分组搜索结果通常有助于获取每个组的匹配计数或其他聚合。例如,它对于创建一个图表来说明每个月匹配的博客文章数量或按站点分组网页搜索结果或按作者分组论坛帖子等非常有用。
Manticore 支持按单个或多个列和计算表达式对搜索结果进行分组。结果可以:
- 在组内排序
- 每个组返回多于一行
- 过滤组
- 对组进行排序
- 使用聚合函数进行聚合
- SQL
- JSON
通用语法
SELECT {* | SELECT_expr [, SELECT_expr ...]}
...
GROUP BY {field_name | alias } [, ...]
[HAVING where_condition]
[WITHIN GROUP ORDER BY field_name {ASC | DESC} [, ...]]
...
SELECT_expr: { field_name | function_name(...) }
where_condition: {aggregation expression alias | COUNT(*)}JSON 查询格式目前支持基本的分组,可以检索聚合值及其 count(*)。
{
"table": "<table_name>",
"limit": 0,
"aggs": {
"<aggr_name>": {
"terms": {
"field": "<attribute>",
"size": <int value>
}
}
}
}标准查询输出返回未分组的结果集,可以使用 limit(或 size)隐藏。
聚合需要为组的结果集大小设置 size。
分组非常简单 - 只需在 SELECT 查询的末尾添加 "GROUP BY smth"。smth 可以是:
- 表中的任何非全文字段:整数、浮点数、字符串、MVA(多值属性)
- 或者如果你在
SELECT列表中使用了别名,你也可以按它分组
在 SELECT 列表中可以省略任何 聚合函数,它仍然会工作:
- SQL
SELECT release_year FROM films GROUP BY release_year LIMIT 5;+--------------+
| release_year |
+--------------+
| 2004 |
| 2002 |
| 2001 |
| 2005 |
| 2000 |
+--------------+然而,在大多数情况下,你可能希望为每个组获取一些聚合数据,例如:
COUNT(*)以简单地获取每个组中的元素数量- 或
AVG(field)计算组内字段的平均值
对于 HTTP JSON 请求,使用主查询级别的单个 aggs 桶并设置 limit=0 与 SQL 查询中的 GROUP BY 和 COUNT(*) 具有类似的行为和性能。
- SQL1
- SQL2
- JSON
- PHP
- Python
- Python-asyncio
- Javascript
- Java
- C#
- Rust
- TypeScript
- Go
SELECT release_year, count(*) FROM films GROUP BY release_year LIMIT 5;SELECT release_year, AVG(rental_rate) FROM films GROUP BY release_year LIMIT 5;POST /search -d '
{
"table" : "films",
"limit": 0,
"aggs" :
{
"release_year" :
{
"terms" :
{
"field":"release_year",
"size":100
}
}
}
}
'$index->setName('films');
$search = $index->search('');
$search->limit(0);
$search->facet('release_year','release_year',100);
$results = $search->get();
print_r($results->getFacets());res =searchApi.search({"table":"films","limit":0,"aggs":{"release_year":{"terms":{"field":"release_year","size":100}}}})res = await searchApi.search({"table":"films","limit":0,"aggs":{"release_year":{"terms":{"field":"release_year","size":100}}}})res = await searchApi.search({"table":"films","limit":0,"aggs":{"release_year":{"terms":{"field":"release_year","size":100}}}});HashMap<String,Object> aggs = new HashMap<String,Object>(){{
put("release_year", new HashMap<String,Object>(){{
put("terms", new HashMap<String,Object>(){{
put("field","release_year");
put("size",100);
}});
}});
}};
searchRequest = new SearchRequest();
searchRequest.setIndex("films");
searchRequest.setLimit(0);
query = new HashMap<String,Object>();
query.put("match_all",null);
searchRequest.setQuery(query);
searchRequest.setAggs(aggs);
searchResponse = searchApi.search(searchRequest);var agg = new Aggregation("release_year", "release_year");
agg.Size = 100;
object query = new { match_all=null };
var searchRequest = new SearchRequest("films", query);
searchRequest.Aggs = new List<Aggregation> {agg};
var searchResponse = searchApi.Search(searchRequest);let query = SearchQuery::new();
let aggTerms1 = AggTerms::new {
fields: "release_year".to_string(),
size: Some(100),
};
let agg1 = Aggregation {
terms: Some(Box::new(aggTerms1)),
..Default::default(),
};
let mut aggs = HashMap::new();
aggs.insert("release_year".to_string(), agg1);
let search_req = SearchRequest {
table: "films".to_string(),
query: Some(Box::new(query)),
aggs: serde_json::json!(aggs),
..Default::default(),
};
let search_res = search_api.search(search_req).await;res = await searchApi.search({
index: 'test',
limit: 0,
aggs: {
cat_id: {
terms: { field: "cat", size: 1 }
}
}
});query := map[string]interface{} {};
searchRequest.SetQuery(query);
aggTerms := manticoreclient.NewAggregationTerms()
aggTerms.SetField("cat")
aggTerms.SetSize(1)
aggregation := manticoreclient.NewAggregation()
aggregation.setTerms(aggTerms)
searchRequest.SetAggregation(aggregation)
res, _, _ := apiClient.SearchAPI.Search(context.Background()).SearchRequest(*searchRequest).Execute()+--------------+----------+
| release_year | count(*) |
+--------------+----------+
| 2004 | 108 |
| 2002 | 108 |
| 2001 | 91 |
| 2005 | 93 |
| 2000 | 97 |
+--------------+----------++--------------+------------------+
| release_year | avg(rental_rate) |
+--------------+------------------+
| 2004 | 2.78629661 |
| 2002 | 3.08259249 |
| 2001 | 3.09989142 |
| 2005 | 2.90397978 |
| 2000 | 3.17556739 |
+--------------+------------------+{
"took": 2,
"timed_out": false,
"hits": {
"total": 10000,
"hits": [
]
},
"release_year": {
"group_brand_id": {
"buckets": [
{
"key": 2004,
"doc_count": 108
},
{
"key": 2002,
"doc_count": 108
},
{
"key": 2000,
"doc_count": 97
},
{
"key": 2005,
"doc_count": 93
},
{
"key": 2001,
"doc_count": 91
}
]
}
}
}Array
(
[release_year] => Array
(
[buckets] => Array
(
[0] => Array
(
[key] => 2009
[doc_count] => 99
)
[1] => Array
(
[key] => 2008
[doc_count] => 102
)
[2] => Array
(
[key] => 2007
[doc_count] => 93
)
[3] => Array
(
[key] => 2006
[doc_count] => 103
)
[4] => Array
(
[key] => 2005
[doc_count] => 93
)
[5] => Array
(
[key] => 2004
[doc_count] => 108
)
[6] => Array
(
[key] => 2003
[doc_count] => 106
)
[7] => Array
(
[key] => 2002
[doc_count] => 108
)
[8] => Array
(
[key] => 2001
[doc_count] => 91
)
[9] => Array
(
[key] => 2000
[doc_count] => 97
)
)
)
){'aggregations': {u'release_year': {u'buckets': [{u'doc_count': 99,
u'key': 2009},
{u'doc_count': 102,
u'key': 2008},
{u'doc_count': 93,
u'key': 2007},
{u'doc_count': 103,
u'key': 2006},
{u'doc_count': 93,
u'key': 2005},
{u'doc_count': 108,
u'key': 2004},
{u'doc_count': 106,
u'key': 2003},
{u'doc_count': 108,
u'key': 2002},
{u'doc_count': 91,
u'key': 2001},
{u'doc_count': 97,
u'key': 2000}]}},
'hits': {'hits': [], 'max_score': None, 'total': 1000},
'profile': None,
'timed_out': False,
'took': 0}{'aggregations': {u'release_year': {u'buckets': [{u'doc_count': 99,
u'key': 2009},
{u'doc_count': 102,
u'key': 2008},
{u'doc_count': 93,
u'key': 2007},
{u'doc_count': 103,
u'key': 2006},
{u'doc_count': 93,
u'key': 2005},
{u'doc_count': 108,
u'key': 2004},
{u'doc_count': 106,
u'key': 2003},
{u'doc_count': 108,
u'key': 2002},
{u'doc_count': 91,
u'key': 2001},
{u'doc_count': 97,
u'key': 2000}]}},
'hits': {'hits': [], 'max_score': None, 'total': 1000},
'profile': None,
'timed_out': False,
'took': 0}{"took":0,"timed_out":false,"aggregations":{"release_year":{"buckets":[{"key":2009,"doc_count":99},{"key":2008,"doc_count":102},{"key":2007,"doc_count":93},{"key":2006,"doc_count":103},{"key":2005,"doc_count":93},{"key":2004,"doc_count":108},{"key":2003,"doc_count":106},{"key":2002,"doc_count":108},{"key":2001,"doc_count":91},{"key":2000,"doc_count":97}]}},"hits":{"total":1000,"hits":[]}}class SearchResponse {
took: 0
timedOut: false
aggregations: {release_year={buckets=[{key=2009, doc_count=99}, {key=2008, doc_count=102}, {key=2007, doc_count=93}, {key=2006, doc_count=103}, {key=2005, doc_count=93}, {key=2004, doc_count=108}, {key=2003, doc_count=106}, {key=2002, doc_count=108}, {key=2001, doc_count=91}, {key=2000, doc_count=97}]}}
hits: class SearchResponseHits {
maxScore: null
total: 1000
hits: []
}
profile: null
}class SearchResponse {
took: 0
timedOut: false
aggregations: {release_year={buckets=[{key=2009, doc_count=99}, {key=2008, doc_count=102}, {key=2007, doc_count=93}, {key=2006, doc_count=103}, {key=2005, doc_count=93}, {key=2004, doc_count=108}, {key=2003, doc_count=106}, {key=2002, doc_count=108}, {key=2001, doc_count=91}, {key=2000, doc_count=97}]}}
hits: class SearchResponseHits {
maxScore: null
total: 1000
hits: []
}
profile: null
}class SearchResponse {
took: 0
timedOut: false
aggregations: {release_year={buckets=[{key=2009, doc_count=99}, {key=2008, doc_count=102}, {key=2007, doc_count=93}, {key=2006, doc_count=103}, {key=2005, doc_count=93}, {key=2004, doc_count=108}, {key=2003, doc_count=106}, {key=2002, doc_count=108}, {key=2001, doc_count=91}, {key=2000, doc_count=97}]}}
hits: class SearchResponseHits {
maxScore: null
total: 1000
hits: []
}
profile: null
}{
"took":0,
"timed_out":false,
"aggregations":
{
"cat_id":
{
"buckets":
[{
"key":1,
"doc_count":1
}]
}
},
"hits":
{
"total":5,
"hits":[]
}
}{
"took":0,
"timed_out":false,
"aggregations":
{
"cat_id":
{
"buckets":
[{
"key":1,
"doc_count":1
}]
}
},
"hits":
{
"total":5,
"hits":[]
}
}- SQL
SELECT release_year, count(*) from films GROUP BY release_year ORDER BY release_year asc limit 5;+--------------+----------+
| release_year | count(*) |
+--------------+----------+
| 2000 | 97 |
| 2001 | 91 |
| 2002 | 108 |
| 2003 | 106 |
| 2004 | 108 |
+--------------+----------+或者,你可以按聚合进行排序:
- 按
count(*)显示具有最多元素的组首先 - 按
avg(rental_rate)展示评分最高的电影首先。注意,在示例中,它是通过别名完成的:avg(rental_rate)首先映射到avg在SELECT列表中,然后我们只需做ORDER BY avg
- SQL1
- SQL2
SELECT release_year, count(*) FROM films GROUP BY release_year ORDER BY count(*) desc LIMIT 5;SELECT release_year, AVG(rental_rate) avg FROM films GROUP BY release_year ORDER BY avg desc LIMIT 5;+--------------+----------+
| release_year | count(*) |
+--------------+----------+
| 2004 | 108 |
| 2002 | 108 |
| 2003 | 106 |
| 2006 | 103 |
| 2008 | 102 |
+--------------+----------++--------------+------------+
| release_year | avg |
+--------------+------------+
| 2006 | 3.26184368 |
| 2000 | 3.17556739 |
| 2001 | 3.09989142 |
| 2002 | 3.08259249 |
| 2008 | 2.99000049 |
+--------------+------------+- SQL
- JSON
SELECT category_id, release_year, count(*) FROM films GROUP BY category_id, release_year ORDER BY category_id ASC, release_year ASC;POST /search -d '
{
"size": 0,
"table": "films",
"aggs": {
"cat_release": {
"composite": {
"size":5,
"sources": [
{ "category": { "terms": { "field": "category_id" } } },
{ "release year": { "terms": { "field": "release_year" } } }
]
}
}
}
}
'+-------------+--------------+----------+
| category_id | release_year | count(*) |
+-------------+--------------+----------+
| 1 | 2000 | 5 |
| 1 | 2001 | 2 |
| 1 | 2002 | 6 |
| 1 | 2003 | 6 |
| 1 | 2004 | 5 |
| 1 | 2005 | 10 |
| 1 | 2006 | 4 |
| 1 | 2007 | 5 |
| 1 | 2008 | 7 |
| 1 | 2009 | 14 |
| 2 | 2000 | 10 |
| 2 | 2001 | 5 |
| 2 | 2002 | 6 |
| 2 | 2003 | 6 |
| 2 | 2004 | 10 |
| 2 | 2005 | 4 |
| 2 | 2006 | 5 |
| 2 | 2007 | 8 |
| 2 | 2008 | 8 |
| 2 | 2009 | 4 |
+-------------+--------------+----------+{
"took": 0,
"timed_out": false,
"hits": {
"total": 1000,
"total_relation": "eq",
"hits": []
},
"aggregations": {
"cat_release": {
"after_key": {
"category": 1,
"release year": 2007
},
"buckets": [
{
"key": {
"category": 1,
"release year": 2008
},
"doc_count": 7
},
{
"key": {
"category": 1,
"release year": 2009
},
"doc_count": 14
},
{
"key": {
"category": 1,
"release year": 2005
},
"doc_count": 10
},
{
"key": {
"category": 1,
"release year": 2004
},
"doc_count": 5
},
{
"key": {
"category": 1,
"release year": 2007
},
"doc_count": 5
}
]
}
}
}有时查看每个组的不止一行是有用的。这可以通过 GROUP N BY 辅助轻松实现。例如,在以下情况下,我们将为每一年获取两部电影而不是只有一部,而简单的 GROUP BY release_year 将返回。
- SQL
SELECT release_year, title FROM films GROUP 2 BY release_year ORDER BY release_year DESC LIMIT 6;+--------------+-----------------------------+
| release_year | title |
+--------------+-----------------------------+
| 2009 | ALICE FANTASIA |
| 2009 | ALIEN CENTER |
| 2008 | AMADEUS HOLY |
| 2008 | ANACONDA CONFESSIONS |
| 2007 | ANGELS LIFE |
| 2007 | ARACHNOPHOBIA ROLLERCOASTER |
+--------------+-----------------------------+另一个关键的分析需求是在组内对元素进行排序。要实现这一点,请使用 WITHIN GROUP ORDER BY ... {ASC|DESC} 子句。例如,让我们获取每一年评分最高的电影。请注意,它与仅 ORDER BY 并行工作:
WITHIN GROUP ORDER BY对组内的结果进行排序- 而仅
GROUP BY对组本身进行排序
这两个完全独立地工作。
- SQL
SELECT release_year, title, rental_rate FROM films GROUP BY release_year WITHIN GROUP ORDER BY rental_rate DESC ORDER BY release_year DESC LIMIT 5;+--------------+------------------+-------------+
| release_year | title | rental_rate |
+--------------+------------------+-------------+
| 2009 | AMERICAN CIRCUS | 4.990000 |
| 2008 | ANTHEM LUKE | 4.990000 |
| 2007 | ATTACKS HATE | 4.990000 |
| 2006 | ALADDIN CALENDAR | 4.990000 |
| 2005 | AIRPLANE SIERRA | 4.990000 |
+--------------+------------------+-------------+HAVING expression 是一个有用的子句,用于过滤组。虽然 WHERE 在分组之前应用,但 HAVING 作用于组。例如,让我们保留那些当年电影平均租赁价格高于 3 的年份。我们只得到四个年份:
- SQL
SELECT release_year, avg(rental_rate) avg FROM films GROUP BY release_year HAVING avg > 3;+--------------+------------+
| release_year | avg |
+--------------+------------+
| 2002 | 3.08259249 |
| 2001 | 3.09989142 |
| 2000 | 3.17556739 |
| 2006 | 3.26184368 |
+--------------+------------+注意: 在 搜索查询元信息 中,total_found 值反映了满足 HAVING 条件的组的数量。当使用 HAVING 子句与 GROUP BY 结合时,这使得分页更加准确。
有一个函数 GROUPBY() 返回当前分组的键。它在许多情况下都很有用,特别是当你对 MVA 进行分组 或者对 JSON 值进行分组。
它也可以用在 HAVING 中,例如,仅保留2000年和2002年的数据。
注意,当你一次对多个字段进行 GROUP BY 时,不推荐使用 GROUPBY()。它仍然可以工作,但此时分组键是字段值的复合体,可能不会以你期望的方式出现。
- SQL
SELECT release_year, count(*) FROM films GROUP BY release_year HAVING GROUPBY() IN (2000, 2002);+--------------+----------+
| release_year | count(*) |
+--------------+----------+
| 2002 | 108 |
| 2000 | 97 |
+--------------+----------+Manticore 支持按MVA进行分组。为了演示其工作原理,让我们创建一个名为 "shoes"、包含 MVA 字段 "sizes" 的表,并插入一些文档:
create table shoes(title text, sizes multi);
insert into shoes values(0,'nike',(40,41,42)),(0,'adidas',(41,43)),(0,'reebook',(42,43));
所以我们有:
SELECT * FROM shoes;
+---------------------+----------+---------+
| id | sizes | title |
+---------------------+----------+---------+
| 1657851069130080265 | 40,41,42 | nike |
| 1657851069130080266 | 41,43 | adidas |
| 1657851069130080267 | 42,43 | reebook |
+---------------------+----------+---------+
如果现在对 "sizes" 进行 GROUP BY,它将处理我们所有的多值属性,并为每个大小返回一次聚合,这里仅有计数:
- SQL
- JSON
- PHP
- Python
- Javascript
- Python-asyncio
- Java
- C#
- Rust
- Go
SELECT groupby() gb, count(*) FROM shoes GROUP BY sizes ORDER BY gb asc;POST /search -d '
{
"table" : "shoes",
"limit": 0,
"aggs" :
{
"sizes" :
{
"terms" :
{
"field":"sizes",
"size":100
}
}
}
}
'$index->setName('shoes');
$search = $index->search('');
$search->limit(0);
$search->facet('sizes','sizes',100);
$results = $search->get();
print_r($results->getFacets());res =searchApi.search({"table":"shoes","limit":0,"aggs":{"sizes":{"terms":{"field":"sizes","size":100}}}})res = await searchApi.search({"table":"shoes","limit":0,"aggs":{"sizes":{"terms":{"field":"sizes","size":100}}}});res = await searchApi.search({"table":"shoes","limit":0,"aggs":{"sizes":{"terms":{"field":"sizes","size":100}}}})HashMap<String,Object> aggs = new HashMap<String,Object>(){{
put("release_year", new HashMap<String,Object>(){{
put("terms", new HashMap<String,Object>(){{
put("field","release_year");
put("size",100);
}});
}});
}};
searchRequest = new SearchRequest();
searchRequest.setIndex("films");
searchRequest.setLimit(0);
query = new HashMap<String,Object>();
query.put("match_all",null);
searchRequest.setQuery(query);
searchRequest.setAggs(aggs);
searchResponse = searchApi.search(searchRequest);var agg = new Aggregation("release_year", "release_year");
agg.Size = 100;
object query = new { match_all=null };
var searchRequest = new SearchRequest("films", query);
searchRequest.Limit = 0;
searchRequest.Aggs = new List<Aggregation> {agg};
var searchResponse = searchApi.Search(searchRequest);let query = SearchQuery::new();
let aggTerms1 = AggTerms::new {
fields: "release_year".to_string(),
size: Some(100),
};
let agg1 = Aggregation {
terms: Some(Box::new(aggTerms1)),
..Default::default(),
};
let mut aggs = HashMap::new();
aggs.insert("release_year".to_string(), agg1);
let search_req = SearchRequest {
table: "films".to_string(),
query: Some(Box::new(query)),
aggs: serde_json::json!(aggs),
limit: serde_json::json!(0),
..Default::default(),
};
let search_res = search_api.search(search_req).await;query := map[string]interface{} {};
searchRequest.SetQuery(query);
aggTerms := manticoreclient.NewAggregationTerms()
aggTerms.SetField("mva_field")
aggTerms.SetSize(2)
aggregation := manticoreclient.NewAggregation()
aggregation.setTerms(aggTerms)
searchRequest.SetAggregation(aggregation)
res, _, _ := apiClient.SearchAPI.Search(context.Background()).SearchRequest(*searchRequest).Execute()+------+----------+
| gb | count(*) |
+------+----------+
| 40 | 1 |
| 41 | 2 |
| 42 | 2 |
| 43 | 2 |
+------+----------+{
"took": 0,
"timed_out": false,
"hits": {
"total": 3,
"hits": [
]
},
"aggregations": {
"sizes": {
"buckets": [
{
"key": 43,
"doc_count": 2
},
{
"key": 42,
"doc_count": 2
},
{
"key": 41,
"doc_count": 2
},
{
"key": 40,
"doc_count": 1
}
]
}
}
}Array
(
[sizes] => Array
(
[buckets] => Array
(
[0] => Array
(
[key] => 43
[doc_count] => 2
)
[1] => Array
(
[key] => 42
[doc_count] => 2
)
[2] => Array
(
[key] => 41
[doc_count] => 2
)
[3] => Array
(
[key] => 40
[doc_count] => 1
)
)
)
){'aggregations': {u'sizes': {u'buckets': [{u'doc_count': 2, u'key': 43},
{u'doc_count': 2, u'key': 42},
{u'doc_count': 2, u'key': 41},
{u'doc_count': 1, u'key': 40}]}},
'hits': {'hits': [], 'max_score': None, 'total': 3},
'profile': None,
'timed_out': False,
'took': 0}{'aggregations': {u'sizes': {u'buckets': [{u'doc_count': 2, u'key': 43},
{u'doc_count': 2, u'key': 42},
{u'doc_count': 2, u'key': 41},
{u'doc_count': 1, u'key': 40}]}},
'hits': {'hits': [], 'max_score': None, 'total': 3},
'profile': None,
'timed_out': False,
'took': 0}{"took":0,"timed_out":false,"aggregations":{"sizes":{"buckets":[{"key":43,"doc_count":2},{"key":42,"doc_count":2},{"key":41,"doc_count":2},{"key":40,"doc_count":1}]}},"hits":{"total":3,"hits":[]}}class SearchResponse {
took: 0
timedOut: false
aggregations: {release_year={buckets=[{key=43, doc_count=2}, {key=42, doc_count=2}, {key=41, doc_count=2}, {key=40, doc_count=1}]}}
hits: class SearchResponseHits {
maxScore: null
total: 3
hits: []
}
profile: null
}class SearchResponse {
took: 0
timedOut: false
aggregations: {release_year={buckets=[{key=43, doc_count=2}, {key=42, doc_count=2}, {key=41, doc_count=2}, {key=40, doc_count=1}]}}
hits: class SearchResponseHits {
maxScore: null
total: 3
hits: []
}
profile: null
}class SearchResponse {
took: 0
timedOut: false
aggregations: {release_year={buckets=[{key=43, doc_count=2}, {key=42, doc_count=2}, {key=41, doc_count=2}, {key=40, doc_count=1}]}}
hits: class SearchResponseHits {
maxScore: null
total: 3
hits: []
}
profile: null
}
<!-- request TypeScript -->
``` typescript
res = await searchApi.search({
index: 'test',
aggs: {
mva_agg: {
terms: { field: "mva_field", size: 2 }
}
}
});{
"took":0,
"timed_out":false,
"aggregations":
{
"mva_agg":
{
"buckets":
[{
"key":1,
"doc_count":4
},
{
"key":2,
"doc_count":2
}]
}
},
"hits":
{
"total":4,
"hits":[]
}
}{
"took":0,
"timed_out":false,
"aggregations":
{
"mva_agg":
{
"buckets":
[{
"key":1,
"doc_count":4
},
{
"key":2,
"doc_count":2
}]
}
},
"hits":
{
"total":5,
"hits":[]
}
}If you have a field of type JSON, you can GROUP BY any node from it. To demonstrate this, let's create a table "products" with a few documents, each having a color in the "meta" JSON field:
create table products(title text, meta json);
insert into products values(0,'nike','{"color":"red"}'),(0,'adidas','{"color":"red"}'),(0,'puma','{"color":"green"}');
This gives us:
SELECT * FROM products;
+---------------------+-------------------+--------+
| id | meta | title |
+---------------------+-------------------+--------+
| 1657851069130080268 | {"color":"red"} | nike |
| 1657851069130080269 | {"color":"red"} | adidas |
| 1657851069130080270 | {"color":"green"} | puma |
+---------------------+-------------------+--------+
To group the products by color, we can simply use GROUP BY meta.color, and to display the corresponding group key in the SELECT list, we can use GROUPBY():
- SQL
- JSON
- PHP
- Python
- Javascript
- Java
- C#
- Rust
- TypeScript
- Go
SELECT groupby() color, count(*) from products GROUP BY meta.color;POST /search -d '
{
"table" : "products",
"limit": 0,
"aggs" :
{
"color" :
{
"terms" :
{
"field":"meta.color",
"size":100
}
}
}
}
'$index->setName('products');
$search = $index->search('');
$search->limit(0);
$search->facet('meta.color','color',100);
$results = $search->get();
print_r($results->getFacets());res =searchApi.search({"table":"products","limit":0,"aggs":{"color":{"terms":{"field":"meta.color","size":100}}}})res = await searchApi.search({"table":"products","limit":0,"aggs":{"color":{"terms":{"field":"meta.color","size":100}}}});HashMap<String,Object> aggs = new HashMap<String,Object>(){{
put("color", new HashMap<String,Object>(){{
put("terms", new HashMap<String,Object>(){{
put("field","meta.color");
put("size",100);
}});
}});
}};
searchRequest = new SearchRequest();
searchRequest.setIndex("products");
searchRequest.setLimit(0);
query = new HashMap<String,Object>();
query.put("match_all",null);
searchRequest.setQuery(query);
searchRequest.setAggs(aggs);
searchResponse = searchApi.search(searchRequest);var agg = new Aggregation("color", "meta.color");
agg.Size = 100;
object query = new { match_all=null };
var searchRequest = new SearchRequest("products", query);
searchRequest.Limit = 0;
searchRequest.Aggs = new List<Aggregation> {agg};
var searchResponse = searchApi.Search(searchRequest);let query = SearchQuery::new();
let aggTerms1 = AggTerms::new {
fields: "meta.color".to_string(),
size: Some(100),
};
let agg1 = Aggregation {
terms: Some(Box::new(aggTerms1)),
..Default::default(),
};
let mut aggs = HashMap::new();
aggs.insert("color".to_string(), agg1);
let search_req = SearchRequest {
table: "products".to_string(),
query: Some(Box::new(query)),
aggs: serde_json::json!(aggs),
limit: serde_json::json!(0),
..Default::default(),
};
let search_res = search_api.search(search_req).await;res = await searchApi.search({
index: 'test',
aggs: {
json_agg: {
terms: { field: "json_field.year", size: 1 }
}
}
});query := map[string]interface{} {};
searchRequest.SetQuery(query);
aggTerms := manticoreclient.NewAggregationTerms()
aggTerms.SetField("json_field.year")
aggTerms.SetSize(2)
aggregation := manticoreclient.NewAggregation()
aggregation.setTerms(aggTerms)
searchRequest.SetAggregation(aggregation)
res, _, _ := apiClient.SearchAPI.Search(context.Background()).SearchRequest(*searchRequest).Execute()+-------+----------+
| color | count(*) |
+-------+----------+
| red | 2 |
| green | 1 |
+-------+----------+{
"took": 0,
"timed_out": false,
"hits": {
"total": 3,
"hits": [
]
},
"aggregations": {
"color": {
"buckets": [
{
"key": "green",
"doc_count": 1
},
{
"key": "red",
"doc_count": 2
}
]
}
}
}Array
(
[color] => Array
(
[buckets] => Array
(
[0] => Array
(
[key] => green
[doc_count] => 1
)
[1] => Array
(
[key] => red
[doc_count] => 2
)
)
)
){'aggregations': {u'color': {u'buckets': [{u'doc_count': 1,
u'key': u'green'},
{u'doc_count': 2, u'key': u'red'}]}},
'hits': {'hits': [], 'max_score': None, 'total': 3},
'profile': None,
'timed_out': False,
'took': 0}<!-- request Python-asyncio -->
``` python
res = await searchApi.search({"table":"products","limit":0,"aggs":{"color":{"terms":{"field":"meta.color","size":100}}}}){'aggregations': {u'color': {u'buckets': [{u'doc_count': 1,
u'key': u'green'},
{u'doc_count': 2, u'key': u'red'}]}},
'hits': {'hits': [], 'max_score': None, 'total': 3},
'profile': None,
'timed_out': False,
'took': 0}{"took":0,"timed_out":false,"aggregations":{"color":{"buckets":[{"key":"green","doc_count":1},{"key":"red","doc_count":2}]}},"hits":{"total":3,"hits":[]}}class SearchResponse {
took: 0
timedOut: false
aggregations: {color={buckets=[{key=green, doc_count=1}, {key=red, doc_count=2}]}}
hits: class SearchResponseHits {
maxScore: null
total: 3
hits: []
}
profile: null
}
class SearchResponse {
took: 0
timedOut: false
aggregations: {color={buckets=[{key=green, doc_count=1}, {key=red, doc_count=2}]}}
hits: class SearchResponseHits {
maxScore: null
total: 3
hits: []
}
profile: null
}
class SearchResponse {
took: 0
timedOut: false
aggregations: {color={buckets=[{key=green, doc_count=1}, {key=red, doc_count=2}]}}
hits: class SearchResponseHits {
maxScore: null
total: 3
hits: []
}
profile: null
}
{
"took":0,
"timed_out":false,
"aggregations":
{
"json_agg":
{
"buckets":
[{
"key":2000,
"doc_count":2
},
{
"key":2001,
"doc_count":2
}]
}
},
"hits":
{
"total":4,
"hits":[]
}
}{
"took":0,
"timed_out":false,
"aggregations":
{
"json_agg":
{
"buckets":
[{
"key":2000,
"doc_count":2
},
{
"key":2001,
"doc_count":2
}]
}
},
"hits":
{
"total":4,
"hits":[]
}
}除了返回每组元素数量的 COUNT(*),你还可以使用各种其他聚合函数:
虽然 COUNT(*) 返回组中所有元素的数量,COUNT(DISTINCT field) 返回组中特定字段的唯一值数量,这可能与总数完全不同。例如,你可以在组中有100个元素,但某字段的值全都相同。COUNT(DISTINCT field) 能帮助确定这一点。为演示此功能,我们创建一个名为 "students" 的表,包含学生姓名、年龄和专业:
CREATE TABLE students(name text, age int, major string);
INSERT INTO students values(0,'John',21,'arts'),(0,'William',22,'business'),(0,'Richard',21,'cs'),(0,'Rebecca',22,'cs'),(0,'Monica',21,'arts');
所以我们有:
MySQL [(none)]> SELECT * from students;
+---------------------+------+----------+---------+
| id | age | major | name |
+---------------------+------+----------+---------+
| 1657851069130080271 | 21 | arts | John |
| 1657851069130080272 | 22 | business | William |
| 1657851069130080273 | 21 | cs | Richard |
| 1657851069130080274 | 22 | cs | Rebecca |
| 1657851069130080275 | 21 | arts | Monica |
+---------------------+------+----------+---------+
在示例中,你可以看到如果按 major 分组并同时显示 COUNT(*) 和 COUNT(DISTINCT age),就会清楚地知道选择专业为 "cs" 的有两名学生且年龄各不相同,但对于专业为 "arts" 的也有两名学生,却只有一个唯一年龄。
每个查询中最多只能有一个 COUNT(DISTINCT)。
默认情况下,计数是近似的
实际上,有些计数是精确的,有些则是近似的。下面会详细说明。
Manticore 支持两种计算唯一值计数的算法。一种是使用大量内存通常较慢的传统算法,它收集 {group; value} 对,排序后周期性去重。该方法的优点是保证在普通表中的计数精确。你可以通过将 distinct_precision_threshold 选项设置为 0 来启用它。
另一种算法(默认启用)将计数加载到哈希表中并返回其大小。如果哈希表过大,其内容会被转移到 HyperLogLog。此时计数变为近似,因为 HyperLogLog 是一种概率算法。其优点是每组最大内存使用固定,依赖于 HyperLogLog 的精度。总体内存使用还受 max_matches 设置影响,该值反映组数。
distinct_precision_threshold 选项设定计数保证准确的阈值。HyperLogLog 的精度设置以及“从哈希表到 HyperLogLog”的转换阈值均基于该参数。使用此选项需谨慎,因为将其翻倍会使计数计算所需最大内存翻倍。最大内存使用大致可用公式估算:64 * max_matches * distinct_precision_threshold。注意,这是最坏情况,实际计数计算通常占用明显更少的内存。
对于包含多个磁盘块的分布式表或实时表,COUNT(DISTINCT) 可能返回不准确结果,但对于由相同模式(相同字段集/顺序但分词设置可不同)的本地普通表或实时表组成的分布式表,结果应准确。
- SQL
SELECT major, count(*), count(distinct age) FROM students GROUP BY major;+----------+----------+---------------------+
| major | count(*) | count(distinct age) |
+----------+----------+---------------------+
| arts | 2 | 1 |
| business | 1 | 1 |
| cs | 2 | 2 |
+----------+----------+---------------------+你通常想更好地了解每个分组的内容。你可以使用 GROUP N BY 实现,但它会返回额外的行,可能不想出现在结果中。GROUP_CONCAT() 通过连接组中特定字段的数值来丰富分组内容。继续使用前面的例子,这里改进为显示每组中的所有年龄。
GROUP_CONCAT(field) 返回逗号分隔的值列表。
- SQL
SELECT major, count(*), count(distinct age), group_concat(age) FROM students GROUP BY major+----------+----------+---------------------+-------------------+
| major | count(*) | count(distinct age) | group_concat(age) |
+----------+----------+---------------------+-------------------+
| arts | 2 | 1 | 21,21 |
| business | 1 | 1 | 22 |
| cs | 2 | 2 | 21,22 |
+----------+----------+---------------------+-------------------+- SQL
SELECT release_year year, sum(rental_rate) sum, min(rental_rate) min, max(rental_rate) max, avg(rental_rate) avg FROM films GROUP BY release_year ORDER BY year asc LIMIT 5;+------+------------+----------+----------+------------+
| year | sum | min | max | avg |
+------+------------+----------+----------+------------+
| 2000 | 308.030029 | 0.990000 | 4.990000 | 3.17556739 |
| 2001 | 282.090118 | 0.990000 | 4.990000 | 3.09989142 |
| 2002 | 332.919983 | 0.990000 | 4.990000 | 3.08259249 |
| 2003 | 310.940063 | 0.990000 | 4.990000 | 2.93339682 |
| 2004 | 300.920044 | 0.990000 | 4.990000 | 2.78629661 |
+------+------------+----------+----------+------------+分组使用固定内存,该内存大小依赖于 max_matches 设置。如果 max_matches 足以存储所有找到的组,结果将是 100% 精确的。但若 max_matches 较小,结果的准确性会降低。
涉及并行处理时情况会更复杂。启用 pseudo_sharding 和/或使用包含多个磁盘块的 RT 表时,每个块或伪分片得到的结果集大小都不超过 max_matches。当不同线程结果集合并时,聚合和分组计数可能不准确。为解决这个问题,可以使用更大的 max_matches 值或禁用并行处理。
如果检测到 groupby 可能返回不准确结果,Manticore 会尝试将 max_matches 增加到 max_matches_increase_threshold。检测基于从次级索引(如有)检索到的 groupby 属性的唯一值数量。
为确保在使用 RT 表或 pseudo_sharding 时聚合和分组计数准确,可以启用 accurate_aggregation。这将尝试提升 max_matches 到阈值,如果阈值不足,Manticore 会禁用查询的并行处理。
- SQL
MySQL [(none)]> SELECT release_year year, count(*) FROM films GROUP BY year limit 5;
+------+----------+
| year | count(*) |
+------+----------+
| 2004 | 108 |
| 2002 | 108 |
| 2001 | 91 |
| 2005 | 93 |
| 2000 | 97 |
+------+----------+
MySQL [(none)]> SELECT release_year year, count(*) FROM films GROUP BY year limit 5 option max_matches=1;
+------+----------+
| year | count(*) |
+------+----------+
| 2004 | 76 |
+------+----------+
MySQL [(none)]> SELECT release_year year, count(*) FROM films GROUP BY year limit 5 option max_matches=2;
+------+----------+
| year | count(*) |
+------+----------+
| 2004 | 76 |
| 2002 | 74 |
+------+----------+
MySQL [(none)]> SELECT release_year year, count(*) FROM films GROUP BY year limit 5 option max_matches=3;
+------+----------+
| year | count(*) |
+------+----------+
| 2004 | 108 |
| 2002 | 108 |
| 2001 | 91 |
+------+----------+分面搜索对于现代搜索应用来说,与自动补全、拼写纠正和搜索关键词高亮同样重要,尤其是在电子商务产品中。

当处理大量数据和各种相互关联的属性时,例如尺寸、颜色、制造商或其他因素,分面搜索就派上用场了。在查询海量数据时,搜索结果常常包含许多不符合用户预期的条目。分面搜索使最终用户能够明确定义他们希望搜索结果满足的条件。
在 Manticore Search 中,有一项优化功能,它会保留原始查询的结果集,并在每个分面计算中重复使用。由于聚合操作应用于已计算好的文档子集,因此速度很快,总执行时间通常仅比初始查询稍长一些。分面可以添加到任何查询中,分面可以是任何属性或表达式。分面结果包括分面值和分面计数。可以通过在查询末尾声明分面,使用 SQL SELECT 语句访问分面。
分面值可以来自属性、JSON 属性内的 JSON 属性或表达式。分面值也可以使用别名,但别名在所有结果集(主查询结果集和其他分面结果集)中必须是唯一的。分面值源自聚合的属性/表达式,但也可以来自另一个属性/表达式。
FACET {expr_list} [BY {expr_list} ] [DISTINCT {field_name}] [ORDER BY {expr | FACET()} {ASC | DESC}] [LIMIT [offset,] count]
多个分面声明必须用空格分隔。
分面可以在 aggs 节点中定义:
"aggs" :
{
"group name" :
{
"terms" :
{
"field":"attribute name",
"size": 1000
}
"sort": [ {"attribute name": { "order":"asc" }} ]
}
}
其中:
group name是分配给聚合的别名field值必须包含要进行分面的属性或表达式的名称- 可选的
size指定结果中包含的最大桶数。未指定时,继承主查询的限制。更多详细信息可以在分面结果大小部分找到。 - 可选的
sort指定一个属性数组和/或附加属性,使用与主查询中的"sort"参数相同的语法。
结果集将包含一个 aggregations 节点,其中包含返回的分面,key 是聚合值,doc_count 是聚合计数。
"aggregations": {
"group name": {
"buckets": [
{
"key": 10,
"doc_count": 1019
},
{
"key": 9,
"doc_count": 954
},
{
"key": 8,
"doc_count": 1021
},
{
"key": 7,
"doc_count": 1011
},
{
"key": 6,
"doc_count": 997
}
]
}
}
- SQL
- JSON
- PHP
- Python
- Python-asyncio
- Javascript
- Java
- C#
- Rust
- TypeScript
- Go
SELECT *, price AS aprice FROM facetdemo LIMIT 10 FACET price LIMIT 10 FACET brand_id LIMIT 5;POST /search -d '
{
"table" : "facetdemo",
"query" : {"match_all" : {} },
"limit": 5,
"aggs" :
{
"group_property" :
{
"terms" :
{
"field":"price"
}
},
"group_brand_id" :
{
"terms" :
{
"field":"brand_id"
}
}
}
}
'$index->setName('facetdemo');
$search = $index->search('');
$search->limit(5);
$search->facet('price','price');
$search->facet('brand_id','group_brand_id');
$results = $search->get();res =searchApi.search({"table":"facetdemo","query":{"match_all":{}},"limit":5,"aggs":{"group_property":{"terms":{"field":"price",}},"group_brand_id":{"terms":{"field":"brand_id"}}}})res = await searchApi.search({"table":"facetdemo","query":{"match_all":{}},"limit":5,"aggs":{"group_property":{"terms":{"field":"price",}},"group_brand_id":{"terms":{"field":"brand_id"}}}})res = await searchApi.search({"table":"facetdemo","query":{"match_all":{}},"limit":5,"aggs":{"group_property":{"terms":{"field":"price",}},"group_brand_id":{"terms":{"field":"brand_id"}}}});aggs = new HashMap<String,Object>(){{
put("group_property", new HashMap<String,Object>(){{
put("terms", new HashMap<String,Object>(){{
put("field","price");
}});
}});
put("group_brand_id", new HashMap<String,Object>(){{
put("terms", new HashMap<String,Object>(){{
put("field","brand_id");
}});
}});
}};
searchRequest = new SearchRequest();
searchRequest.setIndex("facetdemo");
searchRequest.setLimit(5);
query = new HashMap<String,Object>();
query.put("match_all",null);
searchRequest.setQuery(query);
searchRequest.setAggs(aggs);
searchResponse = searchApi.search(searchRequest);var agg1 = new Aggregation("group_property", "price");
var agg2 = new Aggregation("group_brand_id", "brand_id");
object query = new { match_all=null };
var searchRequest = new SearchRequest("facetdemo", query);
searchRequest.Limit = 5;
searchRequest.Aggs = new List<Aggregation> {agg1, agg2};
var searchResponse = searchApi.Search(searchRequest);let query = SearchQuery::new();
let aggTerms1 = AggTerms::new("price");
let agg1 = Aggregation {
terms: Some(Box::new(aggTerms1)),
..Default::default(),
};
let aggTerms2 = AggTerms::new("brand_id");
let agg2 = Aggregation {
terms: Some(Box::new(aggTerms2)),
..Default::default(),
};
let mut aggs = HashMap::new();
aggs.insert("group_property".to_string(), agg1);
aggs.insert("group_brand_id".to_string(), agg2);
let search_req = SearchRequest {
table: "facetdemo".to_string(),
query: Some(Box::new(query)),
aggs: serde_json::json!(aggs),
limit: serde_json::json!(5),
..Default::default(),
};
let search_res = search_api.search(search_req).await;res = await searchApi.search({
index: 'test',
query: { match_all:{} },
aggs: {
name_group: {
terms: { field : 'name' }
},
cat_group: {
terms: { field: 'cat' }
}
}
});query := map[string]interface{} {}
searchRequest.SetQuery(query)
aggByName := manticoreclient.NewAggregation()
aggTerms := manticoreclient.NewAggregationTerms()
aggTerms.SetField("name")
aggByName.SetTerms(aggTerms)
aggByCat := manticoreclient.NewAggregation()
aggTerms.SetField("cat")
aggByCat.SetTerms(aggTerms)
aggs := map[string]Aggregation{} { "name_group": aggByName, "cat_group": aggByCat }
searchRequest.SetAggs(aggs)
res, _, _ := apiClient.SearchAPI.Search(context.Background()).SearchRequest(*searchRequest).Execute()+------+-------+----------+---------------------+------------+-------------+---------------------------------------+------------+--------+
| id | price | brand_id | title | brand_name | property | j | categories | aprice |
+------+-------+----------+---------------------+------------+-------------+---------------------------------------+------------+--------+
| 1 | 306 | 1 | Product Ten Three | Brand One | Six_Ten | {"prop1":66,"prop2":91,"prop3":"One"} | 10,11 | 306 |
| 2 | 400 | 10 | Product Three One | Brand Ten | Four_Three | {"prop1":69,"prop2":19,"prop3":"One"} | 13,14 | 400 |
...
| 9 | 560 | 6 | Product Two Five | Brand Six | Eight_Two | {"prop1":90,"prop2":84,"prop3":"One"} | 13,14 | 560 |
| 10 | 229 | 9 | Product Three Eight | Brand Nine | Seven_Three | {"prop1":84,"prop2":39,"prop3":"One"} | 12,13 | 229 |
+------+-------+----------+---------------------+------------+-------------+---------------------------------------+------------+--------+
10 rows in set (0.00 sec)
+-------+----------+
| price | count(*) |
+-------+----------+
| 306 | 7 |
| 400 | 13 |
...
| 229 | 9 |
| 595 | 10 |
+-------+----------+
10 rows in set (0.00 sec)
+----------+----------+
| brand_id | count(*) |
+----------+----------+
| 1 | 1013 |
| 10 | 998 |
| 5 | 1007 |
| 8 | 1033 |
| 7 | 965 |
+----------+----------+
5 rows in set (0.00 sec){
"took": 3,
"timed_out": false,
"hits": {
"total": 10000,
"hits": [
{
"_id": 1,
"_score": 1,
"_source": {
"price": 197,
"brand_id": 10,
"brand_name": "Brand Ten",
"categories": [
10
]
}
},
...
