Elasticsearch

喜讯！极限科技 再次中标 中国移动云 Elasticsearch 自研版技术开发服务项目！ 近日，极限科技再次胜利中标中国移动苏州研发核心 《云能力核心 2023—2024 年挪动云 Elasticsearch 自研版技术开发服务项目》。实现了个性化搜寻及聚合剖析，更稳固牢靠地反对万亿级数据规模，为挪动云零碎提供平安、稳固、牢靠的疾速检索和实时数据摸索剖析能力。

ES根底概念1.1 ElasticSearh是什么Elasticsearch 是一个分布式可扩大的实时搜寻和剖析引擎,一个建设在全文搜索引擎 Apache Lucene(TM) 根底上的搜索引擎，被广泛应用于全文搜寻。 它能够疾速、实时地存储、搜寻和剖析大量数据。 1.2 利用场景作为海量数据的存储工具（亿级），分布式实时文件存储，并将每一个字段都编入索引，使其能够被搜寻。简单的数据分析（ELK技术，elasticsearch+logstash+kibana）分布式搜索引擎。能够扩大到上百台服务器，解决PB级别的结构化或非结构化数据。1.3 集群构造假如集群中有3台es，1台主节点，2台从节点，创立索引的时候，定义了为这个索引创立3个分片shard，1个正本replica 这个索引一共有6个分片，3个shard分片集，每个shard有1个replica正本集，所以一共就有6个分片集shard和对应的replica是不会在同一台机器上的主节点负责节点和索引的增删改查，并不解决文档级别的增删改查1.4 集群原理倒排索引正排索引：通过ID映射到相应的记录。 倒排索引：就是关键词到文档 ID 的映射，每个关键词都对应着一系列的文件，这些文件中都呈现了关键词。 DocIdDoc1谷歌地图之父跳槽 Facebook2谷歌地图之父加盟 Facebook3谷歌地图创始人拉斯来到谷歌加盟 Facebook4谷歌地图之父跳槽 Facebook 与 Wave 我的项目勾销无关5谷歌地图之父拉斯加盟社交网站 Facebook倒排索引：用户输出查问 Facebook ，搜寻零碎查找倒排索引，从中读出蕴含这个单词的文档，这些文档就是提供给用户的搜寻后果。 倒排索引中的所有词项对应一个或多个文档；倒排索引中的词项依据字典程序升序排列WordIdWordDocIds1谷歌1, 2, 3, 4, 52地图1, 2, 3, 4, 53之父1, 2, 4, 54跳槽1, 45Facebook1, 2, 3, 4, 56加盟2, 3, 57创始人38拉斯3, 59来到310与4写入数据客户端抉择一个 node 发送申请过来，这个 node 就是 coordinating node （协调节点）。coordinating node 对 document 进行路由，将申请转发给对应的 node（有 primary shard）。理论的 node 上的 primary shard 解决申请，而后将数据同步到 replica node 。coordinating node 如果发现 primary node 和所有 replica node 都搞定之后，就返回响应后果给客户端。底层原理： ...

背景通常数据库进行分库分表后，目前比拟惯例的作法，是通过将数据异构到Elasticsearch来提供分页列表查问服务；在创立Elasticsearch索引时，根本都是会参考目前的业务需要、关系数据库中的类型以及对数据的相干布局来定义相干字段mapping的类型. 在Elasticsearch的mapping中的列(或则叫属性)，有几个比拟重要的参数(更多参数参考官网文档) 列类型：type 指定了该列的数据类型，罕用的有text,keyword,date,long,double,boolean以及object和nested,不同的类型也有对应的不同查问形式，创立之后是不能批改的； 是否可索引：index 该index选项管制字段值是否被索引。它承受trueorfalse,并且默认为true. 未索引的字段不可查问,当然也不能做为排序字段。 然而在理论的开发过程中，又会有需要对现有的mapping的type进行批改（相似对MySQL数据表的字段进行DDL操作）的诉求。比方商品上的价格price字段，按原来的业务剖析，只须要提供数据返回即可，在创立索引时类型定义了keyword了，并且index设置成了false，这时咱们须要依据价格的范畴查问或则进行排序操作，就心愿对mapping进行调整，将类型批改成数字类型，索引也须要加上；明天针对Elasticsearch的Mapping类型进行批改，探讨几个可行的计划 计划1：使用reindex遇到问题第一工夫，咱们应该是查问官网文档是否有相干的操作阐明，在官网文档中，的确还能找到对已有mapping更新的相干apiput-mapping,通过这个文档，很快能够找到文档中对批改已有mapping的列的形式（参考官网文档），同时也提到的通过reindex的形式来批改已有类型的形式； 除了反对的mapping parameters外，您不能更改现有字段的映射或字段类型。更改现有字段可能会使已编制索引的数据有效。如果您须要更改字段的映射，请应用正确的映射创立一个新索引并将您的数据从新索引reindex到该索引中。如原来索引的mapping如下 PUT /users{ "mappings" : { "properties": { "user_id": { "type": "long" } } }}//加一了两条数据POST /users/_doc?refresh=wait_for{ "user_id" : 12345}POST /users/_doc?refresh=wait_for{ "user_id" : 12346}这时想批改user_id的类型为keyword,咱们间接是批改不了的。 //尝试间接批改type，行不通，会报错PUT /users/_mapping{ "properties": { "user_id": { "type": "keyword" } }}//报错信息{ "error": { "root_cause": [ { "type": "illegal_argument_exception", "reason": "mapper [user_id] of different type, current_type [long], merged_type [keyword]" } ], "type": "illegal_argument_exception", "reason": "mapper [user_id] of different type, current_type [long], merged_type [keyword]" }, "status": 400}按官网文档说的reindex从新索引可按以下步骤操作 ...

一、问题零碎介绍监听商品变更MQ音讯，查问商品最新的信息，调用BulkProcessor批量更新ES集群中的商品字段信息;因为商品数据十分多，所以将商品数据存储到ES集群上，整个ES集群共划分了256个分片，并依据商品的三级类目ID进行分片路由。比方一个SKU的商品名称发生变化，咱们就会收到这个SKU的变更MQ音讯，而后再去查问商品接口，将商品的最新名称查问回来，再依据这个SKU的三级分类ID进行路由，找到对应的ES集群分片，而后更新商品名称字段信息。 因为商品变更MQ音讯量微小，为了晋升更新ES的性能，防止出现MQ音讯积压问题，所以本零碎应用了BulkProcessor进行批量异步更新。 ES客户端版本如下： <dependency> <artifactId>elasticsearch-rest-client</artifactId> <groupId>org.elasticsearch.client</groupId> <version>6.5.3</version> </dependency>BulkProcessor配置伪代码如下： //在这里调用build()办法结构bulkProcessor,在底层实际上是用了bulk的异步操作 this.fullDataBulkProcessor = BulkProcessor.builder((request, bulkListener) -> fullDataEsClient.getClient().bulkAsync(request, RequestOptions.DEFAULT, bulkListener), listener) // 1000条数据申请执行一次bulk .setBulkActions(1000) // 5mb的数据刷新一次bulk .setBulkSize(new ByteSizeValue(5L, ByteSizeUnit.MB)) // 并发申请数量, 0不并发, 1并发容许执行 .setConcurrentRequests(1) // 固定1s必须刷新一次 .setFlushInterval(TimeValue.timeValueSeconds(1L)) // 重试5次，距离1s .setBackoffPolicy(BackoffPolicy.constantBackoff(TimeValue.timeValueSeconds(1L), 5)) .build();二、问题怎么发现的618大促开始后，因为商品变更MQ音讯十分频繁，MQ音讯每天的音讯量更是达到了日常的数倍，而且好多商品还变更了三级类目ID；零碎在更新这些三级类目ID发生变化的SKU商品信息时，依据批改后的三级类目ID路由后的分片更新商品信息时产生了谬误，并且重试了5次，仍然没有胜利；因为在新路由的分片上没有这个商品的索引信息，这些更新申请永远也不会执行胜利，零碎的日志文件中也记录了大量的异样重试日志。商品变更MQ音讯也开始呈现了积压报警，MQ音讯的生产速度显著赶不上生产速度。察看MQ音讯消费者的UMP监控数据，发现生产性能很安稳，没有显著稳定，然而调用次数会在零碎生产MQ一段时间后呈现断崖式降落，由原来的每分钟几万调用量逐步降落到个位数。在重启利用后，零碎又开始生产，UMP监控调用次数复原到失常程度，然而零碎运行一段时间后，还是会呈现生产暂停问题，好像所有生产线程都被暂停了一样。 三、排查问题的具体过程首先找一台暂停生产MQ音讯的容器，查看利用过程ID，应用jstack命令dump利用过程的整个线程堆栈信息，将导出的线程堆栈信息打包上传到 https://fastthread.io/ 进行线程状态剖析。剖析报告如下： 通过剖析报告发现有124个处于BLOCKED状态的线程，而后能够点击查看各线程的具体堆栈信息，堆栈信息如下： 间断查看多个线程的具体堆栈信息，MQ生产线程都是在waiting to lock <0x00000005eb781b10> (a org.elasticsearch.action.bulk.BulkProcessor)，而后依据0x00000005eb781b10去搜寻发现，这个对象锁正在被另外一个线程占用，占用线程堆栈信息如下: 这个线程状态此时正处于WAITING状态，通过线程名称发现，该线程应该是ES客户端外部线程。正是该线程抢占了业务线程的锁，而后又在期待其余条件触发该线程执行，所以导致了所有的MQ生产业务线程始终无奈获取BulkProcessor外部的锁，导致呈现了生产暂停问题。 然而这个线程elasticsearchscheduler为啥不能执行？ 它是什么时候启动的？ 又有什么作用？ 就须要咱们对BulkProcessor进行深入分析,因为BulkProcessor是通过builder模块进行创立的，所以深刻builder源码，理解一下BulkProcessor的创立过程。 public static Builder builder(BiConsumer<BulkRequest, ActionListener<BulkResponse>> consumer, Listener listener) { Objects.requireNonNull(consumer, "consumer"); Objects.requireNonNull(listener, "listener"); final ScheduledThreadPoolExecutor scheduledThreadPoolExecutor = Scheduler.initScheduler(Settings.EMPTY); return new Builder(consumer, listener, (delay, executor, command) -> scheduledThreadPoolExecutor.schedule(command, delay.millis(), TimeUnit.MILLISECONDS), () -> Scheduler.terminate(scheduledThreadPoolExecutor, 10, TimeUnit.SECONDS)); }外部创立了一个工夫调度执行线程池，线程命名规定和上述持有锁的线程名称类似，具体代码如下： ...

前言Elasticsearch 是一个开源的分布式搜寻和剖析引擎，它能够帮忙用户在大规模数据集中疾速、精确地搜寻、剖析和可视化数据。ES 能够解决各种类型的数据，包含结构化、半结构化和非结构化数据，使其十分实用于大数据利用场景。 本文将具体介绍 Elasticsearch Java API 中 Update 写入 null 值有效的问题，以及如何排查、解决此类问题，同时分享笔者的一些反思。 问题形容在应用 Elasticsearch 8.x 的 Java API 时，进行新增数据有以下办法： Create：如果文档不存在，那么就创立它；存在会报错，产生异样报错不会影响其余操作。Index：创立一个新文档或者替换一个现有的文档。Update：局部更新一个文档（设置 upsert 为 true）然而大多数状况下，新增一条数据通常会应用 Update 作为一个操作，因为不心愿做一个更新把整行给笼罩掉，在中间件同步当中通常会遇见多表汇聚的状况，这种状况就强依赖于 Update 更新某一些字段，而不是把整个文档给替换掉。 然而，当笔者在应用 BulkOperation 来结构多个 Update 操作时，将某个值为 null 的字段写入到 Elasticsearch 的 Index 中时，会发现该值无奈被正确地写入到 Index 中。 问题排查笔者首先在 Kibana 中利用控制台进行试验，发现更新一条带有 null 值文档是没有能够的，也就是说明必定是咱们的代码中有问题，上面我将代码拆出来进行了测试. 首先应用 PUT 命令将一条数据写入到 Elasticsearch 中。 PUT index_test/_doc/1{ "age": 12, "name": "John Doe"}而后应用 Java API 来对 _id 为 1 的数据进行批改。 RestClient restClient = RestClient.builder(new HttpHost("xxx", 9200)).build();ElasticsearchTransport transport = new RestClientTransport(restClient, new JacksonJsonpMapper());ElasticsearchClient client8 = new ElasticsearchClient(transport);String indexName = "index_test";String esIdVal = "1";Map<String, Object> doc = new HashMap<>();doc.put("age", null);doc.put("name", null);BulkOperation op = new BulkOperation.Builder().update(i -> i.action(new UpdateAction.Builder<>() .doc(doc) .docAsUpsert(true) .build()).id(esIdVal)).build();List<BulkOperation> list = Collections.singletonList(op); BulkResponse response = client8.bulk(builder -> builder.index(indexName).operations(list));logger.info(response.toString());client.shutdown();运行后果为： ...

从 Elasticsearch 7.x 版本开始，Elasticsearch 发行版包含了本人的 JDK。因而，您不须要独自装置 Java。以下是在 CentOS 7 上装置 Elasticsearch 7.17.7 的残缺步骤：（数据默认保留在/var/lib/elasticsearch下，自行更改）增加 Elasticsearch 仓库 创立一个新repo 文件：vim /etc/yum.repos.d/elasticsearch.repo将以下内容粘贴到文件中：[elasticsearch-7.x]name=Elasticsearch repository for 7.x packagesbaseurl=https://artifacts.elastic.co/packages/7.x/yumgpgcheck=1gpgkey=https://artifacts.elastic.co/GPG-KEY-elasticsearchenabled=1autorefresh=1type=rpm-md装置 Elasticsearch：yum install elasticsearch-7.17.7 -y\配置 Elasticsearch：vim /etc/elasticsearch/elasticsearch.yml依据须要批改一些要害设置，例如：[root@vm11 jdk]# grep -v "#" /etc/elasticsearch/elasticsearch.yml| grep -v "^$" cluster.name: my-clusternode.name: node-1path.data: /opt/elasticsearch/datapath.logs: /opt/elasticsearch/lognetwork.host: 0.0.0.0http.port: 39200discovery.type: single-node对于单节点 Elasticsearch 集群，您能够将 discovery.type 设置为 single-node。这将跳过生产环境查看。要进行此更改，请将以下行增加到 /etc/elasticsearch/elasticsearch.yml 文件 记得 创立 批改后的目录 [root@vm11 ~]# mkdir -pv /opt/elasticsearch/log[root@vm11 ~]# mkdir -pv /opt/elasticsearch/data更改 Elasticsearch 数据和日志目录的所有权： 因为es只能应用普通用户 chown -R elasticsearch:elasticsearch /opt/elasticsearch/启动并启用 Elasticsearch 服务：systemctl start elasticsearchsystemctl enable elasticsearch问题解决日志 呈现 java.security.AccessControlException: access denied ("java.lang.RuntimePermission" "modifyThread") ...

大家好，我是民工哥！ 后面给大家介绍了：关系型数据库 MySQL 、 NoSQL 数据库 Redis 与 MongoDB 常识体系学习的文章。 在当今这样的待业大背景下，卷是必定的，弱小本人也是必须的。所以，学习不能停，必须始终卷上来。所以，从明天开始，让咱们一起卷下一个常识体系的学习之旅：搜索引擎 ElasticSearch 。心愿大家可能从中播种多多！如有帮忙，请点在看、转发反对一波！！！ 边学边实战系列之搜索引擎 ElasticSearch ！ 为什么须要学习 ElasticSearch依据 DB Engine 的排名显示，ElasticSearch 是最受欢迎的企业级搜索引擎。下图红色勾选的是咱们后面的系列详解的，除此之外你能够看到搜寻库ElasticSearch在前十名内： 所以为什么要学习 ElasticSearch 呢？1、在以后软件行业中，搜寻是一个软件系统或平台的基本功能， 学习ElasticSearch就能够为相应的软件打造出良好的搜寻体验。 2、其次，ElasticSearch具备十分强的大数据分析能力。尽管Hadoop也能够做大数据分析，然而ElasticSearch的剖析能力十分高，具备Hadoop不具备的能力。比方有时候用Hadoop剖析一个后果，可能期待的工夫比拟长。 3、ElasticSearch能够很不便的进行应用，能够将其装置在集体的笔记本电脑，也能够在生产环境中，将其进行程度扩大。 4、国内比拟大的互联网公司都在应用，比方小米、滴滴、携程等公司。另外，在腾讯云、阿里云的云平台上，也都有相应的ElasticSearch云产品能够应用。 5、在当今大数据时代，把握近实时的搜寻和剖析能力，能力把握外围竞争力，洞见将来。 明天 ，终终终于卷完了！！！！ 心愿大家可能从中播种多多！如有帮忙，请点在看、转发反对一波！！！ 边学边实战系列（一）：ElasticSearch 根底概念、生态和利用场景 边学边实战系列（二）：ElasticSearch 技术原理图解 边学边实战系列（三）：ElasticSearch 装置与根底应用 边学边实战系列（四）：ElasticSearch 索引治理 边学边实战系列（五）：ElasticSearch DSL 查问原理与实际 边学边实战系列（六）：ElasticSearch 聚合查问原理与实际 边学边实战系列（七）：ElasticSearch 文档索引与读取流程详解 边学边实战系列（八）：ElasticSearch 集群部署、分片与故障转移 边学边实战系列（九）：ElasticSearch 集群布局与运维经验总结 边学边实战系列（十）：ElasticSearch 分片/正本与数据操作流程 边学边实战系列（十一）：ElasticSearch 数据备份与迁徙 边学边实战系列（十二）：ElasticSearch 罕用 Curl 命令实际 边学边实战系列（十三）：ElasticSearch 可视化管理工具 边学边实战系列（十四）：ElasticSearch 性能优化详解 边学边实战系列（十五）：ElasticSearch 性能监控 心愿大家可能从中播种多多！如有帮忙，请点赞、转发反对一波！！！

1 背景京喜达技术部在社区团购场景下采纳JDQ+Flink+Elasticsearch架构来打造实时数据报表。随着业务的倒退Elasticsearch开始暴露出一些弊病，不适宜大批量的数据查问，高频次分页导出导致宕机、存储老本较高。 Elasticsearch的查问语句保护老本较高、在聚合计算场景下呈现数据不准确等问题。Clickhouse是列式数据库，列式型数据库人造适宜OLAP场景，相似SQL语法升高开发和学习老本，采纳疾速压缩算法节俭存储老本，采纳向量执行引擎技术大幅缩减计算耗时。所以做此比照，进行Elasticsearch切换至Clickhouse工作。 2 OLAPOLAP意思是On-Line Analytical Processing 联机剖析解决，Clickhouse就是典型的OLAP联机剖析型数据库管理系统(DBMS)。OLAP次要针对数据进行简单剖析汇总操作，比方咱们业务零碎每天都对当天所有运输团单做汇总统计，计算出每个省区的妥投率，这个操作就属于OLAP类数据处理。与OLAP相似的还有一个OLTP类数据处理，意思是On-Line Transaction Processing 联机事务处理，在OLTP场景中用户并发操作量会很大，要求零碎实时进行数据操作的响应，须要反对事务，Mysql、Oracle、SQLServer等都是OLTP型数据库。 2.1 OLTP场景的特色宽表，即每个表蕴含着大量的列对于读取，从数据库中提取相当多的行，但只提取列的一小部分。查问绝对较少(通常每台服务器每秒查问数百次或更少)查问后果显著小于源数据。换句话说，数据通过过滤或聚合，因而后果适宜于单个服务器的RAM中绝大多数是读申请数据以相当大的批次(> 1000行)更新，而不是单行更新;或者基本没有更新。对于简略查问，容许提早大概50毫秒列中的数据绝对较小：数字和短字符串(例如，每个URL 60个字节)解决单个查问时须要高吞吐量(每台服务器每秒可达数十亿行)事务不是必须的对数据一致性要求低3 个性3.1 Elasticsearch搜寻： 实用倒排索引，每个字段都可被索引且可用于搜寻，海量数据下近实时实现秒级的响应，基于Lucene的开源搜索引擎，为全文检索、高亮、搜寻举荐等提供了检索能力。百度搜寻、淘宝商品搜寻、日志搜寻等数据分析： Elasticsearch提供了大量数据分析的API和丰盛的聚合能力，反对在海量数据的根底上进行数据分析解决。统计订单量、爬虫爬取不同电商的某个商品数据，通过Elasticsearch进行数据分析（各个平台的历史价格、购买力等等）3.2 Clickhouse列式存储压缩算法：采纳lz4和zstd算法数据压缩，高压缩比升高数据大小，升高磁盘IO，升高CPU使用率。索引：依照主键对数据进行排序，clickhouse能在几十毫秒内实现在大量数据对特定数据或范畴数据进行查找。多外围并行处理：ClickHouse会应用服务器上所有可用的资源，来全力实现一次查问。反对SQL：一种基于SQL的申明式查询语言，在许多状况下与ANSI SQL规范雷同。反对group by，order by，from, join，in以及非相干子查问等。向量引擎：为了高效的应用CPU，数据不仅仅按列存储，同时还按向量(列的一部分)进行解决，这样能够更加高效地应用CPU。实时的数据更新：数据总是已增量的形式有序的存储在MergeTree中。数据能够继续一直高效的写入到表中，不会进行任何加锁等操作。写入流量在50M-200M/s适宜在线查问：响应速度快提早极低丰盛的聚合计算函数4 咱们的业务场景大宽表，读大量行少量列进行指标聚合计算查问，后果集比拟小。数据表都是通过Flink加工进去的宽表，列比拟多。在对数据进行查问或者剖析时，常常抉择其中的多数几列作为维度列、对其余多数几列作为指标列，对全表或者肯定范畴内的数据做聚合计算。这个过程会扫描大量的行数据，然而只用了多数几列。大量的列表分页查问与导出Flink中数据大批量追加写入，不做更新操作有时某个指标计算须要全表扫描做聚合计算很少进行全文搜寻论断：数据报表、数据分析场景是典型的OLAP场景，在业务场景上列式存储数据库Clickhouse比Elasticsearch更有劣势，Elasticsearch在全文搜寻上更占优势，然而咱们这种全文搜寻场景较少。 5 老本学习老本：Clickhouse是SQL语法比Elasticsearch的DSL更简略，简直所有后端研发都有Mysql开发教训，比拟相通学习老本更低。开发、测试、保护老本：Clickhouse是SQL语法，与Mysql开发模式类似，更好写单元测试。Elasticsearch是应用Java API拼接查问语句，复杂度较高，不易读不易保护。运维老本：未知，互联网上在日志场景下Clickhouse比Elasticsearch老本更低。服务器老本：Clickhouse的数据压缩比要高于Elasticsearch，雷同业务数据占用的磁盘空间ES占用磁盘空间是Clickhouse的3-10倍，均匀在6倍。 见图1Clickhouse比ES占用更少的CPU和内存 论断：等同数据量状况下，Elasticsearch应用的存储空间是Clickhouse的3-10倍，均匀在6倍。综合学习、开发、测试、保护方面，Clickhouse比Elasticsearch更敌对 6 测试6.1服务器配置以下均基于下图配置进行测试 6.2 写入压测以下基于wms\_order\_sku表，通过Flink在业务安稳状况下双写Elasticsearch和Clickhouse1000W+数据进行测试失去的后果 占用CPU状况：Elasticsearch CPU始终占用很高，Clickhouse占用很少CPU。见 图2 占用内存状况：Elasticsearch 内存升高频繁GC，Clickhouse占用内存较低，比拟安稳。见 图3 写入吞吐量：CH单机写入速度大概为50\~200MB/s，如果写入的数据每行为1kb，写入速度为5-20W/s，图 4(写入吞吐量)为互联网上Elasticsearch与Clickhouse写入数据的比照图，等同数据样本的状况下CH写入性能是Elasticsearch的5倍。因为咱们目前Flink工作为双写，思考到会相互影响，后续补充压测后果。 论断：批量写入数据时Elasticsearch比Clickhouse更吃内存和CPU，Elasticsearch耗费的内存是Clickhouse的5.3倍，耗费的CPU是Clickhouse的27.5倍。吞吐量是Elasticsearch的5倍 6.3 查问性能(单并发测试)以下场景是咱们数据报表以及数据分析中呈现的高频场景，所以基于此进行查问性能测试 数据比照状况 Clickhouse本身在集群配置差一倍的状况下查问性能差别不是很大，CH2(48C 182GB)比CH1(80C 320GB)均匀慢14%。见图 5 Elasticsearch在集群配置差一倍的状况下查问性能受影响较大，ES2(46C 320GB)比ES1(78C 576GB)均匀慢40%。见图 6 ES2(46C 320GB)与CH2(48C 182GB)相比，ES2与CH2 CPU核数相近，ES2内存是CH2 1.75倍的状况下，CH2的响应速度是ES2的响应速度的的12.7倍。见图 7 论断：查问数据时Elasticsearch比Clickhouse慢，在配置相近的状况下Clickhouse的响应速度是Elasticsearch的12.7倍，特地是基于工夫的多字段进行聚合查问是Clickhouse比Elasticsearch快32倍。Clickhouse的查问响应素速度受集群配置大小的影响较小。 6.4 查问压测(高并发测试，数据来源于互联网)因为筹备高并发测试比较复杂耗时多，后续会基于咱们的业务数据以及业务场景进行查问压力测试。以下数据来源于互联网在用户画像场景（数据量262933269）下进行的测试，与咱们的场景十分相似。 论断：Clickhouse对于高并发反对的不够，官网倡议最大QPS为100。高并发状况下吞吐量不如Elasticsearch更敌对 7 总结Clickhouse与Elasticsearch比照Clickhouse的优缺点。 ...

如何计算 CPU 占用率？简略来说，过程的 CPU 占用率指的是 CPU 有多少工夫破费在了运行过程上。在 Linux 零碎里，过程运行的工夫是以jiffies[1]统计的，通过计算jiffies * HZ，就能够失去过程耗费的 CPU 工夫，再除以 CPU 的总工夫，就能够失去过程的 CPU 占用率：jiffies * HZ / total_time。 ps 和 top 的不同之处ps和top是最罕用的两种查看 CPU 占用的形式，都能够用来疾速找到以后 CPU 占用率高的过程。但实际上这两个工具的统计形式是齐全不同的。 咱们用上面这个简略的 Go 程序来测试这两个工具的差异： package mainimport ( "bytes" "fmt" "strconv" "sync" "time")var testData = []byte(`testdata`)func testBuffer(idx int) { m := map[string]*bytes.Buffer{} for i := 0; i < 100; i += 1 { buf, ok := m[strconv.Itoa(i)] if !ok { buf = new(bytes.Buffer) } for j := 0; j < 1024; j += 1 { buf.Write(testData) } m[strconv.Itoa(i)] = buf } fmt.Println("done, ", idx) wg.Done()}var wg sync.WaitGroupfunc main() { for i := 0; i < 10; i += 1 { wg.Add(1) j := i go testBuffer(j) } wg.Wait() fmt.Println("sleeping") time.Sleep(time.Hour)}而后咱们运行这个程序，通过top和ps aux别离查看过程的 CPU 占用状况。 ...

