Bloom Filter是一种空间效率很高的随机数据结构，它利用位数组很简洁地表示一个集合，并能判断一个元素是否属于这个集合。Bloom Filter的这种高效是有一定代价的：在判断一个元素是否属于某个集合时，有可能会把不属于这个集合的元素误认为属于这个集合（false positive）。因此，Bloom Filter不适合那些“零错误”的应用场合。而在能容忍低错误率的应用场合下，Bloom Filter通过极少的错误换取了存储空间的极大节省。
集合表示和元素查询
下面我们具体来看Bloom Filter是如何用位数组表示集合的。初始状态时，Bloom Filter是一个包含m位的位数组，每一位都置为0。
为了表达S={x1, x2,…,xn}这样一个n个元素的集合，Bloom Filter使用k个相互独立的哈希函数（Hash Function），它们分别将集合中的每个元素映射到{1,…,m}的范围中。对任意一个元素x，第i个哈希函数映射的位置hi(x)就会被置为1（1≤i≤k）。注意，如果一个位置多次被置为1，那么只有第一次会起作用，后面几次将没有任何效果。在下图中，k=3，且有两个哈希函数选中同一个位置（从左边数第五位）。
在判断y是否属于这个集合时，我们对y应用k次哈希函数，如果所有hi(y)的位置都是1（1≤i≤k），那么我们就认为y是集合中的元素，否则就认为y不是集合中的元素。下图中y1就不是集合中的元素。y2或者属于这个集合，或者刚好是一个false positive。
错误率估计
前面我们已经提到了，Bloom Filter在判断一个元素是否属于它表示的集合时会有一定的错误率（false positive rate），下面我们就来估计错误率的大小。在估计之前为了简化模型，我们假设kn<m且各个哈希函数是完全随机的。当集合S={x1, x2,…,xn}的所有元素都被k个哈希函数映射到m位的位数组中时，这个位数组中某一位还是0的概率是：
其中1/m表示任意一个哈希函数选中这一位的概率（前提是哈希函数是完全随机的），(1-1/m)表示哈希一次没有选中这一位的概率。要把S完全映射到位数组中，需要做kn次哈希。某一位还是0意味着kn次哈希都没有选中它，因此这个概率就是（1-1/m）的kn次方。令p = e-kn/m是为了简化运算，这里用到了计算e时常用的近似：
令ρ为位数组中0的比例，则ρ的数学期望E(ρ)= p’。在ρ已知的情况下，要求的错误率（false positive rate）为：
(1-ρ)为位数组中1的比例，(1-ρ)k就表示k次哈希都刚好选中1的区域，即false positive rate。上式中第二步近似在前面已经提到了，现在来看第一步近似。p’只是ρ的数学期望，在实际中ρ的值有可能偏离它的数学期望值。M. Mitzenmacher已经证明[2] ，位数组中0的比例非常集中地分布在它的数学期望值的附近。因此，第一步的近似得以成立。分别将p和p’代入上式中，得：
相比p’和f’，使用p和f通常在分析中更为方便。
最优的哈希函数个数
既然Bloom Filter要靠多个哈希函数将集合映射到位数组中，那么应该选择几个哈希函数才能使元素查询时的错误率降到最低呢？这里有两个互斥的理由：如果哈希函数的个数多，那么在对一个不属于集合的元素进行查询时得到0的概率就大；但另一方面，如果哈希函数的个数少，那么位数组中的0就多。为了得到最优的哈希函数个数，我们需要根据上一小节中的错误率公式进行计算。
先用p和f进行计算。注意到f = exp(k ln(1 − e−kn/m))，我们令g = k ln(1 − e−kn/m)，只要让g取到最小，f自然也取到最小。由于p = e-kn/m，我们可以将g写成
