blocks|key|2956188|text|不要紧。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2956189|我不得不查看Lucene文档。似乎我可以使用通配符！:-)|2956190|curl+http://localhost:9200/my_idx/my_type/_search?q=*Doe*|code-block|syntax|javascript|2956191|成功了！|2956192|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|N|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|O|8|@]|9|@]|A|$G|H]]|$1|I|3|J|5|6|7|P|8|@]|9|@]|A|$]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

Nevermind.

I had to look at the Lucene documentation.
Seems I can use wildcards! :-)

<pre><code>curl http://localhost:9200/my_idx/my_type/_search?q=*Doe*
</code></pre>

does the trick!

blocks|key|265210|text|在大型索引上，使用前导和尾随通配符进行搜索将非常慢。如果希望能够按单词前缀进行搜索，请删除前导通配符。如果你真的需要在单词中间找到一个子串，你最好使用ngram标记器。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|265211|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

Searching with leading and trailing wildcards is going to be extremely slow on a large index. If you want to be able to search by word prefix, remove leading wildcard. If you really need to find a substring in a middle of a word, you would be better of using ngram tokenizer.

blocks|key|261329|text|我也在使用nGram。我使用标准的标记器和nGram作为过滤器。下面是我的设置：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|261330|{
++"index":+{
++++"index":+"my_idx",
++++"type":+"my_type",
++++"analysis":+{
++++++"index_analyzer":+{
++++++++"my_index_analyzer":+{
++++++++++"type":+"custom",
++++++++++"tokenizer":+"standard",
++++++++++"filter":+[
++++++++++++"lowercase",
++++++++++++"mynGram"
++++++++++]
++++++++}
++++++},
++++++"search_analyzer":+{
++++++++"my_search_analyzer":+{
++++++++++"type":+"custom",
++++++++++"tokenizer":+"standard",
++++++++++"filter":+[
++++++++++++"standard",
++++++++++++"lowercase",
++++++++++++"mynGram"
++++++++++]
++++++++}
++++++},
++++++"filter":+{
++++++++"mynGram":+{
++++++++++"type":+"nGram",
++++++++++"min_gram":+2,
++++++++++"max_gram":+50
++++++++}
++++++}
++++}
++}
}|code-block|syntax|javascript|261331|让我们找出最多50个字母的单词部分。根据需要调整max_gram。在德语中，可以变得非常大，所以我将其设置为一个很高的值。|261332|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

I'm using nGram, too. I use standard tokenizer and nGram just as a filter. Here is my setup:

<pre><code>{
 "index": {
 "index": "my_idx",
 "type": "my_type",
 "analysis": {
 "index_analyzer": {
 "my_index_analyzer": {
 "type": "custom",
 "tokenizer": "standard",
 "filter": [
 "lowercase",
 "mynGram"
 ]
 }
 },
 "search_analyzer": {
 "my_search_analyzer": {
 "type": "custom",
 "tokenizer": "standard",
 "filter": [
 "standard",
 "lowercase",
 "mynGram"
 ]
 }
 },
 "filter": {
 "mynGram": {
 "type": "nGram",
 "min_gram": 2,
 "max_gram": 50
 }
 }
 }
 }
}
</code></pre>

Let's you find word parts up to 50 letters. Adjust the max_gram as you need. In german words can get really big, so I set it to a high value.

