| /* |
| * Licensed to the Apache Software Foundation (ASF) under one |
| * or more contributor license agreements. See the NOTICE file |
| * distributed with this work for additional information |
| * regarding copyright ownership. The ASF licenses this file |
| * to you 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 org.apache.pig.builtin; |
| |
| import java.io.IOException; |
| import java.util.Iterator; |
| |
| import org.apache.hadoop.util.bloom.BloomFilter; |
| import org.apache.hadoop.util.bloom.Key; |
| |
| import org.apache.pig.Algebraic; |
| import org.apache.pig.EvalFunc; |
| import org.apache.pig.data.DataBag; |
| import org.apache.pig.data.DataByteArray; |
| import org.apache.pig.data.DataType; |
| import org.apache.pig.data.Tuple; |
| import org.apache.pig.data.TupleFactory; |
| import org.apache.pig.impl.logicalLayer.schema.Schema; |
| |
| /** |
| * Build a bloom filter for use later in Bloom. This UDF is intended to run |
| * in a group all job. For example: |
| * define bb BuildBloom('jenkins', '100', '0.1'); |
| * A = load 'foo' as (x, y); |
| * B = group A all; |
| * C = foreach B generate bb(A.x); |
| * store C into 'mybloom'; |
| * The bloom filter can be on multiple keys by passing more than one field |
| * (or the entire bag) to BuildBloom. |
| * The resulting file can then be used in a Bloom filter as: |
| * define bloom Bloom('mybloom'); |
| * A = load 'foo' as (x, y); |
| * B = load 'bar' as (z); |
| * C = filter B by bloom(z); |
| * D = join C by z, A by x; |
| * It uses {@link org.apache.hadoop.util.bloom.BloomFilter}. |
| */ |
| public class BuildBloom extends BuildBloomBase<DataByteArray> implements Algebraic { |
| |
| /** |
| * Build a bloom filter of fixed size and number of hash functions. |
| * @param hashType type of the hashing function (see |
| * {@link org.apache.hadoop.util.hash.Hash}). |
| * @param mode Will be ignored, though by convention it should be |
| * "fixed" or "fixedsize" |
| * @param vectorSize The vector size of this filter. |
| * @param nbHash The number of hash functions to consider. |
| */ |
| public BuildBloom(String hashType, |
| String mode, |
| String vectorSize, |
| String nbHash) { |
| super(hashType, mode, vectorSize, nbHash); |
| } |
| |
| /** |
| * Construct a Bloom filter based on expected number of elements and |
| * desired accuracy. |
| * @param hashType type of the hashing function (see |
| * {@link org.apache.hadoop.util.hash.Hash}). |
| * @param numElements The number of distinct elements expected to be |
| * placed in this filter. |
| * @param desiredFalsePositive the acceptable rate of false positives. |
| * This should be a floating point value between 0 and 1.0, where 1.0 |
| * would be 100% (ie, a totally useless filter). |
| */ |
| public BuildBloom(String hashType, |
| String numElements, |
| String desiredFalsePositive) { |
| super(hashType, numElements, desiredFalsePositive); |
| } |
| |
| @Override |
| public DataByteArray exec(Tuple input) throws IOException { |
| throw new IOException("This must be used with algebraic!"); |
| } |
| |
| public String getInitial() { |
| return Initial.class.getName(); |
| } |
| |
| public String getIntermed() { |
| return Intermediate.class.getName(); |
| } |
| |
| public String getFinal() { |
| return Final.class.getName(); |
| } |
| |
| static public class Initial extends BuildBloomBase<Tuple> { |
| |
| public Initial() { |
| } |
| |
| public Initial(String hashType, |
| String mode, |
| String vectorSize, |
| String nbHash ) { |
| super(hashType, mode, vectorSize, nbHash); |
| } |
| |
| public Initial(String hashType, |
| String numElements, |
| String desiredFalsePositive) { |
| super(hashType, numElements, desiredFalsePositive); |
| } |
| |
| @Override |
| public Tuple exec(Tuple input) throws IOException { |
| if (input == null || input.size() == 0) return null; |
| |
| // Strip off the initial level of bag |
| DataBag values = (DataBag)input.get(0); |
| Iterator<Tuple> it = values.iterator(); |
| Tuple t = it.next(); |
| |
| // If the input tuple has only one field, then we'll extract |
| // that field and serialize it into a key. If it has multiple |
| // fields, we'll serialize the whole tuple. |
| byte[] b; |
| if (t.size() == 1) b = DataType.toBytes(t.get(0)); |
| else b = DataType.toBytes(t, DataType.TUPLE); |
| |
| Key k = new Key(b); |
| filter = new BloomFilter(vSize, numHash, hType); |
| filter.add(k); |
| |
| return TupleFactory.getInstance().newTuple(bloomOut()); |
| } |
| } |
| |
| static public class Intermediate extends BuildBloomBase<Tuple> { |
| |
| public Intermediate() { |
| } |
| |
| public Intermediate(String hashType, |
| String mode, |
| String vectorSize, |
| String nbHash ) { |
| super(hashType, mode, vectorSize, nbHash); |
| } |
| |
| public Intermediate(String hashType, |
| String numElements, |
| String desiredFalsePositive) { |
| super(hashType, numElements, desiredFalsePositive); |
| } |
| |
| |
| @Override |
| public Tuple exec(Tuple input) throws IOException { |
| return TupleFactory.getInstance().newTuple(bloomOr(input)); |
| } |
| } |
| |
| static public class Final extends BuildBloomBase<DataByteArray> { |
| |
| public Final() { |
| } |
| |
| public Final(String hashType, |
| String mode, |
| String vectorSize, |
| String nbHash ) { |
| super(hashType, mode, vectorSize, nbHash); |
| } |
| |
| public Final(String hashType, |
| String numElements, |
| String desiredFalsePositive) { |
| super(hashType, numElements, desiredFalsePositive); |
| } |
| |
| @Override |
| public DataByteArray exec(Tuple input) throws IOException { |
| return bloomOr(input); |
| } |
| } |
| |
| @Override |
| public Schema outputSchema(Schema input) { |
| return new Schema(new Schema.FieldSchema(null, DataType.BYTEARRAY)); |
| } |
| |
| } |