根据对称性法则可以很容易看出当p = 1/2，也就是k = ln2· (m/n)时，g取得最小值。在这种情况下，最小错误率f等于(1/2)k ≈ (0.6185)m/n。另外，注意到p是位数组中某一位仍是0的概率，所以p = 1/2对应着位数组中0和1各一半。换句话说，要想保持错误率低，最好让位数组有一半还空着。
需要强调的一点是，p = 1/2时错误率最小这个结果并不依赖于近似值p和f。同样对于f’ = exp(k ln(1 − (1 − 1/m)kn))，g’ = k ln(1 − (1 − 1/m)kn)，p’ = (1 − 1/m)kn，我们可以将g’写成
同样根据对称性法则可以得到当p’ = 1/2时，g’取得最小值。
位数组的大小
下面我们来看看，在不超过一定错误率的情况下，Bloom Filter至少需要多少位才能表示全集中任意n个元素的集合。假设全集中共有u个元素，允许的最大错误率为є，下面我们来求位数组的位数m。
假设X为全集中任取n个元素的集合，F(X)是表示X的位数组。那么对于集合X中任意一个元素x，在s = F(X)中查询x都能得到肯定的结果，即s能够接受x。显然，由于Bloom Filter引入了错误，s能够接受的不仅仅是X中的元素，它还能够є (u - n)个false positive。因此，对于一个确定的位数组来说，它能够接受总共n + є (u - n)个元素。在n + є (u - n)个元素中，s真正表示的只有其中n个，所以一个确定的位数组可以表示
个集合。m位的位数组共有2m个不同的组合，进而可以推出，m位的位数组可以表示
个集合。全集中n个元素的集合总共有
个，因此要让m位的位数组能够表示所有n个元素的集合，必须有
即：
上式中的近似前提是n和єu相比很小，这也是实际情况中常常发生的。根据上式，我们得出结论：在错误率不大于є的情况下，m至少要等于n log2(1/є)才能表示任意n个元素的集合。
上一小节中我们曾算出当k = ln2· (m/n)时错误率f最小，这时f = (1/2)k = (1/2)mln2 / n。现在令f≤є，可以推出
这个结果比前面我们算得的下界n log2(1/є)大了log2 e ≈ 1.44倍。这说明在哈希函数的个数取到最优时，要让错误率不超过є，m至少需要取到最小值的1.44倍。
总结
在计算机科学中，我们常常会碰到时间换空间或者空间换时间的情况，即为了达到某一个方面的最优而牺牲另一个方面。Bloom Filter在时间空间这两个因素之外又引入了另一个因素：错误率。在使用Bloom Filter判断一个元素是否属于某个集合时，会有一定的错误率。也就是说，有可能把不属于这个集合的元素误认为属于这个集合（False Positive），但不会把属于这个集合的元素误认为不属于这个集合（False Negative）。在增加了错误率这个因素之后，Bloom Filter通过允许少量的错误来节省大量的存储空间。
自从Burton Bloom在70年代提出Bloom Filter之后，Bloom Filter就被广泛用于拼写检查和数据库系统中。近一二十年，伴随着网络的普及和发展，Bloom Filter在网络领域获得了新生，各种Bloom Filter变种和新的应用不断出现。可以预见，随着网络应用的不断深入，新的变种和应用将会继续出现，Bloom Filter必将获得更大的发展。
参考资料
[1] A. Broder and M. Mitzenmacher. Network applications of bloom filters: A survey. Internet Mathematics, 1(4):485–509, 2005.
[2] M. Mitzenmacher. Compressed Bloom Filters. IEEE/ACM Transactions on Networking 10:5 (2002), 604—612.
