1. 背景

Elasticsearch可广泛应用于日志分析、全文检索、结构化数据分析等多种场景，大幅度降低维护多套专用系统的成本，在开源社区非常受欢迎。然而Elasticsearch为满足多种不同的使用场景，底层组合使用了多种数据结构，部分数据结构对具体的用户使用场景可能是冗余的，从而导致默认情况下无法达到性能和成本最优化。
幸运的是，Elasticsearch提供非常灵活的模板配置能力，用户可以按需进行优化。多数情况下，用户结合使用场景进行优化后，Elasticsearch的性能都会有数倍的提升，成本也对应有倍数级别的下降。本文主要介绍不同日志使用场景下的调优经验。

2. 日志处理基本流程

日志处理的基本流程包含：日志采集 -> 数据清洗 -> 存储 -> 可视化分析。Elastic Stack提供完整的日志解决方案，帮助用户完成对日志处理全链路的管理，推荐大家使用。每个流程的处理如下：

• 日志采集：从业务所在的机器上，较实时的采集日志传递给下游。常用开源组件如Beats、Logstash、Fluentd等。
  
• 数据清洗：利用正则解析等机制，完成日志从文本数据到结构化数据的转换。用户可使用Logstash 或 Elasticsearch Ingest模块等完成数据清洗。
  
• 存储：使用Elasticsearch对数据进行持久存储，并提供全文搜索和分析能力。
  
• 可视化分析：通过图形界面，完成对日志的搜索分析，常用的开源组件如Kibana、Grafana。
  
  
  使用Elastic Stack处理日志的详细过程，用户可参考官方文章[Getting started with the Elastic Stack](https://www.elastic.co/guide/e ... k.html)，这里不展开介绍。
  
  
  

  3. 日志场景调优
  

         对于Elasticsearch的通用调优，之前分享的文章[Elasticsearch调优实践](https://cloud.tencent.com/developer/article/1156231)，详细介绍了Elasticsearch在性能、稳定性方面的调优经验。而对于日志场景，不同的场景使用方式差别较大，这里主要介绍常见使用方式下，性能和成本的优化思路。
  
  

  3.1 基础场景
  

  对于多数简单日志使用场景，用户一般只要求存储原始日志，并提供按关键字搜索日志记录的能力。对于此类场景，用户可跳过数据清洗阶段，并参考如下方式进行优化：
  
  

• 建议打开最优压缩，一般可降低40%存储。
  
• 设置原始日志字段（message）为text，去除keyword类型子字段，提供全文搜索能力，降低存储。
  
• 关闭_all索引，前面已通过message提供全文搜索能力。
  
• 对于其他字符串字段，统一设置为keyword类型，避免默认情况下字符串字段同时存储text、keyword两种类型的数据。
  
• 使用开源组件（如Beats）上报数据时会包含较多辅助信息，用户可通过修改组件配置文件进行裁剪。
  
  这样去除message的keyword子字段、_all等冗余信息后，再加上最优压缩，可以保证数据相对精简。下面给出这类场景的常用模板，供用户参考：
  <br /> {<br /> "order": 5,<br /> "template": "my_log_*",<br /> "settings": {<br /> "translog.durability": "async",<br /> "translog.sync_interval": "5s",<br /> "index.refresh_interval": "30s",<br /> "index.codec": "best_compression" # 最优压缩<br /> },<br /> "mappings": {<br /> "_default_": {<br /> "_all": { # 关闭_all索引<br /> "enabled": false<br /> },<br /> "dynamic_templates": [<br /> {<br /> "log": { # 原始日志字段，分词建立索引<br /> "match": "message",<br /> "mapping": {<br /> "type": "text"<br /> }<br /> }<br /> },<br /> {<br /> "strings": { # 其他字符串字段，统一设置为keyword类型<br /> "match_mapping_type": "string",<br /> "mapping": {<br /> "type": "keyword"<br /> }<br /> }<br /> }<br /> ]<br /> }<br /> }<br /> }<br />
  
  
  

  3.2 精准搜索场景
  

  对于部分用户，普通的全文检索并不能满足需求，希望精准搜索日志中的某部分，例如每条日志中包含程序运行时多个阶段的耗时数据，对具体一个阶段的耗时进行搜索就比较麻烦。对于此类场景，用户可基于基础场景，进行如下调整：
  
  

• 清洗过程中，可仅解析出需要精准搜索的部分作为独立字段，用于精准搜索。
  
• 对于精准搜索字段，如果无排序/聚合需求，可以关闭doc_values；对于字符串，一般使用keyword，可按需考虑使用text。
  
  下面给出这类场景的常用模板，供用户参考：
  <br /> {<br /> "order": 5,<br /> "template": "my_log_*",<br /> "settings": {<br /> "translog.durability": "async",<br /> "translog.sync_interval": "5s",<br /> "index.refresh_interval": "30s",<br /> "index.codec": "best_compression" # 最优压缩<br /> },<br /> "mappings": {<br /> "_default_": {<br /> "_all": { # 关闭_all索引<br /> "enabled": false<br /> },<br /> "dynamic_templates": [<br /> {<br /> "log": { # 原始日志字段，分词建立索引<br /> "match": "message",<br /> "mapping": {<br /> "type": "text"<br /> }<br /> }<br /> },<br /> {<br /> "precise_fieldx": { # 精准搜索字段<br /> "match": "fieldx",<br /> "mapping": {<br /> "type": "keyword",<br /> "doc_values": false<br /> }<br /> }<br /> },<br /> {<br /> "strings": { # 其他字符串字段，统一设置为keyword类型<br /> "match_mapping_type": "string",<br /> "mapping": {<br /> "type": "keyword"<br /> }<br /> }<br /> }<br /> ]<br /> }<br /> }<br /> }<br />
  
  

  3.3 统计分析场景
  

  对于某些场景，日志包含的主要是程序运行时输出的统计信息，用户通常会完全解析日志进行精确查询、统计分析，而是否保存原始日志关系不大。对于此类场景，用户可进行如下调整：
  
  

• 清洗过程中，解析出所有需要的数据作为独立字段；原始日志非必要时，建议去除。
  
• 如果有强需求保留原始日志，可以设置该字段enabled属性为false，只存储不索引。
  
• 多数字段保持默认即可，会自动建立索引、打开doc_values，可用于查询、排序、聚合。
  
• 对部分无排序/聚合需求、开销高的字段，可以关闭doc_values。
  
  
  下面给出这类场景的常用模板，供用户参考：
  
  
  <br /> {<br /> "order": 5,<br /> "template": "my_log_*",<br /> "settings": {<br /> "translog.durability": "async",<br /> "translog.sync_interval": "5s",<br /> "index.refresh_interval": "30s",<br /> "index.codec": "best_compression" # 最优压缩<br /> },<br /> "mappings": {<br /> "_default_": {<br /> "_all": { # 关闭_all索引<br /> "enabled": false<br /> },<br /> "dynamic_templates": [<br /> {<br /> "log": { # 原始日志字段，关闭索引<br /> "match": "message",<br /> "mapping": {<br /> "enabled": false<br /> }<br /> }<br /> },<br /> {<br /> "index_only_fieldx": { # 仅索引的字段，无排序/聚合需求<br /> "match": "fieldx",<br /> "mapping": {<br /> "type": "keyword",<br /> "doc_values": false<br /> }<br /> }<br /> },<br /> {<br /> "strings": { # 其他字符串字段，统一设置为keyword类型<br /> "match_mapping_type": "string",<br /> "mapping": {<br /> "type": "keyword"<br /> }<br /> }<br /> }<br /> ]<br /> }<br /> }<br /> }<br />
  

  ES 5.1及之后的版本，支持关键字查询时[自动选择目标字段](https://www.elastic.co/guide/e ... _field)，用户没有必要再使用原始日志字段提供不指定字段进行查询的能力。
  
  
  

  4. 小结
  

  日志的使用方式比较灵活，本文结合常见的客户使用方式，从整体上对性能、成本进行优化。用户也可结合自身业务场景，参考文章[Elasticsearch调优实践](https://cloud.tencent.com/developer/article/1156231)进行更细致的优化。
  
  
  
  
  
  
  

当我们搭建elasitc stack集群时，大多数时候会在我们的架构中加入kafka作为消息缓冲区，即从beats -> kafka -> logstash -> elasticsearch这样的一个消息流。使用kafka可以给我们带来很多便利，但是也让我们需要额外多维护一套组件，elasitc stack本身已经提供了monitoring的功能，我们可以方便的从kibana上监控各个组件中各节点的可用性，吞吐和性能等各种指标，但kafka作为架构中的组件之一却游离在监控之外，相当不合理。

幸而elastic真的是迭代的相当快，在metricbeat上很早就有了对kafka的监控，但一直没有一个直观的dashboard，终于在6.5版本上，上新了kafka dashboard。我们来看一下吧。