blocks|key|3170384|text|尝试使用此处描述的解决方案：Exact+Substring+Searches+in+ElasticSearch|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|3170385|{
++++"mappings":+{
++++++++"my_type":+{
++++++++++++"index_analyzer":"index_ngram",
++++++++++++"search_analyzer":"search_ngram"
++++++++}
++++},
++++"settings":+{
++++++++"analysis":+{
++++++++++++"filter":+{
++++++++++++++++"ngram_filter":+{
++++++++++++++++++++"type":+"ngram",
++++++++++++++++++++"min_gram":+3,
++++++++++++++++++++"max_gram":+8
++++++++++++++++}
++++++++++++},
++++++++++++"analyzer":+{
++++++++++++++++"index_ngram":+{
++++++++++++++++++++"type":+"custom",
++++++++++++++++++++"tokenizer":+"keyword",
++++++++++++++++++++"filter":+[+"ngram_filter",+"lowercase"+]
++++++++++++++++},
++++++++++++++++"search_ngram":+{
++++++++++++++++++++"type":+"custom",
++++++++++++++++++++"tokenizer":+"keyword",
++++++++++++++++++++"filter":+"lowercase"
++++++++++++++++}
++++++++++++}
++++++++}
++++}
}|code-block|syntax|javascript|3170386|为了解决磁盘使用问题和搜索词过长的问题，使用了短8个字符的长ngram(配置为："max_gram"：8)。要搜索超过8个字符的术语，请将搜索转换为布尔查询，并在该字符串中查找每个不同的8字符子字符串。例如，如果用户搜索large+yard+(10个字符的字符串)，则搜索将为：|3170387|“呀！|3170388|entityMap|0|LINK|mutability|MUTABLE|url|https://web.archive.org/web/20131216221809/http://blog.rnf.me/2013/exact-substring-search-in-elasticsearch.html^0|E|15|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@]|9|@$A|V|B|W|1|X]]|C|$]]|$1|D|3|E|5|F|7|Y|8|@]|9|@]|C|$G|H]]|$1|I|3|J|5|6|7|Z|8|@]|9|@]|C|$]]|$1|K|3|L|5|6|7|10|8|@]|9|@]|C|$]]|$1|M|3|-4|5|6|7|11|8|@]|9|@]|C|$]]]|N|$O|$5|P|Q|R|C|$S|T]]]]

Try the solution with is described here: <a href="https://web.archive.org/web/20131216221809/http://blog.rnf.me/2013/exact-substring-search-in-elasticsearch.html" rel="nofollow noreferrer">Exact Substring Searches in ElasticSearch</a>
<pre><code>{
 &quot;mappings&quot;: {
 &quot;my_type&quot;: {
 &quot;index_analyzer&quot;:&quot;index_ngram&quot;,
 &quot;search_analyzer&quot;:&quot;search_ngram&quot;
 }
 },
 &quot;settings&quot;: {
 &quot;analysis&quot;: {
 &quot;filter&quot;: {
 &quot;ngram_filter&quot;: {
 &quot;type&quot;: &quot;ngram&quot;,
 &quot;min_gram&quot;: 3,
 &quot;max_gram&quot;: 8
 }
 },
 &quot;analyzer&quot;: {
 &quot;index_ngram&quot;: {
 &quot;type&quot;: &quot;custom&quot;,
 &quot;tokenizer&quot;: &quot;keyword&quot;,
 &quot;filter&quot;: [ &quot;ngram_filter&quot;, &quot;lowercase&quot; ]
 },
 &quot;search_ngram&quot;: {
 &quot;type&quot;: &quot;custom&quot;,
 &quot;tokenizer&quot;: &quot;keyword&quot;,
 &quot;filter&quot;: &quot;lowercase&quot;
 }
 }
 }
 }
}
</code></pre>
To solve the disk usage problem and the too-long search term problem short 8 characters long ngrams are used (configured with: &quot;max_gram&quot;: 8). To search for terms with more than 8 characters, turn your search into a boolean AND query looking for every distinct 8-character substring in that string. For example, if a user searched for large yard (a 10-character string), the search would be:
&quot;arge ya AND arge yar AND rge yard.