前言：为了实际一下ElasticSearch的自定义相关度评分，应用了Expression脚本。然而在执行过程中却报错了，最初通过批改索引映射，增加别名和同步旧索引数据得以解决，所以以下也围绕这几项问题开展。 波及常识：索引映射批改索引别名应用重建索引相关度评分  问题：评分脚本执行报错。    (以下都是用Kibana的图形化开发工具测试ES的API)，以后索引是一个一般的产品数据，包含产品ID，产品名称，产品别名，产品品类，价格，排序编号。上面只是想搜寻某产品名，依照排序编号的相关度相乘的排序形式，然而却报错。 (注：_search下取文档的值是doc, 其余状况下ctx)GET products_new/_search{ "query": { "function_score": { "query": { "match": { "goods_name": 12 } }, "script_score": { "script": { "lang": "expression", "source": "_score * doc['goods_sort']" } } } }} 批改索引映射报错。        依据报错提醒是因为脚本中执行的字段非数据，非日期和非地理位置类型，所以就须要对之前索引的字段类型进行批改。然而索引映射对字段的批改只能增加属性不能批改，mapper [goods_sort] of different type, current_type [keyword], merged_type [long]，解决这个就须要重建索引。索引重建给旧索引增加别名。 POST /_aliases{ "actions": [ { "add": { "index": "products", "alias": "products_alias" } } ]}依据旧索引创立新字段类型的映射。PUT products_new { "mappings": { "properties": { "class_id": { "type": "long" }, "goods_name": { "type": "text", "analyzer": "ik_smart" }, "goods_sort": { "type": "long" }, "id": { "type": "long" }, "price": { "type": "text", "fields": { "keyword": { "type": "keyword", "ignore_above": 256 } } }, "single_goods_name": { "type": "text", "analyzer": "ik_max_word" }, "state": { "type": "long" } } }}同步旧索引数据到新索引。POST /_reindex{ "source": { "index": "products" }, "dest": { "index": "products_new" }}给新索引增加和旧索引雷同的别名并移除旧索引别名。 ...

前言：上一篇实际了通过Logstash同步MySQL的几张关联表到Elasticsearch中。为了实现同一种业务需要，嵌套文档在资源开销和查问速度上要优于父子文档（针对大量数据的状况）。所以以下就实际一下嵌套文档的根本应用和，以及Logstash如何同步一对多关系表到ElasticSearch的嵌套文档中。  RESTful模仿：以下以博客内容和博客评论为例，从映射创立，到增，删，改，查，聚合演示嵌套文档的应用办法，索引名 “blog_new”。 1. 创立映射PUT blog_new{ "mappings": { "properties": { "title": { "type": "text" }, "body": { "type": "text" }, "tags": { "type": "keyword" }, "published_on": { "type": "keyword" }, "comments": { "type": "nested", "properties": { "name": { "type": "text" }, "comment": { "type": "text" }, "age": { "type": "short" }, "rating": { "type": "short" }, "commented_on": { "type": "text" } } } } }}2. 增加POST blog_new/blog/2{ "title": "Hero", "body": "Hero test body...", "tags": ["Heros", "happy"], "published_on": "6 Oct 2018", "comments": [ { "name": "steve", "age": 24, "rating": 18, "comment": "Nice article..", "commented_on": "3 Nov 2018" } ]}3.  删除POST blog_new/blog/1/_update{ "script": { "lang": "painless", "source": "ctx._source.comments.removeIf(it -> it.name == 'John');" }}4. 批改POST blog_new/blog/2/_update{ "script": { "source": "for(e in ctx._source.comments){if (e.name == 'steve') {e.age = 25; e.comment= 'very very good article...';}}" }}5. 查问GET /blog_new/_search?pretty{ "query": { "bool": { "must": [ { "nested": { "path": "comments", "query": { "bool": { "must": [ { "match": { "comments.name": "William" } }, { "match": { "comments.age": 34 } } ] } } } } ] } }}6. 聚合GET blog_new/_search{ "size": 0, "aggs": { "comm_aggs": { "nested": { "path": "comments" }, "aggs": { "min_age": { "min": { "field": "comments.age" } } } } }}Logstash同步：同步到ES的嵌套文档和后面的父子文档就有点不一样了，这里只须要一个jdbc。合并次要是通过关联查问出后果，而后聚合导入到ElasticSearch中。以下还是以博客和评论为例，创立索引映射和其余MySQL表之类的就省略，间接看运行命令。 ...

前言：目前大部分业务开发中，ElasticSearch次要还是用来做搜寻。而撑持搜寻性能的数据结构比拟繁多，不会有数据嵌套或者多种关联之类的。只管没有，然而有些小众需要可能还会有一对多查问的场景。为了实现和MySQL的Join相似的查问形式，以下以ES的父子文档形式贮存，并具体演示Logstash如何将MySQL的多张有关联的表同步到ES的父子文档。  手动演示：以下以restful形式创立父子文档索引，并以简略的形式查问相似join的数据返回。上面所有演示的索引名称都为 "my_join_index"。 1. 创立父子关联索引PUT my_join_index{ "mappings": { "properties": { "my_join_field": { "type": "join", "relations": { "question": "answer" } } } }}2. 创立父文档PUT my_join_index/_doc/1?refreshPUT my_join_index/_doc/1?refresh{ "text": "This is a question", "my_join_field": "question" }PUT my_join_index/_doc/2?refresh{ "text": "This is another question", "my_join_field": "question"}3. 创立子文档PUT my_join_index/_doc/3?routing=1&refreshPUT my_join_index/_doc/3?routing=1&refresh { "text": "This is an answer", "my_join_field": { "name": "answer", "parent": "1" }}PUT my_join_index/_doc/4?routing=1&refresh{ "text": "This is another answer2", "my_join_field": { "name": "answer", "parent": "2" }}4. 全局检索GET my_join_index/_searchGET my_join_index/_search{ "query": { "match_all": {} }, "sort": ["_id"]}5. 依据父文档查找子文档GET my_join_index/_searchGET my_join_index/_search{ "query": { "has_parent" : { "parent_type" : "question", "query" : { "match" : { "text" : "This is" } } } }}6. 依据子文档查找父文档GET my_join_index/_searchGET my_join_index/_search{"query": { "has_child" : { "type" : "answer", "query" : { "match" : { "text" : "This is question" } } } }}7. Join聚合GET my_join_index/_searchGET my_join_index/_search{ "query": { "parent_id": { "type": "answer", "id": "1" } }, "aggs": { "parents": { "terms": { "field": "my_join_field#question", "size": 10 } } }, "script_fields": { "parent": { "script": { "source": "doc['my_join_field#question']" } } }}8. 单条联结查问, 能够是一条父文档对应多个子文档GET my_join_index/_searchGET my_join_index/_search{ "query": { "bool": { "must": [ { "match": { "title": "历史圈" } }, { "has_child": { "type": "answer", "query": { "match": { "text":"是的" } }, "inner_hits":{} } } ] } }}Logstash同步：以下以文章分类表和文章表为例，二者系一对多的关系。同步文档时，文章分类作为父文档，文章作为子文档，关联字段为 “my_join_field”。  ...

前言：上一篇文章也简略的介绍了Logstash同步MySQL到ElasticSearch。批量同步虽说就配置文件不一样，然而实际操作的时候，也还会遇到不少的问题，比方reader不容许特殊字符(0x0)等等。上面也次要以几个问题来演示批量同步的过程，以及启动命令时如何排查报错的办法。  批量同步配置：input { stdin { } jdbc { # mysql 数据库链接,shop为数据库名 jdbc_connection_string => "jdbc:mysql://127.0.0.1:3306/rebuild?characterEncoding=UTF-8&useSSL=false" # 用户名和明码 jdbc_user => "root" jdbc_password => "root" # 驱动 jdbc_driver_library => "E:/2setsoft/1dev/logstash-7.8.0/mysqletc/mysql-connector-java-5.1.7-bin.jar" # 驱动类名 jdbc_driver_class => "com.mysql.jdbc.Driver" jdbc_paging_enabled => "true" jdbc_page_size => "50000" parameters => {"number" => "200"} # 执行的sql 文件门路+名称 #statement_filepath => "E:/2setsoft/1dev/logstash-7.8.0/mysqletc/user.sql" statement => "SELECT * FROM `hhyp_article` WHERE delete_time = 0" # 是否将字段名转换为小写，默认true（如果有数据序列化、反序列化需要，倡议改为false）； lowercase_column_names => false # Value can be any of: fatal,error,warn,info,debug，默认info； sql_log_level => warn # 是否记录上次执行后果，true示意会将上次执行后果的tracking_column字段的值保留到last_run_metadata_path指定的文件中； record_last_run => true # 须要记录查问后果某字段的值时，此字段为true，否则默认tracking_column为timestamp的值； use_column_value => true # 须要记录的字段，用于增量同步，需是数据库字段 tracking_column => "ModifyTime" # Value can be any of: numeric,timestamp，Default value is "numeric" tracking_column_type => timestamp # record_last_run上次数据寄存地位； last_run_metadata_path => "E:/2setsoft/1dev/logstash-7.8.0/mysqletc/last_id.txt" # 是否革除last_run_metadata_path的记录，须要增量同步时此字段必须为false； clean_run => false # 设置监听距离 各字段含意（由左至右）分、时、天、月、年，全副为*默认含意为每分钟都更新 schedule => "* * * * *" # 索引类型 type => "article" } jdbc { # mysql 数据库链接,shop为数据库名 jdbc_connection_string => "jdbc:mysql://127.0.0.1:3306/rebuild?characterEncoding=UTF-8&useSSL=false" # 用户名和明码 jdbc_user => "root" jdbc_password => "root" # 驱动 jdbc_driver_library => "E:/2setsoft/1dev/logstash-7.8.0/mysqletc/mysql-connector-java-5.1.7-bin.jar" # 驱动类名 jdbc_driver_class => "com.mysql.jdbc.Driver" jdbc_paging_enabled => "true" jdbc_page_size => "50000" parameters => {"number" => "200"} # 执行的sql 文件门路+名称 #statement_filepath => "E:/2setsoft/1dev/logstash-7.8.0/mysqletc/goods_class.sql" statement => "SELECT * FROM `hhyp_article_cate` WHERE delete_time = 0" # 是否将字段名转换为小写，默认true（如果有数据序列化、反序列化需要，倡议改为false）； lowercase_column_names => false # Value can be any of: fatal,error,warn,info,debug，默认info； sql_log_level => warn # 设置监听距离 各字段含意（由左至右）分、时、天、月、年，全副为*默认含意为每分钟都更新 schedule => "* * * * *" # 索引类型 type => "article_cate" } } filter { json { source => "message" remove_field => ["message"] }} output { if[type] == "article" { elasticsearch { hosts => ["127.0.0.1:9200"] index => "hhyp_article" document_id => "%{id}" } } if[type] == "article_cate" { elasticsearch { hosts => ["127.0.0.1:9200"] index => "hhyp_article_cate" document_id => "%{id}" } } stdout { codec => json_lines }}注意事项：1. 索引type。ES从6版本后一个索引下不能有多个type，所以在多张表同步时，最好一张表建设一个索引。所以在每个jdbc里type指定了一个名称。能够拿该名称在output时做判断。 ...

前言：最近又从新在看ElasticSearch的文档，发现那些DSL语法全都遗记了，所以筹备写一个用ES做贮存的demo小我的项目。其实是用DSL代替之前我的项目的SQL，然而数据以及一些字段还是须要，所以就须要将以前的MySQL数据导入到ElasticSearch中。        以前的做法是写一个脚本，通过创立索引，创立文档，将MySQL数据插入到ElasticSearch中，当初想通过Elastic中的成员之一——Logstash，来实现初步的导入工作。上面就从根底简略介绍该形式的导入过程吧。  工具清单：ElasticSearchLogstashJava JDK4. mysql-connector-java 环境搭建：首先能够先装置JDK，配置环境变量，版本1.8之类的都能够。ElasticSearch和Logstash能够在Elastic官网下载最新版本，最好是两个工具的版本保持一致，目前我这里应用的是7.8版本。mysql-connector-java是一个同步驱动，只须要在启动logstash指定目录就能够。  配置MySQL同步：input { stdin { } jdbc { # mysql 数据库链接,shop为数据库名 jdbc_connection_string => "jdbc:mysql://127.0.0.1:3306/rebuild?characterEncoding=UTF-8&useSSL=false" # 用户名和明码 jdbc_user => "root" jdbc_password => "root" # 驱动 jdbc_driver_library => "E:/2setsoft/1dev/logstash-7.8.0/mysqletc/mysql-connector-java-5.1.7-bin.jar" # 驱动类名 jdbc_driver_class => "com.mysql.jdbc.Driver" jdbc_paging_enabled => "true" jdbc_page_size => "50000" # 执行的sql 文件门路+名称 statement_filepath => "E:/2setsoft/1dev/logstash-7.8.0/mysqletc/run.sql" # 设置监听距离 各字段含意（由左至右）分、时、天、月、年，全副为*默认含意为每分钟都更新 schedule => "* * * * *" # 索引类型 type => "content" } } filter { json { source => "message" remove_field => ["message"] }} output { elasticsearch { hosts => ["127.0.0.1:9200"] index => "hhyp" document_type => "room" document_id => "%{id}" } stdout { codec => json_lines } }1. jdbc_connection_string配置示例：jdbc:mysql://IP地址:端口/数据库名?字符集&其余配置参数。  ...

前言：ElasticSearch是一个一个分布式的实时文档存储，每一个字段都能够被索引与搜寻，并且能反对PB级别的结构化或者非结构化数据。晚期咱们利用的全局搜寻是简略的SQL含糊查问，为了分担数据库压力所以用了ES，抉择他的起因除了以上几点外，还有其提供的API形式简略，于任何对接的编程语言都实用。以下将以PHP的业务场景欠缺搜寻性能。 环境：ThinkPHP5.1ElasticSearch7.8PHP7.3 性能迭代简介：最开始的ES取代办法解决了搜寻速度问题，起初新增的ik分词器，解决了搜寻词条繁多问题。单也正是中文分词起因，对每句话都是拆解成指定粒度的词。当遇到单词时，个别只会对一句话里的单词进行拆解，而搜寻时须要输出某个字母，心愿输入的是带字母前面的词条，也就是含糊查问。通过翻阅文档，发现了即时搜寻。        即时搜寻或输出即搜寻（search-as-you-type），就是当用户习惯在输完查问内容之前，就能为他们展示搜寻后果，不仅能在更短的工夫内失去搜寻后果，也能疏导用户搜寻索引中实在存在的后果。例如，输出 dvd r ，即时搜寻会失去：dvd r9s 和 dvd r9sk等 , 以下将通过残缺示例演示成果。  配置索引的映射：对于ElasticSearch环境搭建和基本操作在后面文章有阐明，这里就伪装曾经创立了索引，以下是索引的映射。还有文档的增加也是依据你本人的需要定义了，不论是定时工作还是业务节点触发，亦或是采集工具同步等。 { "mappings": { "_doc": { "properties": { "class_id": { "type": "long" }, "goods_name": { "type": "text", "analyzer": "ik_smart" }, "goods_sort": { "type": "keyword" }, "id": { "type": "keyword" }, "price": { "type": "long" }, "single_goods_name": { "type": "text", "analyzer": "ik_max_word" }, "state": { "type": "keyword" }, "v": { "type": "long" } } } }}前台搜寻：1.  即搜寻示例{ "match_phrase_prefix" : { "brand" : { "query": "walker johnnie bl", "slop": 10 } }}2. 业务代码public function queryData($key, $sort, $from = 0, $size = 10){ $from = $from * $size; $indexName = Env::get('elasticsearch.goods_index') ?? 'products'; $params = [ 'index' => $indexName, 'client' => [ 'timeout' => 10, 'connect_timeout' => 10 ], 'body' => [ 'from' => $from, 'size' => $size, 'query' => [ 'bool' => [ 'should' => [ [ 'multi_match' => [ 'query' => $key, 'fields' => [ 'goods_name^2', 'single_goods_name' ], ], ], [ 'wildcard' => [ 'single_goods_name' => "$key*" ] ], [ 'fuzzy' => [ 'single_goods_name' => [ 'value' => $key ] ] ], [ 'match_phrase_prefix' => [ 'single_goods_name' => "$key" ] ], ], ], ], 'sort' => [ ['_score' => "desc"], ['goods_sort' => 'desc'] ], ] ]; return $this->es->search($params);}3. 成果演示 ...

前言：之前也有讲到过es的根本应用，次要是通过PHP代码对es索引，文档等的增删改以及搜寻优化的解决方案。而当初为了进一步满足搜寻的全新性能，所以就须要一个中文分词器，而为了方面查看或治理es文档，能够应用图形化面板。所以接下来次要介绍es中文分词器的装置和es的web面板。  elasticsearch-analysis-ik分词：es自带分词，然而没有中文分词，在一众elasticsearch的中文分词插件中，我举荐应用analysis-ik。ik能够对中文语义化分词，有粗略力度分词和最大化分词。最大化能够将一小段句子拆分成单个字，词语的幂集，如果本地的es没有装置该插件，对某文档的字段应用则会报上面谬误。    所以，装置办法就来了，以下全副以elasticsearch7.8.0版本进行介绍和演示。本地装置了es后，找到其装置的根目录，会发现有一个plugins目录，外面就是放分词插件的中央。接下来就是按上面地址，抉择与本人es版本绝对的下载。       下载实现后，将解压包放入es的plugins目录中，重启es，通过postman测试分词就标识胜利了。 elasticsearch-head:elasticsearch-head是一个简略的es web面板，能够连贯指定地址，查看es节点情况，创立索引，查看索引列表，文档，模仿搜寻等。上面就简略介绍一下这个工具的装置。工具地址能够网络上搜寻，我这里就不贴，在github也有。      因为这个工具是基于NodeJs，所以装置的前提须要有Node环境，而且还要装置了cnpm，cnpm具体的办法能够网络自行查找。这些都筹备好了之后，通过cmd进入到elasticsearch-head根目录。    第一步先执行cnpm install。期待将所有的依赖包装置结束后，执行cnpm run start就默认开启了本地的一个端口为9100的 http服务，最初拜访浏览器就能够了。        浏览器进入到127.0.0.1:9100，就能够看到上面这个页面，默认是没有连贯9200的(es的默认端口)。然而第一次连贯会报跨域的谬误，所以须要找到es的配置，也就是config目录下elasticsearch.yml，在结尾增加两行代码，最初重启es再用面板连贯就能够胜利了。http.cors.enabled: truehttp.cors.allow-origin: "*"    因为这个工具有创立索引的性能，所以接下来我就次要通过postman演示创立文档和批量创立的办法，而后刷新面板的对应索引查看数据。 创立索引：给指定索引增加一个文档：给指定索引批量增加文档：   通过postman批量增加文档有两种形式，一种是以参数形式，另一种是以上传json文件形式。然而二者在书写文档时(也就是json)，在最初肯定要加一个换行，不然会报错。(1). 传参形式。(2). 上传文件形式。搜寻：    以下就是通过postman测试增加的文档，还有一个是es从7.0版本后，一个索引只能有一个type，并且都为_doc，所以面板中的类型都是_doc。

PolarDB-X 为了不便用户体验，提供了收费的试验环境，您能够在试验环境里体验 PolarDB-X 的装置部署和各种内核个性。除了收费的试验，PolarDB-X 也提供收费的视频课程，手把手教你玩转 PolarDB-X 分布式数据库。 本期试验将领导您如何采集PolarDB-X SQL日志到ElasticSearch 本期收费试验地址 本期教学视频地址 前置筹备假如曾经依据前一讲内容实现了PolarDB-X的搭建部署，应用PolarDB-X Operator装置PolarDB-X，并且能够胜利链接上PolarDB-X数据库。 装置PolarDB-X LogCollector本步骤将领导您装置PolarDB-X LogCollector组件，并将PolarDB-X日志投递到Elastic Search中。 1.执行如下命令，创立一个名为polardbx-logcollector的命名空间。 kubectl create namespace polardbx-logcollector2.执行如下命令，装置 PolarDB-X LogCollector。 helm install --namespace polardbx-logcollector polardbx-logcollector polardbx/polardbx-logcollector --version=1.3.0-alpha.13.执行如下命令，期待 PolarDB-X LogCollector 的组件ready。 kubectl get pods -n polardbx-logcollector4.PolarDB-X 日志采集性能是默认敞开的，执行如下命令，为第三步创立的 PolarDB-X 集群开启日志采集性能。 kubectl patch pxc polardb-x --patch '{"spec":{"config":{"cn":{"enableAuditLog":true}}}}' --type merge5.在 LogStash 规范输入查看 PolarDB-X 日志。 阐明：logstash pod name须要替换为 polardbx-logcollector 命名空间下的 logstash pod 名称。 kubectl logs -n polardbx-logcollector {logstash pod name} -f部署Elastic Search本步骤将领导您如何在K8s上部署一个Elastic Search集群。 ...

作者：京东科技 王长春 业务问题小编工作中负责业务的一个服务端零碎，应用了 Elasticsearch 服务做数据存储，业务经营人员反馈，用户在应用该产品时发现，用户后盾统计的订单笔数和导出的订单笔数不统一！ 交易订单笔数不对，呈现过错订单了？这一听极为震撼！呈现这样的问题，在金融科技公司外面是相对不容许产生的，得马上定位问题并解决！ 小编马上联系业务和相干人员，通过梳理上游零碎的调用关系，发现业务零碎应用到的是我这边的 ES 的存储服务，而后对线上状况进行复现，根本理解问题的景象： 用户操作后盾里的订单总笔数：商户页面的"订单总笔数"，"订单总笔数"应用的是小编 ES 存储服务中 ES 的统计聚合性能，其中订单总笔数是应用了 cardinality 操作，并且应用的是 orderId(订单编号)进行统计去重。导出性能里的订单总笔数：导出性能应用的是 ES 存储服务中的 ES 条件查问性能，导出性能是进行分页查问的。问题定位这两个查问数量不统一，首先看查问条件是否统一呢？ 通过一番排查，业务零碎在调用查问订单总数和导出订单总数的这两个查问条件是统一的，也就是申请到我这边 ES 服务时，统计聚合的查问和分页导出的查问条件是统一的，然而为什么会在 ES 外面查问的后果是不统一的呢？难道 ES 外面的数据不全？统计聚合或分页导出的其中有一个不准了？ 为了具体排查哪个操作可能存在问题，于是通过雷同条件下查询数据库的总数和 ES 外面的数据进行比照。发现雷同条件下，数据库外面的数据和 ES 条件查问的总数是统一的, 同时业务的 orerId 字段是没有反复，所以能够确定的是：通过 orderId 进行统计聚合去重的操作是有问题的。 数据库查问：数据库是做分库分表，此处数据库查问应用的是公司内的数据部河汉大表——公司数据部会 T+1日从业务从库数据库中抽取 T 日的增量数据放在建设的"大表"中, 不便各业务进行数据应用。 经营后盾查问：经营后盾查问是间接查问 ES 存储服务。 数据部大表数量 = MySQL 数据库分库分表表里数量 = 经营控制台查问数量 = ES 存储文档数量 问题定位： ES 存储服务对外给业务提供的: 通过 orderId 进行统计聚合去重（cardinality）的性能应该是有问题的。 ES 的 cardinality 原理探索下面说过，小编负责的 ES 存储服务对外给业务提供了通过指定业务字段进行统计聚合去重的性能，统计聚合去重应用的是 ES 的 cardinality 性能。通过业务的查问的条件，应用 ES 的聚合性能 cardinality 操作，映射到 ES 层的操作命令如下代码所示， ...

一、我的项目介绍一款全源码，可二开，可基于云部署、公有部署的企业级知识库云平台，一款让企业常识变为实打实的数字财产的零碎，利用在须要进行文档整顿、分类、归集、检索、剖析的场景。获取形式+q:262086839为什么建设知识库平台？ 助力企业常识资产无效积淀和利用，防止随文档负责人变动而反复制作，无纸化数字治理更加迷信与绿色，为企业衰弱倒退赋能。 撑持各岗位，各类型员工疾速能力晋升， 帮忙企业将所有扩散的常识集中，分门别类对立无效治理，不便共享与再次查问利用。二、我的项目所用技术前端技术栈：vue，elementui，tinyMce（弱小的富文本） 后端技术栈：springboot，activiti（工作流引擎），ElasticSearch，redis，neo4j（常识图谱） 数据库：MySQL 三、我的项目核心技术介绍 全文检索简略操作，高效检索。企业版的“百度一下”。反对文章题目检索，反对文章内容全局检索，以及反对附件内容检索（弱小）。2.常识图谱采纳neo4j技术，常识图谱智能剖析，疾速读懂您的趣味点。 3.工作流审批文章拟写，提交，审阅，公布，全过程审核机制。 富文本编辑器采纳国外富文本编辑器，满足word、Excel等一键粘贴百分之百格局还原，反对视频、图文等。其余性能反对文档珍藏，点赞，反对文档分类和打标签，文章热榜等。

作者：京东保险 管顺利 开篇最近应用Elasticsearch实现画像零碎，实现的dmp的数据中台能力。同时调研了竞品的架构选型。以及重温了redis原理等。特此做一次es的总结和回顾。网上没看到有人用Elasticsearch来实现画像的。我来做第一次尝试。 背景说完，咱们先思考一件事，应用内存零碎做数据库。他的长处是什么？他的痛点是什么？ 一、原理这里不在论述全貌。只聊聊通信、内存、长久化三局部。 通信es集群最小单元是三个节点。两个从节点搭配保障其高可用也是集群化的根底。那么节点之间RPC通信用的是什么？必然是netty，es基于netty实现了Netty4Transport的通信包。初始化Transport后建设Bootstrap，通过MessageChannelHandler实现接管和转发。es里辨别server和client，如图1。序列化应用的json。es在rpc设计上偏差于易用、通用、易了解。而不是单谋求性能。 图1 有了netty的保驾护航使得es释怀是应用json序列化。 内存 图2 es内存分为两局部【on heap】和【off heap】。on heap这部分由es的jvm治理。off heap则是由lucene治理。on heap 被分为两局部，一部分能够回收，一部分不能回收。 能回收的局部index buffer存储新的索引文档。当被填满时，缓冲区的文档会被写入到磁盘segment上。node上共享所有shards。 不能被回收的有node query cache、shard request cache、file data cache、segments cache node query cache是node级缓存，过滤后保留在每个node上，被所有shards共享，应用bitset数据结构（布隆优化版）关掉了评分。应用的LRU淘汰策略。GC无奈回收。 shard request cache是shard级缓存，每个shard都有。默认状况下该缓存只存储request后果size等于0的查问。所以该缓存不会被hits，但却缓存hits.total，aggregations，suggestions。能够通过clear cache api革除。应用的LRU淘汰策略。GC无奈回收。 file data cache 是把聚合、排序后的data缓存起来。初期es是没有doc values的，所以聚合、排序后须要有一个file data来缓存，防止磁盘IO。如果没有足够内存存储file data，es会一直地从磁盘加载数据到内存，并删除旧的数据。这些会造成磁盘IO和引发GC。所以2.x之后版本引入doc values个性，把文档构建在indextime上，存储到磁盘，通过memory mapped file形式拜访。甚至如果只关怀hits.total，只返回doc id，关掉doc values。doc values反对keyword和数值类型。text类型还是会创立file data。 segments cache是为了减速查问，FST永驻堆内内存。FST能够了解为前缀树，减速查问。but！！es 7.3版本开始把FST交给了堆外内存，能够让节点反对更多的数据。FST在磁盘上也有对应的长久化文件。 off heap 即Segments Memory，堆外内存是给Lucene应用的。 所以倡议至多留一半的内存给lucene。 es 7.3版本开始把tip（terms index）通过mmp形式加载，交由零碎的pagecache治理。除了tip，nvd（norms），dvd（doc values）， tim（term dictionary），cfs（compound）类型的文件都是由mmp形式加载传输，其余都是nio形式。tip off heap后的成果jvm占用量降落了78%左右。能够应用_cat/segments API 查看 segments.memory内存占用量。 因为对外内存是由操作系统pagecache治理内存的。如果产生回收时，FST的查问会牵扯到磁盘IO上，对查问效率影响比拟大。能够参考linux pagecache的回收策略应用双链策略。 ...