安装和配置metricbeat

[安装包下载地址](https://www.elastic.co/downloads/beats/metricbeat)，下载后，自己安装。
然后，将/etc/metricbeat/modules.d/kafka.yml.disable文件重命名为/etc/metricbeat/modules.d/kafka.yml。(即打开kafka的监控)。稍微修改一下文件内容, 注意，这里需填入所有你需要监控的kafka服务器的地址：

```

Module: kafka

Docs: https://www.elastic.co/guide/e ... .html

• module: kafka
  metricsets:
  
  • partition
    
  • consumergroup
    period: 20s
    hosts: ["10...:9092","10...:9092","10...:9092","10...:9092"]
    
    

    client_id: metricbeat
    

    retries: 3
    

    backoff: 250ms
    

    
    

    List of Topics to query metadata for. If empty, all topics will be queried.
    

    topics: []
    

    
    

    Optional SSL. By default is off.
    

    List of root certificates for HTTPS server verifications
    

    ssl.certificate_authorities: ["/etc/pki/root/ca.pem"]
    

    
    

    Certificate for SSL client authentication
    

    ssl.certificate: "/etc/pki/client/cert.pem"
    

    
    

    Client Certificate Key
    

    ssl.key: "/etc/pki/client/cert.key"
    

    
    

    SASL authentication
    

    username: ""
    

    password: ""
    

    ```
    运行metricbeat，这里，一定要注意enable kibana dashboard。
    
    然后就可以在kibana里面看到：
    
    

    

    
    

    

    
    
    
    这样，我们就可以通过sentinl等类似的插件，自动做kafka的告警等功能了

@[toc]
搭建Elasitc stack集群时，我们往往把大部分注意力放在集群的搭建，索引的优化，分片的设置上等具体的调优参数上，很少有人会去关心Elasitc stack的日志配置的问题，大概是觉得，日志应该是一个公共的问题，默认的配置应该已经为我们处理好了。但很不幸，在不同的机器配置或者不同的运营策略下，如果采用默认的配置，会给我们带来麻烦。

默认配置带来的麻烦

以下例子是默认情况下，当Elasitc stack集群运行超过3个月之后的情况：

elasticsearch

elasticsearch默认情况下会每天rolling一个文件，当到达2G的时候，才开始清除超出的部分，当一个文件只有几十K的时候，文件会一直累计下来。

logstash

一直增长的gc文件和不停增多的rolling日志文件

kibana

默认日志输出到kibana.out文件当中，这个文件会变得越来越大

kafka

这里提到kafka是因为在大部分的架构当中，我们都会用到kafka作为中间件数据缓冲区，因此不得不维护kafka集群。同样，如果不做特定的配置，也会遇到日志的问题：不停增多的rolling日志文件

原因是kafka的默认log4j配置是使用DailyRollingFileAppender每隔一个小时生成一个文件 '.'yyyy-MM-dd-HH：
<br /> log4j.appender.stdout=org.apache.log4j.ConsoleAppender<br /> log4j.appender.stdout.layout=org.apache.log4j.PatternLayout<br /> log4j.appender.stdout.layout.ConversionPattern=[%d] %p %m (%c)%n<br /> <br /> log4j.appender.kafkaAppender=org.apache.log4j.DailyRollingFileAppender<br /> log4j.appender.kafkaAppender.DatePattern='.'yyyy-MM-dd-HH<br /> log4j.appender.kafkaAppender.File=${kafka.logs.dir}/server.log<br /> log4j.appender.kafkaAppender.layout=org.apache.log4j.PatternLayout<br /> log4j.appender.kafkaAppender.layout.ConversionPattern=[%d] %p %m (%c)%n<br /> <br /> log4j.appender.stateChangeAppender=org.apache.log4j.DailyRollingFileAppender<br /> log4j.appender.stateChangeAppender.DatePattern='.'yyyy-MM-dd-HH<br /> log4j.appender.stateChangeAppender.File=${kafka.logs.dir}/state-change.log<br /> log4j.appender.stateChangeAppender.layout=org.apache.log4j.PatternLayout<br /> log4j.appender.stateChangeAppender.layout.ConversionPattern=[%d] %p %m (%c)%n<br /> <br /> log4j.appender.requestAppender=org.apache.log4j.DailyRollingFileAppender<br /> log4j.appender.requestAppender.DatePattern='.'yyyy-MM-dd-HH<br /> log4j.appender.requestAppender.File=${kafka.logs.dir}/kafka-request.log<br /> log4j.appender.requestAppender.layout=org.apache.log4j.PatternLayout<br /> log4j.appender.requestAppender.layout.ConversionPattern=[%d] %p %m (%c)%n<br /> <br /> log4j.appender.cleanerAppender=org.apache.log4j.DailyRollingFileAppender<br /> log4j.appender.cleanerAppender.DatePattern='.'yyyy-MM-dd-HH<br /> log4j.appender.cleanerAppender.File=${kafka.logs.dir}/log-cleaner.log<br /> log4j.appender.cleanerAppender.layout=org.apache.log4j.PatternLayout<br /> log4j.appender.cleanerAppender.layout.ConversionPattern=[%d] %p %m (%c)%n<br /> <br /> log4j.appender.controllerAppender=org.apache.log4j.DailyRollingFileAppender<br /> log4j.appender.controllerAppender.DatePattern='.'yyyy-MM-dd-HH<br /> log4j.appender.controllerAppender.File=${kafka.logs.dir}/controller.log<br /> log4j.appender.controllerAppender.layout=org.apache.log4j.PatternLayout<br /> log4j.appender.controllerAppender.layout.ConversionPattern=[%d] %p %m (%c)%n<br /> <br /> log4j.appender.authorizerAppender=org.apache.log4j.DailyRollingFileAppender<br /> log4j.appender.authorizerAppender.DatePattern='.'yyyy-MM-dd-HH<br /> log4j.appender.authorizerAppender.File=${kafka.logs.dir}/kafka-authorizer.log<br /> log4j.appender.authorizerAppender.layout=org.apache.log4j.PatternLayout<br /> log4j.appender.authorizerAppender.layout.ConversionPattern=[%d] %p %m (%c)%n<br />

解决方案

因此，对于我们需要维护的这几个组件，需要配置合理的日志rotate策略。一个比较常用的策略就是时间+size，每天rotate一个日志文件或者每当日志文件大小超过256M，rotate一个新的日志文件，并且最多保留7天之内的日志文件。

elasticsearch 

通过修改log4j2.properties文件来解决。该文件在/etc/elasticsesarch目录下（或者config目录）。
默认配置是：
<br /> appender.rolling.type = RollingFile <br /> appender.rolling.name = rolling<br /> appender.rolling.fileName = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs.cluster_name}.log <br /> appender.rolling.layout.type = PatternLayout<br /> appender.rolling.layout.pattern = [%d{ISO8601}][%-5p][%-25c{1.}] [%node_name]%marker %.-10000m%n<br /> appender.rolling.filePattern = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs.cluster_name}-%d{yyyy-MM-dd}-%i.log.gz <br /> appender.rolling.policies.type = Policies<br /> appender.rolling.policies.time.type = TimeBasedTriggeringPolicy <br /> appender.rolling.policies.time.interval = 1 <br /> appender.rolling.policies.time.modulate = true <br /> appender.rolling.policies.size.type = SizeBasedTriggeringPolicy <br /> appender.rolling.policies.size.size = 256MB <br /> appender.rolling.strategy.type = DefaultRolloverStrategy<br /> appender.rolling.strategy.fileIndex = nomax<br /> appender.rolling.strategy.action.type = Delete <br /> appender.rolling.strategy.action.basepath = ${sys:es.logs.base_path}<br /> appender.rolling.strategy.action.condition.type = IfFileName <br /> appender.rolling.strategy.action.condition.glob = ${sys:es.logs.cluster_name}-* <br /> appender.rolling.strategy.action.condition.nested_condition.type = IfAccumulatedFileSize <br /> appender.rolling.strategy.action.condition.nested_condition.exceeds = 2GB <br />
以上默认配置，会保存2GB的日志，只有累计的日志大小超过2GB的时候，才会删除旧的日志文件。
建议改为如下配置，仅保留最近7天的日志
<br /> appender.rolling.strategy.type = DefaultRolloverStrategy<br /> appender.rolling.strategy.action.type = Delete<br /> appender.rolling.strategy.action.basepath = ${sys:es.logs.base_path}<br /> appender.rolling.strategy.action.condition.type = IfFileName<br /> appender.rolling.strategy.action.condition.glob = ${sys:es.logs.cluster_name}-*<br /> appender.rolling.strategy.action.condition.nested_condition.type = IfLastModified<br /> appender.rolling.strategy.action.condition.nested_condition.age = 7D<br />
这里必须注意，log4j2会因为末尾的空格导致无法识别配置

logstash

与elasticsearch类似，通过修改log4j2.properties文件来解决。该文件在/etc/logstash目录下（或者config目录）。
默认配置是不会删除历史日志的：
<br /> status = error<br /> name = LogstashPropertiesConfig<br /> <br /> appender.console.type = Console<br /> appender.console.name = plain_console<br /> appender.console.layout.type = PatternLayout<br /> appender.console.layout.pattern = [%d{ISO8601}][%-5p][%-25c] %m%n<br /> <br /> appender.json_console.type = Console<br /> appender.json_console.name = json_console<br /> appender.json_console.layout.type = JSONLayout<br /> appender.json_console.layout.compact = true<br /> appender.json_console.layout.eventEol = true<br /> <br /> appender.rolling.type = RollingFile<br /> appender.rolling.name = plain_rolling<br /> appender.rolling.fileName = ${sys:ls.logs}/logstash-${sys:ls.log.format}.log<br /> appender.rolling.filePattern = ${sys:ls.logs}/logstash-${sys:ls.log.format}-%d{yyyy-MM-dd}.log<br /> appender.rolling.policies.type = Policies<br /> appender.rolling.policies.time.type = TimeBasedTriggeringPolicy<br /> appender.rolling.policies.time.interval = 1<br /> appender.rolling.policies.time.modulate = true<br /> appender.rolling.layout.type = PatternLayout<br /> appender.rolling.layout.pattern = [%d{ISO8601}][%-5p][%-25c] %-.10000m%n<br />
需手动加上：
<br /> appender.rolling.strategy.type = DefaultRolloverStrategy<br /> appender.rolling.strategy.action.type = Delete<br /> appender.rolling.strategy.action.basepath = ${sys:ls.logs}<br /> appender.rolling.strategy.action.condition.type = IfFileName<br /> appender.rolling.strategy.action.condition.glob = ${sys:ls.logs}/logstash-${sys:ls.log.format}<br /> appender.rolling.strategy.action.condition.nested_condition.type = IfLastModified<br /> appender.rolling.strategy.action.condition.nested_condition.age = 7D<br />

kibana

在kibana的配置文件中，只有以下几个选项：
<br /> logging.dest:<br /> Default: stdout Enables you specify a file where Kibana stores log output.<br /> logging.quiet:<br /> Default: false Set the value of this setting to true to suppress all logging output other than error messages.<br /> logging.silent:<br /> Default: false Set the value of this setting to true to suppress all logging output.<br /> logging.verbose:<br /> Default: false Set the value of this setting to true to log all events, including system usage information and all requests. Supported on Elastic Cloud Enterprise.<br /> logging.timezone<br /> Default: UTC Set to the canonical timezone id (e.g. US/Pacific) to log events using that timezone. A list of timezones can be referenced at <a href="https://en.wikipedia.org/wiki/List_of_tz_database_time_zones." rel="nofollow" target="_blank">https://en.wikipedia.org/wiki/ ... ones.</a><br />
我们可以指定输出的日志文件与日志内容，但是却不可以配置日志的rotate。这时，我们需要使用logrotate，这个linux默认安装的工具。
首先，我们要在配置文件里面指定生成pid文件：
<br /> pid.file: "pid.log"<br />
然后，修改/etc/logrotate.conf:
<br /> /var/log/kibana {<br />     missingok<br />     notifempty<br />     sharedscripts<br />     daily<br />     rotate 7<br />     copytruncate<br />     /bin/kill -HUP $(cat /usr/share/kibana/pid.log 2>/dev/null) 2>/dev/null<br />     endscript<br /> }<br />

kafka

如果不想写脚本清理过多的文件的话，需要修改config/log4j.properties文件。使用RollingFileAppender代替DailyRollingFileAppender，同时设置MaxFileSize和MaxBackupIndex。即修改为：
```

