\"pythondata\":[\"Employees = pd.DataFrame(columns=['employee_id', 'employee_name', 'manager_id', 'salary']).astype({\\n    'employee_id': pd.Int64Dtype(),\\n    'employee_name': 'str',\\n    'manager_id': pd.Int64Dtype(),\\n    'salary': pd.Int64Dtype()\\n})\"],\"database_schema\":{\"employees\":{\"employee_id\":\"INT\",\"employee_name\":\"VARCHAR(100)\",\"manager_id\":\"INT\",\"salary\":\"INT\"}}}",
+            "judgerAvailable": true,
+            "judgeType": "large",
+            "mysqlSchemas": [
+                "Create table if not exists employees(employee_id int, employee_name varchar(100), manager_id int, salary int)",
+                "Truncate table Employees",
+                "insert into Employees (employee_id, employee_name, manager_id, salary) values ('1', 'Alice', 'None', '150000')",
+                "insert into Employees (employee_id, employee_name, manager_id, salary) values ('2', 'Bob', '1', '120000')",
+                "insert into Employees (employee_id, employee_name, manager_id, salary) values ('3', 'Charlie', '1', '110000')",
+                "insert into Employees (employee_id, employee_name, manager_id, salary) values ('4', 'David', '2', '105000')",
+                "insert into Employees (employee_id, employee_name, manager_id, salary) values ('5', 'Eve', '2', '100000')",
+                "insert into Employees (employee_id, employee_name, manager_id, salary) values ('6', 'Frank', '3', '95000')",
+                "insert into Employees (employee_id, employee_name, manager_id, salary) values ('7', 'Grace', '3', '98000')",
+                "insert into Employees (employee_id, employee_name, manager_id, salary) values ('8', 'Helen', '5', '90000')"
+            ],
+            "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\":[\"employee_id\",\"employee_name\",\"manager_id\",\"salary\"]},\"rows\":{\"Employees\":[[1,\"Alice\",null,150000],[2,\"Bob\",1,120000],[3,\"Charlie\",1,110000],[4,\"David\",2,105000],[5,\"Eve\",2,100000],[6,\"Frank\",3,95000],[7,\"Grace\",3,98000],[8,\"Helen\",5,90000]]}}",
+            "__typename": "QuestionNode"
+        }
+    }
+}

