The 'Speed Code' of Sorting Algorithms: Time Complexity and Bubble Sort
This article focuses on the "speed code" of sorting algorithms, with a core emphasis on time complexity and bubble sort. Time complexity is measured using the Big O notation, with common types including O(n) (linear, growing proportionally with data size), O(n²) (quadratic, extremely inefficient for large datasets), and O(log n) (logarithmic, extremely fast). It serves as a key indicator for judging the efficiency of algorithms. Bubble sort, a fundamental algorithm, works by comparing and swapping adjacent elements to "float" smaller elements upward and "sink" larger elements downward. Using the array [5, 3, 8, 4, 2] as an example, it repeatedly traverses and compares adjacent elements until the array is sorted. Its time complexity is O(n²), with a space complexity of O(1) (in-place sorting). Its advantages include simplicity, intuitive logic, while its main drawback is low efficiency, making it extremely time-consuming for large datasets. In summary, despite its slowness (O(n²)), bubble sort is a valuable introductory tool that helps understand sorting principles and time complexity, laying the foundation for learning more efficient algorithms like quicksort (optimized to O(n log n)).
Read MoreLearning Insertion Sort: Principles and Code, Just Like Organizing Your Desktop
This article analogizes "sorting the desktop" to insertion sort, with the core idea being inserting elements one by one into their correct positions within the already sorted portion. The basic steps are: initializing the first element as sorted, starting from the second element, comparing it backward with the sorted portion to find the insertion position, shifting elements, and then inserting the current element. Taking the array `[5,3,8,2,4]` as an example: initially sorted as `[5]`, inserting 3 (after shifting 5) results in `[3,5]`; inserting 8 (directly appending) gives `[3,5,8]`; inserting 2 (shifting 8, 5, and 3 sequentially, then inserting at the beginning) yields `[2,3,5,8]`; inserting 4 (shifting 8 and 5, then inserting after 3) completes the sorting. The Python code implementation uses a double loop: the outer loop iterates over elements to be inserted, and the inner loop compares backward and shifts elements. It has a time complexity of O(n²), space complexity of O(1), and is suitable for small-scale data or nearly sorted data. It is an in-place sorting algorithm with no additional space required. This sorting analogy intuitively reflects the essence of "inserting one by one" and aids in understanding the sorting logic.
Read MoreLearning Bubble Sort from Scratch: Step-by-Step Teaching and Code Implementation
### Summary of Bubble Sort Sorting is the process of rearranging unordered data according to a set of rules. Bubble Sort is a fundamental sorting algorithm whose core principle is to gradually "bubble up" larger elements to the end of the array through adjacent element comparisons and swaps. **Core Idea**: In each iteration, start from the beginning of the array and compare adjacent elements sequentially. If a preceding element is larger than the following one, swap them. After each iteration, the largest element "sinks" to its correct position at the end, reducing the length of the unsorted portion by 1. Repeat until all elements are sorted. **Steps**: The outer loop controls the number of iterations (n-1 times, where n is the array length). The inner loop compares and swaps adjacent elements in each iteration. An optimization is to terminate early if no swaps occur in a round. **Complexity**: Time complexity is O(n²) in the worst case (completely reversed order) and O(n) in the best case (already sorted). Space complexity is O(1) (in-place sorting). **Characteristics and Applications**: It is simple to implement but inefficient (O(n²)). Suitable for small datasets or scenarios with low efficiency requirements (e.g., teaching demonstrations). By embodying the comparison-swap paradigm, Bubble Sort lays the foundation for understanding more complex sorting algorithms.
Read MoreWhat is Time Complexity O(n)? A Must-Learn Efficiency Concept for Data Structure Beginners
The article explains the necessity of time complexity: due to differences in hardware and compilers, direct timing is impractical, and an abstract description of the algorithm's efficiency trend is required. The core is linear time complexity O(n), which indicates that the number of operations is proportional to the input size n (such as the length of an array). Scenarios like traversing an array to find a target or printing all elements require n operations. Big O notation ignores constants and lower-order terms, focusing only on the growth trend (e.g., O(2n+5) is still O(n)). Comparing common complexities (O(1) constant, O(n) linear, O(n²) quadratic, O(log n) logarithmic), O(n) is more efficient than O(n²) but less efficient than O(1) or O(log n). Understanding O(n) is fundamental to algorithms, helping optimize code and avoid timeout issues caused by "brute-force solutions."
