What is a Heap? A Detailed Explanation of Basic Operations on Heaps in Data Structures
A heap is a special structure based on a complete binary tree, stored in an array, and satisfies the properties of a max heap (parent node value ≥ child node) or a min heap (parent node value ≤ child node). It can efficiently retrieve the maximum or minimum value and is widely used in algorithms. The array indices map parent-child relationships: left child is at 2i+1, right child at 2i+2, and parent is at (i-1)//2. A max heap has the largest root (e.g., [9,5,7,3,6,2,4]), while a min heap has the smallest root (e.g., [3,6,5,9,7,2,4]). Core operations include insertion (appending new element to the end and adjusting upward to satisfy heap property), deletion (swapping root with last element and adjusting downward), heap construction (adjusting from the last non-leaf node downward), and retrieving the root (directly accessing the root node). It is applied in priority queues, heap sort, and Top K problems. The efficient structure and operations of heaps are crucial for understanding algorithms, and beginners can start with array simulation to master them.
Read MoreBinary Search: How Much Faster Than Linear Search? Search Techniques in Data Structures
This article introduces search algorithms in computers, focusing on linear search and binary search. Linear search (sequential search) is a basic method that checks data one by one from the beginning to the end. It has a time complexity of O(n) and is suitable for scenarios with small data volumes or unordered data. In the worst case, it needs to traverse all data. Binary search, on the other hand, requires an ordered array. Its core is to eliminate half of the data each time, with a time complexity of O(log n). When the data volume is large, it is far more efficient than linear search (e.g., for n=1 million, binary search only needs 20 times, while linear search needs 1 million times). The two have different applicable scenarios: binary search is suitable for ordered, large - data - volume, and frequently searched scenarios; linear search is suitable for unordered, small - data - volume, or dynamically changing data. In summary, binary search significantly improves efficiency through "half - by - half elimination" and is an efficient choice for large - volume ordered data. Linear search is more flexible in scenarios with small data volumes or unordered data.
Read MoreBubble Sort: The Simplest Sorting Algorithm, Easy to Learn in 3 Minutes
Bubble Sort is a basic sorting algorithm that simulates the "bubble rising" process to gradually "bubble up" the largest element to the end of the array for sorting. The core idea is to repeatedly compare adjacent elements, swapping them if the preceding element is larger than the following one. After each round of traversal, the largest element is placed in its correct position. If no swaps occur in a round, the algorithm can terminate early. Taking the array [5, 3, 8, 4, 2] as an example: In the first round, adjacent elements are compared, and the largest number 8 "bubbles up" to the end, resulting in the array [3, 5, 4, 2, 8]. In the second round, the first four elements are compared, and the second largest number 5 moves to the second-to-last position, changing the array to [3, 4, 2, 5, 8]. In the third round, the first three elements are compared, and the third largest number 4 moves to the third-to-last position, resulting in [3, 2, 4, 5, 8]. In the fourth round, the first two elements are compared, and the fourth largest number 3 moves to the fourth-to-last position, resulting in [2, 3, 4, 5, 8]. Finally, no swaps occur in the last round, and the sorting is complete. A key optimization is early termination to avoid unnecessary traversals. The worst-case and average time complexity is O(n²), with a space complexity of O(1). Despite its low efficiency, Bubble Sort is simple and easy to understand, making it a foundational example for sorting algorithm beginners.
Read MoreHow Does a Hash Table Store Data? Hash Functions Simplify Lookup
The article uses the analogy of searching for a book to introduce the problem: sequential search of data (such as arrays) is inefficient, while a hash table is an efficient storage tool. The core of a hash table is the hash function, which maps data to "buckets" (array positions) to enable fast access and storage. The hash function converts data into a hash value (bucket number), for example, taking the last two digits of a student ID to get the hash value. When storing, the hash value is first calculated to locate the bucket. If multiple data items have the same hash value (collision), it can be resolved using the linked list method (chaining within the bucket) or open addressing (finding the next empty bucket). When searching, the hash value is directly calculated to locate the bucket, and then the search is performed within the bucket, eliminating the need to traverse all data, resulting in extremely fast speed. Hash tables are widely used (such as in address books and caches), with their core being the use of a hash function to transform the search from "scanning through everything" to "direct access".
