2519 lines
69 KiB
Markdown
2519 lines
69 KiB
Markdown
- [ElasticSearch](#elasticsearch)
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- [简介](#简介)
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- [用例场景](#用例场景)
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- [安装](#安装)
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- [add elasticsearch GPG keys](#add-elasticsearch-gpg-keys)
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- [Add Elasticsearch 8.x APT Repository](#add-elasticsearch-8x-apt-repository)
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- [install elastic search](#install-elastic-search)
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- [Indices, documents, and fields](#indices-documents-and-fields)
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- [Documents and fields](#documents-and-fields)
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- [metadata field](#metadata-field)
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- [Mappings and data types](#mappings-and-data-types)
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- [向ElasticSearch中添加数据](#向elasticsearch中添加数据)
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- [General content](#general-content)
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- [Timestamped data](#timestamped-data)
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- [查询和分析数据](#查询和分析数据)
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- [Rest Api](#rest-api)
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- [query language](#query-language)
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- [Query DSL](#query-dsl)
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- [Query DSL分析](#query-dsl分析)
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- [ES | QL](#es--ql)
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- [使用ElasticSearch Api索引和查询数据](#使用elasticsearch-api索引和查询数据)
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- [创建索引](#创建索引)
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- [向索引中添加数据](#向索引中添加数据)
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- [向索引中添加单个document](#向索引中添加单个document)
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- [向索引中添加多个document](#向索引中添加多个document)
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- [定义mapping和data type](#定义mapping和data-type)
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- [使用dynamic mapping](#使用dynamic-mapping)
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- [手动指定索引的mapping](#手动指定索引的mapping)
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- [将dynamic mapping和手动指定mapping相结合](#将dynamic-mapping和手动指定mapping相结合)
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- [搜索索引](#搜索索引)
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- [搜索所有文档](#搜索所有文档)
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- [match请求](#match请求)
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- [删除索引](#删除索引)
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- [全文本搜索和过滤](#全文本搜索和过滤)
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- [创建索引](#创建索引-1)
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- [批量插入数据](#批量插入数据)
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- [执行full-text search](#执行full-text-search)
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- [`match`](#match)
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- [contains all terms](#contains-all-terms)
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- [应匹配terms的最小个数](#应匹配terms的最小个数)
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- [search across mutli fields](#search-across-mutli-fields)
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- [filter \& exact matches](#filter--exact-matches)
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- [bool query](#bool-query)
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- [date range](#date-range)
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- [exact match](#exact-match)
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- [组合搜索](#组合搜索)
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- [使用Query DSL分析数据](#使用query-dsl分析数据)
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- [get metrics](#get-metrics)
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- [计算订单的平均值](#计算订单的平均值)
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- [在单个请求中计算订单的多个metrics](#在单个请求中计算订单的多个metrics)
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- [根据category对订单进行分组](#根据category对订单进行分组)
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- [date\_histogram](#date_histogram)
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- [Calendar intervals](#calendar-intervals)
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- [compare category performance](#compare-category-performance)
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- [analyze daily sale performance](#analyze-daily-sale-performance)
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- [pipeline aggregation demo](#pipeline-aggregation-demo)
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# ElasticSearch
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## 简介
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ElasticSearch是一个分布式的搜索和分析引擎、可缩放的数据存储、矢量数据库（vector database）。
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### 用例场景
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如下是ElasticSearch的用例场景
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- 日志：es可以用于收集、存储和分析日志
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- full-text search：通过倒排索引，es可以用于构建全文本搜索方案
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### 安装
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下列安装示例基于Ubuntu 22.04
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#### add elasticsearch GPG keys
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```bash
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wget -q https://artifacts.elastic.co/GPG-KEY-elasticsearch -O- | sudo gpg --dearmor -o /usr/share/keyrings/elasticsearch-keyring.gpg
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```
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#### Add Elasticsearch 8.x APT Repository
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```bash
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echo "deb [signed-by=/usr/share/keyrings/elasticsearch-keyring.gpg] https://artifacts.elastic.co/packages/8.x/apt stable main" | sudo tee /etc/apt/sources.list.d/elastic-8.x.list
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```
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#### install elastic search
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```bash
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sudo apt update && sudo apt install elasticsearch
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```
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### Indices, documents, and fields
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在ES中，index是存储的基本单元，是存储数据的逻辑namespace，位于同一index下的存储数据共享相似的特征。
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在ES服务部署之后，需要创建index，并在index中存储数据。
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index是一系列document的集合，通过`name`或`alias`唯一标识，在查询或其他操作中，通过`unique name`来定位index。
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#### Documents and fields
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ElasticSearch以json文档的格式来序列化和存储数据。一个document是fields的集合，field则是对应的key-value pair。每个document都有一个唯一的id，文档id可以手动指定，也可以让ES自动生成。
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一个ES文档的格式如下所示：
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```json
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{
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"_index": "my-first-elasticsearch-index",
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"_id": "DyFpo5EBxE8fzbb95DOa",
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"_version": 1,
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"_seq_no": 0,
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"_primary_term": 1,
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"found": true,
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"_source": {
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"email": "john@smith.com",
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"first_name": "John",
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"last_name": "Smith",
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"info": {
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"bio": "Eco-warrior and defender of the weak",
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"age": 25,
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"interests": [
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"dolphins",
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"whales"