[3] www.cs.jhu.edu/~fabian/courses/CS600.624/slides/bloomslides.pdf
[4] http://166.111.248.20/seminar/2006_11_23/hash_2_yaxuan.ppt
===============================java实现=================================================
摘自 https://github.com/MagnusS/Java-BloomFilter
| /** | |
| * This program is free software: you can redistribute it and/or modify | |
| * it under the terms of the GNU Lesser General Public License as published by | |
| * the Free Software Foundation, either version 3 of the License, or | |
| * (at your option) any later version. | |
| * | |
| * This program is distributed in the hope that it will be useful, | |
| * but WITHOUT ANY WARRANTY; without even the implied warranty of | |
| * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | |
| * GNU Lesser General Public License for more details. | |
| * | |
| * You should have received a copy of the GNU Lesser General Public License | |
| * along with this program. If not, see <http://www.gnu.org/licenses/>. | |
| */ | |
| package com.skjegstad.utils; | |
| import java.io.Serializable; | |
| import java.nio.charset.Charset; | |
| import java.security.MessageDigest; | |
| import java.security.NoSuchAlgorithmException; | |
| import java.util.BitSet; | |
| import java.util.Collection; | |
| /** | |
| * Implementation of a Bloom-filter, as described here: | |
| * http://en.wikipedia.org/wiki/Bloom_filter | |
| * | |
| * For updates and bugfixes, see http://github.com/magnuss/java-bloomfilter | |
| * | |
| * Inspired by the SimpleBloomFilter-class written by Ian Clarke. This | |
| * implementation provides a more evenly distributed Hash-function by | |
| * using a proper digest instead of the Java RNG. Many of the changes | |
| * were proposed in comments in his blog: | |
| * http://blog.locut.us/2008/01/12/a-decent-stand-alone-java-bloom-filter-implementation/ | |
| * | |
| * @param <E> Object type that is to be inserted into the Bloom filter, e.g. String or Integer. | |
| * @author Magnus Skjegstad <[email protected]> | |
| */ | |
| public class BloomFilter<E> implements Serializable { | |
| private BitSet bitset; | |
| private int bitSetSize; | |
| private double bitsPerElement; | |
| private int expectedNumberOfFilterElements; // expected (maximum) number of elements to be added | |
| private int numberOfAddedElements; // number of elements actually added to the Bloom filter | |
| private int k; // number of hash functions | |
| static final Charset charset = Charset.forName("UTF-8"); // encoding used for storing hash values as strings | |
| static final String hashName = "MD5"; // MD5 gives good enough accuracy in most circumstances. Change to SHA1 if it's needed | |
| static final MessageDigest digestFunction; | |
| static { // The digest method is reused between instances | |
| MessageDigest tmp; | |
| try { | |
| tmp = java.security.MessageDigest.getInstance(hashName); | |
| } catch (NoSuchAlgorithmException e) { | |
| tmp = null; | |
| } | |
| digestFunction = tmp; | |
| } | |
| /** | |
| * Constructs an empty Bloom filter. The total length of the Bloom filter will be | |
| * c*n. | |
| * | |
| * @param c is the number of bits used per element. | |
| * @param n is the expected number of elements the filter will contain. | |
| * @param k is the number of hash functions used. | |
| */ | |
| public BloomFilter(double c, int n, int k) { | |
| this.expectedNumberOfFilterElements = n; | |
| this.k = k; | |
| this.bitsPerElement = c; | |
| this.bitSetSize = (int)Math.ceil(c * n); | |
| numberOfAddedElements = 0; | |
| this.bitset = new BitSet(bitSetSize); | |
| } | |
| /** | |
| * Constructs an empty Bloom filter. The optimal number of hash functions (k) is estimated from the total size of the Bloom | |
| * and the number of expected elements. | |
| * | |
| * @param bitSetSize defines how many bits should be used in total for the filter. | |
| * @param expectedNumberOElements defines the maximum number of elements the filter is expected to contain. | |