对于事实中一对多的场景比方一个商品除了题目、形容等根本信息外还会有尺寸色彩等多个属性，一篇博文可能会有多条评论信息，在ES有方法反对这种一对多的数据结构吗？答案是有的，比拟常见的形式有： 嵌套对象嵌套文档(nested)嵌套对象订单场景中会有主单与子单的概念，一个主单可能有多个子单，在ES中如果咱们应用嵌套对象进行存储那是怎么的呢？咱们先给出示例索引的mapping: { "order_index":{ "mappings":{ "_doc":{ "properties":{ "orderId":{ "type":"keyword" }, "orderNo":{ "type":"keyword" }, "orderUserName":{ "type":"keyword" }, "orderItems":{ "properties":{ "orderItemId":{ "type":"keyword" }, "orderId":{ "type":"keyword" }, "productName":{ "type":"keyword" }, "brandName":{ "type":"keyword" }, "sellPrice":{ "type":"keyword" } } } } } } }}其中orderItems是子单列表的详情字段，它本身也是一个对象。增加示例数据后： [ { "_index":"order_index", "_type":"_doc", "_id":"1", "_score":1, "_source":{ "orderId":"1", "orderNo":"123456", "orderUserName":"张三", "orderItems":[ { "orderItemId":"12234", "orderId":"1", "productName":"火腿肠", "brandName":"双汇", "sellPrice":"28" }, { "orderItemId":"12235", "orderId":"1", "productName":"果冻", "brandName":"汇源", "sellPrice":"12" } ] } }]当初咱们想查问商品名称为‘火腿肠’并且品牌为‘汇源’的订单，DSL语句如下： POST order_index/_search{ "query":{ "bool":{ "must":[ { "match":{ "orderItems.productName":"火腿肠" } }, { "match":{ "orderItems.brandName":"汇源" } } ] } }}查问后果： ...

一、我的项目介绍一款全源码，可二开，可基于云部署、公有部署的企业级知识库云平台，一款让企业常识变为实打实的数字财产的零碎，利用在须要进行文档整顿、分类、归集、检索、剖析的场景。获取形式+q:262086839为什么建设知识库平台？ 助力企业常识资产无效积淀和利用，防止随文档负责人变动而反复制作，无纸化数字治理更加迷信与绿色，为企业衰弱倒退赋能。 撑持各岗位，各类型员工疾速能力晋升， 帮忙企业将所有扩散的常识集中，分门别类对立无效治理，不便共享与再次查问利用。二、我的项目所用技术前端技术栈：vue，elementui，tinyMce（弱小的富文本） 后端技术栈：springboot，activiti（工作流引擎），ElasticSearch，redis，neo4j（常识图谱） 数据库：MySQL 三、我的项目核心技术介绍 全文检索简略操作，高效检索。企业版的“百度一下”。反对文章题目检索，反对文章内容全局检索，以及反对附件内容检索（弱小）。2.常识图谱采纳neo4j技术，常识图谱智能剖析，疾速读懂您的趣味点。 3.工作流审批文章拟写，提交，审阅，公布，全过程审核机制。 富文本编辑器采纳国外富文本编辑器，满足word、Excel等一键粘贴百分之百格局还原，反对视频、图文等。其余性能反对文档珍藏，点赞，反对文档分类和打标签，文章热榜等。

作者：京东物流 李洪吉 提到ES，大多数爱好者想到的都是搜索引擎，然而明确一点，ES不等同于搜索引擎。不论是谷歌、百度、必应、搜狗为代表的自然语言解决(NLP)、爬虫、网页解决、大数据处理的全文搜索引擎，还是有明确搜寻目标的搜寻行为，如各大电商网站、OA、站内搜索、视频网站的垂直搜索引擎，他们或多或少都应用到了ES。 作为搜索引擎的一部分，ES天然具备速度快、后果精确、后果丰盛等特点，那么ES是如何达到“搜索引擎”级别的查问效率呢？首先是索引，其次是压缩算法，接下来咱们就一起理解下ES的索引构造和压缩算法 1 构造1.1 MysqlMysql下的data目录寄存的文件就是mysql相干数据，mysql文件夹对应的就是数据库mysql。 其中表columns\_priv对应了3个文件：columns\_priv.frm、columns\_priv.MYD、columns\_priv.MYI。 .frm：表构造；.MYD：myisam存储引擎原数据；.MYI：myisam存储引擎索引；.ibd：innodb存储引擎数据 1.2 Elasticsearch cfe为索引文，cfs 为数据文件，cfe文件保留Lucene各文件在.cfs文件的地位信息 cfs、cfe 在segment还很小的时候，将segment的所有文件都存在在cfs中，在cfs逐步变大时，大小超过shard的10%，则会拆分为其余文件，如tim、dvd、fdt等文件 1.3 存储构造倒排索引构造分为倒排表、词项字典、词项索引 倒排表蕴含某个词项的所有id的数据存储了在.doc文件中 词项字典蕴含了index field的所有通过解决之后的词项数据，最终存储在.tim文件中 1.4 构造比照咱们以某商城的手机为例，左侧为es倒排索引构造，右侧为原始数据。左侧图示只是为了展现倒排索引构造，并不是说es中倒排表就是简略的数组 以下面构造比照示例图为例，如果共有10亿条数据须要存储在ES中(上图右)，分词后存储的倒排表(上图左)大略蕴含分词term以及对应的id数组等，在10亿条数据中，分词“小米”相干的数据有100万条，也就是说分词“小米”对应的数组Posting List长度是100万 id是int类型的有序主键，分词“小米”在数组Posting List中100万int类型数字总长度=100万✖每个int占4字节=400万Byte≈4MB。1个分词占4MB空间,如果10亿条数据有500万个分词，总空间=4MB✖500万=2千万MB，磁盘空间间接爆炸 2 算法分词对应的数组Posting List理论就是一个个有序数组，而有序数值数组是比拟容易进行压缩解决的，而且一般来说压缩效益也不错，如果能对其进行压缩是可能大大节约空间资源的 ES中倒排索引的压缩算法次要有FOR算法(Frame Of Reference)和RBM算法(RoaringBitMap) 2.1 FORFOR算法的核心思想是用减法来削减数值大小，从而达到升高空间存储。 假如V(n)示意数组中第n个字段的值，那么通过FOR算法压缩的数值V(n)=V(n)-V(n-1)。也就是说存储的是后一位减去前一位的差值。存储是也不再依照int来计算了，而是看这个数组的最大值须要占用多少bit来计算 咱们依照差值计算的形式来保留数据，初始值为1，2与1的差值为1，3与2的差值为1……最终咱们就将原始Posting List数据转化为100万个1，每个1咱们能够用1bit来记录，总空间=1bit✖100万=100万bit，相比原有400万Byte=3200bit，空间压缩了32倍 在理论生产中，不可能呈现一个term的Posting List是这种差值均为1的状况，所以咱们以通用示例举例。如果原数据为[73，300，302，332，343，372],数组中6个数字占据总空间为24字节。依照差值形式记录，数组转化为[73,227,2,30,11,29],最大数字为227，大于2的7次方128，小于2的8次方256，所以每个数字能够应用8bit即1Byte来保留，占据总空间为1Byte*6 + 1Byte=7Byte 在此基础上，咱们将差值数组依照密集度划分为[73,227]和[2,30,11,29]，其中[73,227]中最大值227介于2的7次方和2的8次方之间，所以用8bit=1Byte作为切割分段，[2,30,11,29]中最大数30介于2的4次方和2的5次方之间，所以用5bit作为切割分段。 数组[73,227]占据总空间为8bit✖2个=16bit=2Byte 数组[2,30,11,29]占据总空间为5bit✖4个=20bit=3Byte 为什么20bit=3Byte呢？因为8bit=1Byte，小于8bit也会占据1个字节空间，所以17bit到24bit均为3Byte所以，最终占据总空间=1+2+1+3=7Byte 疑难一：既然原数组[73，300，302，332，343，372]要依照密集度拆分为[73,227]和[2,30,11,29]两个数组，那为什么不持续往下拆分，间接拆分到每个数字是一个数组，这样应用bit记录时占据总空间会更少？ 答：如果持续拆分数组，空间的确会应用更少，然而，之前咱们提到搜索引擎速度快的形式有两种：高效的压缩算法和疾速的编码解码速度，单个数字存储的确压缩了空间，然而咱们无奈再通过解码的形式将源数据还原 疑难二：为什么源数据应用差值记录占据6Byte，拆分数组后占据7Byte，拆分后占据空间不变，有时候甚至会变大，为什么？ 答：数据量小的状况下的确会呈现该状况，因为咱们须要拆分数组并记录拆分数组的长度（如下面示例中的8bit和5bit），在原数据存储空间根底上还要存储拆分长度，所以数据量小的状况下会呈现比间接存储占据空间大的状况。然而不论是搜索引擎还是Elasticsearch更多解决的是海量数据，数据量越多，差值数组拆分的形式节俭空间越显著 2.2 RBM咱们曾经理解了FOR压缩算法，算法外围是将PostingList依照差值密集度转化成两个差值数组。在这里咱们要思考一种状况就是：在大数据中，10亿条数据分词500万个，如果分词“小米”所在PostList比拟扩散且差值很大，此时应用FOR算法成果就会大打折扣。所以稠密的数组，不适宜应用FOR算法 在这里咱们以[1000，62101，131385，132052，191173，196658]为例，如果依照FOR算法，转化成的差值数组为[1000，61101，69284，667，59121，5485]密集度很低。咱们采纳RBM算法 源数据PostingList是由int类型组成的数组，int类型=4Byte=32bit，最大值=2的32次方-1=4294967295≈43亿。当数据较大且稠密时，咱们将32bit拆分为16bit和16bit，16bit最大值=65535，前16bit寄存商，后16bit寄存余数，所以商和余数都不会超过65535.咱们将源数组的值除以65536，失去的商和余数别离寄存在前16bit和后16bit。 以数字196658为例，转化为2进制，前16位=3，后16位=50 失去的后果以K-V寄存。Key最大值为16bit，所以以short[]数组寄存，Value以Container寄存。 因为源数组为有序数组，所以依照高下16位转化后，商和余数都是从小到大排列 通过看Container源码，咱们能够看到Container有3种：ArrayContainer、BitmapContainer、RunContainer。 ArrayContainer实质为汇合，所以随着数组中数量越多，占用空间越多，呈正向增长。当数组种数量为4096时，占据总空间=4096个✖16bit(即2Byte)➗1024=8KB ...

本文曾经收录到Github仓库，该仓库蕴含计算机根底、Java根底、多线程、JVM、数据库、Redis、Spring、Mybatis、SpringMVC、SpringBoot、分布式、微服务、设计模式、架构、校招社招分享等外围知识点，欢送star~ Github地址 如果拜访不了Github，能够拜访gitee地址。 gitee地址 跟大家分享Elasticsearch的基础知识，它是做什么的以及它的应用和基本原理。 一、生存中的数据搜索引擎是对数据的检索，所以咱们先从生存中的数据说起。咱们生存中的数据总体分为两种： 结构化数据非结构化数据结构化数据： 也称作行数据，是由二维表构造来逻辑表白和实现的数据，严格地遵循数据格式与长度标准，次要通过关系型数据库进行存储和治理。指具备固定格局或无限长度的数据，如数据库，元数据等。 非结构化数据： 又可称为全文数据，不定长或无固定格局，不适于由数据库二维表来体现，包含所有格局的办公文档、XML、HTML、Word 文档，邮件，各类报表、图片和咅频、视频信息等。 阐明：如果要更粗疏的辨别的话，XML、HTML 可划分为半结构化数据。因为它们也具备本人特定的标签格局，所以既能够依据须要按结构化数据来解决，也可抽取出纯文本按非结构化数据来解决。最全面的Java面试网站 依据两种数据分类，搜寻也相应的分为两种： 结构化数据搜寻非结构化数据搜寻对于结构化数据，因为它们具备特定的构造，所以咱们个别都是能够通过关系型数据库（MySQL，Oracle 等）的二维表（Table）的形式存储和搜寻，也能够建设索引。 对于非结构化数据，也即对全文数据的搜寻次要有两种办法： 程序扫描全文检索程序扫描： 通过文字名称也可理解到它的大略搜寻形式，即依照程序扫描的形式查问特定的关键字。 例如给你一张报纸，让你找到该报纸中“安全”的文字在哪些地方呈现过。你必定须要从头到尾把报纸浏览扫描一遍而后标记出关键字在哪些版块呈现过以及它的呈现地位。 这种形式无疑是最耗时的最低效的，如果报纸排版字体小，而且版块较多甚至有多份报纸，等你扫描完你的眼睛也差不多了。 全文搜寻： 对非结构化数据程序扫描很慢，咱们是否能够进行优化？把咱们的非结构化数据想方法弄得有肯定构造不就行了吗？ 将非结构化数据中的一部分信息提取进去，从新组织，使其变得有肯定构造，而后对此有肯定构造的数据进行搜寻，从而达到搜寻绝对较快的目标。 这种形式就形成了全文检索的基本思路。这部分从非结构化数据中提取出的而后从新组织的信息，咱们称之为索引。 这种形式的次要工作量在后期索引的创立，然而对于前期搜寻却是疾速高效的。 二、先说说 Lucene通过对生存中数据的类型作了一个简短理解之后，咱们晓得关系型数据库的 SQL 检索是解决不了这种非结构化数据的。 这种非结构化数据的解决须要依赖全文搜寻，而目前市场上凋谢源代码的最好全文检索引擎工具包就属于 Apache 的 Lucene了。 然而 Lucene 只是一个工具包，它不是一个残缺的全文检索引擎。Lucene 的目标是为软件开发人员提供一个简略易用的工具包，以不便的在指标零碎中实现全文检索的性能，或者是以此为根底建设起残缺的全文检索引擎。 目前以 Lucene 为根底建设的开源可用全文搜索引擎次要是 Solr 和 Elasticsearch。 Solr 和 Elasticsearch 都是比拟成熟的全文搜索引擎，能实现的性能和性能也根本一样。 然而 ES 自身就具备分布式的个性和易装置应用的特点，而 Solr 的分布式须要借助第三方来实现，例如通过应用 ZooKeeper 来达到分布式协调治理。 不论是 Solr 还是 Elasticsearch 底层都是依赖于 Lucene，而 Lucene 能实现全文搜寻次要是因为它实现了倒排索引的查问构造。 如何了解倒排索引呢？ 如果现有三份数据文档，文档的内容如下别离是： Java is the best programming language.PHP is the best programming language.Javascript is the best programming language.为了创立倒排索引，咱们通过分词器将每个文档的内容域拆分成独自的词（咱们称它为词条或 Term），创立一个蕴含所有不反复词条的排序列表，而后列出每个词条呈现在哪个文档。 ...

[TOC]一、elastic stack 根底平安1. 生成证书1.1 机会: 启动Elasticsearch之前1.2 借助certutil工具-生成ca证书./bin/elasticsearch-certutil ca证书文件名为: elastic-stack-ca.p12 1.3 借助certutil工具-生成证书(在任一节点)./bin/elasticsearch-certutil cert --ca elastic-stack-ca.p12证书名为: elastic-certificates.p12 1.4 将elastic-certificates.p12证书copy到所有node的config下2. 利用生成证书-加密节点间通信2.1 elasticsearch.yml:配置cluster.name# 集群内的节点name雷同cluster.name: my-cluster2.2 elasticsearch.yml:配置node.name# 每个节点不同node.name: node12.3 elasticsearch.yml:退出certificate配置:因为应用了ca证书制作颁发的certificates证书,verification mode就指定它, 前面还有ca生成的http证书, 配置就是http的(前面再说)xpack.security.transport.ssl.enabled: truexpack.security.transport.ssl.verification_mode: certificate xpack.security.transport.ssl.client_authentication: requiredxpack.security.transport.ssl.keystore.path: elastic-certificates.p12xpack.security.transport.ssl.truststore.path: elastic-certificates.p122.4. 后面3个步骤在所有节点上操作完3. 在所有的节点上, start elasticsearch4.对于证书文件的阐明4.1 elastic-stack-ca.p12 蕴含你的CA的公共证书你要为各节点签订证书要用的私钥4.2 elastic-certificates.p12这个文件是一个 keystore 文件, 蕴含: 节点证书节点私钥keyCA证书(可易了解为前面有我就不必ca了)4.3 keystore 是什么密钥（key）和证书（certificates）存在一个文件中称为keystore。在keystore里，蕴含两种数据： 密钥实体（Key entity）——密钥（secret key）又或者是私钥和配对公钥（采纳非对称加密）可信赖的证书实体（trusted certificate entries）——只蕴含公钥Keystore能够了解为寄存利用签名的文件。

本文首发于公众号：Hunter后端 原文链接：es笔记一之es装置与介绍 首先介绍一下 es，全名为 Elasticsearch，它定义上不是一种数据库，是一种搜索引擎。 咱们能够把海量数据都放到 es 里而后提供搜寻操作，然而 MySQL 也同样能够提供搜寻，为什么要用 es 呢？ 一个是因为它搜寻快，应用倒排索引的形式将数据分词之后存储数据，查问的时候间接依据搜寻关键词找到相干数据。 另一个是因为它能够提供一些 MySQL 做不到或者比拟耗时的含糊搜寻，这个咱们后续再介绍。 1、es 和 kibana 的装置es 和 kibana 的装置这里能够间接看我之前的一篇博客文章，讲的很具体，或者去官网找相应的装置步骤。 es和kibana装置 这里咱们应用的是 7.6.0 版本。 kibana 是一个界面工具，能够用于在浏览器界面间接操作 es。 2、es 层级和构造介绍首先，咱们进入 kibana 界面，创立一条数据，而后依据这条数据来介绍 es 中构造的层级关系。 进入 kibana 的输出界面，输出： PUT /customer/_doc/1{ "name": "John Doe"}点击 PUT 命令同行左边的三角形，或者按住 ctrl + enter 确认，咱们就实现了一次数据的创立。 而后输出上面的命令查看这条数据的详情： GET /customer/_doc/1通过数据的存储和查看，咱们能够差不多理解 es 中数据的操作形式是 restful 格调的，能够通过 GET、PUT、POST、DELETE 的形式来实现数据的增删改查。 而后看一下咱们通过 GET 形式取得的数据状况，如下： { "_index" : "customer", "_type" : "_doc", "_id" : "1", "_version" : 1, "_seq_no" : 0, "_primary_term" : 1, "found" : true, "_source" : { "name" : "John Doe" }}在这个数据结构中，有一些货色是能够和 MySQL 等数据库的构造对应起来的，比方 index 对应 MySQL 中的 database，type 对应 MySQL 中的 table，而 source 中的数据则对应于 MySQL 中一条条的数据。 ...

一、前言ElasticSearch的Java客户端中，spring-data-elasticsearch 简化了 增删改、建索引等，没有简化 简单查问 编码。easy-es API 相似 Mybatis-Plus，大幅升高开发门槛，缩小代码量，反对 自定义排序、权重、原生查问，留下了 广大的调整空间 二、Java客户端 Java Transport Client：官网已弃用，二进制协定，版本强绑定，不倡议应用Jest：4年多没公布新版本，不保护了，不倡议应用Java REST Client：7.15 以下版本实用Elasticsearch Java API Client：7.16 版本公布测试版，倡议es 8.0 以上应用spring-data-elasticsearch：简化了 增删改、建索引等，没有简化 简单查问 编码easy-es：API 相似 Mybatis-Plus，大幅升高开发门槛，缩小代码量，反对 自定义排序、权重、原生查问，留下了 广大的调整空间三、easy-es MySQL和 easy-es 语法比照 | Easy-Es MySQLEasy-Eses-DSL/es java apiandandmustororshould=eqterm!=neboolQueryBuilder.mustNot(queryBuilder)>gtQueryBuilders.rangeQuery('es field').gt()>=ge.rangeQuery('es field').gte()<lt.rangeQuery('es field').lt()<=le.rangeQuery('es field').lte()like '%field%'likeQueryBuilders.wildcardQuery(field,value)... spring-boot配置 留神：adress 不能以 http(s):// 结尾 在 application.yml 配置文件中增加： easy-es: enable: true # 是否开启EE主动配置 默认开启,可缺省 address: 域名:9200 username: elastic password: 明码 banner: false # 默认为true 打印banner 若您不冀望打印banner,可配置为false global-config: process-index-mode: smoothly #索引解决模式,smoothly:平滑模式,默认开启此模式, not_smoothly:非平滑模式, manual:手动模式 print-dsl: false # 开启控制台打印通过本框架生成的DSL语句，默认为开启，测试稳固后的生产环境倡议敞开，以晋升大量性能 async-process-index-blocking: false # 异步解决索引是否阻塞主线程 默认阻塞 数据量过大时调整为非阻塞异步进行 我的项目启动更快 db-config: table-prefix: # 索引前缀,可用于辨别环境 默认为空 用法和MP的tablePrefix一样的作用和用法 field-strategy: not_null # 字段更新策略 默认为not_null refresh-policy: none # 默认为不刷新，wait_until对写入性能影响也很大增加依赖 ...

1，text当一个字段的内容须要被全文检索时，能够应用text类型，它反对长内容的存储，如文章内容、商品信息等，该类型的字段在保留时会被分词器剖析，并拆分成多个词项，而后依据拆分后的词项生成对应的索引。须要留神的是text类型的字段无奈进行准确匹配，也不能间接用于排序、聚合，也被称为ananlyzed字符串。 2，keywordkeyword类型的字段内容不会被分词器剖析、拆分，而是依据原始文本间接生成倒排索引，所以keyword类型的字段能够间接通过原始文本准确的检索到。该类型的字段个别用于过滤、排序、聚合操作。 3，日期类型ES中的date类型黑夜反对如下两种格局：strict_date_optional_time，示意 yyyy-MM-dd'T'HH:mm:ss.SSSSSSZ 或者 yyyy-MM-dd 格局的日期epoch_millis，示意从 1970.1.1 零点到当初的毫秒数，除了指定date类型外，尽量指定相应的格局，如下： PUT blog{ "mappings": { "properties": { "publishDate":{ "type": "date", "format": "yyyy-MM-dd HH:mm:ss||yyyy-MM-dd||epoch_millis" } } }}这里补充下，字段类型如果是date类型，在应用canal同步mysql数据到ES的时候会现出工夫格局转换问题。具体如下： java.lang.RuntimeException: ES sync commit error ElasticsearchException[Elasticsearch exception [type=mapper_parsing_exception, reason=failed to parse field [createTime] of type [date] in document with id 'XXX']]; nested: ElasticsearchException[Elasticsearch exception [type=illegal_argument_exception, reason=Invalid format: "2020-08-25T15:27:01+08:00" is malformed at "-08-25T15:27:01+08:00"]];很多的解决方案是通过批改canal源码解决的。具体阐明可查看canal同步数据到ES工夫格局问题。我之前应用ES只是通过工夫进行排序，所以我间接将相应的工夫字段以long类型即工夫戳的格局存储的。在数据同步的时候将数据表里的工夫字段转成了工夫戳后再进行同步。 4，布尔类型bolean类型，有true、false两个值 5，数值类型 类型取值范畴byte-2^7 ~ 2^7-1short-2^15 ~ 2^15-1integer-2^31 ~ 2^31-1long-2^63 ~ 2^63-1float32位单精度IEEE 754浮点类型double64位双精度IEEE 754浮点类型half_float16位半精度IEEE 754浮点类型scaled_float缩放类型的的浮点数个别状况下，优先应用范畴小的类型 ...

1. 节点节点依照职责能够分为 master节点、数据节点 和 协调节点，每个节点类型能够进行独自配置。默认状况下，集群不会对节点角色进行划分，所有节点都是平等的，能够负责所有的职责。然而在生产环境中须要对这些节点的角色进行最优划分，否则在高并发申请的状况下，集群容易呈现服务阻塞超时甚至服务解体的隐患。 1. master节点负责保护整个集群的相干工作，治理集群的变更，如创立/删除索引、节点衰弱状态监测、节点上/下线等。 master节点是由集群节点通过选举算法选举进去的，一个集群中只有一个节点能够成为master节点，然而能够有一个或多个节点参加master节点的选举。在默认状况下，任意节点都能够作为master的候选节点，能够通过配置项node.master对以后节点是否作为master的候选节点进行管制。 为了防止选举时产生脑裂，倡议master的候选节点数量是 2n+1。 2. 数据节点次要负责索引数据的保留工作，此外也执行数据的其余操作，如文档的删除、批改和查问操作。 数据节点的很多工作是调用Lucene库进行Lucene索引操作，因而这种节点对于内存和I/O的耗费比拟大，生产环境中应多留神数据节点的计算机负载状况。 3. 协调节点客户端能够向ES集群的节点发动申请，这个节点叫作协调节点。 在默认状况下，协调节点能够是集群中的任意节点，此时它的生命周期是和一个独自的申请相干的。也就是说，当客户端向集群中的某个节点发动申请时，此时该节点被称为以后申请的协调节点；当它将响应后果返回给客户端后，该协调节点的生命周期就完结了。 当然，为了升高集群的负载，能够设置某些节点作为独自的协调节点。在节点的配置文件中设置 node.master 和 node.data 配置项为 false，此时，这个节点就不会被选中为master节点并且不再负责数据节点，而客户端就能够把这类节点作为协调节点来应用，把所有的申请都散发到这些节点上。 2. 分片一个分片(shard)是一个最小级别“工作单元(worker unit)”，它只是保留了索引中所有数据的一部分。 分片能够是主分片(primary shard) 或者是 复制分片(replica shard)（或副分片）。 比方，一个具备10亿文档的索引占据1TB的磁盘空间，而任一节点都没有这样大的磁盘空间；或者单个节点解决搜寻申请，响应太慢。为了解决这个问题，ES 提供了将索引划分成多份的能力，这些份就叫做 分片。当你创立一个索引的时候，你能够指定你想要的分片的数量。每个分片自身也是一个功能完善并且独立的“索引”，这个“索引”能够被搁置到集群中的任何节点上。分片很重要，次要有两方面的起因： 容许你程度宰割/扩大你的内容容量。容许你在分片（潜在地，位于多个节点上）之上进行分布式的、并行的操作，进而进步性能/吞吐量。至于一个分片怎么散布，它的文档怎么聚合回搜寻申请，是齐全由 ES 治理的，对于作为用户的你来说，这些都是通明的。 分片还有 正本 replica shard，在一个网络/云的环境里，失败随时都可能产生，在某个分片/节点不知怎么的就处于离线状态，或者因为任何起因隐没了，这种状况下，有一个故障转移机制是十分有用并且是强烈推荐的。为此目标，ES 容许你创立分片的一份或多份拷贝，这些拷贝叫做 复制分片，或者间接叫复制。 2.1. 主分片与副分片1. 是否前期可批改主分片数量，在创立索引时指定。创立索引后不能批改。副分片数量，在创立索引后，仍然能批改。因为数据存储时，会拿hash值，依据索引中主分片数量取余，定位所在分片的地址。如果后续批改了主分片数量，会导致数据的地址谬误。 2. 数量主分片：默认数量为5，单个分片的大小倡议在20G～50G。主分片的数量决定了索引最多能存储多少数据（理论的数量取决于数据、硬件和利用场景）。副分片：默认数量为1。副分片的数量保证数据的高可靠性，避免数据失落。每个主分片都有一个或多个正本分片，当主分片异样时，正本能够提供数据的查问等操作。 主分片和对应的正本分片是不会在同一个节点上的，所以正本分片数的最大值是 n -1（其中 n 为节点数）。 3. 意义主分片：可程度宰割/扩大的内容容量；可在分片之上进行分布式的、并行的操作，进而提供性能/吞吐量。副分片：在分片/节点失败的状况下，提供了高可用性。（留神：正本不能与对应的主分片置于同一节点上）；扩大搜寻量/吞吐量，因为搜寻能够在所有的正本上并行运行。4. 副分片选举生成主分片es为了更好地稳定性和容灾，除了进行必要的索引备份外，正本的增加能够更好地维持集群数据完整性。 当呈现某个节点从集群脱离，在集群其余节点的正本，此时会选举出主分片，所以这里就有主分片和正本之间的数据同步问题。 5. 主从复制当有数据写入时，申请节点收到申请，先转给主分片所在节点上执行申请，如果胜利，它转发申请到相应的几个复制分片所在的节点上。当所有的复制分片报告胜利，主分片所在节点报告胜利到申请的节点，申请的节点再报告给客户端。 6. 增减节点时，分片主动负载平衡假如有6台机器总共7个分片, 必定就会有1台机器有2个分片, 此时给 es 集群新增1台机器进来,那么有2个分片的那台机器会主动给1个分片调配给新退出的机器下来. 2.2. 集群状态ES 的集群监控信息中蕴含了许多的统计数据，其中最为重要的一项就是集群衰弱。集群衰弱存储在status字段中，次要包含 green、yellow、red 三种状态。它的三种色彩含意如下： ...

