HADOOP-1757 Bloomfilters: single argument constructor, use enum for bloom filter types
git-svn-id: https://svn.apache.org/repos/asf/lucene/hadoop/trunk/src/contrib/hbase@570270 13f79535-47bb-0310-9956-ffa450edef68
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@ -25,6 +25,8 @@ Trunk (unreleased changes)
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IMPROVEMENTS
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HADOOP-1737 Make HColumnDescriptor data publically members settable
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HADOOP-1746 Clean up findbugs warnings
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HADOOP-1757 Bloomfilters: single argument constructor, use enum for bloom
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filter types
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Below are the list of changes before 2007-08-18
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@ -27,40 +27,88 @@ import org.apache.hadoop.io.WritableComparable;
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/**
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* Supplied as a parameter to HColumnDescriptor to specify what kind of
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* bloom filter to use for a column, and its configuration parameters
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* bloom filter to use for a column, and its configuration parameters.
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*
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* There is no way to automatically determine the vector size and the number of
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* hash functions to use. In particular, bloom filters are very sensitive to the
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* number of elements inserted into them. For HBase, the number of entries
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* depends on the size of the data stored in the column. Currently the default
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* region size is 64MB, so the number of entries is approximately
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* 64MB / (average value size for column).
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*
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* If m denotes the number of bits in the Bloom filter (vectorSize),
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* n denotes the number of elements inserted into the Bloom filter and
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* k represents the number of hash functions used (nbHash), then according to
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* Broder and Mitzenmacher,
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*
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* ( http://www.eecs.harvard.edu/~michaelm/NEWWORK/postscripts/BloomFilterSurvey.pdf )
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*
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* the probability of false positives is minimized when k is approximately
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* m/n ln(2).
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*
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*/
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public class BloomFilterDescriptor implements WritableComparable {
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private static final double DEFAULT_NUMBER_OF_HASH_FUNCTIONS = 4.0;
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/*
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* Specify the kind of bloom filter that will be instantiated
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*/
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/** The type of bloom filter */
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public static enum BloomFilterType {
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/** <i>Bloom filter</i>, as defined by Bloom in 1970. */
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BLOOMFILTER,
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/**
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* <i>Bloom filter</i>, as defined by Bloom in 1970.
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* <i>Counting Bloom filter</i>, as defined by Fan et al. in a ToN 2000 paper.
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*/
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public static final int BLOOMFILTER = 1;
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COUNTING_BLOOMFILTER,
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/**
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* <i>counting Bloom filter</i>, as defined by Fan et al. in a ToN 2000 paper.
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* <i>Retouched Bloom filter</i>, as defined in the CoNEXT 2006 paper.
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*/
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public static final int COUNTING_BLOOMFILTER = 2;
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/**
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* <i>retouched Bloom filter</i>, as defined in the CoNEXT 2006 paper.
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*/
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public static final int RETOUCHED_BLOOMFILTER = 3;
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RETOUCHED_BLOOMFILTER
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}
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/** Default constructor - used in conjunction with Writable */
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public BloomFilterDescriptor() {
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super();
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}
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/**
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* Creates a BloomFilterDescriptor for the specified type of filter, fixes
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* the number of hash functions to 4 and computes a vector size using:
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*
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* vectorSize = ceil((4 * n) / ln(2))
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*
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* @param type
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* @param numberOfEntries
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*/
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public BloomFilterDescriptor(final BloomFilterType type,
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final int numberOfEntries) {
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switch(type) {
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case BLOOMFILTER:
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case COUNTING_BLOOMFILTER:
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case RETOUCHED_BLOOMFILTER:
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this.filterType = type;
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break;
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default:
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throw new IllegalArgumentException("Invalid bloom filter type: " + type);
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}
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this.nbHash = (int) DEFAULT_NUMBER_OF_HASH_FUNCTIONS;
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this.vectorSize = (int) Math.ceil(
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(DEFAULT_NUMBER_OF_HASH_FUNCTIONS * (1.0 * numberOfEntries)) /
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Math.log(2.0));
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}
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/**
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* @param type The kind of bloom filter to use.
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* @param vectorSize The vector size of <i>this</i> filter.
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* @param nbHash The number of hash functions to consider.
