HugeGraph 图数据库索引介绍 - 范围索引，全文索引

目录

HugeGraph 索引介绍

二级索引

组合索引

范围索引

全文索引

 

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HugeGraph 索引介绍

二级索引

创建schema和添加数据

schema.propertyKey("name").asText().ifNotExist().create();schema.propertyKey("uid").asLong().ifNotExist().create();schema.propertyKey("city").asText().ifNotExist().create();schema.propertyKey("mid").asText().ifNotExist().create();schema.propertyKey("url").asText().ifNotExist().create();schema.propertyKey("date").asText().ifNotExist().create();schema.vertexLabel("article").properties("name", "uid", "city","mid","url").useCustomizeStringId().ifNotExist().create(); schema.indexLabel("articleByNameSecondary").onV("article").by("name").secondary().ifNotExist().create();graph.addVertex(T.label, "article",T.id,"id7", "mid", "lakjdflkjalskdjfl", "name", "北京三联WiFi熟练度附近", "uid", 1234567897,"city","未知","url","http://test.com//IodUhh2bV");graph.addVertex(T.label, "article",T.id,"id2", "mid", "lakjdflkjalskdjfl", "name", "北京三联法律文件而来反馈", "uid", 1234567891,"city","未知","url","http://test.com//IodUhh2bV");graph.addVertex(T.label, "article",T.id,"id3", "mid", "lakjdflkjalskdjfl", "name", "深圳广州WiFi熟练度附近", "uid", 1234567892,"city","未知","url","http://test.com//IodUhh2bV");graph.addVertex(T.label, "article",T.id,"id4", "mid", "lakjdflkjalskdjfl", "name", "深圳WiFi熟练度附近", "uid", 1234567893,"city","未知","url","http://test.com//IodUhh2bV");graph.addVertex(T.label, "article",T.id,"id5", "mid", "lakjdflkjalskdjfl", "name", "广州测试", "uid", 1234567894,"city","未知","url","http://test.com//IodUhh2bV");graph.addVertex(T.label, "article",T.id,"id6", "mid", "lakjdflkjalskdjfl", "name", "数据", "uid", 1234567895,"city","未知","url","http://test.com//IodUhh2bV");

根据name查询，该方法只能全不值匹配，不能模糊匹配

模糊匹配使用该方法，不推荐这么使用，filter过滤数据，当数据量很大的时候性能会下降

g.V().hasLabel("article").filter{it.get().property("name").value().contains("北京")}; 或者使用下面这个方法进行正则匹配 g.V().hasLabel("article").filter{it.get().property("name").value().matches("北京(.*)")};

 可以对结果进行过滤

组合索引

待补充

范围索引

待补充

全文索引

创建索引标签

设置定点article的name字段为全文索引（可以模糊检索）

schema.indexLabel("articleByNameSearch").onV("article").by("name").search().ifNotExist().create();

其他schema设置

schema.propertyKey("name").asText().ifNotExist().create();schema.propertyKey("uid").asLong().ifNotExist().create();schema.propertyKey("city").asText().ifNotExist().create();schema.propertyKey("mid").asText().ifNotExist().create();schema.propertyKey("url").asText().ifNotExist().create();schema.propertyKey("date").asText().ifNotExist().create();schema.vertexLabel("article").properties("name", "uid", "city","mid","url")// .primaryKeys("name").useCustomizeStringId().ifNotExist().create();

 添加数据

graph.addVertex(T.label, "article",T.id,"id7", "mid", "lakjdflkjalskdjfl", "name", "北京三联WiFi熟练度附近", "uid", 1234567897,"city","未知","url","http://test.com//IodUhh2bV"); graph.addVertex(T.label, "article",T.id,"id2", "mid", "lakjdflkjalskdjfl", "name", "北京三联法律文件而来反馈", "uid", 1234567891,"city","未知","url","http://test.com//IodUhh2bV"); graph.addVertex(T.label, "article",T.id,"id3", "mid", "lakjdflkjalskdjfl", "name", "深圳广州WiFi熟练度附近", "uid", 1234567892,"city","未知","url","http://test.com//IodUhh2bV"); graph.addVertex(T.label, "article",T.id,"id4", "mid", "lakjdflkjalskdjfl", "name", "深圳WiFi熟练度附近", "uid", 1234567893,"city","未知","url","http://test.com//IodUhh2bV"); graph.addVertex(T.label, "article",T.id,"id5", "mid", "lakjdflkjalskdjfl", "name", "广州测试", "uid", 1234567894,"city","未知","url","http://test.com//IodUhh2bV"); graph.addVertex(T.label, "article",T.id,"id6", "mid", "lakjdflkjalskdjfl", "name", "数据", "uid", 1234567895,"city","未知","url","http://test.com//IodUhh2bV");

 数据添加成功，查看数据

idlabelproperties

id2	article	{"name":"北京三联法律文件而来反馈","uid":1234567891,"city":"未知","mid":"lakjdflkjalskdjfl","url":"http://test.com//IodUhh2bV"}
id3	article	{"name":"深圳广州WiFi熟练度附近","uid":1234567892,"city":"未知","mid":"lakjdflkjalskdjfl","url":"http://test.com//IodUhh2bV"}
id4	article	{"name":"深圳WiFi熟练度附近","uid":1234567893,"city":"未知","mid":"lakjdflkjalskdjfl","url":"http://test.com//IodUhh2bV"}
id5	article	{"name":"广州测试","uid":1234567894,"city":"未知","mid":"lakjdflkjalskdjfl","url":"http://test.com//IodUhh2bV"}
id6	article	{"name":"数据","uid":1234567895,"city":"未知","mid":"lakjdflkjalskdjfl","url":"http://test.com//IodUhh2bV"}
id7	article	{"name":"北京三联WiFi熟练度附近","uid":1234567897,"city":"未知","mid":"lakjdflkjalskdjfl","url":"http://test.com//IodUhh2bV"}

查询 

添加索引的数据会进行分词，我们在HugeGraph中可以看到配置文件 hugegraph.properties 中有配置分词项，这里使用jieba分词

search.text_analyzer=jieba search.text_analyzer_mode=INDEX

执行查询

g.V().hasLabel("article").has("name", Text.contains("熟练度"))

查询结果： 

idlabelproperties

id3	article	{"name":"深圳广州WiFi熟练度附近","uid":1234567892,"city":"未知","mid":"lakjdflkjalskdjfl","url":"http://test.com//IodUhh2bV"}
id4	article	{"name":"深圳WiFi熟练度附近","uid":1234567893,"city":"未知","mid":"lakjdflkjalskdjfl","url":"http://test.com//IodUhh2bV"}
id7	article	{"name":"北京三联WiFi熟练度附近","uid":1234567897,"city":"未知","mid":"lakjdflkjalskdjfl","url":"http://test.com//IodUhh2bV"}

我们可以看出可以模糊查询了

 我们再查询“熟练”，发现数据也可以出来

当查询“熟”一个字的时候发现没有数据，搜索“练度”也没有数据，原因是索引将数据进行分词，将分词后的每个词进行索引，如果你搜索的词在分词的时候没有分词出来，最终搜索的结果就会搜索不到，“熟”，“练度”这两个字（词），在JieBa分词中没有，所以搜索不到。

所以查询使用使用Text.contains()，也并非我们开发中判断字符串中是否包含该字符串的意思。

 

 

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