1. 全副角色masterdata (包含：data_content, data_hot, data_warm, data_cold, data_frozen)ingestmlremote_cluster_clienttransform仅协调角色2. 各个角色简介2.1. master eligible node有资格被选为master来治理cluster 专用的master节点专一于治理集群健康状况十分重要2.2. data node增删改查、搜寻和聚合 CRUD, search, and aggregations2.3. ingest nodeingest节点可能对文档利用ingest pipeline，以便在index之前转换和润色文档。在ingest负载较重的状况下，单设专用的ingest节点(不设置master/data)十分有用； 堆栈监控和ingest pipeline须要ingest的role;2.4. remote_cluster_client noderemote_cluster_client角色使节点有资格充当近程客户端；跨集群搜寻(ccs)和跨集群复制(ccr)，须要remote_cluster_client的role; ccs= 跨集群搜寻：cross-cluster searchccr= 跨集群复制：cross-cluster replication2.5. ml机器学习须要ml角色node.roles: [ ml, remote_cluster_client] remote_cluster_client角色为可选角色，但强烈推荐应用。否则，在机器学习作业或数据提要中应用跨集群搜寻将失败。2.6. transformnode.roles: [ transform, remote_cluster_client ]Fleet、Elastic Security应用程序和转换都须要transform角色 remote_cluster_client为可选角色，但强烈推荐应用。否则，在转换中应用跨集群搜寻将失败。2.7 仅协调节点：Coordinating only nodenode.roles: [ ] 路由：smart load balancers收集整个集群的状态，而后将申请间接路由到适合的节点大型集群或者可能须要 They join the cluster and receive the full cluster state, like every other node, and they use the cluster state to route requests directly to the appropriate place(s).

1. 单节点模式To ensure your cluster can report a green status, override the default by setting index.number_of_replicas to 0 on every index.2.两个节点的集群两个节点都作为数据节点； recommend they both be data nodesindex.number_of_replicas: 1 每个索引都设置备份数为1； setting index.number_of_replicas to 1 on every index其中一个节点设置 node.master: false，确保一个节点不是master备选节点； set node.master: false on one of your two nodes so that it is not master-eligible(默认每个节点都有全副的role) 双节点时，设置其中一个不要作为备选master的节点!(node.master: false) By default, each node is assigned every role. We recommend you assign both nodes all other roles except master eligibility. If one node fails, the other node can handle its tasks.不举荐双节点-弹性力有余 ...

1. 数据管理定义满足条件的索引 index定义并应用满足条件的索引模板 index template定义并应用满足条件的动静模板 dynamic template为工夫序列索引定义一个ILM策略定义创立新的数据流的索引模板-index template+data stream2. 检索数据执行查问query: terms and/or phrases 在一个索引的单个或多个field中;执行查问query: 应用bool组合多个查问和过滤器 bool filter 多个查问;写异步检索: async search;metric and bucket aggregations有子聚合的聚合;跨集群检索: searches across multiple clusters应用运行时字段的检索: search that utilizes a runtime field3. 研发检索利用高亮检索后果中的term词;按需要对检索后果进行sort;检索后果分页;定义并应用索引别名: alias;定义并应用检索模板 search template4. 数据处理定义满足条件的mapping;定义并应用满足条件的定制analyzer: custom analyzer ;multi-fields: 不同data type, analyzers的multi-fields; (Define and use multi-fields with different data types and/or analyzers)应用reindex api+update by query来reindex和更新文档;定义和应用ingest pipeline, 蕴含无痛批改document; (Define and use an ingest pipeline that satisfies a given set of requirements, including the use of Painless to modify documents)通过 Painless scripting 定义runtime fields来获取自定义values ...

问题形容： 应用elasticsearch做分页查问时，当查问记录超过10000时，会报如下谬误：Result window is too large, from + size must be less than or equal to: [10000] but was [10025].起因剖析： Elasticsearch中**index.max_result_window**默认值是10000，而咱们的查问超过了这个默认值，所以就会呈现上述谬误。解决办法： 这里须要批改一下**index.max_result_window**的值，把这个设置到适合的值即可，我这里设置100000，大家能够依据本人的须要进行设置。PUT my_index/_settings{ "index":{ "max_result_window":100000 }}设置胜利后，返回"acknowledged": true { "acknowledged": true}设置胜利后再去查问就不会报那个错了。

Elasticsearch 是一款基于 Lunece 的分布式、可扩大、实时的搜寻与数据分析引擎。 版本变更版本号公布工夫内容0.42010-02第一次公布1.x2014-01 2.x2015-10 5.x2016-10Lucene 6.x，默认打分机制从 TF-IDF 改为 BM 25新性能：反对 Keyword 类型性能优化：移除竞争锁、反对分片上聚合的缓存6.x2017-10Lucene 7.x新性能：跨集群复制、索引生命周期治理、反对 SQL性能优化：索引时进行排序，无效存储稠密字段，升高了存储老本7.x2019-04Lucene 8.0新性能：新的集群协调实现、Script score query性能优化：默认的主分片数从 5 改为 18.x2022-02Lucene 9.0新性能：原生反对 NLP、原生矢量搜寻Elastic Stack 生态圈Elasticsearch Best Practice Architecture 1. Logstash：数据处理管道开源的服务器端数据处理管道，反对从不同起源采集数据，转换数据，并将数据发送到不同的存储库中。 Logstash，由 Jordan Sisel 创立于 2009 年，最后用来做日志的采集与解决，在 2013 年被 Elasticsearch 收买。 个性： 实时解析和转换数据，从 IP 地址破译出地理坐标；将 PII 数据匿名化，齐全排除敏感字段。可扩大，200 多个插件（日志、数据库、Arcsigh、Netflow）可靠性安全性，Logstash 会通过长久化队列来保障至多将运行中的事件送达一次；数据传输加密监控2. Kibana（Kibana = Kiwifruit + Banana）：可视化剖析数据可视化工具，帮忙用户解开对数据的任何疑难Kibana，创始人是 Rashid Khan。Kibana 是基于 Logstash 的工具，2013 年退出 Elastic 公司。 3. Beats：轻量的数据采集器Go 语言开发 4. X-Pack：商业化套件6.3 之前的版本，X-Pack 以插件形式装置 ...

当咱们建设一个 index 的时候，能够为 index 创立专属的 analyzer 这个 analyzer 的只是该 index 可见，而不是全局的 PUT /my_index{ "mappings": { "properties": { "title": { "type": "text", "analyzer": "my_analyzer" }, "content": { "type": "text" } } }, "settings": { "analysis": { "analyzer": { "my_analyzer": { "type": "custom", "tokenizer": "standard", "filter": [ "lowercase", "my_synonyms" ] } }, "filter": { "my_synonyms": { "type": "synonym", "synonyms_path": "analysis/synonyms.txt" } } } }}有这么一个需要，咱们心愿，应用 python sdk 查看 analyzer 的后果，怎么办？ 很简略: es.indices.analyze( index=index_name, body={ "analyzer": "sentence_analyzer", "text": content, })加上 index=index_name 参数就好了，不然会遇到报错： failed to find global analyzer [sentence_analyzer] ...

本文重点为介绍一个计算广告的匹配算法，来自 Indexing Boolean Expression 。这种匹配算法能够匹配较为简单的布尔表达式。 尽量以说人话的形式解释这种算法。 不波及权重排序等规定。 概念倒排索引：Inverted Index，反向索引，依据内容查找文档记录 如 document1: { a: [1,2] }，document2: { a:[1], b: [9] }建设倒排索引为 a.1: [document1, document2], a.2: [document1]，b.1: [document2]能疾速找到到内容所在的文档记录析取：Disjunctive，逻辑或合取：Conjunctive，逻辑与，后文称为 且析取范式： （一个或多个且）的或被认为是一个 DNF如：(A)、(B) ∪ (C)、(D ∩ E) ∪ (F)合取范式： （一个或多个析取）的且被认为是一个 CNF如：(A)、(B) ∩ (C)、(D ∪ E) ∩ (F)所有逻辑公式都能够转换成合取范式 CNF断言：在后续的形容中，咱们将最小的逻辑单元称为一个断言，如 (a=1 ∩ b=2) ∪ c!=3 中的，a=1、b=2、c!=3指标既然所有的逻辑公式都能够转为 CNF，那么咱们的指标就是实现一个可疾速查找指标所匹配的 CNF 布尔表达式（boolean expressions）的算法。 DNF 算法先来匹配 DNF 表达式 首先有几个匹配规定： BE1：(a=1 且 b=2 且 c=1)BE2：(a=2 且 b=3) 或 c=1BE3: (a=1 且 b!=2) 或 c=1BE4：(a=1 且 b!=3)BE5：b!=2有一个指标 s1，其属性为 a=1、b=3对所有的匹配规定中的断言建设倒排索引 ...

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1 装置ES下载地址：https://www.elastic.co/cn/dow... 抉择Windows版本，我下载的是7.17.0。解压后即可实现装置。 进入bin文件， 双击执行 elasticsearch.bat，而后关上浏览器，进入页面： http://localhost:9200，看到以下输入，示意启动胜利。 2 装置Nodejs下载地址：https://nodejs.org/en/ 下载安装包，并装置。 验证命令： node -v能够查看nodejs的版本，输入版本号代表装置胜利。 3 装置 Grunt 工具装置命令： npm install -g grunt-cli 验证命令： grunt -version 能够查看Grunt的版本，输入版本号代表装置胜利。 4 装置 Head 插件安装包下载地址：https://github.com/mobz/elast... 解压后cd进入文件夹，运行装置命令： npm install装置实现后执行启动命令： npm run start拜访地址：http://localhost:9100/，即可看到Head插件已启动： 批改 Elasticsearch 应用的配置文件：elasticsearch-7.11.1\config\elasticsearch.yml 。 在 elasticsearch.yml 配置文件中，增加如下配置： http.cors.enabled: true http.cors.allow-origin: "*"设置参数的时候前面要有空格。 重启Elasticsearch ，而后在地址http://localhost:9100/中点击“连贯”，即可，连上Elasticsearch： 如上所示，装置胜利。 5 装置 Kibana下载地址：https://www.elastic.co/cn/dow... Kibana 的版本和 Elasticsearch 的版本必须统一，我也抉择了7.17.0。 进入 bin 目录下，双击执行 kibana.bat 文件，即可启动 kibana。 通过浏览器进入：http://localhost:5601/ 能够看到 kibana 启动胜利。 ...

因为ES是以后搜寻零碎的标杆，在做搜寻零碎时，有时候须要拿ES的实现形式来作为咱们的搜寻零碎实现形式的参考，这时就须要对ES接口的每个参数进行具体的拆解，搞清楚每个参数的作用机制及外部的实现形式，避免接口API在未来产生频繁变动，在具体实现时少走弯路。这个过程中仅仅是ES的文档就有点不够了，ES的文档诚然十分优良，但也不是每个参数都会解说的八面玲珑，有时候咱们须要在源码中寻找答案，上面就ES本地调试环境的搭建做简略的介绍。 第一步，将ES的源码clone 到本地，这里成文时es的最新版本是7.9.0。 git clone git@github.com:elastic/elasticsearch.git导入到IDE中，这里应用的是idea，ES是应用gradle构建的，能够应用idea的主动导入不便的导入到IDE中，这里要留神的一点是构建过程中会从maven仓库下载相干依赖，找一个速度比拟快的maven仓库能够减速导入，比方阿里的maven仓库，还须要一个vpn，因为构建过程中须要从aws下载一些资源，须要fq。导入胜利后，idea的run configuration中会主动呈现一个"Debug Elasticsearch"的配置，能够先将这个跑起来，前面会用到执行命令 gradle run --debug-jvm期待启动胜利，启动比较慢，期待启动胜利后就能够通过http（如postman）工具进行拜访了，当然也能够应用kibana。默认会有basic auth，账号是elastic:password能够先建一个索引 //PUT http://127.0.0.1:9200/test{ "mappings": { "properties": { "name": { "type": "text" } } }}写入一些数据 //POST http://127.0.0.1:9200/_bulk{ "index" : { "_index" : "test", "_id" : "1" } } { "name" : "trying out elastiksearch" }{ "index" : { "_index" : "test", "_id" : "2" } } { "name" : "trylng out elastiksearch" }{ "index" : { "_index" : "test", "_id" : "3" } } { "name" : "trying oute elasticsearch" }{ "index" : { "_index" : "test", "_id" : "4" } } { "name" : "trying outr elasticsearch" }搜寻 ...

fuzzy在es中能够了解为含糊查问，搜寻自身很多时候是不准确的，很多时候咱们须要在用户的查问词中有局部谬误的状况下也能召回正确的后果，然而计算机无法了解自然语言，因而咱们只能通过一些算法代替语言理解能力实现相似的事件，前缀查问的实现比较简单但成果很难令人满意，就含糊查问而言es的fuzzy实现了一种复杂度和成果比拟折中的查问能力。 字符的类似度-编辑间隔编辑间隔是对两个字符串差别长度的量化，及一个字符至多须要解决多少次能力变成另一个字符，比方lucene和lucece只差了一个字符他们的编辑间隔是1。 莱文斯坦间隔(Levenshtein distance)编辑间隔的一种，指两个字符串之间，由一个转成另一个所需的起码编辑操作次数。容许的编辑包含： 将一个字符替换成另一个字符插入一个字符删除一个字符Damerau–Levenshtein distance莱文斯坦间隔的一个扩大版 ，将相邻地位的两个字符的调换当做一次编辑，而在经典的莱文斯坦间隔计算中地位调换是2次编辑。 ElasticSearch反对经典的Levenshtein间隔和Damerau-Levenshtein间隔，在es中对含糊查问的反对有两种形式match query和fuzzy query。 match query应用形式如下所示： GET index_name/_search{ "query": { "match": { "name": { "query": "elastic search", "fuzziness": 0, "prefix_length": 0, "max_expansions": 50, "transpositions": true } } }}上面对他反对的参数进行一些介绍： fuzziness本次查问容许的最大编辑间隔，默认不开启含糊查问，相当于fuzziness=0。 反对的格局 能够是数字（0、1、2）代表固定的最大编辑间隔主动模式，AUTO:[low],[high]的格局，含意为： 查问词长度在[0-low)范畴内编辑间隔为0（即强匹配)[low, high)范畴内容许编辑一次\>high容许编辑2次 也能够只写AUTO代表默认的主动模式，相当于AUTO:3,6 prefix_length管制两个字符串匹配的最小雷同的前缀大小，也即是前n个字符不容许编辑，必须与查问词雷同，默认是0，大于0时能够显著晋升查问性能，须要留神的是这里的 prefix_length作用在分词后的 term 级，也就是作用在每个分词的词根上而不是整个查问词上，对于下面的例子 elastic search 来说就是须要 elastic 和 search 都会严格匹配前两个字符来召回，是不是很意外。 max_expansions这个参数比拟蛊惑，查问了相当的文档都对这个参数模糊不清，通过对Lucene源码的debug跟踪得出以下论断： Lucene的fuzzy 查问是通过query改写实现的，查问时会将倒排索引词典中的term汇合和查问term做编辑间隔计算，获取topN个间隔最小的term作为query改写的词，这N个词会组成一组或查问，即改写为boolean query (改写词作为term query放在should clause)max_expansions影响的是这里的或查问的元素数目，实际上这个数目会被两个参数影响，另外一个是indices.query.bool.max_clause_count，最终取indices.query.bool.max_clause_count和max_expansions的最小值倒排中的term总数会影响fuzzy查问的性能，term越多性能越差，文档总数不是影响fuzzy查问性能的关键因素。值得注意的是分词后的term自身也算一次max_expansions计数，也就是说 max_expansions=1 时相当于不扩大（分词term级准确查问）max_expansions 是作用在分片级参数，如果分片数比拟多的话，查问后果看起来和max_expansions设置数目不统一也是失常的transpositions将相邻地位字符调换算作一次编辑间隔，如 ab -> ba，即应用Damerau–Levenshtein间隔算法，默认开启，设置 transpositions=false将应用经典莱文斯坦间隔算法。 minimum_should_match 的作用机制minimum_should_match作用在分词后的term级，即分词后的term无论是通过准确查找或者含糊查找命中算且算一次计数，对于同一个term扩大进去的term1、term2不做反复计数。当operator 为 and 时，minimum_should_match >0 时会导致查不到后果，这是因为minimum_should_match的计算方法是should clause命中的个数，operator为and时无should clause命中数永远不会 >0，所以无后果。参见BooleanWeight.java#L390minimum_should_match为百分比时ES的计算方法 ...

如何选型ETL产品的选型工作始终以来都是困扰架构师的一块心病，国外付费产品用不起，国外收费产品学习老本高、不易施行。 少数软件又为什么逐步国产化？落地我的项目中真正的需要又是什么？真的是靠利落拽就能解决所有问题吗？轻量级 跨平台 易集成go语言开发，部署简略，开箱即用。不只是数据交换工具，还是能够嵌入并解析go语言脚本的开发工具。欠缺的ETL解决方案，提供可视化工作设计器、调度配置及日志查看性能。反对丰盛的数据源关系型数据库Oralce、MySQL、PostgreSQL、Sqlite非关系型数据库ElasticSearch、Redis= 时序数据库InfluxDB、ClickHouse、Prometheus消息中间件Kafka、RocketMQ文件Excel、CSV资源地址收费下载 etl-engine使用手册 etl-crontab使用手册 嵌入脚本开发

京东物流：康睿 姚再毅 李振 刘斌 王北永 阐明：以下全副均基于 ElasticSearch 8.1 版本 一.跨集群检索 - ccr官网文档地址： https://www.elastic.co/guide/en/elasticsearch/reference/8.1/modules-cross-cluster-search.html跨集群检索的背景和意义 跨集群检索定义 跨集群检索环境搭建官网文档地址： https://www.elastic.co/guide/en/elasticsearch/reference/8.1/modules-cross-cluster-search.html 步骤1：搭建两个本地单节点集群，本地练习可勾销平安配置 步骤2：每个集群都执行以下命令 PUT \_cluster/settings { "persistent": { "cluster": { "remote": { "cluster\_one": { "seeds": [ "172.21.0.14:9301" ] },"cluster_two": { "seeds": [ "172.21.0.14:9302" ] } } } } } 步骤3：验证集群之间是否互通 计划1：Kibana 可视化查看：stack Management -> Remote Clusters -> status 应该是 connected！ 且必须打上绿色的对号。 计划2：GET _remote/info 跨集群查问演练# 步骤1 在集群 1 中增加数据如下PUT test01/_bulk{"index":{"_id":1}}{"title":"this is from cluster01..."}# 步骤2 在集群 2 中增加数据如下：PUT test01/_bulk{"index":{"_id":1}}{"title":"this is from cluster02..."}# 步骤 3：执行跨集群检索如下: 语法：POST 集群名称1:索引名称,集群名称2:索引名称/_searchPOST cluster_one:test01,cluster_two:test01/_search{ "took" : 7, "timed_out" : false, "num_reduce_phases" : 3, "_shards" : { "total" : 2, "successful" : 2, "skipped" : 0, "failed" : 0 }, "_clusters" : { "total" : 2, "successful" : 2, "skipped" : 0 }, "hits" : { "total" : { "value" : 2, "relation" : "eq" }, "max_score" : 1.0, "hits" : [ { "_index" : "cluster_two:test01", "_id" : "1", "_score" : 1.0, "_source" : { "title" : "this is from cluster02..." } }, { "_index" : "cluster_one:test01", "_id" : "1", "_score" : 1.0, "_source" : { "title" : "this is from cluster01..." } } ] }}二.跨集群复制 - ccs - 该性能需付费官网文档地址： https://www.elastic.co/guide/en/elasticsearch/reference/current/xpack-ccr.html如何保障集群的高可用正本机制快照和复原跨集群复制（相似mysql 主从同步）跨集群复制概述 ...

number_of_replicas 的默认值是 1 参考: number_of_replicasDynamic index settingsnumber_of_shards 的默认值，elasticsearch version >= 7 是1 ; elasticsearch version <=7 是 5 参考：如何在 Elasticsearch 中为每个索引抉择正确的分片数

es 的 url 退出账号密码 把账号密码加到 url 中 curl http://yourusername:yourpassword@host:9200/yourindexname/_settings把账号密码加到 curl 的 user 参数中 curl --user yourusername:yourpassword http://yourusername:yourpassword@host:9200/yourindexname/_settings

背景：在 Mac本下，通过 docker 实现一个 es 集群（3台-或者可多台）搭建。（后续如果有实在的机器，只须要又该对应的 ip 地址即可，须要关注的是，机器间是能够相互 ping通的，并且 iptable是放开的端口）具体步骤：首先创立一个新的 docker 网络docker network create -d bridge estestnet查看创立以后的网络是否创立胜利，能够看到下图是创立胜利的，docker network list设置三个 es.yml, 通过文件资源共享，es 执行的时候会主动调用。Node1 节点 es1.yml cluster.name: elasticsearch-clusternode.name: es-node1network.bind_host: 0.0.0.0# 切记 docker 容器中不能通过127.0.0.1 进行通信，须要改成本人电脑本机的 ip# 本机本人的 ip 下方的 discovery.zen.ping.unicast.hosts保持一致network.publish_host: 172.25.168.63http.port: 9200transport.tcp.port: 9300http.cors.enabled: truehttp.cors.allow-origin: "*"node.master: truenode.data: truediscovery.zen.ping.unicast.hosts: ["172.25.168.63:9300","172.25.168.63:9301","172.25.168.63:9302"]discovery.zen.minimum_master_nodes: 2# 初始化执行时，默认执行的节点cluster.initial_master_nodes: es-node1Node2 节点 es2.yml cluster.name: elasticsearch-clusternode.name: es-node2network.bind_host: 0.0.0.0# 本机本人的 ip 下方的 discovery.zen.ping.unicast.hosts保持一致network.publish_host: 172.25.168.63http.port: 9201transport.tcp.port: 9301http.cors.enabled: truehttp.cors.allow-origin: "*"node.master: truenode.data: truediscovery.zen.ping.unicast.hosts: ["172.25.168.63:9300","172.25.168.63:9301","172.25.168.63:9302"]discovery.zen.minimum_master_nodes: 2cluster.initial_master_nodes: es-node1Node3 节点 es3.yml ...

[toc] 1. win10命令行乱码1.1 elasticsearch命令行中文乱码win10命令行启动elasticsearch时,命令行字符乱码,须要批改编码格局:有两种, 一种是长期, 一种是永恒批改注册表: 1.2 长期批改输出【win+r】->chcp 65001->确定1.3 批改注册表1. 关上注册表: 输出【win+r】，regedit 确定;2. 门路【HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Command Processor】3. 【新建->字符串值】名称=autorun, 值=chcp 650012. es生成证书2.1 签发CA证书./bin/elasticsearch-certutil ca一路回车, 目录下生成: elastic-stack-ca.p122.2 用CA证书生成节点证书./bin/elasticsearch-certutil cert --ca elastic-stack-ca.p12一路回车不要明码, 会生成: elastic-certificates.p122.3 将CA证书和节点证书mv到config/certs下mv *.p12 config/certs/2.4 签发HTTP证书交互过程如下: (centos7) ./bin/elasticsearch-certutil http# 不须要csr, 输出nGenerate a CSR? [y/N]n# 应用生成的CA整肃, 输出yUse an existing CA? [y/N]y# 输出CA门路: 从certs开始CA Path: certs/elastic-stack-ca.p12# 没有CA明码,间接回车Password for elastic-stack-ca.p12:# 设置5年,默认,输出:5yFor how long should your certificate be valid? [5y] 5y# 是否须要每个节点都生成证书:输出nGenerate a certificate per node? [y/N]n# 输出node名称: hostname, 输出后y确认ZB-PF2P9LED# 输出ip: , 输出后y确认192.168.0.102# 方才这些配置还须要批改吗? 输出nDo you wish to change any of these options? [y/N]n# 不必明码, 回车Provide a password for the "http.p12" file: [<ENTER> for none]# 问要不要给http证书改名, 间接回车What filename should be used for the output zip file? [D:\devs\elastic-safe\es8.5.2\elasticsearch-ssl-http.zip]#最初:Zip file written to D:\devs\elastic-safe\es8.5.2\elasticsearch-ssl-http.zip2.5 证书放到certs目录下unzip elasticsearch-ssl-http.zip elasticsearch-ssl-http/mv elasticsearch/http.p12 kibana/elasticsearch-ca.pem config/certs/# 其余的文件删掉即可3. 配置elasticsearch.ymlcluster.name: es-clusternode.name: es-node-1path.data: D:/devs/elastic-safe/es8.5.2/datapath.logs: D:/devs/elastic-safe/es8.5.2/logs# 设置网络拜访节点network.host: ZB-PF2P9LED# 设置网络拜访端口http.port: 9200# 初始种子节点#discovery.seed_hosts: ["ZB-PF2P9LED"]# 平安认证xpack.security.enabled: truexpack.security.enrollment.enabled: true# http的认证xpack.security.http.ssl: enabled: true keystore.path: D:/devs/elastic-safe/es8.5.2/config/certs/http.p12 truststore.path: D:/devs/elastic-safe/es8.5.2/config/certs/http.p12# 传输认证xpack.security.transport.ssl: enabled: true verification_mode: certificate keystore.path: D:/devs/elastic-safe/es8.5.2/config/certs/elastic-certificates.p12 truststore.path: D:/devs/elastic-safe/es8.5.2/config/certs/elastic-certificates.p12# 此处留神, es-node-1是下面配置的节点名称cluster.initial_master_nodes: ["es-node-1"]http.host: [_local_, _site_]ingest.geoip.downloader.enabled: falsexpack.security.http.ssl.client_authentication: none而后启动, 即可! ...