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]
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},
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"join_date": "2024/05/01"
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}
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}
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```
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#### metadata field
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一个indexed document包含data和metadata。
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metadata fields为系统fields，用于存储document的相关信息，在elastic search中，metadata field以下划线`_`开头，例如，如下field为metadata field：
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- `_id`：文档id，文档id在每个index中都是唯一的
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- `_index`：标识该文档存储在哪个index中
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#### Mappings and data types
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每个index都有mapping或schema，用于指定document中的fields如何被索引。
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一个`mapping`定义了每个field的data type，以及该field如何被索引、该field如何被存储。
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在将document添加到index时，对于`mapping`有如下两种选择：
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- `Dynamic Mapping`：让ES自动检测data type并创建mapping。在引入dynamic mapping后，可能会针对某些用例场景产生次优结果
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- `Explicit Mapping`：针对每个field手动指定data type
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### 向ElasticSearch中添加数据
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#### General content
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General content是不包含时间戳的数据，对于general content，可以通过如下方式添加到ES中：
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- API：可以通过HTTP API向ES中添加数据
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#### Timestamped data
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Timestamped data代表包含timestamp field的数据，如果使用了`Elastic Common Schema(ECS)`，那么timestamp field的名称为`@timestamp`，这些数据可能是`logs, metrics, traces`。
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### 查询和分析数据
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可以通过如下方式来查询和分析数据
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#### Rest Api
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可以通过rest api来管理elastic search集群，并索引和查询数据。
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#### query language
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ES提供了多种查询语言来和数据进行交互
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- Query DSL: ES主要的查询语言
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- ES|QL: 8.11中新增的piped query language和计算引擎
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##### Query DSL
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query DSL是一种json格式的查询语言，支持复杂的查询、过滤、聚合操作，是ES最原始也是功能最强的查询语言
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`_search` endpoint接收Query DSL格式的查询
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query DSL支持如下查询：
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- 全文本搜索：搜索已经被分析和索引过的文本，支持短语或临近查询、模糊匹配等
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- 关键词查询：支持精确的关键词匹配
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- 语义查询
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- 向量查询
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- 地理位置查询
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##### Query DSL分析
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如果要通过Query DSL对elastic search数据进行分析，那么Aggregations是主要的工具。
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Aggregations允许根据数据构建复杂的数据摘要，并获取指标、模式和趋势。
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aggregations利用了和查询相同的数据结构，故而聚合的速度十分快，可以实时的对数据进行分析和可视化。
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在使用ES时，可以在同一时刻对相同的数据同时进行文档查询、结果过滤、数据分析操作，聚合是在查询请求的上下文中进行计算的。
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ES支持如下类型的Aggregations：
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- Metric：计算metrics，例如field的总和或平均
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- Bucket：基于field value、范围或其他指标对文档进行分组
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- Pipeline：在其他聚合操作结果集的基础上执行聚合操作
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##### ES | QL
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Elasticsearch Query Language是一个piped query language，用于对数据进行过滤、transforming、分析。ES|QL基于新的计算引擎，查询、聚合、transformation方法是直接在Elasticsearch中执行的。在Kibana工具中可以使用ES|QL语法。
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ES|QL支持Query DSL中的部分特性，例如聚合、过滤、transformation
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## 使用ElasticSearch Api索引和查询数据
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### 创建索引
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可以通过如下方式来创建一个名为`books`的索引：
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```
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PUT /books
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```
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返回相应结构如下，代表索引创建成功：
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```json
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{
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"acknowledged": true,
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"shards_acknowledged": true,
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"index": "books"
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}
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```
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### 向索引中添加数据
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可以向ElasticSearch中添加json形式的数据，json格式数据被称为document。ElasticSearch将添加的数据保存到可搜索的索引中。
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#### 向索引中添加单个document
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```
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POST books/_doc
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{
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"name": "Snow Crash",
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"author": "Neal Stephenson",
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"release_date": "1992-06-01",
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"page_count": 470
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}
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```
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该请求的返回体中包含ElasticSearch为该document生成的元数据，包含索引范围内唯一的`_id`，在索引范围内唯一标识该document。
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```json
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{
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"_index": "books",
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"_id": "O0lG2IsBaSa7VYx_rEia",
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"_version": 1,
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"result": "created",
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"_shards": {
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"total": 2,
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"successful": 2,
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"failed": 0
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},
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"_seq_no": 0,
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"_primary_term": 1
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}
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```
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#### 向索引中添加多个document
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可以使用`/_bulk`接口来在单个请求中添加多个document。`_bulk`请求的请求体由多个json串组成，json串之间通过换行符分隔。
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bulk请求示例如下所示：
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```
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POST /_bulk
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{ "index" : { "_index" : "books" } }
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{"name": "Revelation Space", "author": "Alastair Reynolds", "release_date": "2000-03-15", "page_count": 585}
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{ "index" : { "_index" : "books" } }
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{"name": "1984", "author": "George Orwell", "release_date": "1985-06-01", "page_count": 328}
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{ "index" : { "_index" : "books" } }
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{"name": "Fahrenheit 451", "author": "Ray Bradbury", "release_date": "1953-10-15", "page_count": 227}
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{ "index" : { "_index" : "books" } }
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{"name": "Brave New World", "author": "Aldous Huxley", "release_date": "1932-06-01", "page_count": 268}