| */ | |
| public BloomFilter(int bitSetSize, int expectedNumberOElements) { | |
| this(bitSetSize / (double)expectedNumberOElements, | |
| expectedNumberOElements, | |
| (int) Math.round((bitSetSize / (double)expectedNumberOElements) * Math.log(2.0))); | |
| } | |
| /** | |
| * Constructs an empty Bloom filter with a given false positive probability. The number of bits per | |
| * element and the number of hash functions is estimated | |
| * to match the false positive probability. | |
| * | |
| * @param falsePositiveProbability is the desired false positive probability. | |
| * @param expectedNumberOfElements is the expected number of elements in the Bloom filter. | |
| */ | |
| public BloomFilter(double falsePositiveProbability, int expectedNumberOfElements) { | |
| this(Math.ceil(-(Math.log(falsePositiveProbability) / Math.log(2))) / Math.log(2), // c = k / ln(2) | |
| expectedNumberOfElements, | |
| (int)Math.ceil(-(Math.log(falsePositiveProbability) / Math.log(2)))); // k = ceil(-log_2(false prob.)) | |
| } | |
| /** | |
| * Construct a new Bloom filter based on existing Bloom filter data. | |
| * | |
| * @param bitSetSize defines how many bits should be used for the filter. | |
| * @param expectedNumberOfFilterElements defines the maximum number of elements the filter is expected to contain. | |
| * @param actualNumberOfFilterElements specifies how many elements have been inserted into the <code>filterData</code> BitSet. | |
| * @param filterData a BitSet representing an existing Bloom filter. | |
| */ | |
| public BloomFilter(int bitSetSize, int expectedNumberOfFilterElements, int actualNumberOfFilterElements, BitSet filterData) { | |
| this(bitSetSize, expectedNumberOfFilterElements); | |
| this.bitset = filterData; | |
| this.numberOfAddedElements = actualNumberOfFilterElements; | |
| } | |
| /** | |
| * Generates a digest based on the contents of a String. | |
| * | |
| * @param val specifies the input data. | |
| * @param charset specifies the encoding of the input data. | |
| * @return digest as long. | |
| */ | |
| public static int createHash(String val, Charset charset) { | |
| return createHash(val.getBytes(charset)); | |
| } | |
| /** | |
| * Generates a digest based on the contents of a String. | |
| * | |
| * @param val specifies the input data. The encoding is expected to be UTF-8. | |
| * @return digest as long. | |
| */ | |
| public static int createHash(String val) { | |
| return createHash(val, charset); | |
| } | |
| /** | |
| * Generates a digest based on the contents of an array of bytes. | |
| * | |
| * @param data specifies input data. | |
| * @return digest as long. | |
| */ | |
| public static int createHash(byte[] data) { | |
| return createHashes(data, 1)[0]; | |
| } | |
| /** | |
| * Generates digests based on the contents of an array of bytes and splits the result into 4-byte int's and store them in an array. The | |
| * digest function is called until the required number of int's are produced. For each call to digest a salt | |
| * is prepended to the data. The salt is increased by 1 for each call. | |
| * | |
| * @param data specifies input data. | |
| * @param hashes number of hashes/int's to produce. | |
| * @return array of int-sized hashes | |
| */ | |
| public static int[] createHashes(byte[] data, int hashes) { | |
| int[] result = new int[hashes]; | |
| int k = 0; | |
| byte salt = 0; | |
| while (k < hashes) { | |
| byte[] digest; | |
| synchronized (digestFunction) { | |
| digestFunction.update(salt); | |
| salt++; | |
| digest = digestFunction.digest(data); | |
| } | |
| for (int i = 0; i < digest.length/4 && k < hashes; i++) { | |
| int h = 0; | |
| for (int j = (i*4); j < (i*4)+4; j++) { | |
| h <<= 8; | |
| h |= ((int) digest[j]) & 0xFF; | |
| } | |
| result[k] = h; | |
| k++; | |
| } | |
| } | |
| return result; | |
| } | |
| /** | |
| * Compares the contents of two instances to see if they are equal. | |
| * | |