{
"_id": 5,
"_score": 1,
"_source": {
"price": 805,
"brand_id": 7,
"brand_name": "Brand Seven",
"categories": [
11,
12,
13
]
}
}
]
},
"aggregations": {
"group_property": {
"buckets": [
{
"key": 1000,
"doc_count": 11
},
{
"key": 999,
"doc_count": 12
},
...
{
"key": 991,
"doc_count": 7
}
]
},
"group_brand_id": {
"buckets": [
{
"key": 10,
"doc_count": 1019
},
{
"key": 9,
"doc_count": 954
},
{
"key": 8,
"doc_count": 1021
},
{
"key": 7,
"doc_count": 1011
},
{
"key": 6,
"doc_count": 997
}
]
}
}
}Array
(
[price] => Array
(
[buckets] => Array
(
[0] => Array
(
[key] => 1000
[doc_count] => 11
)
[1] => Array
(
[key] => 999
[doc_count] => 12
)
[2] => Array
(
[key] => 998
[doc_count] => 7
)
[3] => Array
(
[key] => 997
[doc_count] => 14
)
[4] => Array
(
[key] => 996
[doc_count] => 8
)
)
)
[group_brand_id] => Array
(
[buckets] => Array
(
[0] => Array
(
[key] => 10
[doc_count] => 1019
)
[1] => Array
(
[key] => 9
[doc_count] => 954
)
[2] => Array
(
[key] => 8
[doc_count] => 1021
)
[3] => Array
(
[key] => 7
[doc_count] => 1011
)
[4] => Array
(
[key] => 6
[doc_count] => 997
)
)
)
){'aggregations': {u'group_brand_id': {u'buckets': [{u'doc_count': 1019,
u'key': 10},
{u'doc_count': 954,
u'key': 9},
{u'doc_count': 1021,
u'key': 8},
{u'doc_count': 1011,
u'key': 7},
{u'doc_count': 997,
u'key': 6}]},
u'group_property': {u'buckets': [{u'doc_count': 11,
u'key': 1000},
{u'doc_count': 12,
u'key': 999},
{u'doc_count': 7,
u'key': 998},
{u'doc_count': 14,
u'key': 997},
{u'doc_count': 8,
u'key': 996}]}},
'hits': {'hits': [{u'_id': u'1',
u'_score': 1,
u'_source': {u'brand_id': 10,
u'brand_name': u'Brand Ten',
u'categories': [10],
u'price': 197,
u'property': u'Six',
u'title': u'Product Eight One'}},
{u'_id': u'2',
u'_score': 1,
u'_source': {u'brand_id': 6,
u'brand_name': u'Brand Six',
u'categories': [12, 13, 14],
u'price': 671,
u'property': u'Four',
u'title': u'Product Nine Seven'}},
{u'_id': u'3',
u'_score': 1,
u'_source': {u'brand_id': 3,
u'brand_name': u'Brand Three',
u'categories': [13, 14, 15],
u'price': 92,
u'property': u'Six',
u'title': u'Product Five Four'}},
{u'_id': u'4',
u'_score': 1,
u'_source': {u'brand_id': 10,
u'brand_name': u'Brand Ten',
u'categories': [11],
u'price': 713,
u'property': u'Five',
u'title': u'Product Eight Nine'}},
{u'_id': u'5',
u'_score': 1,
u'_source': {u'brand_id': 7,
u'brand_name': u'Brand Seven',
u'categories': [11, 12, 13],
u'price': 805,
u'property': u'Two',
u'title': u'Product Ten Three'}}],
'max_score': None,
'total': 10000},
'profile': None,
'timed_out': False,
'took': 4}{'aggregations': {u'group_brand_id': {u'buckets': [{u'doc_count': 1019,
u'key': 10},
{u'doc_count': 954,
u'key': 9},
{u'doc_count': 1021,
u'key': 8},
{u'doc_count': 1011,
u'key': 7},
{u'doc_count': 997,
u'key': 6}]},
u'group_property': {u'buckets': [{u'doc_count': 11,
u'key': 1000},
{u'doc_count': 12,
u'key': 999},
{u'doc_count': 7,
u'key': 998},
{u'doc_count': 14,
u'key': 997},
{u'doc_count': 8,
u'key': 996}]}},
'hits': {'hits': [{u'_id': u'1',
u'_score': 1,
u'_source': {u'brand_id': 10,
u'brand_name': u'Brand Ten',
u'categories': [10],
u'price': 197,
u'property': u'Six',
u'title': u'Product Eight One'}},
{u'_id': u'2',
u'_score': 1,
u'_source': {u'brand_id': 6,
u'brand_name': u'Brand Six',
u'categories': [12, 13, 14],
u'price': 671,
u'property': u'Four',
u'title': u'Product Nine Seven'}},
{u'_id': u'3',
u'_score': 1,
u'_source': {u'brand_id': 3,
u'brand_name': u'Brand Three',
u'categories': [13, 14, 15],
u'price': 92,
u'property': u'Six',
u'title': u'Product Five Four'}},
{u'_id': u'4',
u'_score': 1,
u'_source': {u'brand_id': 10,
u'brand_name': u'Brand Ten',
u'categories': [11],
u'price': 713,
u'property': u'Five',
u'title': u'Product Eight Nine'}},
{u'_id': u'5',
u'_score': 1,
u'_source': {u'brand_id': 7,
u'brand_name': u'Brand Seven',
u'categories': [11, 12, 13],
u'price': 805,
u'property': u'Two',
u'title': u'Product Ten Three'}}],
'max_score': None,
'total': 10000},
'profile': None,
'timed_out': False,
'took': 4}{"took":0,"timed_out":false,"hits":{"total":10000,"hits":[{"_id": 1,"_score":1,"_source":{"price":197,"brand_id":10,"brand_name":"Brand Ten","categories":[10],"title":"Product Eight One","property":"Six"}},{"_id": 2,"_score":1,"_source":{"price":671,"brand_id":6,"brand_name":"Brand Six","categories":[12,13,14],"title":"Product Nine Seven","property":"Four"}},{"_id": 3,"_score":1,"_source":{"price":92,"brand_id":3,"brand_name":"Brand Three","categories":[13,14,15],"title":"Product Five Four","property":"Six"}},{"_id": 4,"_score":1,"_source":{"price":713,"brand_id":10,"brand_name":"Brand Ten","categories":[11],"title":"Product Eight Nine","property":"Five"}},{"_id": 5,"_score":1,"_source":{"price":805,"brand_id":7,"brand_name":"Brand Seven","categories":[11,12,13],"title":"Product Ten Three","property":"Two"}}]}}class SearchResponse {
took: 0
timedOut: false
aggregations: {group_property={buckets=[{key=1000, doc_count=11}, {key=999, doc_count=12}, {key=998, doc_count=7}, {key=997, doc_count=14}, {key=996, doc_count=8}]}, group_brand_id={buckets=[{key=10, doc_count=1019}, {key=9, doc_count=954}, {key=8, doc_count=1021}, {key=7, doc_count=1011}, {key=6, doc_count=997}]}}
hits: class SearchResponseHits {
maxScore: null
total: 10000
hits: [{_id=1, _score=1, _source={price=197, brand_id=10, brand_name=Brand Ten, categories=[10], title=Product Eight One, property=Six}}, {_id=2, _score=1, _source={price=671, brand_id=6, brand_name=Brand Six, categories=[12, 13, 14], title=Product Nine Seven, property=Four}}, {_id=3, _score=1, _source={price=92, brand_id=3, brand_name=Brand Three, categories=[13, 14, 15], title=Product Five Four, property=Six}}, {_id=4, _score=1, _source={price=713, brand_id=10, brand_name=Brand Ten, categories=[11], title=Product Eight Nine, property=Five}}, {_id=5, _score=1, _source={price=805, brand_id=7, brand_name=Brand Seven, categories=[11, 12, 13], title=Product Ten Three, property=Two}}]
}
profile: null
}class SearchResponse {
took: 0
timedOut: false
aggregations: {group_property={buckets=[{key=1000, doc_count=11}, {key=999, doc_count=12}, {key=998, doc_count=7}, {key=997, doc_count=14}, {key=996, doc_count=8}]}, group_brand_id={buckets=[{key=10, doc_count=1019}, {key=9, doc_count=954}, {key=8, doc_count=1021}, {key=7, doc_count=1011}, {key=6, doc_count=997}]}}
hits: class SearchResponseHits {
maxScore: null
total: 10000
hits: [{_id=1, _score=1, _source={price=197, brand_id=10, brand_name=Brand Ten, categories=[10], title=Product Eight One, property=Six}}, {_id=2, _score=1, _source={price=671, brand_id=6, brand_name=Brand Six, categories=[12, 13, 14], title=Product Nine Seven, property=Four}}, {_id=3, _score=1, _source={price=92, brand_id=3, brand_name=Brand Three, categories=[13, 14, 15], title=Product Five Four, property=Six}}, {_id=4, _score=1, _source={price=713, brand_id=10, brand_name=Brand Ten, categories=[11], title=Product Eight Nine, property=Five}}, {_id=5, _score=1, _source={price=805, brand_id=7, brand_name=Brand Seven, categories=[11, 12, 13], title=Product Ten Three, property=Two}}]
}
profile: null
}class SearchResponse {
took: 0
timedOut: false
aggregations: {group_property={buckets=[{key=1000, doc_count=11}, {key=999, doc_count=12}, {key=998, doc_count=7}, {key=997, doc_count=14}, {key=996, doc_count=8}]}, group_brand_id={buckets=[{key=10, doc_count=1019}, {key=9, doc_count=954}, {key=8, doc_count=1021}, {key=7, doc_count=1011}, {key=6, doc_count=997}]}}
hits: class SearchResponseHits {
maxScore: null
total: 10000
hits: [{_id=1, _score=1, _source={price=197, brand_id=10, brand_name=Brand Ten, categories=[10], title=Product Eight One, property=Six}}, {_id=2, _score=1, _source={price=671, brand_id=6, brand_name=Brand Six, categories=[12, 13, 14], title=Product Nine Seven, property=Four}}, {_id=3, _score=1, _source={price=92, brand_id=3, brand_name=Brand Three, categories=[13, 14, 15], title=Product Five Four, property=Six}}, {_id=4, _score=1, _source={price=713, brand_id=10, brand_name=Brand Ten, categories=[11], title=Product Eight Nine, property=Five}}, {_id=5, _score=1, _source={price=805, brand_id=7, brand_name=Brand Seven, categories=[11, 12, 13], title=Product Ten Three, property=Two}}]
}
profile: null
}{
"took": 0,
"timed_out": false,
"hits": {
"total": 5,
"hits": [
{
"_id": 1,
"_score": 1,
"_source": {
"content": "Text 1",
"name": "Doc 1",
"cat": 1
}
},
...
{
"_id": 5,
"_score": 1,
"_source": {
"content": "Text 5",
"name": "Doc 5",
"cat": 4
}
}
]
},
"aggregations": {
"name_group": {
"buckets": [
{
"key": "Doc 1",
"doc_count": 1
},
...
{
"key": "Doc 5",
"doc_count": 1
}
]
},
"cat_group": {
"buckets": [
{
"key": 1,
"doc_count": 2
},
...
{
"key": 4,
"doc_count": 1
}
]
}
}
}{
"took": 0,
"timed_out": false,
"hits": {
"total": 5,
"hits": [
{
"_id": 1,
"_score": 1,
"_source": {
"content": "Text 1",
"name": "Doc 1",
"cat": 1
}
},
...
{
"_id": 5,
"_score": 1,
"_source": {
"content": "Text 5",
"name": "Doc 5",
"cat": 4
}
}
]
},
"aggregations": {
"name_group": {
"buckets": [
{
"key": "Doc 1",
"doc_count": 1
},
...
{
"key": "Doc 5",
"doc_count": 1
}
]
},
"cat_group": {
"buckets": [
{
"key": 1,
"doc_count": 2
},
...
{
"key": 4,
"doc_count": 1
}
]
}
}
}可以通过聚合另一个属性或表达式对数据进行分面。例如,如果文档同时包含品牌ID和名称,我们可以在分面中返回品牌名称,但聚合品牌ID。这可以通过使用 FACET {expr1} BY {expr2} 来实现。
- SQL
SELECT * FROM facetdemo FACET brand_name by brand_id;+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+
| id | price | brand_id | title | brand_name | property | j | categories |
+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+
| 1 | 306 | 1 | Product Ten Three | Brand One | Six_Ten | {"prop1":66,"prop2":91,"prop3":"One"} | 10,11 |
| 2 | 400 | 10 | Product Three One | Brand Ten | Four_Three | {"prop1":69,"prop2":19,"prop3":"One"} | 13,14 |
....
| 19 | 855 | 1 | Product Seven Two | Brand One | Eight_Seven | {"prop1":63,"prop2":78,"prop3":"One"} | 10,11,12 |
| 20 | 31 | 9 | Product Four One | Brand Nine | Ten_Four | {"prop1":79,"prop2":42,"prop3":"One"} | 12,13,14 |
+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+
20 rows in set (0.00 sec)
+-------------+----------+
| brand_name | count(*) |
+-------------+----------+
| Brand One | 1013 |
| Brand Ten | 998 |
| Brand Five | 1007 |
| Brand Nine | 944 |
| Brand Two | 990 |
| Brand Six | 1039 |
| Brand Three | 1016 |
| Brand Four | 994 |
| Brand Eight | 1033 |
| Brand Seven | 965 |
+-------------+----------+
10 rows in set (0.00 sec)如果需要从 FACET 返回的桶中移除重复项,可以使用 DISTINCT field_name,其中 field_name 是您希望用于去重的字段。如果您对分布式表进行 FACET 查询,并且不确定表中是否有唯一的ID(表应该是本地的且具有相同的模式),也可以是 id(这是默认值)。
如果查询中有多个 FACET 声明,field_name 在所有声明中应该相同。
DISTINCT 会在 count(*) 列之前返回一个额外的列 count(distinct ...),使您无需进行另一个查询即可获得两个结果。
- SQL
- JSON
SELECT brand_name, property FROM facetdemo FACET brand_name distinct property;POST /sql -d 'SELECT brand_name, property FROM facetdemo FACET brand_name distinct property'+-------------+----------+
| brand_name | property |
+-------------+----------+
| Brand Nine | Four |
| Brand Ten | Four |
| Brand One | Five |
| Brand Seven | Nine |
| Brand Seven | Seven |
| Brand Three | Seven |
| Brand Nine | Five |
| Brand Three | Eight |
| Brand Two | Eight |
| Brand Six | Eight |
| Brand Ten | Four |
| Brand Ten | Two |
| Brand Four | Ten |
| Brand One | Nine |
| Brand Four | Eight |
| Brand Nine | Seven |
| Brand Four | Five |
| Brand Three | Four |
| Brand Four | Two |
| Brand Four | Eight |
+-------------+----------+
20 rows in set (0.00 sec)
+-------------+--------------------------+----------+
| brand_name | count(distinct property) | count(*) |
+-------------+--------------------------+----------+
| Brand Nine | 3 | 3 |
| Brand Ten | 2 | 3 |
| Brand One | 2 | 2 |
| Brand Seven | 2 | 2 |
| Brand Three | 3 | 3 |
| Brand Two | 1 | 1 |
| Brand Six | 1 | 1 |
| Brand Four | 4 | 5 |
+-------------+--------------------------+----------+
8 rows in set (0.00 sec){
"took": 0,
"timed_out": false,
"hits": {
"total": 20,
"total_relation": "eq",
"hits": [
{
"_score": 1,
"_source": {
"brand_name": "Brand Nine",
"property": "Four"
}
},
{
"_score": 1,
"_source": {
"brand_name": "Brand Ten",
"property": "Four"
}
},
...
{
"_score": 1,
"_source": {
"brand_name": "Brand Four",
"property": "Eight"
}
}
]
},
"aggregations": {
"brand_name": {
"buckets": [
{
"key": "Brand Nine",
"doc_count": 3,
"count(distinct property)": 3
},
{
"key": "Brand Ten",
"doc_count": 3,
"count(distinct property)": 2
},
...
{
"key": "Brand Two",
"doc_count": 1,
"count(distinct property)": 1
},
{
"key": "Brand Six",
"doc_count": 1,
"count(distinct property)": 1
},
{
"key": "Brand Four",
"doc_count": 5,
"count(distinct property)": 4
}
]
}
}
}- SQL
- JSON
- PHP
- Python
- Python-asyncio
- Javascript
- Java
- C#
- Rust
- TypeScript
- Go
SELECT * FROM facetdemo FACET INTERVAL(price,200,400,600,800) AS price_range ;POST /search -d '
{
"table": "facetdemo",
"query":
{
"match_all": {}
},
"expressions":
{
"price_range": "INTERVAL(price,200,400,600,800)"
},
"aggs":
{
"group_property":
{
"terms":
{
"field": "price_range"
}
}
}
}$index->setName('facetdemo');
$search = $index->search('');
$search->limit(5);
$search->expression('price_range','INTERVAL(price,200,400,600,800)');
$search->facet('price_range','group_property');
$results = $search->get();
print_r($results->getFacets());res =searchApi.search({"table":"facetdemo","query":{"match_all":{}},"expressions":{"price_range":"INTERVAL(price,200,400,600,800)"},"aggs":{"group_property":{"terms":{"field":"price_range"}}}})res = await searchApi.search({"table":"facetdemo","query":{"match_all":{}},"expressions":{"price_range":"INTERVAL(price,200,400,600,800)"},"aggs":{"group_property":{"terms":{"field":"price_range"}}}})res = await searchApi.search({"table":"facetdemo","query":{"match_all":{}},"expressions":{"price_range":"INTERVAL(price,200,400,600,800)"},"aggs":{"group_property":{"terms":{"field":"price_range"}}}});searchRequest = new SearchRequest();
expressions = new HashMap<String,Object>(){{
put("price_range","INTERVAL(price,200,400,600,800)");
}};
searchRequest.setExpressions(expressions);
aggs = new HashMap<String,Object>(){{
put("group_property", new HashMap<String,Object>(){{
put("terms", new HashMap<String,Object>(){{
put("field","price_range");
}});
}});
}};
searchRequest.setIndex("facetdemo");
searchRequest.setLimit(5);
query = new HashMap<String,Object>();
query.put("match_all",null);
searchRequest.setQuery(query);
searchRequest.setAggs(aggs);
searchResponse = searchApi.search(searchRequest);var expr = new Dictionary<string, string> { {"price_range", "INTERVAL(price,200,400,600,800"} } ;
var agg = new Aggregation("group_property", "price_range");
object query = new { match_all=null };
var searchRequest = new SearchRequest("facetdemo", query);
searchRequest.Limit = 5;
searchRequest.Expressions = new List<Object> {expr};
searchRequest.Aggs = new List<Aggregation> {agg};
var searchResponse = searchApi.Search(searchRequest);let query = SearchQuery::new();
let aggTerms1 = AggTerms::new("price_range");
let agg1 = Aggregation {
terms: Some(Box::new(aggTerms1)),
..Default::default(),
};
let mut aggs = HashMap::new();
aggs.insert("group_property".to_string(), agg1);
let mut expr = HashMap::new();
expr.insert("price_range".to_string(), "INTERVAL(price,200,400,600,800");
let expressions: [HashMap; 1] = [expr];
let search_req = SearchRequest {
table: "facetdemo".to_string(),
query: Some(Box::new(query)),
expressions: serde_json::json!(expressions),
aggs: serde_json::json!(aggs),
limit: serde_json::json!(5),
..Default::default(),
};
let search_res = search_api.search(search_req).await;res = await searchApi.search({
index: 'test',
query: { match_all:{} },
expressions: { cat_range: "INTERVAL(cat,1,3)" }
aggs: {
expr_group: {
terms: { field : 'cat_range' }
}
}
});query := map[string]interface{} {}
searchRequest.SetQuery(query)
exprs := map[string]string{} { "cat_range": "INTERVAL(cat,1,3)" }
searchRequest.SetExpressions(exprs)
aggByExpr := manticoreclient.NewAggregation()
aggTerms := manticoreclient.NewAggregationTerms()
aggTerms.SetField("cat_range")
aggByExpr.SetTerms(aggTerms)
aggs := map[string]Aggregation{} { "expr_group": aggByExpr }
searchRequest.SetAggs(aggs)
res, _, _ := apiClient.SearchAPI.Search(context.Background()).SearchRequest(*searchRequest).Execute()+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+-------------+
| id | price | brand_id | title | brand_name | property | j | categories | price_range |
+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+-------------+
| 1 | 306 | 1 | Product Ten Three | Brand One | Six_Ten | {"prop1":66,"prop2":91,"prop3":"One"} | 10,11 | 1 |
...
+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+-------------+
20 rows in set (0.00 sec)
+-------------+----------+
| price_range | count(*) |
+-------------+----------+
| 0 | 1885 |
| 3 | 1973 |
| 4 | 2100 |
| 2 | 1999 |
| 1 | 2043 |
+-------------+----------+
5 rows in set (0.01 sec){
"took": 3,
"timed_out": false,
"hits": {
"total": 10000,
"hits": [
{
"_id": 1,
"_score": 1,
"_source": {
"price": 197,
"brand_id": 10,
"brand_name": "Brand Ten",
"categories": [
10
],
"price_range": 0
}
},
...