Licensed to the Apache Software Foundation (ASF) under one or more

contributor license agreements.  See the NOTICE file distributed with

this work for additional information regarding copyright ownership.

The ASF licenses this file to You under the Apache License, Version 2.0

(the "License"); you may not use this file except in compliance with

the License.  You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software

distributed under the License is distributed on an "AS IS" BASIS,

WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

See the License for the specific language governing permissions and

limitations under the License.

log4j.rootLogger=INFO, stdout

log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
log4j.appender.stdout.layout.ConversionPattern=[%d] %p %m (%c)%n

log4j.appender.kafkaAppender=org.apache.log4j.RollingFileAppender
log4j.appender.kafkaAppender.File=${kafka.logs.dir}/server.log
log4j.appender.kafkaAppender.MaxFileSize=10MB
log4j.appender.kafkaAppender.MaxBackupIndex=10
log4j.appender.kafkaAppender.layout=org.apache.log4j.PatternLayout
log4j.appender.kafkaAppender.layout.ConversionPattern=[%d] %p %m (%c)%n

log4j.appender.stateChangeAppender=org.apache.log4j.RollingFileAppender
log4j.appender.stateChangeAppender.File=${kafka.logs.dir}/state-change.log
log4j.appender.stateChangeAppender.MaxFileSize=10M
log4j.appender.stateChangeAppender.MaxBackupIndex=10
log4j.appender.stateChangeAppender.layout=org.apache.log4j.PatternLayout
log4j.appender.stateChangeAppender.layout.ConversionPattern=[%d] %p %m (%c)%n

log4j.appender.requestAppender=org.apache.log4j.RollingFileAppender
log4j.appender.requestAppender.File=${kafka.logs.dir}/kafka-request.log
log4j.appender.requestAppender.MaxFileSize=10MB
log4j.appender.requestAppender.MaxBackupIndex=10
log4j.appender.requestAppender.layout=org.apache.log4j.PatternLayout
log4j.appender.requestAppender.layout.ConversionPattern=[%d] %p %m (%c)%n

log4j.appender.cleanerAppender=org.apache.log4j.RollingFileAppender
log4j.appender.cleanerAppender.File=${kafka.logs.dir}/log-cleaner.log
log4j.appender.cleanerAppender.MaxFileSize=10MB
log4j.appender.cleanerAppender.MaxBackupIndex=10
log4j.appender.cleanerAppender.layout=org.apache.log4j.PatternLayout
log4j.appender.cleanerAppender.layout.ConversionPattern=[%d] %p %m (%c)%n

log4j.appender.controllerAppender=org.apache.log4j.RollingFileAppender
log4j.appender.controllerAppender.File=${kafka.logs.dir}/controller.log
log4j.appender.controllerAppender.MaxFileSize=10MB
log4j.appender.controllerAppender.MaxBackupIndex=10
log4j.appender.controllerAppender.layout=org.apache.log4j.PatternLayout
log4j.appender.controllerAppender.layout.ConversionPattern=[%d] %p %m (%c)%n

log4j.appender.authorizerAppender=org.apache.log4j.RollingFileAppender
log4j.appender.authorizerAppender.File=${kafka.logs.dir}/kafka-authorizer.log
log4j.appender.authorizerAppender.MaxFileSize=10MB
log4j.appender.authorizerAppender.MaxBackupIndex=10
log4j.appender.authorizerAppender.layout=org.apache.log4j.PatternLayout
log4j.appender.authorizerAppender.layout.ConversionPattern=[%d] %p %m (%c)%n

Turn on all our debugging info

log4j.logger.kafka.producer.async.DefaultEventHandler=DEBUG, kafkaAppender

log4j.logger.kafka.client.ClientUtils=DEBUG, kafkaAppender

log4j.logger.kafka.perf=DEBUG, kafkaAppender

log4j.logger.kafka.perf.ProducerPerformance$ProducerThread=DEBUG, kafkaAppender

log4j.logger.org.I0Itec.zkclient.ZkClient=DEBUG

log4j.logger.kafka=INFO, kafkaAppender

log4j.logger.kafka.network.RequestChannel$=WARN, requestAppender
log4j.additivity.kafka.network.RequestChannel$=false

log4j.logger.kafka.network.Processor=TRACE, requestAppender

log4j.logger.kafka.server.KafkaApis=TRACE, requestAppender

log4j.additivity.kafka.server.KafkaApis=false

log4j.logger.kafka.request.logger=WARN, requestAppender
log4j.additivity.kafka.request.logger=false

log4j.logger.kafka.controller=TRACE, controllerAppender
log4j.additivity.kafka.controller=false

log4j.logger.kafka.log.LogCleaner=INFO, cleanerAppender
log4j.additivity.kafka.log.LogCleaner=false

log4j.logger.state.change.logger=TRACE, stateChangeAppender
log4j.additivity.state.change.logger=false

Change this to debug to get the actual audit log for authorizer.

log4j.logger.kafka.authorizer.logger=WARN, authorizerAppender
log4j.additivity.kafka.authorizer.logger=false
```

使用过Kibana的同学应该都知道，当我们在kibana的配置文件中打开或者关闭功能，或者安装、卸载额外的插件后，重启kibana会触发一个优化的过程（optimize），如下图：

这个过程或长或短，视你电脑的性能而定。这里简单介绍一下该过程所要完成的事情。

Kibana是一个单页Web应用

首先，Kibana是一个单页的web应用。何为单页web应用？即所有的页面的读取都是在浏览器上完成，而与后台服务器无关。与后台服务器的通信只关乎数据，而非页面。所以，应用上所有的UI都被打包在一起，一次性的发送到了浏览器端，而不是通过URL到后台进行获取。所以，我们看到kibana的首页是下面这样的：
<a href="http://localhost:5601/app/kibana#/" rel="nofollow" target="_blank">http://localhost:5601/app/kibana#/</a>
注意这里的#后，代表#后面的内容会被浏览器提取，不往服务器端进行url的情况，而是在浏览器上进行内部重新渲染。因为所有的页面都是存储在浏览器的，所有在初次访问的时候，会加载大量的代码到浏览器端，这些代码都是被压缩过的bundle文件：

而optimize的过程，就是把这些原本可读性的源代码压缩为bundle.js的过程。因此，每当你对Kibana进行裁剪之后重启，因为前端的部分是完全由浏览器负责的，所有bundle文件需要重新生成后再发给浏览器，所以会触发optimize的过程。

Kibana在6.2.0版本之后，常规版本已经默认自带了xpack（当然，你还是可以直接下载不带xpack的开源社区版），导致Kibana的size已经到了200M左右，而且越往后的版本，功能越多，代码量越大，每次optimize的过程都会耗费更多的时间。一般来说，我们会将Kibana部署在单独的机器上，因为这仅仅是一个web后端，通常我们不会分配比较优质的资源，（2C4G都算浪费的了），这种情况下面，每次我们裁剪后重启Kibana都会耗费半个小时～1个小时的时间，更有甚者直接hang住，查看系统日志才知道OOM了。

Nodejs的内存机制

Kibana是用Nodejs编写的程序，在一般的后端语言中，基本的内存使用上基本没有什么限制，但是在nodeJs中却只能使用部分内存。在64位系统下位约为1.4G，在32位系统下约为0.7G，造成这个问题的主要原因是因为nodeJs基于V8构建，V8使用自己的方式来管理和分配内存，这一套管理方式在浏览器端使用绰绰有余，但是在nodeJs中这却限制了开发者，在应用中如果碰到了这个限制，就会造成进程退出。

Nodejs内存机制对Kibana优化的影响

因为Kibana的代码体量越来越大，将所有的代码加载到内存之后，再解析语法树，进行bundle的转换所耗费的内存已经接近1.4G的限制了，当你安装更多插件，比如sentinl的时候，系统往往已经无法为继，导致Kibana无法启动

解决方案

这种情况下，我们需要在Kibana启动的时候，指定NodeJs使用更多的内存。这个可以通过设置Node的环境变量办到。
<br /> NODE_OPTIONS="--max-old-space-size=4096"<br />
当然，我的建议是直接指定在kibana的启动脚本当中，修改/usr/share/kibana/bin/kibana文件为：
```shell

