一、Pattern Analyzer简介
elasticsearch在索引和搜索之前都需要对输入的文本进行分词，elasticsearch提供的pattern analyzer使得我们可以通过正则表达式的简单方式来定义分隔符，从而达到自定义分词的处理逻辑；
内置的的pattern analyzer的名字为pattern，其使用的模式是W+，即除了字母和数字之外的所有非单词字符；
analyzers.add(new PreBuiltAnalyzerProviderFactory("pattern", CachingStrategy.ELASTICSEARCH,
() -> new PatternAnalyzer(Regex.compile("\\W+" /*PatternAnalyzer.NON_WORD_PATTERN*/, null), true,
CharArraySet.EMPTY_SET)));
作为全局的pattern analyzer，我们可以直接使用
POST _analyze
{
"analyzer": "pattern",
"text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
}
{
"tokens" : [
{
"token" : "the",
"start_offset" : 0,
"end_offset" : 3,
"type" : "word",
"position" : 0
},
{
"token" : "2",
"start_offset" : 4,
"end_offset" : 5,
"type" : "word",
"position" : 1
},
{
"token" : "quick",
"start_offset" : 6,
"end_offset" : 11,
"type" : "word",
"position" : 2
},
{
"token" : "brown",
"start_offset" : 12,
"end_offset" : 17,
"type" : "word",
"position" : 3
},
{
"token" : "foxes",
"start_offset" : 18,
"end_offset" : 23,
"type" : "word",
"position" : 4
},
{
"token" : "jumped",
"start_offset" : 24,
"end_offset" : 30,
"type" : "word",
"position" : 5
},
{
"token" : "over",
"start_offset" : 31,
"end_offset" : 35,
"type" : "word",
"position" : 6
},
{
"token" : "the",
"start_offset" : 36,
"end_offset" : 39,
"type" : "word",
"position" : 7
},
{
"token" : "lazy",
"start_offset" : 40,
"end_offset" : 44,
"type" : "word",
"position" : 8
},
{
"token" : "dog",
"start_offset" : 45,
"end_offset" : 48,
"type" : "word",
"position" : 9
},
{
"token" : "s",
"start_offset" : 49,
"end_offset" : 50,
"type" : "word",
"position" : 10
},
{
"token" : "bone",
"start_offset" : 51,
"end_offset" : 55,
"type" : "word",
"position" : 11
}
]
}
二、自定义Pattern Analyzer
我们可以通过以下方式自定pattern analyzer，并设置分隔符为所有的空格符号；
PUT my_pattern_test_space_analyzer
{
"settings": {
"analysis": {
"analyzer": {
"my_pattern_test_space_analyzer": {
"type": "pattern",
"pattern": "[\\p{Space}]",
"lowercase": true
}
}
}
}
}
我们使用自定义的pattern analyzer测试一下效果
POST my_pattern_test_space_analyzer/_analyze
{
"analyzer": "my_pattern_test_space_analyzer",
"text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
}
{
"tokens" : [
{
"token" : "the",
"start_offset" : 0,
"end_offset" : 3,
"type" : "word",
"position" : 0
},
{
"token" : "2",
"start_offset" : 4,
"end_offset" : 5,
"type" : "word",
"position" : 1
},
{
"token" : "quick",
"start_offset" : 6,
"end_offset" : 11,
"type" : "word",
"position" : 2
},
{
"token" : "brown-foxes",
"start_offset" : 12,
"end_offset" : 23,
"type" : "word",
"position" : 3
},
{
"token" : "jumped",
"start_offset" : 24,
"end_offset" : 30,
"type" : "word",
"position" : 4
},
{
"token" : "over",
"start_offset" : 31,
"end_offset" : 35,
"type" : "word",
"position" : 5
},
{
"token" : "the",
"start_offset" : 36,
"end_offset" : 39,
"type" : "word",
"position" : 6
},
{
"token" : "lazy",
"start_offset" : 40,
"end_offset" : 44,
"type" : "word",
"position" : 7
},
{
"token" : "dog's",
"start_offset" : 45,
"end_offset" : 50,
"type" : "word",
"position" : 8
},
{
"token" : "bone.",
"start_offset" : 51,
"end_offset" : 56,
"type" : "word",
"position" : 9
}
]
}
三、常用的Java中的正则表达式
elasticsearch的Pattern Analyzer使用的Java Regular Expressions，只有了解Java中一些常用的正则表达式才能更好的自定义pattern analyzer；
单字符定义
x The character x
\\ The backslash character
\0n The character with octal value 0n (0 <= n <= 7)
\0nn The character with octal value 0nn (0 <= n <= 7)
\0mnn The character with octal value 0mnn (0 <= m <= 3, 0 <= n <= 7)
\xhh The character with hexadecimal value 0xhh
\uhhhh The character with hexadecimal value 0xhhhh
\x{h...h} The character with hexadecimal value 0xh...h (Character.MIN_CODE_POINT <= 0xh...h <= Character.MAX_CODE_POINT)
\t The tab character ('\u0009')
\n The newline (line feed) character ('\u000A')
\r The carriage-return character ('\u000D')
\f The form-feed character ('\u000C')
\a The alert (bell) character ('\u0007')
\e The escape character ('\u001B')
\cx The control character corresponding to x