{
"_id": 20,
"_score": 1,
"_source": {
"price": 227,
"brand_id": 3,
"brand_name": "Brand Three",
"categories": [
12,
13
],
"price_range": 1
}
}
]
},
"aggregations": {
"group_property": {
"buckets": [
{
"key": 4,
"doc_count": 2100
},
{
"key": 3,
"doc_count": 1973
},
{
"key": 2,
"doc_count": 1999
},
{
"key": 1,
"doc_count": 2043
},
{
"key": 0,
"doc_count": 1885
}
]
}
}
}Array
(
[group_property] => Array
(
[buckets] => Array
(
[0] => Array
(
[key] => 4
[doc_count] => 2100
)
[1] => Array
(
[key] => 3
[doc_count] => 1973
)
[2] => Array
(
[key] => 2
[doc_count] => 1999
)
[3] => Array
(
[key] => 1
[doc_count] => 2043
)
[4] => Array
(
[key] => 0
[doc_count] => 1885
)
)
)
){'aggregations': {u'group_brand_id': {u'buckets': [{u'doc_count': 1019,
u'key': 10},
{u'doc_count': 954,
u'key': 9},
{u'doc_count': 1021,
u'key': 8},
{u'doc_count': 1011,
u'key': 7},
{u'doc_count': 997,
u'key': 6}]},
u'group_property': {u'buckets': [{u'doc_count': 11,
u'key': 1000},
{u'doc_count': 12,
u'key': 999},
{u'doc_count': 7,
u'key': 998},
{u'doc_count': 14,
u'key': 997},
{u'doc_count': 8,
u'key': 996}]}},
'hits': {'hits': [{u'_id': u'1',
u'_score': 1,
u'_source': {u'brand_id': 10,
u'brand_name': u'Brand Ten',
u'categories': [10],
u'price': 197,
u'property': u'Six',
u'title': u'Product Eight One'}},
{u'_id': u'2',
u'_score': 1,
u'_source': {u'brand_id': 6,
u'brand_name': u'Brand Six',
u'categories': [12, 13, 14],
u'price': 671,
u'property': u'Four',
u'title': u'Product Nine Seven'}},
{u'_id': u'3',
u'_score': 1,
u'_source': {u'brand_id': 3,
u'brand_name': u'Brand Three',
u'categories': [13, 14, 15],
u'price': 92,
u'property': u'Six',
u'title': u'Product Five Four'}},
{u'_id': u'4',
u'_score': 1,
u'_source': {u'brand_id': 10,
u'brand_name': u'Brand Ten',
u'categories': [11],
u'price': 713,
u'property': u'Five',
u'title': u'Product Eight Nine'}},
{u'_id': u'5',
u'_score': 1,
u'_source': {u'brand_id': 7,
u'brand_name': u'Brand Seven',
u'categories': [11, 12, 13],
u'price': 805,
u'property': u'Two',
u'title': u'Product Ten Three'}}],
'max_score': None,
'total': 10000},
'profile': None,
'timed_out': False,
'took': 0}{'aggregations': {u'group_brand_id': {u'buckets': [{u'doc_count': 1019,
u'key': 10},
{u'doc_count': 954,
u'key': 9},
{u'doc_count': 1021,
u'key': 8},
{u'doc_count': 1011,
u'key': 7},
{u'doc_count': 997,
u'key': 6}]},
u'group_property': {u'buckets': [{u'doc_count': 11,
u'key': 1000},
{u'doc_count': 12,
u'key': 999},
{u'doc_count': 7,
u'key': 998},
{u'doc_count': 14,
u'key': 997},
{u'doc_count': 8,
u'key': 996}]}},
'hits': {'hits': [{u'_id': u'1',
u'_score': 1,
u'_source': {u'brand_id': 10,
u'brand_name': u'Brand Ten',
u'categories': [10],
u'price': 197,
u'property': u'Six',
u'title': u'Product Eight One'}},
{u'_id': u'2',
u'_score': 1,
u'_source': {u'brand_id': 6,
u'brand_name': u'Brand Six',
u'categories': [12, 13, 14],
u'price': 671,
u'property': u'Four',
u'title': u'Product Nine Seven'}},
{u'_id': u'3',
u'_score': 1,
u'_source': {u'brand_id': 3,
u'brand_name': u'Brand Three',
u'categories': [13, 14, 15],
u'price': 92,
u'property': u'Six',
u'title': u'Product Five Four'}},
{u'_id': u'4',
u'_score': 1,
u'_source': {u'brand_id': 10,
u'brand_name': u'Brand Ten',
u'categories': [11],
u'price': 713,
u'property': u'Five',
u'title': u'Product Eight Nine'}},
{u'_id': u'5',
u'_score': 1,
u'_source': {u'brand_id': 7,
u'brand_name': u'Brand Seven',
u'categories': [11, 12, 13],
u'price': 805,
u'property': u'Two',
u'title': u'Product Ten Three'}}],
'max_score': None,
'total': 10000},
'profile': None,
'timed_out': False,
'took': 0}{"took":0,"timed_out":false,"hits":{"total":10000,"hits":[{"_id": 1,"_score":1,"_source":{"price":197,"brand_id":10,"brand_name":"Brand Ten","categories":[10],"title":"Product Eight One","property":"Six","price_range":0}},{"_id": 2,"_score":1,"_source":{"price":671,"brand_id":6,"brand_name":"Brand Six","categories":[12,13,14],"title":"Product Nine Seven","property":"Four","price_range":3}},{"_id": 3,"_score":1,"_source":{"price":92,"brand_id":3,"brand_name":"Brand Three","categories":[13,14,15],"title":"Product Five Four","property":"Six","price_range":0}},{"_id": 4,"_score":1,"_source":{"price":713,"brand_id":10,"brand_name":"Brand Ten","categories":[11],"title":"Product Eight Nine","property":"Five","price_range":3}},{"_id": 5,"_score":1,"_source":{"price":805,"brand_id":7,"brand_name":"Brand Seven","categories":[11,12,13],"title":"Product Ten Three","property":"Two","price_range":4}},{"_id": 6,"_score":1,"_source":{"price":420,"brand_id":2,"brand_name":"Brand Two","categories":[10,11],"title":"Product Two One","property":"Six","price_range":2}},{"_id": 7,"_score":1,"_source":{"price":412,"brand_id":9,"brand_name":"Brand Nine","categories":[10],"title":"Product Four Nine","property":"Eight","price_range":2}},{"_id": 8,"_score":1,"_source":{"price":300,"brand_id":9,"brand_name":"Brand Nine","categories":[13,14,15],"title":"Product Eight Four","property":"Five","price_range":1}},{"_id": 9,"_score":1,"_source":{"price":728,"brand_id":1,"brand_name":"Brand One","categories":[11],"title":"Product Nine Six","property":"Four","price_range":3}},{"_id": 10,"_score":1,"_source":{"price":622,"brand_id":3,"brand_name":"Brand Three","categories":[10,11],"title":"Product Six Seven","property":"Two","price_range":3}},{"_id": 11,"_score":1,"_source":{"price":462,"brand_id":5,"brand_name":"Brand Five","categories":[10,11],"title":"Product Ten Two","property":"Eight","price_range":2}},{"_id": 12,"_score":1,"_source":{"price":939,"brand_id":7,"brand_name":"Brand Seven","categories":[12,13],"title":"Product Nine Seven","property":"Six","price_range":4}},{"_id": 13,"_score":1,"_source":{"price":948,"brand_id":8,"brand_name":"Brand Eight","categories":[12],"title":"Product Ten One","property":"Six","price_range":4}},{"_id": 14,"_score":1,"_source":{"price":900,"brand_id":9,"brand_name":"Brand Nine","categories":[12,13,14],"title":"Product Ten Nine","property":"Three","price_range":4}},{"_id": 15,"_score":1,"_source":{"price":224,"brand_id":3,"brand_name":"Brand Three","categories":[13],"title":"Product Two Six","property":"Four","price_range":1}},{"_id": 16,"_score":1,"_source":{"price":713,"brand_id":10,"brand_name":"Brand Ten","categories":[12],"title":"Product Two Four","property":"Six","price_range":3}},{"_id": 17,"_score":1,"_source":{"price":510,"brand_id":2,"brand_name":"Brand Two","categories":[10],"title":"Product Ten Two","property":"Seven","price_range":2}},{"_id": 18,"_score":1,"_source":{"price":702,"brand_id":10,"brand_name":"Brand Ten","categories":[12,13],"title":"Product Nine One","property":"Three","price_range":3}},{"_id": 19,"_score":1,"_source":{"price":836,"brand_id":4,"brand_name":"Brand Four","categories":[10,11,12],"title":"Product Four Five","property":"Two","price_range":4}},{"_id": 20,"_score":1,"_source":{"price":227,"brand_id":3,"brand_name":"Brand Three","categories":[12,13],"title":"Product Three Four","property":"Ten","price_range":1}}]}}class SearchResponse {
took: 0
timedOut: false
aggregations: {group_property={buckets=[{key=4, doc_count=2100}, {key=3, doc_count=1973}, {key=2, doc_count=1999}, {key=1, doc_count=2043}, {key=0, doc_count=1885}]}}
hits: class SearchResponseHits {
maxScore: null
total: 10000
hits: [{_id=1, _score=1, _source={price=197, brand_id=10, brand_name=Brand Ten, categories=[10], title=Product Eight One, property=Six, price_range=0}}, {_id=2, _score=1, _source={price=671, brand_id=6, brand_name=Brand Six, categories=[12, 13, 14], title=Product Nine Seven, property=Four, price_range=3}}, {_id=3, _score=1, _source={price=92, brand_id=3, brand_name=Brand Three, categories=[13, 14, 15], title=Product Five Four, property=Six, price_range=0}}, {_id=4, _score=1, _source={price=713, brand_id=10, brand_name=Brand Ten, categories=[11], title=Product Eight Nine, property=Five, price_range=3}}, {_id=5, _score=1, _source={price=805, brand_id=7, brand_name=Brand Seven, categories=[11, 12, 13], title=Product Ten Three, property=Two, price_range=4}}]
}
profile: null
}class SearchResponse {
took: 0
timedOut: false
aggregations: {group_property={buckets=[{key=4, doc_count=2100}, {key=3, doc_count=1973}, {key=2, doc_count=1999}, {key=1, doc_count=2043}, {key=0, doc_count=1885}]}}
hits: class SearchResponseHits {
maxScore: null
total: 10000
hits: [{_id=1, _score=1, _source={price=197, brand_id=10, brand_name=Brand Ten, categories=[10], title=Product Eight One, property=Six, price_range=0}}, {_id=2, _score=1, _source={price=671, brand_id=6, brand_name=Brand Six, categories=[12, 13, 14], title=Product Nine Seven, property=Four, price_range=3}}, {_id=3, _score=1, _source={price=92, brand_id=3, brand_name=Brand Three, categories=[13, 14, 15], title=Product Five Four, property=Six, price_range=0}}, {_id=4, _score=1, _source={price=713, brand_id=10, brand_name=Brand Ten, categories=[11], title=Product Eight Nine, property=Five, price_range=3}}, {_id=5, _score=1, _source={price=805, brand_id=7, brand_name=Brand Seven, categories=[11, 12, 13], title=Product Ten Three, property=Two, price_range=4}}]
}
profile: null
}class SearchResponse {
took: 0
timedOut: false
aggregations: {group_property={buckets=[{key=4, doc_count=2100}, {key=3, doc_count=1973}, {key=2, doc_count=1999}, {key=1, doc_count=2043}, {key=0, doc_count=1885}]}}
hits: class SearchResponseHits {
maxScore: null
total: 10000
hits: [{_id=1, _score=1, _source={price=197, brand_id=10, brand_name=Brand Ten, categories=[10], title=Product Eight One, property=Six, price_range=0}}, {_id=2, _score=1, _source={price=671, brand_id=6, brand_name=Brand Six, categories=[12, 13, 14], title=Product Nine Seven, property=Four, price_range=3}}, {_id=3, _score=1, _source={price=92, brand_id=3, brand_name=Brand Three, categories=[13, 14, 15], title=Product Five Four, property=Six, price_range=0}}, {_id=4, _score=1, _source={price=713, brand_id=10, brand_name=Brand Ten, categories=[11], title=Product Eight Nine, property=Five, price_range=3}}, {_id=5, _score=1, _source={price=805, brand_id=7, brand_name=Brand Seven, categories=[11, 12, 13], title=Product Ten Three, property=Two, price_range=4}}]
}
profile: null
}{
"took": 0,
"timed_out": false,
"hits": {
"total": 5,
"hits": [
{
"_id": 1,
"_score": 1,
"_source": {
"content": "Text 1",
"name": "Doc 1",
"cat": 1,
"cat_range": 1
}
},
...
{
"_id": 5,
"_score": 1,
"_source": {
"content": "Text 5",
"name": "Doc 5",
"cat": 4,
"cat_range": 2,
}
}
]
},
"aggregations": {
"expr_group": {
"buckets": [
{
"key": 0,
"doc_count": 0
},
{
"key": 1,
"doc_count": 3
},
{
"key": 2,
"doc_count": 2
}
]
}
}
}{
"took": 0,
"timed_out": false,
"hits": {
"total": 5,
"hits": [
{
"_id": 1,
"_score": 1,
"_source": {
"content": "Text 1",
"name": "Doc 1",
"cat": 1,
"cat_range": 1
}
},
...
{
"_id": 5,
"_score": 1,
"_source": {
"content": "Text 5",
"name": "Doc 5",
"cat": 4,
"cat_range": 2
}
}
]
},
"aggregations": {
"expr_group": {
"buckets": [
{
"key": 0,
"doc_count": 0
},
{
"key": 1,
"doc_count": 3
},
{
"key": 2,
"doc_count": 2
}
]
}
}
}- SQL
SELECT *,INTERVAL(price,200,400,600,800) AS price_range FROM facetdemo
FACET price_range AS price_range,brand_name ORDER BY brand_name asc;+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+-------------+
| id | price | brand_id | title | brand_name | property | j | categories | price_range |
+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+-------------+
| 1 | 306 | 1 | Product Ten Three | Brand One | Six_Ten | {"prop1":66,"prop2":91,"prop3":"One"} | 10,11 | 1 |
...
+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+-------------+
20 rows in set (0.00 sec)
+--------------+-------------+----------+
| fprice_range | brand_name | count(*) |
+--------------+-------------+----------+
| 1 | Brand Eight | 197 |
| 4 | Brand Eight | 235 |
| 3 | Brand Eight | 203 |
| 2 | Brand Eight | 201 |
| 0 | Brand Eight | 197 |
| 4 | Brand Five | 230 |
| 2 | Brand Five | 197 |
| 1 | Brand Five | 204 |
| 3 | Brand Five | 193 |
| 0 | Brand Five | 183 |
| 1 | Brand Four | 195 |
...Facets可以通过构造固定大小的桶来对值进行直方图聚合。 键函数是:
key_of_the_bucket = interval + offset * floor ( ( value - offset ) / interval )
直方图参数interval必须为正数,直方图参数offset必须为正数且小于interval。默认情况下,桶以数组形式返回。直方图参数keyed使得响应以字典形式返回桶键。
- SQL
- JSON
- JSON 2
SELECT COUNT(*), HISTOGRAM(price, {hist_interval=100}) as price_range FROM facets GROUP BY price_range ORDER BY price_range ASC;POST /search -d '
{
"size": 0,
"table": "facets",
"aggs": {
"price_range": {
"histogram": {
"field": "price",
"interval": 300
}
}
}
}'POST /search -d '
{
"size": 0,
"table": "facets",
"aggs": {
"price_range": {
"histogram": {
"field": "price",
"interval": 300,
"keyed": true
}
}
}
}'+----------+-------------+
| count(*) | price_range |
+----------+-------------+
| 5 | 0 |
| 5 | 100 |
| 1 | 300 |
| 4 | 400 |
| 1 | 500 |
| 3 | 700 |
| 1 | 900 |
+----------+-------------+{
"took": 0,
"timed_out": false,
"hits": {
"total": 20,
"total_relation": "eq",
"hits": []
},
"aggregations": {
"price_range": {
"buckets": [
{
"key": 0,
"doc_count": 10
},
{
"key": 300,
"doc_count": 6
},
{
"key": 600,
"doc_count": 3
},
{
"key": 900,
"doc_count": 1
}
]
}
}
}{
"took": 0,
"timed_out": false,
"hits": {
"total": 20,
"total_relation": "eq",
"hits": []
},
"aggregations": {
"price_range": {
"buckets": {
"0": {
"key": 0,
"doc_count": 10
},
"300": {
"key": 300,
"doc_count": 6
},
"600": {
"key": 600,
"doc_count": 3
},
"900": {
"key": 900,
"doc_count": 1
}
}
}
}
}Facets可以对日期直方图值进行聚合,这与普通直方图类似。不同之处在于,区间由日期或时间表达式指定。此类表达式需要特殊支持,因为区间长度不总是固定的。值会根据以下键函数四舍五入到最近的桶:
key_of_the_bucket = interval * floor ( value / interval )
直方图参数calendar_interval理解月份具有不同的天数。
与calendar_interval不同,fixed_interval参数使用固定数量的单位,无论其在日历中的位置如何,都不会偏离。但是fixed_interval无法处理如周或月这样的单位,因为月不是一个固定数量。尝试为fixed_interval指定如周或月这样的单位将导致错误。
接受的区间在日期直方图表达式中描述。默认情况下,桶以数组形式返回。直方图参数keyed使得响应以字典形式返回桶键。
- SQL
- JSON
SELECT count(*), DATE_HISTOGRAM(tm, {calendar_interval='month'}) AS months FROM idx_dates GROUP BY months ORDER BY months ASCPOST /search -d '
{
"table": "idx_dates",
"size": 0,
"aggs": {
"months": {
"date_histogram": {
"field": "tm",
"keyed": true,
"calendar_interval": "month"
}
}
}
}'+----------+------------+
| count(*) | months |
+----------+------------+
| 442 | 1485907200 |
| 744 | 1488326400 |
| 720 | 1491004800 |
| 230 | 1493596800 |
+----------+------------+{
"timed_out": false,
"hits": {
"total": 2136,
"total_relation": "eq",
"hits": []
},
"aggregations": {
"months": {
"buckets": {
"2017-02-01T00:00:00": {
"key": 1485907200,
"key_as_string": "2017-02-01T00:00:00",
"doc_count": 442
},
"2017-03-01T00:00:00": {
"key": 1488326400,
"key_as_string": "2017-03-01T00:00:00",
"doc_count": 744
},
"2017-04-01T00:00:00": {
"key": 1491004800,
"key_as_string": "2017-04-01T00:00:00",
"doc_count": 720
},
"2017-05-01T00:00:00": {
"key": 1493596800,
"key_as_string": "2017-05-01T00:00:00",
"doc_count": 230
}
}
}
}
}- SQL
- JSON
- JSON 2
SELECT COUNT(*), RANGE(price, {range_to=150},{range_from=150,range_to=300},{range_from=300}) price_range FROM facets GROUP BY price_range ORDER BY price_range ASC;POST /search -d '
{
"size": 0,
"table": "facets",
"aggs": {
"price_range": {
"range": {
"field": "price",
"ranges": [
{
"to": 99
},
{
"from": 99,
"to": 550
},
{
"from": 550
}
]
}
}
}
}'POST /search -d '
{
"size":0,
"table":"facets",
"aggs":{
"price_range":{
"range":{
"field":"price",
"keyed":true,
"ranges":[
{
"from":100,
"to":399
},
{
"from":399
}
]
}
}
}
}'+----------+-------------+
| count(*) | price_range |
+----------+-------------+
| 8 | 0 |
| 2 | 1 |
| 10 | 2 |
+----------+-------------+{
"took": 0,
"timed_out": false,
"hits": {
"total": 20,
"total_relation": "eq",
"hits": []
},
"aggregations": {
"price_range": {
"buckets": [
{
"key": "*-99",
"to": 99,
"doc_count": 5
},
{
"key": "99-550",
"from": 99,
"to": 550,
"doc_count": 11
},
{
"key": "550-*",
"from": 550,
"doc_count": 4
}
]
}
}
}{
"took": 0,
"timed_out": false,
"hits": {
"total": 20,
"total_relation": "eq",
"hits": []
},
"aggregations": {
"price_range": {
"buckets": {
"100-399": {
"from": 100,
"to": 399,
"doc_count": 6
},
"399-*": {
"from": 399,
"doc_count": 9
}
}
}
}
}Facets可以对一组日期范围进行聚合,这与普通范围类似。不同之处在于,from和to值可以使用日期数学表达式表示。此聚合包括from值并排除每个范围的to值。将keyed属性设置为true使得响应以字典形式返回桶键,而不是数组。
- SQL
- JSON
SELECT COUNT(*), DATE_RANGE(tm, {range_to='2017||+2M/M'},{range_from='2017||+2M/M',range_to='2017||+5M/M'},{range_from='2017||+5M/M'}) AS points FROM idx_dates GROUP BY points ORDER BY points ASC;POST /search -d '
{
"table": "idx_dates",
"size": 0,
"aggs": {
"points": {
"date_range": {
"field": "tm",
"keyed": true,
"ranges": [
{
"to": "2017||+2M/M"
},
{
"from": "2017||+2M/M",
"to": "2017||+4M/M"
},
{
"from": "2017||+4M/M",
"to": "2017||+5M/M"
},
{
"from": "2017||+5M/M"
}
]
}
}
}
}'+----------+--------+
| count(*) | points |
+----------+--------+
| 442 | 0 |
| 1464 | 1 |
| 230 | 2 |
+----------+--------+{
"timed_out": false,
"hits": {
"total": 2136,
"total_relation": "eq",
"hits": []
},
"aggregations": {
"points": {
"buckets": {
"*-2017-03-01T00:00:00": {
"to": "2017-03-01T00:00:00",
"doc_count": 442
},
"2017-03-01T00:00:00-2017-04-01T00:00:00": {
"from": "2017-03-01T00:00:00",
"to": "2017-04-01T00:00:00",
"doc_count": 744
},
"2017-04-01T00:00:00-2017-05-01T00:00:00": {
"from": "2017-04-01T00:00:00",
"to": "2017-05-01T00:00:00",
"doc_count": 720
},
"2017-05-01T00:00:00-*": {
"from": "2017-05-01T00:00:00",
"doc_count": 230
}
}
}
}
}Facets支持ORDER BY子句,就像标准查询一样。每个Facet可以有自己的排序方式,Facet的排序不会影响主结果集的排序,这由主查询的ORDER BY决定。排序可以基于属性名、计数(使用COUNT(*)、COUNT(DISTINCT attribute_name))或特殊的FACET()函数,该函数提供聚合数据值。默认情况下,带有ORDER BY COUNT(*)的查询将按降序排序。
- SQL
- JSON
SELECT * FROM facetdemo
FACET brand_name BY brand_id ORDER BY FACET() ASC
FACET brand_name BY brand_id ORDER BY brand_name ASC
FACET brand_name BY brand_id order BY COUNT(*) DESC;
FACET brand_name BY brand_id order BY COUNT(*);POST /search -d '
{
"table":"table_name",
"aggs":{
"group_property":{
"terms":{
"field":"a"
},
"sort":[
{
"count(*)":{
"order":"desc"
}
}
]
}
}
}'+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+
| id | price | brand_id | title | brand_name | property | j | categories |
+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+
| 1 | 306 | 1 | Product Ten Three | Brand One | Six_Ten | {"prop1":66,"prop2":91,"prop3":"One"} | 10,11 |
...
