Performance 弹性搜索速度问题

我们正在使用ElasticSearch搜索15万条记录。这些记录被分成不同的索引大小，其中一些索引有150万条记录

我们有足够的RAM 80 GB，60 GB的整个索引都适合RAM。根据ElasticSearch的响应时间统计，查询执行耗时7毫秒，但我们从ElasticSearch获得的结果耗时300毫秒。这里怎么了？我们可以在哪里寻找，我们的时间将走向何方

ES设置：

2 Nodes on 2 different hosts

Each index has 1 primary shard we have 2 shards each index 

3,762 Total Shards

3,762 Successful Shards

85 Indices

20,347,989 Documents

40.5GB Size

elasticsearch.yml

index.cache.field.type: soft

indices.cache.filter.size: 50%

index.fielddata.cache: soft

index.cache.field.expire: 60m

indices.fielddata.cache.size: 50%

indices.fielddata.cache.expire : 60m

index.store.type: mmapfs

transport.tcp.compress: true;

bootstrap.mlockall: true

index.search.slowlog.threshold.query.warn: 10s

index.search.slowlog.threshold.query.info: 5s

index.search.slowlog.threshold.query.debug: 2s

index.search.slowlog.threshold.query.trace: 500ms

例如：我们有一个国家DE的索引，有150万个文档。此索引有2个碎片

启动ES：

/usr/lib/jvm/java-7-openjdk-amd64//bin/java -Xms32g -Xmx32g -Xss256k -Djava.awt.headless=true -XX:+UseParNewGC -XX:+UseConcMarkSweepGC -XX:CMSInitiatingOccupancyFraction=75 -XX:+UseCMSInitiatingOccupancyOnly -XX:+HeapDumpOnOutOfMemoryError -Delasticsearch -Des.pidfile=/var/run/elasticsearch.pid -Des.path.home=/usr/share/elasticsearch -cp :/usr/share/elasticsearch/lib/elasticsearch-1.1.2.jar:/usr/share/elasticsearch/lib/*:/usr/share/elasticsearch/lib/sigar/* -Des.default.config=/etc/elasticsearch/elasticsearch.yml -Des.default.path.home=/usr/share/elasticsearch -Des.default.path.logs=/var/log/elasticsearch -Des.default.path.data=/var/lib/elasticsearch -Des.default.path.work=/tmp/elasticsearch -Des.default.path.conf=/etc/elasticsearch org.elasticsearch.bootstrap.Elasticsearch

操作系统：

示例查询：搜索某个城市，我们在其中向ES提供位置\u id:

{
    "query": {
        "match_all": {}
    },
    "sort": {},
    "facets": {
        "location_id": {
            "facet_filter": {
                "bool": {
                    "must": [{
                        "terms": {
                            "sponsored": [
                                1,
                                0
                            ]
                        }
                    }, {
                        "geo_distance": {
                            "distance": "50km",
                            "geo_point": {
                                "lat": -33.42628,
                                "lon": -70.56656
                            }
                        }
                    }]
                }
            },
            "terms": {
                "field": "location_facet",
                "all_terms": true,
                "size": 100,
                "script": "doc['geo_point'].empty ? null : ceil(doc['geo_point'].arcDistanceInKm(-33.42628,    -70.56656)) + '|' + doc['location_facet'].value\n + '|' + doc['location_id'].value"
            }
        },
        "company_id": {
            "facet_filter": {
                "bool": {
                    "must": [{
                        "terms": {
                            "sponsored": [
                                1,
                                0
                            ]
                        }
                    }, {
                        "geo_distance": {
                            "distance": "50km",
                            "geo_point": {
                                "lat": -33.42628,
                                "lon": -70.56656
                            }
                        }
                    }, {
                        "terms": {
                            "location_id": [
                                25717
                            ]
                        }
                    }]
                }
            },
            "terms": {
                "field": "company_facet",
                "order": "count",
                "script": "doc['company_facet'].value + '|' + doc['company_id'].value"
            }
        },
        "job_type_id": {
            "facet_filter": {
                "bool": {
                    "must": [{
                        "terms": {
                            "sponsored": [
                                1,
                                0
                            ]
                        }
                    }, {
                        "geo_distance": {
                            "distance": "50km",
                            "geo_point": {
                                "lat": -33.42628,
                                "lon": -70.56656
                            }
                        }
                    }]
                }
            },
            "terms": {
                "field": "jobtype_facet",
                "order": "term",
                "all_terms": true
            }
        }
    },
    "filter": {},
    "size": 10,
    "from": 0,
    "explain": false,
    "highlight": {
        "order": "score",
        "require_field_match": false,
        "pre_tags": [
            "<b>"
        ],
        "post_tags": [
            "</b>"
        ],
        "fields": {
            "description": {
                "type": "fvh",
                "force_source": true,
                "no_match_size": 200,
                "index_options": "offsets",
                "fragment_size": 200,
                "number_of_fragments": 2,
                "matched_fields": [
                    "description",
                    "title"
                ]
            }
        }
    }
}