blocks|key|2956408|text|在不更改索引映射的情况下，您可以执行一个简单的前缀查询，该查询将执行您所希望的部分搜索|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2956409|即。|2956410|{
++"query":+{+
++++"prefix"+:+{+"name"+:+"Doe"+}
++}
}|code-block|syntax|javascript|2956411|https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-prefix-query.html|offset|length|2956412|entityMap|0|LINK|mutability|MUTABLE|url^0|0|0|0|0|2J|0|0^^$0|@$1|2|3|4|5|6|7|T|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|U|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|V|8|@]|9|@]|A|$G|H]]|$1|I|3|J|5|6|7|W|8|@]|9|@$K|X|L|Y|1|Z]]|A|$]]|$1|M|3|-4|5|6|7|10|8|@]|9|@]|A|$]]]|N|$O|$5|P|Q|R|A|$S|J]]]]

without changing your index mappings you could do a simple prefix query that will do partial searches like you are hoping for

ie.

<pre><code>{
 "query": { 
 "prefix" : { "name" : "Doe" }
 }
}
</code></pre>

<a href="https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-prefix-query.html" rel="noreferrer">https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-prefix-query.html</a>

blocks|key|266916|text|我认为没有必要更改任何映射。尝试使用query_string，它是完美的。所有场景都将使用默认标准分析器：|type|unstyled|depth|inlineStyleRanges|offset|length|style|BOLD|entityRanges|data|266917|我们有数据：|266918|{"_id"+:+"1","name"+:+"John+Doeman","function"+:+"Janitor"}
{"_id"+:+"2","name"+:+"Jane+Doewoman","function"+:+"Teacher"}|code-block|syntax|javascript|266919|场景1:|266920|{"query":+{
++++"query_string"+:+{"default_field"+:+"name",+"query"+:+"*Doe*"}
}+}|266921|响应：|266922|{"_id"+:+"1","name"+:+"John+Doeman","function"+:+"Janitor"}
{"_id"+:+"2","name"+:+"Jane+Doewoman","function"+:+"Teacher"}|266923|场景2:|266924|{"query":+{
++++"query_string"+:+{"default_field"+:+"name",+"query"+:+"*Jan*"}
}+}|266925|266926|{"_id"+:+"1","name"+:+"John+Doeman","function"+:+"Janitor"}|266927|场景3:|266928|{"query":+{
++++"query_string"+:+{"default_field"+:+"name",+"query"+:+"*oh*+*oe*"}
}+}|266929|266930|{"_id"+:+"1","name"+:+"John+Doeman","function"+:+"Janitor"}
{"_id"+:+"2","name"+:+"Jane+Doewoman","function"+:+"Teacher"}|266931|编辑-与spring+data+elastic+search+https://stackoverflow.com/a/43579948/2357869实现相同|266932|另一个解释query_string比其他https://stackoverflow.com/a/43321606/2357869更好的原因|266933|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/a/43579948/2357869|1|https://stackoverflow.com/a/43321606/2357869^0|I|C|0|0|0|0|4|0|0|0|0|0|4|0|0|0|0|0|4|0|0|0|0|V|18|0|0|K|18|1|0^^$0|@$1|2|3|4|5|6|7|1M|8|@$9|1N|A|1O|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|1P|8|@]|D|@]|E|$]]|$1|H|3|I|5|J|7|1Q|8|@]|D|@]|E|$K|L]]|$1|M|3|N|5|6|7|1R|8|@$9|1S|A|1T|B|C]]|D|@]|E|$]]|$1|O|3|P|5|J|7|1U|8|@]|D|@]|E|$K|L]]|$1|Q|3|R|5|6|7|1V|8|@]|D|@]|E|$]]|$1|S|3|T|5|J|7|1W|8|@]|D|@]|E|$K|L]]|$1|U|3|V|5|6|7|1X|8|@$9|1Y|A|1Z|B|C]]|D|@]|E|$]]|$1|W|3|X|5|J|7|20|8|@]|D|@]|E|$K|L]]|$1|Y|3|R|5|6|7|21|8|@]|D|@]|E|$]]|$1|Z|3|10|5|J|7|22|8|@]|D|@]|E|$K|L]]|$1|11|3|12|5|6|7|23|8|@$9|24|A|25|B|C]]|D|@]|E|$]]|$1|13|3|14|5|J|7|26|8|@]|D|@]|E|$K|L]]|$1|15|3|R|5|6|7|27|8|@]|D|@]|E|$]]|$1|16|3|17|5|J|7|28|8|@]|D|@]|E|$K|L]]|$1|18|3|19|5|6|7|29|8|@]|D|@$9|2A|A|2B|1|2C]]|E|$]]|$1|1A|3|1B|5|6|7|2D|8|@]|D|@$9|2E|A|2F|1|2G]]|E|$]]|$1|1C|3|-4|5|6|7|2H|8|@]|D|@]|E|$]]]|1D|$1E|$5|1F|1G|1H|E|$1I|1J]]|1K|$5|1F|1G|1H|E|$1I|1L]]]]