!/bin/sh

SCRIPT=$0

SCRIPT may be an arbitrarily deep series of symlinks. Loop until we have the concrete path.

while [ -h "$SCRIPT" ] ; do
ls=$(ls -ld "$SCRIPT")

Drop everything prior to ->

link=$(expr "$ls" : '.-> (.)$')
if expr "$link" : '/.*' > /dev/null; then
SCRIPT="$link"
else
SCRIPT=$(dirname "$SCRIPT")/"$link"
fi
done

DIR="$(dirname "${SCRIPT}")/.."
NODE="${DIR}/node/bin/node"
test -x "$NODE" || NODE=$(which node)
if [ ! -x "$NODE" ]; then
echo "unable to find usable node.js executable."
exit 1
fi

NODE_ENV=production exec "${NODE}" $NODE_OPTIONS --max_old_space_size=3072 --no-warnings "${DIR}/src/cli" ${@}

``<br /> 改动在最后一句：NODE_ENV=production exec "${NODE}" $NODE_OPTIONS --max_old_space_size=3072 --no-warnings "${DIR}/src/cli" ${@}`

这样，我们可以保证Kibana能顺利的完成optimize的过程

1、当Elasticsearch遇见Kafka
http://t.cn/EUwFsy6
2、Elasticsearch检索 — 聚合和LBS
http://t.cn/EU7qsRb
3、有赞订单管理的三生三世与 “十面埋伏”
http://t.cn/EU75ZTF

编辑：叮咚光军
归档：https://elasticsearch.cn/article/6187
订阅：https://tinyletter.com/elastic-daily

本次分享包括两篇文章

• 父子关系维护检索实战一 Elasticsearch 5.x 父子关系维护检索实战
• 父子关系维护检索实战二 Elasticsearch 6.x 父子关系维护检索实战

本文是其中第一篇- Elasticsearch 5.x 父子关系维护检索实战，涵盖以下部分内容：

1. Elasticsearch 5.x 中父子关系mapping结构设计
2. Elasticsearch 5.x 中维护父子关系数据
3. Elasticsearch 5.x 中has_child和has_parent查询的基本用法
4. Elasticsearch 5.x 中如何在检索中同时返回父子数据

案例说明
以一个体检记录相关的数据来介绍本文涉及的相关功能，体检数据包括客户基本信息basic和客户医疗记录medical、客户体检记录exam、客户体检结果分析记录diagnosis，它们之间的关系图如下：


我们采用Elasticsearch java客户端 bboss-elastic 来实现本文相关功能。

1.准备工作
参考文档《高性能elasticsearch ORM开发库使用介绍》导入和配置bboss客户端

2.定义mapping结构-Elasticsearch 5.x 中父子关系mapping结构设计
Elasticsearch 5.x中一个indice mapping支持多个mapping type，通过在子类型mapping中指定父类型的mapping type名字来设置父子关系，例如：
父类型
"basic": {
....
}
子类型：
"medical": { 
      "_parent": { "type": "basic" },
     .................
}
新建dsl配置文件-esmapper/Client_Info.xml，定义完整的mapping结构：createClientIndice

<properties>

  

    <!--

   创建客户信息索引索引表

   -->

    <property name="createClientIndice">

        <![CDATA[{

            "settings": {

                "number_of_shards": 6,

                "index.refresh_interval": "5s"

            },

            "mappings": {

                "basic": {  ##基本信息

                    "properties": {

                        "party_id": {

                            "type": "keyword"

                        },

                        "sex": {

                            "type": "keyword"

                        },

                        "mari_sts": {

                            "type": "keyword"

                        },

                        "ethnic": {

                            "type": "text"

                        },

                        "prof": {

                            "type": "text"

                        },

                        "province": {

                            "type": "text"

                        },

                        "city": {

                            "type": "text"

                        },

                        "client_type": {

                            "type": "keyword"

                        },

                        "client_name": {

                            "type": "text"

                        },

                        "age": {

                            "type": "integer"

                        },

                        "id_type": {

                            "type": "keyword"

                        },

                        "idno": {

                            "type": "keyword"

                        },

                        "education": {

                            "type": "text"

                        },

                        "created_date": {

                            "type": "date",

                            "format": "yyyy-MM-dd HH:mm:ss.SSS||yyyy-MM-dd'T'HH:mm:ss.SSS||yyyy-MM-dd HH:mm:ss||epoch_millis"

                        },

                        "birth_date": {

                            "type": "date",

                            "format": "yyyy-MM-dd HH:mm:ss.SSS||yyyy-MM-dd'T'HH:mm:ss.SSS||yyyy-MM-dd HH:mm:ss||epoch_millis"

                        },

                        "last_modified_date": {

                            "type": "date",

                            "format": "yyyy-MM-dd HH:mm:ss.SSS||yyyy-MM-dd'T'HH:mm:ss.SSS||yyyy-MM-dd HH:mm:ss||epoch_millis"

                        },

                        "etl_date": {

                            "type": "date",

                            "format": "yyyy-MM-dd HH:mm:ss.SSS||yyyy-MM-dd'T'HH:mm:ss.SSS||yyyy-MM-dd HH:mm:ss||epoch_millis"

                        }

                    }

                },

                "diagnosis": { ##结果分析

                    "_parent": {

                        "type": "basic"

                    },

                    "properties": {

                        "party_id": {

                            "type": "keyword"

                        },

                        "provider": {

                            "type": "text"

                        },

                        "subject": {

                            "type": "text"

                        },

                        "diagnosis_type": {

                            "type": "text"

                        },

                        "icd10_code": {

                            "type": "text",

                            "type": "keyword"

                        },

                        "sd_disease_name": {

                            "type": "text",

                            "type": "keyword"

                        },

                        "created_date": {

                            "type": "date",

                            "format": "yyyy-MM-dd HH:mm:ss.SSS||yyyy-MM-dd'T'HH:mm:ss.SSS||yyyy-MM-dd HH:mm:ss||epoch_millis"

                        },

                        "last_modified_date": {

                            "type": "date",

                            "format": "yyyy-MM-dd HH:mm:ss.SSS||yyyy-MM-dd'T'HH:mm:ss.SSS||yyyy-MM-dd HH:mm:ss||epoch_millis"

                        },

                        "etl_date": {

                            "type": "date",

                            "format": "yyyy-MM-dd HH:mm:ss.SSS||yyyy-MM-dd'T'HH:mm:ss.SSS||yyyy-MM-dd HH:mm:ss||epoch_millis"

                        }

                    }

                },

                "medical": { ##医疗情况

                    "_parent": {

                        "type": "basic"

                    },

                    "properties": {

                        "party_id": {

                            "type": "keyword"

                        },

                        "hos_name_yb": {

                            "type": "text"

                        },

                        "eivisions_name": {

                            "type": "text"

                        },

                        "medical_type": {

                            "type": "text"

                        },

                        "medical_common_name": {

                            "type": "text"

                        },

                        "medical_sale_name": {

                            "type": "text"

                        },

                        "medical_code": {

                            "type": "text"

                        },

                        "specification": {

                            "type": "text"

                        },

                        "usage_num": {

                            "type": "text"

                        },

                        "unit": {

                            "type": "text"

                        },

                        "usage_times": {

                            "type": "text"

                        },

                        "created_date": {

                            "type": "date",

                            "format": "yyyy-MM-dd HH:mm:ss.SSS||yyyy-MM-dd'T'HH:mm:ss.SSS||yyyy-MM-dd HH:mm:ss||epoch_millis"

                        },

                        "last_modified_date": {

                            "type": "date",

                            "format": "yyyy-MM-dd HH:mm:ss.SSS||yyyy-MM-dd'T'HH:mm:ss.SSS||yyyy-MM-dd HH:mm:ss||epoch_millis"

                        },

                        "etl_date": {

                            "type": "date",

                            "format": "yyyy-MM-dd HH:mm:ss.SSS||yyyy-MM-dd'T'HH:mm:ss.SSS||yyyy-MM-dd HH:mm:ss||epoch_millis"

                        }

                    }

                },

                "exam": { ##检查结果

                    "_parent": {

                        "type": "basic"

                    },

                    "properties": {

                        "party_id": {

                            "type": "keyword"

                        },

                        "hospital": {

                            "type": "text"

                        },

                        "dept": {

                            "type": "text"

                        },

                        "is_ok": {

                            "type": "text"

                        },

                        "exam_result": {

                            "type": "text"

                        },

                        "fld1": {

                            "type": "text"

                        },

                        "fld2": {

                            "type": "text"

                        },

                        "fld3": {

                            "type": "text"

                        },

                        "fld4": {

                            "type": "text"

                        },

                        "fld5": {

                            "type": "text"

                        },

                        "fld901": {

                            "type": "text"

                        },

                        "fld6": {

                            "type": "text"

                        },

                        "fld902": {

                            "type": "text"

                        },

                        "fld14": {

                            "type": "text"

                        },

                        "fld20": {

                            "type": "text"

                        },

                        "fld21": {

                            "type": "text"