| * @param obj is the object to compare to. | |
| * @return True if the contents of the objects are equal. | |
| */ | |
| @Override | |
| public boolean equals(Object obj) { | |
| if (obj == null) { | |
| return false; | |
| } | |
| if (getClass() != obj.getClass()) { | |
| return false; | |
| } | |
| final BloomFilter<E> other = (BloomFilter<E>) obj; | |
| if (this.expectedNumberOfFilterElements != other.expectedNumberOfFilterElements) { | |
| return false; | |
| } | |
| if (this.k != other.k) { | |
| return false; | |
| } | |
| if (this.bitSetSize != other.bitSetSize) { | |
| return false; | |
| } | |
| if (this.bitset != other.bitset && (this.bitset == null || !this.bitset.equals(other.bitset))) { | |
| return false; | |
| } | |
| return true; | |
| } | |
| /** | |
| * Calculates a hash code for this class. | |
| * @return hash code representing the contents of an instance of this class. | |
| */ | |
| @Override | |
| public int hashCode() { | |
| int hash = 7; | |
| hash = 61 * hash + (this.bitset != null ? this.bitset.hashCode() : 0); | |
| hash = 61 * hash + this.expectedNumberOfFilterElements; | |
| hash = 61 * hash + this.bitSetSize; | |
| hash = 61 * hash + this.k; | |
| return hash; | |
| } | |
| /** | |
| * Calculates the expected probability of false positives based on | |
| * the number of expected filter elements and the size of the Bloom filter. | |
| * <br /><br /> | |
| * The value returned by this method is the <i>expected</i> rate of false | |
| * positives, assuming the number of inserted elements equals the number of | |
| * expected elements. If the number of elements in the Bloom filter is less | |
| * than the expected value, the true probability of false positives will be lower. | |
| * | |
| * @return expected probability of false positives. | |
| */ | |
| public double expectedFalsePositiveProbability() { | |
| return getFalsePositiveProbability(expectedNumberOfFilterElements); | |
| } | |
| /** | |
| * Calculate the probability of a false positive given the specified | |
| * number of inserted elements. | |
| * | |
| * @param numberOfElements number of inserted elements. | |
| * @return probability of a false positive. | |
| */ | |
| public double getFalsePositiveProbability(double numberOfElements) { | |
| // (1 - e^(-k * n / m)) ^ k | |
| return Math.pow((1 - Math.exp(-k * (double) numberOfElements | |
| / (double) bitSetSize)), k); | |
| } | |
| /** | |
| * Get the current probability of a false positive. The probability is calculated from | |
| * the size of the Bloom filter and the current number of elements added to it. | |
| * | |
| * @return probability of false positives. | |
| */ | |
| public double getFalsePositiveProbability() { | |
| return getFalsePositiveProbability(numberOfAddedElements); | |
| } | |
| /** | |
| * Returns the value chosen for K.<br /> | |
| * <br /> | |
| * K is the optimal number of hash functions based on the size | |
| * of the Bloom filter and the expected number of inserted elements. | |
| * | |
| * @return optimal k. | |
| */ | |
| public int getK() { | |
| return k; | |
| } | |
| /** | |
| * Sets all bits to false in the Bloom filter. | |
| */ | |
| public void clear() { | |
| bitset.clear(); | |
| numberOfAddedElements = 0; | |
| } | |
| /** | |
| * Adds an object to the Bloom filter. The output from the object's | |
| * toString() method is used as input to the hash functions. | |
| * | |
| * @param element is an element to register in the Bloom filter. | |
| */ | |
| public void add(E element) { | |
| add(element.toString().getBytes(charset)); | |
| } | |
| /** | |
| * Adds an array of bytes to the Bloom filter. | |
| * | |
| * @param bytes array of bytes to add to the Bloom filter. | |
| */ | |
| public void add(byte[] bytes) { | |
| int[] hashes = createHashes(bytes, k); | |
| for (int hash : hashes) | |
| bitset.set(Math.abs(hash % bitSetSize), true); | |
| numberOfAddedElements ++; | |
| } | |
| /** | |
| * Adds all elements from a Collection to the Bloom filter. | |
