Elasticsearch-mapper 基于注解方式生成mapping(2.0以上)

Elasticsearch生成mapping的方式上有多种方式，我们可以把mapping做成配置文件，也可以用spring-data-elasticsearch基于注解生成。

在基于注解生成这种方式上spring-data的注解还是不错的，但是如果想深度定制化一些参数spring-data却是不支持的，比如针对分词的string类型字段的fielddata加载设置。

又如果项目中不想引入spring但又想使用基于注解方式生成mapping，这时spring-data就不行了，这里有另一种选择：elasticsearch-mapper。

elasticsearch-mapper支持绝大部分数据类型和相关参数的设置，使用是请参考官网对各种数据类型和相关参数：ES2.x官网mapping设置

下面是使用示例：

 1 @Document(type = "book", _timestamp = true, _ttl = @TTL(enabled = true, _default = "5m"))  
 2 public class Book {  
 3     /*ID,只能是Long或者String类型*/  
 4     @Id  
 5     private Long id;  
 6   
 7     /*数值类型*/  
 8     @Field(type = FieldType.Double, ignoreMalformed = true)  
 9     private Double price;  
10   
11     /*数值类型*/  
12     @Field(type = FieldType.Integer)  
13     private Integer pageCount;  
14   
15     /*未分词String型*/  
16     @Field(type = FieldType.String, index = FieldIndex.not_analyzed)  
17     private String isnNo;  
18   
19     /*bool型*/  
20     @Field(type = FieldType.Boolean, nullValue = "false")  
21     private Boolean isValid;  
22   
23     /*日期类型*/  
24     @Field(type = FieldType.Date, format = DateFormat.basic_time_no_millis)  
25     private Date publishDate;  
26   
27     /*分词String类型,并设置fielddata加载限制(当然也可不设置用默认)*/  
28     @Field(  
29             type = FieldType.String,  
30             index = FieldIndex.analyzed,  
31             analyzer = "ik_max_word",  
32             searchAnalyzer = "ik_smart",  
33             termVector = TermVector.with_positions_offsets,  
34             fielddata = @Fielddata(  
35                     format = FielddataFormat.paged_bytes,  
36                     frequency = @FielddataFrequencyFilter(  
37                             enable = true,  
38                             min = 0.001,  
39                             max = 1.2,  
40                             minSegmentSize = 500  
41                     ),  
42                     loading = FielddataLoading.eager_global_ordinals  
43             )  
44   
45     )  
46     private String author;  
47   
48     /*multi field 类型(用于多字段搜索)*/  
49     @MultiField(  
50             mainField = @Field(type = FieldType.String, index = FieldIndex.analyzed, analyzer = "ik_max_word", searchAnalyzer = "ik_smart"),  
51             otherFields = {  
52                     @MultiNestedField(dotSuffix = "pinyin", nestedField = @Field(  
53                             type = FieldType.String,  
54                             index = FieldIndex.analyzed,  
55                             analyzer = "lc_index",  
56                             searchAnalyzer = "lc_search")  
57                     ),  
58                     @MultiNestedField(dotSuffix = "english", nestedField = @Field(  
59                             type = FieldType.String,  
60                             index = FieldIndex.analyzed,  
61                             analyzer = "standard")  
62                     )  
63             }  
64     )  
65     private String title;  
66   
67     /*Completion Context Suggester配置(如果不配置CompletionContext则是Completion Suggester)*/  
68     @CompletionField(analyzer = "ik", payloads = true, context = {  
69             @CompletionContext(name = "bookType", type = CompletionContextType.category, defaultVal = {"algorithm"}),  
70             @CompletionContext(name = "bookColor", type = CompletionContextType.category, defaultVal = {"red"})  
71     })  
72     private String suggestContextField;  
73   
74     /*二进制类型*/  
75     @Field(type = FieldType.Binary)  
76     private byte[] pdf;  
77   
78     /*内嵌类型*/  
79     @NestedObject(clazz = SalesArea.class)  
80     private SalesArea salesArea;  
81       
82 }

[Reference]

[1] http://blog.csdn.net/chennanymy/article/details/52663589