| 20 | 31 | 9 | Product Four One | Brand Nine | Ten_Four | {"prop1":79,"prop2":42,"prop3":"One"} | 12,13,14 |
+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+
20 rows in set (0.01 sec)
+-------------+----------+
| brand_name | count(*) |
+-------------+----------+
| Brand One | 1013 |
| Brand Two | 990 |
| Brand Three | 1016 |
| Brand Four | 994 |
| Brand Five | 1007 |
| Brand Six | 1039 |
| Brand Seven | 965 |
| Brand Eight | 1033 |
| Brand Nine | 944 |
| Brand Ten | 998 |
+-------------+----------+
10 rows in set (0.01 sec)
+-------------+----------+
| brand_name | count(*) |
+-------------+----------+
| Brand Eight | 1033 |
| Brand Five | 1007 |
| Brand Four | 994 |
| Brand Nine | 944 |
| Brand One | 1013 |
| Brand Seven | 965 |
| Brand Six | 1039 |
| Brand Ten | 998 |
| Brand Three | 1016 |
| Brand Two | 990 |
+-------------+----------+
10 rows in set (0.01 sec)
+-------------+----------+
| brand_name | count(*) |
+-------------+----------+
| Brand Six | 1039 |
| Brand Eight | 1033 |
| Brand Three | 1016 |
| Brand One | 1013 |
| Brand Five | 1007 |
| Brand Ten | 998 |
| Brand Four | 994 |
| Brand Two | 990 |
| Brand Seven | 965 |
| Brand Nine | 944 |
+-------------+----------+
10 rows in set (0.01 sec){
"took": 0,
"timed_out": false,
"hits": {
"total": 6,
"total_relation": "eq",
"hits": [
{
"_id": 1515697460415037554,
"_score": 1,
"_source": {
"a": 1
}
},
{
"_id": 1515697460415037555,
"_score": 1,
"_source": {
"a": 2
}
},
{
"_id": 1515697460415037556,
"_score": 1,
"_source": {
"a": 2
}
},
{
"_id": 1515697460415037557,
"_score": 1,
"_source": {
"a": 3
}
},
{
"_id": 1515697460415037558,
"_score": 1,
"_source": {
"a": 3
}
},
{
"_id": 1515697460415037559,
"_score": 1,
"_source": {
"a": 3
}
}
]
},
"aggregations": {
"group_property": {
"buckets": [
{
"key": 3,
"doc_count": 3
},
{
"key": 2,
"doc_count": 2
},
{
"key": 1,
"doc_count": 1
}
]
}
}
}默认情况下,每个Facet结果集仅限于20个值。可以通过LIMIT子句单独为每个Facet控制Facet值的数量,提供返回值的数量格式LIMIT count或使用偏移量LIMIT offset, count。
返回的最大Facet值数量受查询的max_matches设置限制。如果您想实现动态max_matches(限制max_matches为偏移量+每页以提高性能),必须考虑到过低的max_matches值可能会影响Facet值的数量。在这种情况下,应使用足以覆盖Facet值数量的最小max_matches值。
- SQL
- JSON
- PHP
- Python
- Python-asyncio
- Javascript
- Java
- C#
- Rust
- TypeScript
- Go
SELECT * FROM facetdemo
FACET brand_name BY brand_id ORDER BY FACET() ASC LIMIT 0,1
FACET brand_name BY brand_id ORDER BY brand_name ASC LIMIT 2,4
FACET brand_name BY brand_id order BY COUNT(*) DESC LIMIT 4;POST /search -d '
{
"table" : "facetdemo",
"query" : {"match_all" : {} },
"limit": 5,
"aggs" :
{
"group_property" :
{
"terms" :
{
"field":"price",
"size":1,
}
},
"group_brand_id" :
{
"terms" :
{
"field":"brand_id",
"size":3
}
}
}
}
'$index->setName('facetdemo');
$search = $index->search('');
$search->limit(5);
$search->facet('price','price',1);
$search->facet('brand_id','group_brand_id',3);
$results = $search->get();
print_r($results->getFacets());res =searchApi.search({"table":"facetdemo","query":{"match_all":{}},"limit":5,"aggs":{"group_property":{"terms":{"field":"price","size":1,}},"group_brand_id":{"terms":{"field":"brand_id","size":3}}}})res = await searchApi.search({"table":"facetdemo","query":{"match_all":{}},"limit":5,"aggs":{"group_property":{"terms":{"field":"price","size":1,}},"group_brand_id":{"terms":{"field":"brand_id","size":3}}}})res = await searchApi.search({"table":"facetdemo","query":{"match_all":{}},"limit":5,"aggs":{"group_property":{"terms":{"field":"price","size":1,}},"group_brand_id":{"terms":{"field":"brand_id","size":3}}}});searchRequest = new SearchRequest();
aggs = new HashMap<String,Object>(){{
put("group_property", new HashMap<String,Object>(){{
put("terms", new HashMap<String,Object>(){{
put("field","price");
put("size",1);
}});
}});
put("group_brand_id", new HashMap<String,Object>(){{
put("terms", new HashMap<String,Object>(){{
put("field","brand_id");
put("size",3);
}});
}});
}};
searchRequest.setIndex("facetdemo");
searchRequest.setLimit(5);
query = new HashMap<String,Object>();
query.put("match_all",null);
searchRequest.setQuery(query);
searchRequest.setAggs(aggs);
searchResponse = searchApi.search(searchRequest);var agg1 = new Aggregation("group_property", "price");
agg1.Size = 1;
var agg2 = new Aggregation("group_brand_id", "brand_id");
agg2.Size = 3;
agg2.Size = 100;
object query = new { match_all=null };
var searchRequest = new SearchRequest("facetdemo", query);
searchRequest.Aggs = new List<Aggregation> {agg1, agg2};
var searchResponse = searchApi.Search(searchRequest);let query = SearchQuery::new();
let aggTerms1 = AggTerms {
field: "price".to_string(),
size: Some(1),
};
let agg1 = Aggregation {
terms: Some(Box::new(aggTerms1)),
..Default::default(),
};
let aggTerms1 = AggTerms {
field: "brand_id".to_string(),
size: Some(3),
};
let agg2 = Aggregation {
terms: Some(Box::new(aggTerms2)),
..Default::default(),
};
let mut aggs = HashMap::new();
aggs.insert("group_property".to_string(), agg1);
aggs.insert("group_brand_id".to_string(), agg2);
let search_req = SearchRequest {
table: "facetdemo".to_string(),
query: Some(Box::new(query)),
aggs: serde_json::json!(aggs),
limit: serde_json::json!(5),
..Default::default(),
};
let search_res = search_api.search(search_req).await;res = await searchApi.search({
index: 'test',
query: { match_all:{} },
aggs: {
name_group: {
terms: { field : 'name', size: 1 }
},
cat_group: {
terms: { field: 'cat' }
}
}
});query := map[string]interface{} {}
searchRequest.SetQuery(query)
aggByName := manticoreclient.NewAggregation()
aggTerms := manticoreclient.NewAggregationTerms()
aggTerms.SetField("name")
aggByName.SetTerms(aggTerms)
aggByName.SetSize(1)
aggByCat := manticoreclient.NewAggregation()
aggTerms.SetField("cat")
aggByCat.SetTerms(aggTerms)
aggs := map[string]Aggregation{} { "name_group": aggByName, "cat_group": aggByCat }
searchRequest.SetAggs(aggs)
res, _, _ := apiClient.SearchAPI.Search(context.Background()).SearchRequest(*searchRequest).Execute()+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+
| id | price | brand_id | title | brand_name | property | j | categories |
+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+
| 1 | 306 | 1 | Product Ten Three | Brand One | Six_Ten | {"prop1":66,"prop2":91,"prop3":"One"} | 10,11 |
...
| 20 | 31 | 9 | Product Four One | Brand Nine | Ten_Four | {"prop1":79,"prop2":42,"prop3":"One"} | 12,13,14 |
+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+
20 rows in set (0.01 sec)
+-------------+----------+
| brand_name | count(*) |
+-------------+----------+
| Brand One | 1013 |
+-------------+----------+
1 rows in set (0.01 sec)
+-------------+----------+
| brand_name | count(*) |
+-------------+----------+
| Brand Four | 994 |
| Brand Nine | 944 |
| Brand One | 1013 |
| Brand Seven | 965 |
+-------------+----------+
4 rows in set (0.01 sec)
+-------------+----------+
| brand_name | count(*) |
+-------------+----------+
| Brand Six | 1039 |
| Brand Eight | 1033 |
| Brand Three | 1016 |
+-------------+----------+
3 rows in set (0.01 sec){
"took": 3,
"timed_out": false,
"hits": {
"total": 10000,
"hits": [
{
"_id": 1,
"_score": 1,
"_source": {
"price": 197,
"brand_id": 10,
"brand_name": "Brand Ten",
"categories": [
10
]
}
},
...
{
"_id": 5,
"_score": 1,
"_source": {
"price": 805,
"brand_id": 7,
"brand_name": "Brand Seven",
"categories": [
11,
12,
13
]
}
}
]
},
"aggregations": {
"group_property": {
"buckets": [
{
"key": 1000,
"doc_count": 11
}
]
},
"group_brand_id": {
"buckets": [
{
"key": 10,
"doc_count": 1019
},
{
"key": 9,
"doc_count": 954
},
{
"key": 8,
"doc_count": 1021
}
]
}
}
}Array
(
[price] => Array
(
[buckets] => Array
(
[0] => Array
(
[key] => 1000
[doc_count] => 11
)
)
)
[group_brand_id] => Array
(
[buckets] => Array
(
[0] => Array
(
[key] => 10
[doc_count] => 1019
)
[1] => Array
(
[key] => 9
[doc_count] => 954
)
[2] => Array
(
[key] => 8
[doc_count] => 1021
)
)
)
){'aggregations': {u'group_brand_id': {u'buckets': [{u'doc_count': 1019,
u'key': 10},
{u'doc_count': 954,
u'key': 9},
{u'doc_count': 1021,
u'key': 8}]},
u'group_property': {u'buckets': [{u'doc_count': 11,
u'key': 1000}]}},
'hits': {'hits': [{u'_id': u'1',
u'_score': 1,
u'_source': {u'brand_id': 10,
u'brand_name': u'Brand Ten',
u'categories': [10],
u'price': 197,
u'property': u'Six',
u'title': u'Product Eight One'}},
{u'_id': u'2',
u'_score': 1,
u'_source': {u'brand_id': 6,
u'brand_name': u'Brand Six',
u'categories': [12, 13, 14],
u'price': 671,
u'property': u'Four',
u'title': u'Product Nine Seven'}},
{u'_id': u'3',
u'_score': 1,
u'_source': {u'brand_id': 3,
u'brand_name': u'Brand Three',
u'categories': [13, 14, 15],
u'price': 92,
u'property': u'Six',
u'title': u'Product Five Four'}},
{u'_id': u'4',
u'_score': 1,
u'_source': {u'brand_id': 10,
u'brand_name': u'Brand Ten',
u'categories': [11],
u'price': 713,
u'property': u'Five',
u'title': u'Product Eight Nine'}},
{u'_id': u'5',
u'_score': 1,
u'_source': {u'brand_id': 7,
u'brand_name': u'Brand Seven',
u'categories': [11, 12, 13],
u'price': 805,
u'property': u'Two',
u'title': u'Product Ten Three'}}],
'max_score': None,
'total': 10000},
'profile': None,
'timed_out': False,
'took': 0}{'aggregations': {u'group_brand_id': {u'buckets': [{u'doc_count': 1019,
u'key': 10},
{u'doc_count': 954,
u'key': 9},
{u'doc_count': 1021,
u'key': 8}]},
u'group_property': {u'buckets': [{u'doc_count': 11,
u'key': 1000}]}},
'hits': {'hits': [{u'_id': u'1',
u'_score': 1,
u'_source': {u'brand_id': 10,
u'brand_name': u'Brand Ten',
u'categories': [10],
u'price': 197,
u'property': u'Six',
u'title': u'Product Eight One'}},
{u'_id': u'2',
u'_score': 1,
u'_source': {u'brand_id': 6,
u'brand_name': u'Brand Six',
u'categories': [12, 13, 14],
u'price': 671,
u'property': u'Four',
u'title': u'Product Nine Seven'}},
{u'_id': u'3',
u'_score': 1,
u'_source': {u'brand_id': 3,
u'brand_name': u'Brand Three',
u'categories': [13, 14, 15],
u'price': 92,
u'property': u'Six',
u'title': u'Product Five Four'}},
{u'_id': u'4',
u'_score': 1,
u'_source': {u'brand_id': 10,
u'brand_name': u'Brand Ten',
u'categories': [11],
u'price': 713,
u'property': u'Five',
u'title': u'Product Eight Nine'}},
{u'_id': u'5',
u'_score': 1,
u'_source': {u'brand_id': 7,
u'brand_name': u'Brand Seven',
u'categories': [11, 12, 13],
u'price': 805,
u'property': u'Two',
u'title': u'Product Ten Three'}}],
'max_score': None,
'total': 10000},
'profile': None,
'timed_out': False,
'took': 0}{"took":0,"timed_out":false,"hits":{"total":10000,"hits":[{"_id": 1,"_score":1,"_source":{"price":197,"brand_id":10,"brand_name":"Brand Ten","categories":[10],"title":"Product Eight One","property":"Six"}},{"_id": 2,"_score":1,"_source":{"price":671,"brand_id":6,"brand_name":"Brand Six","categories":[12,13,14],"title":"Product Nine Seven","property":"Four"}},{"_id": 3,"_score":1,"_source":{"price":92,"brand_id":3,"brand_name":"Brand Three","categories":[13,14,15],"title":"Product Five Four","property":"Six"}},{"_id": 4,"_score":1,"_source":{"price":713,"brand_id":10,"brand_name":"Brand Ten","categories":[11],"title":"Product Eight Nine","property":"Five"}},{"_id": 5,"_score":1,"_source":{"price":805,"brand_id":7,"brand_name":"Brand Seven","categories":[11,12,13],"title":"Product Ten Three","property":"Two"}}]}}class SearchResponse {
took: 0
timedOut: false
aggregations: {group_property={buckets=[{key=1000, doc_count=11}]}, group_brand_id={buckets=[{key=10, doc_count=1019}, {key=9, doc_count=954}, {key=8, doc_count=1021}]}}
hits: class SearchResponseHits {
maxScore: null
total: 10000
hits: [{_id=1, _score=1, _source={price=197, brand_id=10, brand_name=Brand Ten, categories=[10], title=Product Eight One, property=Six}}, {_id=2, _score=1, _source={price=671, brand_id=6, brand_name=Brand Six, categories=[12, 13, 14], title=Product Nine Seven, property=Four}}, {_id=3, _score=1, _source={price=92, brand_id=3, brand_name=Brand Three, categories=[13, 14, 15], title=Product Five Four, property=Six}}, {_id=4, _score=1, _source={price=713, brand_id=10, brand_name=Brand Ten, categories=[11], title=Product Eight Nine, property=Five}}, {_id=5, _score=1, _source={price=805, brand_id=7, brand_name=Brand Seven, categories=[11, 12, 13], title=Product Ten Three, property=Two}}]
}
profile: null
}class SearchResponse {
took: 0
timedOut: false
aggregations: {group_property={buckets=[{key=1000, doc_count=11}]}, group_brand_id={buckets=[{key=10, doc_count=1019}, {key=9, doc_count=954}, {key=8, doc_count=1021}]}}
hits: class SearchResponseHits {
maxScore: null
total: 10000
hits: [{_id=1, _score=1, _source={price=197, brand_id=10, brand_name=Brand Ten, categories=[10], title=Product Eight One, property=Six}}, {_id=2, _score=1, _source={price=671, brand_id=6, brand_name=Brand Six, categories=[12, 13, 14], title=Product Nine Seven, property=Four}}, {_id=3, _score=1, _source={price=92, brand_id=3, brand_name=Brand Three, categories=[13, 14, 15], title=Product Five Four, property=Six}}, {_id=4, _score=1, _source={price=713, brand_id=10, brand_name=Brand Ten, categories=[11], title=Product Eight Nine, property=Five}}, {_id=5, _score=1, _source={price=805, brand_id=7, brand_name=Brand Seven, categories=[11, 12, 13], title=Product Ten Three, property=Two}}]
}
profile: null
}class SearchResponse {
took: 0
timedOut: false
aggregations: {group_property={buckets=[{key=1000, doc_count=11}]}, group_brand_id={buckets=[{key=10, doc_count=1019}, {key=9, doc_count=954}, {key=8, doc_count=1021}]}}
hits: class SearchResponseHits {
maxScore: null
total: 10000
hits: [{_id=1, _score=1, _source={price=197, brand_id=10, brand_name=Brand Ten, categories=[10], title=Product Eight One, property=Six}}, {_id=2, _score=1, _source={price=671, brand_id=6, brand_name=Brand Six, categories=[12, 13, 14], title=Product Nine Seven, property=Four}}, {_id=3, _score=1, _source={price=92, brand_id=3, brand_name=Brand Three, categories=[13, 14, 15], title=Product Five Four, property=Six}}, {_id=4, _score=1, _source={price=713, brand_id=10, brand_name=Brand Ten, categories=[11], title=Product Eight Nine, property=Five}}, {_id=5, _score=1, _source={price=805, brand_id=7, brand_name=Brand Seven, categories=[11, 12, 13], title=Product Ten Three, property=Two}}]
}
profile: null
}{
"took": 0,
"timed_out": false,
"hits": {
"total": 5,
"hits": [
{
"_id": 1,
"_score": 1,
"_source": {
"content": "Text 1",
"name": "Doc 1",
"cat": 1
}
},
...
{
"_id": 5,
"_score": 1,
"_source": {
"content": "Text 5",
"name": "Doc 5",
"cat": 4
}
}
]
},
"aggregations": {
"name_group": {
"buckets": [
{
"key": "Doc 1",
"doc_count": 1
}
]
},
"cat_group": {
"buckets": [
{
"key": 1,
"doc_count": 2
},
...
{
"key": 4,
"doc_count": 1
}
]
}
}
}{
"took": 0,
"timed_out": false,
"hits": {
"total": 5,
"hits": [
{
"_id": 1,
"_score": 1,
"_source": {
"content": "Text 1",
"name": "Doc 1",
"cat": 1
}
},
...
{
"_id": 5,
"_score": 1,
"_source": {
"content": "Text 5",
"name": "Doc 5",
"cat": 4
}
}
]
},
"aggregations": {
"name_group": {
"buckets": [
{
"key": "Doc 1",
"doc_count": 1
}
]
},
"cat_group": {
"buckets": [
{
"key": 1,
"doc_count": 2
},
...
{
"key": 4,
"doc_count": 1
}
]
}
}
}当使用SQL时,带有 facets 的搜索会返回多个结果集。MySQL客户端/库/连接器所使用的 必须 支持多个结果集,以便访问 facets 结果集。
内部,FACET 是执行多查询的一个快捷方式,其中第一个查询包含主要搜索查询,批次中的其余查询各自包含聚类。正如多查询的情况一样,对于带有 facets 的搜索,常见的查询优化可以启动,这意味着搜索查询只执行一次,而 facets 则在搜索查询结果上操作,每个 facet 只增加总查询时间的一小部分。
要检查 facets 搜索是否以优化模式运行,可以在 查询日志 中查找,其中所有记录的查询将包含一个 xN 字符串,N 是优化组中运行的查询数量。或者,可以检查 SHOW META 语句的输出,该语句将显示一个 multiplier 指标:
- SQL
SELECT * FROM facetdemo FACET brand_id FACET price FACET categories;
SHOW META LIKE 'multiplier';+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+
| id | price | brand_id | title | brand_name | property | j | categories |
+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+
| 1 | 306 | 1 | Product Ten Three | Brand One | Six_Ten | {"prop1":66,"prop2":91,"prop3":"One"} | 10,11 |
...
+----------+----------+
| brand_id | count(*) |
+----------+----------+
| 1 | 1013 |
...
+-------+----------+
| price | count(*) |
+-------+----------+
| 306 | 7 |
...
+------------+----------+
| categories | count(*) |
+------------+----------+
| 10 | 2436 |
...
+---------------+-------+
| Variable_name | Value |
+---------------+-------+
| multiplier | 4 |
+---------------+-------+
1 row in set (0.00 sec)Manticore Search 最伟大的功能之一是能够结合全文搜索与地理定位。例如,零售商可以提供一个搜索功能,用户可以查找产品,结果集可以指示最近的商店是否有库存,这样用户可以去店内取货。旅游网站可以根据特定区域进行搜索,并按距离某个点的距离对结果进行排序(例如,“搜索酒店附近的博物馆”)。
要执行地理搜索,文档需要包含经纬度坐标对。这些坐标可以存储为浮点数属性。如果文档中有多个位置,使用 JSON 属性存储坐标对可能会更方便。
table myrt
{
...
rt_attr_float = lat
rt_attr_float = lon
...