I think there's no need to change any mapping.
Try to use query_string, it's perfect. All scenarios will work with default standard analyzer:

We have data:

<pre><code>{"_id" : "1","name" : "John Doeman","function" : "Janitor"}
{"_id" : "2","name" : "Jane Doewoman","function" : "Teacher"}
</code></pre>

Scenario 1:

<pre><code>{"query": {
 "query_string" : {"default_field" : "name", "query" : "*Doe*"}
} }
</code></pre>

Response:

<pre><code>{"_id" : "1","name" : "John Doeman","function" : "Janitor"}
{"_id" : "2","name" : "Jane Doewoman","function" : "Teacher"}
</code></pre>

Scenario 2:

<pre><code>{"query": {
 "query_string" : {"default_field" : "name", "query" : "*Jan*"}
} }
</code></pre>

Response:

<pre><code>{"_id" : "1","name" : "John Doeman","function" : "Janitor"}
</code></pre>

Scenario 3:

<pre><code>{"query": {
 "query_string" : {"default_field" : "name", "query" : "*oh* *oe*"}
} }
</code></pre>

Response:

<pre><code>{"_id" : "1","name" : "John Doeman","function" : "Janitor"}
{"_id" : "2","name" : "Jane Doewoman","function" : "Teacher"}
</code></pre>

EDIT -
Same implementation with spring data elastic search 
<a href="https://stackoverflow.com/a/43579948/2357869">https://stackoverflow.com/a/43579948/2357869</a>

One more explanation how query_string is better than others
<a href="https://stackoverflow.com/a/43321606/2357869">https://stackoverflow.com/a/43321606/2357869</a>

blocks|key|3170589|text|如果你想实现自动补全功能，那么Completion+Suggester是最好的解决方案。下一个blog+post包含了一个非常清晰的描述如何工作。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|3170590|简而言之，它是一种内存中的数据结构，称为FST，它包含有效的建议，并针对快速检索和内存使用进行了优化。从本质上讲，它只是一个图表。例如，包含单词hotel、marriot、mercure、munchen和munich的FST将如下所示：|style|CODE|3170591|​|3170592|📷|atomic|3170593|3170594|entityMap|0|LINK|mutability|MUTABLE|url|https://www.elastic.co/guide/en/elasticsearch/reference/current/search-suggesters-completion.html|1|https://www.elastic.co/blog/you-complete-me|2|IMAGE|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/2215bd83c01431dce42de1b66555940e.png|imageAlt^0|F|K|0|1B|9|1|0|20|5|26|7|2E|7|2M|7|2U|6|0|0|0|1|2|0|0^^$0|@$1|2|3|4|5|6|7|13|8|@]|9|@$A|14|B|15|1|16]|$A|17|B|18|1|19]]|C|$]]|$1|D|3|E|5|6|7|1A|8|@$A|1B|B|1C|F|G]|$A|1D|B|1E|F|G]|$A|1F|B|1G|F|G]|$A|1H|B|1I|F|G]|$A|1J|B|1K|F|G]]|9|@]|C|$]]|$1|H|3|I|5|6|7|1L|8|@]|9|@]|C|$]]|$1|J|3|K|5|L|7|1M|8|@]|9|@$A|1N|B|1O|1|1P]]|C|$]]|$1|M|3|I|5|6|7|1Q|8|@]|9|@]|C|$]]|$1|N|3|-4|5|6|7|1R|8|@]|9|@]|C|$]]]|O|$P|$5|Q|R|S|C|$T|U]]|V|$5|Q|R|S|C|$T|W]]|X|$5|Y|R|Z|C|$10|11|12|-4]]]]