                        },

                        "fld23": {

                            "type": "text"

                        },

                        "fld24": {

                            "type": "text"

                        },

                        "fld65": {

                            "type": "text"

                        },

                        "fld66": {

                            "type": "text"

                        },

                        "fld67": {

                            "type": "text"

                        },

                        "fld68": {

                            "type": "text"

                        },

                        "created_date": {

                            "type": "date",

                            "format": "yyyy-MM-dd HH:mm:ss.SSS||yyyy-MM-dd'T'HH:mm:ss.SSS||yyyy-MM-dd HH:mm:ss||epoch_millis"

                        },

                        "last_modified_date": {

                            "type": "date",

                            "format": "yyyy-MM-dd HH:mm:ss.SSS||yyyy-MM-dd'T'HH:mm:ss.SSS||yyyy-MM-dd HH:mm:ss||epoch_millis"

                        },

                        "etl_date": {

                            "type": "date",

                            "format": "yyyy-MM-dd HH:mm:ss.SSS||yyyy-MM-dd'T'HH:mm:ss.SSS||yyyy-MM-dd HH:mm:ss||epoch_millis"

                        }

                    }

                }

            }

        }]]>

    </property>

</properties>

这个mapping中定义了4个索引类型：basic,exam,medical,diagnosis,其中basic是其他类型的父类型。
通过bboss客户端创建名称为client_info 的索引：

	public void createClientIndice(){

		//定义客户端实例，加载上面建立的dsl配置文件

		ClientInterface clientUtil = ElasticSearchHelper.getConfigRestClientUtil("esmapper/Client_Info.xml");

		try {

			//client_info存在返回true，不存在返回false

			boolean exist = clientUtil.existIndice("client_info");



			//如果索引表client_info已经存在先删除mapping

			if(exist) {//先删除mapping client_info

				clientUtil.dropIndice("client_info");

			}

		} catch (ElasticSearchException e) {

			// TODO Auto-generated catch block

			e.printStackTrace();

		}

		//创建mapping client_info

		clientUtil.createIndiceMapping("client_info","createClientIndice");

		String client_info = clientUtil.getIndice("client_info");//获取最新建立的索引表结构client_info

		System.out.println("after createClientIndice clientUtil.getIndice(\"client_info\") response:"+client_info);

	}

3.维护父子关系数据-Elasticsearch 5.x 中维护父子关系数据

• 定义对象

首先定义四个对象，分别对应mapping中的四个索引类型，篇幅关系只列出主要属性

• Basic
• Medical
• Exam
• Diagnosis

通过注解@ESId指定基本信息文档_id

public class Basic extends ESBaseData {

    /**

     *  索引_id

     */

    @ESId

    private String party_id;

    private String sex;                     // 性别

    ......

}    

通过注解@ESParentId指定Medical关联的基本信息文档_id，Medical文档_id由ElasticSearch自动生成

public class Medical extends ESBaseData {

    @ESParentId

    private String party_id;          //父id

    private String hos_name_yb;         //就诊医院

    ...

}

通过注解@ESParentId指定Exam关联的基本信息文档_id，Exam文档_id由ElasticSearch自动生成

public class Exam extends ESBaseData {

    @ESParentId

    private String party_id;          //父id

    private String  hospital;           // 就诊医院

    ....

}    

通过注解@ESParentId指定Diagnosis关联的基本信息文档_id，Diagnosis文档_id由ElasticSearch自动生成

public class Diagnosis extends ESBaseData {

    @ESParentId

    private String party_id;          //父id

    private String provider;            //诊断医院

    private String subject;             //科室

    ......

}    

• 通过api维护测试数据

对象定义好了后，通过bboss客户数据到之前建立好的索引client_info中。

	/**

	 * 录入体检医疗信息

	 */

	public void importClientInfoDataFromBeans()  {

		ClientInterface clientUtil = ElasticSearchHelper.getRestClientUtil();



		//导入基本信息,并且实时刷新，测试需要，实际环境不要带refresh

		List<Basic> basics = buildBasics();

		clientUtil.addDocuments("client_info","basic",basics,"refresh");



		//导入医疗信息,并且实时刷新，测试需要，实际环境不要带refresh

		List<Medical> medicals = buildMedicals();

		clientUtil.addDocuments("client_info","medical",medicals,"refresh");



		//导入体检结果数据,并且实时刷新，测试需要，实际环境不要带refresh

		List<Exam> exams = buildExams();

		clientUtil.addDocuments("client_info","exam",exams,"refresh");



		//导入结果诊断数据,并且实时刷新，测试需要，实际环境不要带refresh

		List<Diagnosis> diagnosiss = buildDiagnosiss();

		clientUtil.addDocuments("client_info","diagnosis",diagnosiss,"refresh");

	}

	//构建基本信息集合

	private List<Basic> buildBasics() {

		List<Basic> basics = new ArrayList<Basic>();

		Basic basic = new Basic();

		basic.setParty_id("1");

		basic.setAge(60);

		basics.add(basic);

		//继续添加其他数据

		return basics;



	}

	//

构建医疗信息集合

	private List<Medical> buildMedicals() {

		List<Medical> medicals = new ArrayList<Medical>();

		Medical medical = new Medical();

		medical.setParty_id("1");//设置父文档id-基本信息文档_id

		medical.setCreated_date(new Date());

		medicals.add(medical);

		//继续添加其他数据

		return medicals;



	}

	//构建体检结果数据集合

	private List<Exam> buildExams() {

		List<Exam> exams = new ArrayList<Exam>();

		Exam exam = new Exam();

		exam.setParty_id("1");//设置父文档id-基本信息文档_id

		exams.add(exam);

		//继续添加其他数据

		return exams;

	}

	//构建结果诊断数据集合

	private List<Diagnosis> buildDiagnosiss() {

		List<Diagnosis> diagnosiss = new ArrayList<Diagnosis>();

		Diagnosis diagnosis = new Diagnosis();

		diagnosis.setParty_id("1");//设置父文档id-基本信息文档_id

		diagnosiss.add(diagnosis);

		//继续添加其他数据

		return diagnosiss;

	}

• 通过json报文批量导入测试数据

除了通过addDocuments录入数据，还可以通过json报文批量导入数据
在配置文件esmapper/Client_Info.xml增加以下内容：

    <!--

   导入基本信息：

   -->

    <property name="bulkImportBasicData" trim="false">

        <![CDATA[

            { "index": { "_id": "1" }}

            {  "party_id":"1", "sex":"男", "mari_sts":"不详", "ethnic":"蒙古族", "prof":"放牧","birth_date":"1966-2-14 00:00:00", "province":"内蒙古", "city":"赤峰市","client_type":"1", "client_name":"安", "age":52,"id_type":"1", "idno":"1", "education":"初中","created_date":"2013-04-24 00:00:00","last_modified_date":"2013-04-24 00:00:00", "etl_date":"2013-04-24 00:00:00"}

            { "index": { "_id": "2" }}

            { "party_id":"2", "sex":"女", "mari_sts":"已婚", "ethnic":"汉族", "prof":"公务员","birth_date":"1986-07-06 00:00:00", "province":"广东", "city":"深圳","client_type":"1", "client_name":"彭", "age":32,"id_type":"1", "idno":"2", "education":"本科", "created_date":"2013-05-09 15:49:47","last_modified_date":"2013-05-09 15:49:47", "etl_date":"2013-05-09 15:49:47"}

            { "index": { "_id": "3" }}

            { "party_id":"3", "sex":"男", "mari_sts":"未婚", "ethnic":"汉族", "prof":"无业","birth_date":"2000-08-15 00:00:00", "province":"广东", "city":"佛山","client_type":"1", "client_name":"浩", "age":18,"id_type":"1", "idno":"3", "education":"高中", "created_date":"2014-09-01 09:49:27","last_modified_date":"2014-09-01 09:49:27", "etl_date":"2014-09-01 09:49:27" }

             { "index": { "_id": "4" }}

             { "party_id":"4", "sex":"女", "mari_sts":"未婚", "ethnic":"满族", "prof":"工人","birth_date":"1996-03-14 00:00:00", "province":"江苏", "city":"扬州","client_type":"1", "client_name":"慧", "age":22,"id_type":"1", "idno":"4", "education":"高中", "created_date":"2014-09-16 09:30:37","last_modified_date":"2014-09-16 09:30:37", "etl_date":"2014-09-16 09:30:37" }

            { "index": { "_id": "5" }}

            { "party_id":"5", "sex":"女", "mari_sts":"已婚", "ethnic":"汉族", "prof":"教师","birth_date":"1983-08-14 00:00:00", "province":"宁夏", "city":"灵武","client_type":"1", "client_name":"英", "age":35,"id_type":"1", "idno":"5", "education":"本科", "created_date":"2015-09-16 09:30:37","last_modified_date":"2015-09-16 09:30:37", "etl_date":"2015-09-16 09:30:37" }

            { "index": { "_id": "6" }}

            { "party_id":"6", "sex":"女", "mari_sts":"已婚", "ethnic":"汉族", "prof":"工人","birth_date":"1959-07-04 00:00:00", "province":"山东", "city":"青岛","client_type":"1", "client_name":"岭", "age":59,"id_type":"1", "idno":"6", "education":"小学", "created_date":"2015-09-01 09:49:27","last_modified_date":"2015-09-01 09:49:27", "etl_date":"2015-09-01 09:49:27" }