Read MoreDrawing Binary Trees Step by Step: The First Lesson in Data Structure Fundamentals
A binary tree is a fundamental data structure where each node has at most two child nodes (left and right), and nodes with no descendants are called leaves. Core terms include: root node (the topmost starting point), leaf node (node with no children), child node (a node on the next level below its parent), and left/right subtrees (the left/right children and their descendants of a node). Construction starts from the root node, with child nodes added incrementally. Each node can have at most two children, and child positions are ordered (left vs. right). A binary tree must satisfy: each node has ≤2 children, and child positions are clearly defined (left or right). Traversal methods include pre-order (root → left → right), in-order (left → root → right), and post-order (left → right → root). Drawing the tree is crucial for understanding core relationships, as it intuitively visualizes node connections and forms the foundation for complex structures (e.g., heaps, red-black trees) and algorithms (sorting, searching).
Read MoreThe Art of Queuing: Applications of Queues in Data Structures
This article introduces the queue data structure. In daily life, queuing (such as getting meals in a cafeteria) embodies the "first-come, first-served" principle, which is the prototype of a queue. A queue is a data structure that follows the "First-In-First-Out" (FIFO) principle. Its core operations include enqueue (adding an element to the tail of the queue) and dequeue (removing the earliest added element from the front of the queue). Additionally, operations like viewing the front element and checking if the queue is empty are also supported. Queues differ from stacks (which follow the "Last-In-First-Out" (LIFO) principle); the former adheres to "first-come, first-served," while the latter reflects "last-come, first-served." Queues have wide applications: In computer task scheduling, systems process multiple tasks in a queue (e.g., programs opened earlier receive CPU time first); the BFS (Breadth-First Search) algorithm uses a queue to expand nodes level by level, enabling shortest path searches in mazes; during e-commerce promotions, queues buffer user requests to prevent system overload; in multi-threading, producers add data to a queue, and consumers process it in order, facilitating asynchronous collaboration. Learning queues helps solve problems such as processing data in sequence and avoiding resource conflicts, making it a fundamental tool in programming and algorithms. Understanding the "First-In-First-Out" principle contributes to efficiently solving practical problems.
Read MoreStacks in Daily Life: Why Are Stacks the First Choice for Data Structure Beginners?
The article introduces "stack" through daily scenarios such as stacking plates and browser backtracking, with its core feature being "Last-In-First-Out" (LIFO). A stack is a container that can only be operated on from the top, with core operations being "Push" (pushing onto the stack) and "Pop" (popping from the stack). As a first choice for data structure introduction, the stack has a simple logic (only the LIFO rule), clear operations (only two basic operations), extensive applications (scenarios like bracket matching, browser backtracking, recursion, etc.), and can be easily implemented using arrays or linked lists. It serves as a foundation for learning subsequent structures like queues and trees, helps establish clear programming thinking, and is a "stepping stone" for understanding data structures.
Read MoreLinked List vs Array: Key Differences for Beginners in Data Structures
Arrays and linked lists are among the most fundamental data structures in programming. Understanding their differences and applicable scenarios is crucial for writing efficient code. **Array Features**: - Stores elements in contiguous memory locations, allowing random access via index (O(1) time complexity). - Requires a fixed initial size; inserting/deleting elements in the middle demands shifting elements (O(n) time complexity). - Ideal for scenarios with known fixed sizes and high-frequency random access (e.g., grade sheets, map coordinates). **Linked List Features**: - Elements are scattered in memory, with each node containing data and a pointer/reference. - No random access (requires traversing from the head, O(n) time complexity). - Offers flexible dynamic expansion; inserting/deleting elements in the middle only requires modifying pointers (O(1) time complexity). - Suitable for dynamic data and high-frequency insertion/deletion scenarios (e.g., queues, linked hash tables). **Core Differences**: - Arrays rely on contiguous memory but have restricted operations. - Linked lists use scattered storage but suffer from slower access speeds. - Key distinctions lie in storage method, access speed, and insertion/deletion efficiency. Selection should be based on specific requirements. Mastering their underlying logic enables more efficient code implementation.