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

@@ -0,0 +1,66 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3539",
+            "questionFrontendId": "3230",
+            "categoryTitle": "Database",
+            "boundTopicId": 2852426,
+            "title": "Customer Purchasing Behavior Analysis",
+            "titleSlug": "customer-purchasing-behavior-analysis",
+            "content": null,
+            "translatedTitle": "客户购买行为分析",
+            "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\": \"55\", \"totalSubmission\": \"162\", \"totalAcceptedRaw\": 55, \"totalSubmissionRaw\": 162, \"acRate\": \"34.0%\"}",
+            "hints": [],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\":{\"Transactions\":[\"transaction_id\",\"customer_id\",\"product_id\",\"transaction_date\",\"amount\"],\"Products\":[\"product_id\",\"category\",\"price\"]},\"rows\":{\"Transactions\":[[1,101,1,\"2023-01-01\",100.00],[2,101,2,\"2023-01-15\",150.00],[3,102,1,\"2023-01-01\",100.00],[4,102,3,\"2023-01-22\",200.00],[5,101,3,\"2023-02-10\",200.00]],\"Products\":[[1,\"A\",100.00],[2,\"B\",150.00],[3,\"C\",200.00]]}}",
+            "metaData": "{\"mysql\":[\"CREATE TABLE if not exists Transactions (\\n    transaction_id INT,\\n    customer_id INT,\\n    product_id INT,\\n    transaction_date DATE,\\n    amount DECIMAL(10, 2)\\n)\",\"CREATE TABLE if not exists Products (\\n    product_id INT ,\\n    category VARCHAR(255),\\n    price DECIMAL(10, 2)\\n)\\n\"],\"mssql\":[\"CREATE TABLE Transactions (\\n    transaction_id INT ,\\n    customer_id INT,\\n    product_id INT,\\n    transaction_date DATE,\\n    amount DECIMAL(10, 2)\\n)\\n\",\"\\nCREATE TABLE Products (\\n    product_id INT ,\\n    category VARCHAR(255),\\n    price DECIMAL(10, 2)\\n)\"],\"oraclesql\":[\"CREATE TABLE Transactions (\\n    transaction_id NUMBER ,\\n    customer_id NUMBER,\\n    product_id NUMBER,\\n    transaction_date DATE,\\n    amount NUMBER(10, 2)\\n)\",\"CREATE TABLE Products (\\n    product_id NUMBER ,\\n    category VARCHAR2(255),\\n    price NUMBER(10, 2)\\n)\",\"ALTER SESSION SET nls_date_format='YYYY-MM-DD'\"],\"database\":true,\"name\":\"analyze_customer_behavior\",\"postgresql\":[\"CREATE TABLE IF NOT EXISTS Transactions (\\n    transaction_id INTEGER,\\n    customer_id INTEGER,\\n    product_id INTEGER,\\n    transaction_date DATE,\\n    amount NUMERIC(10, 2)\\n);\\n\",\"CREATE TABLE IF NOT EXISTS Products (\\n    product_id INTEGER,\\n    category VARCHAR(255),\\n    price NUMERIC(10, 2)\\n);\\n\"],\"pythondata\":[\"Transactions = pd.DataFrame(columns=['transaction_id', 'customer_id', 'product_id', 'transaction_date', 'amount']).astype({'transaction_id': 'int', 'customer_id': 'int', 'product_id': 'int', 'transaction_date': 'datetime64[ns]', 'amount': 'float'})\",\"Products = pd.DataFrame(columns=['product_id', 'category', 'price']).astype({'product_id': 'int', 'category': 'str', 'price': 'float'})\"],\"database_schema\":{\"Transactions\":{\"transaction_id\":\"INT\",\"customer_id\":\"INT\",\"product_id\":\"INT\",\"transaction_date\":\"DATE\",\"amount\":\"DECIMAL(10, 2)\"},\"Products\":{\"product_id\":\"INT\",\"category\":\"VARCHAR(255)\",\"price\":\"DECIMAL(10, 2)\"}}}",
+            "judgerAvailable": true,
+            "judgeType": "large",
+            "mysqlSchemas": [
+                "CREATE TABLE if not exists Transactions (\n    transaction_id INT,\n    customer_id INT,\n    product_id INT,\n    transaction_date DATE,\n    amount DECIMAL(10, 2)\n)",
+                "CREATE TABLE if not exists Products (\n    product_id INT ,\n    category VARCHAR(255),\n    price DECIMAL(10, 2)\n)\n",
+                "Truncate table Transactions",
+                "insert into Transactions (transaction_id, customer_id, product_id, transaction_date, amount) values ('1', '101', '1', '2023-01-01', '100.0')",
+                "insert into Transactions (transaction_id, customer_id, product_id, transaction_date, amount) values ('2', '101', '2', '2023-01-15', '150.0')",
+                "insert into Transactions (transaction_id, customer_id, product_id, transaction_date, amount) values ('3', '102', '1', '2023-01-01', '100.0')",
+                "insert into Transactions (transaction_id, customer_id, product_id, transaction_date, amount) values ('4', '102', '3', '2023-01-22', '200.0')",
+                "insert into Transactions (transaction_id, customer_id, product_id, transaction_date, amount) values ('5', '101', '3', '2023-02-10', '200.0')",
+                "Truncate table Products",
+                "insert into Products (product_id, category, price) values ('1', 'A', '100.0')",
+                "insert into Products (product_id, category, price) values ('2', 'B', '150.0')",
+                "insert into Products (product_id, category, price) values ('3', 'C', '200.0')"
+            ],
+            "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,
+            "isSubscribed": false,
+            "isDailyQuestion": false,
+            "dailyRecordStatus": null,
+            "editorType": "CKEDITOR",
+            "ugcQuestionId": null,
+            "style": "LEETCODE",
+            "exampleTestcases": "{\"headers\":{\"Transactions\":[\"transaction_id\",\"customer_id\",\"product_id\",\"transaction_date\",\"amount\"],\"Products\":[\"product_id\",\"category\",\"price\"]},\"rows\":{\"Transactions\":[[1,101,1,\"2023-01-01\",100.00],[2,101,2,\"2023-01-15\",150.00],[3,102,1,\"2023-01-01\",100.00],[4,102,3,\"2023-01-22\",200.00],[5,101,3,\"2023-02-10\",200.00]],\"Products\":[[1,\"A\",100.00],[2,\"B\",150.00],[3,\"C\",200.00]]}}",
+            "__typename": "QuestionNode"
+        }
+    }
+}