If you want to implement autocomplete functionality, then <a href="https://www.elastic.co/guide/en/elasticsearch/reference/current/search-suggesters-completion.html" rel="nofollow noreferrer">Completion Suggester</a> is the most neat solution. The next <a href="https://www.elastic.co/blog/you-complete-me" rel="nofollow noreferrer">blog post</a> contains a very clear description how this works.

In two words, it's an in-memory data structure called an FST which contains valid suggestions and is optimised for fast retrieval and memory usage. Essentially, it is just a graph. For instance, and FST containing the words <code>hotel</code>, <code>marriot</code>, <code>mercure</code>, <code>munchen</code> and <code>munich</code> would look like this:

<a href="https://i.stack.imgur.com/wGUFs.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/wGUFs.png" alt="enter image description here"></a>

blocks|key|3170630|text|您可以使用regexp。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3170631|{+"_id"+:+"1",+"name"+:+"John+Doeman"+,+"function"+:+"Janitor"}
{+"_id"+:+"2",+"name"+:+"Jane+Doewoman","function"+:+"Teacher"++}
{+"_id"+:+"3",+"name"+:+"Jimmy+Jackal"+,"function"+:+"Student"++}+|code-block|syntax|javascript|3170632|如果您使用此查询：|3170633|{
++"query":+{
++++"regexp":+{
++++++"name":+"J.*"
++++}
++}
}|3170634|您将给定其名称以“J”开头的所有数据，您只希望接收其名称以“.Consider”结尾的前两条记录，因此您可以使用以下查询：|3170635|{
++"query":+{+
++++"regexp":+{
++++++"name":+".*man"
++++}
++}
}|3170636|如果您希望接收其名称中存在"m“的所有记录，则可以使用以下查询：|3170637|{
++"query":+{+
++++"regexp":+{
++++++"name":+".*m.*"
++++}
++}
}|3170638|这对我很有效，.And，我希望我的答案适用于解决你的问题。|3170639|entityMap^0|0|0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|W|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|X|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Y|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|Z|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|10|8|@]|9|@]|A|$]]|$1|M|3|N|5|D|7|11|8|@]|9|@]|A|$E|F]]|$1|O|3|P|5|6|7|12|8|@]|9|@]|A|$]]|$1|Q|3|R|5|D|7|13|8|@]|9|@]|A|$E|F]]|$1|S|3|T|5|6|7|14|8|@]|9|@]|A|$]]|$1|U|3|-4|5|6|7|15|8|@]|9|@]|A|$]]]|V|$]]

you can use regexp.

<pre><code>{ "_id" : "1", "name" : "John Doeman" , "function" : "Janitor"}
{ "_id" : "2", "name" : "Jane Doewoman","function" : "Teacher" }
{ "_id" : "3", "name" : "Jimmy Jackal" ,"function" : "Student" } 
</code></pre>

if you use this query :

<pre><code>{
 "query": {
 "regexp": {
 "name": "J.*"
 }
 }
}
</code></pre>

you will given all of data that their name start with "J".Consider you want to receive just the first two record that their name end with "man" so you can use this query :

<pre><code>{
 "query": { 
 "regexp": {
 "name": ".*man"
 }
 }
}
</code></pre>

and if you want to receive all record that in their name exist "m" , you can use this query :