Read MoreLearning Data Structures from Scratch: What Exactly Is an Array?
An array is an ordered collection of data elements of the same type, accessed via indices (starting from 0), with elements stored contiguously. It is used to efficiently manage a large amount of homogeneous data. For example, class scores can be represented by the array `scores = [90, 85, 95, 78, 92]` instead of multiple individual variables, facilitating overall operations. In Python, array declaration and initialization can be done with `scores = [90, 85, 95, 78, 92]` or `[0] * 5` (declaring an array of length 5). Elements are accessed using `scores[index]`, and it's important to note the index range (0 to length-1), as out-of-bounds indices will cause errors. Basic operations include traversal with loops (`for score in scores: print(score)`), while insertion and deletion require shifting subsequent elements (with a time complexity of O(n)). Core characteristics of arrays are: same element type, 0-based indexing, and contiguous storage. Their advantages include fast access speed (O(1)), but disadvantages are lower efficiency for insertions/deletions and fixed size. As a foundational data structure, understanding the core idea of arrays—"indexed access and contiguous storage"—is crucial for learning more complex structures like linked lists and hash tables, making arrays a fundamental tool for data management.
Read MoreMySQL WHERE Clause: A Beginner's Guide to Mastering Basic Data Filtering Methods
This article introduces the usage of the WHERE clause in MySQL, which is part of the SELECT statement used to filter records that meet specific conditions. The core content includes: 1. **Basic Conditions**: Equality (=) and inequality (!= or <>) apply to numeric values and strings (strings must be enclosed in single quotes). 2. **Range Conditions**: >, <, >=, <=, or the more concise BETWEEN...AND... (includes both endpoints). 3. **Logical Combinations**: AND (all conditions met), OR (any condition met), NOT (negation). Note that AND has higher precedence than OR; parentheses can be used for complex logic. 4. **Fuzzy Query**: LIKE combined with % (any characters) or _ (single character), e.g., %张% matches names containing "Zhang". 5. **Null Value Handling**: Use IS NULL / IS NOT NULL to check for null values; = or != cannot be used. Notes: Strings must be enclosed in single quotes, BETWEEN includes endpoints, and avoid direct null judgment with = or !=. The WHERE clause is the core of data filtering; mastering condition types and special handling allows flexible extraction of target data.
Read MoreMySQL Foreign Key Constraints: How to Avoid Data Errors in Table Relationships?
MySQL foreign key constraints are used to ensure the integrity of multi - table associated data, avoiding invalid references (such as non - existent user IDs in orders) and data inconsistencies (such as residual orders after a user is deleted). A foreign key constraint is a table - level constraint, which requires that the foreign key field of the child table references the primary key or unique key of the parent table. When creating, the parent table must be created first, and then in the child table, the association is specified using `FOREIGN KEY (foreign key field) REFERENCES parent_table(primary key field)`. The behavior can be set through `ON DELETE/ON UPDATE`, such as `CASCADE` (cascade operation), `SET NULL` (set to NULL), or `RESTRICT` (operation is prohibited by default). The functions of foreign key constraints are: preventing incorrect references, maintaining data consistency, and clarifying table relationships. Precautions for use: The referenced field in the parent table must be a primary key/unique key, the data types of the foreign key and the parent table field must be consistent, and when deleting records in the parent table, the child table associations must be processed first. Although it may affect performance, it can be ignored for small and medium - sized projects. Foreign key constraints are a core tool for multi - table association. It is recommended to use them first when designing related tables. Mastering the syntax and behavior settings can ensure data reliability.
