基本操作
操作索引
1.新建索引
curl -XPUT localhost:9200/index01
2.查看索引
curl -XGET http://192.168.168.101:9200/index01/_settings
curl -XGET http://192.168.168.101:9200/index01,blog/_settings
3.删除索引
curl -XDELETE http://192.168.168.101:9200/index02
4.打开关闭索引
curl -XPOST http://192.168.168.101:9200/index01/_close
curl -XPOST http://192.168.168.101:9200/index01/_open
文档管理
1.新建文档
curl -XPUT -d ‘{‘id’:1,‘title’:‘es简介’}’ http://localhost:9200/index01/article/1
2.获取文档
curl -XGET http://192.168.168.101:9200/index01/article/1
3.删除文档
curl -XDELETE http://192.168.168.101:9200/index01/article/1
查询操作
类Lucene查询
_exists_:execution_completed_time
__type:company_extended_business
weibo_type:18 OR weibo_type:24 OR weibo_type:25
NOT company_id:442966
first_consume_time:{'2019-01-03 00:00:00' TO '2019-01-03 00:00:00'}
基本查询
指定请求头
–header “content-Type:application/json”
准备数据
curl -XPUT -d '{"id":1,"title":"es简介","content":"es好用好用真好用"}' http://192.168.168.101:9200/index01/article/1
curl -XPUT -d '{"id":1,"title":"java编程思想","content":"这就是个工具书"}' http://192.168.168.101:9200/index01/article/2
curl -XPUT -d '{"id":1,"title":"大数据简介","content":"你知道什么是大数据吗，就是大数据"}' http://192.168.168.101:9200/index01/article/3
term query
curl -XGET http://192.168.168.101:9200/index01/_search -d {'query':{'term':{'title':'你好'}}}
查询的字段只有一个值得时候，应该使用term而不是terms，在查询字段包含多个的时候才使用terms，使用terms语法，json中必须包含数组
match在匹配时会对所查找的关键词进行分词，然后按分词匹配查找，而term会直接对关键词进行查找。一般**模糊查找的时候，多用match，而精确查找时可以使用term
terms query
{
'query':{
'terms':{
'tag':["search",'nosql','hello']
}
}
}
match query
{'query':{'match':{'title':'你好'}}}
{
"query": {
"match": {
"__type": "info"
}
},
"sort": [
{
"campaign_end_time": {
"order": "desc"
}
}
]
}
match_all
{'query':{'match_all':{'title':'标题一样'}}}
multi match
多值匹配查询
{
"query": {
"multi_match": {
"query": "运动 上衣",
"fields": [
"brandName^100",
"brandName.brandName_pinyin^100",
"brandName.brandName_keyword^100",
"sortName^80",
"sortName.sortName_pinyin^80",
"productName^60",
"productKeyword^20"
],
"type": <multi-match-type>,
"operator": "AND"
}
}
}
Bool query
bool查询包含四个子句，must，filter，should，must_not
{
'query':{
'bool':{
'must':[{
'term':{
'_type':{
'value':'age'
}
}
},{
'term':{
'account_grade':{
'value':'23'
}
}
}
]
}
}
}
{
"bool":{
"must":{
"term":{"user":"lucy"}
},
"filter":{
"term":{"tag":"teach"}
},
"should":[
{"term":{"tag":"wow"}},
{"term":{"tag":"elasticsearch"}}
],
"mininum_should_match":1,
"boost":1.0
}
}
Filter query
query和filter的区别：query查询的时候，会先比较查询条件，然后计算分值，最后返回文档结果；而filter是先判断是否满足查询条件，如果不满足会缓存查询结果（记录该文档不满足结果），满足的话，就直接缓存结果
filter快在：对结果进行缓存，避免计算分值
{
"query": {
"bool": {
"must": [
{"match_all": {}}
],
"filter": {
"range": {
"create_admin_id": {
"gte": 10,
"lte": 20
}
}
}
}
}
}
range query
{
'query':{
'range':{
'age':{
'gte':'30',
'lte':'20'
}
}
}
}
通配符查询
{
'query':{
'wildcard':{
'title':'cr?me'
}
}
}
正则表达式查询
{
'query':{
'regex':{
'title':{
'value':'cr.m[ae]',
'boost':10.0
}
}
}
}
前缀查询
{
'query':{
'match_phrase_prefix':{
'title':{
'query':'crime punish',
'slop':1
}
}
}
}
query_string
{
'query':{
'query_string':{
'query':'title:crime^10 +title:punishment -otitle:cat +author:(+Fyodor +dostoevsky)'
}
}
}
聚合查询
聚合提供了用户进行分组和数理统计的能力，可以把聚合理解成SQL中的GROUP BY和分组函数
