深度解析HashMap：探秘Java中的键值存储魔法

Insert image description here

文章目录

一、前言

1.1 介绍HashMap在Java中的重要性

HashMap是Java中一个非常重要的数据结构，它属于Java集合框架的一部分，用于存储键值对。

HashMap在Java中的一些重要性：

高效的查找操作： HashMap基于哈希表实现，可以在常数时间内执行查找操作，这使得它在大数据集合中非常高效。
灵活性： HashMap允许存储不同类型的键和值，包括自定义对象。这使得它非常灵活，适用于各种场景。
无序性： HashMap中的元素是无序的，不像List那样有顺序。这对于不需要特定顺序的场景非常有用。
允许空键值： HashMap允许存储空键和空值，这在某些情况下是很有用的。
扩展性： HashMap的大小是动态可调整的，可以根据需要进行扩展。这有助于在不同规模的数据集上保持高效性能。
基于哈希表的性能： 在平均情况下，HashMap提供了很好的性能。它允许快速插入、删除和查找操作。
实现了Map接口： HashMap实现了Map接口，这使得它能够与其他Java集合框架交互，并且易于使用和理解。
自动处理哈希冲突： 哈希表中可能存在冲突，即两个不同的键可能映射到相同的哈希桶。HashMap使用链表或红黑树来处理这种冲突，确保在冲突发生时也能够保持较好的性能。

1.2 引出本文将深入挖掘HashMap的内部机制

从以下几个方面深入挖掘：

基本原理： 首先介绍HashMap的基本原理，即它是如何工作的。HashMap是一种基于哈希表的数据结构，它通过将键映射到表中的位置来实现快速的数据检索。探讨哈希函数的选择和冲突解决策略对HashMap性能的影响。
内部结构： 探讨HashMap的内部结构，包括桶（buckets）和链表（或树）。HashMap使用数组来存储数据，每个数组的元素是一个桶，每个桶可以包含一个链表或树的数据结构，用于处理哈希冲突。
哈希函数： 深入了解哈希函数的作用和设计原则。合适的哈希函数能够将键均匀地分布到桶中，减少冲突的概率，提高HashMap的性能。
扩容机制： 讨论HashMap是如何处理负载因子和扩容的。负载因子是指HashMap中已使用的桶的比例，当负载因子超过某个阈值时，HashMap会进行扩容，重新调整大小并重新分配元素，以保持性能。
并发性： 考虑HashMap在多线程环境中的并发性问题。了解HashMap的线程安全性和可能的并发性优化，例如ConcurrentHashMap。
JDK版本差异： 注意不同版本的JDK中HashMap的实现可能有所不同，了解这些差异有助于理解HashMap的演进过程。

二、 HashMap的基本概念

2.1 什么是HashMap？

HashMap是一种用于存储键值对的数据结构，它提供了快速的数据检索能力。在HashMap中，每个键都映射到一个唯一的值。
它基于哈希表（Hash Table）实现，通过将键映射到数组的特定位置来实现快速的查找。
HashMap的基本原理是使用哈希函数将键转换成数组索引，然后在数组的相应位置存储对应的值。
当需要查找一个键对应的值时，HashMap会使用相同的哈希函数来计算出数组索引，然后直接访问该位置以获取值，这样可以在平均情况下实现O(1)的时间复杂度。
在Java中，HashMap是Java集合框架中的一部分，位于java.util包下。它允许存储null键和null值，但是在并发环境中使用时需要注意同步问题。
HashMap是非同步的，如果在多线程环境中使用，可以考虑使用ConcurrentHashMap。

简单的Java示例，展示如何使用HashMap：

import java.util.HashMap;

public class HashMapExample {
    
    
    public static void main(String[] args) {
    
    
        // 创建HashMap
        HashMap<String, Integer> hashMap = new HashMap<>();

        // 添加键值对
        hashMap.put("One", 1);
        hashMap.put("Two", 2);
        hashMap.put("Three", 3);

        // 获取值
        int value = hashMap.get("Two");
        System.out.println("Value for key 'Two': " + value);