}
坐标可以存储为度或弧度。
如果为纬度和经度属性生成了二级索引,那么在 成本基优化器 决定使用它们的情况下,它们可能会自动用于加快地理搜索速度。
要找出两个点之间的距离,可以使用 GEODIST() 函数。GEODIST 需要作为前四个参数的两个坐标对。
GEODIST 的第五个参数可以以简化 JSON 格式配置某些方面。默认情况下,GEODIST 假定坐标为弧度,但可以添加 in=degrees 以允许使用度作为输入。我们执行地理距离的坐标类型必须与表中存储的坐标类型相同;否则,结果会误导人。
计算出的距离默认以米为单位,但可以通过 out 选项将其转换为千米、英尺或英里。最后,默认使用一种称为 adaptive 的计算方法。基于 haversine 算法的另一种方法可用,但这种方法较慢且不够精确。
函数的结果——距离——可以用于 ORDER BY 子句以对结果进行排序:
SELECT *, GEODIST(40.7643929, -73.9997683, lat, lon, {in=degrees, out=miles}) AS distance FROM myindex WHERE MATCH('...') ORDER BY distance ASC, WEIGHT() DESC;
或者限制结果为某个点周围的圆形区域:
SELECT *,GEODIST(40.7643929, -73.9997683, lat,lon, {in=degrees, out=miles}) AS distance FROM myindex WHERE MATCH('...') AND distance <1000 ORDER BY WEIGHT(), DISTANCE ASC;
另一个地理搜索功能是确定某个位置是否在指定区域内。有一个特殊函数构建一个多边形对象,然后使用另一个函数测试一组坐标是否包含在该多边形内。
有两种函数可用于创建多边形:
- GEOPOLY2D() - 考虑地球曲率创建多边形
- POLY2D() - 在平面空间中创建简单多边形
POLY2D 适用于多边形的边长小于 500km(对于 3-4 边的多边形;对于更多边的多边形,应考虑较低的值)的地理搜索。对于边长较长的区域,必须使用 GEOPOLY2D 以保持结果的准确性。GEOPOLY2D 预期坐标为度数的纬度/经度对;使用弧度将导致平面空间中的结果(类似于 POLY2D)。
CONTAINS() 接受一个多边形和一组坐标作为输入,并输出 1 如果点在多边形内,否则输出 0。
SELECT *,CONTAINS(GEOPOLY2D(40.76439, -73.9997, 42.21211, -73.999, 42.21211, -76.123, 40.76439, -76.123), 41.5445, -74.973) AS inside FROM myindex WHERE MATCH('...') AND inside=1;
Percolate 查询也被称为持久查询、前瞻搜索、文档路由、逆向搜索和反向搜索。
传统的搜索方式是存储文档并在其中执行搜索查询。然而,在某些情况下,我们希望将查询应用于新来的文档以发出匹配信号。这种需求出现在监控系统中,这些系统收集数据并向用户通知特定事件,例如某个度量标准达到某个阈值或监控数据中出现特定值。另一个例子是新闻聚合,用户可能只想收到特定类别或主题的通知,甚至特定的“关键词”。
在这种情况下,传统的搜索方式并不适用,因为它假设所需的搜索是在整个集合上进行的。这个过程会随着用户的数量而增加,导致许多查询在整集中运行,从而造成显著的额外负载。本节中描述的另一种方法是将查询存储起来,并在新文档或一批文档中测试它们。
Google Alerts、AlertHN、彭博终端和其他允许用户订阅特定内容的系统使用类似的技术。
- 请参阅 percolate 以获取创建 PQ 表的信息。
- 请参阅 向 percolate 表添加规则 以了解如何添加 percolate 规则(也称为 PQ 规则)。这里有一个快速示例:
关于 percolate 查询需要记住的关键点是,你的搜索查询已经存储在表中。你需要提供的是一些文档,以检查它们是否与任何存储的规则匹配。
你可以通过 SQL 或 JSON 接口,或者使用编程语言客户端来执行 percolate 查询。SQL 方法提供了更多的灵活性,而 HTTP 方法则更简单,并提供了你所需的大部功能。下表可以帮助你理解它们之间的差异。
| 需求行为 | SQL | HTTP |
|---|---|---|
| 提供单个文档 | CALL PQ('tbl', '{doc1}') |
query.percolate.document{doc1} |
| 提供单个文档(替代) | CALL PQ('tbl', 'doc1', 0 as docs_json) |
- |
| 提供多个文档 | CALL PQ('tbl', ('doc1', 'doc2'), 0 as docs_json) |
- |
| 提供多个文档(替代) | CALL PQ('tbl', ('{doc1}', '{doc2}')) |
- |
| 提供多个文档(替代) | CALL PQ('tbl', '[{doc1}, {doc2}]') |
- |
| 返回匹配文档 id | 0/1 as docs (默认禁用) | 默认启用 |
| 使用文档自身的 id 显示在结果中 | 'id field' as docs_id (默认禁用) | 无 |
| 考虑输入文档是 JSON | 1 as docs_json (默认 1) | 默认启用 |
| 考虑输入文档是纯文本 | 0 as docs_json (默认 1) | 无 |
| 稀疏分布模式 | 默认 | 默认 |
| 分片分布模式 | sharded as mode | 无 |
| 返回匹配查询的所有信息 | 1 as query (默认 0) | 默认启用 |
| 跳过无效 JSON | 1 as skip_bad_json (默认 0) | 无 |
| 在 SHOW META 中提供扩展信息 | 1 as verbose (默认 0) | 无 |
| 定义如果未提供 docs_id 字段将添加到文档 id 的数字(主要适用于 分布式 PQ 模式) | 1 as shift (默认 0) | 无 |
为了演示如何操作,这里有一些示例。让我们创建一个具有两个字段的 PQ 表:
- title (文本)
- color (字符串)
并在此表中添加三个规则:
- 仅全文搜索。查询:
@title bag - 全文搜索和过滤。查询:
@title shoes。过滤:color='red' - 全文搜索和更复杂的过滤。查询:
@title shoes。过滤:color IN('blue', 'green')
- SQL
- JSON
- PHP
- Python
- Python-asyncio
- javascript
- Java
- C#
- Rust
- TypeScript
- Go
CREATE TABLE products(title text, color string) type='pq';
INSERT INTO products(query) values('@title bag');
INSERT INTO products(query,filters) values('@title shoes', 'color=\'red\'');
INSERT INTO products(query,filters) values('@title shoes', 'color in (\'blue\', \'green\')');
select * from products;PUT /pq/products/doc/
{
"query": {
"match": {
"title": "bag"
}
},
"filters": ""
}
PUT /pq/products/doc/
{
"query": {
"match": {
"title": "shoes"
}
},
"filters": "color='red'"
}
PUT /pq/products/doc/
{
"query": {
"match": {
"title": "shoes"
}
},
"filters": "color IN ('blue', 'green')"
}$index = [
'table' => 'products',
'body' => [
'columns' => [
'title' => ['type' => 'text'],
'color' => ['type' => 'string']
],
'settings' => [
'type' => 'pq'
]
]
];
$client->indices()->create($index);
$query = [
'table' => 'products',
'body' => [ 'query'=>['match'=>['title'=>'bag']]]
];
$client->pq()->doc($query);
$query = [
'table' => 'products',
'body' => [ 'query'=>['match'=>['title'=>'shoes']],'filters'=>"color='red'"]
];
$client->pq()->doc($query);
$query = [
'table' => 'products',
'body' => [ 'query'=>['match'=>['title'=>'shoes']],'filters'=>"color IN ('blue', 'green')"]
];
$client->pq()->doc($query);utilsApi.sql('create table products(title text, color string) type=\'pq\'')
indexApi.insert({"table" : "products", "doc" : {"query" : "@title bag" }})
indexApi.insert({"table" : "products", "doc" : {"query" : "@title shoes", "filters": "color='red'" }})
indexApi.insert({"table" : "products", "doc" : {"query" : "@title shoes","filters": "color IN ('blue', 'green')" }})await utilsApi.sql('create table products(title text, color string) type=\'pq\'')
await indexApi.insert({"table" : "products", "doc" : {"query" : "@title bag" }})
await indexApi.insert({"table" : "products", "doc" : {"query" : "@title shoes", "filters": "color='red'" }})
await indexApi.insert({"table" : "products", "doc" : {"query" : "@title shoes","filters": "color IN ('blue', 'green')" }})res = await utilsApi.sql('create table products(title text, color string) type=\'pq\'');
res = indexApi.insert({"table" : "products", "doc" : {"query" : "@title bag" }});
res = indexApi.insert({"table" : "products", "doc" : {"query" : "@title shoes", "filters": "color='red'" }});
res = indexApi.insert({"table" : "products", "doc" : {"query" : "@title shoes","filters": "color IN ('blue', 'green')" }});utilsApi.sql("create table products(title text, color string) type='pq'", true);
doc = new HashMap<String,Object>(){{
put("query", "@title bag");
}};
newdoc = new InsertDocumentRequest();
newdoc.index("products").setDoc(doc);
indexApi.insert(newdoc);
doc = new HashMap<String,Object>(){{
put("query", "@title shoes");
put("filters", "color='red'");
}};
newdoc = new InsertDocumentRequest();
newdoc.index("products").setDoc(doc);
indexApi.insert(newdoc);
doc = new HashMap<String,Object>(){{
put("query", "@title shoes");
put("filters", "color IN ('blue', 'green')");
}};
newdoc = new InsertDocumentRequest();
newdoc.index("products").setDoc(doc);
indexApi.insert(newdoc);utilsApi.Sql("create table products(title text, color string) type='pq'", true);
Dictionary<string, Object> doc = new Dictionary<string, Object>();
doc.Add("query", "@title bag");
InsertDocumentRequest newdoc = new InsertDocumentRequest(index: "products", doc: doc);
indexApi.Insert(newdoc);
doc = new Dictionary<string, Object>();
doc.Add("query", "@title shoes");
doc.Add("filters", "color='red'");
newdoc = new InsertDocumentRequest(index: "products", doc: doc);
indexApi.Insert(newdoc);
doc = new Dictionary<string, Object>();
doc.Add("query", "@title bag");
doc.Add("filters", "color IN ('blue', 'green')");
newdoc = new InsertDocumentRequest(index: "products", doc: doc);
indexApi.Insert(newdoc);utils_api.sql("create table products(title text, color string) type='pq'", Some(true)).await;
let mut doc1 = HashMap::new();
doc1.insert("query".to_string(), serde_json::json!("@title bag"));
let insert_req1 = InsertDocumentRequest::new("products".to_string(), serde_json::json!(doc1));
index_api.insert(insert_req1).await;
let mut doc2 = HashMap::new();
doc2.insert("query".to_string(), serde_json::json!("@title shoes"));
doc2.insert("filters".to_string(), serde_json::json!("color='red'"));
let insert_req2 = InsertDocumentRequest::new("products".to_string(), serde_json::json!(doc2));
index_api.insert(insert_req2).await;
let mut doc3 = HashMap::new();
doc3.insert("query".to_string(), serde_json::json!("@title bag"));
doc3.insert("filters".to_string(), serde_json::json!("color IN ('blue', 'green')"));
let insert_req3 = InsertDocumentRequest::new("products".to_string(), serde_json::json!(doc3));
index_api.insert(insert_req3).await;res = await utilsApi.sql("create table test_pq(title text, color string) type='pq'");
res = indexApi.insert({
index: 'test_pq',
doc: { query : '@title bag' }
});
res = indexApi.insert(
index: 'test_pq',
doc: { query: '@title shoes', filters: "color='red'" }
});
res = indexApi.insert({
index: 'test_pq',
doc: { query : '@title shoes', filters: "color IN ('blue', 'green')" }
});apiClient.UtilsAPI.Sql(context.Background()).Body("create table test_pq(title text, color string) type='pq'").Execute()
indexDoc := map[string]interface{} {"query": "@title bag"}
indexReq := manticoreclient.NewInsertDocumentRequest("test_pq", indexDoc)
apiClient.IndexAPI.Insert(context.Background()).InsertDocumentRequest(*indexReq).Execute();
indexDoc = map[string]interface{} {"query": "@title shoes", "filters": "color='red'"}
indexReq = manticoreclient.NewInsertDocumentRequest("test_pq", indexDoc)
apiClient.IndexAPI.Insert(context.Background()).InsertDocumentRequest(*indexReq).Execute();
indexDoc = map[string]interface{} {"query": "@title shoes", "filters": "color IN ('blue', 'green')"}
indexReq = manticoreclient.NewInsertDocumentRequest("test_pq", indexDoc)
apiClient.IndexAPI.Insert(context.Background()).InsertDocumentRequest(*indexReq).Execute();+---------------------+--------------+------+---------------------------+
| id | query | tags | filters |
+---------------------+--------------+------+---------------------------+
| 1657852401006149635 | @title shoes | | color IN ('blue, 'green') |
| 1657852401006149636 | @title shoes | | color='red' |
| 1657852401006149637 | @title bag | | |
+---------------------+--------------+------+---------------------------+{
"table": "products",
"type": "doc",
"_id": 1657852401006149661,
"result": "created"
}
{
"table": "products",
"type": "doc",
"_id": 1657852401006149662,
"result": "created"
}
{
"table": "products",
"type": "doc",
"_id": 1657852401006149663,
"result": "created"
}Array(
[table] => products
[type] => doc
[_id] => 1657852401006149661
[result] => created
)
Array(
[table] => products
[type] => doc
[_id] => 1657852401006149662
[result] => created
)
Array(
[table] => products
[type] => doc
[_id] => 1657852401006149663
[result] => created
){'created': True,
'found': None,
'id': 0,
'table': 'products',
'result': 'created'}
{'created': True,
'found': None,
'id': 0,
'table': 'products',
'result': 'created'}
{'created': True,
'found': None,
'id': 0,
'table': 'products',
'result': 'created'}{'created': True,
'found': None,
'id': 0,
'table': 'products',
'result': 'created'}
{'created': True,
'found': None,
'id': 0,
'table': 'products',
'result': 'created'}
{'created': True,
'found': None,
'id': 0,
'table': 'products',
'result': 'created'}"table":"products","_id":0,"created":true,"result":"created"}
{"table":"products","_id":0,"created":true,"result":"created"}
{"table":"products","_id":0,"created":true,"result":"created"}{total=0, error=, warning=}
class SuccessResponse {
index: products
id: 0
created: true
result: created
found: null
}
class SuccessResponse {
index: products
id: 0
created: true
result: created
found: null
}
class SuccessResponse {
index: products
id: 0
created: true
result: created
found: null
}{total=0, error="", warning=""}
class SuccessResponse {
index: products
id: 0
created: true
result: created
found: null
}
class SuccessResponse {
index: products
id: 0
created: true
result: created
found: null
}
class SuccessResponse {
index: products
id: 0
created: true
result: created
found: null
}{total=0, error="", warning=""}
class SuccessResponse {
index: products
id: 0
created: true
result: created
found: null
}
class SuccessResponse {
index: products
id: 0
created: true
result: created
found: null
}
class SuccessResponse {
index: products
id: 0
created: true
result: created
found: null
}{
"table":"test_pq",
"_id":1657852401006149661,
"created":true,
"result":"created"
}
{
"table":"test_pq",
"_id":1657852401006149662,
"created":true,
"result":"created"
}
{
"table":"test_pq",
"_id":1657852401006149663,
"created":true,
"result":"created"
}{
"table":"test_pq",
"_id":1657852401006149661,
"created":true,
"result":"created"
}
{
"table":"test_pq",
"_id":1657852401006149662,
"created":true,
"result":"created"
}
{
"table":"test_pq",
"_id":1657852401006149663,
"created":true,
"result":"created"
}第一个文档没有匹配任何规则。它可以匹配前两个规则,但它们需要额外的过滤条件。
第二个文档匹配一个规则。请注意,CALL PQ 默认期望文档是一个 JSON,但如果你使用 0 as docs_json,你可以传递一个纯字符串。
- SQL
- JSON
- PHP
- Python
- Python-asyncio
- javascript
- Java
- C#
- Rust
- TypeScript
- Go
CALL PQ('products', 'Beautiful shoes', 0 as docs_json);
CALL PQ('products', 'What a nice bag', 0 as docs_json);
CALL PQ('products', '{"title": "What a nice bag"}');POST /pq/products/search
{
"query": {
"percolate": {
"document": {
"title": "What a nice bag"
}
}
}
}$percolate = [
'table' => 'products',
'body' => [
'query' => [
'percolate' => [
'document' => [
'title' => 'What a nice bag'
]
]
]
]
];
$client->pq()->search($percolate);searchApi.percolate('products',{"query":{"percolate":{"document":{"title":"What a nice bag"}}}})await searchApi.percolate('products',{"query":{"percolate":{"document":{"title":"What a nice bag"}}}})res = await searchApi.percolate('products',{"query":{"percolate":{"document":{"title":"What a nice bag"}}}});PercolateRequest percolateRequest = new PercolateRequest();
query = new HashMap<String,Object>(){{
put("percolate",new HashMap<String,Object >(){{
put("document", new HashMap<String,Object >(){{
put("title","what a nice bag");
}});
}});
}};
percolateRequest.query(query);
searchApi.percolate("test_pq",percolateRequest);Dictionary<string, Object> percolateDoc = new Dictionary<string, Object>();
percolateDoc.Add("document", new Dictionary<string, Object> {{ "title", "what a nice bag" }});
Dictionary<string, Object> query = new Dictionary<string, Object> {{ "percolate", percolateDoc }};
PercolateRequest percolateRequest = new PercolateRequest(query=query);
searchApi.Percolate("test_pq",percolateRequest);let mut percolate_doc_fields = HashMap::new();
percolate_doc_fileds.insert("title".to_string(), "what a nice bag");
let mut percolate_doc = HashMap::new();
percolate_doc.insert("document".to_string(), percolate_doc_fields);
let percolate_query = PercolateRequestQuery::new(serde_json::json!(percolate_doc));
let percolate_req = PercolateRequest::new(percolate_query);
search_api.percolate("test_pq", percolate_req).await;res = await searchApi.percolate('test_pq', { query: { percolate: { document : { title : 'What a nice bag' } } } } );query := map[string]interface{} {"title": "what a nice bag"}
percolateRequestQuery := manticoreclient.NewPercolateQuery(query)
percolateRequest := manticoreclient.NewPercolateRequest(percolateRequestQuery)
res, _, _ := apiClient.SearchAPI.Percolate(context.Background(), "test_pq").PercolateRequest(*percolateRequest).Execute()+---------------------+
| id |
+---------------------+
| 1657852401006149637 |
+---------------------+
+---------------------+
| id |
+---------------------+
| 1657852401006149637 |
+---------------------+{
"took": 0,
"timed_out": false,
"hits": {
"total": 1,
"max_score": 1,
"hits": [
{
"table": "products",
"_type": "doc",
"_id": 1657852401006149644,
"_score": "1",
"_source": {
"query": {
"ql": "@title bag"
}
},
"fields": {
"_percolator_document_slot": [
1
]
}
}
]
}
}Array
(
[took] => 0
[timed_out] =>
[hits] => Array
(
[total] => 1
[max_score] => 1
[hits] => Array
(
[0] => Array
(
[_index] => products
[_type] => doc
[_id] => 1657852401006149644
[_score] => 1
[_source] => Array
(
[query] => Array
(
[match] => Array
(
[title] => bag
)
)
)
[fields] => Array
(
[_percolator_document_slot] => Array
(
[0] => 1
)
)
)
)
)
){'hits': {'hits': [{u'_id': u'2811025403043381480',
u'table': u'products',
u'_score': u'1',
u'_source': {u'query': {u'ql': u'@title bag'}},
u'_type': u'doc',
u'fields': {u'_percolator_document_slot': [1]}}],
'total': 1},
'profile': None,
'timed_out': False,
'took': 0}{'hits': {'hits': [{u'_id': u'2811025403043381480',
u'table': u'products',
u'_score': u'1',
u'_source': {u'query': {u'ql': u'@title bag'}},
u'_type': u'doc',
u'fields': {u'_percolator_document_slot': [1]}}],
'total': 1},
'profile': None,
'timed_out': False,
'took': 0}{
"took": 0,
"timed_out": false,
"hits": {
"total": 1,
"hits": [
{
"table": "products",
"_type": "doc",
"_id": 2811045522851233808,
"_score": "1",
"_source": {
"query": {
"ql": "@title bag"
}
},
"fields": {
"_percolator_document_slot": [
1
]
}
}
]
}
}class SearchResponse {
took: 0
timedOut: false
hits: class SearchResponseHits {
total: 1
maxScore: 1
hits: [{_index=products, _type=doc, _id=2811045522851234109, _score=1, _source={query={ql=@title bag}}, fields={_percolator_document_slot=[1]}}]
aggregations: null
}
profile: null
}class SearchResponse {
took: 0
timedOut: false
hits: class SearchResponseHits {
total: 1
maxScore: 1
hits: [{_index=products, _type=doc, _id=2811045522851234109, _score=1, _source={query={ql=@title bag}}, fields={_percolator_document_slot=[1]}}]
aggregations: null
}
profile: null
}class SearchResponse {
took: 0
timedOut: false
hits: class SearchResponseHits {
total: 1
maxScore: 1
hits: [{_index=products, _type=doc, _id=2811045522851234109, _score=1, _source={query={ql=@title bag}}, fields={_percolator_document_slot=[1]}}]
aggregations: null
}
profile: null
}{
"took": 0,
"timed_out": false,
"hits": {
"total": 1,
"hits": [
{
"table": "test_pq",
"_type": "doc",
"_id": 1657852401006149661,
"_score": "1",
"_source": {
"query": {
"ql": "@title bag"
}
},
"fields": {
"_percolator_document_slot": [
1
]
}
}
]
}
}{
"took": 0,
"timed_out": false,
"hits": {
"total": 1,
"hits": [
{
"table": "test_pq",
"_type": "doc",
"_id": 1657852401006149661,
"_score": "1",
"_source": {
"query": {
"ql": "@title bag"
}
},
"fields": {
"_percolator_document_slot": [
1
]
}
}
]
}
}- SQL
- JSON
- PHP
- Python
- Python-asyncio
- javascript
- Java
- C#
- Rust
- TypeScript
- Go
CALL PQ('products', '{"title": "What a nice bag"}', 1 as query);POST /pq/products/search
{
"query": {
"percolate": {
"document": {
"title": "What a nice bag"
}
}
}
}$percolate = [
'table' => 'products',
'body' => [
'query' => [
'percolate' => [
'document' => [
'title' => 'What a nice bag'
]
]
]
]
];
$client->pq()->search($percolate);searchApi.percolate('products',{"query":{"percolate":{"document":{"title":"What a nice bag"}}}})await searchApi.percolate('products',{"query":{"percolate":{"document":{"title":"What a nice bag"}}}})res = await searchApi.percolate('products',{"query":{"percolate":{"document":{"title":"What a nice bag"}}}});PercolateRequest percolateRequest = new PercolateRequest();
query = new HashMap<String,Object>(){{
put("percolate",new HashMap<String,Object >(){{
put("document", new HashMap<String,Object >(){{
put("title","what a nice bag");
}});
}});
}};
percolateRequest.query(query);
searchApi.percolate("test_pq",percolateRequest);Dictionary<string, Object> percolateDoc = new Dictionary<string, Object>();
percolateDoc.Add("document", new Dictionary<string, Object> {{ "title", "what a nice bag" }});
Dictionary<string, Object> query = new Dictionary<string, Object> {{ "percolate", percolateDoc }};
PercolateRequest percolateRequest = new PercolateRequest(query=query);
searchApi.Percolate("test_pq",percolateRequest);let mut percolate_doc_fields = HashMap::new();
percolate_doc_fileds.insert("title".to_string(), "what a nice bag");
let mut percolate_doc = HashMap::new();
percolate_doc.insert("document".to_string(), percolate_doc_fields);
let percolate_query = PercolateRequestQuery::new(serde_json::json!(percolate_doc));
let percolate_req = PercolateRequest::new(percolate_query);