Read MoreMySQL Character Sets and Collations: Essential Basic Configurations for Beginners
This article introduces MySQL character sets and collations. A character set is an encoding rule for storing characters (e.g., utf8mb4 supports full Unicode), while a collation determines how characters are compared and sorted (e.g., utf8mb4_general_ci is case-insensitive). Improper configuration can lead to garbled text, incorrect sorting (e.g., abnormal sorting of "张三"), or compatibility issues (e.g., old utf8 not supporting emojis). Configuration hierarchy priority: Column-level > Table-level > Database-level > Server-level, with default following server-level configuration. Commands like SHOW VARIABLES (for character set/collation), SHOW CREATE DATABASE/ TABLE are used to check configurations. Recommended configurations: Prioritize utf8mb4 character set. Modify my.cnf/ini file at server-level, and specify character sets/collations for databases/tables/columns using CREATE/ALTER statements. Common issues: Garbled text requires unified character set; emoji not displaying should switch to utf8mb4; incorrect sorting can be resolved by choosing a more precise collation. Best practices: Use utf8mb4 character set and collation (utf8mb4_general_ci for better performance or unicode_ci for precision). Avoid individual column-level configurations and regularly check configurations for consistency.
Read MoreIntroduction to MySQL Transactions: Understanding Basic Transaction Characteristics and Use Cases
MySQL transactions are a set of SQL operations that must all succeed (commit) or fail (rollback) simultaneously to ensure data integrity. Their core ACID properties include Atomicity (operations are indivisible), Consistency (compliance with business rules), Isolation (no interference from concurrent operations), and Durability (permanent storage after commit). Typical scenarios include bank transfers (deduction and addition), e-commerce orders (order placement and inventory deduction), and payment systems (synchronized multi-operation execution). The InnoDB engine supports transactions, which require explicit initiation (START TRANSACTION), followed by COMMIT to confirm or ROLLBACK to undo changes. MySQL defaults to the REPEATABLE READ isolation level. Four isolation levels address concurrency issues like dirty reads, non-repeatable reads, and phantom reads, with selection based on business requirements. It is important to avoid long transactions, reasonably control auto-commit, and balance performance with data security.
Read MoreDetailed Explanation of MySQL Views: Creating and Querying Virtual Tables for Beginners
MySQL views are virtual tables dynamically generated based on SQL query results. They do not store actual data but only retain the query logic. Their core purposes include simplifying repeated queries (such as multi-table joins and conditional filtering), hiding underlying table structures (exposing only necessary fields), and ensuring data security through permission controls. The creation syntax is `CREATE VIEW view_name AS SELECT statement`. For example, a view can be created by joining a student table with a score table. Views are queried similarly to tables, using the `SELECT` operation directly. However, they do not support direct data updates by default; updates must be made indirectly after modifying the underlying tables. Advantages: Reusable query logic, isolation from underlying table complexity, and enhanced data security. Disadvantages: Performance overhead due to dynamically generated results, and potential view invalidation if underlying table structures change. Views are suitable for simplifying complex queries. Beginners should first master creating and querying views. For large datasets or frequently changing table structures, querying tables directly is more efficient.
Read MoreBasics of MySQL Query Optimization: Simple Query Speed-Up Tips for Beginners
This article explains the necessity and practical techniques of SQL query optimization, aiming to enhance system response speed and reduce user waiting time. Common mistakes for beginners include full table scans (without indexes), using SELECT * to return redundant fields, incorrect JOIN operation order, or improper use of functions. Core optimization techniques: 1. Add indexes to frequently queried fields (avoid duplicating primary key indexes and select fields with fewer duplicate values); 2. Clearly specify the required fields in SELECT to avoid redundant data; 3. Use the small table to drive the large table when performing JOINs; 4. Do not use functions on indexed fields (e.g., YEAR(create_time)); 5. Use EXPLAIN to analyze the query plan (focus on the 'type' and 'Extra' columns). Misconceptions to avoid: more indexes are not always better, OR conditions may cause index failure (replace with UNION ALL), and COUNT(DISTINCT) is inefficient. Optimization should first locate issues through EXPLAIN, prioritize mastering basic techniques, and avoid reinventing the wheel by leveraging case studies.