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{ "index" : { "_index" : "books" } }
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{"name": "The Handmaids Tale", "author": "Margaret Atwood", "release_date": "1985-06-01", "page_count": 311}
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```
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如果上述请求被正确处理，将会得到如下返回体：
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```json
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{
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"errors": false,
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"took": 29,
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"items": [
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{
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"index": {
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"_index": "books",
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"_id": "QklI2IsBaSa7VYx_Qkh-",
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"_version": 1,
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"result": "created",
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"_shards": {
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"total": 2,
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"successful": 2,
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"failed": 0
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},
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"_seq_no": 1,
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"_primary_term": 1,
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"status": 201
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}
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},
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{
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"index": {
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"_index": "books",
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"_id": "Q0lI2IsBaSa7VYx_Qkh-",
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"_version": 1,
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"result": "created",
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"_shards": {
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"total": 2,
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"successful": 2,
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"failed": 0
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},
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"_seq_no": 2,
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"_primary_term": 1,
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"status": 201
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}
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},
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{
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"index": {
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"_index": "books",
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"_id": "RElI2IsBaSa7VYx_Qkh-",
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"_version": 1,
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"result": "created",
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||
"_shards": {
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"total": 2,
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"successful": 2,
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"failed": 0
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},
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"_seq_no": 3,
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"_primary_term": 1,
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"status": 201
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}
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},
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{
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"index": {
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"_index": "books",
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"_id": "RUlI2IsBaSa7VYx_Qkh-",
|
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"_version": 1,
|
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"result": "created",
|
||
"_shards": {
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"total": 2,
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"successful": 2,
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"failed": 0
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},
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"_seq_no": 4,
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"_primary_term": 1,
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"status": 201
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}
|
||
},
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{
|
||
"index": {
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"_index": "books",
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"_id": "RklI2IsBaSa7VYx_Qkh-",
|
||
"_version": 1,
|
||
"result": "created",
|
||
"_shards": {
|
||
"total": 2,
|
||
"successful": 2,
|
||
"failed": 0
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},
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"_seq_no": 5,
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"_primary_term": 1,
|
||
"status": 201
|
||
}
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}
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]
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}
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```
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### 定义mapping和data type
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#### 使用dynamic mapping
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当使用dynamic mapping时，elastic search默认情况下将会自动为新field创建mapping。上述示例中向索引中添加的document都使用了dynamic mapping，因为在创建索引时，并没有手动指定mapping。
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可以向`books`索引中新增一个document，新增document中包含当前索引documents中不存在的字段：
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```
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POST /books/_doc
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{
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"name": "The Great Gatsby",
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"author": "F. Scott Fitzgerald",
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"release_date": "1925-04-10",
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"page_count": 180,
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"language": "EN"
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}
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```
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此时，针对`books`索引，新字段`language`之前并不存在，会以`text`的data type被新增到mapping中。
|
||
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可以通过`/{index_uid}/_mapping`请求来查看索引的mapping信息：
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```
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GET /books/_mapping
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```
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其返回的响应为：
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```json
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{
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"books": {
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"mappings": {
|
||
"properties": {
|
||
"author": {
|
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"type": "text",
|
||
"fields": {
|
||
"keyword": {
|
||
"type": "keyword",
|
||
"ignore_above": 256
|
||
}
|
||
}
|
||
},
|
||
"name": {
|
||
"type": "text",
|
||
"fields": {
|
||
"keyword": {
|
||
"type": "keyword",
|
||
"ignore_above": 256
|
||
}
|
||
}
|
||
},
|
||
"new_field": {
|
||
"type": "text",
|
||
"fields": {
|
||
"keyword": {
|
||
"type": "keyword",
|
||
"ignore_above": 256
|
||
}
|
||
}
|
||
},
|
||
"page_count": {
|
||
"type": "long"
|
||
},
|
||
"release_date": {
|
||
"type": "date"
|
||
}
|
||
}
|
||
}
|
||
}
|
||
}
|
||
```
|
||
|
||
#### 手动指定索引的mapping
|
||
如下示例会展示如何在创建索引时手动指定索引的mapping：
|
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```
|
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PUT /my-explicit-mappings-books
|
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{
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"mappings": {
|
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"dynamic": false,
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"properties": {
|
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"name": { "type": "text" },
|
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"author": { "type": "text" },
|
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"release_date": { "type": "date", "format": "yyyy-MM-dd" },
|