| * @param c Collection of elements. | |
| */ | |
| public void addAll(Collection<? extends E> c) { | |
| for (E element : c) | |
| add(element); | |
| } | |
| /** | |
| * Returns true if the element could have been inserted into the Bloom filter. | |
| * Use getFalsePositiveProbability() to calculate the probability of this | |
| * being correct. | |
| * | |
| * @param element element to check. | |
| * @return true if the element could have been inserted into the Bloom filter. | |
| */ | |
| public boolean contains(E element) { | |
| return contains(element.toString().getBytes(charset)); | |
| } | |
| /** | |
| * Returns true if the array of bytes could have been inserted into the Bloom filter. | |
| * Use getFalsePositiveProbability() to calculate the probability of this | |
| * being correct. | |
| * | |
| * @param bytes array of bytes to check. | |
| * @return true if the array could have been inserted into the Bloom filter. | |
| */ | |
| public boolean contains(byte[] bytes) { | |
| int[] hashes = createHashes(bytes, k); | |
| for (int hash : hashes) { | |
| if (!bitset.get(Math.abs(hash % bitSetSize))) { | |
| return false; | |
| } | |
| } | |
| return true; | |
| } | |
| /** | |
| * Returns true if all the elements of a Collection could have been inserted | |
| * into the Bloom filter. Use getFalsePositiveProbability() to calculate the | |
| * probability of this being correct. | |
| * @param c elements to check. | |
| * @return true if all the elements in c could have been inserted into the Bloom filter. | |
| */ | |
| public boolean containsAll(Collection<? extends E> c) { | |
| for (E element : c) | |
| if (!contains(element)) | |
| return false; | |
| return true; | |
| } | |
| /** | |
| * Read a single bit from the Bloom filter. | |
| * @param bit the bit to read. | |
| * @return true if the bit is set, false if it is not. | |
| */ | |
| public boolean getBit(int bit) { | |
| return bitset.get(bit); | |
| } | |
| /** | |
| * Set a single bit in the Bloom filter. | |
| * @param bit is the bit to set. | |
| * @param value If true, the bit is set. If false, the bit is cleared. | |
| */ | |
| public void setBit(int bit, boolean value) { | |
| bitset.set(bit, value); | |
| } | |
| /** | |
| * Return the bit set used to store the Bloom filter. | |
| * @return bit set representing the Bloom filter. | |
| */ | |
| public BitSet getBitSet() { | |
| return bitset; | |
| } | |
| /** | |
| * Returns the number of bits in the Bloom filter. Use count() to retrieve | |
| * the number of inserted elements. | |
| * | |
| * @return the size of the bitset used by the Bloom filter. | |
| */ | |
| public int size() { | |
| return this.bitSetSize; | |
| } | |
| /** | |
| * Returns the number of elements added to the Bloom filter after it | |
| * was constructed or after clear() was called. | |
| * | |
| * @return number of elements added to the Bloom filter. | |
| */ | |
| public int count() { | |
| return this.numberOfAddedElements; | |
| } | |
| /** | |
| * Returns the expected number of elements to be inserted into the filter. | |
| * This value is the same value as the one passed to the constructor. | |
| * | |
| * @return expected number of elements. | |
| */ | |
| public int getExpectedNumberOfElements() { | |
| return expectedNumberOfFilterElements; | |
| } | |
| /** | |
| * Get expected number of bits per element when the Bloom filter is full. This value is set by the constructor | |
| * when the Bloom filter is created. See also getBitsPerElement(). | |
| * | |
| * @return expected number of bits per element. | |
| */ | |
| public double getExpectedBitsPerElement() { | |
| return this.bitsPerElement; | |
| } | |
| /** | |
| * Get actual number of bits per element based on the number of elements that have currently been inserted and the length | |
| * of the Bloom filter. See also getExpectedBitsPerElement(). | |
| * | |
| * @return number of bits per element. | |
| */ | |