Read MoreMySQL Data Backup and Recovery: A Basic Data Security Guide for Beginners
Data backup and recovery are core aspects of MySQL operations and maintenance, preventing data loss. The key tool is `mysqldump`, which can back up an entire database, a single table (e.g., the `users` table), or filter data by conditions (e.g., `age>18`). For advanced needs, `xtrabackup` supports hot backups without service downtime. Recovery is performed via the `mysql` command-line tool, allowing restoration to an existing database or a new instance. To avoid oversight, use `crontab` to set up regular backups (scripts should include compression and cleanup of old backups). Before recovery, verify backup integrity, clear the target database, and disable non-essential services (e.g., foreign key constraints). Common issues like insufficient permissions or non-existent tables can be resolved by checking account credentials and creating the target database. Key points: Proficient use of `mysqldump`, regular backups, monthly recovery testing, and ensuring data security.
Read MoreMySQL JOIN Operations: From Inner Join to Outer Join, A Beginner's Easy Guide
MySQL's JOIN operations are used to combine data from two tables (e.g., a student table and a score table). The core types and their characteristics are as follows: **INNER JOIN**: Returns only matching records from both tables (e.g., Xiaoming, Xiaohong, Xiaogang). The `ON` clause must specify the join condition (e.g., `students.id = scores.student_id`); otherwise, a Cartesian product (incorrect result) will be generated. **LEFT JOIN**: Preserves all records from the left table (student table). If there is no match in the right table (score table), `NULL` is filled (e.g., Xiaoqiang has no score). It is suitable when you need to retain all data from the main table. **RIGHT JOIN**: Preserves all records from the right table (score table). If there is no match in the left table, `NULL` is filled (e.g., scores for student_id=5). It is suitable when you need to retain all data from the secondary table. **FULL JOIN**: Not supported in MySQL. It can be simulated using `LEFT JOIN + UNION`, which includes all students and scores, with `NULL` filling in non-matching parts. Note: The `ON` condition must be written; to filter students with no scores, use `WHERE scores.score IS NULL`; avoid incorrect join conditions that lead to data errors. Core logic: "Retain all from the left table,"
Read MoreIntroduction to MySQL Indexes: Why Should You Understand Indexes Even for Simple Queries?
The article explains why understanding MySQL indexes is necessary even for simple queries. An index is a special data structure (e.g., B+ tree) that maps key field values to data locations, transforming full table scans into precise positioning and significantly improving query efficiency. The reasons why even simple queries require indexes include: slow queries without indexes as data volume grows, requiring proactive planning; beginners often writing inefficient SQL (e.g., redundant conditions); and laying the foundation for complex queries (e.g., multi-table joins). Common index types include primary key, regular, unique, and composite indexes, each suited for different scenarios. Key considerations include avoiding over-indexing (e.g., on frequently updated fields) and ensuring indexes are not invalidated by using functions/expressions. The `EXPLAIN` command can verify index effectiveness. In summary, indexes are core to performance optimization; appropriate indexes should be designed based on usage scenarios to accommodate data growth and complex queries.