        // 遍历HashMap
        for (String key : hashMap.keySet()) {
    
    
            System.out.println("Key: " + key + ", Value: " + hashMap.get(key));
        }
    }
}

2.2 为什么HashMap在Java中如此流行？

HashMap受欢迎的原因：

快速的查找时间复杂度： HashMap基于哈希表实现，它允许通过键直接访问值，而不需要按顺序搜索。在平均情况下，查找操作的时间复杂度是O(1)，即常数时间，这使得HashMap非常高效。
灵活的存储容量： HashMap的大小可以根据需要动态调整，而不是固定的。这意味着它可以自动调整以适应存储的元素数量，从而减少内存浪费。
键值对存储： HashMap存储数据的方式是键值对形式，这使得它适用于许多不同的应用场景。每个元素都由一个键和一个值组成，通过键来唯一标识元素，这有助于组织和检索数据。
可接受的性能： 尽管在某些特定情况下，HashMap的性能可能受到哈希碰撞的影响，但Java的HashMap实现已经做了很多优化以尽量减少这种情况的发生。此外，Java 8及更高版本引入了红黑树来优化处理哈希碰撞的性能。
API丰富： HashMap提供了丰富的API，使得开发者能够方便地执行插入、删除、更新和查询等操作。它还实现了Map接口，使得它可以与其他集合框架无缝集成。
Widely used: Due to its efficient performance and flexible features, HashMap is widely used in Java to implement caching, indexing, data retrieval, etc.各种场景, making it an important part of the Java collection framework.

3. Internal structure of HashMap

3.1 Combination of array and linked list: Buckets

Bucket meaning:

Buckets are a data structure that can be viewed as a combination of arrays and linked lists.
Buckets are often used in the implementation of hash tables, where data is stored in multiple buckets, and each bucket can contain one or more elements. This helps resolve hash collision issues.

Bucket usage:

In a hash table, a key is mapped to a specific bucket through a hash function, and the corresponding value is then looked up or stored in the bucket.
Due to the mapping of hash functions, multiple keys may be mapped to the same bucket, which is哈希冲突.
Buckets can be implemented using arrays or linked lists.
- In the array implementation, each bucket is an array element and can直接通过索引访问.
- In the linked list implementation, each bucket is a linked list for存储哈希冲突的元素.

The design of this combination enablesbuckets to have the fast random access characteristics of arrays and the dynamic size and flexibility of linked lists. The choice of bucket depends on the specific application scenario and the design requirements of the hash table.

3.2 Hash algorithm: how key values are mapped to buckets

In a hash table, the Hash algorithm is used to map key values to buckets.

A hash table is a data structure that uses 哈希函数来将键映射到索引 and then stores values in buckets corresponding to indexes.

The general process of hash algorithm:

Calculate the hash value: First, calculate the hash value of the key through the hash function. The hash function accepts a key as input and produces a fixed size 哈希码. Ideally, the hash function should produce different hash codes for different keys to reduce collisions.
maps to bucket: Next, map the hash code to a bucket index by pairing 哈希码取模运算. This usually involves using 哈希码除以桶的数量，然后取余数. For example, if the hash code is h and the number of buckets is N, then the index of the bucket is h mod N.
Handling collisions: Due to limitations of the hash function, 两个不同的键具有相同的哈希码 may occur, which is 冲突. There are many ways to resolve conflicts, two common ones are the linked list method and the open addressing method.
- Linked list method: Use a linked list or other data structure in each bucket to store key-value pairs with the same hash code. If a conflict occurs, new key-value pairs can be added to the end of the linked list.
- Open addressing: If a conflict occurs, try to find an empty slot elsewhere in the hash table and insert the key-value pair into the first empty slot found. in the trough. This may involve methods such as线性探测、二次探测.

In this way,a hash table allows a fast lookup of a key to retrieve the value associated with it without the need to traverse the entire data structure.

4. Parsing the put() method of HashMap

4.1 Basic process of put() method

HashMap is one of the commonly used data structures in Java. It implements the Map interface and provides storage and retrieval of key-value pairs. HashMap'sput() method is used to add key-value pairs to HashMap.