[toc] 1. 不生成证书间接启动1.1 残缺的控制台信息间接启动后控制台会展现记录一段要害信息 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━✅ Elasticsearch security features have been automatically configured!✅ Authentication is enabled and cluster connections are encrypted.ℹ️ Password for the elastic user (reset with `bin/elasticsearch-reset-password -u elastic`): +H2hnvwZ2DmAHY1Eim_Mℹ️ HTTP CA certificate SHA-256 fingerprint: d924ffb43dd09829e6f25156b2264dc5b8f5b1d119ac7e1bdde2dd0104776836ℹ️ Configure Kibana to use this cluster:• Run Kibana and click the configuration link in the terminal when Kibana starts.• Copy the following enrollment token and paste it into Kibana in your browser (valid for the next 30 minutes): eyJ2ZXIiOiI4LjUuMiIsImFkciI6WyIyLjAuMC4xOjU5MjAwIl0sImZnciI6ImQ5MjRmZmI0M2RkMDk4MjllNmYyNTE1NmIyMjY0ZGM1YjhmNWIxZDExOWFjN2UxYmRkZTJkZDAxMDQ3NzY4MzYiLCJrZXkiOiJMWFZJVjRVQlVQN1dJOFdZREM2LTpRaTd6WGVHWFFTdS1hZEU1RFJ4Z3Z3In0=ℹ️ Configure other nodes to join this cluster:• On this node: ⁃ Create an enrollment token with `bin/elasticsearch-create-enrollment-token -s node`. ⁃ Uncomment the transport.host setting at the end of config/elasticsearch.yml. ⁃ Restart Elasticsearch.• On other nodes: ⁃ Start Elasticsearch with `bin/elasticsearch --enrollment-token <token>`, using the enrollment token that you generated.━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━1.2 整体阐明elastic账号的默认明码HTTP CA证书kibana拜访es的token其余node退出以后node的cluster的token1.3 忘性不好我忘了,怎么办问题1. elastic账号默认明码我没看到,清了, 怎么办?问题2. kibana拜访的token超过30分钟了,怎么办?1.4 重置明码和token1.4.1 重置明码运行中另开一个窗口, 应用下列命令能够重置一个明码; ...

1.问题系列:1.es的match/match_phrase/match_phrase_prefix的区别

GET position2/_search{ "query": { "match_all": {} }}DELETE position2PUT position2 { "mappings":{ "properties":{ "title":{ "type":"text", "analyzer": "ik_max_word", "search_analyzer": "ik_smart" }, "description":{ "type":"text", "analyzer": "ik_max_word", "search_analyzer": "ik_smart" }, "price":{ "type":"double" } } }} POST position2/_doc { "title": "中华人民共和国", "price":10.1,"description":"小明" }POST position2/_doc { "title": "中国", "price":10.2,"description":"北京" }POST position2/_doc { "title": "小明", "price":10.3,"description":"上海" }GET position2/_search{ "query": { "match": { "title": { "query": "中国", "analyzer": "ik_smart", "fuzziness": 2 } } }}GET /_analyze{ "analyzer": "ik_max_word", "text":"中华人民共和国"}GET /_analyze{ "analyzer": "ik_smart", "text":"中华人民共和国"}GET /_analyze{ "analyzer": "ik_smart", "text":"中国人民"}GET /_analyze{ "analyzer": "ik_max_word", "text":"中国"}GET /_analyze{ "analyzer": "ik_smart", "text":"小明"}POST _analyze{ "analyzer": "standard", "text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."}POST books/_analyzer{ "field":"title", "text":"中华人民共和国"}GET position1/_search{ "query": { "match": { "title": { "query": "中华", "operator": "and" } } }}GET position1/_search{ "query": { "match": { "title": "小明" } }}GET position1/_search{ "query": { "range": { "price": { "gte": 10.5, "lte": 11 } } }}GET position1/_search{ "query": { "bool": { "must": [ {"term": {"title": "小"} } ], "filter": [ {"range": {"price": {"gte": 10.15, "lte": 11}}} ] } }}GET position1/_search{ "query": { "bool": { "must": [ {"match_all": {}} ], "filter": [ {"range": {"price": {"gte": 10.15, "lte": 11}}} ] } }}PUT position?prettyGET _cat/indices?vGET position1/_mapping?prettyDELETE .kibana_task_manager_1 DELETE .kibana_1POST position1/_doc { "title": "小明", "price":10.2, "description":"b" }POST position1/_doc { "title": "中华人民共和国", "price":10.9,"description":"dsdsf" }PUT position1 { "mappings":{ "properties":{ "title":{ "type":"text" }, "description":{ "type":"text" }, "price":{ "type":"double" }, "onSale":{ "type":"boolean" }, "type":{ "type":"integer" }, "createDate":{ "type":"date" } } }}

日期作者版本备注2022-10-03dingbinthu@163.comv1.0 本文讲述在centos 7操作系统上从无到有实现IDEA+Kibana+elasticsearch本地调试elaticsearch源码的操作过程。 1. Git稳当起见，降级git到比拟新的版本，如git version 2.24.1。 在linux执行git clone github上开源代码会产生卡死或半天没反馈等景象，个别设置如下即可： git config --global --unset https.proxy git config --global --unset http.proxy git config --global http.sslVerify "false" git config --global http.postBuffer 524288000 #最管用的 2. 下载Elasticsearch源码本文调试的是Elasticsearch 7.10.2版本，留神后文kibana抉择肯定要与此版本完全一致才行。 mkdir es-source-dir cd es-source-dir git clone https://github.com/elastic/elasticsearch.git elasticsearch-7.10.git cd elasticsearch-7.10.git git branch -a | grep 7. git tag | grep 7.10 git checkout v7.10.2 源码门路根门路记作$es_code_root，不便下文应用 3.设置 Jdkcd $es_code_root vim CONTRIBUTING.md 浏览得悉此版本的elasticsearch运行须要jdk14 ...

可能不少小伙伴都留神到了，从 ElasticSearch7.17 这个版本开始，原先的 Java 高级客户端 Java High Level REST Client 废除了，不反对了。诚实说，ElasticSearch 算是我用过的所有 Java 工具中，更新最为激进的一个了，在 Es7 中废除了 TransportClient，7.17 又废除了 TransportClient，那么当初用啥呢？当初的客户端叫做 Elasticsearch Java API Client。 始终偷懒抉择忽视 Elasticsearch Java API Client，不过最近工作中用到了，所以还是整篇文章和小伙伴们简略梳理一下 Elasticsearch Java API Client 的玩法。 上面的介绍我次要从索引操作和文档操作两个方面来给大家介绍。 不过须要跟大家强调的是，ElasticSearch 的 Java 客户端想要用的 6，必须要相熟 ElasticSearch 的查问脚本，大家平时在工作中遇到 Es 相干的问题，我也都是倡议先在 Kibana 中把操作脚本写好，而后再翻译成 Java 代码，或者间接拷贝到 Java 代码中，十分不倡议上来就整 Java 代码，那样很容易出错。 如果你对 Es 的操作不相熟，松哥录了收费的视频教程，大家能够参考： https://www.bilibili.com/video/BV1ft4y1e7tq/ 不想看视频，也能够在微信公众号后盾回复 es，有文档教程。 1. Elasticsearch Java API ClientElasticsearch Java API Client 是 Elasticsearch 的官网 Java API，这个客户端为所有 Elasticsearch APIs 提供强类型的申请和响应。 ...

实战场景如何在 PHP 中应用 Elasticsearch API 接口 知识点•PHP•Elasticsearch•Restful 接口菜鸟实战装置 Elasticsearch 官网 PHP SDK https://github.com/elastic/el... composer require elasticsearch/elasticsearch创立 ES Client use Elastic\Elasticsearch\ClientBuilder; public function info(Request $request){ // 初始化$client = ClientBuilder::create() ->setHosts(['localhost:9200']) ->build();$response = $client->info();// 响应格式化$info = $response->asArray();$data = [ 'es_info' => $info,];$this->success($data);} 执行返回后果 JSON{ "code": 0, "message": "ok", "data": { "es_info": { "name": "xqdsvtvnyje9zxeo", "cluster_name": "elasticsearch", "cluster_uuid": "tNPisFAxTVaRdXHKATIUWQ", "version": { "number": "8.5.1", "build_flavor": "default", "build_type": "tar", "build_hash": "c1310c45fc534583afe2c1c03046491efba2bba2", "build_date": "2022-11-09T21:02:20.169855900Z", "build_snapshot": false, "lucene_version": "9.4.1", "minimum_wire_compatibility_version": "7.17.0", "minimum_index_compatibility_version": "7.0.0" }, "tagline": "You Know, for Search" } }}通过上述步骤，就将 php 与 Elasticsearch 连通了，接下来应用 sdk 中的 api 即可。 ...

如何装置 Elasticsearch实战场景如何装置 Elasticsearch 知识点• CentOS • Java• Elasticsearch 装置• Kibana 装置 菜鸟实战Elasticsearch 是一个基于 Lucene 的搜寻服务器。它提供了一个分布式多用户能力的全文搜索引擎，基于 RESTful web 接口。Elasticsearch 是用 Java 语言开发的，并作为 Apache 许可条款下的开放源码公布，是一种风行的企业级搜索引擎。Elasticsearch 用于云计算中，可能达到实时搜寻，稳固，牢靠，疾速，装置使用方便。官网客户端在 Java、.NET（C#）、PHP、Python、Apache Groovy、Ruby 和许多其余语言中都是可用的。 前置环境确认 java 环境Elasticsearch 运行于Java JDK 环境, 所以须要确认该环境 确认服务器架构[root@xqdsvtvnyje9zxeo ~]# uname -aLinux xqdsvtvnyje9zxeo 4.18.0-80.el8.x86_64 #1 SMP Tue Jun 4 09:19:46 UTC 2019 x86_64 x86_64 x86_64 GNU/Linux新增 Elasticsearch 账户ElasticSearch5及以上版本，基于平安思考，强制规定不能以root身份运行，必须创立一个非root用户来运行ElasticSearch。 useradd esuserpasword esuser下载文件下载地址  https://www.elastic.co/cn/dow... 下载wget https://artifacts.elastic.co/... 解压tar -zxvf elasticsearch-8.5.1-linux-x86_64.tar.gz 运行环境配置零碎环境vim /etc/security/limits.conf elasticsearch soft nofile 65536elasticsearch hard nofile 65536elasticsearch soft nproc 4096elasticsearch hard nproc 4096 ...

有一种类似度匹配需要，须要以匹配到的标签个数优先，这种状况就须要用到自定义查问语句。 先上代码，这里我用的是PHP的数组构造，最终提交的时候是会转成json格局的，暂且不表： 'query' => [ 'script_score' => [ "query" => [], 'script' => [ 'source' => ' def matches = 0; for (t in params.tags) { if(doc["tags"].contains(t)) { matches += 1; } } return _score * matches * matches; ', "params" => [ "tags" => $tags, ], ] ],]首先最外层的数组就是咱们通常写的query语句，放在body中进行申请的，次要看query外面的构造，这种须要自定义脚本解决评分的，query中只放了一个script_score： script_score蕴含了两个局部，一部分是query，另外一部分是自定义的script，这外面的query，就是失常本来写在外层的query中的查问构造，本来怎么写还是怎么写，比方这外面可能还会须要一个标签匹配的，那么持续加一个terms来查问。 而后来看script局部，这外面又分为两个货色： source 自定义的评分排序脚本params 自定义的评分脚本参数首先看params，这里我传递了$tags，这是一个标签数组，最终会在source里进行调用，source局部会放到elasticsearch中进行编译，造成相似于函数的货色，而后params就是一个参数，在source的代码中须要调用tags这个参数，采纳params.tags来进行调用。 而后再看source代码局部，这外面会把搜寻到的后果doc中的tags字段（这doc中的tags也是一个数组）与所冀望的tags进行校验比照，最初失去理论匹配的个数：matches。 最初看source中的return局部，这里会返回评分后果，这里的_score是搜寻自身计算的类似度评分，而后这个中央_score * matches * matches，这里是采纳标签个数对评分后果进行放大，标签数匹配越多，天然放大后果就越大。 举个例子： 匹配文档_score标签个数后果分排序地位文档A4021602文档B3032701可见最终受到标签影响更大，实现了标签后果数优先的排序。 当然，至于放大模式，比如说，评分一样，以标签数优先，那么不必进行标签平方解决，另外所冀望的放大后果依据具体情况能够自行处理。 另外，在文章开端帖上elasticsearch的脚本语法链接： https://www.elastic.co/guide/...

后面的文章外面次要讲的是查问的用法，还是连续之前的文章格局，这里讲讲批改。 1. 单文档批改1.1. insert其实在数据筹备阶段曾经有新增的例子了。 DSLPOST /operation_log/_doc{ "ip": "0.0.0.0", "module": "测试数据"}spring OperationLog operationLog=new OperationLog(); operationLog.setIp("0.0.0.0"); operationLog.setModule("测试数据"); return esRestTemplate.save(operationLog);1.2. update-(save)新增时，springboot 用到的是 save 办法，更新时也一样能够。不过得拿到文档的id，这里id=13OkA4QBMgWicIn2wBwM。 DSLPUT /operation_log/_doc/13OkA4QBMgWicIn2wBwM{ "ip": "0.0.0.0", "module": "测试数据1"}springesRestTemplate.save(operationLog);1.3. update-(document)DSLPOST /operation_log/_update/13OkA4QBMgWicIn2wBwM{ "doc": { "module":"测试数据1" }}spring Document document = Document.create(); document.put("module", "测试数据1"); UpdateQuery updateQuery = UpdateQuery .builder(id) .withDocument(document) .build(); esRestTemplate.update(updateQuery,IndexCoordinates.of("operation_log"));1.4. update-(script)DSLPOST /operation_log/_update/13OkA4QBMgWicIn2wBwM{ "script": { "source": "ctx._source.module = params.module", "params": { "module": "测试数据1" } }}spring Map<String, Object> params = new HashMap<>(); params.put("module", "测试数据1"); UpdateQuery updateQuery = UpdateQuery .builder(id) .withScript("ctx._source.module = params.module") .withParams(params) .build(); esRestTemplate.update(updateQuery, IndexCoordinates.of("operation_log"));1.5. deleteDSLDELETE /operation_log/_doc/13OkA4QBMgWicIn2wBwMspring esRestTemplate.delete(id, OperationLog.class);2. 批量批改 bulk批量新增 DSLPOST /operation_log/_bulk{"create":{"_index":"operation_log"}}{"ip":"0.0.0.0","module":"测试数据1"}{"create":{"_index":"operation_log"}}{"ip":"0.0.0.0","module":"测试数据2"}{"create":{"_index":"operation_log"}}{"ip":"0.0.0.0","module":"测试数据3"}批量更新 DSLPOST /operation_log/_bulk{"update":{"_id":"2HP9A4QBMgWicIn26BzR"}}{"doc":{"module":"测试数据11"}}{"update":{"_id":"2XP9A4QBMgWicIn26BzR"}}{"script":{"source":"ctx._source.module = params.module","params":{"module":"测试数据22"}}}批量删除 DSLPOST /operation_log/_bulk{"delete":{"_id":"2HP9A4QBMgWicIn26BzR"}}{"delete":{"_id":"2XP9A4QBMgWicIn26BzR"}}{"delete":{"_id":"2nP9A4QBMgWicIn26BzR"}}不知是否留神到，在批量更新的语句中，反对同时 doc、script 两种更新形式。实际上来说，_bulk 其实反对同时将上述的三种语句一起提交执行。不过我的项目上个别不会如此利用，都是独自离开来。像批量新增，save 办法就反对批量新增操作，尽管底层代码还是调用 bulkOperation。 ...

01 Elasticsearch 宽泛应用带来的老本问题Elasticsearch（下文简称“ES”）是一个分布式的搜索引擎，还可作为分布式数据库来应用，罕用于日志解决、剖析和搜寻等场景；在运维排障层面，ES 组成的 ELK（Elasticsearch+ Logstash+ Kibana）解决方案，简略易用、响应速度快，并且提供了丰盛的报表；高可用方面， ES 提供了分布式和横向扩大；数据层面，反对分片和多正本。 ES 的应用便捷，生态残缺，在企业之中失去了宽泛的利用。 随之而来的是物理资源和费用的减少，如何升高 ES 场景的老本成为了大家广泛关怀的话题。 如何升高 ES 的老本ES 的次要的老本是主机老本，主机老本又分为计算资源和存储资源。 计算资源简略了解就是 CPU 和内存，如果升高 CPU 和主机的数量，意味着计算能力会降落，因而通常会采纳冷热节点；热节点应用高配的机器，冷节点应用低配的机器；比方 CPU 内存比从 1:4 升高为 1:8，如8C32G->4C32G。然而没有升高内存，因为 ES 对内存有更高的需要，来进步响应速度。或者采纳低频的 CPU，更老的硬件。 存储老本是远大于计算成本的，是要重点思考的老本。当初的存储介质通常是 SSD 和 HDD 这种两种介质，在云厂商 SSD 的老本是 0.8元/G，HDD 的老本是 0.35元/G，对象存储的价格是 0.12元/G。但前两种设施是块设施，提供的是文件系统的协定，但对象存储反对是 S3 协定的，彼此之间不兼容。 如何将对象存储和 ES 联合起来，咱们调研了两种计划。 第一种计划，批改 ES 存储引擎，适配对象存储调用。这种形式须要批改 ES 的源码，团队要投入很多的人力来做开发设计调研以及最初的验证，投入产出比是非常低的。 第二种计划是将对象存储作为磁盘来应用，将其挂载到操作系统。把 ES 分为 hot 和 warm 节点。hot节点存储热数据，挂载的是块设施。warm 节点应用对象存储。 02 对象存储文件系统选型文件系统选型的时候次要思考了三个方面。第一个是性能，首先要满足最根本的性能需要，第二个是性能，第三个是可靠性。咱们调研了 s3fs、 goofys 和 JuiceFS。 性能方面， s3fs 和 goofys 在 read 和 write 方面没有本地缓存，其性能是依附 s3 的性能来撑持的，这两个文件系统整体的性能相比JuiceFS 会低一些。 ...

Index用不好，麻烦事不会少；一、治理形式ElasticSearch作为最罕用的搜索引擎组件，在零碎架构中施展极其重要的能力，能够极大的晋升数据的加载和检索效率；但不可否认的是，在长期的利用实际中，也发现很多不好解决的流程和场景； 从直观感觉上说，业务中对索引的应用次要波及如图的几个流程，其外围也就是索引的构造保护与数据的流动治理两个模块； 如果数据结构比较简单且体量小，那么应用起来可能很棘手；如果数据主体简单且会动静扩大，并且体量偏大，那么就很容易踩中一些比拟坑的点； 比方：索引中字段一旦有误，调整的流程十分复杂；数据流向索引中的形式，须要依据场景灵便抉择；以及数据查问时的深度分页问题；上面将围绕这些问题来总结下应答策略； 顺带补充一句，其实很多组件在利用的时候都有不太合乎预期的中央，所以在集成时能够思考编写自定义的管理程序，来解决应用时可能存在的问题； 二、构造保护对于ES索引的构造保护，数据主体如果绝对简略的话，能够思考手动治理，但实际上应用索引时，通常主体构造都比较复杂，字段个数超过三五十都很常见，所以基于流程化的治理很有必要； 构造映射：将须要构建索引的主体构造，在字段库中对立保护，值得注意的是字段名称和类型，字段能够与关系型数据库的查问统一，然而不同组件类型的形容不一样，尤其对ES来说，如果字段类型不合理，会影响搜寻的应用； 索引构造：在理论的业务场景中，字段的信息是会动态变化的，这就会给索引构造的保护带来很多麻烦，字段的增减都好治理，然而如果波及类型的变动，则存在索引重建的过程，会导致数据屡次从新调度，这也是危险较高的操作； 程序保护：这种构造保护的机制，其外围目标是把整个流程进行程序化治理，防止人工进行干涉，以此来确保索引构造的稳固扩大； 不得不提的一个经验教训，已经在治理业务日志的索引构造时，呈现过一次误删动作，好在能够从新构建和数据备份复原，然而仍旧给心里留下了几厘米的暗影，尔后也将保护流程彻底程序化，防止失误动作产生； 三、数据调度1、同步计划数据的调度治理，其本质就是将数据从一个容器向另一个容器搬运或者拷贝，其外围操作就是读和写两个动作，然而为了让流程具备容错和稳定性，通常须要做策略和计划的设计； 同步双写：对数据的实时性要求极高，通常在一个事务中实现数据的双写动作，保证数据层面的强一致性； 异步解耦：在实现数据库的写动作之后，基于MQ音讯解耦索引的写入，流程存在轻微的提早，如果生产失败会导致数据缺失； 定时工作：通过任务调度的形式，以指定的工夫周期执行新增数据的同步机制，存在显著的时效问题； 组件同步：采纳适合的同步组件，比方官网提供的组件或者一些第三方开源的组件，在原理上与工作同步相似； 数据同步的选型计划有多种，如何抉择齐全看具体的场景，在过往的应用过程中，对于外围业务会采纳同步双写，对于外部的流动类业务会采纳异步的形式，对于业务日志会采纳任务调度，对于零碎的监控或执行日志则多是依赖同步组件； 2、中断和复原无论采纳何种形式将数据同步到索引中，都不得不面对一个灵魂问题，如果流程忽然异常中断，复原后如何保障索引数据不失落？这个问题适应于很多简单的流程； 容错性是掂量一个简单流程的外围指标，比方在索引数据同步的过程，须要短暂性的暂停，或者流程被迫中断时，都应该具备复原后主动修复索引中数据缺失的能力； ES实际中一个十分经典的问题，批改索引的构造时须要进行索引重建，此时要将以后索引迁入长期索引中，在实现索引结构调整之后，须要从长期索引中迁回数据，在此过程中，能够对服务交互的索引名称动静调整； 当然也能够间接应用长期索引作为交互索引，防止一次迁徙动作，这种动静的辨认须要在服务中嵌入，在整个reindex过程中要防止手动干涉，集体还是更置信程序的安全性和准确性； 四、刷新策略在向ES索引中写数据时，存在三种不同的数据刷新机制，查看6.8版本的设置中，参数refresh_interval设置的是1s工夫，即执行写入动作1秒后数据才能够被搜寻到，防止频繁写入耗费过多的资源； NONE：默认的刷新策略，申请提交之后不会期待数据刷新，升高资源耗费但数据实时性低； IMMEDIATE：申请提交后立刻刷新索引，数据的实时性很高然而资源耗费过大，API文档中倡议测试应用； WAIT_UNTIL：申请提交之后会期待索引刷新实现才会完结，相对来说是一种比拟均衡的策略； 刷新机制对于索引的数据保护来说，次要在增删改的动作中，对即时查问有间接的影响，至于如何抉择还是要联合具体的场景，尤其与同步计划关联亲密，也能够在索引交互中动静保护策略，来应答不时之需； 五、深度分页对于数据查问来说，简直都存在分页的需要，在常见的利用中，一直下拉的性能都是存在最大的极限值； ES中罕用From/Size进行分页查问，然而存在一个限度，在索引的设置中存在max_result_window分页深度的限度，6.8版本默认值是10000条，即10000之后的数据无奈应用From/Size翻页； 先从理论利用场景来剖析，大多数的翻页需要最多也就前10页左右，所以从这个角度思考，ES的翻页限度在正当区间，在实践中也存在对局部索引调高的状况，暂未呈现显著问题； 再从技术角度来思考一下，如果翻页的参数过大意味着更多的数据过滤，那计算资源的占用也会升高，ES引擎的弱小在于搜寻能力，检索出符合要求的数据即可； 不论是ES还是其它相似的分布式存储组件，甚至是MySQL分库分表模式，其本质都是数据分布在不同服务节点的不同数据片上；惯例的执行原理都是给申请调配一个主节点，协调各个节点执行雷同的查问，并实现后果汇总和响应，深度分页时计算资源的占用天然十分高； 如果肯定须要深度分页，在6.8的版本中提供了Scroll或Search-After两种其余的形式，用法参考相干文档即可。 六、参考源码编程文档：https://gitee.com/cicadasmile/butte-java-note利用仓库：https://gitee.com/cicadasmile/butte-flyer-parent

Elasticsearch在db_ranking 的排名又（双叒叕）回升了一位,如图1-1所示;由此可见es在存储畛域曾经蔚然成风且占有十分重要的位置。 随着Elasticsearch越来越受欢迎，企业破费在ES建设上的老本天然也不少。那如何缩小ES的老本呢？明天咱们就特地来聊聊ES降本增效的常见办法： 弹性伸缩分级存储其余：（1）数据压缩（2）off heap 图 1-1 Elasticsearch db_ranking 1 弹性伸缩所谓弹性伸缩翻译成大白话就是随时疾速瘦身与增肥，并且是头痛医头，按需动静调整资源。当计算能力有余的时候咱们能够疾速裁减出计算资源，业届比拟有代表性的两个ES相干产品阿里云Indexing Service 和 滴滴的ES-Fastloader； 当存储资源有余时，可能疾速扩容磁盘，业届比拟代表性es产品:阿里云的ES日志增强版。 1-1 计算存储拆散ES应用计算存储拆散架构之后，解决了资源预留而造成资源节约的问题。在晚期大家认为的计算存储拆散的实现形式为：应用云盘代替本地盘，这种实现形式能够进步数据的可靠性、能够疾速弹扩磁盘资源和计算资源，然而es本身弹性需要是无奈解决，即秒级shard搬迁和replica扩容。 那么如何解决es本身的弹性呢？本文该局部将给出答案。 共享存储版ES本文该局部将介绍咱们京东云-中间件搜寻团队，研发的共享存储版本ES；计算存储拆散架构如图1-2所示 图 1-2 计算存储拆散架构(共享) 如图1-2所示，咱们只存储一份数据，primary shard负责读写，replica只负责读；当咱们须要扩容replica的时候无需进行数据搬迁,间接跳过原生es的peer recover两阶段，秒级实现replica的弹扩。 当主分片产生relocating时,能够间接跳过原生es的peer recover第一阶段(该阶段最为耗时)，同时也不须要原生es的第二阶段发送translog。 共享版本的计算存储拆散ES，绝对于原生的ES和一般版本的计算存储拆散，具备如下突出的劣势： 数据只保留一份，存储老本倍数级升高存储容量按需主动拓展，简直无空间节约按理论用量计费，无需容量布局性能测试数据集为esrally提供的http_logs共享版ES7.10.2: 3个data节点(16C64GB)原生ES7.10.2: 3个data节点(16C64GB) 表 1-1 正本性能测试比照 咱们的初步性能测试后果如表1-1所示；正本数越多，共享版本的es越具备劣势; 从表1-1所示咱们能够看出性能仿佛晋升的不是特地现实，目前咱们正从两个方面进行优化晋升： 底层依赖的云海存储，目前正在有打算地进行着性能晋升源码侧，咱们也在正在优化ing在研发es计算存储拆散的过程中，咱们攻克了很多的问题,后续将输入更加具体的文章进行介绍，比方：主写副只读的具体实现，replica的拜访近实时问题，ES的主分片切换脏写问题等等。目前共享版本的ES正在外部进行公测，欢送在云搜平台进行试用。 1-2 内部构建Segment对于有大量写入的场景，通常不会继续的高流量写入，而只有1-2个小时写入流量洪峰；在写入过程中最消耗工夫的过程并不是写磁盘而是构建segment,既然构建segment如此耗时，那么咱们是否能够将该局部性能独自进去，造成一个可疾速扩大的资源（防止去间接改变es源码而引入其余问题）。 目前业界曾经有比拟好的案例如阿里云的Indexing Service 和滴滴开源的 ES-Fastloader 内部构建Segment，绝对于共享存储版的es实现起来更简略；它的外围解决方案应用了spark或者map reduce这种批处理引擎进行批量计算解决，而后将构建好的segment搬运到对应的索引shard即可。它的具体实现,我这里就不做搬运工了，大家感兴趣能够参考滴滴公布的文章《滴滴离线索引疾速构建FastIndex架构实际》 内部构建segment的性能也在咱们的布局中。 2 分级存储ES实现降本增效的另外一个方向：分级存储，该解决方案次要是针对数据量大查问少且对查问耗时不太敏感的业务。分级存储，比拟成熟的解决方案有es冷热架构和可搜寻快照。 2-1 冷热架构冷热架构实用场景：时序型数据或者同一集群中同时存在这两个索引（一个热数据，另外一个冷数据） es冷热架构架构，该性能曾经在京东云上线有一段时间了，欢送大家依据本人的业务状态进行试用，冷数据节点开启如图2-1所示 图 2-1 冷数据节点开启 倡议如果索引表是按天/小时，这种周期存储的数据，且数据查问具备冷热性，倡议开启冷节点；开启冷节点后你可能会取得如下的收益： 开启冷节点后能够升高你的存储老本，因为寄存冷节点的索引咱们能够抉择缩小正本数、冷节点的存储介质更便宜集群能够寄存更多的数据冷数据forcemerge,晋升冷数据的查问性能冷数据从热节点迁徙走之后，缩小热节点的资源占用，从而使热查问更快冷热架构的核心技术为 shard-allocation-filtering; 冷热架构实现原理： es的hot节点减少如下配置 node.attr.box_type: hot es的warm节点减少如下配置 node.attr.box_type: warm 热数据索引setting减少如下配置，即可限度shard调配在hot节点 "index.routing.allocation.require.box_type": "hot"当数据查问削弱，咱们通过如下配置，即可使数据由hot节点迁徙到warm节点 ...

