系统信息相关API
// 查看ec版本配置信息
http://localhost:9200
// 查看所有插件
http://localhost:9200/_cat/plugins?v
// 查看所有索引
http://localhost:9200/_cat/indices?v
// 对ES进行健康检查
http://localhost:9200/_cat/health?v
// 查看当前的磁盘占用率
http://localhost:9200/_cat/allocation?v
创建索引 yii2blog/articles
curl -v -X PUT "http://localhost:9200/yii2blog?pretty=true" -d "json"
curl -v -X POST "http://localhost:9200/yii2blog?pretty=true" -d "json"
{
"settings":{
"refresh_interval": "5s", //5秒后刷新
"number_of_shards": 1, //分片，目前1台机器，所以为1
"number_of_replicas": 0 //副本为0
},
"mappings": {
"_default_": {
"_all": {
"enabled": true //所有数据都索引
}
},
"articles": { //名称可自定义，可定义为表名
"dynamic": false , //动态映射
"properties": {
"article_id": {
"type": "long"
},
"post_title": {
"type": "string",
"index": "analyzed",
"analyzer": "ik"
},
"post_excerpt": {
"type": "string",
"index": "analyzed",
"analyzer": "ik"
}
}
}
}
}
删除索引 yii2blog/articles
curl -v -X DELETE "localhost:9200/yii2blog?pretty=true"
添加记录
curl -v X PUT "http://localhost:9200/yii2blog/articles/1?pretty=true" -d "json"
curl -v X POST "http://localhost:9200/yii2blog/articles/1?pretty=true" -d "json"
{
"article_id" : 1,
"post_title" : "这是文章标题",
"post_excerpt" : "这是文章描述"
}
查看记录
curl -v X GET "http://localhost:9200/yii2blog/articles/1?pretty=true"
删除记录
curl -v X DELETE "http://localhost:9200/yii2blog/articles/1?pretty=true"
更新记录
curl -v X PUT "http://localhost:9200/yii2blog/articles/1?pretty=true" -d "json"
curl -v X POST "http://localhost:9200/yii2blog/articles/1?pretty=true" -d "json"
{
"article_id" : 1,
"post_title" : "更新文章标题",
"post_excerpt" : "更新文章描述"
}
数据查询
1. 返回 elastic 中所有记录
curl -v X GET "http://localhost:9200/_search?pretty=true"
2. 返回 yii2blog 中所有记录
curl -v X GET "http://localhost:9200/yii2blog/_search?pretty=true"
3. 返回 yii2blog/articles 中所有记录
curl -v X GET "http://localhost:9200/yii2blog/articles/_search?pretty=true"
全文搜索
1. 使用 Match 查询，指定的匹配条件是 post_excerpt 字段里面包含"描述"这个词
curl -v X POST "http://localhost:9200/yii2blog/articles/_search?pretty=true" -d "json"
{
"query" : { "match" : { "post_excerpt" : "描述" }}
}
2. 返回2两条记录（Elastic 默认一次返回10条结果）
curl -v X POST "http://localhost:9200/yii2blog/articles/_search?pretty=true" -d "json"
{
"query" : { "match" : { "post_excerpt" : "描述" }},
"size": 2
}
3. 从位置3开始（默认是从位置0开始），只返回5条结果。
curl -v X POST "http://localhost:9200/yii2blog/articles/_search?pretty=true" -d "json"
{
"query" : { "match" : { "post_excerpt" : "描述" }},
"from": 3,
"size": 5
}
4. 搜索的是 "描述" or "文章"。
curl -v X POST "http://localhost:9200/yii2blog/articles/_search?pretty=true" -d "json"
{
"query" : { "match" : { "post_excerpt" : "描述 文章" }}
}
5. 搜索的是 "描述" and "文章"，必须使用布尔查询。
curl -v X POST "http://localhost:9200/yii2blog/articles/_search?pretty=true" -d "json"
{
"query": {
"bool": {
"must": [
{ "match": { "post_excerpt": "描述" } },
{ "match": { "post_excerpt": "文章" } }
]
}
}
}
6. 使用 Match 查询，指定的匹配条件是 post_excerpt 字段里面包含带高亮的"描述"这个词
curl -v X POST "http://localhost:9200/yii2blog/articles/_search?pretty=true" -d "json"
{
"query": {
"multi_match": {
"query": "描述",
"fields": [
"post_excerpt"
]
}
},
"highlight": {
"pre_tags": [
"<b class=\"highlight\">"
],
"post_tags": [