| public double getBitsPerElement() { | |
| return this.bitSetSize / (double)numberOfAddedElements; | |
| } | |
| } |
| /** | |
| * This program is free software: you can redistribute it and/or modify | |
| * it under the terms of the GNU Lesser General Public License as published by | |
| * the Free Software Foundation, either version 3 of the License, or | |
| * (at your option) any later version. | |
| * | |
| * This program is distributed in the hope that it will be useful, | |
| * but WITHOUT ANY WARRANTY; without even the implied warranty of | |
| * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | |
| * GNU Lesser General Public License for more details. | |
| * | |
| * You should have received a copy of the GNU Lesser General Public License | |
| * along with this program. If not, see <http://www.gnu.org/licenses/>. | |
| */ | |
| package com.skjegstad.utils; | |
| import java.util.ArrayList; | |
| import java.util.List; | |
| import java.util.Random; | |
| /** | |
| * A (very) simple benchmark to evaluate the performance of the Bloom filter class. | |
| * | |
| * @author Magnus Skjegstad | |
| */ | |
| public class BloomfilterBenchmark { | |
| static int elementCount = 50000; // Number of elements to test | |
| public static void printStat(long start, long end) { | |
| double diff = (end - start) / 1000.0; | |
| System.out.println(diff + "s, " + (elementCount / diff) + " elements/s"); | |
| } | |
| public static void main(String[] argv) { | |
| final Random r = new Random(); | |
| // Generate elements first | |
| List<String> existingElements = new ArrayList(elementCount); | |
| for (int i = 0; i < elementCount; i++) { | |
| byte[] b = new byte[200]; | |
| r.nextBytes(b); | |
| existingElements.add(new String(b)); | |
| } | |
| List<String> nonExistingElements = new ArrayList(elementCount); | |
| for (int i = 0; i < elementCount; i++) { | |
| byte[] b = new byte[200]; | |
| r.nextBytes(b); | |
| nonExistingElements.add(new String(b)); | |
| } | |
| BloomFilter<String> bf = new BloomFilter<String>(0.001, elementCount); | |
| System.out.println("Testing " + elementCount + " elements"); | |
| System.out.println("k is " + bf.getK()); | |
| // Add elements | |
| System.out.print("add(): "); | |
| long start_add = System.currentTimeMillis(); | |
| for (int i = 0; i < elementCount; i++) { | |
| bf.add(existingElements.get(i)); | |
| } | |
| long end_add = System.currentTimeMillis(); | |
| printStat(start_add, end_add); | |
| // Check for existing elements with contains() | |
| System.out.print("contains(), existing: "); | |
| long start_contains = System.currentTimeMillis(); | |
| for (int i = 0; i < elementCount; i++) { | |
| bf.contains(existingElements.get(i)); | |
| } | |
| long end_contains = System.currentTimeMillis(); | |
| printStat(start_contains, end_contains); | |
| // Check for existing elements with containsAll() | |
| System.out.print("containsAll(), existing: "); | |
| long start_containsAll = System.currentTimeMillis(); | |
| for (int i = 0; i < elementCount; i++) { | |
| bf.contains(existingElements.get(i)); | |
| } | |
| long end_containsAll = System.currentTimeMillis(); | |
| printStat(start_containsAll, end_containsAll); | |
| // Check for nonexisting elements with contains() | |
| System.out.print("contains(), nonexisting: "); | |
| long start_ncontains = System.currentTimeMillis(); | |
| for (int i = 0; i < elementCount; i++) { | |
| bf.contains(nonExistingElements.get(i)); | |
| } | |
| long end_ncontains = System.currentTimeMillis(); | |
| printStat(start_ncontains, end_ncontains); | |
| // Check for nonexisting elements with containsAll() | |
| System.out.print("containsAll(), nonexisting: "); | |
| long start_ncontainsAll = System.currentTimeMillis(); | |
| for (int i = 0; i < elementCount; i++) { | |
| bf.contains(nonExistingElements.get(i)); | |
| } | |
| long end_ncontainsAll = System.currentTimeMillis(); | |
| printStat(start_ncontainsAll, end_ncontainsAll); | |
| } | |
| } |