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*/
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public BloomFilterDescriptor(int type, int vectorSize, int nbHash) {
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public BloomFilterDescriptor(final BloomFilterType type, final int vectorSize,
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final int nbHash) {
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switch(type) {
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case BLOOMFILTER:
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case COUNTING_BLOOMFILTER:
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@ -75,7 +123,7 @@ public class BloomFilterDescriptor implements WritableComparable {
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this.nbHash = nbHash;
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}
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int filterType;
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BloomFilterType filterType;
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int vectorSize;
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int nbHash;
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@ -113,7 +161,7 @@ public class BloomFilterDescriptor implements WritableComparable {
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/** {@inheritDoc} */
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@Override
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public int hashCode() {
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int result = Integer.valueOf(this.filterType).hashCode();
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int result = this.filterType.hashCode();
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result ^= Integer.valueOf(this.vectorSize).hashCode();
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result ^= Integer.valueOf(this.nbHash).hashCode();
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return result;
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@ -123,14 +171,15 @@ public class BloomFilterDescriptor implements WritableComparable {
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/** {@inheritDoc} */
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public void readFields(DataInput in) throws IOException {
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filterType = in.readInt();
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int ordinal = in.readInt();
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this.filterType = BloomFilterType.values()[ordinal];
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vectorSize = in.readInt();
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nbHash = in.readInt();
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}
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/** {@inheritDoc} */
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public void write(DataOutput out) throws IOException {
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out.writeInt(filterType);
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out.writeInt(filterType.ordinal());
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out.writeInt(vectorSize);
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out.writeInt(nbHash);
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}
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@ -140,7 +189,7 @@ public class BloomFilterDescriptor implements WritableComparable {
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/** {@inheritDoc} */
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public int compareTo(Object o) {
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BloomFilterDescriptor other = (BloomFilterDescriptor)o;
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int result = this.filterType - other.filterType;
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int result = this.filterType.ordinal() - other.filterType.ordinal();
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if(result == 0) {
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result = this.vectorSize - other.vectorSize;
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@ -31,6 +31,11 @@ import org.apache.hadoop.io.WritableComparable;
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/**
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* An HColumnDescriptor contains information about a column family such as the
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* number of versions, compression settings, etc.
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*
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* It is used as input when creating a table or adding a column. Once set, the
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* parameters that specify a column cannot be changed without deleting the
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* column and recreating it. If there is data stored in the column, it will be
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* deleted when the column is deleted.
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*/
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public class HColumnDescriptor implements WritableComparable {
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@ -317,17 +317,20 @@ class HStore implements HConstants {
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LOG.debug("loading bloom filter for " + this.storeName);
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}
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switch(family.getBloomFilter().filterType) {
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BloomFilterDescriptor.BloomFilterType type =
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family.getBloomFilter().filterType;
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case BloomFilterDescriptor.BLOOMFILTER:
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switch(type) {
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case BLOOMFILTER:
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bloomFilter = new BloomFilter();
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break;
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case BloomFilterDescriptor.COUNTING_BLOOMFILTER:
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case COUNTING_BLOOMFILTER:
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bloomFilter = new CountingBloomFilter();
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break;
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case BloomFilterDescriptor.RETOUCHED_BLOOMFILTER:
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case RETOUCHED_BLOOMFILTER:
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bloomFilter = new RetouchedBloomFilter();
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}
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FSDataInputStream in = fs.open(filterFile);
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LOG.debug("creating bloom filter for " + this.storeName);
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}
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switch(family.getBloomFilter().filterType) {
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BloomFilterDescriptor.BloomFilterType type =
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family.getBloomFilter().filterType;
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case BloomFilterDescriptor.BLOOMFILTER:
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switch(type) {
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case BLOOMFILTER:
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bloomFilter = new BloomFilter(family.getBloomFilter().vectorSize,
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family.getBloomFilter().nbHash);
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break;
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case BloomFilterDescriptor.COUNTING_BLOOMFILTER:
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case COUNTING_BLOOMFILTER:
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bloomFilter =
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new CountingBloomFilter(family.getBloomFilter().vectorSize,
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family.getBloomFilter().nbHash);
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break;
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case BloomFilterDescriptor.RETOUCHED_BLOOMFILTER:
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case RETOUCHED_BLOOMFILTER:
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bloomFilter =
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new RetouchedBloomFilter(family.getBloomFilter().vectorSize,
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family.getBloomFilter().nbHash);
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@ -19,17 +19,15 @@
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*/
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package org.apache.hadoop.hbase;
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import org.apache.log4j.Level;
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import org.apache.log4j.Logger;
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import org.apache.commons.logging.Log;
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import org.apache.commons.logging.LogFactory;
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import org.apache.hadoop.io.Text;
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/** Tests per-column bloom filters */
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public class TestBloomFilters extends HBaseClusterTestCase {
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private static final Text CONTENTS = new Text("contents:");
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static final Log LOG = LogFactory.getLog(TestBloomFilters.class);
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private HTableDescriptor desc = null;
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private HTable table = null;
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private static final Text CONTENTS = new Text("contents:");
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private static final Text[] rows = {
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new Text("wmjwjzyv"),
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/** constructor */
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public TestBloomFilters() {