在第一篇文章中，咱们曾经能够通过 docker 装置 elasticsearch 和 kibana 了。那么这次就间接进入实战演练。 咱们会先筹备数据，针对不同常见利用场景，而后别离通过 Query DSL 和 Spring Data JPA 来实现。 Query DSL：ElasticSearch提供了一个能够执行的JSON格调的DSL(domain-specific language 畛域特定语言)，这个被称为Query DSL。 1. 筹备1.1. 索引数据筹备上面就是通过 Query DSL 保护了一个名为 operation_log 的索引，用于记录零碎中各个模块的操作日志。 1. 创立索引PUT /operation_log2. 保护mapping构造PUT /operation_log/_mapping{ "properties": { "ip": { "type": "keyword" }, "trace_id": { "type": "keyword" }, "operation_time": { "type": "date", "format": "yyyy-MM-dd HH:mm:ss" }, "module": { "type": "keyword" }, "action_code": { "type": "keyword" }, "location": { "type": "text", "analyzer": "ik_max_word", "fields": { "keyword": { "type": "keyword" } } }, "object_id": { "type": "keyword" }, "object_name": { "type": "text", "analyzer": "ik_max_word", "fields": { "keyword": { "type": "keyword" } } }, "operator_id": { "type": "keyword" }, "operator_name": { "type": "keyword" }, "operator_dept_id": { "type": "keyword" }, "operator_dept_name": { "type": "text", "analyzer": "ik_max_word", "fields": { "keyword": { "type": "keyword" } } }, "changes": { "type": "nested", "properties": { "field_name": { "type": "keyword" }, "old_value": { "type": "keyword" }, "new_value": { "type": "keyword" } } } }}3. 新建文档上面一个个文档一一的新增，其实也是能够通过 _bulk 批量新增的，这里还是先依照根底的来。 ...

本文将为你介绍 Apache APISIX 的 elasticsearch-logger 插件的相干信息，并通过此插件获取 APISIX 的实时日志。背景信息Apache APISIX 是一个动静、实时、高性能的 API 网关，提供了负载平衡、动静上游、灰度公布、服务熔断、身份认证、可观测性等丰盛的流量治理性能。作为 API 网关，Apache APISIX 不仅领有丰盛的插件，而且反对插件的热加载。 Elasticsearch 是一个基于 Lucene 库的搜索引擎。它提供了分布式、RESTful 格调的搜寻和数据分析引擎，具备可扩展性、可分布式部署和可进行相关度搜寻等特点，可能解决不断涌现出的各种用例。同时还能够集中存储用户数据，帮忙用户发现意料之中以及意料之外的状况。 插件介绍APISIX 以 HTTP 申请的形式向 Elasticsearch 发送 APISIX 的 Runtime 日志。插件 elasticsearch-logger 采纳 bulk 的格局进行日志上报，这容许 APISIX 能够将多条日志合并后再进行上报，这使得 APISIX 在对 Elasticsearch 进行日志上报方面更加灵便并且具备较好的性能。你能够参考文档 APISIX 批处理器 对日志合进行更加粗疏的配置。 配置步骤首先，你须要装置实现 APISIX，本文所有步骤基于 Centos 7.5 零碎进行。具体的装置步骤参考 APISIX 装置指南。 步骤1：启动 Elasticsearch本示例只演示了通过 docker-compose 启动 Elasticsearch 单节点的形式，其它启动形式可参考 Elasticsearch 官网文档。 # 应用 docker-compose 启动 1 个 Elasticsearch 节点, 1 个 kibanaversion: '3.8'services: elasticsearch: image: docker.elastic.co/elasticsearch/elasticsearch:7.17.1 container_name: elasticsearch environment: ES_JAVA_OPTS: -Xms512m -Xmx512m discovery.type: single-node xpack.security.enabled: 'false' networks: - es-net ports: - "9200:9200" - "9300:9300" kibana: image: docker.elastic.co/kibana/kibana:7.17.1 container_name: kibana environment: ELASTICSEARCH_HOSTS: http://elasticsearch:9200 I18N_LOCALE: zh-CN networks: - es-net depends_on: - elasticsearch ports: - "5601:5601"networks: es-net: driver: bridge步骤2：创立路由并配置插件APISIX 默认配置文件中已启用 elasticsearch-logger 插件，所以你只须要通过下方命令创立路由并配置 elasticsearch-logger 插件就能够在 APISIX 中失常应用了。 ...

明天咱们要探讨的主题会让很多老手感到困惑，因为自身这个问题就挺难了解的。但如果你对 ES 比拟相熟，明天的内容也能够测试一下你对 ES 数据类型方面的常识是否可靠。 一、默认的 Numeric 类型首先，咱们创立一个索引： PUT orders/_doc/1{ "order_id": 1}如上示例，利用 Dynamic Mapping，零碎会为主动为 orders 索引创立一个 Mapping，那么默认的状况下，order_id 这个字段会被推断为啥类型呢？ 上面是这个 orders 索引的 Mapping： # 获取 MappingGET orders/_mapping# 后果：{ "orders" : { "mappings" : { "properties" : { "order_id" : { "type" : "long" } } } }}如上示例，很显著 Dynamic Mapping 推断的 order_id 为 long 类型。 那如果咱们对这个 order_id 字段写入一些非整型数字会产生啥状况呢？在 Kibana 中执行以下命令来插入文档： PUT orders/_doc/2{ "order_id": 2}PUT orders/_doc/3{ "order_id": "3"}PUT orders/_doc/4{ "order_id": 4.5}PUT orders/_doc/5{ "order_id": "5.5"}PUT orders/_doc/6{ "order_id": "六"}文档 2 跟文档 1 类似，所以必定能够插入。但当咱们尝试把一个字符串、float，甚至字符串模式的 float 值存储到 long 类型字段时会产生啥呢？能够必定的是，除了文档 6，其余都能够胜利存储，但为啥呢？ ...

企业数据越存越多，存储容量与查问性能、以及存储老本之间的矛盾对于技术团队来说是个广泛难题。这个难题在 Elasticsearch 与 ClickHouse 这两个场景中尤为突出，为了应答不同热度数据对查问性能的要求，这两个组件在架构设计上就有一些将数据进行分层的策略。 同时，在存储介质方面，随着云计算的倒退，对象存储以低廉的价格和弹性伸缩的空间取得了企业的青眼。越来越多的企业将温、冷数据迁徙至对象存储。但如果将索引、剖析组件间接对接至对象存储时会产生查问性能、兼容性等问题。 这篇文章将为大家介绍这两个场景中冷热数据分层的基本原理，以及如何通过应用 JuiceFS 来应答在对象存储上存在的问题。 01- Elasticsearch 数据分层构造详解在介绍 ES 如何实现冷热数据分层策略之前先来理解三个相干的概念：Data Stream，Index Lifecycle Management 和 Node Role。 Data StreamData Stream（数据流）是 ES 中一个重要概念，它有如下特色： 流式写入：它是一个流式写入的数据集，而不是一个固定大小的汇合；仅追加写：它是用追加写的形式将数据更新进去，且不须要批改历史数据；工夫戳：每一条新增的数据都会有一个工夫戳记录是什么时候产生的；多个索引：在 ES 里有一个索引的概念，每一条数据最终会落到它对应的一个索引中，然而数据流是一个更下层、更大的概念，一个数据流背地可能会有很多索引，这些索引是依据不同的规定来生成的。一个数据流尽管由很多的索引来形成，然而只有最新的索引才是可写的，历史索引是只读的，一旦固化好之后就不能再批改。日志数据就是合乎数据流特色的一类数据，它是只追加写，同时也得有工夫戳，用户会依据不同的维度，比方按天或者按其余的维度来生成新的索引。 下图是一个数据流建设索引的简略示例，在用数据流的过程中，ES 会间接写到最新的索引，而不是历史索引，历史索引不会被批改。随着后续更多新的数据生成，这个索引也会积淀成为一个老的索引。 下图，当用户往 ES 外面去写数据时，大抵分为两个阶段： 阶段 1：数据会先写到内存的 In-memory buffer 缓冲区；阶段 2：缓冲区依据肯定的规定和工夫，再落到本地磁盘上，就是下图绿色的长久化的数据，在 ES 中叫做 Segment。这个过程中可能会有一些时间差，在长久化的过程中，如果去触发查问， 新创建的Segment 不能被搜寻到。一旦这个 Segment 长久化实现之后，就能够立刻被下层的查问引擎搜寻。 Index Lifecycle ManagementIndex Lifecycle Management，简称 ILM，就是索引的生命周期治理。ILM 将索引的生命周期定义为 5 个阶段： 热数据（Hot）：须要频繁更新或者查问的数据；温数据（Warm）：不再更新，但仍会被频繁查问的数据；冷数据（Cold）：不再更新，且查问频率较低的数据；极冷数据（Frozen）：不再更新，且简直不会被查问的数据。能够比拟释怀地把这类数据放在一个绝对最低速最便宜的存储介质中；删除数据（Delete) : 不再须要用到，能够释怀删除的数据。一个索引里的数据，不论是 index 还是 segment，都会经验这些阶段，这个分类的规定很好地帮忙用户去治理 ES 里的数据，用户能够本人定义不同阶段的规定。 Node Role在 ES 中，每一个部署节点都会有一个 Node Role，也就是节点角色。每一个 ES 节点会调配不同的角色，比方 master、data、ingest 等。用户能够联合节点角色，以及上文提到的不同生命周期的阶段来组合进行数据管理。 ...

ElasticSearch常用命令场景：随着我的项目版本迭代，导致ES索引构造也会跟着变动，而ES又没有重命名这一说，所以每次批改索引构造原有字段就须要从新建索引，而后再做数据迁徙。 如果间接应用索引名称，在做数据迁徙的时候会变得很繁琐，能够看我上篇文章，这么繁琐的步骤操作起来就容易出错。而如果应用ES别名，间接操作别名就不便很多了。 #查看所有索引GET _cat/indices{}#查看索引构造GET live_new/_mapping{}#查看指定索引别名GET live_new/_alias/*{}GET live_new/_mapping{}#给索引增加别名POST /_aliases{ "actions": [ { "add":{ "index": "live_new", "alias": "datacube_live" }} ]}POST _reindex{ "source": { "index": "live" }, "dest": { "index": "live_new" }}#移除就索引增加新索引POST /_aliases{ "actions": [ { "remove": { "index": "live_new", "alias": "datacube_live" }}, { "add": { "index": "live_new_v2", "alias": "datacube_live" }} ]}PUT live_new{ "mappings": { "properties": { "room_id": { "type": "keyword" }, "room_name": { "type": "text", "analyzer": "ik_max_word", "search_analyzer": "ik_smart" }, "shop_ids": { "type": "keyword" }, "live_date": { "type": "keyword" }, "start_time": { "type": "keyword" }, "end_time": { "type": "keyword" }, "goods_list": { "type": "nested", "properties": { "goods_id": { "type": "keyword" }, "goods_name": { "type": "text" }, "sku_code": { "type": "keyword" }, "category_id": { "type": "keyword" }, "activity_type": { "type": "keyword" }, "lecture_type": { "type": "keyword" }, "area_ids": { "type": "keyword" } } }, "is_empty": { "type": "boolean" }, "is_callback": { "type": "boolean" }, "is_pal_15": { "type": "boolean" }, "creator_id": { "type": "keyword" }, "push_type": { "type": "keyword" }, "create_at": { "type": "keyword" }, "is_del": { "type": "boolean" } } }}

Elasticsearch SQL 是一个 X-Pack 组件，容许用户应用相似 SQL 的语法在 ES 中进行查问。用户能够在 REST、JDBC、命令行中应用 SQL 在 ES 执行数据检索和数据聚合操作。ES SQL 有以下几个特点： 本地集成，SQL 模块是 ES 本人构建的，间接集成到公布的版本中。不须要内部的组件，应用 SQL 模块不须要额定的依赖，如硬件、运行时库等。轻量高效，SQL 模块不形象 ES 和其搜寻能力，而是裸露 SQL 接口，容许以雷同的申明性、简洁的形式进行适当的全文搜寻。上面的内容咱们基于 ES 7.13 来学习一下 Elasticsearch SQL 模块提供的性能。 如果你对 ES 感兴趣，欢送订阅我的Elasticsearch 从入门到实际小册，咱们一起学习提高！ 一、Elasticsearch SQL 应用在开始应用 SQL 模块提供的性能前，在 kibana 执行以下指令来创立数据： PUT /library/_bulk?refresh{"index":{"_id": "Leviathan Wakes"}}{"name": "Leviathan Wakes", "author": "James S.A. Corey", "release_date": "2011-06-02", "page_count": 561}{"index":{"_id": "Hyperion"}}{"name": "Hyperion", "author": "Dan Simmons", "release_date": "1989-05-26", "page_count": 482}{"index":{"_id": "Dune"}}{"name": "Dune", "author": "Frank Herbert", "release_date": "1965-06-01", "page_count": 604}导入数据实现后，能够执行上面的 SQL 进行数据搜寻了： ...

在以前的一个我的项目中，对系统进行架构设计后，须要把es当做惟一存储源，记录下其中踩到的坑：1，首先es不反对事务，所以在架构设计的时候肯定要思考这一点。       特地的，es在生产环境个别不容许应用脚本，更新操作都是在业务Java零碎内存中去更新，而后再刷新到es数据库，所以当多个线程并发批改时，只会有最初一条更新胜利（其实其余的线程也更新胜利了，只是被最初一个线程笼罩了），解决办法是上接口的调用者加分布式锁，或者把申请放到保障串行的音讯队列（比方kafka的同一个分区） 2，es不反对动静批改mapping，在做具体的index设计时，应该把业务须要的字段都剖析完，什么字段应用什么类型 3，非非凡状况，所有字段都应该采纳keyword类型，否则将产生劫难！！！在mapping一个index的时候，肯定要查看业务Java要操作的字段，在mapping中是否都有定义。比方新建一个index，在JavaBean中定义了一个Person类，有个 String  name的字段，在mapping的时候，漏掉了name，于是在我的项目跑起来后，第一个插入的会把它mapping成text类型，后续依照trem查问的时候一条都查不出，或者查出谬误的数据，所以肯定要器重mapping！！！   另外须要留神的是，在我的项目启动的时候，应该先查看es对应的index是否存在，而后在运行我的项目(能够利用spring的InitializingBean接口)，这样根本就能保障mapping是正确的了。 @Configuration@ConfigurationProperties(prefix = "elastic.search.index")public class ElasticSearchIndexConfig implements InitializingBean { @Override public void afterPropertiesSet() { // ... this.checkAllIndexExist(); // ... } /** * 服务器启动的时候，查看所有index是否都在 */ private void checkAllIndexExist() { // 判断index，不存在就抛出异样 }}4，举荐应用elasticsearch-rest-high-level-client作为Java操作es的api，尽管写起来比拟繁琐，然而查不到数据，能够debug出他拼装的查问语句，而后到kibana工具台上执行，进而发现问题，须要留神版本。 5，依照条件查问的时候（排除含糊查问），倡议应用trem API。 6，保留数据后，保障立即再次可见，须要把同步刷新策略改为：IMMEDIATE（强制刷新），es作为一个分布式的搜索引擎，不会对每条数据都实时刷新到磁盘，而是先缓存，再依据策略去刷新。所以改成了强制刷新能保障保留胜利后数据可见性，然而他对性能影响重大，须要依据业务去抉择。 /** * Don't refresh after this request. The default. */ NONE("false"), /** * Force a refresh as part of this request. This refresh policy does not scale for high indexing or search throughput but is useful * to present a consistent view to for indices with very low traffic. And it is wonderful for tests! */ IMMEDIATE("true"), /** * Leave this request open until a refresh has made the contents of this request visible to search. This refresh policy is * compatible with high indexing and search throughput but it causes the request to wait to reply until a refresh occurs. */ WAIT_UNTIL("wait_for");7，es的bulk操作不反对刷新策略，他们都是默认的，之所以这样是因为bulk不晓得你要操作的类型是什么和你要操作的数据量。比方应用了一个indexRequest之前为了保证数据可见性，策略改为强制刷新，前面有个批量插入的需要，复用了这个indexRequest，就会报异样，须要小心。 ...

ES集体罕用的一些命令 查问相干命令 #查看所有的cat操作GET /_cat#查看节点信息GET /_cat/nodes?v #查看集群以后状态：红、黄、绿GET /_cluster/health#查看集群详情GET /_cluster/stats#查看集群期待工作GET /_cluster/pending_tasks#查看指定索引的全副信息GET /indextest001#查看指定索引的配置信息GET /indextest001/_settings?#查看指定索引的属性信息GET /indextest001/_mapping?#查看索引全副文档GET rollover_test-000001/_search{ "query": { "match_all": {} }}#查看索引生命周期GET /indextest001/_ilm/explain#列出所有索引详情GET /_cat/indices?v#查看指定索引详情GET /_cat/indices/my-index-000003?v#查看各索引分片的详细情况GET /_cat/shards?v#查看指定索引分片的详细情况GET /_cat/shards/my-index-000003?v#查看master节点信息GET /_cat/master?v#查看索引的文档数量GET /_cat/count/career_plan_sku_index_52?v#查看所有index的段信息GET /_cat/segments?v#查看所有index的别名信息GET /_cat/aliases?v#查看索引别名有哪些索引援用GET /_cat/aliases/rollover_test_alias一些批改相干实例操作 #批改索引分片数量PUT /indextest001/_settings{ "settings": { "number_of_replicas": 0 }}#创立索引PUT indextest001#删除索引DELETE indextest001#查看mappingGET indextest001/_mapping#创立mappingPOST indextest001/_mapping{ "properties": { "title": { "type": "text" }, "description": { "type": "text", "index": "false" }, "price": { "type": "double" }, "onSale": { "type": "boolean" }, "type": { "type": "integer" }, "createDate": { "type": "date" } }}#查看索引对应了哪些别名GET indextest001/_alias#新增索引别名POST _aliases{ "actions": [ { "add": { "index": "indextest001", "alias": "shop" } } ]}#删除索引别名POST _aliases{ "actions": [ { "remove": { "index": "indextest001", "alias": "shop" } } ]}#查看分词器分词后果GET /_analyze{ "analyzer": "ik_max_word", "text": "博文视点"}GET /career_plan_sku_suggest/_analyze{ "text": "猫窝宠物窝垫夏季保暖宠物床⼩猫窝猫⽤品", "analyzer": "ik_and_pinyin_analyzer"}#查看索引模板GET /_template/ilm-history#查看集群配置GET _cluster/settings#批改生命周期刷新频率配置PUT _cluster/settings{ "persistent": { "indices.lifecycle.poll_interval": "10s" }}#新增滚动生命周期策略PUT _ilm/policy/rollover_test_policy{ "policy": { "phases": { "hot": { "min_age": "0", "actions": { "set_priority": { "priority": 100 }, "rollover": { "max_docs": 5 } } }, "warm": { "min_age": "0", "actions": { "set_priority": { "priority": 50 }, "forcemerge": { "max_num_segments": 1 } } }, "cold": { "min_age": "1d", "actions": { "set_priority": { "priority": 0 }, "freeze": {} } }, "delete": { "min_age": "1d", "actions": { "delete": {} } } } }}#新增索引模板PUT _template/rollover_test_template{ "index_patterns": [ "rollover_test-*" ], "settings": { "index.lifecycle.name": "rollover_test_policy", "index.lifecycle.rollover_alias": "rollover_test_alias", "number_of_replicas": 0, "number_of_shards": 1 }}#应用模板创立索引PUT rollover_test-000001 { "aliases": { "rollover_test_alias":{ "is_write_index": true } }}#查看索引生命周期GET rollover_test-000001/_ilm/explain#查看索引全副文档GET rollover_test-000001/_search{ "query": { "match_all": {} }}#查看索引别名GET _alias/rollover_test_alias#索引减少文档POST /rollover_test-000001/_doc{ "name": "caodewang"}#应用DSL语法搜寻文档GET /career_plan_sku_index_52/_search{ "query": { "bool": { "must": [ { "term": { "category": { "value": "手机" } } } ], "filter": [ { "range": { "basePrice": { "gte": 1000, "lte": 3000 } } } ] } }, "from": 0, "size": 10, "sort": [ { "basePrice": { "order": "desc" } } ]}

测试过程批改ilm检测周期indices.lifecycle.poll_interval 默认是 10min， 批改为10sPUT _cluster/settings{ "persistent": { "indices.lifecycle.poll_interval": "10s" }}创立测试策略和模版（1）创立策略，每写入5个文档，即触发滚动策略，滚出新的索引PUT _ilm/policy/rollover_test_policy{ "policy": { "phases": { "hot": { "min_age": "0", "actions": { "set_priority": { "priority": 100 }, "rollover": { "max_docs": 5 } } }, "warm": { "min_age": "0", "actions": { "set_priority": { "priority": 50 }, "forcemerge": { "max_num_segments": 1 } } }, "cold": { "min_age": "1d", "actions": { "set_priority": { "priority": 0 }, "freeze": {} } }, "delete": { "min_age": "1d", "actions": { "delete": {} } } } }}创立模版，应用下面创立的生命周期管理策略PUT _template/rollover_test_template{ "index_patterns": [ "rollover_test-*" ], "settings": { "index.lifecycle.name": "rollover_test_policy", "index.lifecycle.rollover_alias": "rollover_test_alias", "number_of_replicas": 0, "number_of_shards": 1 }}创立初始的索引PUT rollover_test-000001 { "aliases": { "rollover_test_alias":{ "is_write_index": true } }} 由下面信息，能够失去 rollover_test-000001处于 hot阶段的rollover步骤查看中 ...