Read MoreComprehensive Analysis of MySQL CRUD: A Quick Guide for Beginners to Master Data Insert, Update, Delete, and Query
This article introduces MySQL CRUD operations (Create, Read, Update, Delete), which are fundamental to data management. The four core operations correspond to: Create (insertion), Read (query), Update (modification), and Delete (removal). First, the preparation work: create a `students` table (with auto-incrementing primary key `id`, `name`, `age`, and `class` fields) and insert 4 test records. **Create (Insert)** : Use the `INSERT` statement, which supports single-row or batch insertion. Ensure field and value correspondence; strings should be enclosed in single quotes, and auto-incrementing primary keys can be specified as `NULL` (e.g., `INSERT INTO students VALUES (NULL, 'Xiao Fang', 15, 'Class 4')`). **Read (Query)** : Use the `SELECT` statement. The basic syntax is `SELECT 字段 FROM 表`, supporting conditional filtering (`WHERE`), sorting (`ORDER BY`), fuzzy queries (`LIKE`), etc. For example: `SELECT * FROM students WHERE age > 18`. **Update (Update)** : Use the `UPDATE` statement with syntax `UPDATE 表 SET 字段=值 WHERE 条件`. **Without a `WHERE` clause, the entire table will be modified** (e.g., `UPDATE students SET age=18 WHERE name='Xiao Gang'`). **Delete (Delete)** :
Read MoreMySQL Installation and Environment Configuration: A Step-by-Step Guide to Setting Up a Local Database
This article introduces basic information about MySQL and a guide to its installation and usage. MySQL is an open-source relational database management system (RDBMS) known for its stability and ease of use, making it suitable for local practice and small project development. Before installation, the operating system (Windows/Linux) should be confirmed, and the community edition installation package should be downloaded from the official website. The minimum hardware requirement is 1GB of memory. For Windows installation: Download the community edition installer, choose between typical or custom installation. During configuration, set a root password (at least 8 characters), and select the utf8mb4 character set (to avoid Chinese garbled characters). Verify the version using `mysql -V` and log in with `mysql -u root -p`. For Linux (Ubuntu), install via `sudo apt`, followed by executing the security configuration (changing the root password). Common issues include port conflicts (resolve by closing conflicting services), incorrect passwords (root password can be reset on Windows), and Chinese garbled characters (check character set configuration). It is recommended to use tools like Navicat or the command line to practice SQL, and regularly back up data using `mysqldump`. After successful installation, users can proceed to learn SQL syntax and database design.
Read MoreMySQL Primary Key and Foreign Key: Establishing Table Relationships in Simple Terms for Beginners
This article explains the necessity of primary keys and foreign keys for database orderliness. A primary key is a field within a table that uniquely identifies data (e.g., `class_id` in a class table), ensuring data uniqueness and non-nullability, similar to an "ID card." A foreign key is a field in a child table that references the primary key of a parent table (e.g., `class_id` in a student table), establishing relationships between tables and preventing invalid child table data (e.g., a student belonging to a non-existent class). The core table relationship is **one-to-many**: a class table (parent table) corresponds to multiple student records (child table), with the foreign key dependent on the existence of the parent table's primary key. Key considerations: foreign keys must have the same data type as primary keys, the InnoDB engine must be used, and data in the parent table must be inserted first. Summary: Primary keys ensure data uniqueness within a table, while foreign keys maintain relationships between tables. In a one-to-many relationship, the parent table's primary key and the child table's foreign key are central, resulting in a clear and efficient database structure.
Read MoreDetailed Explanation of MySQL Data Types: Basic Type Selection for Beginners
Data types are fundamental in MySQL; choosing the wrong one can lead to issues like data overflow or wasted space, making it crucial for writing effective SQL. This article explains from three aspects: importance, type classification, and selection principles. **Numeric Types**: Integers (TINYINT/SMALLINT/INT/BIGINT, with increasing ranges; use UNSIGNED to avoid negative value waste); Floats (FLOAT/DOUBLE, low precision, suitable for non-financial scenarios); Fixed-point numbers (DECIMAL, high precision, for exact calculations like amounts). **String Types**: Fixed-length CHAR(M) (suitable for short fixed text but space-wasting); Variable-length VARCHAR(M) (space-efficient but requires extra length storage); TEXT (stores ultra-long text, no default values allowed). **Date and Time**: DATE (date only); DATETIME (full date and time); TIMESTAMP (4 bytes, short range but auto-updates, suitable for time-sensitive data). **Other Types**: TINYINT(1) as a boolean alternative; ENUM (single selection from predefined values); SET (multiple selections from predefined values). **Selection Principles**: Prioritize the smallest appropriate type; choose based on requirements (e.g., VARCHAR for phone numbers, DECIMAL for amounts); avoid overusing NULL; strictly prohibit incorrect use of INT for phone numbers, etc.