<pre><code>{
 "query": { 
 "regexp": {
 "name": ".*m.*"
 }
 }
}
</code></pre>

This works for me .And I hope my answer be suitable for solve your problem.

blocks|key|3170650|text|使用通配符(*)防止计算分数|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3170651|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

Using wilcards (*) prevent the calc of a score

blocks|key|3170661|text|我正在使用这个并让我开始工作|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3170662|3170663|“查询”：{+"query_string“：{”查询“：”*测试*“，”字段“："field1"，"field2"，"analyze_wildcard”：true，"allow_leading_wildcard"：true+}+}|blockquote|3170664|3170665|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|J|8|@]|9|@]|A|$]]|$1|C|3|D|5|E|7|K|8|@]|9|@]|A|$]]|$1|F|3|-4|5|6|7|L|8|@]|9|@]|A|$]]|$1|G|3|-4|5|6|7|M|8|@]|9|@]|A|$]]]|H|$]]

I am using this and got I worked

<blockquote>
<pre><code>"query": {
 "query_string" : {
 "query" : "*test*",
 "fields" : ["field1","field2"],
 "analyze_wildcard" : true,
 "allow_leading_wildcard": true
 }
 }
</code></pre>
</blockquote>

blocks|key|3170774|text|虽然有很多答案都集中在解决手头的问题上，但在选择一个特定的答案之前，不要过多地谈论人们需要做出的各种权衡。因此，让我尝试添加有关此视角的更多细节。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3170775|部分搜索现在是一个非常常见和重要的功能，如果没有正确地实现，可能会导致糟糕的用户体验和糟糕的性能，所以首先要知道与我在this+detailed+SO+answer中提到的这个功能相关的应用程序功能和非功能需求。|offset|length|style|BOLD|3170776|现在有多种方法，如查询时间、索引时间、完成提示和输入时搜索等--在最新版本的中添加了这些类型的数据。|3170777|现在，想要快速实现解决方案的人可以使用下面的端到端工作解决方案。|3170778|索引映射|3170779|{
++"settings":+{
++++"analysis":+{
++++++"filter":+{
++++++++"autocomplete_filter":+{
++++++++++"type":+"ngram",
++++++++++"min_gram":+1,
++++++++++"max_gram":+10
++++++++}
++++++},
++++++"analyzer":+{
++++++++"autocomplete":+{+
++++++++++"type":+"custom",
++++++++++"tokenizer":+"standard",
++++++++++"filter":+[
++++++++++++"lowercase",
++++++++++++"autocomplete_filter"
++++++++++]
++++++++}
++++++}
++++},
++++"index.max_ngram_diff"+:+10
++},
++"mappings":+{
++++"properties":+{
++++++"title":+{
++++++++"type":+"text",
++++++++"analyzer":+"autocomplete",+
++++++++"search_analyzer":+"standard"+
++++++}
++++}
++}
}|code-block|syntax|javascript|3170780|给定示例文档的索引|3170781|{
++"title"+:+"John+Doeman"
++
}

