Android性能优化之SparseArray

Android性能优化之使用SparseArray代替HashMap

最近在重构one的项目，其中用HashMap来缓存ActivityGroup加载过的View，Android Studio给出了一个警告，之前考虑项目进度没怎么在意，这次仔细看了下提示，如下：

Use new SparseArray<View> (...) instead for better performance

意思就是说用SparseArray来替代，以获取更好的性能。对SparseArray根本不熟悉，甚至都没听过，第一感觉应该是Android提供的类，于是ctl+鼠标左键进入SparseArray的源码，果不其然，确实是Android提供的一个工具类,部分源码如下：

/*
 * Copyright (C) 2006 The Android Open Source Project
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package android.util;

import com.android.internal.util.ArrayUtils;
import com.android.internal.util.GrowingArrayUtils;

import libcore.util.EmptyArray;

/**
 * SparseArrays map integers to Objects.  Unlike a normal array of Objects,
 * there can be gaps in the indices.  It is intended to be more memory efficient
 * than using a HashMap to map Integers to Objects, both because it avoids
 * auto-boxing keys and its data structure doesn't rely on an extra entry object
 * for each mapping.
 *
 * <p>Note that this container keeps its mappings in an array data structure,
 * using a binary search to find keys.  The implementation is not intended to be appropriate for
 * data structures
 * that may contain large numbers of items.  It is generally slower than a traditional
 * HashMap, since lookups require a binary search and adds and removes require inserting
 * and deleting entries in the array.  For containers holding up to hundreds of items,
 * the performance difference is not significant, less than 50%.</p>
 *
 * <p>To help with performance, the container includes an optimization when removing
 * keys: instead of compacting its array immediately, it leaves the removed entry marked
 * as deleted.  The entry can then be re-used for the same key, or compacted later in
 * a single garbage collection step of all removed entries.  This garbage collection will
 * need to be performed at any time the array needs to be grown or the the map size or
 * entry values are retrieved.</p>
 *
 * <p>It is possible to iterate over the items in this container using
 * {@link #keyAt(int)} and {@link #valueAt(int)}. Iterating over the keys using
 * <code>keyAt(int)</code> with ascending values of the index will return the
 * keys in ascending order, or the values corresponding to the keys in ascending
 * order in the case of <code>valueAt(int)</code>.</p>
 */
public class SparseArray<E> implements Cloneable {
    private static final Object DELETED = new Object();
    private boolean mGarbage = false;

    private int[] mKeys;
    private Object[] mValues;
    private int mSize;

    /**
     * Creates a new SparseArray containing no mappings.
     */
    public SparseArray() {
        this(10);
    }

    /**
     * Creates a new SparseArray containing no mappings that will not
     * require any additional memory allocation to store the specified
     * number of mappings.  If you supply an initial capacity of 0, the
     * sparse array will be initialized with a light-weight representation
     * not requiring any additional array allocations.
     */
    public SparseArray(int initialCapacity) {
        if (initialCapacity == 0) {
            mKeys = EmptyArray.INT;
            mValues = EmptyArray.OBJECT;
        } else {
            mValues = ArrayUtils.newUnpaddedObjectArray(initialCapacity);
            mKeys = new int[mValues.length];
        }
        mSize = 0;
    }

    @Override
    @SuppressWarnings("unchecked")
    public SparseArray<E> clone() {
        SparseArray<E> clone = null;
        try {
            clone = (SparseArray<E>) super.clone();
            clone.mKeys = mKeys.clone();
            clone.mValues = mValues.clone();
        } catch (CloneNotSupportedException cnse) {
            /* ignore */
        }
        return clone;
    }

    /**
     * Gets the Object mapped from the specified key, or <code>null</code>
     * if no such mapping has been made.
     */
    public E get(int key) {
        return get(key, null);
    }

    /**
     * Gets the Object mapped from the specified key, or the specified Object
     * if no such mapping has been made.
     */
    @SuppressWarnings("unchecked")
    public E get(int key, E valueIfKeyNotFound) {
        int i = ContainerHelpers.binarySearch(mKeys, mSize, key);

        if (i < 0 || mValues[i] == DELETED) {
            return valueIfKeyNotFound;
        } else {
            return (E) mValues[i];
        }
    }

    /**
     * Removes the mapping from the specified key, if there was any.
     */
    public void delete(int key) {
        int i = ContainerHelpers.binarySearch(mKeys, mSize, key);

        if (i >= 0) {
            if (mValues[i] != DELETED) {
                mValues[i] = DELETED;
                mGarbage = true;
            }
        }
    }

    /**
     * @hide
     * Removes the mapping from the specified key, if there was any, returning the old value.
     */
    public E removeReturnOld(int key) {
        int i = ContainerHelpers.binarySearch(mKeys, mSize, key);

        if (i >= 0) {
            if (mValues[i] != DELETED) {
                final E old = (E) mValues[i];
                mValues[i] = DELETED;
                mGarbage = true;
                return old;
            }
        }
        return null;
    }

    /**
     * Alias for {@link #delete(int)}.
     */
    public void remove(int key) {
        delete(key);
    }

    /**
     * Removes the mapping at the specified index.
     */
    public void removeAt(int index) {
        if (mValues[index] != DELETED) {
            mValues[index] = DELETED;
            mGarbage = true;
        }
    }