Read MoreLearning MySQL from Scratch: Mastering Data Extraction with Query Statements
This article introduces the basics of MySQL. First, it explains that MySQL is an open-source relational database used for storing structured data (such as users, orders, etc.). Before use, it needs to be installed and run, and then connected through graphical tools or command lines. Data is stored in the form of "tables", which are composed of "fields" (e.g., id, name). For example, a student table includes fields like student ID and name. Core query operations include: basic queries (`SELECT * FROM table_name` to retrieve all columns, `SELECT column_name` to specify columns, `AS` to set aliases); conditional queries (`WHERE` combined with comparison operators, logical operators, and `LIKE` for fuzzy matching to filter data); sorting (`ORDER BY`, default ascending `ASC`, descending with `DESC`); limiting results (`LIMIT` to control the number of returned rows); and deduplication (`DISTINCT` to exclude duplicates). It also provides comprehensive examples and practice suggestions, emphasizing familiarizing oneself with query logic through table creation testing and combined conditions. The core of MySQL queries: clarify requirements → select table → specify columns → add conditions → sort/limit. With more practice, one can master query operations proficiently.
Read MoreSQL Introduction: How to Create and Manipulate Data Tables in MySQL?
A data table is a "table" for storing structured data in a database, composed of columns (defining data types) and rows (recording specific information). For example, a "student table" contains columns such as student ID and name, with each row corresponding to a student's information. To create a table, use the `CREATE TABLE` statement, which requires defining the table name, column names, data types, and constraints (e.g., primary key `PRIMARY KEY`, non-null `NOT NULL`, default value `DEFAULT`). Common data types include integer `INT`, string `VARCHAR(length)`, and date `DATE`. Constraints like auto-increment primary key `AUTO_INCREMENT` ensure uniqueness. To view the table structure, use `DESCRIBE` or `SHOW COLUMNS`, which display column names, types, and whether null values are allowed. Operations include: - Insertion: `INSERT INTO` (specify column names to avoid order errors), - Query: `SELECT` (`*` for all columns, `WHERE` for conditional filtering), - Update: `UPDATE` (must include `WHERE` to avoid full-table modification), - Deletion: `DELETE` (similarly requires `WHERE`, otherwise the entire table is cleared). Notes: Strings use single quotes; `UPDATE`/`DELETE` must include `WHERE`; primary keys are unique and non-null.
Read MoreGit Version Control: Understanding the Underlying Logic of Snapshots and Version Evolution
This article introduces the core knowledge of version control and Git. Version control is used to securely preserve code history, enabling backtracking, collaboration, and experimentation, while resolving code conflicts in multi - person collaboration. Git is a distributed version control system where each developer has a complete local copy of the code history, eliminating the need for continuous internet connection and enhancing development flexibility. Git's core design consists of "snapshots" (each commit is a complete copy of the code state for easy backtracking) and "branches" (managing development in parallel through pointers, such as the main branch and feature branches). Its three core areas are the working directory (where code is modified), the staging area (temporarily storing changes to be committed), and the local repository (storing snapshots). The operation process is "writing code → adding to the staging area → committing to the repository". Basic operations include initialization (git init), status checking (status), committing (add + commit), history viewing (log), branch management (branch + checkout + merge), version rollback using reset, and collaboration through remote repositories (push/pull). Essentially, Git is "snapshots + branches". By understanding the core areas and basic operations, one can master Git, which supports clear code evolution and team collaboration.
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