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"page_count": { "type": "integer" }
|
||
}
|
||
}
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||
}
|
||
```
|
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|
||
上述示例中请求体含义如下：
|
||
- `"dynamic": false`: 在索引中禁用dynamic mapping，如果提交的document中包含了mapping中不存在的field，那么该提交的document将会被拒绝
|
||
- `"properties"`：properties属性定义了document中的fields及其数据类型
|
||
|
||
#### 将dynamic mapping和手动指定mapping相结合
|
||
如果在创建索引时手动指定了索引的mapping，那么在向索引中添加document时，document必须符合索引的定义。
|
||
|
||
如果要结合dynamic mapping和手动指定mapping，有如下两种方式：
|
||
- 使用update mapping Api
|
||
- 手动指定mapping时，将dynamic设置为true，此时向document中添加new field时无需对mapping执行update
|
||
|
||
### 搜索索引
|
||
#### 搜索所有文档
|
||
```
|
||
GET books/_search
|
||
```
|
||
上述请求将会搜索`books`索引中所有的文档
|
||
|
||
响应如下：
|
||
```json
|
||
{
|
||
"took": 2,
|
||
"timed_out": false,
|
||
"_shards": {
|
||
"total": 5,
|
||
"successful": 5,
|
||
"skipped": 0,
|
||
"failed": 0
|
||
},
|
||
"hits": {
|
||
"total": {
|
||
"value": 7,
|
||
"relation": "eq"
|
||
},
|
||
"max_score": 1,
|
||
"hits": [
|
||
{
|
||
"_index": "books",
|
||
"_id": "CwICQpIBO6vvGGiC_3Ls",
|
||
"_score": 1,
|
||
"_source": {
|
||
"name": "Brave New World",
|
||
"author": "Aldous Huxley",
|
||
"release_date": "1932-06-01",
|
||
"page_count": 268
|
||
}
|
||
},
|
||
... (truncated)
|
||
]
|
||
}
|
||
}
|
||
```
|
||
|
||
其中，响应体的字段含义如下：
|
||
- `took`：es执行该搜索请求花费的时间，单位为ms
|
||
- `time_out`：代表该请求是否超时
|
||
- `_shards`：代表该请求的分片数和成功数
|
||
- `hits`：hits对象中包含了执行结果
|
||
- `total`：total对象中包含了匹配结果的总数信息
|
||
- `max_score`：max_score包含了在所有匹配documents中最高的relavance score
|
||
- `_index`：该字段代表了document所属的索引
|
||
- `_id`：该字段代表document的唯一标识id
|
||
- `_score`：`_score`字段代表当前document的relavance score
|
||
- `_source`：该字段包含了indexing过程中提交的原始json对象
|
||
|
||
#### match请求
|
||
可以通过match请求来查询特定field中包含指定值的documents。这是全文本查询的标准查询。
|
||
|
||
如下示例中会查询索引中`name` field中包含`brave`的文档：
|
||
```
|
||
GET books/_search
|
||
{
|
||
"query": {
|
||
"match": {
|
||
"name": "brave"
|
||
}
|
||
}
|
||
}
|
||
```
|
||
|
||
响应体结构如下：
|
||
```json
|
||
{
|
||
"took": 9,
|
||
"timed_out": false,
|
||
"_shards": {
|
||
"total": 5,
|
||
"successful": 5,
|
||
"skipped": 0,
|
||
"failed": 0
|
||
},
|
||
"hits": {
|
||
"total": {
|
||
"value": 1,
|
||
"relation": "eq"
|
||
},
|
||
"max_score": 0.6931471,
|
||
"hits": [
|
||
{
|
||
"_index": "books",
|
||
"_id": "CwICQpIBO6vvGGiC_3Ls",
|
||
"_score": 0.6931471,
|
||
"_source": {
|
||
"name": "Brave New World",
|
||
"author": "Aldous Huxley",
|
||
"release_date": "1932-06-01",
|
||
"page_count": 268
|
||
}
|
||
}
|
||
]
|
||
}
|
||
}
|
||
```
|
||
|
||
#### 删除索引
|
||
如果要删除创建的索引从头开始，可以使用如下方式：
|
||
```
|
||
DELETE /books
|
||
DELETE /my-explicit-mappings-books
|
||
```
|
||
删除索引将会永久删除其document、shards、元数据。
|
||
|
||
|
||
### 全文本搜索和过滤
|
||
如下示例展示了如何实现cook blog的搜索功能。
|
||
#### 创建索引
|
||
创建`cooking_blog`索引
|
||
```
|
||
PUT /cooking_blog
|
||
```
|
||
|
||
为索引定义mapping：
|
||
```
|
||
PUT /cooking_blog/_mapping
|
||
{
|
||
"properties": {
|
||
"title": {
|
||
"type": "text",
|
||
"analyzer": "standard",
|
||
"fields": {
|
||
"keyword": {
|
||
"type": "keyword",
|
||
"ignore_above": 256
|
||
}
|
||
}
|
||
},
|
||
"description": {
|
||
"type": "text",
|
||
"fields": {
|
||
"keyword": {
|
||
"type": "keyword"
|
||
}
|
||
}
|
||
},
|
||
"author": {
|
||
"type": "text",
|
||
"fields": {
|
||
"keyword": {
|
||
"type": "keyword"
|
||
}
|
||
}
|
||
},
|
||
"date": {
|
||
"type": "date",
|
||
"format": "yyyy-MM-dd"
|
||
},
|
||
"category": {
|
||
"type": "text",
|
||
"fields": {
|
||
"keyword": {
|
||
"type": "keyword"
|
||
}
|
||
}
|
||
},
|
||
"tags": {
|
||
"type": "text",
|
||
"fields": {
|
||
"keyword": {
|
||
"type": "keyword"
|
||
}
|
||
}
|
||
},
|
||
"rating": {
|
||
"type": "float"
|
||
}
|
||
}
|
||
}
|
||
```
|
||
|
||
上述mapping定义含义如下：
|
||
- 对于`text`类型的field，如果analyzer没有指定，那么会默认使用`standard` analyzer
|
||
- 在上述示例中，使用了`multi fields`，将字段既作为`text`来进行全文搜索，又作为`keyword`来进行聚合和排序。在该字段上，既支持全文搜索，又支持青雀匹配和过滤。如果使用dynamic mapping，那么multi-fields将会自动被创建。
|
||
- `ignore_above`不会索引`keyword`field中超过256个字符长度的值。默认情况下，keyword field其ignore_above的值为256
|
||
|
||
> #### multi-field
|
||
> 对同一个字段按不同的方式进行索引有时候很必要，对于multi-fields，一个字符串类型字段可以被映射到`text`类型用于全文索引，也可以被映射到`keyword`类型用作排序和聚合。
|
||
>
|
||
> 示例如下：
|
||
> ```
|
||
> PUT my-index-000001
|
||
> {
|
||
> "mappings": {
|
||
> "properties": {
|
||
> "city": {
|
||
> "type": "text",
|
||
> "fields": {
|
||
> "raw": {
|
||
> "type": "keyword"
|
||
> }
|
||
> }
|
||
> }
|
||
> }
|
||
> }
|
||
> }
|
||
>
|
||
> PUT my-index-000001/_doc/1
|
||
> {
|
||
> "city": "New York"
|
||
> }
|
||
>
|
||
> PUT my-index-000001/_doc/2
|
||
> {
|
||
> "city": "York"
|
||
> }
|
||
>
|
||
> GET my-index-000001/_search
|
||
> {
|
||
> "query": {
|
||
> "match": {
|
||
> "city": "york"
|
||
> }
|
||
> },
|
||
> "sort": {
|
||
> "city.raw": "asc"
|
||
> },
|
||
> "aggs": {
|
||
> "Cities": {
|
||
> "terms": {
|
||
> "field": "city.raw"
|
||
> }
|
||
> }
|
||
> }
|
||
> }
|
||
> ```
|
||
|
||
> #### ignore_above
|
||
> 在`keyword`中指定`ignore_above`为256，将避免索引长度大于256的字段值。当字段长度大于256时，该字段将不会被索引，`但是被忽略字段将会包含在_source中`
|
||
>
|
||
> 当`ignore_above`没有显式指定时，其值默认为256.
|
||
|
||
#### 批量插入数据
|
||
再创建索引后，可以向索引中批量插入文档数据：
|
||
```
|
||
POST /cooking_blog/_bulk?refresh=wait_for
|
||
{"index":{"_id":"1"}}
|
||
{"title":"Perfect Pancakes: A Fluffy Breakfast Delight","description":"Learn the secrets to making the fluffiest pancakes, so amazing you won't believe your tastebuds. This recipe uses buttermilk and a special folding technique to create light, airy pancakes that are perfect for lazy Sunday mornings.","author":"Maria Rodriguez","date":"2023-05-01","category":"Breakfast","tags":["pancakes","breakfast","easy recipes"],"rating":4.8}
|
||
{"index":{"_id":"2"}}
|
||
{"title":"Spicy Thai Green Curry: A Vegetarian Adventure","description":"Dive into the flavors of Thailand with this vibrant green curry. Packed with vegetables and aromatic herbs, this dish is both healthy and satisfying. Don't worry about the heat - you can easily adjust the spice level to your liking.","author":"Liam Chen","date":"2023-05-05","category":"Main Course","tags":["thai","vegetarian","curry","spicy"],"rating":4.6}
|
||
{"index":{"_id":"3"}}
|
||
{"title":"Classic Beef Stroganoff: A Creamy Comfort Food","description":"Indulge in this rich and creamy beef stroganoff. Tender strips of beef in a savory mushroom sauce, served over a bed of egg noodles. It's the ultimate comfort food for chilly evenings.","author":"Emma Watson","date":"2023-05-10","category":"Main Course","tags":["beef","pasta","comfort food"],"rating":4.7}
|
||
{"index":{"_id":"4"}}
|
||
{"title":"Vegan Chocolate Avocado Mousse","description":"Discover the magic of avocado in this rich, vegan chocolate mousse. Creamy, indulgent, and secretly healthy, it's the perfect guilt-free dessert for chocolate lovers.","author":"Alex Green","date":"2023-05-15","category":"Dessert","tags":["vegan","chocolate","avocado","healthy dessert"],"rating":4.5}
|
||
{"index":{"_id":"5"}}
|
||
{"title":"Crispy Oven-Fried Chicken","description":"Get that perfect crunch without the deep fryer! This oven-fried chicken recipe delivers crispy, juicy results every time. A healthier take on the classic comfort food.","author":"Maria Rodriguez","date":"2023-05-20","category":"Main Course","tags":["chicken","oven-fried","healthy"],"rating":4.9}
|
||
```
|
||
|
||
#### 执行full-text search
|
||
full-text search会在一个或多个document fields之间执行基于文本的查询。这些查询会为每个匹配的文档计算relevance score，relevance score的计算基于文档内容和search terms的关联程度。
|
||
|
||
ES支持多种查询类型，每种查询类型都有其`matching text`和`relevance scoring`的方法。
|
||
|
||
##### `match`
|
||
match是针对full-text的标准查询，基于每个字段上配置的analyzer，query text将会被分析。
|
||
|
||
```
|
||
GET /cooking_blog/_search
|
||
{
|
||
"query": {
|
||
"match": {
|
||
"description": {
|
||
"query": "fluffy pancakes"
|
||
}
|
||
}
|
||
}
|
||
}
|
||
```
|
||
默认情况下，`match query`在resulting tokens间使用`or`，故而在上述的查询中，会查找description中包含`fluffy`或`pancakes`任一的document。
|
||
|
||
其会返回结果如下：
|
||
```
|
||
{
|
||
"took": 0,
|
||
"timed_out": false,
|
||
"_shards": {
|
||
"total": 1,
|
||
"successful": 1,
|
||
"skipped": 0,
|
||
"failed": 0
|
||
},
|
||
"hits": {
|
||
"total": {
|
||
"value": 1,
|
||
"relation": "eq"
|
||
},
|
||
"max_score": 1.8378843,
|
||
"hits": [
|
||
{
|
||
"_index": "cooking_blog",
|
||
"_id": "1",
|
||
"_score": 1.8378843,
|
||
"_source": {
|
||
"title": "Perfect Pancakes: A Fluffy Breakfast Delight",
|
||
"description": "Learn the secrets to making the fluffiest pancakes, so amazing you won't believe your tastebuds. This recipe uses buttermilk and a special folding technique to create light, airy pancakes that are perfect for lazy Sunday mornings.",
|
||
"author": "Maria Rodriguez",
|
||
"date": "2023-05-01",
|
||
"category": "Breakfast",
|
||
"tags": [
|
||
"pancakes",
|
||
"breakfast",
|
||
"easy recipes"
|
||
],
|
||