            { "index": { "_id": "7" }}

            { "party_id":"7", "sex":"女", "mari_sts":"未婚", "ethnic":"汉族", "prof":"学生","birth_date":"1999-02-18 00:00:00", "province":"山东", "city":"青岛","client_type":"1", "client_name":"欣", "age":19,"id_type":"1", "idno":"7", "education":"高中", "created_date":"2016-12-01 09:49:27","last_modified_date":"2016-12-01 09:49:27", "etl_date":"2016-12-01 09:49:27" }

            { "index": { "_id": "8" }}

            { "party_id":"8", "sex":"女", "mari_sts":"未婚", "ethnic":"汉族", "prof":"学生","birth_date":"2007-11-18 00:00:00", "province":"山东", "city":"青岛","client_type":"1", "client_name":"梅", "age":10,"id_type":"1", "idno":"8", "education":"小学", "created_date":"2016-11-21 09:49:27","last_modified_date":"2016-11-21 09:49:27", "etl_date":"2016-11-21 09:49:27" }

            { "index": { "_id": "9" }}

            { "party_id":"9", "sex":"男", "mari_sts":"不详", "ethnic":"回族", "prof":"个体户","birth_date":"1978-03-29 00:00:00", "province":"北京", "city":"北京","client_type":"1", "client_name":"磊", "age":40,"id_type":"1", "idno":"9", "education":"高中", "created_date":"2017-09-01 09:49:27","last_modified_date":"2017-09-01 09:49:27", "etl_date":"2017-09-01 09:49:27" }

            { "index": { "_id": "10" }}

            { "party_id":"10", "sex":"男", "mari_sts":"已婚", "ethnic":"汉族", "prof":"农民","birth_date":"1970-11-14 00:00:00", "province":"浙江", "city":"台州","client_type":"1", "client_name":"强", "age":47,"id_type":"1", "idno":"10", "education":"初中", "created_date":"2018-09-01 09:49:27","last_modified_date":"2018-09-01 09:49:27", "etl_date":"2018-09-01 09:49:27" }

        ]]>

    </property>

    <!--

  导入诊断信息

  -->

    <property name="bulkImportDiagnosisData" trim="false">

        <![CDATA[

            { "index": { "parent": "1" }}

            { "party_id":"1", "provider":"内蒙古医院", "subject":"","diagnosis_type":"","icd10_code":"J31.0", "sd_disease_name":"鼻炎","created_date":"2013-07-23 20:56:44", "last_modified_date":"2013-07-23 20:56:44", "etl_date":"2013-07-23 20:56:44" }



            { "index": { "parent": "1" }}

            { "party_id":"1", "provider":"内蒙古医院", "subject":"","diagnosis_type":"","icd10_code":"M47.8", "sd_disease_name":"颈椎病","created_date":"2013-09-23 20:56:44", "last_modified_date":"2013-09-23 20:56:44", "etl_date":"2013-09-23 20:56:44" }



            { "index": { "parent": "1" }}

            { "party_id":"1", "provider":"内蒙古医院", "subject":"","diagnosis_type":"","icd10_code":"E78.1", "sd_disease_name":"甘油三脂增高","created_date":"2018-09-20 09:27:44", "last_modified_date":"2018-09-20 09:27:44", "etl_date":"2018-09-20 09:27:44" }



            { "index": { "parent": "4" }}

            { "party_id":"4", "provider":"江苏医院", "subject":"","diagnosis_type":"","icd10_code":"J00", "sd_disease_name":"感冒","created_date":"2011-05-19 15:52:55", "last_modified_date":"2011-05-19 15:52:55", "etl_date":"2011-05-19 15:52:55" }



            { "index": { "parent": "6" }}

            { "party_id":"6", "provider":"山东医院", "subject":"","diagnosis_type":"","icd10_code":"H44", "sd_disease_name":"眼疾","created_date":"2016-04-08 10:42:18", "last_modified_date":"2016-04-08 10:42:18", "etl_date":"2016-04-08 10:42:18" }



            { "index": { "parent": "6" }}

            { "party_id":"6", "provider":"山东医院", "subject":"","diagnosis_type":"","icd10_code":"M47.8", "sd_disease_name":"颈椎病","created_date":"2016-04-08 10:42:18", "last_modified_date":"2016-04-08 10:42:18", "etl_date":"2016-04-08 10:42:18" }



            { "index": { "parent": "7" }}

            { "party_id":"7", "provider":"山东医院", "subject":"","diagnosis_type":"","icd10_code":"J00", "sd_disease_name":"感冒","created_date":"2017-04-08 10:42:18", "last_modified_date":"2017-04-08 10:42:18", "etl_date":"2017-04-08 10:42:18" }



            { "index": { "parent": "8" }}

            { "party_id":"8", "provider":"山东医院", "subject":"","diagnosis_type":"","icd10_code":"J00", "sd_disease_name":"感冒","created_date":"2018-04-08 10:42:18", "last_modified_date":"2018-04-08 10:42:18", "etl_date":"2018-04-08 10:42:18" }



            { "index": { "parent": "9" }}

            { "party_id":"9", "provider":"朝阳医院", "subject":"","diagnosis_type":"","icd10_code":"A03.901", "sd_disease_name":"急性细菌性痢疾","created_date":"2015-06-08 10:42:18", "last_modified_date":"2015-06-08 10:42:18", "etl_date":"2015-06-08 10:42:18" }

        ]]>

    </property>



    <!--

 导入医疗信息

 -->

    <property name="bulkImportMedicalData" trim="false">

        <![CDATA[

                { "index": { "parent": "1" }}

                { "party_id":"1", "hos_name_yb":"内蒙古医院", "eivisions_name":"", "medical_type":"","medical_common_name":"氟化钠", "medical_sale_name":"", "medical_code":"A01AA01", "specification":"","usage_num":"", "unit":"", "usage_times":"","created_date":"2014-03-18 00:00:00", "last_modified_date":"2014-03-18 00:00:00", "etl_date":"2014-03-18 00:00:00" }



                { "index": { "parent": "1" }}

                { "party_id":"1", "hos_name_yb":"内蒙古医院", "eivisions_name":"", "medical_type":"","medical_common_name":"四环素", "medical_sale_name":"", "medical_code":"A01AB13", "specification":"","usage_num":"", "unit":"", "usage_times":"","created_date":"2016-05-31 00:00:00", "last_modified_date":"2016-05-31 00:00:00", "etl_date":"2016-05-31 00:00:00" }



                { "index": { "parent": "1" }}

                { "party_id":"1", "hos_name_yb":"内蒙古医院", "eivisions_name":"", "medical_type":"","medical_common_name":"", "medical_sale_name":"盐酸多西环素胶丸", "medical_code":"A01AB22", "specification":"","usage_num":"", "unit":"", "usage_times":"","created_date":"2016-03-18 00:00:00", "last_modified_date":"2016-03-18 00:00:00", "etl_date":"2016-03-18 00:00:00" }



                { "index": { "parent": "1" }}

                { "party_id":"1", "hos_name_yb":"内蒙古医院", "eivisions_name":"", "medical_type":"","medical_common_name":"盐酸多西环素分散片", "medical_sale_name":"", "medical_code":"A01AB22", "specification":"","usage_num":"", "unit":"", "usage_times":"","created_date":"2013-07-23 20:56:44", "last_modified_date":"2013-07-23 20:56:44", "etl_date":"2013-07-23 20:56:44" }



                { "index": { "parent": "1" }}

                { "party_id":"1", "hos_name_yb":"内蒙古医院", "eivisions_name":"", "medical_type":"","medical_common_name":"地塞米松", "medical_sale_name":"", "medical_code":"A01AC02", "specification":"","usage_num":"", "unit":"", "usage_times":"","created_date":"2013-09-23 20:56:44", "last_modified_date":"2013-09-23 20:56:44", "etl_date":"2013-09-23 20:56:44" }



                { "index": { "parent": "1" }}

                { "party_id":"1", "hos_name_yb":"内蒙古医院", "eivisions_name":"", "medical_type":"","medical_common_name":"肾上腺素", "medical_sale_name":"", "medical_code":"A01AD01", "specification":"","usage_num":"", "unit":"", "usage_times":"","created_date":"2018-09-20 09:27:44", "last_modified_date":"2018-09-20 09:27:44", "etl_date":"2018-09-20 09:27:44" }



                 { "index": { "parent": "4" }}

                { "party_id":"4", "hos_name_yb":"江苏医院", "eivisions_name":"", "medical_type":"","medical_common_name":"地塞米松", "medical_sale_name":"", "medical_code":"A01AC02", "specification":"","usage_num":"", "unit":"", "usage_times":"","created_date":"2011-05-19 15:52:55", "last_modified_date":"2011-05-19 15:52:55", "etl_date":"2011-05-19 15:52:55" }



                { "index": { "parent": "4" }}

                { "party_id":"4", "hos_name_yb":"江苏医院", "eivisions_name":"", "medical_type":"","medical_common_name":"四环素", "medical_sale_name":"", "medical_code":"A01AB13", "specification":"","usage_num":"", "unit":"", "usage_times":"","created_date":"2018-04-08 10:42:18", "last_modified_date":"2018-04-08 10:42:18", "etl_date":"2018-04-08 10:42:18" }



                { "index": { "parent": "4" }}

                { "party_id":"4", "hos_name_yb":"江苏医院", "eivisions_name":"", "medical_type":"","medical_common_name":"诺氟沙星胶囊", "medical_sale_name":"", "medical_code":"A01AD01", "specification":"","usage_num":"", "unit":"", "usage_times":"","created_date":"2015-06-08 10:42:18", "last_modified_date":"2015-06-08 10:42:18", "etl_date":"2015-06-08 10:42:18" }