{
++"title"+:+"Jane+Doewoman"
++
}

{
++"title"+:+"Jimmy+Jackal"
++
}|3170782|和搜索查询|3170783|{
++++"query":+{
++++++++"match":+{
++++++++++++"title":+"Doe"
++++++++}
++++}
}|3170784|返回预期搜索结果的|3170785|+"hits":+[
++++++++++++{
++++++++++++++++"_index":+"6467067",
++++++++++++++++"_type":+"_doc",
++++++++++++++++"_id":+"1",
++++++++++++++++"_score":+0.76718915,
++++++++++++++++"_source":+{
++++++++++++++++++++"title":+"John+Doeman"
++++++++++++++++}
++++++++++++},
++++++++++++{
++++++++++++++++"_index":+"6467067",
++++++++++++++++"_type":+"_doc",
++++++++++++++++"_id":+"2",
++++++++++++++++"_score":+0.76718915,
++++++++++++++++"_source":+{
++++++++++++++++++++"title":+"Jane+Doewoman"
++++++++++++++++}
++++++++++++}
++++++++]|3170786|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/a/60584211/4039431^0|0|0|1C|1N|N|0|0|U|8|0|0|0|4|0|0|7|2|0|0|0|5|0|0|0|8|0|0^^$0|@$1|2|3|4|5|6|7|1C|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|1D|8|@$D|1E|E|1F|F|G]]|9|@$D|1G|E|1H|1|1I]]|A|$]]|$1|H|3|I|5|6|7|1J|8|@$D|1K|E|1L|F|G]]|9|@]|A|$]]|$1|J|3|K|5|6|7|1M|8|@]|9|@]|A|$]]|$1|L|3|M|5|6|7|1N|8|@$D|1O|E|1P|F|G]]|9|@]|A|$]]|$1|N|3|O|5|P|7|1Q|8|@]|9|@]|A|$Q|R]]|$1|S|3|T|5|6|7|1R|8|@$D|1S|E|1T|F|G]]|9|@]|A|$]]|$1|U|3|V|5|P|7|1U|8|@]|9|@]|A|$Q|R]]|$1|W|3|X|5|6|7|1V|8|@$D|1W|E|1X|F|G]]|9|@]|A|$]]|$1|Y|3|Z|5|P|7|1Y|8|@]|9|@]|A|$Q|R]]|$1|10|3|11|5|6|7|1Z|8|@$D|20|E|21|F|G]]|9|@]|A|$]]|$1|12|3|13|5|P|7|22|8|@]|9|@]|A|$Q|R]]|$1|14|3|-4|5|6|7|23|8|@]|9|@]|A|$]]]|15|$16|$5|17|18|19|A|$1A|1B]]]]

While there are a lot of answers which focuses on solving the issue at hand but don't talk much about the various trade-off which someone needs to make before choosing a particular answer. So let me try to add a few more details on this perspective.
Partial search is now a day a very common and important feature and if not implemented properly can lead to poor user experience and bad performance, so first know your application function and non-function requirement related to this feature which I talked about in my <a href="https://stackoverflow.com/a/60584211/4039431">this detailed SO answer</a>.
Now there are various approaches, like query time, index time, completion suggester and search as you type data-types added in recent version of elasticsarch.
Now people who quickly want to just implement a solution can use below end to end working solution.
Index mapping
<pre><code>{
 &quot;settings&quot;: {
 &quot;analysis&quot;: {
 &quot;filter&quot;: {
 &quot;autocomplete_filter&quot;: {
 &quot;type&quot;: &quot;ngram&quot;,
 &quot;min_gram&quot;: 1,
 &quot;max_gram&quot;: 10
 }
 },
 &quot;analyzer&quot;: {
 &quot;autocomplete&quot;: { 
 &quot;type&quot;: &quot;custom&quot;,
 &quot;tokenizer&quot;: &quot;standard&quot;,
 &quot;filter&quot;: [
 &quot;lowercase&quot;,
 &quot;autocomplete_filter&quot;
 ]
 }
 }
 },
 &quot;index.max_ngram_diff&quot; : 10
 },
 &quot;mappings&quot;: {
 &quot;properties&quot;: {
 &quot;title&quot;: {
 &quot;type&quot;: &quot;text&quot;,
 &quot;analyzer&quot;: &quot;autocomplete&quot;, 
 &quot;search_analyzer&quot;: &quot;standard&quot; 
 }
 }
 }
}
</code></pre>
Index given sample docs
<pre><code>{
 &quot;title&quot; : &quot;John Doeman&quot;
 
}

{
 &quot;title&quot; : &quot;Jane Doewoman&quot;
 
}

{
 &quot;title&quot; : &quot;Jimmy Jackal&quot;
 