"rating": 4.8
|
||
}
|
||
}
|
||
]
|
||
}
|
||
}
|
||
```
|
||
|
||
> ##### track total hits
|
||
> 如果想要精确计算hit count，通常需要遍历所有的匹配文档，这将会带来很大开销。
|
||
>
|
||
> `track_total_hists`参数允许对`如何计算hit count`进行控制。
|
||
> - 如果设置为true，那么会精确的计算匹配数量，`total.relation`会一直为`eq`，代表`total.value`和实际hit count相同
|
||
> - 如果该值为其他值，例如其默认值`10000`，则该查询数量的`下限`为`10000`
|
||
> - 如果`total.relation`为`eq`，则`total.value`代表实际hit count
|
||
> - 如果`total.relation`为`gte`， 则`total.value`为hit count的下界，实际hit count大于或等于`total.value`
|
||
|
||
> ##### max score
|
||
> `max_score`代表所有matching documents中最高的relevance score
|
||
|
||
> ##### _score
|
||
> _score为指定文档的relevance score，分数越高，匹配越好。
|
||
|
||
##### contains all terms
|
||
如上所示，如果在`query.match.description.query`中指定了多个由空格分隔的词，那么可以为`query.match.description.operator`指定`and`，那么其会查询description中包含所有分词的文档。
|
||
```
|
||
GET /cooking_blog/_search
|
||
{
|
||
"query": {
|
||
"match": {
|
||
"description": {
|
||
"query": "fluffy pancakes",
|
||
"operator": "and"
|
||
}
|
||
}
|
||
}
|
||
}
|
||
```
|
||
|
||
##### 应匹配terms的最小个数
|
||
通过`minimum_should_match`参数，可以指定在search result中最少应该匹配的terms个数。
|
||
|
||
如下实例中，查询文本中包含三个分词，`"fluffy", "pancakes", or "breakfast"`，但是查询结果中至少应该包含3个中的2个：
|
||
```
|
||
GET /cooking_blog/_search
|
||
{
|
||
"query": {
|
||
"match": {
|
||
"title": {
|
||
"query": "fluffy pancakes breakfast",
|
||
"minimum_should_match": 2
|
||
}
|
||
}
|
||
}
|
||
}
|
||
```
|
||
##### search across mutli fields
|
||
当用户输入查询文本时，其通常不知道是否查询文本出现在文档中的哪个字段。`multi_match`允许同时跨多个字段来进行查询。
|
||
|
||
mutli_match查询示例如下：
|
||
```
|
||
GET /cooking_blog/_search
|
||
{
|
||
"query": {
|
||
"multi_match": {
|
||
"query": "vegetarian curry",
|
||
"fields": ["title", "description", "tags"]
|
||
}
|
||
}
|
||
}
|
||
```
|
||
在上述示例中，将会在`title, description, tags`三个字段中查询`vegetarian curry`，其中每个字段的重要性都相同。
|
||
|
||
但是，在某种场景下，在部分字段中的匹配比其他字段中的匹配更加重要（例如，相关论文检索时，关键词出现在摘要中要比出现在正文中更加重要）。
|
||
|
||
因此，可以在fields中针对多个字段进行重要性加权的调整：
|
||
```
|
||
GET /cooking_blog/_search
|
||
{
|
||
"query": {
|
||
"multi_match": {
|
||
"query": "vegetarian curry",
|
||
"fields": ["title^3", "description^2", "tags"]
|
||
}
|
||
}
|
||
}
|
||
```
|
||
如上述示例所示，`^`语法应用于指定字段：
|
||
- `title^3`：title的重要性为非增强字段的3倍
|
||
- `description^2`：description的重要性为非增强字段的2倍
|
||
- `tags`：非增强字段
|
||
|
||
#### filter & exact matches
|
||
filter允许根据指定条件来缩小查询结果的范围。和full-text search不同的是，filter是二进制的，并且不会影响relevance score。
|
||
|
||
filter相较于query执行的更快，因为被排除的result不会计算score。
|
||
|
||
##### bool query
|
||
在如下示例中，`bool query`只会返回category为`Breakfast`中的文档：
|
||
```
|
||
GET /cooking_blog/_search
|
||
{
|
||
"query": {
|
||
"bool": {
|
||
"filter": [
|
||
{ "term": { "category.keyword": "Breakfast" } }
|
||
]
|
||
}
|
||
}
|
||
}
|
||
```
|
||
在针对`category`进行filter时，指定的是`category.keyword`，其类型为`keyword`，为`精确、区分大小写的匹配`。
|
||
|
||
> `category.keyword`字段其`multi field`类型为`keyword`，代表了字段`没有经过分析的版本`（代表字段没有指定analyzer），故而其进行的是精确、大小写敏感的匹配。
|
||
>
|
||
> 如下两种场景将会使用`.keyword`：
|
||
> 1. 使用了针对`text`类型的字段使用了dynamic mappings，此时es会自动创建一个名为`.keyword`的`multi-field`
|
||
> 2. 当手动指定`text`类型字段的`.keyword`为`keyword`类型时
|
||
|
||
##### date range
|
||
当想要查询一定时间范围内的文档时，可以按如下方式来编写查询：
|
||
```
|
||
GET /cooking_blog/_search
|
||
{
|
||
"query": {
|
||
"range": {
|
||
"date": {
|
||
"gte": "2023-05-01",
|
||
"lte": "2023-05-31"
|
||
}
|
||
}
|
||
}
|
||
}
|
||
```
|
||
##### exact match
|
||
可以通过`term`来搜索精确术语，term将会在字段中精确的搜索，`并不会对传入的文本进行分析`。
|
||
|
||
针对指定term的精确、大小写敏感搜索通常基于keyword进行搜索。
|
||
|
||
term搜索示例如下所示，搜索作者为`Maria Rodriguez`的文档：
|
||
```
|
||
GET /cooking_blog/_search
|
||
{
|
||
"query": {
|
||
"term": {
|
||
"author.keyword": "Maria Rodriguez"
|
||
}
|
||
}
|
||
}
|
||
```
|
||
|
||
> 应该避免在`text`类型的字段上使用`term`查询，因为`text`类型的field会被analyzer进行分析和转换
|
||
|
||
#### 组合搜索
|
||
`bool query`允许将多个子查询组合为复杂的查询。
|
||
|
||
bool query示例如下：
|
||
```
|
||
GET /cooking_blog/_search
|
||
{
|
||
"query": {
|
||
"bool": {
|
||
"must": [
|
||
{ "term": { "tags": "vegetarian" } },
|
||
{
|
||
"range": {
|
||
"rating": {
|
||
"gte": 4.5
|
||
}
|
||
}
|
||
}
|
||
],
|
||
"should": [
|
||
{
|
||
"term": {
|
||
"category": "Main Course"
|
||
}
|
||
},
|
||
{
|
||
"multi_match": {
|
||
"query": "curry spicy",
|
||
"fields": [
|
||
"title^2",
|
||
"description"
|
||
]
|
||
}
|
||
},
|
||
{
|
||
"range": {
|
||
"date": {
|
||
"gte": "now-1M/d"
|
||
}
|
||
}
|
||
}
|
||
],
|
||
"must_not": [
|
||
{
|
||
"term": {
|
||
"category.keyword": "Dessert"
|
||
}
|
||
}
|
||
]
|
||
}
|
||
}
|
||
}
|
||
```
|
||
上述查询含义如下：
|
||
- tag中必须精确匹配vegetarian
|
||
- rating必须大于或等于4.5
|
||
- 在title或者description中应该包含`curry`或`spicy`
|
||
- category应该精确匹配`Main Course`
|
||
- date字段应该大于或等于上个月
|
||
- category不能为Dessert
|
||
|
||
> ##### `must_not`
|
||
> `must_not`会淘汰不满足指定条件的文档
|
||
|
||
## 使用Query DSL分析数据
|
||
在使用kibana导入`sample ecommerce orders`的数据集后，其会创建一个名为`kibana_sample_data_ecommerce`的索引，其索引结构如下：
|
||
```
|
||
{
|
||
"kibana_sample_data_ecommerce": {
|
||
"mappings": {
|
||
"properties": {
|
||
"category": {
|
||
"type": "text",
|
||
"fields": {
|
||
"keyword": {
|
||
"type": "keyword"
|
||
}
|
||
}
|
||
},
|
||
"currency": {
|
||
"type": "keyword"
|
||
},
|
||
"customer_birth_date": {
|
||
"type": "date"
|
||
},
|
||
"customer_first_name": {
|
||
"type": "text",
|
||
"fields": {
|
||
"keyword": {
|
||
"type": "keyword",
|
||
"ignore_above": 256
|
||
}
|
||
}
|
||
},
|
||
"customer_full_name": {
|
||
"type": "text",
|
||
"fields": {
|
||
"keyword": {
|
||
"type": "keyword",
|
||
"ignore_above": 256
|
||
}
|
||
}
|
||
},
|
||
"customer_gender": {
|
||
"type": "keyword"
|
||
},
|
||
"customer_id": {
|
||
"type": "keyword"
|
||
},
|
||
"customer_last_name": {
|
||
"type": "text",
|
||
"fields": {
|
||
"keyword": {
|
||
"type": "keyword",
|
||
"ignore_above": 256
|
||
}
|
||
}
|
||
},
|
||
"customer_phone": {
|
||
"type": "keyword"
|
||
},
|
||
"day_of_week": {
|
||
"type": "keyword"
|
||
},
|
||
"day_of_week_i": {
|
||
"type": "integer"
|
||
},
|
||
"email": {
|
||
"type": "keyword"
|
||
},
|
||
"event": {
|
||
"properties": {
|
||
"dataset": {
|
||
"type": "keyword"
|
||
}
|
||
}
|
||
},
|
||
"geoip": {
|
||
"properties": {
|
||
"city_name": {
|
||
"type": "keyword"
|
||
},
|
||
"continent_name": {
|
||
"type": "keyword"
|
||
},
|
||
"country_iso_code": {
|
||
"type": "keyword"
|
||
},
|
||
"location": {
|
||
"type": "geo_point"
|
||
},
|
||
"region_name": {
|
||
"type": "keyword"
|
||
}
|
||
}
|
||
},
|
||
"manufacturer": {
|
||
"type": "text",
|
||
"fields": {
|
||
"keyword": {
|
||
"type": "keyword"
|
||
}
|
||
}
|
||
},
|
||
"order_date": {
|
||
"type": "date"
|
||
},
|
||
"order_id": {
|
||
"type": "keyword"
|
||
},
|
||
"products": {
|
||
"properties": {
|
||
"_id": {
|
||
"type": "text",
|
||
"fields": {
|
||
"keyword": {
|
||
"type": "keyword",
|
||
"ignore_above": 256
|
||
}
|
||
}
|
||
},
|
||
"base_price": {
|
||
"type": "half_float"
|
||
},
|
||
"base_unit_price": {
|
||
"type": "half_float"
|
||
},
|
||
"category": {
|
||
"type": "text",
|
||
"fields": {
|
||
"keyword": {
|
||
"type": "keyword"
|
||
}
|
||
}
|
||
},
|
||
"created_on": {
|
||
"type": "date"
|
||
},
|
||
"discount_amount": {
|
||
"type": "half_float"
|
||
},
|
||
"discount_percentage": {
|
||
"type": "half_float"
|
||
},
|
||
"manufacturer": {
|
||
"type": "text",
|
||
"fields": {
|
||
"keyword": {
|
||
"type": "keyword"
|
||
}
|
||
}
|
||
},
|
||
"min_price": {
|
||
"type": "half_float"
|
||
},
|
||
"price": {
|
||
"type": "half_float"
|
||
},
|
||
"product_id": {
|
||
"type": "long"
|
||
},
|
||
"product_name": {
|
||
"type": "text",
|
||
"fields": {
|
||
"keyword": {
|
||
"type": "keyword"
|
||
}
|
||
},
|
||
"analyzer": "english"
|
||
},
|
||
"quantity": {
|
||
"type": "integer"
|
||
},
|
||
"sku": {
|
||
"type": "keyword"
|
||
},
|
||
"tax_amount": {
|
||
"type": "half_float"
|
||
},
|
||
"taxful_price": {
|
||
"type": "half_float"
|
||
},
|
||
"taxless_price": {
|
||
"type": "half_float"
|
||
},
|
||
"unit_discount_amount": {
|
||
"type": "half_float"
|
||
}
|
||
}
|
||
},
|
||
"sku": {
|
||
"type": "keyword"
|
||
},
|
||
"taxful_total_price": {
|
||
"type": "half_float"
|
||
},
|
||
"taxless_total_price": {
|
||
"type": "half_float"
|
||
},
|
||
"total_quantity": {
|
||
"type": "integer"
|
||
},
|
||
"total_unique_products": {
|
||
"type": "integer"
|
||
},
|
||
"type": {
|
||
"type": "keyword"
|
||
},
|
||
"user": {
|
||
"type": "keyword"
|
||
}
|
||
}
|
||
}
|
||
}
|
||
}
|
||
```
|
||
其中，`geoip.properties, products.properties`都为嵌套的object类型，而`geo_point`类型则是用于地理坐标。
|
||
|
||
### get metrics
|
||
#### 计算订单的平均值
|
||
通过如下请求，可以计算数据集中所有订单的平均值：
|
||
```
|
||
GET kibana_sample_data_ecommerce/_search
|
||
{
|
||
"size": 0,
|
||
"aggs": {
|
||
"avg_order_value": {
|
||
"avg": {
|
||
"field": "taxful_total_price"
|
||
}
|
||
}
|
||
}
|
||
}
|
||
```
|
||
|
||
在上述请求体中，各属性分别代表如下含义：
|
||
- size: 将`size`设置为0可以避免在返回的结果中包含`匹配的文档`，size设置为0后，返回结果中只会包含聚合的结果
|
||
- `avg_order_value`为该项metric的name
|
||