search_api.percolate("test_pq", percolate_req).await;res = await searchApi.percolate('test_pq', { query: { percolate: { document : { title : 'What a nice bag' } } } } );query := map[string]interface{} {"title": "what a nice bag"}
percolateRequestQuery := manticoreclient.NewPercolateQuery(query)
percolateRequest := manticoreclient.NewPercolateRequest(percolateRequestQuery)
res, _, _ := apiClient.SearchAPI.Percolate(context.Background(), "test_pq").PercolateRequest(*percolateRequest).Execute()+---------------------+------------+------+---------+
| id | query | tags | filters |
+---------------------+------------+------+---------+
| 1657852401006149637 | @title bag | | |
+---------------------+------------+------+---------+{
"took": 0,
"timed_out": false,
"hits": {
"total": 1,
"max_score": 1,
"hits": [
{
"table": "products",
"_type": "doc",
"_id": 1657852401006149644,
"_score": "1",
"_source": {
"query": {
"ql": "@title bag"
}
},
"fields": {
"_percolator_document_slot": [
1
]
}
}
]
}
}Array
(
[took] => 0
[timed_out] =>
[hits] => Array
(
[total] => 1
[max_score] => 1
[hits] => Array
(
[0] => Array
(
[_index] => products
[_type] => doc
[_id] => 1657852401006149644
[_score] => 1
[_source] => Array
(
[query] => Array
(
[match] => Array
(
[title] => bag
)
)
)
[fields] => Array
(
[_percolator_document_slot] => Array
(
[0] => 1
)
)
)
)
)
){'hits': {'hits': [{u'_id': u'2811025403043381480',
u'table': u'products',
u'_score': u'1',
u'_source': {u'query': {u'ql': u'@title bag'}},
u'_type': u'doc',
u'fields': {u'_percolator_document_slot': [1]}}],
'total': 1},
'profile': None,
'timed_out': False,
'took': 0}{'hits': {'hits': [{u'_id': u'2811025403043381480',
u'table': u'products',
u'_score': u'1',
u'_source': {u'query': {u'ql': u'@title bag'}},
u'_type': u'doc',
u'fields': {u'_percolator_document_slot': [1]}}],
'total': 1},
'profile': None,
'timed_out': False,
'took': 0}{
"took": 0,
"timed_out": false,
"hits": {
"total": 1,
"hits": [
{
"table": "products",
"_type": "doc",
"_id": 2811045522851233808,
"_score": "1",
"_source": {
"query": {
"ql": "@title bag"
}
},
"fields": {
"_percolator_document_slot": [
1
]
}
}
]
}
}class SearchResponse {
took: 0
timedOut: false
hits: class SearchResponseHits {
total: 1
maxScore: 1
hits: [{_index=products, _type=doc, _id=2811045522851234109, _score=1, _source={query={ql=@title bag}}, fields={_percolator_document_slot=[1]}}]
aggregations: null
}
profile: null
}class SearchResponse {
took: 0
timedOut: false
hits: class SearchResponseHits {
total: 1
maxScore: 1
hits: [{_index=products, _type=doc, _id=2811045522851234109, _score=1, _source={query={ql=@title bag}}, fields={_percolator_document_slot=[1]}}]
aggregations: null
}
profile: null
}class SearchResponse {
took: 0
timedOut: false
hits: class SearchResponseHits {
total: 1
maxScore: 1
hits: [{_index=products, _type=doc, _id=2811045522851234109, _score=1, _source={query={ql=@title bag}}, fields={_percolator_document_slot=[1]}}]
aggregations: null
}
profile: null
}{
"took": 0,
"timed_out": false,
"hits": {
"total": 1,
"hits": [
{
"table": "test_pq",
"_type": "doc",
"_id": 1657852401006149661,
"_score": "1",
"_source": {
"query": {
"ql": "@title bag"
}
},
"fields": {
"_percolator_document_slot": [
1
]
}
}
]
}
}{
"took": 0,
"timed_out": false,
"hits": {
"total": 1,
"hits": [
{
"table": "test_pq",
"_type": "doc",
"_id": 1657852401006149661,
"_score": "1",
"_source": {
"query": {
"ql": "@title bag"
}
},
"fields": {
"_percolator_document_slot": [
1
]
}
}
]
}
}请注意,使用 CALL PQ 时,你可以通过不同方式提供多个文档:
- 作为普通文档数组,使用圆括号如
('doc1', 'doc2')。这需要0 as docs_json - 作为 JSON 数组,使用圆括号如
('{doc1}', '{doc2}') - 或者作为标准 JSON 数组
'[{doc1}, {doc2}]'
- SQL
- JSON
- PHP
- Python
- Python-asyncio
- javascript
- Java
- C#
- Rust
- TypeScript
- Go
CALL PQ('products', ('nice pair of shoes', 'beautiful bag'), 1 as query, 0 as docs_json);
CALL PQ('products', ('{"title": "nice pair of shoes", "color": "red"}', '{"title": "beautiful bag"}'), 1 as query);
CALL PQ('products', '[{"title": "nice pair of shoes", "color": "blue"}, {"title": "beautiful bag"}]', 1 as query);POST /pq/products/search
{
"query": {
"percolate": {
"documents": [
{"title": "nice pair of shoes", "color": "blue"},
{"title": "beautiful bag"}
]
}
}
}$percolate = [
'table' => 'products',
'body' => [
'query' => [
'percolate' => [
'documents' => [
['title' => 'nice pair of shoes','color'=>'blue'],
['title' => 'beautiful bag']
]
]
]
]
];
$client->pq()->search($percolate);searchApi.percolate('products',{"query":{"percolate":{"documents":[{"title":"nice pair of shoes","color":"blue"},{"title":"beautiful bag"}]}}})await searchApi.percolate('products',{"query":{"percolate":{"documents":[{"title":"nice pair of shoes","color":"blue"},{"title":"beautiful bag"}]}}})res = await searchApi.percolate('products',{"query":{"percolate":{"documents":[{"title":"nice pair of shoes","color":"blue"},{"title":"beautiful bag"}]}}});percolateRequest = new PercolateRequest();
query = new HashMap<String,Object>(){{
put("percolate",new HashMap<String,Object >(){{
put("documents", new ArrayList<Object>(){{
add(new HashMap<String,Object >(){{
put("title","nice pair of shoes");
put("color","blue");
}});
add(new HashMap<String,Object >(){{
put("title","beautiful bag");
}});
}});
}});
}};
percolateRequest.query(query);
searchApi.percolate("products",percolateRequest);var doc1 = new Dictionary<string, Object>();
doc1.Add("title","nice pair of shoes");
doc1.Add("color","blue");
var doc2 = new Dictionary<string, Object>();
doc2.Add("title","beautiful bag");
var docs = new List<Object> {doc1, doc2};
Dictionary<string, Object> percolateDoc = new Dictionary<string, Object> {{ "documents", docs }};
Dictionary<string, Object> query = new Dictionary<string, Object> {{ "percolate", percolateDoc }};
PercolateRequest percolateRequest = new PercolateRequest(query=query);
searchApi.Percolate("products",percolateRequest);let mut percolate_doc_fields1 = HashMap::new();
percolate_doc_fields1.insert("title".to_string(), "nice pair of shoes");
percolate_doc_fields1.insert("color".to_string(), "blue");
let mut percolate_doc_fields2 = HashMap::new();
percolate_doc_fields2.insert("title".to_string(), "beautiful bag");
let mut percolate_doc_fields_list: [HashMap; 2] = [percolate_doc_fields1, percolate_doc_fields2];
let mut percolate_doc = HashMap::new();
percolate_doc.insert("documents".to_string(), percolate_doc_fields_list);
let percolate_query = PercolateRequestQuery::new(serde_json::json!(percolate_doc));
let percolate_req = PercolateRequest::new(percolate_query);
search_api.percolate("products", percolate_req).await;docs = [ {title : 'What a nice bag'}, {title : 'Really nice shoes'} ];
res = await searchApi.percolate('test_pq', { query: { percolate: { documents : docs } } } );doc1 := map[string]interface{} {"title": "What a nice bag"}
doc2 := map[string]interface{} {"title": "Really nice shoes"}
query := []interface{} {doc1, doc2}
percolateRequestQuery := manticoreclient.NewPercolateQuery(query)
percolateRequest := manticoreclient.NewPercolateRequest(percolateRequestQuery)
res, _, _ := apiClient.SearchAPI.Percolate(context.Background(), "test_pq").PercolateRequest(*percolateRequest).Execute()+---------------------+------------+------+---------+
| id | query | tags | filters |
+---------------------+------------+------+---------+
| 1657852401006149637 | @title bag | | |
+---------------------+------------+------+---------+
+---------------------+--------------+------+-------------+
| id | query | tags | filters |
+---------------------+--------------+------+-------------+
| 1657852401006149636 | @title shoes | | color='red' |
| 1657852401006149637 | @title bag | | |
+---------------------+--------------+------+-------------+
+---------------------+--------------+------+---------------------------+
| id | query | tags | filters |
+---------------------+--------------+------+---------------------------+
| 1657852401006149635 | @title shoes | | color IN ('blue, 'green') |
| 1657852401006149637 | @title bag | | |
+---------------------+--------------+------+---------------------------+{
"took": 0,
"timed_out": false,
"hits": {
"total": 2,
"max_score": 1,
"hits": [
{
"table": "products",
"_type": "doc",
"_id": 1657852401006149644,
"_score": "1",
"_source": {
"query": {
"ql": "@title bag"
}
},
"fields": {
"_percolator_document_slot": [
2
]
}
},
{
"table": "products",
"_type": "doc",
"_id": 1657852401006149646,
"_score": "1",
"_source": {
"query": {
"ql": "@title shoes"
}
},
"fields": {
"_percolator_document_slot": [
1
]
}
}
]
}
}Array
(
[took] => 23
[timed_out] =>
[hits] => Array
(
[total] => 2
[max_score] => 1
[hits] => Array
(
[0] => Array
(
[_index] => products
[_type] => doc
[_id] => 2810781492890828819
[_score] => 1
[_source] => Array
(
[query] => Array
(
[match] => Array
(
[title] => bag
)
)
)
[fields] => Array
(
[_percolator_document_slot] => Array
(
[0] => 2
)
)
)
[1] => Array
(
[_index] => products
[_type] => doc
[_id] => 2810781492890828821
[_score] => 1
[_source] => Array
(
[query] => Array
(
[match] => Array
(
[title] => shoes
)
)
)
[fields] => Array
(
[_percolator_document_slot] => Array
(
[0] => 1
)
)
)
)
)
){'hits': {'hits': [{u'_id': u'2811025403043381494',
u'table': u'products',
u'_score': u'1',
u'_source': {u'query': {u'ql': u'@title bag'}},
u'_type': u'doc',
u'fields': {u'_percolator_document_slot': [2]}},
{u'_id': u'2811025403043381496',
u'table': u'products',
u'_score': u'1',
u'_source': {u'query': {u'ql': u'@title shoes'}},
u'_type': u'doc',
u'fields': {u'_percolator_document_slot': [1]}}],
'total': 2},
'profile': None,
'timed_out': False,
'took': 0}{'hits': {'hits': [{u'_id': u'2811025403043381494',
u'table': u'products',
u'_score': u'1',
u'_source': {u'query': {u'ql': u'@title bag'}},
u'_type': u'doc',
u'fields': {u'_percolator_document_slot': [2]}},
{u'_id': u'2811025403043381496',
u'table': u'products',
u'_score': u'1',
u'_source': {u'query': {u'ql': u'@title shoes'}},
u'_type': u'doc',
u'fields': {u'_percolator_document_slot': [1]}}],
'total': 2},
'profile': None,
'timed_out': False,
'took': 0}{
"took": 6,
"timed_out": false,
"hits": {
"total": 2,
"hits": [
{
"table": "products",
"_type": "doc",
"_id": 2811045522851233808,
"_score": "1",
"_source": {
"query": {
"ql": "@title bag"
}
},
"fields": {
"_percolator_document_slot": [
2
]
}
},
{
"table": "products",
"_type": "doc",
"_id": 2811045522851233810,
"_score": "1",
"_source": {
"query": {
"ql": "@title shoes"
}
},
"fields": {
"_percolator_document_slot": [
1
]
}
}
]
}
}class SearchResponse {
took: 0
timedOut: false
hits: class SearchResponseHits {
total: 2
maxScore: 1
hits: [{_index=products, _type=doc, _id=2811045522851234133, _score=1, _source={query={ql=@title bag}}, fields={_percolator_document_slot=[2]}}, {_index=products, _type=doc, _id=2811045522851234135, _score=1, _source={query={ql=@title shoes}}, fields={_percolator_document_slot=[1]}}]
aggregations: null
}
profile: null
}class SearchResponse {
took: 0
timedOut: false
hits: class SearchResponseHits {
total: 2
maxScore: 1
hits: [{_index=products, _type=doc, _id=2811045522851234133, _score=1, _source={query={ql=@title bag}}, fields={_percolator_document_slot=[2]}}, {_index=products, _type=doc, _id=2811045522851234135, _score=1, _source={query={ql=@title shoes}}, fields={_percolator_document_slot=[1]}}]
aggregations: null
}
profile: null
}class SearchResponse {
took: 0
timedOut: false
hits: class SearchResponseHits {
total: 2
maxScore: 1
hits: [{_index=products, _type=doc, _id=2811045522851234133, _score=1, _source={query={ql=@title bag}}, fields={_percolator_document_slot=[2]}}, {_index=products, _type=doc, _id=2811045522851234135, _score=1, _source={query={ql=@title shoes}}, fields={_percolator_document_slot=[1]}}]
aggregations: null
}
profile: null
}{
"took": 0,
"timed_out": false,
"hits": {
"total": 2,
"hits": [
{
"table": "test_pq",
"_type": "doc",
"_id": 1657852401006149661,
"_score": "1",
"_source": {
"query": {
"ql": "@title bag"
}
},
"fields": {
"_percolator_document_slot": [
1
]
}
},
{
"table": "test_pq",
"_type": "doc",
"_id": 1657852401006149662,
"_score": "1",
"_source": {
"query": {
"ql": "@title shoes"
}
},
"fields": {
"_percolator_document_slot": [
1
]
}
}
]
}
}{
"took": 0,
"timed_out": false,
"hits": {
"total": 2,
"hits": [
{
"table": "test_pq",
"_type": "doc",
"_id": 1657852401006149661,
"_score": "1",
"_source": {
"query": {
"ql": "@title bag"
}
},
"fields": {
"_percolator_document_slot": [
1
]
}
},
{
"table": "test_pq",
"_type": "doc",
"_id": 1657852401006149662,
"_score": "1",
"_source": {
"query": {
"ql": "@title shoes"
}
},
"fields": {
"_percolator_document_slot": [
1
]
}
}
]
}
}- SQL
- JSON
- PHP
- Python
- Python-asyncio
- javascript
- Java
- C#
- Rust
- TypeScript
- Go
CALL PQ('products', '[{"title": "nice pair of shoes", "color": "blue"}, {"title": "beautiful bag"}]', 1 as query, 1 as docs);POST /pq/products/search
{
"query": {
"percolate": {
"documents": [
{"title": "nice pair of shoes", "color": "blue"},
{"title": "beautiful bag"}
]
}
}
}$percolate = [
'table' => 'products',
'body' => [
'query' => [
'percolate' => [
'documents' => [
['title' => 'nice pair of shoes','color'=>'blue'],
['title' => 'beautiful bag']
]
]
]
]
];
$client->pq()->search($percolate);searchApi.percolate('products',{"query":{"percolate":{"documents":[{"title":"nice pair of shoes","color":"blue"},{"title":"beautiful bag"}]}}})await searchApi.percolate('products',{"query":{"percolate":{"documents":[{"title":"nice pair of shoes","color":"blue"},{"title":"beautiful bag"}]}}})res = await searchApi.percolate('products',{"query":{"percolate":{"documents":[{"title":"nice pair of shoes","color":"blue"},{"title":"beautiful bag"}]}}});percolateRequest = new PercolateRequest();
query = new HashMap<String,Object>(){{
put("percolate",new HashMap<String,Object >(){{
put("documents", new ArrayList<Object>(){{
add(new HashMap<String,Object >(){{
put("title","nice pair of shoes");
put("color","blue");
}});
add(new HashMap<String,Object >(){{
put("title","beautiful bag");
}});
}});
}});
}};
percolateRequest.query(query);
searchApi.percolate("products",percolateRequest);var doc1 = new Dictionary<string, Object>();
doc1.Add("title","nice pair of shoes");
doc1.Add("color","blue");
var doc2 = new Dictionary<string, Object>();
doc2.Add("title","beautiful bag");
var docs = new List<Object> {doc1, doc2};
Dictionary<string, Object> percolateDoc = new Dictionary<string, Object> {{ "documents", docs }};
Dictionary<string, Object> query = new Dictionary<string, Object> {{ "percolate", percolateDoc }};
PercolateRequest percolateRequest = new PercolateRequest(query=query);
searchApi.Percolate("products",percolateRequest);let mut percolate_doc_fields1 = HashMap::new();
percolate_doc_fields1.insert("title".to_string(), "nice pair of shoes");
percolate_doc_fields1.insert("color".to_string(), "blue");
let mut percolate_doc_fields2 = HashMap::new();
percolate_doc_fields2.insert("title".to_string(), "beautiful bag");
let mut percolate_doc_fields_list: [HashMap; 2] = [percolate_doc_fields1, percolate_doc_fields2];
let mut percolate_doc = HashMap::new();
percolate_doc.insert("documents".to_string(), percolate_doc_fields_list);
let percolate_query = PercolateRequestQuery::new(serde_json::json!(percolate_doc));
let percolate_req = PercolateRequest::new(percolate_query);
search_api.percolate("products", percolate_req).await;docs = [ {title : 'What a nice bag'}, {title : 'Really nice shoes'} ];
res = await searchApi.percolate('test_pq', { query: { percolate: { documents : docs } } } );doc1 := map[string]interface{} {"title": "What a nice bag"}
doc2 := map[string]interface{} {"title": "Really nice shoes"}
query := []interface{} {doc1, doc2}
percolateRequestQuery := manticoreclient.NewPercolateQuery(query)
percolateRequest := manticoreclient.NewPercolateRequest(percolateRequestQuery)
res, _, _ := apiClient.SearchAPI.Percolate(context.Background(), "test_pq").PercolateRequest(*percolateRequest).Execute()+---------------------+-----------+--------------+------+---------------------------+
| id | documents | query | tags | filters |
+---------------------+-----------+--------------+------+---------------------------+
| 1657852401006149635 | 1 | @title shoes | | color IN ('blue, 'green') |
| 1657852401006149637 | 2 | @title bag | | |
+---------------------+-----------+--------------+------+---------------------------+{
"took": 0,
"timed_out": false,
"hits": {
"total": 2,
"max_score": 1,
"hits": [
{
"table": "products",
"_type": "doc",
"_id": 1657852401006149644,
"_score": "1",
"_source": {
"query": {
"ql": "@title bag"
}
},
"fields": {
"_percolator_document_slot": [
2
]
}
},
{
"table": "products",
"_type": "doc",
"_id": 1657852401006149646,
"_score": "1",
"_source": {
"query": {
"ql": "@title shoes"
}
},
"fields": {
"_percolator_document_slot": [
1
]
}
}
]
}
}Array
(
[took] => 23
[timed_out] =>
[hits] => Array
(
[total] => 2
[max_score] => 1
[hits] => Array
(
[0] => Array
(
[_index] => products
[_type] => doc
[_id] => 2810781492890828819
[_score] => 1
[_source] => Array
(
[query] => Array
(
[match] => Array
(
[title] => bag
)
)
)
[fields] => Array
(
[_percolator_document_slot] => Array
(
[0] => 2
)
)
)
[1] => Array
(
[_index] => products
[_type] => doc
[_id] => 2810781492890828821
[_score] => 1
[_source] => Array
(
[query] => Array
(
[match] => Array
(
[title] => shoes
)
)
)
[fields] => Array
(
[_percolator_document_slot] => Array
(
[0] => 1
)
)
)
)
)
){'hits': {'hits': [{u'_id': u'2811025403043381494',
u'table': u'products',
u'_score': u'1',
u'_source': {u'query': {u'ql': u'@title bag'}},
u'_type': u'doc',
u'fields': {u'_percolator_document_slot': [2]}},
{u'_id': u'2811025403043381496',
u'table': u'products',
u'_score': u'1',
u'_source': {u'query': {u'ql': u'@title shoes'}},
u'_type': u'doc',
u'fields': {u'_percolator_document_slot': [1]}}],
'total': 2},
'profile': None,
'timed_out': False,
'took': 0}{'hits': {'hits': [{u'_id': u'2811025403043381494',
u'table': u'products',
u'_score': u'1',
u'_source': {u'query': {u'ql': u'@title bag'}},
u'_type': u'doc',
u'fields': {u'_percolator_document_slot': [2]}},
{u'_id': u'2811025403043381496',
u'table': u'products',
u'_score': u'1',
u'_source': {u'query': {u'ql': u'@title shoes'}},
u'_type': u'doc',
u'fields': {u'_percolator_document_slot': [1]}}],
'total': 2},
'profile': None,
'timed_out': False,
'took': 0}{
"took": 6,
"timed_out": false,
"hits": {
"total": 2,
"hits": [
{
"table": "products",
"_type": "doc",
"_id": 2811045522851233808,
"_score": "1",
"_source": {
"query": {
"ql": "@title bag"
}
},
"fields": {
"_percolator_document_slot": [
2
]
}
},
{
"table": "products",
"_type": "doc",
"_id": 2811045522851233810,
"_score": "1",
"_source": {
"query": {
"ql": "@title shoes"
}
},
"fields": {
"_percolator_document_slot": [
1
]
}
}
]
}
}class SearchResponse {
took: 0
timedOut: false
hits: class SearchResponseHits {
total: 2
maxScore: 1
hits: [{_index=products, _type=doc, _id=2811045522851234133, _score=1, _source={query={ql=@title bag}}, fields={_percolator_document_slot=[2]}}, {_index=products, _type=doc, _id=2811045522851234135, _score=1, _source={query={ql=@title shoes}}, fields={_percolator_document_slot=[1]}}]
aggregations: null
}
profile: null
}class SearchResponse {
took: 0
timedOut: false
hits: class SearchResponseHits {