指标聚合/桶聚合
Metrics（度量/指标）：简单的对过滤出来的数据集进行avg，max操作，是一个单一的数值
Bucket（桶）：将过滤出来的数据集按条件分成多个小数据集，然后Metrics会分别作用在这些小数据集上
max/min/avg/sum/stats
{
'aggs':{c
'group_sum':{
'sum':{
'field':'money'
}
}
}
}
{
"aggs":{
"avg_fees":{
"avg":{
"field":"fees"
}
}
}
}
terms聚合
terms根据字段值项分组聚合.field按什么字段分组,size指定返回多少个分组,shard_size指定每个分片上返回多少个分组,order排序方式.可以指定include和exclude正则筛选表达式的值,指定missing设置缺省值
{
'aggs':{
'group_by_type':{
'terms':{
'field':'_type'
}
}
}
}
{
"size": 0,
"aggs": {
"terms":{
"terms": {
"field": "__type",
"size": 10
}
}
}
}
{
"size": 0,
"aggs": {
"terms":{
"terms": {
"field": "__type",
"size": 10,
"order": {
"_count": "asc"
}
}
}
}
}
{
"size": 0,
"aggs": {
"agg_terms": {
"terms": {
"field": "cost",
"order": {
"_count": "asc"
}
},
"aggs": {
"max_balance": {
"max": {
"field": "cost"
}
}
}
}
}
}
{
"size": 0,
"aggs": {
"agg_terms": {
"terms": {
"field": "cost",
"include": ".*",
"exclude": ".*"
}
}
}
}
cardinality去重
{
"size": 0,
"aggs": {
"count_type": {
"cardinality": {
"field": "__type"
}
}
}
}
cardinality
percentiles百分比
percentiles对指定字段（脚本）的值按从小到大累计每个值对应的文档数的占比（占所有命中文档数的百分比），返回指定占比比例对应的值。默认返回[ 1, 5, 25, 50, 75, 95, 99 ]分位上的值
{
"size": 0,
"aggs": {
"age_percents":{
"percentiles": {
"field": "age",
"percents": [
1,
5,
25,
50,
75,
95,
99
]
}
}
}
}
{
"size": 0,
"aggs": {
"states": {
"terms": {
"field": "gender"
},
"aggs": {
"banlances": {
"percentile_ranks": {
"field": "balance",
"values": [
20000,
40000
]
}
}
}
}
}
percentiles rank
统计小于等于指定值得文档比
{
"size": 0,
"aggs": {
"tests": {
"percentile_ranks": {
"field": "age",
"values": [
10,
15
]
}
}
}
}
filter聚合
filter对满足过滤查询的文档进行聚合计算,在查询命中的文档中选取过滤条件的文档进行聚合,先过滤在聚合
{
"size": 0,
"aggs": {
"agg_filter":{
"filter": {
"match":{"gender":"F"}
},
"aggs": {
"avgs": {
"avg": {
"field": "age"
}
}
}
}
}
}
filtters聚合
多个过滤组聚合计算
{
"size": 0,
"aggs": {
"message": {
"filters": {
"filters": {
"errors": {
"exists": {
"field": "__type"
}
},
"warring":{
"term": {
"__type": "info"
}
}
}
}
}
}
}
range聚合
{
"aggs": {
"agg_range": {
"range": {
"field": "cost",
"ranges": [
{
"from": 50,
"to": 70
},
{
"from": 100
}
]
},
"aggs": {
"bmax": {
"max": {
"field": "cost"
}
}
}
}
}
}
date_range聚合
{
"aggs": {
"date_aggrs": {
"date_range": {
"field": "accepted_time",
"format": "MM-yyy",
"ranges": [
{
"from": "now-10d/d",
"to": "now"
}
]
}
}
}
}
date_histogram
时间直方图聚合,就是按天、月、年等进行聚合统计。可按 year (1y), quarter (1q), month (1M), week (1w), day (1d), hour (1h), minute (1m), second (1s) 间隔聚合或指定的时间间隔聚合
{
"aggs": {
"sales_over_time": {
"date_histogram": {
"field": "accepted_time",
"interval": "quarter",
"min_doc_count" : 0, //可以返回没有数据的月份
"extended_bounds" : { //强制返回数据的范围
"min" : "2014-01-01",
"max" : "2014-12-31"
}
}
}
}
}
missing聚合
{
"aggs": {
"account_missing": {
"missing": {
"field": "__type"
}
}
}
}
LogStash操作
启动logStash
logstash -e ‘input{stdin{}}output{stdout{codec=>rubydebug}}’
IK分词器
curl -XPOST http://192.168.168.101:9200/_analyze -d ‘{“analyzer”:“ik”,“text”:“JAVA编程思想”}’
http://192.168.168.101:9200/index01/_analyze?analyzer=ik&text=中华人民共和国
IK分词器
curl -XPUT -d ‘{“id”:1,“kw”:“我们都爱中华人民共和国”}’ http://192.168.168.101:9200/haha1/haha/1
Mapping
查看mapping
curl -XGET http://192.168.168.101:9200/jtdb_item/tb_item/_mapping