Basic process:

Compute the hash value of the key: First, calculate the hash value of the key by the key's hashCode() method. HashMap uses this hash value to determine where the key-value pair is stored in the internal array.
Calculate array index: Convert the calculated hash value into an array index through a series of 位运算. The specific conversion process usually involves a modulo operation (%) and some bit operations to ensure that the index value is within a reasonable range.
Check whether there is already an element at the index position: If the corresponding index position in the array is empty, it means that there is no key-value pair at that position, and the new key-value pair is directly inserted into this location.
Handling collision (Collision): If one or more key-value pairs already exist at the calculated index position, that is, a collision has occurred, usually 链地址法 (Separate Chaining) or 开放地址法 (Open Addressing) and other strategies to solve.
- Chain address method: Maintain a linked list (or other data structure) at the location of the collision, and add new key-value pairs to the linked list. That's why HashMap allows multiple keys to have the same hash value.
- Open address method: In the case of collision, find the next available position through certain rules and insert the key-value pair there.
Update a value or insert a new key-value pair: If the location to be inserted is determined after the collision is resolved, check whether the same key already exists at that location. If it exists, the corresponding value is updated; if it does not exist, the new key-value pair is inserted.
Check whether expansion is required: After inserting the key-value pair, it will check whether the size of the current HashMap exceeds 阈值. If it exceeds, it will trigger扩容操作 and resize the array to maintain the performance of HashMap.

4.2 Methods to deal with hash collisions

When dealing with hash conflicts, HashMap usually uses the followingseveral methods:

Separate Chaining: This is one of the most common methods to resolve hash conflicts. In this method, each bucket of HashMap is no longer a single location, but a linked list. When a hash conflict occurs, a new key-value pair is added to the linked list of the corresponding bucket. In this way, each bucket can hold multiple key-value pairs, which共享同一个哈希值.
Open Addressing: In this method, all elements are stored in the table without using additional data structures (such as linked lists). When a hash collision occurs, this method attempts to find a 空的槽 elsewhere in the hash table to store the conflicting element. This can be achieved by 线性探测、二次探测 etc.
Rehashing: When 元素数量达到一定阈值时 in HashMap, 再哈希操作 will be triggered. Rehash usually does 扩大散列表的大小 and remaps the existing elements into a new, larger hash table. This helps reduce the probability of hash collisions and improves the performance of HashMap.
Conversion between linked list and red-black tree: InJava 8及之后的版本, when the length of the linked list reaches a certain threshold, the linked list will be converted into a red-black tree . This is to improve the efficiency of finding elements in the linked list, because 红黑树的查找复杂度为O(log n) and 链表的为O(n). This optimization is mainly to deal with performance issues in extreme cases.

In Java, the implementation of HashMap may change in different versions, so checking the specific version源代码 can provide more detailed information.

4.3 Capacity expansion mechanism: how to maintain efficient performance

Performance reasons:

HashMap may need to be expanded when storing large amounts of data to maintain较低的负载因子 and ensure efficient performance.
The choice of load factor is a trade-off,较低的负载因子 can reduce hash collisions, but will result in more frequent expansions.
较高的负载因子It can reduce the number of expansions, but it may cause the length of the linked list to be too long, affecting query performance.
Choosing the appropriate 初始容量 and 负载因子 is an important factor to consider when using HashMap.

A brief analysis of HashMap expansion mechanism:

Initial Capacity: When creating a HashMap object, you can specify the initial capacity. HashMap internally maintains an array (called a bucket), and the initial capacity represents the size of the array.
Load Factor: Load factor refers to the threshold at which capacity expansion is performed. In a HashMap, the load factor is a floating point number between 0 and 1, 默认为 0.75. When the number of elements in the HashMap reaches the capacity multiplied by the load factor, the expansion operation is triggered.
Expansion operation (Rehashing): When HashMap needs to be expanded, it will create a new array, usually 原数组的两倍大小. Then, it will重新分配 the elements in the original array into the new array. This process involves重新计算每个元素的哈希值 to determine its position in the new array.
Recalculate the hash value: The hash value is recalculated to ensure that the element is in the new array均匀分布. The hash function used by HashMap is usually (n is the length of the new array) to ensure that the calculation result is within the valid range of the new array. 原始哈希值与 (n - 1) 与运算
Data migration: When reassigning elements to a new array, it may happen that multiple elements are mapped to the same position in the new array (hash collision occurs). In this case, each position of the new array is usually a 链表或树结构, used to store multiple elements mapped to the same position.
Concurrent processing: In a multi-threaded environment, HashMap uses the 分段锁 (Segment) mechanism to improve concurrency performance. During the expansion operation, only 被迁移的段相关的锁 will be obtained, and access to other segments will not be blocked.

