The nodes of a linked list, in contrast, are allocated at arbitrary locations in memory, i.e., they can be distributed over the entire memory. Thus, once we have accessed one array element, we can access the neighboring elements very quickly. When accessing main memory, all array elements on the same memory page are loaded into the CPU cache simultaneously. Since the memory for an array is allocated in one piece, its elements are located at consecutive memory addresses. ![]() To answer the question "Which is faster – an array or a linked list?" we need to consider one more factor: the principle of locality. In summary: for the same length, a linked list requires at least twice as much memory as an array – and even six times as much in Java! However, with varying lengths, an array-based data structure can block unused memory, so you must weigh these two factors against each other. Linked lists are thus more memory efficient than arrays. For a linked list, it gets immediately deleted (or released for deletion by the garbage collector). In an array-based data structure, the removed field is usually left free for future insert operations. For example, an array of int primitives requires 4 bytes per entry: In an array, each field requires as much memory as the data type it contains. Thus, accessing an element (reading or writing) and determining length is cheaper with an array – inserting and removing, on the other hand, with a linked list. The following table summarizes the time complexities of the various operations: Operation Therefore, we can query the size of such data structures in constant time, i.e., O(1). Some data structures based on linked lists (e.g., the Java LinkedList) additionally store the size in a field, which they update on insertion and removal. Thus, the time complexity for determining the length of a linked list is: O(n) The longer the list, the longer the counting takes. In the case of a linked list, we have to run through the entire list and count the list nodes. Thus, the time complexity for determining the length of an array is: O(1) ![]() The effort for this is independent of the length of the array, so it is constant. The size of an array is known and can be queried, for example, in Java via array.length. That is how the Java ArrayDeque is implemented, for example. With a circular array, we can also reduce the time complexity for insertion and removal at the beginning of an array-based data structure to O(1). ![]() The time complexity is, therefore: O(n)ĭata structures such as Java's ArrayList have a strategy for reducing the average time complexity of inserting and removing elements: By reserving space in the array for new elements, both when creating and when expanding, they can reduce the time complexity – at least for insertion and removal at the end of an array-based data structure – to O(1). The time required is proportional to the array length. In the array example, it makes no difference whether we access the "a" or the "p": In terms of effort, it makes no difference how long the array is or at which position we read or write an element. With an array, we can address each element directly. (You can find an introduction to time complexity in the article " Big O Notation and Time Complexity – Easily Explained".) Accessing a Specific Element ("Random Access") ![]() Let's start with the cost of the various operations. ¹ A data element can be a primitive element, such as an int, double, or char – or a reference to an object. The following sections compare the consequences of the layout of the two data structures in terms of the time required to insert and remove elements, the memory required, and the principle of locality (I'll explain what this means in the corresponding section). Array – singly linked list – doubly linked listĪn array is a contiguous block of memory that directly contains the data elements¹.Ī linked list consists of list nodes, each containing a data element¹ and a reference to the next node (and – in the case of a doubly linked list – to the previous node).
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