{
“查询”：{
“全部匹配”：{}
},
“排序”：{}，
“方面”：{
“位置标识”：{
“面_过滤器”：{
“布尔”：{
“必须”：[{
“条款”：{
“赞助”：[
1.
0
]
}
}, {
“地理距离”：{
“距离”：“50公里”，
“地理点”：{
“lat”：-33.42628，
“lon”：-70.56656
}
}
}]
}
},
“条款”：{
“字段”：“位置”，
“所有术语”：正确，
“尺寸”：100，
“脚本”：“doc['geo_-point'].empty？null:ceil（doc['geo_-point'].arcdanceinkm（-33.42628，-70.56656））+'|'+doc['location_-facet'].value\n+'|'+doc['location_-id'].value”
}
},
“公司id”：{
“面_过滤器”：{
“布尔”：{
“必须”：[{
“条款”：{
“赞助”：[
1.
0
]
}
}, {
“地理距离”：{
“距离”：“50公里”，
“地理点”：{
“lat”：-33.42628，
“lon”：-70.56656
}
}
}, {
“条款”：{
“位置标识”：[
25717
]
}
}]
}
},
“条款”：{
“字段”：“公司”，
“订单”：“计数”，
“脚本”：“doc['company_facet'].value+'|'+doc['company_id'].value”
}
},
“作业类型\u id”：{
“面_过滤器”：{
“布尔”：{
“必须”：[{
“条款”：{
“赞助”：[
1.
0
]
}
}, {
“地理距离”：{
“距离”：“50公里”，
“地理点”：{
“lat”：-33.42628，
“lon”：-70.56656
}
}
}]
}
},
“条款”：{
“字段”：“作业类型”，
“订单”：“期限”，
“所有术语”：正确
}
}
},
“筛选器”：{}，
“尺寸”：10，
“from”：0，
“解释”：错误，
“亮点”：{
“顺序”：“得分”，
“要求字段匹配”：false，
“pre_标签”：[
""
],
“post_标签”：[
""
],
“字段”：{
“说明”：{
“类型”：“fvh”，
“力量来源”：没错，
“无匹配尺寸”：200，
“索引选项”：“偏移量”，
“碎片大小”：200，
“碎片的数量”：2，
“匹配的_字段”：[
“说明”，
“标题”
]
}
}
}
}

---

此查询的响应时间：>400ms，非常慢。我们也禁用了面，但没有任何更改。

对于单点，“geo_bounding_box”过滤器可能比“geo_distance”过滤器更快。

很难这样说。您应该检查以下内容：*网络延迟如何？*有效负载大，网络传输需要时间吗？*你在使用HTTP吗？尝试Thrift模块，因为您将能够利用TCP的较低开销和Thrift的压缩。了解更多有关集群的信息将很有帮助。有多少节点？有多少个碎片（主碎片和副本碎片）？你的JVM设置是什么？CPU和核心数？磁盘空间？磁盘类型？您是否在查询期间测量CPU和磁盘性能？您是同时插入的吗？你从哪里得到7毫秒和300毫秒的计时？@vaidik：谢谢你的快速响应！我们没有任何延迟问题。我们的服务器位于专用网络中。我还测试了来自外部服务器的请求，下载时间仅为9ms。返回文档的大小为47300字节。真正的问题来自ES，因为在响应中返回的是花费的时间。@John Petrone:谢谢您的快速响应。我会把你需要的资料寄给你。我认为这是ES中的一些设置问题，因为我们在文档中有不同大小的不同索引，但实际上我们使用相同数量的碎片=2。例如，一个索引有150万行。你认为2个碎片要少吗？一个多大的碎片

{
    "query": {
        "match_all": {}
    },
    "sort": {},
    "facets": {
        "location_id": {
            "facet_filter": {
                "bool": {
                    "must": [{
                        "terms": {
                            "sponsored": [
                                1,
                                0
                            ]
                        }
                    }, {
                        "geo_distance": {
                            "distance": "50km",
                            "geo_point": {
                                "lat": -33.42628,
                                "lon": -70.56656
                            }
                        }
                    }]
                }
            },
            "terms": {
                "field": "location_facet",
                "all_terms": true,
                "size": 100,
                "script": "doc['geo_point'].empty ? null : ceil(doc['geo_point'].arcDistanceInKm(-33.42628,    -70.56656)) + '|' + doc['location_facet'].value\n + '|' + doc['location_id'].value"
            }
        },
        "company_id": {
            "facet_filter": {
                "bool": {
                    "must": [{
                        "terms": {
                            "sponsored": [
                                1,
                                0
                            ]
                        }
                    }, {
                        "geo_distance": {
                            "distance": "50km",
                            "geo_point": {
                                "lat": -33.42628,
                                "lon": -70.56656
                            }
                        }
                    }, {
                        "terms": {
                            "location_id": [
                                25717
                            ]
                        }
                    }]
                }
            },
            "terms": {
                "field": "company_facet",
                "order": "count",
                "script": "doc['company_facet'].value + '|' + doc['company_id'].value"
            }
        },
        "job_type_id": {
            "facet_filter": {
                "bool": {
                    "must": [{
                        "terms": {
                            "sponsored": [
                                1,
                                0
                            ]
                        }
                    }, {
                        "geo_distance": {
                            "distance": "50km",
                            "geo_point": {
                                "lat": -33.42628,
                                "lon": -70.56656
                            }
                        }
                    }]
                }
            },
            "terms": {
                "field": "jobtype_facet",
                "order": "term",
                "all_terms": true
            }
        }
    },
    "filter": {},
    "size": 10,
    "from": 0,
    "explain": false,
    "highlight": {
        "order": "score",
        "require_field_match": false,
        "pre_tags": [
            "<b>"
        ],
        "post_tags": [
            "</b>"
        ],
        "fields": {
            "description": {
                "type": "fvh",
                "force_source": true,
                "no_match_size": 200,
                "index_options": "offsets",
                "fragment_size": 200,
                "number_of_fragments": 2,
                "matched_fields": [
                    "description",
                    "title"
                ]
            }
        }
    }
}