- Dependency
- Client
- Client - Transport Client
- Client - XPack Transport Client
- Document APIs
- Document APIs - Index API
- Document APIs - Get API
- Document APIs - Delete API
- Document APIs - Delete By Query API
- Document APIs - Update API
- Document APIs - Multi Get API
- Document APIs - Bulk API
- Document APIs - Using Bulk Processor
- Search API
- Search API - Using scrolls in Java
- Search API - MultiSearch API
- Search API - Using Aggregations
- Search API - Terminate After
- Search API - Search Template
- Aggregations
- Aggregations - Structuring aggregations
- Aggregations - Metrics aggregations
- Aggregations - Bucket aggregations
- Query DSL
- Query DSL - Match All Query
- Query DSL - Full text queries
- Query DSL - Term level queries
- Query DSL - Compound queries
- Query DSL - Joining queries
- Query DSL - Geo queries
- Query DSL - Specialized queries
- Query DSL - Span queries
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Query DSL - Specialized queries
Specialized queries
- more_like_this query(相似度查询)
这个查询能检索到与指定文本、文档或者文档集合相似的文档。
String[] fields = {"name.first", "name.last"}; //fields
String[] texts = {"text like this one"}; //text
Item[] items = null;
QueryBuilder qb = moreLikeThisQuery(fields, texts, items)
.minTermFreq(1) //ignore threshold
.maxQueryTerms(12); //max num of Terms in generated queries
- script query
该查询允许脚本充当过滤器。 另请参阅 function_score query 。
QueryBuilder qb = scriptQuery(
new Script("doc['num1'].value > 1") //inlined script
);
如果已经在每个数据节点上存储名为 `myscript.painless 的脚本,请执行以下操作:
doc['num1'].value > params.param1
然后使用:
QueryBuilder qb = scriptQuery(
new Script(
ScriptType.FILE, //脚本类型 ScriptType.FILE, ScriptType.INLINE, ScriptType.INDEXED
"painless", //Scripting engine 脚本引擎
"myscript", //Script name 脚本名
Collections.singletonMap("param1", 5)) //Parameters as a Map of <String, Object>
);
- Percolate Query
Settings settings = Settings.builder().put("cluster.name", "elasticsearch").build();
TransportClient client = new PreBuiltTransportClient(settings);
client.addTransportAddress(new InetSocketTransportAddress(new InetSocketAddress(InetAddresses.forString("127.0.0.1"), 9300)));
在可以使用percolate
查询之前,应该添加percolator
映射,并且应该对包含percolator
查询的文档建立索引:
// create an index with a percolator field with the name 'query':
client.admin().indices().prepareCreate("myIndexName")
.addMapping("query", "query", "type=percolator")
.addMapping("docs", "content", "type=text")
.get();
//This is the query we're registering in the percolator
QueryBuilder qb = termQuery("content", "amazing");
//Index the query = register it in the percolator
client.prepareIndex("myIndexName", "query", "myDesignatedQueryName")
.setSource(jsonBuilder()
.startObject()
.field("query", qb) // Register the query
.endObject())
.setRefreshPolicy(RefreshPolicy.IMMEDIATE) // Needed when the query shall be available immediately
.get();
在上面的index中query名为 myDesignatedQueryName
为了检查文档注册查询,使用这个代码:
//Build a document to check against the percolator
XContentBuilder docBuilder = XContentFactory.jsonBuilder().startObject();
docBuilder.field("content", "This is amazing!");
docBuilder.endObject(); //End of the JSON root object
PercolateQueryBuilder percolateQuery = new PercolateQueryBuilder("query", "docs", docBuilder.bytes());
// Percolate, by executing the percolator query in the query dsl:
SearchResponse response = client().prepareSearch("myIndexName")
.setQuery(percolateQuery))
.get();
//Iterate over the results
for(SearchHit hit : response.getHits()) {
// Percolator queries as hit
}
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