total: 2
maxScore: 1
hits: [{_index=products, _type=doc, _id=2811045522851234133, _score=1, _source={query={ql=@title bag}}, fields={_percolator_document_slot=[2]}}, {_index=products, _type=doc, _id=2811045522851234135, _score=1, _source={query={ql=@title shoes}}, fields={_percolator_document_slot=[1]}}]
aggregations: null
}
profile: null
}class SearchResponse {
took: 0
timedOut: false
hits: class SearchResponseHits {
total: 2
maxScore: 1
hits: [{_index=products, _type=doc, _id=2811045522851234133, _score=1, _source={query={ql=@title bag}}, fields={_percolator_document_slot=[2]}}, {_index=products, _type=doc, _id=2811045522851234135, _score=1, _source={query={ql=@title shoes}}, fields={_percolator_document_slot=[1]}}]
aggregations: null
}
profile: null
}{
"took": 0,
"timed_out": false,
"hits": {
"total": 2,
"hits": [
{
"table": "test_pq",
"_type": "doc",
"_id": 1657852401006149661,
"_score": "1",
"_source": {
"query": {
"ql": "@title bag"
}
},
"fields": {
"_percolator_document_slot": [
1
]
}
},
{
"table": "test_pq",
"_type": "doc",
"_id": 1657852401006149662,
"_score": "1",
"_source": {
"query": {
"ql": "@title shoes"
}
},
"fields": {
"_percolator_document_slot": [
1
]
}
}
]
}
}{
"took": 0,
"timed_out": false,
"hits": {
"total": 2,
"hits": [
{
"table": "test_pq",
"_type": "doc",
"_id": 1657852401006149661,
"_score": "1",
"_source": {
"query": {
"ql": "@title bag"
}
},
"fields": {
"_percolator_document_slot": [
1
]
}
},
{
"table": "test_pq",
"_type": "doc",
"_id": 1657852401006149662,
"_score": "1",
"_source": {
"query": {
"ql": "@title shoes"
}
},
"fields": {
"_percolator_document_slot": [
1
]
}
}
]
}
}默认情况下,匹配文档的 id 对应于你提供列表中的相对编号。然而,在某些情况下,每个文档已经有它自己的 id。对于这种情况,CALL PQ 提供了选项 'id field name' as docs_id。
请注意,如果通过提供的字段名找不到 id,结果中将不会显示该 PQ 规则。
此选项仅适用于通过 SQL 调用 CALL PQ。
- SQL
CALL PQ('products', '[{"id": 123, "title": "nice pair of shoes", "color": "blue"}, {"id": 456, "title": "beautiful bag"}]', 1 as query, 'id' as docs_id, 1 as docs);+---------------------+-----------+--------------+------+---------------------------+
| id | documents | query | tags | filters |
+---------------------+-----------+--------------+------+---------------------------+
| 1657852401006149664 | 456 | @title bag | | |
| 1657852401006149666 | 123 | @title shoes | | color IN ('blue, 'green') |
+---------------------+-----------+--------------+------+---------------------------+当使用 CALL PQ 处理多个单独的 JSON 时,可以使用选项 1 as skip_bad_json 来跳过输入中的任何无效 JSON。下面的例子中,第 2 个查询因无效 JSON 而失败,但第 3 个查询通过使用 1 as skip_bad_json 避免了错误。请记住,当通过 HTTP 发送 JSON 查询时,这个选项不可用,因为此时整个 JSON 查询必须是有效的。
- SQL
CALL PQ('products', ('{"title": "nice pair of shoes", "color": "blue"}', '{"title": "beautiful bag"}'));
CALL PQ('products', ('{"title": "nice pair of shoes", "color": "blue"}', '{"title": "beautiful bag}'));
CALL PQ('products', ('{"title": "nice pair of shoes", "color": "blue"}', '{"title": "beautiful bag}'), 1 as skip_bad_json);+---------------------+
| id |
+---------------------+
| 1657852401006149635 |
| 1657852401006149637 |
+---------------------+
ERROR 1064 (42000): Bad JSON objects in strings: 2
+---------------------+
| id |
+---------------------+
| 1657852401006149635 |
+---------------------+渗透查询的设计初衷是处理高吞吐量和大数据量。为了优化性能以实现更低的延迟和更高的吞吐量,请考虑以下方面。
渗透表的分布有两种模式,渗透查询可以针对这些模式工作:
- 稀疏模式(默认)。 适用于:文档数量多,镜像渗透表。当你的文档集很大,但存储在渗透表中的查询集很小时,稀疏模式是有益的。在这种模式下,你传递的文档批次将被分配到多个代理中,因此每个节点只处理你请求中的一部分文档。Manticore 会分割你的文档集,并将块分配给各个镜像。一旦代理处理完查询,Manticore 会收集并合并结果,返回一个最终的查询集,就像它来自单个表一样。使用复制来辅助此过程。
- 分片模式。 适用于:渗透规则数量多,规则分散在多个渗透表中。在这种模式下,整个文档集会被广播到分布式渗透表的所有表中,而不会初始分割文档。当推送相对较小的文档集,但存储的查询数量很大时,这种模式是有益的。在这种情况下,更合适的是在每个节点上只存储一部分渗透规则,然后合并从处理相同文档集但针对不同渗透规则集的节点返回的结果。这种模式必须显式设置,因为它意味着网络负载的增加,并且期望表具有不同的渗透规则集,而复制无法直接实现这一点。
假设你有一个表 pq_d2 定义如下:
table pq_d2
{
type = distributed
agent = 127.0.0.1:6712:pq
agent = 127.0.0.1:6712:ptitle
}
'pq' 和 'ptitle' 各自包含:
- SQL
- JSON
- PHP
- Python
- Python-asyncio
- javascript
- Java
- C#
- Rust
- TypeScript
- Go
SELECT * FROM pq;POST /pq/pq/search$params = [
'table' => 'pq',
'body' => [
]
];
$response = $client->pq()->search($params);searchApi.search({"table":"pq","query":{"match_all":{}}})await searchApi.search({"table":"pq","query":{"match_all":{}}})res = await searchApi.search({"table":"pq","query":{"match_all":{}}});Map<String,Object> query = new HashMap<String,Object>();
query.put("match_all",null);
SearchRequest searchRequest = new SearchRequest();
searchRequest.setIndex("pq");
searchRequest.setQuery(query);
SearchResponse searchResponse = searchApi.search(searchRequest);object query = new { match_all=null };
SearchRequest searchRequest = new SearchRequest("pq", query);
SearchResponse searchResponse = searchApi.Search(searchRequest);let query = SearchQuery::new();
let search_req = SearchRequest {
table: "pq".to_string(),
query: Some(Box::new(query)),
..Default::default(),
};
let search_res = search_api.search(search_req).await;res = await searchApi.search({"table":"test_pq","query":{"match_all":{}}});query := map[string]interface{} {}
percolateRequestQuery := manticoreclient.NewPercolateRequestQuery(query)
percolateRequest := manticoreclient.NewPercolateRequest(percolateRequestQuery)
res, _, _ := apiClient.SearchAPI.Percolate(context.Background(), "test_pq").PercolateRequest(*percolateRequest).Execute()+------+-------------+------+-------------------+
| id | query | tags | filters |
+------+-------------+------+-------------------+
| 1 | filter test | | gid>=10 |
| 2 | angry | | gid>=10 OR gid<=3 |
+------+-------------+------+-------------------+
2 rows in set (0.01 sec){
"took":0,
"timed_out":false,
"hits":{
"total":2,
"hits":[
{
"_id": 1,
"_score":1,
"_source":{
"query":{ "ql":"filter test" },
"tags":"",
"filters":"gid>=10"
}
},
{
"_id": 2,
"_score":1,
"_source":{
"query":{"ql":"angry"},
"tags":"",
"filters":"gid>=10 OR gid<=3"
}
}
]
}
}(
[took] => 0
[timed_out] =>
[hits] =>
(
[total] => 2
[hits] =>
(
[0] =>
(
[_id] => 1
[_score] => 1
[_source] =>
(
[query] =>
(
[ql] => filter test
)
[tags] =>
[filters] => gid>=10
)
),
[1] =>
(
[_id] => 1
[_score] => 1
[_source] =>
(
[query] =>
(
[ql] => angry
)
[tags] =>
[filters] => gid>=10 OR gid<=3
)
)
)
)
){'hits': {'hits': [{u'_id': u'2811025403043381501',
u'_score': 1,
u'_source': {u'filters': u"gid>=10",
u'query': u'filter test',
u'tags': u''}},
{u'_id': u'2811025403043381502',
u'_score': 1,
u'_source': {u'filters': u"gid>=10 OR gid<=3",
u'query': u'angry',
u'tags': u''}}],
'total': 2},
'profile': None,
'timed_out': False,
'took': 0}{'hits': {'hits': [{u'_id': u'2811025403043381501',
u'_score': 1,
u'_source': {u'filters': u"gid>=10",
u'query': u'filter test',
u'tags': u''}},
{u'_id': u'2811025403043381502',
u'_score': 1,
u'_source': {u'filters': u"gid>=10 OR gid<=3",
u'query': u'angry',
u'tags': u''}}],
'total': 2},
'profile': None,
'timed_out': False,
'took': 0}{"hits": {"hits": [{"_id": 2811025403043381501,
"_score": 1,
"_source": {"filters": u"gid>=10",
"query": "filter test",
"tags": ""}},
{"_id": 2811025403043381502,
"_score": 1,
"_source": {"filters": u"gid>=10 OR gid<=3",
"query": "angry",
"tags": ""}}],
"total": 2},
"timed_out": false,
"took": 0}class SearchResponse {
took: 0
timedOut: false
hits: class SearchResponseHits {
total: 2
maxScore: null
hits: [{_id=2811045522851233962, _score=1, _source={filters=gid>=10, query=filter test, tags=}}, {_id=2811045522851233951, _score=1, _source={filters=gid>=10 OR gid<=3, query=angry,tags=}}]
aggregations: null
}
profile: null
}class SearchResponse {
took: 0
timedOut: false
hits: class SearchResponseHits {
total: 2
maxScore: null
hits: [{_id=2811045522851233962, _score=1, _source={filters=gid>=10, query=filter test, tags=}}, {_id=2811045522851233951, _score=1, _source={filters=gid>=10 OR gid<=3, query=angry,tags=}}]
aggregations: null
}
profile: null
}class SearchResponse {
took: 0
timedOut: false
hits: class SearchResponseHits {
total: 2
maxScore: null
hits: [{_id=2811045522851233962, _score=1, _source={filters=gid>=10, query=filter test, tags=}}, {_id=2811045522851233951, _score=1, _source={filters=gid>=10 OR gid<=3, query=angry,tags=}}]
aggregations: null
}
profile: null
}{
'hits':
{
'hits':
[{
'_id': '2811025403043381501',
'_score': 1,
'_source':
{
'filters': "gid>=10",
'query': 'filter test',
'tags': ''
}
},
{
'_id':
'2811025403043381502',
'_score': 1,
'_source':
{
'filters': "gid>=10 OR gid<=3",
'query': 'angry',
'tags': ''
}
}],
'total': 2
},
'profile': None,
'timed_out': False,
'took': 0
}{
'hits':
{
'hits':
[{
'_id': '2811025403043381501',
'_score': 1,
'_source':
{
'filters': "gid>=10",
'query': 'filter test',
'tags': ''
}
},
{
'_id':
'2811025403043381502',
'_score': 1,
'_source':
{
'filters': "gid>=10 OR gid<=3",
'query': 'angry',
'tags': ''
}
}],
'total': 2
},
'profile': None,
'timed_out': False,
'took': 0
}并且你在分布式表上执行 CALL PQ,并传入几个文档。
- SQL
- JSON
- PHP
- Python
- Python-asyncio
- javascript
- Java
- C#
- Rust
- TypeScript
- Go
CALL PQ ('pq_d2', ('{"title":"angry test", "gid":3 }', '{"title":"filter test doc2", "gid":13}'), 1 AS docs);POST /pq/pq/search -d '
"query":
{
"percolate":
{
"documents" : [
{ "title": "angry test", "gid": 3 },
{ "title": "filter test doc2", "gid": 13 }
]
}
}
'$params = [
'table' => 'pq',
'body' => [
'query' => [
'percolate' => [
'documents' => [
[
'title'=>'angry test',
'gid' => 3
],
[
'title'=>'filter test doc2',
'gid' => 13
],
]
]
]
]
];
$response = $client->pq()->search($params);searchApi.percolate('pq',{"percolate":{"documents":[{"title":"angry test","gid":3},{"title":"filter test doc2","gid":13}]}})await searchApi.percolate('pq',{"percolate":{"documents":[{"title":"angry test","gid":3},{"title":"filter test doc2","gid":13}]}})res = await searchApi.percolate('pq',{"percolate":{"documents":[{"title":"angry test","gid":3},{"title":"filter test doc2","gid":13}]}});percolateRequest = new PercolateRequest();
query = new HashMap<String,Object>(){{
put("percolate",new HashMap<String,Object >(){{
put("documents", new ArrayList<Object>(){{
add(new HashMap<String,Object >(){{
put("title","angry test");
put("gid",3);
}});
add(new HashMap<String,Object >(){{
put("title","filter test doc2");
put("gid",13);
}});
}});
}});
}};
percolateRequest.query(query);
searchApi.percolate("pq",percolateRequest);var doc1 = new Dictionary<string, Object>();
doc1.Add("title","angry test");
doc1.Add("gid",3);
var doc2 = new Dictionary<string, Object>();
doc2.Add("title","filter test doc2");
doc2.Add("gid",13);
var docs = new List<Object> {doc1, doc2};
Dictionary<string, Object> percolateDoc = new Dictionary<string, Object> {{ "documents", docs }};
Dictionary<string, Object> query = new Dictionary<string, Object> {{ "percolate", percolateDoc }};
PercolateRequest percolateRequest = new PercolateRequest(query=query);
searchApi.Percolate("pq",percolateRequest);let mut percolate_doc_fields1 = HashMap::new();
percolate_doc_fields1.insert("title".to_string(), "angry test");
percolate_doc_fields1.insert("gid".to_string(), 3);
let mut percolate_doc_fields2 = HashMap::new();
percolate_doc_fields2.insert("title".to_string(), "filter test doc2");
percolate_doc_fields2.insert("gid".to_string(), 13);
let mut percolate_doc_fields_list: [HashMap; 2] = [percolate_doc_fields1, percolate_doc_fields2];
let mut percolate_doc = HashMap::new();
percolate_doc.insert("documents".to_string(), percolate_doc_fields_list);
let percolate_query = PercolateRequestQuery::new(serde_json::json!(percolate_doc));
let percolate_req = PercolateRequest::new(percolate_query);
search_api.percolate("pq", percolate_req).await;docs = [ {title : 'What a nice bag'}, {title : 'Really nice shoes'} ];
res = await searchApi.percolate('test_pq', { query: { percolate: { documents : docs } } } );doc1 := map[string]interface{} {"title": "What a nice bag"}
doc2 := map[string]interface{} {"title": "Really nice shoes"}
query := []interface{} {doc1, doc2}
percolateRequestQuery := manticoreclient.NewPercolateQuery(query)
percolateRequest := manticoreclient.NewPercolateRequest(percolateRequestQuery)
res, _, _ := apiClient.SearchAPI.Percolate(context.Background(), "test_pq").PercolateRequest(*percolateRequest).Execute()+------+-----------+
| id | documents |
+------+-----------+
| 1 | 2 |
| 2 | 1 |
+------+-----------+{
"took":0,
"timed_out":false,
"hits":{
"total":2,"hits":[
{
"_id": 2,
"_score":1,
"_source":{
"query":{"title":"angry"},
"tags":"",
"filters":"gid>=10 OR gid<=3"
}
}
{
"_id": 1,
"_score":1,
"_source":{
"query":{"ql":"filter test"},
"tags":"",
"filters":"gid>=10"
}
},
]
}
}(
[took] => 0
[timed_out] =>
[hits] =>
(
[total] => 2
[hits] =>
(
[0] =>
(
[_index] => pq
[_type] => doc
[_id] => 2
[_score] => 1
[_source] =>
(
[query] =>
(
[ql] => angry
)
[tags] =>
[filters] => gid>=10 OR gid<=3
),
[fields] =>
(
[_percolator_document_slot] =>
(
[0] => 1
)
)
),
[1] =>
(
[_index] => pq
[_id] => 1
[_score] => 1
[_source] =>
(
[query] =>
(
[ql] => filter test
)
[tags] =>
[filters] => gid>=10
)
[fields] =>
(
[_percolator_document_slot] =>
(
[0] => 0
)
)
)
)
)
){'hits': {'hits': [{u'_id': u'2811025403043381480',
u'table': u'pq',
u'_score': u'1',
u'_source': {u'query': {u'ql': u'angry'},u'tags':u'',u'filters':u"gid>=10 OR gid<=3"},
u'_type': u'doc',
u'fields': {u'_percolator_document_slot': [1]}},
{u'_id': u'2811025403043381501',
u'table': u'pq',
u'_score': u'1',
u'_source': {u'query': {u'ql': u'filter test'},u'tags':u'',u'filters':u"gid>=10"},
u'_type': u'doc',
u'fields': {u'_percolator_document_slot': [1]}}],
'total': 2},
'profile': None,
'timed_out': False,
'took': 0}{'hits': {'hits': [{u'_id': u'2811025403043381480',
u'table': u'pq',
u'_score': u'1',
u'_source': {u'query': {u'ql': u'angry'},u'tags':u'',u'filters':u"gid>=10 OR gid<=3"},
u'_type': u'doc',
u'fields': {u'_percolator_document_slot': [1]}},
{u'_id': u'2811025403043381501',
u'table': u'pq',
u'_score': u'1',
u'_source': {u'query': {u'ql': u'filter test'},u'tags':u'',u'filters':u"gid>=10"},
u'_type': u'doc',
u'fields': {u'_percolator_document_slot': [1]}}],
'total': 2},
'profile': None,
'timed_out': False,
'took': 0}{'hits': {'hits': [{u'_id': u'2811025403043381480',
u'table': u'pq',
u'_score': u'1',
u'_source': {u'query': {u'ql': u'angry'},u'tags':u'',u'filters':u"gid>=10 OR gid<=3"},
u'_type': u'doc',
u'fields': {u'_percolator_document_slot': [1]}},
{u'_id': u'2811025403043381501',
u'table': u'pq',
u'_score': u'1',
u'_source': {u'query': {u'ql': u'filter test'},u'tags':u'',u'filters':u"gid>=10"},
u'_type': u'doc',
u'fields': {u'_percolator_document_slot': [1]}}],
'total': 2},
'profile': None,
'timed_out': False,
'took': 0}class SearchResponse {
took: 10
timedOut: false
hits: class SearchResponseHits {
total: 2
maxScore: 1
hits: [{_index=pq, _type=doc, _id=2811045522851234165, _score=1, _source={query={ql=@title angry}}, fields={_percolator_document_slot=[1]}}, {_index=pq, _type=doc, _id=2811045522851234166, _score=1, _source={query={ql=@title filter test doc2}}, fields={_percolator_document_slot=[2]}}]
aggregations: null
}
profile: null
}class SearchResponse {
took: 10
timedOut: false
hits: class SearchResponseHits {
total: 2
maxScore: 1
hits: [{_index=pq, _type=doc, _id=2811045522851234165, _score=1, _source={query={ql=@title angry}}, fields={_percolator_document_slot=[1]}}, {_index=pq, _type=doc, _id=2811045522851234166, _score=1, _source={query={ql=@title filter test doc2}}, fields={_percolator_document_slot=[2]}}]
aggregations: null
}
profile: null
}class SearchResponse {
took: 10
timedOut: false
hits: class SearchResponseHits {
total: 2
maxScore: 1
hits: [{_index=pq, _type=doc, _id=2811045522851234165, _score=1, _source={query={ql=@title angry}}, fields={_percolator_document_slot=[1]}}, {_index=pq, _type=doc, _id=2811045522851234166, _score=1, _source={query={ql=@title filter test doc2}}, fields={_percolator_document_slot=[2]}}]
aggregations: null
}
profile: null
}{
"took": 0,
"timed_out": false,
"hits": {
"total": 2,
"hits": [
{
"table": "test_pq",
"_type": "doc",
"_id": 1657852401006149661,
"_score": "1",
"_source": {
"query": {
"ql": "@title bag"
}
},
"fields": {
"_percolator_document_slot": [
1
]
}
},
{
"table": "test_pq",
"_type": "doc",
"_id": 1657852401006149662,
"_score": "1",
"_source": {
"query": {
"ql": "@title shoes"
}
},
"fields": {
"_percolator_document_slot": [
1
]
}
}
]
}
}{
"took": 0,
"timed_out": false,
"hits": {
"total": 2,
"hits": [
{
"table": "test_pq",
"_type": "doc",
"_id": 1657852401006149661,
"_score": "1",
"_source": {
"query": {
"ql": "@title bag"
}
},
"fields": {
"_percolator_document_slot": [
1
]
}
},
{
"table": "test_pq",
"_type": "doc",
"_id": 1657852401006149662,
"_score": "1",
"_source": {
"query": {
"ql": "@title shoes"
}
},
"fields": {
"_percolator_document_slot": [
1
]
}
}
]
}
}在前面的例子中,我们使用了默认的稀疏模式。为了演示分片模式,让我们创建一个由 2 个本地渗透表组成的分布式渗透表,并向 "products1" 添加 2 个文档,向 "products2" 添加 1 个文档:
create table products1(title text, color string) type='pq';
create table products2(title text, color string) type='pq';
create table products_distributed type='distributed' local='products1' local='products2';
INSERT INTO products1(query) values('@title bag');
INSERT INTO products1(query,filters) values('@title shoes', 'color=\'red\'');
INSERT INTO products2(query,filters) values('@title shoes', 'color in (\'blue\', \'green\')');
现在,如果你在 CALL PQ 中添加 'sharded' as mode,它会将文档发送到所有代理的表(在这个例子中,只是本地表,但它们也可以是远程的,以利用外部硬件)。这种模式不通过 JSON 接口提供。
- SQL
CALL PQ('products_distributed', ('{"title": "nice pair of shoes", "color": "blue"}', '{"title": "beautiful bag"}'), 'sharded' as mode, 1 as query);+---------------------+--------------+------+---------------------------+
| id | query | tags | filters |
+---------------------+--------------+------+---------------------------+
| 1657852401006149639 | @title bag | | |
| 1657852401006149643 | @title shoes | | color IN ('blue, 'green') |
+---------------------+--------------+------+---------------------------+请注意,配置中的代理镜像语法(当多个主机分配给一个 agent 行,用 | 分隔时)与 CALL PQ 查询模式无关。每个 agent 始终代表一个节点,无论为该代理指定了多少个 HA 镜像。
在某些情况下,你可能希望获取更多关于渗透查询性能的详细信息。为此,有一个选项 1 as verbose,它仅通过 SQL 可用,允许你保存更多的性能指标。你可以使用 SHOW META 查询来查看它们,该查询可以在 CALL PQ 之后运行。更多信息请参见 SHOW META。
- 1 as verbose
- 0 as verbose
CALL PQ('products', ('{"title": "nice pair of shoes", "color": "blue"}', '{"title": "beautiful bag"}'), 1 as verbose); show meta;CALL PQ('products', ('{"title": "nice pair of shoes", "color": "blue"}', '{"title": "beautiful bag"}'), 0 as verbose); show meta;+---------------------+
| id |
+---------------------+
| 1657852401006149644 |
| 1657852401006149646 |
+---------------------+
+-------------------------+-----------+
| Variable name | Value |
+-------------------------+-----------+
| total | 0.000 sec |
| setup | 0.000 sec |
| queries_matched | 2 |
| queries_failed | 0 |
| document_matched | 2 |
| total_queries_stored | 3 |
| term_only_queries | 3 |
| fast_rejected_queries | 0 |
| time_per_query | 27, 10 |
| time_of_matched_queries | 37 |
+-------------------------+-----------++---------------------+
| id |
+---------------------+
| 1657852401006149644 |
| 1657852401006149646 |
+---------------------+
+-----------------------+-----------+
| Variable name | Value |
+-----------------------+-----------+
| total | 0.000 sec |
| queries_matched | 2 |
| queries_failed | 0 |
| document_matched | 2 |
| total_queries_stored | 3 |
| term_only_queries | 3 |
| fast_rejected_queries | 0 |
+-----------------------+-----------+