5. Interpretation of the get() method of HashMap

5.1 Internal implementation of get() method

The HashMap's get() method is a method used to obtain the value corresponding to the specified key.

Brief internal implementation analysis:

Calculate the hash value: First, the get() method will receive the incoming key object and pass the key object's hashCode() method calculates a hash value. This hash value is used to determine the position of the key-value pair in the hash table.
Calculate the index position: Next, by performing a series of operations on the hash value, such as取余数, etc., calculate the key-value pair in the array index position in . This index position is the storage location of the key-value pair in the hash table.
Looking up a linked list or red-black tree: Since the hash values of different keys may be the same, there may be a hash conflict. In this case, key-value pairs with the same hash value are stored in a linked list or red-black tree at the same array index. get() The method will search on the linked list or red-black tree at this position.
Compare key values: In a linked list or red-black tree, 遍历每个节点 will compare key values until a matching key-value pair is found, or Make sure there are no matching key-value pairs.
Return result: If a matching key-value pair is found, the corresponding value is returned; if no matching key-value pair is found, then null.

6. Thread safety issues

6.1 Thread safety analysis of HashMap

HashMap is not thread-safe, which means that operating on HashMap in a multi-threaded environment will result in undefined behavior. This is because HashMap does not have a built-in synchronization mechanism to ensure its thread safety.

In a multi-threaded environment,the following problems may occur:

Race Condition (Race Condition): Multiple threads read and write HashMap at the same time, which may lead to data inconsistency or loss.
Inconsistent traversal: When one thread is traversing the HashMap, another thread makes structural modifications to it (adding or deleting elements), which may lead to ConcurrentModificationException Exception or inconsistency during traversal.

To address these issues, Java provides somethread-safe alternatives:

Use Collections.synchronizedMap() method to create a thread-safe HashMap. The Map object returned by this method will synchronize all accesses , but the performance is relatively low.
UseConcurrentHashMap class, which is a thread-safe version of HashMap. It adopts 分段锁 method to improve concurrency performance and is suitable for use in multi-threaded environments. .

// 创建线程安全的 HashMap
Map<String, Integer> synchronizedMap = Collections.synchronizedMap(new HashMap<>());

// 创建 ConcurrentHashMap
ConcurrentMap<String, Integer> concurrentMap = new ConcurrentHashMap<>();

In a multi-threaded environment, it is recommended to use ConcurrentHashMap or 手动在需要同步的地方进行加锁操作 to ensure the thread safety of HashMap.

6.2 Introduction to ConcurrentHashMap

ConcurrentHashMap is a thread-safe hash table implementation provided by Java. It is a thread-safe version of HashMap, specially designed for efficient concurrent operations in a multi-threaded environment. ConcurrentHashMap was introduced in JDK 1.5 and has been improved and optimized in subsequent versions.

ConcurrentHashMap mainly has the following features and advantages:

Segment lock mechanism:ConcurrentHashMap is used internally分段锁 (Segment), each segment There is a lock, 不同的键值对会被映射到不同的分段上, so that during multi-threaded operation, only a certain segment will be locked instead of the entire structure, thus improving the performance of concurrent access.
Thread safety: In a multi-threaded environment, ConcurrentHashMap provides better thread safety and supports 并发的读取操作, while ensuring 写入操作的一致性和可见性.
Supports high concurrency: Compared with traditional synchronization containers (such as synchronized Maps obtained through Collections.synchronizedMap),ConcurrentHashMap in high concurrency situations Have better performance.
Null value is not supported:ConcurrentHashMap The key or value is not supportednull because null is treated as a>特殊标识 to indicate that the key or value does not exist.

