ES6.3.2 使用profile分析match查询发现Collector时间过长，如何优化？
Elasticsearch | 作者 hapjin | 发布于2019年03月13日 | 阅读数：4939
索引名称为user_v1，配置如下：
profile查询如下：
发现[user_v1][1]分片的 collector time长达7.9s
而[user_v1][4]分片的collector time 却只有几百毫秒
有一点可肯定的是：[user_v1][4]是SSD硬盘，而[user_v1][1]是机械硬盘。如果是硬盘的原因，那这个差距也太大了吧？
求问，Collector时间具体是指哪些操作的时间呢？还有其他原因导致 collector time相差如此之大的原因么？怎么解决呢？
关于Collector time：解释，有点不明白。
Collectors are the processes which are responsible for gathering raw results, combining them, filtering, sorting etc.
Each profile also contains a section about the Lucene Collectors which run the search
{
"user_v1": {
"settings": {
"index": {
"refresh_interval": "30s",
"number_of_shards": "5",
"provided_name": "user_v1",
//....
"number_of_replicas": "1"
}
}
}
}{
"user_v1": {
"mappings": {
"profile": {
"properties": {
"created": {
"type": "date",
"format": "yyyy-MM-dd HH:mm:ss"
},
"details": {
"type": "text",
"fields": {
"raw": {
"type": "keyword"
}
},
"analyzer": "hanlp_standard"
},
"nick": {
"type": "text",
"fields": {
"raw": {
"type": "keyword"
}
},
"analyzer": "hanlp_standard"
},
"signature": {
"type": "text",
"fields": {
"raw": {
"type": "keyword"
}
},
"analyzer": "hanlp_standard"
},
//.....profile查询如下：
发现[user_v1][1]分片的 collector time长达7.9s
{
"id": "[wx0dqdubRkiqJJ-juAqH4A][user_v1][1]",
"searches": [
{
"query": [
{
"type": "BooleanQuery",
"description": "nick:微信 nick: nick:黄色",
"time": "674.2ms",
"time_in_nanos": 674296440,
"breakdown": {
"score": 345336336,
"build_scorer_count": 41,
"match_count": 0,
"create_weight": 44350,
"next_doc": 318735380,
"match": 0,
"create_weight_count": 1,
"next_doc_count": 5003310,
"score_count": 4666082,
"build_scorer": 510940,
"advance": 0,
"advance_count": 0
},
"children": [
{
"type": "TermQuery",
"description": "nick:微信",
"time": "43ms",
"time_in_nanos": 43092821,
"breakdown": {
"score": 649883,
"build_scorer_count": 59,
"match_count": 0,
"create_weight": 17762,
"next_doc": 42284755,
"match": 0,
"create_weight_count": 1,
"next_doc_count": 7030,
"score_count": 5800,
"build_scorer": 127531,
"advance": 0,
"advance_count": 0
}
},
{
"type": "TermQuery",
"description": "nick: ",
"time": "391.2ms",
"time_in_nanos": 391264139,
"breakdown": {
"score": 197751347,
"build_scorer_count": 61,
"match_count": 0,
"create_weight": 4578,
"next_doc": 183813681,
"match": 0,
"create_weight_count": 1,
"next_doc_count": 4996728,
"score_count": 4660658,
"build_scorer": 37085,
"advance": 0,
"advance_count": 0
}
},
{
"type": "TermQuery",
"description": "nick:黄色",
"time": "117.4ms",
"time_in_nanos": 117471823,
"breakdown": {
"score": 149981,
"build_scorer_count": 39,
"match_count": 0,
"create_weight": 3613,
"next_doc": 117297171,
"match": 0,
"create_weight_count": 1,
"next_doc_count": 997,
"score_count": 771,
"build_scorer": 19250,
"advance": 0,
"advance_count": 0
}
}
]
}
],
"rewrite_time": 1363467792,
"collector": [
{
"name": "CancellableCollector",
"reason": "search_cancelled",
"time": "8.1s",
"time_in_nanos": 8115275034,
"children": [
{
"name": "SimpleTopScoreDocCollector",
"reason": "search_top_hits",
"time": "7.9s",
"time_in_nanos": 7918401119
}
]
}
]
}
],
"aggregations": []
},而[user_v1][4]分片的collector time 却只有几百毫秒
{
"id": "[yFnfouyXTvONUxhOmMp--A][user_v1][4]",
"searches": [
{
"query": [
{
"type": "BooleanQuery",
"description": "nick:微信 nick: nick:黄色",
"time": "870.6ms",
"time_in_nanos": 870672781,
"breakdown": {
"score": 571167502,
"build_scorer_count": 50,
"match_count": 0,
"create_weight": 76714,
"next_doc": 289187438,
"match": 0,
"create_weight_count": 1,
"next_doc_count": 5057254,
"score_count": 4667487,
"build_scorer": 516335,
"advance": 0,
"advance_count": 0
},
"children": [
{
"type": "TermQuery",
"description": "nick:微信",
"time": "1.4ms",
"time_in_nanos": 1472493,
"breakdown": {
"score": 942180,
"build_scorer_count": 70,
"match_count": 0,
"create_weight": 30118,
"next_doc": 306063,
"match": 0,
"create_weight_count": 1,
"next_doc_count": 7108,
"score_count": 5647,
"build_scorer": 181306,
"advance": 0,
"advance_count": 0
}
},
{
"type": "TermQuery",
"description": "nick: ",
"time": "590ms",
"time_in_nanos": 590014764,
"breakdown": {
"score": 398076512,
"build_scorer_count": 72,
"match_count": 0,
"create_weight": 9719,
"next_doc": 182139208,
"match": 0,
"create_weight_count": 1,
"next_doc_count": 5050613,
"score_count": 4662107,
"build_scorer": 76532,
"advance": 0,
"advance_count": 0
}
},
{
"type": "TermQuery",
"description": "nick:黄色",
"time": "277.2micros",
"time_in_nanos": 277285,
"breakdown": {
"score": 183306,
"build_scorer_count": 54,
"match_count": 0,
"create_weight": 7504,
"next_doc": 42644,
"match": 0,
"create_weight_count": 1,
"next_doc_count": 893,
"score_count": 774,
"build_scorer": 42109,
"advance": 0,
"advance_count": 0
}
}
]
}
],
"rewrite_time": 572186,
"collector": [
{
"name": "CancellableCollector",
"reason": "search_cancelled",
"time": "862.4ms",
"time_in_nanos": 862446475,
"children": [
{
"name": "SimpleTopScoreDocCollector",
"reason": "search_top_hits",
"time": "598.8ms",
"time_in_nanos": 598822927
}
]
}
]
}
],
"aggregations": []
}
]
}有一点可肯定的是：[user_v1][4]是SSD硬盘，而[user_v1][1]是机械硬盘。如果是硬盘的原因，那这个差距也太大了吧？
求问，Collector时间具体是指哪些操作的时间呢？还有其他原因导致 collector time相差如此之大的原因么？怎么解决呢？
关于Collector time：解释，有点不明白。
Collectors are the processes which are responsible for gathering raw results, combining them, filtering, sorting etc.
Each profile also contains a section about the Lucene Collectors which run the search
2 个回复
rochy - rochy_he
赞同来自:
Collector 中进行合并、过滤、排序以及获取数据内容的操作
上述 search_top_hits 也就是获取最后数据的耗时高达 600ms，有可能是 SSD 硬盘的缘故
zmc - ES PAAS、JuiceFS
赞同来自: