数组
数据结构的构成主要是有数组和链表构建。 数组侧重的内存的随机寻址,方便快速搜索和定位。链表主要是通过逻辑关联,便于逻辑结构映射到物理结构,适合新增,更新和删除操作。
随着项目做的越多,越发现底层的逻辑代码是是非常的完美,并且执行效率更高。 如果自己去实现,时间和复杂的都差很多。 那怎么利用好已有的SDK的方法能力,很考验对于源码的理解能力。
1. ArrayList
功能需求
- 对于数组元素进行动态的管理
源码分析
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构造数组
public ArrayList(int initialCapacity) { if (initialCapacity > 0) { this.elementData = new Object[initialCapacity]; } else if (initialCapacity == 0) { this.elementData = EMPTY_ELEMENTDATA; } else { throw new IllegalArgumentException("Illegal Capacity: "+ initialCapacity); } }
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申请数组空间,默认是10数组
public void ensureCapacity(int minCapacity) { int minExpand = (elementData != DEFAULTCAPACITY_EMPTY_ELEMENTDATA) // any size if not default element table ? 0 // larger than default for default empty table. It's already // supposed to be at default size. : DEFAULT_CAPACITY; if (minCapacity > minExpand) { ensureExplicitCapacity(minCapacity); } }
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对于数组空间不够,采用复制数组的方法
- 具体使用System.arraycopy 中的 Arrays.copyOf(elementData, newCapacity);
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数组的增删改成的操作
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增加
public boolean add(E e) { ensureCapacityInternal(size + 1); // Increments modCount!! elementData[size++] = e; return true; }
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删除
public E remove(int index) { rangeCheck(index); modCount++; E oldValue = elementData(index); int numMoved = size - index - 1; if (numMoved > 0) System.arraycopy(elementData, index+1, elementData, index, numMoved); elementData[--size] = null; // clear to let GC do its work return oldValue; }
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修改
public E set(int index, E element) { rangeCheck(index); E oldValue = elementData(index); elementData[index] = element; return oldValue; }
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查询
public E get(int index) { rangeCheck(index); return elementData(index); }
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排序
public static <T> void sort(T[] a, int fromIndex, int toIndex, Comparator<? super T> c) { if (c == null) { sort(a, fromIndex, toIndex); } else { rangeCheck(a.length, fromIndex, toIndex); if (LegacyMergeSort.userRequested) legacyMergeSort(a, fromIndex, toIndex, c); else TimSort.sort(a, fromIndex, toIndex, c, null, 0, 0); } }
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数组采用二分法排序
private TimSort(T[] a, Comparator<? super T> c, T[] work, int workBase, int workLen) { this.a = a; this.c = c; // Allocate temp storage (which may be increased later if necessary) int len = a.length; int tlen = (len < 2 * INITIAL_TMP_STORAGE_LENGTH) ? len >>> 1 : INITIAL_TMP_STORAGE_LENGTH; if (work == null || workLen < tlen || workBase + tlen > work.length) { @SuppressWarnings({"unchecked", "UnnecessaryLocalVariable"}) T[] newArray = (T[])java.lang.reflect.Array.newInstance (a.getClass().getComponentType(), tlen); tmp = newArray; tmpBase = 0; tmpLen = tlen; } else { tmp = work; tmpBase = workBase; tmpLen = workLen; } int stackLen = (len < 120 ? 5 : len < 1542 ? 10 : len < 119151 ? 24 : 49); runBase = new int[stackLen]; runLen = new int[stackLen]; }
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2. Arrays 排序操作
功能需求
源码分析
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快速排序(DualPivotQuicksort)
- /** * Sorts the specified range of the array using the given * workspace array slice if possible for merging * * @param a the array to be sorted * @param left the index of the first element, inclusive, to be sorted * @param right the index of the last element, inclusive, to be sorted * @param work a workspace array (slice) * @param workBase origin of usable space in work array * @param workLen usable size of work array */ static void sort(int[] a, int left, int right, int[] work, int workBase, int workLen) { // Use Quicksort on small arrays if (right - left < QUICKSORT_THRESHOLD) { sort(a, left, right, true); return; } /* * Index run[i] is the start of