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

@@ -0,0 +1,76 @@
+{
+    "data": {
+        "question": {
+            "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,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": null,
+            "topicTags": [
+                {
+                    "name": "Database",
+                    "slug": "database",
+                    "translatedName": "数据库",
+                    "__typename": "TopicTagNode"
+                }
+            ],
+            "companyTagStats": null,
+            "codeSnippets": null,
+            "stats": "{\"totalAccepted\": \"70\", \"totalSubmission\": \"132\", \"totalAcceptedRaw\": 70, \"totalSubmissionRaw\": 132, \"acRate\": \"53.0%\"}",
+            "hints": [],
+            "solution": null,
+            "status": null,
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+        }
+    }
+}

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

@@ -0,0 +1,71 @@
+{
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+        "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,
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+            "hints": [],
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+                "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')"
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+}

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

@@ -0,0 +1,62 @@
+{
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+            "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,
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+                "insert into EmployeeShifts (employee_id, start_time, end_time) values ('1', '2023-10-01 16:00:00', '2023-10-02 00:00:00')",
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+                "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')"
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+}

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

@@ -0,0 +1,59 @@
+{
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+            "questionId": "3574",
+            "questionFrontendId": "3262",
+            "categoryTitle": "Database",
+            "boundTopicId": 2887671,
+            "title": "Find Overlapping Shifts",
+            "titleSlug": "find-overlapping-shifts",
+            "content": null,
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+            "difficulty": "Medium",
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+                "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')",
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+                "insert into EmployeeShifts (employee_id, start_time, end_time) values ('4', '08:00:00', '10:00:00')",
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+}

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

@@ -0,0 +1,72 @@
+{
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+            "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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+            "__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",
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+            "codeSnippets": null,
+            "stats": "{\"totalAccepted\": \"32\", \"totalSubmission\": \"34\", \"totalAcceptedRaw\": 32, \"totalSubmissionRaw\": 34, \"acRate\": \"94.1%\"}",
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+            "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\"]]}}",
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+            "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')"
+            ],
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+            "__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",
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+            "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\"}}}",
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+            "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')",
+                "insert into Passes (pass_from, time_stamp, pass_to) values ('1', '00:25', '2')",
+                "insert into Passes (pass_from, time_stamp, pass_to) values ('2', '00:27', '3')"
+            ],
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+            "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>\"]}",
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+            "__typename": "QuestionNode"
+        }
+    }
+}