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super();
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conf.set("hbase.hregion.maxunflushed", "90"); // flush cache every 100 writes
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conf.set("hbase.hregion.memcache.flush.size", "100");// flush cache every 100 bytes
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conf.set("hbase.regionserver.maxlogentries", "90"); // and roll log too
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Logger.getLogger(HRegion.class).setLevel(Level.DEBUG);
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Logger.getLogger(HStore.class).setLevel(Level.DEBUG);
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}
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/** {@inheritDoc} */
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@Override
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public void setUp() {
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/** Test that specifies explicit parameters for the bloom filter */
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public void testExplicitParameters() {
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HTable table = null;
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try {
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super.setUp();
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this.desc = new HTableDescriptor("test");
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desc.addFamily(
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new HColumnDescriptor(CONTENTS, 1, HColumnDescriptor.CompressionType.NONE,
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false, Integer.MAX_VALUE,
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// Setup
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HTableDescriptor desc = new HTableDescriptor(getName());
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BloomFilterDescriptor bloomFilter =
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new BloomFilterDescriptor( // if we insert 1000 values
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BloomFilterDescriptor.BLOOMFILTER, // plain old bloom filter
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BloomFilterDescriptor.BloomFilterType.BLOOMFILTER, // plain old bloom filter
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12499, // number of bits
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4 // number of hash functions
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))); // false positive = 0.0000001
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);
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desc.addFamily(
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new HColumnDescriptor(CONTENTS, // Column name
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1, // Max versions
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HColumnDescriptor.CompressionType.NONE, // no compression
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HColumnDescriptor.DEFAULT_IN_MEMORY, // not in memory
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HColumnDescriptor.DEFAULT_MAX_VALUE_LENGTH,
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bloomFilter
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)
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);
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// Create the table
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HBaseAdmin admin = new HBaseAdmin(conf);
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admin.createTable(desc);
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// Open table
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table = new HTable(conf, desc.getName());
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// Store some values
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e.printStackTrace();
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fail();
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}
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try {
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// Give cache flusher and log roller a chance to run
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// Otherwise we'll never hit the bloom filter, just the memcache
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Thread.sleep(conf.getLong(HConstants.THREAD_WAKE_FREQUENCY, 10 * 1000) * 2);
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} catch (InterruptedException e) {
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// ignore
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}
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/** the test */
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public void testBloomFilters() {
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try {
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if (table != null) {
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for(int i = 0; i < testKeys.length; i++) {
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byte[] value = table.get(testKeys[i], CONTENTS);
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if(value != null && value.length != 0) {
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LOG.info("non existant key: " + testKeys[i] + " returned value: " +
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new String(value, HConstants.UTF8_ENCODING));
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}
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}
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}
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} catch (Exception e) {
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e.printStackTrace();
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fail();
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}
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}
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/** Test that uses computed for the bloom filter */
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public void testComputedParameters() {
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HTable table = null;
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try {
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// Setup
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HTableDescriptor desc = new HTableDescriptor(getName());
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BloomFilterDescriptor bloomFilter =
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new BloomFilterDescriptor(
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BloomFilterDescriptor.BloomFilterType.BLOOMFILTER, // plain old bloom filter
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1000 // estimated number of entries
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);
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LOG.info("vector size: " + bloomFilter.vectorSize);
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desc.addFamily(
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new HColumnDescriptor(CONTENTS, // Column name
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1, // Max versions
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HColumnDescriptor.CompressionType.NONE, // no compression
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HColumnDescriptor.DEFAULT_IN_MEMORY, // not in memory
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HColumnDescriptor.DEFAULT_MAX_VALUE_LENGTH,
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bloomFilter
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)
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);
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// Create the table
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HBaseAdmin admin = new HBaseAdmin(conf);
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admin.createTable(desc);
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// Open table
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table = new HTable(conf, desc.getName());
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// Store some values
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for(int i = 0; i < 100; i++) {
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Text row = rows[i];
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String value = row.toString();
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long lockid = table.startUpdate(rows[i]);
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table.put(lockid, CONTENTS, value.getBytes(HConstants.UTF8_ENCODING));
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table.commit(lockid);
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}
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} catch (Exception e) {
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e.printStackTrace();
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fail();
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}
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try {
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// Give cache flusher and log roller a chance to run
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// Otherwise we'll never hit the bloom filter, just the memcache
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}
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try {
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if (table != null) {
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for(int i = 0; i < testKeys.length; i++) {
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byte[] value = table.get(testKeys[i], CONTENTS);
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if(value != null && value.length != 0) {
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System.err.println("non existant key: " + testKeys[i] +
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" returned value: " + new String(value, HConstants.UTF8_ENCODING));
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LOG.info("non existant key: " + testKeys[i] + " returned value: " +
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new String(value, HConstants.UTF8_ENCODING));
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}
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}
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}
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} catch (Exception e) {
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