i-th run * (ascending or descending sequence). */ int[] run = new int[MAX_RUN_COUNT + 1]; int count = 0; run[0] = left; // Check if the array is nearly sorted for (int k = left; k < right; run[count] = k) { if (a[k] < a[k + 1]) { // ascending while (++k <= right && a[k - 1] <= a[k]); } else if (a[k] > a[k + 1]) { // descending while (++k <= right && a[k - 1] >= a[k]); for (int lo = run[count] - 1, hi = k; ++lo < --hi; ) { int t = a[lo]; a[lo] = a[hi]; a[hi] = t; } } else { // equal for (int m = MAX_RUN_LENGTH; ++k <= right && a[k - 1] == a[k]; ) { if (--m == 0) { sort(a, left, right, true); return; } } } /* * The array is not highly structured, * use Quicksort instead of merge sort. */ if (++count == MAX_RUN_COUNT) { sort(a, left, right, true); return; } } // Check special cases // Implementation note: variable "right" is increased by 1. if (run[count] == right++) { // The last run contains one element run[++count] = right; } else if (count == 1) { // The array is already sorted return; } // Determine alternation base for merge byte odd = 0; for (int n = 1; (n <<= 1) < count; odd ^= 1); // Use or create temporary array b for merging int[] b; // temp array; alternates with a int ao, bo; // array offsets from 'left' int blen = right - left; // space needed for b if (work == null || workLen < blen || workBase + blen > work.length) { work = new int[blen]; workBase = 0; } if (odd == 0) { System.arraycopy(a, left, work, workBase, blen); b = a; bo = 0; a = work; ao = workBase - left; } else { b = work; ao = 0; bo = workBase - left; } // Merging for (int last; count > 1; count = last) { for (int k = (last = 0) + 2; k <= count; k += 2) { int hi = run[k], mi = run[k - 1]; for (int i = run[k - 2], p = i, q = mi; i < hi; ++i) { if (q >= hi || p < mi && a[p + ao] <= a[q + ao]) { b[i + bo] = a[p++ + ao]; } else { b[i + bo] = a[q++ + ao]; } } run[++last] = hi; } if ((count & 1) != 0) { for (int i = right, lo = run[count - 1]; --i >= lo; b[i + bo] = a[i + ao] ); run[++last] = right; } int[] t = a; a = b; b = t; int o = ao; ao = bo; bo = o; } }
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归并排序
public static void parallelSort(float[] a) { int n = a.length, p, g; if (n <= MIN_ARRAY_SORT_GRAN || (p = ForkJoinPool.getCommonPoolParallelism()) == 1) DualPivotQuicksort.sort(a, 0, n - 1, null, 0, 0); else new ArraysParallelSortHelpers.FJFloat.Sorter (null, a, new float[n], 0, n, 0, ((g = n / (p << 2)) <= MIN_ARRAY_SORT_GRAN) ? MIN_ARRAY_SORT_GRAN : g).invoke(); }
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二分法搜索
private static int binarySearch0(long[] a, int fromIndex, int toIndex, long key) { int low = fromIndex; int high = toIndex - 1; while (low <= high) { int mid = (low + high) >>> 1; long midVal = a[mid]; if (midVal < key) low = mid + 1; else if (midVal > key) high = mid - 1; else return mid; // key found } return -(low + 1); // key not found. }
3. ArrayDeque 数组队列
功能需求
源码分析
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申请元素
private void allocateElements(int numElements) { int initialCapacity = MIN_INITIAL_CAPACITY; // Find the best power of two to hold elements. // Tests "<=" because arrays aren't kept full. if (numElements >= initialCapacity) { initialCapacity = numElements; initialCapacity |= (initialCapacity >>> 1); initialCapacity |= (initialCapacity >>> 2); initialCapacity |= (initialCapacity >>> 4); initialCapacity |= (initialCapacity >>> 8); initialCapacity |= (initialCapacity >>> 16); initialCapacity++; if (initialCapacity < 0) // Too many elements, must back off initialCapacity >>>= 1;// Good luck allocating 2 ^ 30 elements } elements = new Object[initialCapacity]; }
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插入队列元素
public void addFirst(E e) { if (e == null) throw new NullPointerException(); elements[head = (head - 1) & (elements.length - 1)] = e; if (head == tail) doubleCapacity(); }
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移除队列元素
public E removeFirst() { E x = pollFirst(); if (x == null) throw new NoSuchElementException(); return x; }