1 需要剖析1.1 剖析压测对象1）什么是ClickHouse 和ElasticsearchClickHouse 是一个真正的列式数据库管理系统（DBMS)。在 ClickHouse 中，数据始终是按列存储的，包含矢量（向量或列块）执行的过程。只有有可能，操作都是基于矢量进行分派的，而不是单个的值，这被称为«矢量化查问执行»，它有利于升高理论的数据处理开销。 Elasticsearch是一个开源的分布式、RESTful 格调的搜寻和数据分析引擎，它的底层是开源库Apache Lucene。 它能够被这样精确地形容： 一个分布式的实时文档存储，每个字段能够被索引与搜寻一个分布式实时剖析搜索引擎能胜任上百个服务节点的扩大，并反对 PB 级别的结构化或者非结构化数据 2）为什么要对他们进行压测家喻户晓，ClickHouse在根本场景体现十分优良，性能优于ES，然而咱们理论的业务查问中有很多是简单的业务查问场景，甚至是大数量的查问，所以为了在双十一业务峰值来到前，确保大促流动峰值业务稳定性，针对ClickHouse 和Elasticsearch在咱们理论业务场景中是否领有优良的抗压能力，通过这次性能压测，探测系统中的性能瓶颈点，进行针对性优化，从而晋升零碎性能。 1.2 制订压测指标 为什么会抉择这个（queryOBBacklogData）接口呢？ 1）从复杂度来看，接口（queryOBBacklogData）查问了5次，代码如下： /** * 切ck-queryOBBacklogData * @param queryBO * @return */public OutboundBacklogRespBO queryOBBacklogDataCKNew(OutboundBacklogQueryBO queryBO) { log.info(" queryOBBacklogDataCK入参：{}", JSON.toJSONString(queryBO)); // 公共条件-卡最近十天工夫 String commonStartTime = DateUtils.getTime(DateUtil.format(new Date(), DateUtil.FORMAT_DATE), DateUtils.ELEVEN_AM, 1, -10); String commonEndTime = DateUtils.getTime(DateUtil.format(new Date(), DateUtil.FORMAT_DATE), DateUtils.ELEVEN_AM, 1, 1); // 越库信息-待越库件数&待越库工作数 WmsObCrossDockQueryBo wmsObCrossDockQueryBo = wmsObCrossDockQueryBoBuilder(queryBO,commonStartTime, commonEndTime); log.info("queryOBBacklogDataCK-wmsObCrossDockQueryBo: {}", JSON.toJSONString(wmsObCrossDockQueryBo)); CompletableFuture<OutboundBacklogRespBO> preCrossDockInfoCF = CompletableFuture.supplyAsync( () -> wmsObCrossDockMapper.preCrossDockInfo(wmsObCrossDockQueryBo), executor); // 汇合工作信息-待调配订单 WmsObAssignOrderQueryBo wmsObAssignOrderQueryBo = wmsObAssignOrderQueryBoBuilder(queryBO, commonStartTime, commonEndTime); log.info("queryOBBacklogDataCK-wmsObAssignOrderQueryBo: {}", JSON.toJSONString(wmsObAssignOrderQueryBo)); CompletableFuture<Integer> preAssignOrderQtyCF = CompletableFuture.supplyAsync( () -> wmsObAssignOrderMapper.preAssignOrderInfo(wmsObAssignOrderQueryBo), executor); // 拣货信息-待拣货件数&待拣货工作数 WmsPickTaskQueryBo wmsPickTaskQueryBo = wmsPickTaskQueryBoBuilder(queryBO, commonStartTime, commonEndTime); log.info("queryOBBacklogDataCK-wmsPickTaskQueryBo: {}", JSON.toJSONString(wmsPickTaskQueryBo)); CompletableFuture<OutboundBacklogRespBO> prePickingInfoCF = CompletableFuture.supplyAsync( () -> wmsPickTaskMapper.pickTaskInfo(wmsPickTaskQueryBo), executor); // 分播信息-待分播件数&待分播工作 WmsCheckTaskDetailQueryBo wmsCheckTaskDetailQueryBo = wmsCheckTaskDetailQueryBoBuilder(queryBO, commonStartTime, commonEndTime); log.info("queryOBBacklogDataCK-wmsCheckTaskDetailQueryBo: {}", JSON.toJSONString(wmsCheckTaskDetailQueryBo)); CompletableFuture<OutboundBacklogRespBO> preSowInfoCF = CompletableFuture.supplyAsync( () -> wmsCheckTaskDetailMapper.checkTaskDetailInfo(wmsCheckTaskDetailQueryBo), executor); // 发货信息-待发货件数 WmsOrderSkuQueryBo wmsOrderSkuQueryBo = wmsOrderSkuQueryBoBuilder(queryBO, commonStartTime, commonEndTime); log.info("queryOBBacklogDataCK-wmsOrderSkuQueryBo: {}", JSON.toJSONString(wmsOrderSkuQueryBo)); CompletableFuture<Integer> preDispatchCF = CompletableFuture.supplyAsync( () -> wmsOrderSkuMapper.preDispatchInfo(wmsOrderSkuQueryBo), executor); return processResult(preCrossDockInfoCF, preAssignOrderQtyCF, prePickingInfoCF, preSowInfoCF, preDispatchCF);}2）接口（queryOBBacklogData），总共查问了5个表，如下： ...

支流电商平台有一个很常见的性能：“猜你喜爱”智能举荐，对用户做了精准画像，算法简单，而一般的我的项目中，因为内容规模无限，便用 ElasticSearch 根本能够实现根底需要 在咱们开发的 店熵SAAS平台（shopfai.com）我的项目中应用了本文中的计划，比较稳定，用户体验还不错。 实现思路：拜访某个商品时，提取此商品的关键词，写入ES 历史关键词（索引名示例：history_keywords）聚合ES 历史关键词 中的所有关键词，提取呈现最多的前几个用提取出这几个关键词再次查问商品，作为“猜你喜爱” 举荐后果几个外围控制点：ES 历史关键词 须要管制总数量，只保留最近拜访的数据，能够用Redis实现一个循环记数器，计数器的值作为ES记录的ID写入，实现ES数据的循环写入，这个数量取多少适合，跟据具体我的项目调整优化，1000亦可，10000亦可，会间接影响举荐后果聚合 ES历史关键词时 ，具体取几个需理论优化调整，咱们我的项目中取了 10 个当用户首次拜访时，尚无历史关键词数据，这时候能够取 “热搜词”（热搜词性能需另外实现）简化流程图：   外围的 ES 查问语法：提取商品名称里的关键词：POST /_analyze{ "tokenizer" : "ik_smart", "text": "商品名称ABC"}因为不同语言封装办法不同，这里只贴了 原生 诘法，分词器中文用了ik_smart，依据具体情况调整 从历史关键词中提取呈现最多的前几个：POST /history_keywords{ "size":0, "query":{ "bool":{ "filter":[ { "term":{ "user_id":"123" } } ] } }, "aggs":{ "topN":{ "terms":{ "field":"keyword", "size":10 } } }}history_keywords 里只有两个字段 用户ID（user_id），关键词（keyword） 最初，用这几个关键词查问商品，代码不贴了。 本文原始网址：https://www.liu12.com/article/es-guess-you-like，转载请保留出处

名词定义分片一个 分片 是一个底层的 工作单元 ，它仅保留了全副数据中的一部分。 在分片外部机制中，咱们将具体介绍分片是如何工作的，而当初咱们只需晓得一个分片是一个 Lucene 的实例，以及它自身就是一个残缺的搜索引擎。 咱们的文档被存储和索引到分片内，然而应用程序是间接与索引而不是与分片进行交互。 Elasticsearch 是利用分片将数据散发到集群内各处的。分片是数据的容器，文档保留在分片内，分片又被调配到集群内的各个节点里。 当你的集群规模扩充或者放大时， Elasticsearch 会主动的在各节点中迁徙分片，使得数据依然均匀分布在集群里。 一个分片能够是 主 分片或者 正本 分片。 索引内任意一个文档都归属于一个主分片，所以主分片的数目决定着索引可能保留的最大数据量。 正本分片一个正本分片只是一个主分片的拷贝。正本分片作为硬件故障时爱护数据不失落的冗余备份，并为搜寻和返回文档等读操作提供服务。 正本分片的次要目标就是为了故障转移，如果持有主分片的节点挂掉了，一个正本分片就会降职为主分片的角色。在索引写入时，正本分片做着与主分片雷同的工作。新文档首先被索引进主分片而后再同步到其它所有的正本分片。减少正本数并不会减少索引容量。 在索引建设的时候就曾经确定了主分片数，然而正本分片数能够随时批改。 ES集群外围三个的参数ES集群的服务器个数，ES集群的总CPU外围个数，ES集群的总内存预估要存入ES总数据量（数据+索引）艰深了解分片数（number_of_shards）就是主分片个数正本数（number_of_replicas） 能够了解成主分片的拷贝个数如果主分片数是5，则每个正本都有5个分片因而ES集群总的分片数 = 分片数 * (正本数 + 1)正本数调配算法： 正本数 <=  ES集群的服务器个数 - 1  正本除了晋升查问效率，次要目标为了容灾，数据冗余存储，保障数据安全，因而，正本数应跟据ES集群中的数据库数量（N-1）进行配置，如果ES集群中有3台服务器，则正本数设置为2，减 1 因为主分片自身也算一份。正本数设置为2，数据实际上有3份，扩散到3台服务器中。  预估要存入ES总数据量 * (正本数+1)  <= ES集群的总内存 / 2 存入ES搜索引擎 的数据能够预估，比方预估存入的数据有 100G，则实际上会占用的内存为  100G * (正本数+1)  加 1 因为计入了主分片，数量里超限了也能够存入，只有磁盘够大，然而计算时就可能用到了磁盘，磁盘的IO性能会让整个ES集群性能大副降落。 ES集群的总内存只取了一半，因为考虚 ES 的 java. 程序自身占用的内存，数据聚合运算耗费的内存，如果理论我的项目中大量简单运算，占用的内存须要更多。 分片数调配算法： 分片数 * (正本数 + 1) <= ES集群的总CPU外围个数  分片内的检索运算聚合须要 CPU， 为了充分发挥多核CPU的性能，且又防止CPU频繁调度，因而，ES集群内总的分片数应小于等于总的CPU外围个数。 以上是集体了解，因程度无限，不免有不妥之处，不可全信，欢送交换切磋，欢送斧正。 ...

在应用ES的时候，给原有index的增加、删除、批改等操作都是比拟常见的景象。因而，记录一些罕用的用法对咱们开发效率将有很大晋升。 后面的文章曾经介绍了如何创立ES索引的，本次实例讲持续应用应用datacube_live索引来做演示。 为了不便观看，依然将datacube_live的现有构造展示进去，本次用来演示在datacube_live增加is_del字段，来给es数据做软删除标记，将is_del类型定义为boolean类型。 1.获取datacube_live以后构造体：GET http://xxxx:9200/datacube_live/_mapping { "datacube_live": { "mappings": { "properties": { "create_at": { "type": "keyword" }, "creator_id": { "type": "keyword" }, "goods_list": { "type": "nested", "properties": { "activity_type": { "type": "keyword" }, "area_ids": { "type": "keyword" }, "category_id": { "type": "keyword" }, "goods_id": { "type": "keyword" }, "goods_name": { "type": "text" }, "lecture_type": { "type": "keyword" }, "sku_code": { "type": "keyword" } } }, "is_callback": { "type": "boolean" }, "is_empty": { "type": "boolean" }, "is_pal_15": { "type": "boolean" }, "live_date": { "type": "keyword" }, "live_start_time": { "type": "keyword" }, "push_type": { "type": "keyword" }, "room_id": { "type": "keyword" }, "room_name": { "type": "text" }, "shop_ids": { "type": "keyword" } } } }}2.定义is_del类型在定义is_del类型的时候，须要将其放入properties属性当中去，构造如下所示。 ...

术语 文档doc：数据项（如：一条订单数据），json格局，每个文档都有一个惟一Id索引index（名词）：类似文档的汇合；索引indexing（动词）：保留一个文档到es的过程类型：每个索引里都能够有一个或多个type，type是索引中的一个逻辑数据分类，一个type下的文档都有雷同的字段集群：es集群其实是一个分布式系统、要满足高可用性，高可用就是当集群中有节点服务进行响应的时候，整个服务还能失常工作，也就是服务可用性 不同的集群是通过不同的名字来辨别的，默认的名字：elasticsearch，能够在配置文件中进行批改或者在命令行中应用-E cluster.name=wupx进行设置 节点：节点就是一个es实例，实质上是一个java过程分片：能够将一个索引中的数据切分为多个分片映射：定义索引中的文档的轮廓，定义了数据类型以及文档中显示的字段的格局和规定，以管制动静增加字段的映射外围数据类型：文本、关键字、日期、长整型、双精度、布尔、ip简单数据类型：json对象和嵌套数据类型API文档API（可进行插入、更新）,id为空时会主动生成 申请： PUT schools/_doc/5{ name":"City School", "description":"ICSE", "street":"West End", "city":"Meerut", "state":"UP", "zip":"250002", "location":[28.9926174, 77.692485], "fees":3500, "tags":["fully computerized"], "rating":"4.5"}响应： { "_index" : "schools", "_type" : "_doc", "_id" : "5", "_version" : 1, "result" : "created", "_shards" : { "total" : 2, "successful" : 1, "failed" : 0 }, "_seq_no" : 2, "_primary_term" : 1}搜寻API POST /schools/_search{ "query":{ "query_string":{ "query":"up" } }}索引API 创立索引PUT colleges删除索引DELETE /colleges获取索引get colleges索引存在head colleges索引设置get /colleges/_settings

简介当 Elasticsearch 集群中有新节点退出，或者有节点离线，Elasticsearch 会主动在节点间进行平衡。当有新节点退出集群，集群中现有的分片和正本会从新平衡散布。当有节点离线，须要从新从失落的分片对应的正本列表中抉择一个成为新的分片，并对失落的正本从新生成和数据恢复，最初进行分片和正本在节点间的平衡散布。 新增节点当有新的节点退出集群，Elasticsearch 会主动进行分片的平衡散布，确保不会呈现热点节点。比方，在一个本来有三个分片和一个正本的两个节点组成的集群中，新增一个节点，Node 1 和 Node 2 上各有一个分片被迁徙到了新的 Node 3 节点，当初每个节点上都领有2个分片，而不是之前的3个。每个节点的硬件资源（CPU, RAM, I/O）将被更少的分片所共享，每个分片的性能将会失去晋升。 节点离线当有节点从集群离线，Elasticsearch 会在集群内提拔其余领有以后节点下面的分片对应的正本为分片。在正本被提拔为分片当前，master 节点开始执行复原操作来重建缺失的正本。集群中的节点之间相互拷贝分片数据，这个过程还有可能会触发小规模的分片挪动，最终，分片将在节点间迁徙来达到一个最佳的均衡状态。 勾销迁徙对于节点临时离线，在短时间后又从新上线，而此时集群还没有实现分片的平衡和迁徙的这种状况，Elasticsearch 会查看有没有文档变更，包含删除、新增和批改，如果没有，那么 master 将会勾销正在进行的再均衡并复原该机器磁盘上的数据。因为，本地磁盘的复原永远要比网络间传输要快，并且保障了分片数据是一样的。 如果文档有变更（比方：节点离线之后又索引了新的文档），那么复原过程会持续依照失常流程进行。重新加入的节点会删除本地的、过期的数据，而后从新获取一份新的。 参考https://www.elastic.co/guide/...

原文To prevent writes from taking place on the "wrong" side of a network partition, by default, index operations only succeed if a quorum (>replicas/2+1) of active shards are available. This default can be overridden on a node-by-node basis using the action.write_consistency setting. To alter this behavior per-operation, the consistency request parameter can be used. Valid write consistency values are one, quorum, and all. Note, for the case where the number of replicas is 1 (total of 2 copies of the data), then the default behavior is to succeed if 1 copy (the primary) can perform the write. ...

因为本文篇幅较长，想要获取PDF，请关注‘公众号-菜鸟成长学习笔记’回复"es手册"即可支付文件。es概括Elaticsearch，简称为 ES， ES 是一个开源的高扩大的分布式全文搜索引擎，Elasticsearch 是面向文档型数据库，一条数据在这里就是一个文档。 基本要素ES是一个文档型数据库，在与传统的关系型数据库上，存在着肯定的差别。上面将ES外面波及到的元素与关系型数据库进行一一对应。 ElasticSearch索引(index)类型(type)文档(document)字段(field)MySQL数据库(database)数据表(table)数据行(row)数据列(column)索引操作创立索引向 ES 服务器发 PUT 申请 ： http://127.0.0.1:9200/shopping。创立索引只能应用PUT申请，PUT是幂等性的，也就是说不存在的时候就会创立，存在的时候就不会从新创立而是返回索引曾经存在的信息。 { "acknowledged": true,//响应后果 "shards_acknowledged": true,//分片后果 "index": "shopping"//索引名称}查问索引向 ES 服务器发 GET 申请 ： http://127.0.0.1:9200/shopping。 { "shopping": {//索引名 "aliases": {},//别名 "mappings": {},//映射 "settings": {//设置 "index": {//设置 - 索引 "creation_date": "1617861426847",//设置 - 索引 - 创立工夫 "number_of_shards": "1",//设置 - 索引 - 主分片数量 "number_of_replicas": "1",//设置 - 索引 - 主分片数量 "uuid": "J0WlEhh4R7aDrfIc3AkwWQ",//设置 - 索引 - 主分片数量 "version": {//设置 - 索引 - 主分片数量 "created": "7080099" }, "provided_name": "shopping"//设置 - 索引 - 主分片数量 } } }}查看所有索引向 ES 服务器发 GET 申请 ： http://127.0.0.1:9200/_cat/indices?v。 ...

前言随着时间推移，基于工夫数据的相关度逐步升高。有可能咱们会想要查看上周、上个月甚至上一年度产生了什么，然而大多数状况，咱们只关怀以后产生的。历史旧数据的拜访热度变的很低，甚至曾经没有了搜寻的需要，但仍然占用存储空间，占用系统资源。针对这些数据，有必要进行资源的开释。 删除旧索引基于时序的数据个别是依照工夫范畴来创立索引的，按工夫范畴索引带来的益处是能够不便地删除旧数据。删除整个索引比删除单个文档要更加高效：Elasticsearch 只须要删除整个文件夹。删除索引是终极伎俩。 迁徙旧索引随着数据被记录，很有可能存在一个热点索引——今日的索引。所有新文档都会写入这个索引，简直所有查问也都以它为指标。这个索引对 IO 和 CPU 就有比拟高的要求，该当应用最好的硬件，倡议应用 SSD。历史的旧的数据简直是只读，不会写入，并且搜寻的频率也比拟小，该当应用绝对较差的硬件，倡议比拟大的硬盘 Elasticsearch 是如何得悉哪台是最好的服务器呢？通过给每台服务器指定任意的标签来通知它。 在 Elasticsearch 的 yml 文件中配置 node.attr 属性，标记以后节点是一个热节点： node.attr.my_node_type=hot其中 my_node_type 是一个任意的标签名称。同理，标记一个冷节点： node.attr.my_node_type=warm通过给节点打标签，Elasticsearch 就晓得了哪些节点是热（Hot）节点，哪些是冷（Warm）节点，接下来通过对索引进行设置，Elasticsearch 就会主动的依照对应关系，把索引调配到对应的节点上。 创立索引时，指定索引创立在 Hot 节点上： PUT logs-2022-06-27{ "settings":{ "number_of_shards":2, "number_of_replicas":0, "index.routing.allocation.require.my_node_type":"hot" }}随着时间推移，索引可能变得不再热门，将其调配到 Warm 节点上： PUT logs-2022-06-27/_settings{ "index.routing.allocation.require.my_node_type":"warm"}段文件合并优化历史的索引不大可能会扭转，比方日志事件是动态的。将每个分片中的小段合并至一个大段，会占用更少的资源更快地响应查问。合并通过 optimize API 来做到。 历史的索引有可能领有正本分片。如果下发一个优化（Optimize）申请，它会优化主分片和正本分片，这有些节约。能够长期移除正本分片，进行优化，而后再复原正本分片： POST /logs-2022-06-27/_settings{ "number_of_replicas": 0 }POST /logs-2022-06-27/_optimize?max_num_segments=1POST /logs-2022-06-27/_settings{ "number_of_replicas": 1 }敞开旧索引当索引变得更“老”，到了简直不会再被拜访的工夫点。能够在这个阶段删除它们，也能够抉择敞开。被敞开的索引，还会存在于集群中，但它们不会耗费磁盘空间以外的资源（比方：内存）。另外，从新关上一个索引要比从备份中复原快得多。 在敞开之前，须要刷新索引来确保没有事务残留在事务日志中。一个空白的事务日志会使得索引在从新关上时复原得更快： POST /logs-2022-01-*/_flush POST /logs-2022-01-*/_close POST /logs-2022-01-*/_open 归档旧索引历史十分旧的索引，能够通过归档至硬盘封存。归档后就能够将索引从集群中删除，开释集群空间资源了。 参考https://www.elastic.co/guide/...

数据扩容当存储达到下限的时候，就须要思考扩容，个别有两种状况： 单分片的数据量达到了下限（文档数量下限 2^31 或者容量下限 50GB）单节点数据容量超载，存储吃紧第 1 种状况，需思考减少分片的数量，减小单个分片的数据存储量。第 2 种状况，须要减少新节点，减小单节点上的数据量，缓解单节点数据容量吃紧的压力。当有新的节点退出集群，Elasticsearch 会主动挪动分片，且在分片挪动过程中，所有的索引搜寻申请均在失常运行。 Elasticsearch 在索引创立时须要指定分片数量，分片的数量一旦确定，就不能进行批改了，因为分片的数量是文档路由算法的计算元素： shard = hash(routing) % number_of_primary_shards所以，在索引创立之初，就须要依据业务理论状况，对分片的数量做一个预调配。提前预调配适合的分片数量。 海量分片分片的预调配不是随便的，海量的分片数量给未来的扩容带来肯定的便当，然而会有大量的没有实际意义的分片存在，而每个分片也是有肯定的老本的： 一个分片的底层即为一个 Lucene 索引，会耗费肯定文件句柄、内存、以及 CPU 运行。每一个搜寻申请都须要命中索引中的每一个分片，海量的分片性能比拟差。所以要防止海量分片，应该依据业务倒退状况，对数据量做评估，来预调配更适合的分片数量。 容量评估1 个分片太少而 1000 个又太多，那么怎么晓得须要多少分片？ 这是一个不好答复的问题。因为切实有太多相干的因素：应用的硬件、文档的大小和复杂度、文档的索引剖析形式、运行的查问类型、执行的聚合以及数据模型等。 单分片的容量评估： 基于筹备用于生产环境的硬件创立一个领有单个节点的集群。创立一个和筹备用于生产环境雷同配置和分析器的索引，并设置只有一个分片无正本。索引理论的文档（或者尽可能靠近理论）。运行理论的查问和聚合（或者尽可能靠近理论）。复制实在环境的应用形式并将数据全副压缩到单个分片上直到它“挂掉”。“挂掉”的定义：申请无响应或者超出忍受。这样就能够定义好了单个分片的适合容量大小。 通常将 50GB 作为分片下限，但还是有必要进行理论场景评估，评估是为了找到一个界线，理论场景中，可能数据量未触达 50GB 下限，然而过了界线就曾经不太可能满足疾速响应速度需要了。 数据总量（GB）的评估：通过下面单分片容量的评估能够计算出绝对精确的单个文档占用多大存储空间，而后依照业务将来某个阶段倒退的预期，评估进去总的文档数据量，计算出来数据总量（GB）：数据总量（GB）= 单文档存储空间 x 文档数据量 分片数量确定：分片数 = 数据总量（GB）/ 单分片的容量另外，联合每 1GB 内存对应最多 20 个分片 磁盘空间（GB）的评估：磁盘空间（BG）= 数据总量（GB）x（正本数量 + 1）x 1.2 数据节点数量评估：数据节点数量 = 向上取整（磁盘空间（GB）/ 单节点的数据量（GB））+ 1 扩容分类减少分片当分片容量快要达到下限，容量达到下限 50GB，或者文档数量达到下限，报错：number of documents in the index can not exceed 2147483519。就须要扩充分片数量以减小单个分片内的数据量。 大规模风行论坛都是从小论坛起步的，随着工夫的推移，论坛的数据量激增到超过了以后分片的下限，就须要扩增分片数量。 扩增分片的步骤：第一步、为论坛创立一个新的索引 baking_new，并为其调配正当的分片数，能够满足以后和将来肯定预期的数据增长： ...

vivo 互联网服务器团队- Shuai Guangying本文梳理了Elasticsearch对于数值索引实现计划的降级和优化思考，从2015年至今数值索引的计划经验了多个版本的迭代，实现思路从最后的字符串模仿到KD-Tree，技术越来越简单，能力越来越弱小，利用场景也越来越丰盛。从地理位置信息建模到多维坐标，数据检索到数据分析洞察都能够看到Elasticsearch的身影。 一、业务背景LBS服务是以后互联网重要的一环，波及餐饮、娱乐、打车、批发等场景。在这些场景中，有很重要的一项根底能力：搜寻左近的POI。比方搜寻左近的美食，搜寻左近的电影院，搜寻左近的专车，搜寻左近的门店。例如：以某个坐标点为核心查问出1km半径范畴的POI坐标，如下图所示： Elasticsearch在地理位置信息检索上具备了毫秒级响应的能力，而毫秒级响应对于用户体验至关重要。下面的问题应用Elasticsearch，只需用到geo_distance查问就能够解决业务问题。应用Elasticsearch的查问语法如下： GET /my_locations/_search{ "query": { "bool": { "must": { "match_all": {} }, "filter": { "geo_distance": { "distance": "1km", "pin.location": { "lat": 40, "lon": 116 } } } } }}工具的应用是一个非常简单的事件，更有意思在于工具解决问题背地的思维。了解了解决问题的思维，就能够超然于工具自身，做到触类旁通。本文基于在海量数据背景下，如何实现毫秒级搜寻左近的POI这个问题，探讨了Elasticsearch的实现计划，以及实现地理位置索引技术的演进过程。 二、背景常识在介绍Elasticsearch的解决计划前，咱们首先须要介绍一些背景常识，次要是3个问题。 如何精确定位一个地址？由经度、纬度和相对高度组成的天文坐标系，可能明确标示出地球上的任何一个地位。地球上的经度范畴[-180， 180]，纬度范畴[-90，90]。通常以本初子午线(经度为0)、赤道(纬度为0)为分界线。对于大多数业务场景，由经纬度组成的二维坐标曾经足以应答业务问题，可能重庆山城会有些例外。 如何计算两个地址间隔？对于立体坐标系，由勾股定理能够不便计算出两个点的间隔。然而因为地球是一个不完满球体，且不同地位有不同海拔高度，所以准确计算两个间隔地位是一个非常复杂的问题。在不思考高度的状况下，二维坐标间隔通常应用Haversine公式。 这个公式非常简单，只需用到arcsin和cos两个高中数学公式。其中和示意两个点纬度和经度的弧度制度量。其中d即为所求两个点的间隔，对应的数学公式如下(参考维基百科)： 程序员更喜爱看代码，对照代码了解公式更简略。相应的代码如下： // 代码摘自lucene-core-8.2.0， SloppyMath工具类 /** * Returns the Haversine distance in meters between two points * given the previous result from {@link #haversinSortKey(double, double, double, double)} * @return distance in meters. */ public static double haversinMeters(double sortKey) { return TO_METERS * 2 * asin(Math.min(1, Math.sqrt(sortKey * 0.5))); } /** * Returns a sort key for distance. This is less expensive to compute than * {@link #haversinMeters(double, double, double, double)}, but it always compares the same. * This can be converted into an actual distance with {@link #haversinMeters(double)}, which * effectively does the second half of the computation. */ public static double haversinSortKey(double lat1, double lon1, double lat2, double lon2) { double x1 = lat1 * TO_RADIANS; double x2 = lat2 * TO_RADIANS; double h1 = 1 - cos(x1 - x2); double h2 = 1 - cos((lon1 - lon2) * TO_RADIANS); double h = h1 + cos(x1) * cos(x2) * h2; // clobber crazy precision so subsequent rounding does not create ties. return Double.longBitsToDouble(Double.doubleToRawLongBits(h) & 0xFFFFFFFFFFFFFFF8L); } // haversin // TODO: remove these for java 9, they fixed Math.toDegrees()/toRadians() to work just like this. public static final double TO_RADIANS = Math.PI / 180D; public static final double TO_DEGREES = 180D / Math.PI; // Earth's mean radius, in meters and kilometers; see http://earth-info.nga.mil/GandG/publications/tr8350.2/wgs84fin.pdf private static final double TO_METERS = 6_371_008.7714D; // equatorial radius private static final double TO_KILOMETERS = 6_371.0087714D; // equatorial radius /** * Returns the Haversine distance in meters between two points * specified in decimal degrees (latitude/longitude). This works correctly * even if the dateline is between the two points. * <p> * Error is at most 4E-1 (40cm) from the actual haversine distance, but is typically * much smaller for reasonable distances: around 1E-5 (0.01mm) for distances less than * 1000km. * * @param lat1 Latitude of the first point. * @param lon1 Longitude of the first point. * @param lat2 Latitude of the second point. * @param lon2 Longitude of the second point. * @return distance in meters. */ public static double haversinMeters(double lat1, double lon1, double lat2, double lon2) { return haversinMeters(haversinSortKey(lat1, lon1, lat2, lon2)); }如何不便在互联网分享经纬度坐标？Geohash是2008-02-26由Gustavo Niemeyer在本人的集体博客上颁布的算法服务。其初衷在于通过对经纬度的编码对外提供简短的URL标识地图地位，不便在电子邮件、论坛和网站中应用。 ...