                { "index": { "parent": "6" }}

                { "party_id":"6", "hos_name_yb":"山东医院", "eivisions_name":"", "medical_type":"","medical_common_name":"盐酸异丙肾上腺素片", "medical_sale_name":"", "medical_code":"A01AD01", "specification":"","usage_num":"", "unit":"", "usage_times":"","created_date":"2014-01-23 20:56:44", "last_modified_date":"2014-01-23 20:56:44", "etl_date":"2014-01-23 20:56:44" }



                { "index": { "parent": "6" }}

                { "party_id":"6", "hos_name_yb":"山东医院", "eivisions_name":"", "medical_type":"","medical_common_name":"甲硝唑栓", "medical_sale_name":"", "medical_code":"A01AB17", "specification":"","usage_num":"", "unit":"", "usage_times":"","created_date":"2018-06-08 10:42:18", "last_modified_date":"2018-06-08 10:42:18", "etl_date":"2018-06-08 10:42:18" }



                { "index": { "parent": "9" }}

                { "party_id":"9", "hos_name_yb":"朝阳医院", "eivisions_name":"", "medical_type":"","medical_common_name":"复方克霉唑乳膏", "medical_sale_name":"", "medical_code":"A01AB18", "specification":"","usage_num":"", "unit":"", "usage_times":"","created_date":"2014-01-23 20:56:44", "last_modified_date":"2014-01-23 20:56:44", "etl_date":"2014-01-23 20:56:44"}

         ]]>

    </property>



    <!--

     导入体检信息

-->

    <property name="bulkImportExamData" trim="false">

        <![CDATA[

            { "index": { "parent": "1" }}

            { "party_id":"1", "hospital":"", "dept":"", "is_ok":"N", "exam_result":"高血压","fld1":"158", "fld2":"63", "fld3":"94", "fld4":"85", "fld5":"131", "fld901":"89", "fld6":"4.9","fld902":"4.8","fld14":"78", "fld21":"78", "fld23":"", "fld24":"5.5", "fld65":"5.5", "fld66":"1.025","fld67":"", "fld68":"","created_date":"2014-03-18 00:00:00", "last_modified_date":"2014-03-18 00:00:00", "etl_date":"2014-03-18 00:00:00" }



            { "index": { "parent": "2" }}

            { "party_id":"2", "hospital":"", "dept":"", "is_ok":"Y", "exam_result":"轻度脂肪肝","fld1":"158", "fld2":"63", "fld3":"94", "fld4":"85", "fld5":"131", "fld901":"89", "fld6":"4.9","fld902":"4.8","fld14":"78", "fld21":"78", "fld23":"", "fld24":"5.5", "fld65":"5.5", "fld66":"1.025","fld67":"", "fld68":"","created_date":"2014-03-18 00:00:00", "last_modified_date":"2014-03-18 00:00:00", "etl_date":"2014-03-18 00:00:00" }



            { "index": { "parent": "3" }}

            { "party_id":"3", "hospital":"", "dept":"", "is_ok":"N", "exam_result":"急性细菌性痢疾","fld1":"158", "fld2":"63", "fld3":"94", "fld4":"85", "fld5":"131", "fld901":"89", "fld6":"4.9","fld902":"4.8","fld14":"78", "fld21":"78", "fld23":"", "fld24":"5.5", "fld65":"5.5", "fld66":"1.025","fld67":"", "fld68":"","created_date":"2014-03-18 00:00:00", "last_modified_date":"2014-03-18 00:00:00", "etl_date":"2014-03-18 00:00:00" }



            { "index": { "parent": "4" }}

            { "party_id":"4", "hospital":"", "dept":"", "is_ok":"N", "exam_result":"感冒","fld1":"158", "fld2":"63", "fld3":"94", "fld4":"85", "fld5":"131", "fld901":"89", "fld6":"4.9","fld902":"4.8","fld14":"78", "fld21":"78", "fld23":"", "fld24":"5.5", "fld65":"5.5", "fld66":"1.025","fld67":"", "fld68":"","created_date":"2014-03-18 00:00:00", "last_modified_date":"2014-03-18 00:00:00", "etl_date":"2014-03-18 00:00:00" }



            { "index": { "parent": "5" }}

            { "party_id":"5", "hospital":"", "dept":"", "is_ok":"N", "exam_result":"感冒","fld1":"158", "fld2":"63", "fld3":"94", "fld4":"85", "fld5":"131", "fld901":"89", "fld6":"4.9","fld902":"4.8","fld14":"78", "fld21":"78", "fld23":"", "fld24":"5.5", "fld65":"5.5", "fld66":"1.025","fld67":"", "fld68":"","created_date":"2014-03-18 00:00:00", "last_modified_date":"2014-03-18 00:00:00", "etl_date":"2014-03-18 00:00:00" }



            { "index": { "parent": "6" }}

            { "party_id":"6", "hospital":"", "dept":"", "is_ok":"N", "exam_result":"感冒","fld1":"158", "fld2":"63", "fld3":"94", "fld4":"85", "fld5":"131", "fld901":"89", "fld6":"4.9","fld902":"4.8","fld14":"78", "fld21":"78", "fld23":"", "fld24":"5.5", "fld65":"5.5", "fld66":"1.025","fld67":"", "fld68":"","created_date":"2014-03-18 00:00:00", "last_modified_date":"2014-03-18 00:00:00", "etl_date":"2014-03-18 00:00:00" }



            { "index": { "parent": "7" }}

            { "party_id":"7", "hospital":"", "dept":"", "is_ok":"N", "exam_result":"颈椎病","fld1":"158", "fld2":"63", "fld3":"94", "fld4":"85", "fld5":"131", "fld901":"89", "fld6":"4.9","fld902":"4.8","fld14":"78", "fld21":"78", "fld23":"", "fld24":"5.5", "fld65":"5.5", "fld66":"1.025","fld67":"", "fld68":"","created_date":"2014-03-18 00:00:00", "last_modified_date":"2014-03-18 00:00:00", "etl_date":"2014-03-18 00:00:00" }



            { "index": { "parent": "8" }}

            { "party_id":"1", "hospital":"", "dept":"", "is_ok":"N", "exam_result":"颈椎病","fld1":"158", "fld2":"63", "fld3":"94", "fld4":"85", "fld5":"131", "fld901":"89", "fld6":"4.9","fld902":"4.8","fld14":"78", "fld21":"78", "fld23":"", "fld24":"5.5", "fld65":"5.5", "fld66":"1.025","fld67":"", "fld68":"","created_date":"2014-03-18 00:00:00", "last_modified_date":"2014-03-18 00:00:00", "etl_date":"2014-03-18 00:00:00" }



            { "index": { "parent": "9" }}

                { "party_id":"9", "hospital":"", "dept":"", "is_ok":"N", "exam_result":"颈椎病","fld1":"158", "fld2":"63", "fld3":"94", "fld4":"85", "fld5":"131", "fld901":"89", "fld6":"4.9","fld902":"4.8","fld14":"78", "fld21":"78", "fld23":"", "fld24":"5.5", "fld65":"5.5", "fld66":"1.025","fld67":"", "fld68":"","created_date":"2014-03-18 00:00:00", "last_modified_date":"2014-03-18 00:00:00", "etl_date":"2014-03-18 00:00:00" }



                { "index": { "parent": "10" }}

                { "party_id":"10", "hospital":"", "dept":"", "is_ok":"N", "exam_result":"颈椎病","fld1":"158", "fld2":"63", "fld3":"94", "fld4":"85", "fld5":"131", "fld901":"89", "fld6":"4.9","fld902":"4.8","fld14":"78", "fld21":"78", "fld23":"", "fld24":"5.5", "fld65":"5.5", "fld66":"1.025","fld67":"", "fld68":"","created_date":"2014-03-18 00:00:00", "last_modified_date":"2014-03-18 00:00:00", "etl_date":"2014-03-18 00:00:00" }

        ]]>

    </property>

通过bboss提供的通用api，导入上面定义的数据：

	/**

	 * 通过读取配置文件中的dsl json数据导入医疗数据

	 */

	public void importClientInfoFromJsonData(){

		ClientInterface clientUtil = ElasticSearchHelper.getConfigRestClientUtil("esmapper/Client_Info.xml");



		clientUtil.executeHttp("client_info/basic/_bulk?refresh","bulkImportBasicData",ClientUtil.HTTP_POST);

		clientUtil.executeHttp("client_info/diagnosis/_bulk?refresh","bulkImportDiagnosisData",ClientUtil.HTTP_POST);

		clientUtil.executeHttp("client_info/medical/_bulk?refresh","bulkImportMedicalData",ClientUtil.HTTP_POST);

		clientUtil.executeHttp("client_info/exam/_bulk?refresh","bulkImportExamData",ClientUtil.HTTP_POST);

统计导入的数据

		

		long basiccount = clientUtil.countAll("client_info/basic");

		System.out.println(basiccount);

		long medicalcount = clientUtil.countAll("client_info/medical");

		System.out.println(medicalcount);

		long examcount = clientUtil.countAll("client_info/exam");

		System.out.println(examcount);

		long diagnosiscount = clientUtil.countAll("client_info/diagnosis");

		System.out.println(diagnosiscount);

	}

4.父子关系查询-Elasticsearch 5.x 中has_child和has_parent查询的基本用法​

• 根据父查子-通过客户名称信息查询客户端体检结果

在配置文件esmapper/Client_Info.xml增加dsl语句：queryExamSearchByClientName

   <!--根据客户名称查询客户体检报告-->

    <property name="queryExamSearchByClientName">

        <![CDATA[

            {

              "query": {

                ## 最多返回size变量对应的记录条数

                "size":#[size],

                "has_parent": {

                  "type": "basic",

                  "query": {

                    "match": {

                      "client_name": #[clientName] ## 通过变量clientName设置客户名称

                    }

                  }

                }

              }

            }

        ]]>

    </property>

 执行查询，通过bboss的searchList 方法获取符合条件的体检报告以及总记录数据，返回size对应的1000条数据

	/**

	 * 根据客户名称查询客户体检报告

	 */

	public void queryExamSearchByClientName(){

		ClientInterface clientUtil = ElasticSearchHelper.getConfigRestClientUtil("esmapper/Client_info.xml");