}
</code></pre>
And search query
<pre><code>{
 &quot;query&quot;: {
 &quot;match&quot;: {
 &quot;title&quot;: &quot;Doe&quot;
 }
 }
}
</code></pre>
which returns expected search results
<pre><code> &quot;hits&quot;: [
 {
 &quot;_index&quot;: &quot;6467067&quot;,
 &quot;_type&quot;: &quot;_doc&quot;,
 &quot;_id&quot;: &quot;1&quot;,
 &quot;_score&quot;: 0.76718915,
 &quot;_source&quot;: {
 &quot;title&quot;: &quot;John Doeman&quot;
 }
 },
 {
 &quot;_index&quot;: &quot;6467067&quot;,
 &quot;_type&quot;: &quot;_doc&quot;,
 &quot;_id&quot;: &quot;2&quot;,
 &quot;_score&quot;: 0.76718915,
 &quot;_source&quot;: {
 &quot;title&quot;: &quot;Jane Doewoman&quot;
 }
 }
 ]
</code></pre>

I've recently started using ElasticSearch and I can't seem to make it search for a part of a word.

Example: I have three documents from my couchdb indexed in ElasticSearch:

<pre><code>{
 "_id" : "1",
 "name" : "John Doeman",
 "function" : "Janitor"
}
{
 "_id" : "2",
 "name" : "Jane Doewoman",
 "function" : "Teacher"
}
{
 "_id" : "3",
 "name" : "Jimmy Jackal",
 "function" : "Student"
} 
</code></pre>

So now, I want to search for all documents containing "Doe"

<pre><code>curl http://localhost:9200/my_idx/my_type/_search?q=Doe
</code></pre>

That doesn't return any hits. But if I search for

<pre><code>curl http://localhost:9200/my_idx/my_type/_search?q=Doeman
</code></pre>

It does return one document (John Doeman).

I've tried setting different analyzers and different filters as properties of my index. I've also tried using a full blown query (for example: 

<pre><code>{
 "query": {
 "term": {
 "name": "Doe"
 }
 }
}
</code></pre>

)
But nothing seems to work.

How can I make ElasticSearch find both John Doeman and Jane Doewoman when I search for "Doe" ?

UPDATE

I tried to use the nGram tokenizer and filter, like Igor proposed, like this:

<pre><code>{
 "index": {
 "index": "my_idx",
 "type": "my_type",
 "bulk_size": "100",
 "bulk_timeout": "10ms",
 "analysis": {
 "analyzer": {
 "my_analyzer": {
 "type": "custom",
 "tokenizer": "my_ngram_tokenizer",
 "filter": [
 "my_ngram_filter"
 ]
 }
 },
 "filter": {
 "my_ngram_filter": {
 "type": "nGram",
 "min_gram": 1,
 "max_gram": 1
 }
 },
 "tokenizer": {
 "my_ngram_tokenizer": {
 "type": "nGram",
 "min_gram": 1,
 "max_gram": 1
 }
 }
 }
 }
}
</code></pre>

The problem I'm having now is that each and every query returns ALL documents.
Any pointers? ElasticSearch documentation on using nGram isn't great...

How to search for a part of a word with ElasticSearch

数据结构

我最近开始使用ElasticSearch，但我似乎不能让它搜索单词的一部分。示例:我在ElasticSearch中索引了couchdb中的三个文档：{  "_id" : "1",  "name" : "John Doeman",  "function" : "Janitor"}{  "_id" : "2",  "nam...

问如何使用ElasticSearch搜索单词的一部分
EN

社区

活动

资源

关于

腾讯云开发者

热门产品

热门推荐

更多推荐

问如何使用ElasticSearch搜索单词的一部分EN

社区

活动

资源

关于

腾讯云开发者

热门产品

热门推荐

更多推荐

问如何使用ElasticSearch搜索单词的一部分
EN