- `avg`为聚合类型，会计算算数平均
|
||
|
||
该请求的返回结果如下所示：
|
||
```
|
||
{
|
||
"took": 0,
|
||
"timed_out": false,
|
||
"_shards": {
|
||
"total": 1,
|
||
"successful": 1,
|
||
"skipped": 0,
|
||
"failed": 0
|
||
},
|
||
"hits": {
|
||
"total": {
|
||
"value": 4675,
|
||
"relation": "eq"
|
||
},
|
||
"max_score": null,
|
||
"hits": []
|
||
},
|
||
"aggregations": {
|
||
"avg_order_value": {
|
||
"value": 75.05542864304813
|
||
}
|
||
}
|
||
}
|
||
```
|
||
返回结果中各属性含义如下所示：
|
||
- `hits.total.value`：代表数据集中的订单数量
|
||
- `hits.hits`为空，因为请求体中设置了`size`为0
|
||
- `aggregations`中包含聚合结果，请求体中为metric指定的结果为`avg_order_value`，故而该项metric位于`aggregations.avg_order_value`
|
||
|
||
#### 在单个请求中计算订单的多个metrics
|
||
如果想要在单个请求中结算多个metrics，可以通过`stats`聚合类型：
|
||
```
|
||
{
|
||
"size":0,
|
||
"aggs":{
|
||
"order_status":{
|
||
"stats":{
|
||
"field":"taxful_total_price"
|
||
}
|
||
}
|
||
}
|
||
}
|
||
```
|
||
其中，`stats`聚合类型会返回count, min, max, avg, sum五个metrics。
|
||
|
||
其返回结果为
|
||
```
|
||
{
|
||
"aggregations": {
|
||
"order_stats": {
|
||
"count": 4675,
|
||
"min": 6.98828125,
|
||
"max": 2250,
|
||
"avg": 75.05542864304813,
|
||
"sum": 350884.12890625
|
||
}
|
||
}
|
||
}
|
||
```
|
||
|
||
#### 根据category对订单进行分组
|
||
可以根据`terms`聚合类型来对订单进行分组，
|
||
```
|
||
GET kibana_sample_data_ecommerce/_search
|
||
{
|
||
"size": 0,
|
||
"aggs": {
|
||
"sales_by_category": {
|
||
"terms": {
|
||
"field": "category.keyword",
|
||
"size": 5,
|
||
"order": { "_count": "desc" }
|
||
}
|
||
}
|
||
}
|
||
}
|
||
```
|
||
`terms`聚合类型会根据该字段的类型对文档进行分组
|
||
|
||
`"size":5`和` "order": { "_count": "desc" }`设置了只会返回最多的5个category
|
||
|
||
|
||
其返回结果如下所示：
|
||
```
|
||
{
|
||
"took": 4,
|
||
"timed_out": false,
|
||
"_shards": {
|
||
"total": 5,
|
||
"successful": 5,
|
||
"skipped": 0,
|
||
"failed": 0
|
||
},
|
||
"hits": {
|
||
"total": {
|
||
"value": 4675,
|
||
"relation": "eq"
|
||
},
|
||
"max_score": null,
|
||
"hits": []
|
||
},
|
||
"aggregations": {
|
||
"sales_by_category": {
|
||
"doc_count_error_upper_bound": 0,
|
||
"sum_other_doc_count": 572,
|
||
"buckets": [
|
||
{
|
||
"key": "Men's Clothing",
|
||
"doc_count": 2024
|
||
},
|
||
{
|
||
"key": "Women's Clothing",
|
||
"doc_count": 1903
|
||
},
|
||
{
|
||
"key": "Women's Shoes",
|
||
"doc_count": 1136
|
||
},
|
||
{
|
||
"key": "Men's Shoes",
|
||
"doc_count": 944
|
||
},
|
||
{
|
||
"key": "Women's Accessories",
|
||
"doc_count": 830
|
||
}
|
||
]
|
||
}
|
||
}
|
||
}
|
||
```
|
||
|
||
> #### doc_count_error_upper_bound
|
||
> 基于es的分布式结构，`terms aggregations`在多个shards上运行时，document计数可能会有小的误差，`doc_count_error_upper_bound`的代表计数的最大可能误差
|
||
|
||
- sum_other_doc_count: 由于当前请求体中设置了`aggs.sales_by_category.terms.size`为5，故而`sum_other_doc_count`代表未包含在返回结果中的文档数量
|
||
- aggregations.buckets： 该字段代表category buckets数组，根据count来排序
|
||
|
||
#### date_histogram
|
||
`date_histogram`类型的聚合会将文档聚合为time-based buckets，其类似于terms，但是其并非基于精确匹配的聚合，而是基于日期的集合。
|
||
|
||
`date_histogram`聚合示例如下：
|
||
```
|
||
GET kibana_sample_data_ecommerce/_search
|
||
{
|
||
"size": 0,
|
||
"aggs": {
|
||
"daily_orders": {
|
||
"date_histogram": {
|
||
"field": "order_date",
|
||
"calendar_interval": "day",
|
||
"format": "yyyy-MM-dd",
|
||
"min_doc_count": 0
|
||
}
|
||
}
|
||
}
|
||
}
|
||
```
|
||
上述示例中，各参数含义如下：
|
||
- `min_doc_count`：当min_doc_count被设置为0时，会返回没有订单的天数
|
||
|
||
请求返回结果如下：
|
||
```
|
||
{
|
||
"took": 2,
|
||
"timed_out": false,
|
||
"_shards": {
|
||
"total": 5,
|
||
"successful": 5,
|
||
"skipped": 0,
|
||
"failed": 0
|
||
},
|
||
"hits": {
|
||
"total": {
|
||
"value": 4675,
|
||
"relation": "eq"
|
||
},
|
||
"max_score": null,
|
||
"hits": []
|
||
},
|
||
"aggregations": {
|
||
"daily_orders": {
|
||
"buckets": [
|
||
{
|
||
"key_as_string": "2024-11-28",
|
||
"key": 1732752000000,
|
||
"doc_count": 146
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-29",
|
||
"key": 1732838400000,
|
||
"doc_count": 153
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-30",
|
||
"key": 1732924800000,
|
||
"doc_count": 143
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-01",
|
||
"key": 1733011200000,
|
||
"doc_count": 140
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-02",
|
||
"key": 1733097600000,
|
||
"doc_count": 139
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-03",
|
||
"key": 1733184000000,
|
||
"doc_count": 157
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-04",
|
||
"key": 1733270400000,
|
||
"doc_count": 145
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-05",
|
||
"key": 1733356800000,
|
||
"doc_count": 152
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-06",
|
||
"key": 1733443200000,
|
||
"doc_count": 163
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-07",
|
||
"key": 1733529600000,
|
||
"doc_count": 141
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-08",
|
||
"key": 1733616000000,
|
||
"doc_count": 151
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-09",
|
||
"key": 1733702400000,
|
||
"doc_count": 143
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-10",
|
||
"key": 1733788800000,
|
||
"doc_count": 143
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-11",
|
||
"key": 1733875200000,
|
||
"doc_count": 142
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-12",
|
||
"key": 1733961600000,
|
||
"doc_count": 161
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-13",
|
||
"key": 1734048000000,
|
||
"doc_count": 144
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-14",
|
||
"key": 1734134400000,
|
||
"doc_count": 157
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-15",
|
||
"key": 1734220800000,
|
||
"doc_count": 158
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-16",
|
||
"key": 1734307200000,
|
||
"doc_count": 144
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-17",
|
||
"key": 1734393600000,
|
||
"doc_count": 151
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-18",
|
||
"key": 1734480000000,
|
||
"doc_count": 145
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-19",
|
||
"key": 1734566400000,
|
||
"doc_count": 157
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-20",
|
||
"key": 1734652800000,
|
||
"doc_count": 158
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-21",
|
||
"key": 1734739200000,
|
||
"doc_count": 153
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-22",
|
||
"key": 1734825600000,
|
||
"doc_count": 165
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-23",
|
||
"key": 1734912000000,
|
||
"doc_count": 153
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-24",
|
||
"key": 1734998400000,
|
||
"doc_count": 158
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-25",
|
||
"key": 1735084800000,
|
||
"doc_count": 160
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-26",
|
||
"key": 1735171200000,
|
||
"doc_count": 159
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-27",
|
||
"key": 1735257600000,
|
||
"doc_count": 152
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-28",
|
||
"key": 1735344000000,
|
||
"doc_count": 142
|
||
}
|
||
]
|
||
}
|
||
}
|
||
}
|
||
```
|
||
上述示例中，`key_as_string`为`human readable`日期字符串，而`key`则是和`key_as_string`代表相同日期的unix时间戳。
|
||
|
||
`doc_count`代表bucket中的文档个数。
|
||
|
||
##### Calendar intervals
|
||
`calendar-aware interval`可以通过`calendar_interval`来进行配置，可以通过时间单位名称`month`或单个时间单位`1M`来进行指定。类似的`day`和`1d`也是等同的，`但是，并不支持2d这种多个时间单位的配置`。
|
||
|
||
可以接受的时间单位为：
|
||
- `minute, 1m`
|
||
- `hour, 1h`
|
||
- `day, 1d`
|
||
- `week, 1w`
|
||
- `month, 1M`
|
||
- `quarter, 1q`
|
||
- `year, 1y`
|
||
|
||
#### compare category performance
|
||
```
|
||
GET kibana_sample_data_ecommerce/_search
|
||
{
|
||
"size": 0,
|
||
"aggs": {
|
||
"categories": {
|
||
"terms": {
|
||
"field": "category.keyword",
|
||
"size": 5,
|
||
"order": { "total_revenue": "desc" }
|
||
},
|
||
"aggs": {
|
||
"total_revenue": {
|
||
"sum": {
|
||
"field": "taxful_total_price"
|
||
}
|
||
},
|
||
"avg_order_value": {
|
||
"avg": {
|
||
"field": "taxful_total_price"
|
||
}
|
||
},
|
||
"total_items": {
|
||
"sum": {
|
||
"field": "total_quantity"
|
||
}
|
||
}
|
||
}
|
||
}
|
||
}
|
||
}
|
||
```
|
||
|
||
如上所示，在按`category.keyword`分组聚合后，其又在`aggs.categories.aggs`中指定了针对每个category计算的metrics。
|
||
|
||
上述请求的返回结果如下所示：
|
||
```
|
||
{
|
||
"aggregations": {
|
||
"categories": {
|
||
"buckets": [
|
||
{
|
||
"key": "Men's Clothing",
|
||
"doc_count": 2179,
|
||
"total_revenue": {
|
||
"value": 156729.453125
|
||
},
|
||
"avg_order_value": {
|
||
"value": 71.92726898715927
|
||
},
|
||
"total_items": {
|
||
"value": 8716
|
||
}
|
||
},
|
||
{
|
||
"key": "Women's Clothing",
|
||
"doc_count": 2262,
|
||
...
|
||
}
|
||
]
|
||
}
|
||
}
|
||
}
|
||
```
|
||
#### analyze daily sale performance
|
||
```
|
||
GET kibana_sample_data_ecommerce/_search
|
||
{
|
||
"size": 0,
|
||
"aggs": {
|
||
"daily_sales": {
|
||
"date_histogram": {
|
||
"field": "order_date",
|
||
"calendar_interval": "day",
|
||
"format": "yyyy-MM-dd"