    /**
     * Remove a range of mappings as a batch.
     *
     * @param index Index to begin at
     * @param size Number of mappings to remove
     */
    public void removeAtRange(int index, int size) {
        final int end = Math.min(mSize, index + size);
        for (int i = index; i < end; i++) {
            removeAt(i);
        }
    }

    private void gc() {
        // Log.e("SparseArray", "gc start with " + mSize);

        int n = mSize;
        int o = 0;
        int[] keys = mKeys;
        Object[] values = mValues;

        for (int i = 0; i < n; i++) {
            Object val = values[i];

            if (val != DELETED) {
                if (i != o) {
                    keys[o] = keys[i];
                    values[o] = val;
                    values[i] = null;
                }

                o++;
            }
        }

        mGarbage = false;
        mSize = o;

        // Log.e("SparseArray", "gc end with " + mSize);
    }
 ……

mSize++;单纯从字面上来理解，SparseArray指的是稀疏数组(Sparse array)，所谓稀疏数组就是数组中大部分的内容值都未被使用（或都为零），在数组中仅有少部分的空间使用。因此造成内存空间的浪费，为了节省内存空间，并且不影响数组中原有的内容值，我们可以采用一种压缩的方式来表示稀疏数组的内容。

继续阅读SparseArray的源码，从构造方法我们可以看出，它和一般的List一样，可以预先设置容器大小，默认的大小是10：

public SparseArray() {

this(10);

}

public SparseArray(int initialCapacity) {

initialCapacity = ArrayUtils.idealIntArraySize(initialCapacity);

mKeys = new int[initialCapacity];

mValues = new Object[initialCapacity];

mSize = 0;

}

再来看看它对数据的“增删改查”。

public void put(int key, E value) {}

public void append(int key, E value){}

修改数据起初以为只有setValueAt(int index, E value)可以修改数据，但后来发现put(int key, E value)也可以修改数据，我们查看put(int key, E value)的源码可知，在put数据之前，会先查找要put的数据是否已经存在，如果存在就是修改，不存在就添加。

public void put(int key, E value) {

int i = binarySearch(mKeys, 0, mSize, key);

if (i >= 0) {

mValues[i] = value;

} else {

i = ~i;

if (i < mSize && mValues[i] == DELETED) {

mKeys[i] = key;

mValues[i] = value;

return;

}

if (mGarbage && mSize >= mKeys.length) {

gc();

// Search again because indices may have changed.

i = ~binarySearch(mKeys, 0, mSize, key);

}

if (mSize >= mKeys.length) {

int n = ArrayUtils.idealIntArraySize(mSize + 1);

int[] nkeys = new int[n];

Object[] nvalues = new Object[n];

// Log.e("SparseArray", "grow " + mKeys.length + " to " + n);

System.arraycopy(mKeys, 0, nkeys, 0, mKeys.length);

System.arraycopy(mValues, 0, nvalues, 0, mValues.length);

mKeys = nkeys;

mValues = nvalues;

}

if (mSize - i != 0) {

// Log.e("SparseArray", "move " + (mSize - i));

System.arraycopy(mKeys, i, mKeys, i + 1, mSize - i);

System.arraycopy(mValues, i, mValues, i + 1, mSize - i);

}

mKeys[i] = key;

mValues[i] = value;

}

所以，修改数据实际也有两种方法：

public void put(int key, E value)

public void setValueAt(int index, E value)

最后再来看看如何查找数据。有两个方法可以查询取值：

public E get(int key)

public E get(int key, E valueIfKeyNotFound)

其中get(int key)也只是调用了 get(int key,E valueIfKeyNotFound),最后一个从传参的变量名就能看出，传入的是找不到的时候返回的值.get(int key)当找不到的时候，默认返回null。

查看第几个位置的键：

public int keyAt(int index)

有一点需要注意的是，查看键所在位置，由于是采用二分法查找键的位置，所以找不到时返回小于0的数值，而不是返回-1。返回的负值是表示它在找不到时所在的位置。

查看第几个位置的值：

public E valueAt(int index)

查看值所在位置，没有的话返回-1：

public int indexOfValue(E value)

最后，发现其核心就是折半查找函数（binarySearch），算法设计的很不错。

private static int binarySearch(int[] a, int start, int len, int key) {

int high = start + len, low = start - 1, guess;

while (high - low > 1) {

guess = (high + low) / 2;

if (a[guess] < key)

low = guess;

else

high = guess;

}

if (high == start + len)

return ~(start + len);

else if (a[high] == key)

return high;

else

return ~high;

}

相应的也有SparseBooleanArray，用来取代HashMap<Integer, Boolean>，SparseIntArray用来取代HashMap<Integer, Integer>，大家有兴趣的可以研究。

SparseArray是android里为<Interger,Object>这样的Hashmap而专门写的类,目的是提高效率，其核心是折半查找函数（binarySearch）。在Android中，当我们需要定义

HashMap<Integer, E> hashMap = new HashMap<Integer, E>();

时，我们可以使用如下的方式来取得更好的性能.

SparseArray<E> sparseArray = new SparseArray<E>();

Android性能优化之SparseArray

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