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

@@ -0,0 +1,66 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3564",
+            "questionFrontendId": "3252",
+            "categoryTitle": "Database",
+            "boundTopicId": 2876919,
+            "title": "Premier League Table Ranking II",
+            "titleSlug": "premier-league-table-ranking-ii",
+            "content": null,
+            "translatedTitle": "英超积分榜排名 II",
+            "translatedContent": null,
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+            "difficulty": "Medium",
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+            "hints": [],
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+            "sampleTestCase": "{\"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]]}}",
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+            "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', 'Chelsea', '22', '13', '2', '7')",
+                "insert into TeamStats (team_id, team_name, matches_played, wins, draws, losses) values ('2', 'Nottingham Forest', '27', '6', '6', '15')",
+                "insert into TeamStats (team_id, team_name, matches_played, wins, draws, losses) values ('3', 'Liverpool', '17', '1', '8', '8')",
+                "insert into TeamStats (team_id, team_name, matches_played, wins, draws, losses) values ('4', 'Aston Villa', '20', '1', '6', '13')",
+                "insert into TeamStats (team_id, team_name, matches_played, wins, draws, losses) values ('5', 'Fulham', '31', '18', '1', '12')",
+                "insert into TeamStats (team_id, team_name, matches_played, wins, draws, losses) values ('6', 'Burnley', '26', '6', '9', '11')",
+                "insert into TeamStats (team_id, team_name, matches_played, wins, draws, losses) values ('7', 'Newcastle United', '33', '11', '10', '12')",
+                "insert into TeamStats (team_id, team_name, matches_played, wins, draws, losses) values ('8', 'Sheffield United', '20', '18', '2', '0')",
+                "insert into TeamStats (team_id, team_name, matches_played, wins, draws, losses) values ('9', 'Luton Town', '5', '4', '0', '1')",
+                "insert into TeamStats (team_id, team_name, matches_played, wins, draws, losses) values ('10', 'Everton', '14', '2', '6', '6')"
+            ],
+            "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]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"
+                }
+            ],
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+            "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>\"]}",
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+            "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": [
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+                    "name": "Database",
+                    "slug": "database",
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+                }
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+            "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]]}}",
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+            "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>\"]}",
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+            "editorType": "CKEDITOR",
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+            "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-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\"]]}}",
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+            "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>\"]}",
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+    }
+}

leetcode-cn/originData/[no content]team-dominance-by-pass-success.json

@@ -0,0 +1,72 @@
+{
+    "data": {
+        "question": {
+            "questionId": "3719",
+            "questionFrontendId": "3384",
+            "categoryTitle": "Database",
+            "boundTopicId": 3014613,
+            "title": "Team Dominance by Pass Success",
+            "titleSlug": "team-dominance-by-pass-success",
+            "content": null,
+            "translatedTitle": "球队传球成功的优势得分",
+            "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\": \"44\", \"totalSubmission\": \"52\", \"totalAcceptedRaw\": 44, \"totalSubmissionRaw\": 52, \"acRate\": \"84.6%\"}",
+            "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,\"Chelsea\"],[5,\"Chelsea\"],[6,\"Chelsea\"]],\"Passes\":[[1,\"00:15\",2],[2,\"00:45\",3],[3,\"01:15\",1],[4,\"00:30\",1],[2,\"46:00\",3],[3,\"46:15\",4],[1,\"46:45\",2],[5,\"46:30\",6]]}}",
+            "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_team_dominance\",\"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\"],\"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\"],\"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",
+            "mysqlSchemas": [
+                "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', 'Chelsea')",
+                "insert into Teams (player_id, team_name) values ('5', 'Chelsea')",
+                "insert into Teams (player_id, team_name) values ('6', 'Chelsea')",
+                "Truncate table Passes",
+                "insert into Passes (pass_from, time_stamp, pass_to) values ('1', '00:15', '2')",
+                "insert into Passes (pass_from, time_stamp, pass_to) values ('2', '00:45', '3')",
+                "insert into Passes (pass_from, time_stamp, pass_to) values ('3', '01:15', '1')",
+                "insert into Passes (pass_from, time_stamp, pass_to) values ('4', '00:30', '1')",
+                "insert into Passes (pass_from, time_stamp, pass_to) values ('2', '46:00', '3')",
+                "insert into Passes (pass_from, time_stamp, pass_to) values ('3', '46:15', '4')",
+                "insert into Passes (pass_from, time_stamp, pass_to) values ('1', '46:45', '2')",
+                "insert into Passes (pass_from, time_stamp, pass_to) values ('5', '46:30', '6')"
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+    }
+}