|
||
},
|
||
"aggs": {
|
||
"revenue": {
|
||
"sum": {
|
||
"field": "taxful_total_price"
|
||
}
|
||
},
|
||
"unique_customers": {
|
||
"cardinality": {
|
||
"field": "customer_id"
|
||
}
|
||
},
|
||
"avg_basket_size": {
|
||
"avg": {
|
||
"field": "total_quantity"
|
||
}
|
||
}
|
||
}
|
||
}
|
||
}
|
||
}
|
||
```
|
||
上述请求首先按照order_date字段按天进行了分组，然后针对每天的订单，继续计算sum, cardinality, avg聚合。
|
||
|
||
其中，cardinality会计算每天不重复的客户个数（distinct）。
|
||
|
||
上述请求返回结构如下所示：
|
||
```
|
||
{
|
||
"took": 119,
|
||
"timed_out": false,
|
||
"_shards": {
|
||
"total": 5,
|
||
"successful": 5,
|
||
"skipped": 0,
|
||
"failed": 0
|
||
},
|
||
"hits": {
|
||
"total": {
|
||
"value": 4675,
|
||
"relation": "eq"
|
||
},
|
||
"max_score": null,
|
||
"hits": []
|
||
},
|
||
"aggregations": {
|
||
"daily_sales": {
|
||
"buckets": [
|
||
{
|
||
"key_as_string": "2024-11-14",
|
||
"key": 1731542400000,
|
||
"doc_count": 146,
|
||
"unique_customers": {
|
||
"value": 42
|
||
},
|
||
"revenue": {
|
||
"value": 10578.53125
|
||
},
|
||
"avg_basket_size": {
|
||
"value": 2.1780821917808217
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-15",
|
||
"key": 1731628800000,
|
||
"doc_count": 153,
|
||
"unique_customers": {
|
||
"value": 44
|
||
},
|
||
"revenue": {
|
||
"value": 10448
|
||
},
|
||
"avg_basket_size": {
|
||
"value": 2.183006535947712
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-16",
|
||
"key": 1731715200000,
|
||
"doc_count": 143,
|
||
"unique_customers": {
|
||
"value": 45
|
||
},
|
||
"revenue": {
|
||
"value": 10283.484375
|
||
},
|
||
"avg_basket_size": {
|
||
"value": 2.111888111888112
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-17",
|
||
"key": 1731801600000,
|
||
"doc_count": 140,
|
||
"unique_customers": {
|
||
"value": 42
|
||
},
|
||
"revenue": {
|
||
"value": 10145.5234375
|
||
},
|
||
"avg_basket_size": {
|
||
"value": 2.142857142857143
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-18",
|
||
"key": 1731888000000,
|
||
"doc_count": 139,
|
||
"unique_customers": {
|
||
"value": 42
|
||
},
|
||
"revenue": {
|
||
"value": 12012.609375
|
||
},
|
||
"avg_basket_size": {
|
||
"value": 2.158273381294964
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-19",
|
||
"key": 1731974400000,
|
||
"doc_count": 157,
|
||
"unique_customers": {
|
||
"value": 43
|
||
},
|
||
"revenue": {
|
||
"value": 11009.45703125
|
||
},
|
||
"avg_basket_size": {
|
||
"value": 2.0955414012738856
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-20",
|
||
"key": 1732060800000,
|
||
"doc_count": 145,
|
||
"unique_customers": {
|
||
"value": 44
|
||
},
|
||
"revenue": {
|
||
"value": 10720.59375
|
||
},
|
||
"avg_basket_size": {
|
||
"value": 2.179310344827586
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-21",
|
||
"key": 1732147200000,
|
||
"doc_count": 152,
|
||
"unique_customers": {
|
||
"value": 43
|
||
},
|
||
"revenue": {
|
||
"value": 11185.3671875
|
||
},
|
||
"avg_basket_size": {
|
||
"value": 2.1710526315789473
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-22",
|
||
"key": 1732233600000,
|
||
"doc_count": 163,
|
||
"unique_customers": {
|
||
"value": 44
|
||
},
|
||
"revenue": {
|
||
"value": 13560.140625
|
||
},
|
||
"avg_basket_size": {
|
||
"value": 2.2576687116564416
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-23",
|
||
"key": 1732320000000,
|
||
"doc_count": 141,
|
||
"unique_customers": {
|
||
"value": 45
|
||
},
|
||
"revenue": {
|
||
"value": 9884.78125
|
||
},
|
||
"avg_basket_size": {
|
||
"value": 2.099290780141844
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-24",
|
||
"key": 1732406400000,
|
||
"doc_count": 151,
|
||
"unique_customers": {
|
||
"value": 44
|
||
},
|
||
"revenue": {
|
||
"value": 11075.65625
|
||
},
|
||
"avg_basket_size": {
|
||
"value": 2.0927152317880795
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-25",
|
||
"key": 1732492800000,
|
||
"doc_count": 143,
|
||
"unique_customers": {
|
||
"value": 41
|
||
},
|
||
"revenue": {
|
||
"value": 10323.8515625
|
||
},
|
||
"avg_basket_size": {
|
||
"value": 2.167832167832168
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-26",
|
||
"key": 1732579200000,
|
||
"doc_count": 143,
|
||
"unique_customers": {
|
||
"value": 44
|
||
},
|
||
"revenue": {
|
||
"value": 10369.546875
|
||
},
|
||
"avg_basket_size": {
|
||
"value": 2.167832167832168
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-27",
|
||
"key": 1732665600000,
|
||
"doc_count": 142,
|
||
"unique_customers": {
|
||
"value": 46
|
||
},
|
||
"revenue": {
|
||
"value": 11711.890625
|
||
},
|
||
"avg_basket_size": {
|
||
"value": 2.1971830985915495
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-28",
|
||
"key": 1732752000000,
|
||
"doc_count": 161,
|
||
"unique_customers": {
|
||
"value": 43
|
||
},
|
||
"revenue": {
|
||
"value": 12612.6640625
|
||
},
|
||
"avg_basket_size": {
|
||
"value": 2.1180124223602483
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-29",
|
||
"key": 1732838400000,
|
||
"doc_count": 144,
|
||
"unique_customers": {
|
||
"value": 42
|
||
},
|
||
"revenue": {
|
||
"value": 10176.87890625
|
||
},
|
||
"avg_basket_size": {
|
||
"value": 2.0347222222222223
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-30",
|
||
"key": 1732924800000,
|
||
"doc_count": 157,
|
||
"unique_customers": {
|
||
"value": 43
|
||
},
|
||
"revenue": {
|
||
"value": 11480.33203125
|
||
},
|
||
"avg_basket_size": {
|
||
"value": 2.159235668789809
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-01",
|
||
"key": 1733011200000,
|
||
"doc_count": 158,
|
||
"unique_customers": {
|
||
"value": 42
|
||
},
|
||
"revenue": {
|
||
"value": 11533.265625
|
||
},
|
||
"avg_basket_size": {
|
||
"value": 2.0822784810126582
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-02",
|
||
"key": 1733097600000,
|
||
"doc_count": 144,
|
||
"unique_customers": {
|
||
"value": 43
|
||
},
|
||
"revenue": {
|
||
"value": 10499.8125
|
||
},
|
||
"avg_basket_size": {
|
||
"value": 2.201388888888889
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-03",
|
||
"key": 1733184000000,
|
||
"doc_count": 151,
|
||
"unique_customers": {
|
||
"value": 40
|
||
},
|
||
"revenue": {
|
||
"value": 12111.6875
|
||
},
|
||
"avg_basket_size": {
|
||
"value": 2.172185430463576
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-04",
|
||
"key": 1733270400000,
|
||
"doc_count": 145,
|
||
"unique_customers": {
|
||
"value": 40
|
||
},
|
||
"revenue": {
|
||
"value": 10530.765625
|
||
},
|
||
"avg_basket_size": {
|
||
"value": 2.0965517241379312
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-05",
|
||
"key": 1733356800000,
|
||
"doc_count": 157,
|
||
"unique_customers": {
|
||
"value": 43
|
||
},
|
||
"revenue": {
|
||
"value": 11872.5625
|
||
},
|
||
"avg_basket_size": {
|
||
"value": 2.1464968152866244
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-06",
|
||
"key": 1733443200000,
|
||
"doc_count": 158,
|
||
"unique_customers": {
|
||
"value": 42
|
||
},
|
||
"revenue": {
|
||
"value": 12109.453125
|
||
},
|
||
"avg_basket_size": {
|
||
"value": 2.151898734177215
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-07",
|
||
"key": 1733529600000,
|
||
"doc_count": 153,
|
||
"unique_customers": {
|
||
"value": 42
|
||
},
|
||
"revenue": {
|
||
"value": 11057.40625
|
||
},
|
||
"avg_basket_size": {
|
||
"value": 2.111111111111111
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-08",
|
||
"key": 1733616000000,
|
||
"doc_count": 165,
|
||
"unique_customers": {
|
||
"value": 42
|
||
},
|
||
"revenue": {
|
||
"value": 13095.609375
|
||
},
|
||
"avg_basket_size": {
|
||
"value": 2.1818181818181817
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-09",
|
||
"key": 1733702400000,
|
||
"doc_count": 153,
|
||
"unique_customers": {
|
||
"value": 41
|
||
},
|
||
"revenue": {
|
||
"value": 12574.015625
|
||
},
|
||
"avg_basket_size": {
|
||
"value": 2.2287581699346406
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-10",
|
||
"key": 1733788800000,
|
||
"doc_count": 158,
|
||
"unique_customers": {
|
||
"value": 42
|
||
},
|
||
"revenue": {
|
||
"value": 11188.1875
|
||
},
|
||
"avg_basket_size": {
|
||
"value": 2.151898734177215
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-11",
|
||
"key": 1733875200000,
|
||
"doc_count": 160,
|
||
"unique_customers": {
|
||
"value": 42
|
||
},
|
||
"revenue": {
|
||
"value": 12117.65625
|
||
},
|
||
"avg_basket_size": {
|
||
"value": 2.20625
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-12",
|
||
"key": 1733961600000,
|
||
"doc_count": 159,
|
||
"unique_customers": {
|
||
"value": 45
|
||
},
|
||
"revenue": {
|
||
"value": 11558.25
|
||
},
|
||
"avg_basket_size": {
|
||
"value": 2.1823899371069184
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-13",
|
||
"key": 1734048000000,
|
||
"doc_count": 152,
|
||
"unique_customers": {
|
||
"value": 45
|
||
},
|
||
"revenue": {
|
||
"value": 11921.1171875
|
||
},