Read MoreHow to Handle Hash Table Collisions? Understand Hash Resolution Methods in Data Structures Simply
Hash tables map keys to array slots via hash functions, and hash collisions occur when different keys map to the same slot. The core solution is to find new positions for colliding elements, with the main methods as follows: 1. **Chaining (Separate Chaining)**: Each slot stores a linked list, where colliding elements are appended to the list. For example, keys 1, 6, and 11 hashing to the same slot form a linked list [1, 6, 11]. **Advantages**: Simple implementation, suitable for dynamic data. **Disadvantages**: Extra space for linked lists; worst-case O(n) lookup. 2. **Open Addressing**: When collisions occur, empty slots are probed. Includes linear probing (i+1, wrapping around) and quadratic probing (i+1², etc.). For example, key 6 hashing to slot 1 (collision) probes slot 2; key 11 probes slot 3. **Advantages**: High space utilization. **Disadvantages**: Linear probing causes primary clustering; quadratic probing requires a larger array. Other methods: Double hashing (multiple hash functions) and common overflow area (primary table + overflow table), suitable for low-collision scenarios. Selection depends on the use case: Chaining (e.g., Java HashMap) suits dynamic, large datasets; Open addressing is better for fixed-size arrays with few collisions.
Read MoreHow to Learn Tree Traversal? Easily Understand Preorder, Inorder, and Postorder Traversals
Trees are fundamental data structures, and traversal is the process of visiting all nodes. This article focuses on explaining the preorder, inorder, and postorder traversals of binary trees, with their core difference lying in the timing of visiting the root node. - **Preorder Traversal (Root → Left → Right)**: Visit the root first, then recursively traverse the left subtree, and finally the right subtree. Example: 1→2→4→5→3→6→7. - **Inorder Traversal (Left → Root → Right)**: Recursively traverse the left subtree first, then visit the root, and finally the right subtree. Example: 4→2→5→1→6→3→7. - **Postorder Traversal (Left → Right → Root)**: Recursively traverse the left subtree first, then the right subtree, and finally visit the root. Example: 4→5→2→6→7→3→1. Memory mnemonic: Preorder has the root first, inorder has the root in the middle, postorder has the root last. In applications, preorder is used for tree copying, inorder sorts binary search trees, and postorder is used for node deletion. Traversal essentially embodies the recursive thought; mastering the order and scenarios enables proficiency.
Read MoreHow to Understand Recursion? Easily Learn Recursion with the Fibonacci Sequence
Recursion is a problem-solving method that "calls itself", breaking down large problems into smaller, similar subproblems until the subproblems can be solved directly, then returning results layer by layer (like disassembling Russian nesting dolls). Its core elements are the **base case** (to avoid infinite loops, e.g., returning fixed values when n=0 or n=1) and the **recursive relation** (decomposing into subproblems, e.g., F(n) = F(n-1) + F(n-2)). Taking the Fibonacci sequence as an example, the recursive function `fib(n)` is implemented through the base case and recursive relation: `fib(0) = 0`, `fib(1) = 1`, and `fib(n) = fib(n-1) + fib(n-2)`. For `fib(5)`, it requires recursively calculating `fib(4)` and `fib(3)`, decomposing layer by layer until `fib(1)` and `fib(0)` are reached, then combining the results in reverse to get the final answer. The recursive process is like "peeling an onion": after reaching the bottom, results "bounce back". Its advantages include clear logic and ease of implementation, but it has redundant calculations (e.g., `fib(3)` is called multiple times), leading to lower efficiency—optimizations like memoization or iteration can be used. In essence, recursion transforms large problems into smaller ones, and smaller problems... (Note: The original Chinese summary appears truncated here; the above translation completes the core description.)
Read MoreWhat 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.
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