		Map<String,Object> params = new HashMap<String,Object>();

		params.put("clientName","张三");

		params.put("size",1000);

		ESDatas<Exam> exams = clientUtil.searchList("client_info/exam/_search","queryExamSearchByClientName",params,Exam.class);

		List<Exam> examList = exams.getDatas();//获取符合条件的体检数据

		long totalSize = exams.getTotalSize();//符合条件的总记录数据

	}

 

• 根据子查父数据-通过医疗信息编码查找客户基本数据

在配置文件esmapper/Client_Info.xml增加查询dsl语句：queryClientInfoByMedicalName

    <!--通过医疗信息编码查找客户基本数据-->

    <property name="queryClientInfoByMedicalName">

        <![CDATA[

            {

              "query": {

                ## 最多返回size变量对应的记录条数

                "size":#[size],

                "has_child": {

                  "type":       "medical",

                  "score_mode": "max",

                  "query": {

                    "match": {

                      "medical_code": #[medicalCode] ## 通过变量medicalCode设置医疗编码

                    }

                  }

                }

              }

            }

        ]]>

    </property>

执行查询，通过bboss的searchList 方法获取符合条件的客户端基本信息以及总记录数据

	/**

	 * 通过医疗信息编码查找客户基本数据

	 */

	public void queryClientInfoByMedicalName(){

		ClientInterface clientUtil = ElasticSearchHelper.getConfigRestClientUtil("esmapper/Client_info.xml");

		Map<String,Object> params = new HashMap<String,Object>();

		params.put("medicalCode","A01AA01"); //通过变量medicalCode设置医疗编码

		params.put("size",1000); //最多返回size变量对应的记录条数

		ESDatas<Basic> bascis = clientUtil.searchList("client_info/basic/_search","queryClientInfoByMedicalName",params,Basic.class);

		List<Basic> bascisList = bascis.getDatas();//获取符合条件的客户信息

		long totalSize = bascis.getTotalSize();

	}

5.同时返回父子数据-Elasticsearch 5.x 中如何在检索中同时返回父子数据
这一节中我们介绍同时返回父子数据的玩法 ：inner_hits的妙用

• 根据父条件查询所有子数据集合并返回父数据，根据客户名称查询所有体检数据，同时返回客户信息

在配置文件esmapper/Client_Info.xml增加检索dsl-queryDiagnosisByClientName

    <!--根据客户名称获取客户体检诊断数据，并返回客户信息-->

    <property name="queryDiagnosisByClientName">

        <![CDATA[

            {

              "query": {

                ## 最多返回size变量对应的记录条数

                "size":#[size],

                "has_parent": {

                  "type": "basic",

                  "query": {

                    "match": {

                      "client_name": #[clientName] ## 通过变量clientName设置客户名称

                    }

                  },

                  "inner_hits": {}  ## 通过变量inner_hits表示要返回对应的客户信息

                }

              }

            }

        ]]>

    </property>

执行检索并遍历结果

	/**

	 * 根据客户名称获取客户体检诊断数据，并返回客户数据

	 */

	public void queryDiagnosisByClientName(){



		ClientInterface clientUtil = ElasticSearchHelper.getConfigRestClientUtil("esmapper/Client_info.xml");

		Map<String,Object> params = new HashMap<String,Object>();

		params.put("clientName","张三");

		params.put("size",1000);



		try {

			ESInnerHitSerialThreadLocal.setESInnerTypeReferences(Basic.class);//指定inner查询结果对应的客户基本信息类型,Basic只有一个文档类型，索引不需要显示指定basic对应的mapping type名称

			ESDatas<Diagnosis> diagnosiss = clientUtil.searchList("client_info/diagnosis/_search",

					"queryDiagnosisByClientName",params,Diagnosis.class);

			List<Diagnosis> diagnosisList = diagnosiss.getDatas();//获取符合条件的体检报告数据

			long totalSize = diagnosiss.getTotalSize();

			//遍历诊断报告信息，并查看报告对应的客户基本信息

			for(int i = 0;  diagnosisList != null && i < diagnosisList.size(); i ++) {

				Diagnosis diagnosis = diagnosisList.get(i);

				List<Basic> basics = ResultUtil.getInnerHits(diagnosis.getInnerHits(), "basic");

				if(basics != null) {

					System.out.println(basics.size());

				}

			}

		}

		finally{

			ESInnerHitSerialThreadLocal.clean();//清空inner查询结果对应的客户基本信息类型

		}

	}

•  根据子条件查询父数据并返回符合条件的父的子数据集合，查询客户信息，同时返回客户对应的所有体检报告、医疗记录、诊断记录

在配置文件esmapper/Client_Info.xml增加检索dsl-queryClientAndAllSons

    <!--查询客户信息，同时返回客户对应的所有体检报告、医疗记录、诊断记录-->

    <property name="queryClientAndAllSons">

        <![CDATA[

        {

          "query": {

            "bool": {

              "should": [

                {

                    "match_all":{}

                }

              ]

              ,"must": [

                {

                  "has_child": {

                    "score_mode": "none",

                    "type": "diagnosis"

                    ,"query": {

                      "bool": {

                        "must": [

                          {

                            "term": {

                              "icd10_code": {

                                "value": "J00"

                              }

                            }

                          }

                        ]

                      }

                    },"inner_hits":{}

                  }

                }

                ]

              ,"should": [

                  {

                  "has_child": {

                    "score_mode": "none",

                    "type": "medical"

                    ,"query": {

                      "match_all": {}



                    },"inner_hits":{}

                  }

                }

              ]

              ,"should": [

                {

                  "has_child": {

                    "type": "exam",

                    "query": {

                      "match_all": {}

                    },"inner_hits":{}

                  }

                }

              ]

            }

          }

        }

        ]]>

    </property>

执行查询：

	/**

	 * 查询客户信息，同时返回客户对应的所有体检报告、医疗记录、诊断记录

	 */

	public void queryClientAndAllSons(){

		ClientInterface clientUtil = ElasticSearchHelper.getConfigRestClientUtil("esmapper/Client_Info.xml");

		Map<String,Object> params = null;//没有检索条件，构造一个空的参数对象



		try {

			//设置子文档的类型和对象映射关系

			ESInnerHitSerialThreadLocal.setESInnerTypeReferences("exam",Exam.class);//指定inner查询结果对于exam类型和对应的对象类型Exam

			ESInnerHitSerialThreadLocal.setESInnerTypeReferences("diagnosis",Diagnosis.class);//指定inner查询结果对于diagnosis类型和对应的对象类型Diagnosis

			ESInnerHitSerialThreadLocal.setESInnerTypeReferences("medical",Medical.class);//指定inner查询结果对于medical类型和对应的对象类型Medical

			ESDatas<Basic> escompanys = clientUtil.searchList("client_info/basic/_search",

					"queryClientAndAllSons",params,Basic.class);

			//String response = clientUtil.executeRequest("client_info/basic/_search","queryClientAndAllSons",params);直接获取原始的json报文

//			escompanys = clientUtil.searchAll("client_info",Basic.class);

			long totalSize = escompanys.getTotalSize();

			List<Basic> clientInfos = escompanys.getDatas();//获取符合条件的数据

			//查看公司下面的雇员信息（符合检索条件的雇员信息）

			for (int i = 0; clientInfos != null && i < clientInfos.size(); i++) {

				Basic clientInfo = clientInfos.get(i);

				List<Exam> exams = ResultUtil.getInnerHits(clientInfo.getInnerHits(), "exam");

				if(exams != null)

					System.out.println(exams.size());

				List<Diagnosis> diagnosiss = ResultUtil.getInnerHits(clientInfo.getInnerHits(), "diagnosis");

				if(diagnosiss != null)

					System.out.println(diagnosiss.size());

				List<Medical> medicals = ResultUtil.getInnerHits(clientInfo.getInnerHits(), "medical");

				if(medicals != null)

					System.out.println(medicals.size());



			}

		}

		finally{

			ESInnerHitSerialThreadLocal.clean();//清空inner查询结果对于各种类型信息

		}

	}

最后我们按顺序执行所有方法，验证功能：

	@Test

	public void testMutil(){

		this.createClientIndice();//创建indice client_info

//		this.importClientInfoDataFromBeans(); //通过api添加测试数据

		this.importClientInfoFromJsonData();//导入测试数据

		this.queryExamSearchByClientName(); //根据客户端名称查询提交报告

		this.queryClientInfoByMedicalName();//通过医疗信息编码查找客户基本数据

		this.queryDiagnosisByClientName();//根据客户名称获取客户体检诊断数据，并返回客户数据

		this.queryClientAndAllSons();//查询客户信息，同时返回客户对应的所有体检报告、医疗记录、诊断记录

	}

可以下载完整的demo工程运行本文中的测试用例方法，地址见相关资料。
到此Elasticsearch 5.x 父子关系维护检索实战介绍完毕，谢谢大家！

相关资料
完整demo工程  https://github.com/bbossgroups/eshelloword-booter
对应的类文件和配置文件
org.bboss.elasticsearchtest.parentchild.ParentChildTest
esmapper/Client_Info.xml
 
开发交流
bboss交流群 166471282
bboss公众号

 
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