一、简介
在ElasticSearch里没有专门的数组类型，任何一个字段都可以有零个和多个值。当字段值的个数大于1时，字段类型就变成了数组。
下面以视频数据为例，介绍ElasticSearch如何索引数组数据，以及如何检索数组中的字段值。
测试视频数据格式如下：
{
"media_id": 88992211,
"tags": ["电影","科技","恐怖","电竞"]
}media_id代表视频id，tags是视频的标签，有多个值。业务上需要按视频标签检索标签下所有的视频。同一个视频有多个标签。
演示使用的ElasticSearch集群的版本是7.6.2。
二、测试演示
2.1 创建索引
PUT test_arrays
{
"settings": {
"number_of_shards": 1
},
"mappings": {
"properties": {
"media_id": {
"type": "long"
},
"tags": {
"type": "text"
}
}
}
}2.2 向test_arrays索引里写入测试数据
POST test_arrays/_doc
{
"media_id": 887722,
"tags": [
"电影",
"科技",
"恐怖",
"电竞"
]
}2.3 查看test_arrays内部如何索引tags字段
{
"tokens" : [
{
"token" : "电",
"start_offset" : 0,
"end_offset" : 1,
"type" : "<IDEOGRAPHIC>",
"position" : 0
},
{
"token" : "影",
"start_offset" : 1,
"end_offset" : 2,
"type" : "<IDEOGRAPHIC>",
"position" : 1
},
{
"token" : "科",
"start_offset" : 3,
"end_offset" : 4,
"type" : "<IDEOGRAPHIC>",
"position" : 102
},
{
"token" : "技",
"start_offset" : 4,
"end_offset" : 5,
"type" : "<IDEOGRAPHIC>",
"position" : 103
},
{
"token" : "恐",
"start_offset" : 6,
"end_offset" : 7,
"type" : "<IDEOGRAPHIC>",
"position" : 204
},
{
"token" : "怖",
"start_offset" : 7,
"end_offset" : 8,
"type" : "<IDEOGRAPHIC>",
"position" : 205
},
{
"token" : "电",
"start_offset" : 9,
"end_offset" : 10,
"type" : "<IDEOGRAPHIC>",
"position" : 306
},
{
"token" : "竞",
"start_offset" : 10,
"end_offset" : 11,
"type" : "<IDEOGRAPHIC>",
"position" : 307
}
]
}从响应结果可以看到，tags数组中的每个值被分词成多个token。
2.4 检索tags数组中的值
POST test_arrays/_search
{
"query": {
"match": {
"tags": "电影"
}
}
}
响应结果：
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 0.68324494,
"hits" : [
{
"_index" : "test_arrays",
"_type" : "_doc",
"_id" : "MyhnpXQBGXOapfjvSpOW",
"_score" : 0.68324494,
"_source" : {
"media_id" : 887722,
"tags" : [
"电影",
"科技",
"恐怖",
"电竞"
]
}
}
]
}
}
模糊检索：
POST test_arrays/_search
{
"query": {
"match": {
"tags": "影"
}
}
}
响应结果
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 0.2876821,
"hits" : [
{
"_index" : "test_arrays",
"_type" : "_doc",
"_id" : "MyhnpXQBGXOapfjvSpOW",
"_score" : 0.2876821,
"_source" : {
"media_id" : 887722,
"tags" : [
"电影",
"科技",
"恐怖",
"电竞"
]
}
}
]
}
}视频数据业务上需要通过标签精确匹配，查询标签下的所有视频。实现这种效果，需要把tags字段类型修改为keyword。test_arrays索引的mappings设置如下：
PUT test_arrays
{
"settings": {
"number_of_shards": 1
},
"mappings": {
"properties": {
"media_id": {
"type": "long"
},
"tags": {
"type": "keyword"
}
}
}
}此时tags字段数组中每一个值对应一个token，可以实现按标签精准查询标签下视频的效果。
{
"tokens" : [
{
"token" : "电影",
"start_offset" : 0,
"end_offset" : 2,
"type" : "word",
"position" : 0
},
{
"token" : "科技",
"start_offset" : 3,
"end_offset" : 5,
"type" : "word",
"position" : 1
},
{
"token" : "恐怖",
"start_offset" : 6,
"end_offset" : 8,
"type" : "word",
"position" : 2
},
{
"token" : "电竞",
"start_offset" : 9,
"end_offset" : 11,
"type" : "word",
"position" : 3
}
]
}实际业务场景中，视频标签的数据可能不是按数组存储的，全部标签存储在一个字符串中，标签之间用逗号分隔。
{
"media_id": 88992211,
"tags": "电影,科技,恐怖,电竞"
}
上面的标签存储格式，通过调整索引字段的类型，同样可以实现精准检索单个标签下视频的效果。test_arrays索引的配置如下：
PUT test_arrays
{
"settings": {
"number_of_shards": 1,
"analysis" : {
"analyzer" : {
"comma_analyzer": {
"tokenizer": "comma_tokenizer"
}
},
"tokenizer" : {
"comma_tokenizer": {
"type": "simple_pattern_split",
"pattern": ","
}
}
}
},
"mappings": {
"properties": {
"media_id": {
"type": "long"
},
"tags": {
"search_analyzer" : "simple",
"analyzer" : "comma_analyzer",
"type" : "text"
}
}
}
}写入一条测试数据到test_arrays索引
POST test_arrays/_doc
{
"media_id": 887722,
"tags": "电影,科技,恐怖,电竞"
}tags字段的索引结构如下，同样实现了一个标签对应一个token。
{
"tokens" : [
{
"token" : "电影",
"start_offset" : 0,
"end_offset" : 2,
"type" : "word",
"position" : 0
},
{
"token" : "科技",
"start_offset" : 3,
"end_offset" : 5,
"type" : "word",
"position" : 1
},
{
"token" : "恐怖",
"start_offset" : 6,
"end_offset" : 8,
"type" : "word",
"position" : 2
},
{
"token" : "电竞",
"start_offset" : 9,
"end_offset" : 11,
"type" : "word",
"position" : 3
}
]
}通过标签精准匹配查询。
请求参数
POST test_arrays/_search
{
"query": {
"match": {
"tags": "电影"
}
}
}
响应结果
{
"took" : 6,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 0.2876821,
"hits" : [
{
"_index" : "test_arrays",
"_type" : "_doc",
"_id" : "3i2ipXQBGXOapfjv3THH",
"_score" : 0.2876821,
"_source" : {
"media_id" : 887722,
"tags" : "电影,科技,恐怖,电竞"
}
}
]
}
}三、总结
ElasticSearch采用的一种数据类型同时支持单值和多值的设计理念，即简化了数据类型的总量，同时也降低索引配置的复杂度，是一种非常优秀的设计。
同时标签数据的组织方式支持数组和分隔符分隔两种形式，体现了ElasticSearch功能的灵活性。