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ARROW-6933: [Java] Suppor linear dictionary encoder
For many scenarios, the distribution of dictionary entries is highly skewed. In other words, a few dictionary entries occurs much more frequently than others. If we can sort the dictionary by the non-increasing order of entry frequencies, and compare each value to encode from the beginning of the dictionary, we get the following benefits: 1) We need no extra memory space or data structure. 2) The search is extremely efficient, as we are likely to find a match in the first few entries of the dictionary. This is the basic idea behind the linear dictionary encoder. When the scenario is right (highly skewed dictionary distribution), it outperforms both search based encoder and hash table based encoders. Closes #5692 from liyafan82/fly_1018_line and squashes the following commits: 78d7196 <liyafan82> Improve Javadocs 2268438 <liyafan82> Suppor linear dictionary encoder Authored-by: liyafan82 <fan_li_ya@foxmail.com> Signed-off-by: Micah Kornfield <emkornfield@gmail.com>
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...lgorithm/src/main/java/org/apache/arrow/algorithm/dictionary/LinearDictionaryEncoder.java
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/* | ||
* 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. | ||
*/ | ||
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package org.apache.arrow.algorithm.dictionary; | ||
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import org.apache.arrow.vector.BaseIntVector; | ||
import org.apache.arrow.vector.ValueVector; | ||
import org.apache.arrow.vector.compare.Range; | ||
import org.apache.arrow.vector.compare.RangeEqualsVisitor; | ||
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/** | ||
* Dictionary encoder based on linear search. | ||
* @param <E> encoded vector type. | ||
* @param <D> decoded vector type, which is also the dictionary type. | ||
*/ | ||
public class LinearDictionaryEncoder<E extends BaseIntVector, D extends ValueVector> { | ||
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/** | ||
* The dictionary for encoding. | ||
*/ | ||
private final D dictionary; | ||
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/** | ||
* A flag indicating if null should be encoded. | ||
*/ | ||
private final boolean encodeNull; | ||
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private RangeEqualsVisitor equalizer; | ||
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private Range range; | ||
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/** | ||
* Constructs a dictionary encoder, with the encode null flag set to false. | ||
* @param dictionary the dictionary. Its entries should be sorted in the non-increasing order of their frequency. | ||
* Otherwise, the encoder still produces correct results, but at the expense of performance overhead. | ||
*/ | ||
public LinearDictionaryEncoder(D dictionary) { | ||
this(dictionary, false); | ||
} | ||
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/** | ||
* Constructs a dictionary encoder. | ||
* @param dictionary the dictionary. Its entries should be sorted in the non-increasing order of their frequency. | ||
* Otherwise, the encoder still produces correct results, but at the expense of performance overhead. | ||
* @param encodeNull a flag indicating if null should be encoded. | ||
* It determines the behaviors for processing null values in the input during encoding. | ||
* When a null is encountered in the input, | ||
* 1) If the flag is set to true, the encoder searches for the value in the dictionary, | ||
* and outputs the index in the dictionary. | ||
* 2) If the flag is set to false, the encoder simply produces a null in the output. | ||
*/ | ||
public LinearDictionaryEncoder(D dictionary, boolean encodeNull) { | ||
this.dictionary = dictionary; | ||
this.encodeNull = encodeNull; | ||
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// temporarily set left and right vectors to dictionary | ||
equalizer = new RangeEqualsVisitor(dictionary, dictionary, false); | ||
range = new Range(0, 0, 1); | ||
} | ||
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/** | ||
* Encodes an input vector by linear search. | ||
* When the dictionary is sorted in the non-increasing order of the entry frequency, | ||
* it will have constant time complexity, with no extra memory requirement. | ||
* @param input the input vector. | ||
* @param output the output vector. Note that it must be in a fresh state. At least, | ||
* all its validity bits should be clear. | ||
*/ | ||
public void encode(D input, E output) { | ||
for (int i = 0; i < input.getValueCount(); i++) { | ||
if (!encodeNull && input.isNull(i)) { | ||
// for this case, we should simply output a null in the output. | ||
// by assuming the output vector is fresh, we do nothing here. | ||
continue; | ||
} | ||
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int index = linearSearch(input, i); | ||
if (index == -1) { | ||
throw new IllegalArgumentException("The data element is not found in the dictionary: " + i); | ||
} | ||
output.setWithPossibleTruncate(i, index); | ||
} | ||
output.setValueCount(input.getValueCount()); | ||
} | ||
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private int linearSearch(D input, int index) { | ||
range.setLeftStart(index); | ||
for (int i = 0; i < dictionary.getValueCount(); i++) { | ||
range.setRightStart(i); | ||
if (input.accept(equalizer, range)) { | ||
return i; | ||
} | ||
} | ||
return -1; | ||
} | ||
} |
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