字符分组
[abc] a, b, or c (simple class)
[^abc] Any character except a, b, or c (negation)
[a-zA-Z] a through z or A through Z, inclusive (range)
[a-d[m-p]] a through d, or m through p: [a-dm-p] (union)
[a-z&&[def]] d, e, or f (intersection)
[a-z&&[^bc]] a through z, except for b and c: [ad-z] (subtraction)
[a-z&&[^m-p]] a through z, and not m through p: [a-lq-z](subtraction)
预定义的字符分组
. Any character (may or may not match line terminators)
\d A digit: [0-9]
\D A non-digit: [^0-9]
\h A horizontal whitespace character: [ \t\xA0\u1680\u180e\u2000-\u200a\u202f\u205f\u3000]
\H A non-horizontal whitespace character: [^\h]
\s A whitespace character: [ \t\n\x0B\f\r]
\S A non-whitespace character: [^\s]
\v A vertical whitespace character: [\n\x0B\f\r\x85\u2028\u2029]
\V A non-vertical whitespace character: [^\v]
\w A word character: [a-zA-Z_0-9]
\W A non-word character: [^\w]
POSIX字符分组
\p{Lower} A lower-case alphabetic character: [a-z]
\p{Upper} An upper-case alphabetic character:[A-Z]
\p{ASCII} All ASCII:[\x00-\x7F]
\p{Alpha} An alphabetic character:[\p{Lower}\p{Upper}]
\p{Digit} A decimal digit: [0-9]
\p{Alnum} An alphanumeric character:[\p{Alpha}\p{Digit}]
\p{Punct} Punctuation: One of !"#$%&'()*+,-./:;<=>?@[\]^_`{|}~
\p{Graph} A visible character: [\p{Alnum}\p{Punct}]
\p{Print} A printable character: [\p{Graph}\x20]
\p{Blank} A space or a tab: [ \t]
\p{Cntrl} A control character: [\x00-\x1F\x7F]
\p{XDigit} A hexadecimal digit: [0-9a-fA-F]
\p{Space} A whitespace character: [ \t\n\x0B\f\r]
以下我们通过正则表达式[\p{Punct}|\p{Space}]可以找出字符串中的标点符号；
import java.util.regex.Matcher;
import java.util.regex.Pattern;
public class Main {
public static void main(String[] args) {
Pattern p = Pattern.compile("[\\p{Punct}|\\p{Space}]");
Matcher matcher = p.matcher("The 2 QUICK Brown-Foxes jumped over the lazy dog's bone.");
while(matcher.find()){
System.out.println("find "+matcher.group()
+" position: "+matcher.start()+"-"+matcher.end());
}
}
}
find position: 3-4
find position: 5-6
find position: 11-12
find - position: 17-18
find position: 23-24
find position: 30-31
find position: 35-36
find position: 39-40
find position: 44-45
find ' position: 48-49
find position: 50-51
find . position: 55-56
四、 Pattern Analyzer的实现
PatternAnalyzer会根据具体的配置信息,使用PatternTokenizer、LowerCaseFilter、StopFilter来组合构建TokenStreamComponents
PatternAnalyzer.java
protected TokenStreamComponents createComponents(String s) {
final Tokenizer tokenizer = new PatternTokenizer(pattern, -1);
TokenStream stream = tokenizer;
if (lowercase) {
stream = new LowerCaseFilter(stream);
}
if (stopWords != null) {
stream = new StopFilter(stream, stopWords);
}
return new TokenStreamComponents(tokenizer, stream);
}
PatternTokenizer里的incrementToken会对输入的文本进行分词处理；由于PatternAnalyzer里初始化PatternTokenizer里的incrementToken会对输入的文本进行分词处理的时候对group设置为-1，所以这里走else分支，最终提取命中符号之间的单词；
PatternTokenizer.java
@Override
public boolean incrementToken() {
if (index >= str.length()) return false;
clearAttributes();
if (group >= 0) {
// match a specific group
while (matcher.find()) {
index = matcher.start(group);
final int endIndex = matcher.end(group);
if (index == endIndex) continue;
termAtt.setEmpty().append(str, index, endIndex);
offsetAtt.setOffset(correctOffset(index), correctOffset(endIndex));
return true;
}
index = Integer.MAX_VALUE; // mark exhausted
return false;
} else {
// String.split() functionality
while (matcher.find()) {
if (matcher.start() - index > 0) {
// found a non-zero-length token
termAtt.setEmpty().append(str, index, matcher.start());
offsetAtt.setOffset(correctOffset(index), correctOffset(matcher.start()));
index = matcher.end();
return true;
}
index = matcher.end();
}
if (str.length() - index == 0) {
index = Integer.MAX_VALUE; // mark exhausted
return false;
}
termAtt.setEmpty().append(str, index, str.length());
offsetAtt.setOffset(correctOffset(index), correctOffset(str.length()));
index = Integer.MAX_VALUE; // mark exhausted
return true;
}
}