|
||
"avg_basket_size": {
|
||
"value": 2.289473684210526
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-14",
|
||
"key": 1734134400000,
|
||
"doc_count": 142,
|
||
"unique_customers": {
|
||
"value": 45
|
||
},
|
||
"revenue": {
|
||
"value": 11135.03125
|
||
},
|
||
"avg_basket_size": {
|
||
"value": 2.183098591549296
|
||
}
|
||
}
|
||
]
|
||
}
|
||
}
|
||
}
|
||
```
|
||
#### pipeline aggregation demo
|
||
可以对其他聚合的返回结果应用pipeline aggregation，示例如下：
|
||
```
|
||
GET kibana_sample_data_ecommerce/_search
|
||
{
|
||
"size": 0,
|
||
"aggs": {
|
||
"daily_sales": {
|
||
"date_histogram": {
|
||
"field": "order_date",
|
||
"calendar_interval": "day"
|
||
},
|
||
"aggs": {
|
||
"daily_revenue": {
|
||
"sum": {
|
||
"field": "taxful_total_price"
|
||
}
|
||
},
|
||
"smoothed_revenue": {
|
||
"moving_fn": {
|
||
"buckets_path": "daily_revenue",
|
||
"window": 3,
|
||
"script": "MovingFunctions.unweightedAvg(values)"
|
||
}
|
||
}
|
||
}
|
||
}
|
||
}
|
||
}
|
||
```
|
||
上述示例中，`aggs.daily_sales.aggs.smoothed_revenue`针对`aggs.daily_sales.aggs.daily_revenue`的聚合结果进行了聚合操作，
|
||
|
||
上述返回结果如下所示：
|
||
```
|
||
{
|
||
"took": 13,
|
||
"timed_out": false,
|
||
"_shards": {
|
||
"total": 5,
|
||
"successful": 5,
|
||
"skipped": 0,
|
||
"failed": 0
|
||
},
|
||
"hits": {
|
||
"total": {
|
||
"value": 4675,
|
||
"relation": "eq"
|
||
},
|
||
"max_score": null,
|
||
"hits": []
|
||
},
|
||
"aggregations": {
|
||
"daily_sales": {
|
||
"buckets": [
|
||
{
|
||
"key_as_string": "2024-11-14T00:00:00.000Z",
|
||
"key": 1731542400000,
|
||
"doc_count": 146,
|
||
"daily_revenue": {
|
||
"value": 10578.53125
|
||
},
|
||
"smoothed_revenue": {
|
||
"value": null
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-15T00:00:00.000Z",
|
||
"key": 1731628800000,
|
||
"doc_count": 153,
|
||
"daily_revenue": {
|
||
"value": 10448
|
||
},
|
||
"smoothed_revenue": {
|
||
"value": 10578.53125
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-16T00:00:00.000Z",
|
||
"key": 1731715200000,
|
||
"doc_count": 143,
|
||
"daily_revenue": {
|
||
"value": 10283.484375
|
||
},
|
||
"smoothed_revenue": {
|
||
"value": 10513.265625
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-17T00:00:00.000Z",
|
||
"key": 1731801600000,
|
||
"doc_count": 140,
|
||
"daily_revenue": {
|
||
"value": 10145.5234375
|
||
},
|
||
"smoothed_revenue": {
|
||
"value": 10436.671875
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-18T00:00:00.000Z",
|
||
"key": 1731888000000,
|
||
"doc_count": 139,
|
||
"daily_revenue": {
|
||
"value": 12012.609375
|
||
},
|
||
"smoothed_revenue": {
|
||
"value": 10292.3359375
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-19T00:00:00.000Z",
|
||
"key": 1731974400000,
|
||
"doc_count": 157,
|
||
"daily_revenue": {
|
||
"value": 11009.45703125
|
||
},
|
||
"smoothed_revenue": {
|
||
"value": 10813.872395833334
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-20T00:00:00.000Z",
|
||
"key": 1732060800000,
|
||
"doc_count": 145,
|
||
"daily_revenue": {
|
||
"value": 10720.59375
|
||
},
|
||
"smoothed_revenue": {
|
||
"value": 11055.86328125
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-21T00:00:00.000Z",
|
||
"key": 1732147200000,
|
||
"doc_count": 152,
|
||
"daily_revenue": {
|
||
"value": 11185.3671875
|
||
},
|
||
"smoothed_revenue": {
|
||
"value": 11247.553385416666
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-22T00:00:00.000Z",
|
||
"key": 1732233600000,
|
||
"doc_count": 163,
|
||
"daily_revenue": {
|
||
"value": 13560.140625
|
||
},
|
||
"smoothed_revenue": {
|
||
"value": 10971.805989583334
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-23T00:00:00.000Z",
|
||
"key": 1732320000000,
|
||
"doc_count": 141,
|
||
"daily_revenue": {
|
||
"value": 9884.78125
|
||
},
|
||
"smoothed_revenue": {
|
||
"value": 11822.033854166666
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-24T00:00:00.000Z",
|
||
"key": 1732406400000,
|
||
"doc_count": 151,
|
||
"daily_revenue": {
|
||
"value": 11075.65625
|
||
},
|
||
"smoothed_revenue": {
|
||
"value": 11543.4296875
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-25T00:00:00.000Z",
|
||
"key": 1732492800000,
|
||
"doc_count": 143,
|
||
"daily_revenue": {
|
||
"value": 10323.8515625
|
||
},
|
||
"smoothed_revenue": {
|
||
"value": 11506.859375
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-26T00:00:00.000Z",
|
||
"key": 1732579200000,
|
||
"doc_count": 143,
|
||
"daily_revenue": {
|
||
"value": 10369.546875
|
||
},
|
||
"smoothed_revenue": {
|
||
"value": 10428.096354166666
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-27T00:00:00.000Z",
|
||
"key": 1732665600000,
|
||
"doc_count": 142,
|
||
"daily_revenue": {
|
||
"value": 11711.890625
|
||
},
|
||
"smoothed_revenue": {
|
||
"value": 10589.684895833334
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-28T00:00:00.000Z",
|
||
"key": 1732752000000,
|
||
"doc_count": 161,
|
||
"daily_revenue": {
|
||
"value": 12612.6640625
|
||
},
|
||
"smoothed_revenue": {
|
||
"value": 10801.763020833334
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-29T00:00:00.000Z",
|
||
"key": 1732838400000,
|
||
"doc_count": 144,
|
||
"daily_revenue": {
|
||
"value": 10176.87890625
|
||
},
|
||
"smoothed_revenue": {
|
||
"value": 11564.700520833334
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-11-30T00:00:00.000Z",
|
||
"key": 1732924800000,
|
||
"doc_count": 157,
|
||
"daily_revenue": {
|
||
"value": 11480.33203125
|
||
},
|
||
"smoothed_revenue": {
|
||
"value": 11500.477864583334
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-01T00:00:00.000Z",
|
||
"key": 1733011200000,
|
||
"doc_count": 158,
|
||
"daily_revenue": {
|
||
"value": 11533.265625
|
||
},
|
||
"smoothed_revenue": {
|
||
"value": 11423.291666666666
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-02T00:00:00.000Z",
|
||
"key": 1733097600000,
|
||
"doc_count": 144,
|
||
"daily_revenue": {
|
||
"value": 10499.8125
|
||
},
|
||
"smoothed_revenue": {
|
||
"value": 11063.4921875
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-03T00:00:00.000Z",
|
||
"key": 1733184000000,
|
||
"doc_count": 151,
|
||
"daily_revenue": {
|
||
"value": 12111.6875
|
||
},
|
||
"smoothed_revenue": {
|
||
"value": 11171.13671875
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-04T00:00:00.000Z",
|
||
"key": 1733270400000,
|
||
"doc_count": 145,
|
||
"daily_revenue": {
|
||
"value": 10530.765625
|
||
},
|
||
"smoothed_revenue": {
|
||
"value": 11381.588541666666
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-05T00:00:00.000Z",
|
||
"key": 1733356800000,
|
||
"doc_count": 157,
|
||
"daily_revenue": {
|
||
"value": 11872.5625
|
||
},
|
||
"smoothed_revenue": {
|
||
"value": 11047.421875
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-06T00:00:00.000Z",
|
||
"key": 1733443200000,
|
||
"doc_count": 158,
|
||
"daily_revenue": {
|
||
"value": 12109.453125
|
||
},
|
||
"smoothed_revenue": {
|
||
"value": 11505.005208333334
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-07T00:00:00.000Z",
|
||
"key": 1733529600000,
|
||
"doc_count": 153,
|
||
"daily_revenue": {
|
||
"value": 11057.40625
|
||
},
|
||
"smoothed_revenue": {
|
||
"value": 11504.260416666666
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-08T00:00:00.000Z",
|
||
"key": 1733616000000,
|
||
"doc_count": 165,
|
||
"daily_revenue": {
|
||
"value": 13095.609375
|
||
},
|
||
"smoothed_revenue": {
|
||
"value": 11679.807291666666
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-09T00:00:00.000Z",
|
||
"key": 1733702400000,
|
||
"doc_count": 153,
|
||
"daily_revenue": {
|
||
"value": 12574.015625
|
||
},
|
||
"smoothed_revenue": {
|
||
"value": 12087.489583333334
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-10T00:00:00.000Z",
|
||
"key": 1733788800000,
|
||
"doc_count": 158,
|
||
"daily_revenue": {
|
||
"value": 11188.1875
|
||
},
|
||
"smoothed_revenue": {
|
||
"value": 12242.34375
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-11T00:00:00.000Z",
|
||
"key": 1733875200000,
|
||
"doc_count": 160,
|
||
"daily_revenue": {
|
||
"value": 12117.65625
|
||
},
|
||
"smoothed_revenue": {
|
||
"value": 12285.9375
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-12T00:00:00.000Z",
|
||
"key": 1733961600000,
|
||
"doc_count": 159,
|
||
"daily_revenue": {
|
||
"value": 11558.25
|
||
},
|
||
"smoothed_revenue": {
|
||
"value": 11959.953125
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-13T00:00:00.000Z",
|
||
"key": 1734048000000,
|
||
"doc_count": 152,
|
||
"daily_revenue": {
|
||
"value": 11921.1171875
|
||
},
|
||
"smoothed_revenue": {
|
||
"value": 11621.364583333334
|
||
}
|
||
},
|
||
{
|
||
"key_as_string": "2024-12-14T00:00:00.000Z",
|
||
"key": 1734134400000,
|
||
"doc_count": 142,
|
||
"daily_revenue": {
|
||
"value": 11135.03125
|
||
},
|
||
"smoothed_revenue": {
|
||
"value": 11865.674479166666
|
||
}
|
||
}
|
||
]
|
||
}
|
||
}
|
||
}
|
||
```
|