"</b>"
],
"fields": {
"post_excerpt": {}
}
}
}
结果：
{
"took": 2,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 0.48288077,
"hits": [
{
"_index": "yii2blog",
"_type": "articles",
"_id": "1",
"_score": 0.48288077,
"_source": {
"article_id": 1,
"post_title": "这是文章标题",
"post_excerpt": "这是文章描述，更新"
},
"highlight": {
"post_excerpt": [
"这是文章描述，<b class="highlight">更新</b>"
]
}
},
{
"_index": "yii2blog",
"_type": "articles",
"_id": "2",
"_score": 0.48288077,
"_source": {
"article_id": 1,
"post_title": "这是文章标题",
"post_excerpt": "这是文章描述，更新"
},
"highlight": {
"post_excerpt": [
"这是文章描述，<b class="highlight">更新</b>"
]
}
}
]
}
}
中文分词设置
首先，安装中文分词插件。这里使用的是 ik，也可以考虑其他插件（比如 smartcn）。
$ ./bin/elasticsearch-plugin install https://github.com/medcl/elasticsearch-analysis-ik/releases/download/v5.5.1/elasticsearch-analysis-ik-5.5.1.zip
上面代码安装的是5.5.1版的插件，与 Elastic 5.5.1 配合使用。
接着，重新启动 Elastic，就会自动加载这个新安装的插件。
然后，新建一个 Index，指定需要分词的字段。这一步根据数据结构而异，下面的命令只针对本文。基本上，凡是需要搜索的中文字段，都要单独设置一下。
$ curl -X PUT 'localhost:9200/accounts' -d '
{
"mappings": {
"person": {
"properties": {
"user": {
"type": "text",
"analyzer": "ik_max_word",
"search_analyzer": "ik_max_word"
},
"title": {
"type": "text",
"analyzer": "ik_max_word",
"search_analyzer": "ik_max_word"
},
"desc": {
"type": "text",
"analyzer": "ik_max_word",
"search_analyzer": "ik_max_word"
}
}
}
}
}'
上面代码中，首先新建一个名称为accounts的 Index，里面有一个名称为person的 Type。person有三个字段。
- user
- title
- desc
这三个字段都是中文，而且类型都是文本（text），所以需要指定中文分词器，不能使用默认的英文分词器。
Elastic 的分词器称为 analyzer。我们对每个字段指定分词器。
"user": {
"type": "text",
"analyzer": "ik_max_word",
"search_analyzer": "ik_max_word"
}
上面代码中，analyzer是字段文本的分词器，search_analyzer是搜索词的分词器。ik_max_word分词器是插件ik提供的，可以对文本进行最大数量的分词。
分词测试
1. 自带分词器 standard
curl -v -X POST "http://localhost:9200/_analyze?analyzer=standard&pretty=true" -d "这是一段汉字"
结果
{
"tokens": [
{
"token": "这",
"start_offset": 1,
"end_offset": 2,
"type": "<IDEOGRAPHIC>",
"position": 0
},
{
"token": "是",
"start_offset": 2,
"end_offset": 3,
"type": "<IDEOGRAPHIC>",
"position": 1
},
{
"token": "一",
"start_offset": 3,
"end_offset": 4,
"type": "<IDEOGRAPHIC>",
"position": 2
},
{
"token": "段",
"start_offset": 4,
"end_offset": 5,
"type": "<IDEOGRAPHIC>",
"position": 3
},
{
"token": "汉",
"start_offset": 5,
"end_offset": 6,
"type": "<IDEOGRAPHIC>",
"position": 4
},
{
"token": "字",
"start_offset": 6,
"end_offset": 7,
"type": "<IDEOGRAPHIC>",
"position": 5
}
]
}
2. 中文分词插件 ik
curl -v -X POST "http://localhost:9200/_analyze?analyzer=ik&pretty=true" -d "这是一段汉字"
结果
{
"tokens": [
{
"token": "这是",
"start_offset": 1,
"end_offset": 3,
"type": "CN_WORD",
"position": 0
},
{
"token": "一段",
"start_offset": 3,
"end_offset": 5,
"type": "CN_WORD",
"position": 1
},
{
"token": "一",
"start_offset": 3,
"end_offset": 4,
"type": "TYPE_CNUM",
"position": 2
},
{
"token": "段",
"start_offset": 4,
"end_offset": 5,
"type": "COUNT",
"position": 3
},
{
"token": "汉字",
"start_offset": 5,
"end_offset": 7,
"type": "CN_WORD",
"position": 4
},
{
"token": "汉",
"start_offset": 5,
"end_offset": 6,
"type": "CN_WORD",
"position": 5
},
{
"token": "字",
"start_offset": 6,
"end_offset": 7,
"type": "CN_CHAR",
"position": 6
}
]
}