ConcurrentHashMap is widely used in practical applications in scenarios that require high concurrent access, such as多线程下的缓存、并发计算, etc. It provides an efficient and safe way to manage key-value pairs, making data operations in concurrent environments more reliable and efficient.

7. Optimization and performance tuning

7.1 How to optimize the performance of HashMap

If you want to optimize the performance of HashMap, you can consider the following aspects:

Settings of initial capacity and load factor: When creating a HashMap, you can use 指定初始容量和负载因子 to optimize performance. The initial capacity represents the number of buckets in the HashMap, and the load factor represents the average number of key-value pairs allowed to be stored in each bucket. Properly setting the initial capacity and load factor can reduce the number of rehashes and improve performance.
Avoid frequent expansion: When the number of elements in the HashMap exceeds the product of the load factor and the initial capacity, the HashMap will expand, which is a time-consuming operation. The appropriate initial capacity can be set by预估the number of elements that HashMap needs to store, thereby reducing the frequency of expansion operations.
Choose a suitable hash algorithm: When customizing an object as the key of a HashMap, make sure to implement the hashCode() method and equals() method, and try to use hashCode() method返回的哈希码分布均匀 to avoid a large number of hash conflicts.
Use concurrent collections appropriately: In a multi-threaded environment, you can consider using ConcurrentHashMap or Collections.synchronizedMap() instead of ordinary HashMap to improve concurrency performance.
Pay attention to choosing the appropriate data structure: In some specific scenarios, there may be a more suitable data structure to replace HashMap, such as usingTreeMap to replace HashMap to obtain有序的键值对 traversal.
Avoid frequent expansion operations: Before adding a large number of elements, you can initialize HashMap through the constructor of HashMap(int initialCapacity), given a larger For the initial capacity, adding the specific data volume estimate and the defined load factor can reduce the number of capacity expansions.
Use local variables: When traversing HashMap, try to put entrySet、keySet或values的结果 into 局部变量 for traversal ,避免反复调用.

7.2 Avoid common pitfalls and mistakes

When using HashMap, there are some common pitfalls and mistakes that need to be avoided to ensure program correctness and performance.

Here are somecommon pitfalls and mistakes and how to avoid them:

The hashCode() and equals() methods are not correctly rewritten: If a custom class is used as the key of a HashMap, the hashCode() and equals() methods must be correctly rewritten to Ensures that equal objects have the same hash code and equal comparison results. Otherwise, the same keys will be stored in the HashMap, resulting in unexpected behavior.

Solution: Ensure that the custom class correctly overrides the hashCode() and equals() methods and adheres to the rules that equal objects have the same hash code and equal comparison results.
Modify HashMap during iteration: When using an iterator to traverse a HashMap, if the structure of the HashMap is modified during the traversal process (such as adding or deleting elements), it will cause ConcurrentModificationExceptionException.

Solution: When iterating, you should 使用迭代器的相关方法来进行元素的移除 instead of directly calling the remove method of HashMap. In addition, you can consider using concurrency-safeConcurrentHashMap to avoid this problem.
Frequent expansion operations: If sufficient initial capacity and load factor of HashMap are not given in advance, frequent expansion operations may occur, affecting performance.

Solution: When creating a HashMap, set the initial capacity and load factor appropriately based on the estimated number of elements to avoid frequent expansion operations.
Use null as key or value: Both keys and values in HashMap can be null, but in some cases, null can be used directly as key or value if not processed. , may cause a null pointer exception or logic error.

Solution: When using HashMap, pay special attention to whether the key or value may be null, and handle it accordingly, such as using the containsKey(key) method to determine the key Whether it exists etc.
Concurrency safety is not considered: Using ordinary HashMap in a multi-threaded environment may cause thread safety issues, such as data inconsistency.

Solution: Consider using concurrency-safe data structures such as ConcurrentHashMap or Collections.synchronizedMap(), or take other appropriate measures Concurrent processing solution.

盈若安好，便是晴天