什么状况下须要更改ES索引？在应用ES做我的项目开发的时候，很多时候回随着版本的迭代呈现ES索引数据的变动，如果是在刚开始开发的时候，那就是删除原来的旧数据从新建索引就能够了，然而我的项目上线并经营了一段时间后，很显然这种形式就不可取了。 这时候咱们就要用到ES提供的_reindex办法了 假如ES曾经创立了名叫live的索引，并且在ES外面live曾经存入了很多数据。mapping构造体为： { "mappings": { "properties": { "room_id": { "type": "keyword" }, "room_name": { "type": "text" }, "shop_ids": { "type": "keyword" }, "live_date": { "type": "keyword" }, "goods_list": { "type": "nested", "properties": { "goods_id": { "type": "keyword" }, "goods_name": { "type": "text" }, "sku_code": { "type": "keyword" }, "category_id": { "type": "keyword" }, "activity_type": { "type": "keyword" }, "lecture_type": { "type": "keyword" }, "area_ids": { "type": "keyword" } } }, "creator_id": { "type": "keyword" }, "push_type": { "type": "keyword" }, "create_at": { "type": "keyword" } } }}索引重建以及数据迁徙假如一个场景，须要更改live对应的mapping构造体，去掉"push_type"字典，另外新增"push_status"字典，然而不影响原来数据。 ...

一、ElasticSearch根底：1、什么是Elasticsearch：Elasticsearch 是基于 Lucene 的 Restful 的分布式实时全文搜索引擎，每个字段都被索引并可被搜寻，能够疾速存储、搜寻、剖析海量的数据。 全文检索是指对每一个词建设一个索引，指明该词在文章中呈现的次数和地位。当查问时，依据当时建设的索引进行查找，并将查找的后果反馈给用户的检索形式。这个过程相似于通过字典中的检索字表查字的过程。 2、Elasticsearch 的基本概念：（1）index 索引：索引相似于mysql 中的数据库，Elasticesearch 中的索引是存在数据的中央，蕴含了一堆有类似构造的文档数据。 （2）type 类型：类型是用来定义数据结构，能够认为是 mysql 中的一张表，type 是 index 中的一个逻辑数据分类 （3）document 文档：相似于 MySQL 中的一行，不同之处在于 ES 中的每个文档能够有不同的字段，然而对于通用字段应该具备雷同的数据类型，文档是es中的最小数据单元，能够认为一个文档就是一条记录。 （4）Field 字段：Field是Elasticsearch的最小单位，一个document外面有多个field （5）shard 分片：单台机器无奈存储大量数据，es能够将一个索引中的数据切分为多个shard，散布在多台服务器上存储。有了shard就能够横向扩大，存储更多数据，让搜寻和剖析等操作散布到多台服务器下来执行，晋升吞吐量和性能。 （6）replica 正本：任何一个服务器随时可能故障或宕机，此时 shard 可能会失落，因而能够为每个 shard 创立多个 replica 正本。replica能够在shard故障时提供备用服务，保证数据不失落，多个replica还能够晋升搜寻操作的吞吐量和性能。primary shard（建设索引时一次设置，不能批改，默认5个），replica shard（随时批改数量，默认1个），默认每个索引10个 shard，5个primary shard，5个replica shard，最小的高可用配置，是2台服务器。 3、什么是倒排索引：在搜索引擎中，每个文档都有一个对应的文档 ID，文档内容被示意为一系列关键词的汇合。例如，某个文档通过分词，提取了 20 个关键词，每个关键词都会记录它在文档中呈现的次数和呈现地位。那么，倒排索引就是 关键词到文档 ID 的映射，每个关键词都对应着一系列的文件，这些文件中都呈现了该关键词。有了倒排索引，搜索引擎能够很不便地响应用户的查问。 要留神倒排索引的两个重要细节： 倒排索引中的所有词项对应一个或多个文档倒排索引中的词项 依据字典程序升序排列4、DocValues的作用：倒排索引也是有缺点的，如果咱们须要对数据做一些聚合操作，比方排序/分组时，lucene外部会遍历提取所有呈现在文档汇合的排序字段，而后再次构建一个最终的排好序的文档汇合list，这个步骤的过程全副维持在内存中操作，而且如果排序数据量微小的话，非常容易就造成solr内存溢出和性能迟缓。 DocValues 就是 es 在构建倒排索引的同时，构建了正排索引，保留了docId到各个字段值的映射，能够看作是以文档为维度，从而实现依据指定字段进行排序和聚合的性能。 另外doc Values 保留在操作系统的磁盘中，当docValues大于节点的可用内存，ES能够从操作系统页缓存中加载或弹出，从而防止产生内存溢出的异样，docValues远小于节点的可用内存，操作系统天然将所有Doc Values存于内存中（堆外内存），有助于快速访问。 5、text 和 keyword类型的区别：两个的区别次要分词的区别：keyword 类型是不会分词的，间接依据字符串内容建设倒排索引，keyword类型的字段只能通过准确值搜寻到；Text 类型在存入 Elasticsearch 的时候，会先分词，而后依据分词后的内容建设倒排索引 6、什么是进展词过滤：进展词能够看成是没有意义的词，比方“的”、“而”，这类词没有必要建设索引 7、query 和 filter 的区别？（1）query：查问操作不仅仅会进行查问，还会计算分值，用于确定相关度； ...

简介Elasticsearch 是基于 Lucene 实现的，Lucene 中引入了按段搜寻的概念，每一个段自身就是一个倒排索引。索引在 Lucene 中除示意所有段的汇合外，还减少了提交点（Commit point）的概念，一个列出了所有已知段的文件的形容如下图：一个 Lucene 索引蕴含一个提交点和三个段。 生成新的文档首先被增加到内存索引缓存中（In-memory buffer）。Elasticsearch 的主动刷新每隔 1s 进行一次，一个新的段被增加到提交点并清空内存索引缓存。当一个查问被触发，所有已知的段按程序被查问。词项统计会对所有段的后果进行聚合，以保障每个词和每个文档的关联都被精确计算。 删除&更新段是不可扭转的，所以既不能把文档从旧的段中移除，也不能批改旧的段来对文档进行更新。取而代之的是，每个提交点会蕴含一个 .del 文件，文件中会列出这些被删除文档的段信息。 当一个文档被 “删除” 时，它实际上只是在 .del 文件中被标记删除。一个被标记删除的文档依然能够被查问匹配到，但它会在最终后果被返回前从后果集中移除。 文档更新也是相似的操作形式：当一个文档被更新时，旧版本文档被标记删除，文档的新版本被索引到一个新的段中。可能两个版本的文档都会被一个查问匹配到，但被删除的那个旧版本文档在后果集返回前就曾经被移除。 合并主动刷新流程每秒会创立一个新的段 ，这样会导致短时间内的段数量暴增。而段数目太多会带来较大的麻烦。每一个段都会耗费文件句柄、内存和 cpu 运行周期。更重要的是，每个搜寻申请都必须轮流查看每个段；所以段越多，搜寻也就越慢。 Elasticsearch 通过在后盾进行段合并来解决这个问题。小的段被合并到大的段，而后这些大的段再被合并到更大的段。 段合并的时候会将那些旧的已删除文档从文件系统中革除。被删除的文档（或被更新文档的旧版本）不会被拷贝到新的大段中。 启动段合并不须要用户做任何事。进行索引和搜寻时会主动进行。段合并的流程： 当索引的时候，刷新（refresh）操作会创立新的段并将段关上以供搜寻应用。合并过程抉择一小部分大小类似的段，在后盾将它们合并到更大的段中且不会中断索引和搜寻。两个已提交（存在于磁盘上）的段和一个未提交（存在于文件系统缓存中）的段合并到一个更大段，如图形容：合并实现后，生成的大段（已排除被标记删除的文档）被关上用来搜寻，较小的段被删除，如图形容： 性能优化段合并的计算量宏大，可能会吃掉大量磁盘 I/O。段合并可能要很长时间能力实现，尤其是比拟大的段，所以合并在后盾定期操作。个别状况下段合并都不会有什么影响，因为大规模段合并的概率是很小的。 不过有时候合并会连累写入速率。如果这个真的产生了，Elasticsearch 会主动限度索引申请到单个线程里。单线程能够缩小段文件的生成速度，防止出现数以百计的段在被合并之前就生成进去。如果 Elasticsearch 发现合并连累索引了，它会记录一个申明有 now throttling indexing 的 INFO 级别信息。 Elasticsearch 的默认设置在这块比拟激进：不心愿搜寻性能被后盾合并影响。不过有时候（尤其是 SSD，或者日志场景）限流阈值太低了。 这个限流阈值默认值是 20 MB/s，对机械磁盘应该是个不错的设置。如果应用的是 SSD，能够思考进步到 100–200 MB/s。测试验证对你的零碎哪个值适合： PUT /_cluster/settings{ "persistent" : { "indices.store.throttle.max_bytes_per_sec" : "100mb" }}如果在做批量导入，齐全不在意搜寻，能够彻底关掉合并限流。这样让索引速度跑到你磁盘容许的极限： PUT /_cluster/settings{ "transient" : { "indices.store.throttle.type" : "none" }}设置限流类型为 none 彻底敞开合并限流。等实现了导入，须要改回 merge 从新关上限流。 ...

前言Elasticsearch 在解决文档变更的时候，首先会把变更记录在索引缓冲区里，而后每隔 1s 将索引缓冲区刷新到文件系统缓存，存在于文件系统缓存中的变更会定时 fsync 到磁盘，文档变更长久化到了硬盘才算不会失落了。 在未长久化到磁盘之前如果产生了意外状况可能会导致数据失落，比方：Elasticsearch 宕机（索引缓冲区未刷新的变更会失落），机房断电导致操作系统重启（文件系统缓存中的变更会失落）。 事务日志简介&作用Elasticsearch 在数据的变更提交过程中减少了事务日志（Translog），采纳追加写的形式。事务日志是磁盘上一块区域内的程序I/O，防止了随机 I/O 须要在磁盘的多个中央挪动磁头，事务日志的形式相对来说要快很多。 一个文档被索引后，会增加到内存缓冲区，同时记录到事务日志。 内存缓冲区 refresh 后，事务日志不清空。 Elasticsearch 索引的每个分片都存在一个事务日志，事务日志记录实现后才会给客户端返回胜利。这就意味着，只有客户端接到胜利响应，文档的变更就曾经长久化到磁盘了，体现在事务日志里。 事务日志提供所有还没有被刷到磁盘的操作的一个长久化纪录。当 Elasticsearch 启动的时候， 它会从磁盘中应用最初一个提交点（Commit point）去复原已知的段，并且会重放 Translog 中所有在最初一次提交后产生的变更操作。 安全性在文档变更被 fsync（fsync 指的是长久化文档变更到事务日志）到磁盘前，变更的文档在重启之后就会失落。默认 Translog 是每 5s 被 fsync 刷新到硬盘，或者在每次写申请实现之后执行(e.g. index, delete, update, bulk)。这个过程在主分片和复制分片都会产生。最终，这意味着在整个申请被 fsync 到主分片和复制分片的 Translog 之前，客户端不会失去一个 200 OK 响应。 在每次申请后都执行一个 fsync 会带来一些性能损失，只管实际表明这种损失绝对较小（特地是bulk 导入，它在一次申请中平摊了大量文档的开销）。 然而对于一些大容量的偶然失落几秒数据问题也并不重大的集群，应用异步的 fsync 还是比拟无益的。比方，写入的数据被缓存到内存中，再每 5s 执行一次 fsync。 这个行为能够通过设置 durability 参数为 async 来启用： PUT /my_index/_settings{ "index.translog.durability": "async", "index.translog.sync_interval": "5s"}这个选项能够针对索引独自设置，并且能够动静进行批改。如果决定应用异步 Translog 的话，就须要能承受在产生 crash 时，失落掉 sync_interval 时间段的数据也无所谓。 ...

前言Elasticsearch 的近实时搜寻是指文档的变动并不是立刻对搜寻可见，默认状况下会有 1s 的提早。 原理当有文档新增或者批改时，Elasticsearch 会先把这种变更记录到索引缓冲区（In-memory buffer）里。 Elasticsearch 每隔 1s 进行一次 refresh，refresh 会把索引缓冲区里记录的变更以段的格局刷新到文件系统缓存，并清空索引缓冲区里的记录。 只有文档变更达到文件系统缓存中，就能够像文件一样被关上和读取了，这也就意味着，文档的变更在此时能够体现在搜寻后果中。文件系统缓存最初通过 fsync 被刷新到硬盘。 总结索引缓冲区的作用是实现批量变更到文件系统缓存，批操作能够进步性能。文件系统缓存的引入是为了解决 fsync 的昂扬代价，如果每次索引一个文档都去执行一次 fsync 的话会造成很大的性能问题，比方：文档变更更加耗时，以及须要更长时间能力体现在搜寻后果中。 参考https://www.elastic.co/guide/...

前言Elasticsearch 是 Java 语言开发，底层的存储引擎是基于Lucene实现，Lucene的倒排索引(Inverted Index)是先在内存里生成，而后定期以段文件(segment file)的模式刷到磁盘的。 每个段文件理论就是一个残缺的倒排索引，并且一旦写到磁盘上就不会做批改。API层面的文档更新和删除实际上是增量写入的一种非凡文档，会保留在新的段里。不变的段文件易于被操作系统cache，热数据简直等效于内存拜访。 内存构造ElasticSearch的内存从大的构造能够分堆内存（On Heap）和堆外内存（Off Heap）。Off Heap局部由Lucene进行治理。On Heap局部存在可GC局部和不可GC局部，可GC局部通过GC回收垃圾对象，从而开释内存。不可GC局部不能通过GC回收垃圾对象，这部分会通过LRU算法进行对象革除并开释内存。更加具体的内存占用与调配如下图： On Heap内存占用这部分内存占用的模块包含：Indexing Buffer、Node Query Cache、Shard Request Cache、Field Data Cache以及Segments Cache。 Indexing Buffer默认调配的内存大小是10% heap size，当缓存满了或者refresh/flush interval到了，就会以segment file的模式写入到磁盘。 Indexing Buffer的存在能够进步文档写入申请的响应速度，取得更高的吞吐量，缩小磁盘IO的拜访频率，节俭了CUP资源。这部分空间是能够通过GC开释被重复利用的。 缓存机会：新文档数据写入的时候生效或者回收：当空间满了的时候会触发GC分明缓存对象，开释空间 Node Query Cache (Filter Cache)节点级别的缓存，节点上的所有分片共享此缓存，是Lucene层面的实现。缓存的是某个filter子查问语句在一个segment上的查问后果。如果一个segment缓存了某个filter子查问的后果，下次能够间接从缓存获取后果，无需再在segment内进行过滤查问。 每个segment有本人的缓存，缓存的key为filter子查问（query clause ），缓存内容为查问后果，这些查问后果是匹配到的document numbers，保留在位图FixedBitSet中。 缓存的构建过程是：对segment执行filter子查问，先获取查问后果中最大的 document number: maxDoc（document number是lucene为每个doc调配的数值编号，fetch的时候也是依据这个编号获取文档内容）。而后创立一个大小为 maxDoc的位图：FixedBitSet，遍历查问命中的doc，将FixedBitSet中对应的bit设置为1。 例如：查问后果的maxDoc是8，那么创立出的FixedBitSet就是：[0,0,0,0,0,0,0,0]，能够了解为是一个长度为8的二值数组，初始值都是0，假如filter查问后果的doc列表是：[1,4,8]，那么FixedBigSet就设置为：[1,0,0,1,0,0,0,1]，当查问有多个filter子查问时，对位图做交并集位运算即可。 用一个例子来阐明Node Query Cache构造。如下图查问语句蕴含两个子查问，别离是对date和age字段的range查问，Lucene在查问过程中遍历每个 segment，查看其各自的LRUQueryCache是否命中filter子查问，segment 8命中了对age和date两个字段的缓存，将会间接返回后果。segment 2只命中了对age字段的缓存，没有命中date字段缓存，将继续执行查问过程。 缓存机会：1.拜访频率大于等于特定阈值之后，query后果才会被缓存2.segment的 doc 数量须要大于10000，并且占整个分片的3%以上 生效或回收：segment合并会导致缓存生效。内存的治理应用LRU算法。 Shard Request CacheShard Request Cache简称Request Cache，他是分片级别的查问缓存，每个分片有本人的缓存，属于ES层面的实现。ES默认状况下最多应用堆内存的1%用作 Request Cache，这是一个节点级别的配置。内存的治理应用LRU算法。 缓存的实现在IndicesRequestCache类中，缓存的key是一个复合构造，次要包含shard，indexreader，以及客户端申请。缓存的value是将查问后果序列化之后的二进制数据。 final Key key = new Key(cacheEntity, reader.getReaderCacheHelper().getKey(), cacheKey);cacheEntity：次要是shard信息，代表该缓存是哪个shard上的查问后果。readerCacheKey：次要用于辨别不同的IndexReader。cacheKey：次要是整个客户端申请的申请体（source）和申请参数（preference、indexRoutings、requestCache等）。 ...

Inverted Index倒排索引（Inverted Index）是实现单词-文档矩阵的一种具体存储模式，在全文检索的场景中，能够实现疾速的搜寻需要，其由两局部形成，别离是 Term Dictionary、Posting List。 Term Dictionary如下三个文档，文档ID别离是001和002，每个文档中有一个字段，用来存储一段文本。 IDtitle001金都嘉怡假日酒店002金都欣欣酒店应用分词器对 title 进行分词，分词后果可能是这样 IDtitle分词后果001金都嘉怡假日酒店金都，嘉怡，假日，酒店002金都欣欣酒店金都，欣欣，酒店去重分词后果，失去一个词语汇合：[金都，嘉怡，欣欣，假日，酒店]，这个词语汇合称之为词典，Term Dictionary 能够简略的认为就是这个词典。 词典是倒排索引中十分重要的组成部分，它用来保护文档汇合中所有的词语汇合，同时记录某个单词对应的倒排列表在倒排文件（存储倒排列表的文件）中的地位信息等。在反对搜寻时，依据用户的查问词，去词典里查问，就能够取得对应的倒排列表，并以此作为后续的排序的根底。 理论的利用场景中，Term Dictionary 是十分大的，搜索引擎须要实现疾速查找能力满足事实中的需要，那么就须要可能疾速的从词典中找到一个词语。为了可能满足疾速查找的需要，Term Dictionary 个别应用 B+ 树结构或者哈希 + 链表构造进行存储。 另外，为了节俭空间，Term Dictionary 在磁盘上是以分 Block 的形式保留的，一个 Block 外部利用公共前缀压缩，比方：将所有以 “pre” 结尾的单词提取 “pre” 作为公共局部，将残余局部原样保留，这样就能够节俭肯定的空间。 Posting List倒排列表（Posting List）中记录了呈现过某个单词的所有文档的文档列表以及单词在该文档中呈现的次数、地位信息。上面联合倒排索引的残缺构造了解下倒排列表： 单词ID单词文档频率倒排列表1金都2(001;1;<1>), (002;1;<1>)2嘉怡2(001;1;<2>)3欣欣2(002;1;<2>)4假日2(001;1;<3>)5酒店2(001;1;<4>), (002;1;<3>)Term Index实现全文检索的速度次要取决于词典查找的速度，尽管 Term Dictionary 应用了查找速度比拟高效的数据结构，然而还是防止不了磁盘随机拜访的性能影响，因为 Term Dictionary 比拟大，如果全副放入内存会有相当大的内存耗费，随着工夫，内存可能会被耗尽。所以 Term Dictionary 不适宜基于内存的查找。 为了进一步晋升 Term Dictionary 的查找速度，于是就有了 Term Index。Term index 应用的是 Trie 树结构。Trie 树不会蕴含所有的 Term，它仅蕴含的是 Term 的一些前缀。通过 Term Index 能够疾速地定位到 Term Dictionary 的 Block 地位，而后基于该 Block 依照 Term Dictionary 外部的数据结构进行查找。 ...

前言技术栈 Elasticsearch 7.17.2python 3.8httpx 0.22.0loguru 0.6.0有时更新 ES 分词器或近程词典后，不确定每个节点是否都已更新到位，没找到间接的命令来校验，故写了一份 Python 脚本来做校验代码创立了 test_{nodeName} 的索引，测试完后手动删除 DELETE test_*代码# encoding: utf8# author: qbit# date: 202-06-16# summary: 遍历 ES 数据节点校验分词后果import pprintimport httpxfrom loguru import loggercoordnode = 'http://192.168.2.67:9200' # ES 协调节点地址esuser = 'elastic' # ES 集群账号espwd = 'xxxx' # ES 集群明码analyzer = "hao_index_mode" # 分词器intext = "燕雀安知鸿鹄之志" # 分词文本 outtext = "燕雀;安;知;鸿鹄之志;鸿鹄" # 分词后果 def GetNodeList(): r""" 获取 ES 集群节点列表 """ url = f'{coordnode}/_cat/nodes?v=true&h=name,ip,master,node.role&s=name&format=json' r = httpx.get(url, auth=(esuser, espwd)) result = r.json() for dic in result: logger.debug(dic) return resultdef CheckOneNodeAnalyzer(nodeDict: dict, expected: str): r""" 在某个节点创立索引，并测试分词 """ nodeName = nodeDict['name'] nodeIP = nodeDict['ip'] indexName = f"test_{nodeName}" url = f"{coordnode}/{indexName}" logger.info(f"{nodeName}, {nodeIP}, {indexName}") dic = { "settings": { "index": { "number_of_shards": 1, "number_of_replicas": 0, "routing.allocation.include._ip": nodeIP } } } r = httpx.put(url, auth=(esuser, espwd), json=dic) # 创立索引 logger.debug(r) url = f"{coordnode}/{indexName}/_analyze" dic = { "analyzer": analyzer, "text": intext } r = httpx.post(url, auth=(esuser, espwd), json=dic) # 验证分词 logger.debug(r) tokenList = list() for dic in r.json()['tokens']: # logger.debug(dic) tokenList.append(dic['token']) tokenLine = ';'.join(tokenList) logger.info(tokenLine) if tokenLine == expected: return ['ok', nodeName, nodeIP, tokenLine] else: return ['no', nodeName, nodeIP, tokenLine]if __name__ == '__main__': nodeList = GetNodeList() okList = list() noList = list() for node in nodeList: if 'd' in node['node.role']: # 数据节点 result = CheckOneNodeAnalyzer(node, outtext) if result[0] == 'ok': okList.append(result) else: noList.append(result) print('------') logger.info(f"okList size: {len(okList)}") pprint.pprint(okList) logger.info(f"noList size: {len(noList)}") pprint.pprint(noList)qbit snap

ElasticSearch根本应用姿态二本文作为elasticsearch 根本应用姿态第二篇，蕴含以下内容 查问指定字段限度返回条数分页查问分组查问高亮主动补全提醒排序返回后果聚合，如统计文档数，某个field value的求和、平均值等更多相干知识点请查看: * ElasticSearch 根本应用姿态 - 一灰灰Blog<!-- more -->0. 数据筹备初始化一个索引，写入一些测试数据 post second-index/_doc{ "@timestamp": "2021-06-10 08:08:08", "url": "/test", "execute": { "args": "id=10&age=20", "cost": 10, "res": "test result" }, "response_code": 200, "app": "yhh_demo"}post second-index/_doc{ "@timestamp": "2021-06-10 08:08:09", "url": "/test", "execute": { "args": "id=20&age=20", "cost": 11, "res": "test result2" }, "response_code": 200, "app": "yhh_demo"}post second-index/_doc{ "@timestamp": "2021-06-10 08:08:10", "url": "/test", "execute": { "args": "id=10&age=20", "cost": 12, "res": "test result2" }, "response_code": 200, "app": "yhh_demo"}post second-index/_doc{ "@timestamp": "2021-06-10 08:08:09", "url": "/hello", "execute": { "args": "tip=welcome", "cost": 2, "res": "welcome" }, "response_code": 200, "app": "yhh_demo"}post second-index/_doc{ "@timestamp": "2021-06-10 08:08:09", "url": "/404", "execute": { "args": "tip=welcome", "cost": 2, "res": "xxxxxxxx" }, "response_code": 404, "app": "yhh_demo"}1. 查问指定字段比方我当初只关怀url返回的状态码, 次要借助_source来指定须要查问的字段，查问的语法和之前介绍的统一 ...

为ELK配置明码拜访， 如果是Docker启动， 须要进入docker容器批改配置:1.配置elasearch开启明码批改, 减少两行elasticsearch.yml: # 开启明码xpack.security.transport.ssl.enabled: truexpack.security.enabled: true重启es或Docker容器 设置明码# 进入docker docker exec -it elk /bin/bashcd /opt/elasticsearch/bin# 手动设置明码./elasticsearch-setup-passwords interactive# 在前面的提醒中配置明码，配置明码即可, 会有很多个明码， 都配一样的为Kibana配置拜访明码kibana.yml: # elk体系有很多的用户组，elastic是默认的用户组之一，能够应用默认的用户，也能够自定义用户elasticsearch.username: "elastic" elasticsearch.password: "1qaz@WSX3edc"重启kibana或Docker容器docker restart elk

Docker装置ELK集成镜像下载ELK镜像搜寻镜像docker search sebp/elk [root@www ~]# docker search sebp/elkNAME DESCRIPTION STARS OFFICIAL AUTOMATEDsebp/elk Collect, search and visualise log data with … 1169 [OK]sebp/elkx Collect, search and visualise log data with … 43 [OK]下载镜像docker pull sebp/elk [root@www ~]# docker pull sebp/elkUsing default tag: latestlatest: Pulling from sebp/elkDigest: sha256:c5f1d0f845ab217ef509b8c6565d0c4a5dd8dea063a411b60dfb7c4508312acaStatus: Image is up to date for sebp/elk:latestdocker.io/sebp/elk:latest批改系统配置批改eleasticsearch用户权限vim /etc/security/limits.conf # 在最初面追加上面内容elk hard nofile65536elk soft nofile65536能够解决ELK启动报: ERROR: Elasticsearch did not exit normally - check the logs at /var/log/elasticsearch/elasticsearch.log错的问题 ...

概述开源的 Elasticsearch （以下简称 Elastic）是目前全文搜索引擎的首选。它能够疾速地贮存、搜寻和剖析海量数据。Elastic 的底层是开源库 Lucene。然而，你没法间接用 Lucene，必须本人写代码去调用它的接口。Elastic 是 Lucene 的封装，提供了 REST API 的操作接口，开箱即用。 装置Elastic 须要 Java 8 环境。如果你的机器还没装置 Java，能够参考这篇文章，留神要保障环境变量JAVA_HOME正确设置。装置完 Java，就能够跟着官网文档装置 Elastic。间接下载压缩包比较简单。 wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-5.5.1.zipunzip elasticsearch-5.5.1.zip #解压cd elasticsearch-5.5.1/ #返回装置目录./bin/elasticsearch #启动服务#./bin/elasticsearch在前台启动服务，若出错会间接显示解决。但启动胜利，会使得命令行无奈执行命令。#因而./bin/elasticsearch -d能够在后盾启动，但后盾启动可能常常失败。启动呈现谬误：ERROR: [1] bootstrap checks failed 执行以下命令解决：sudo sysctl -w vm.max_map_count=262144注：机器每次重启，都最好执行一次。相干谬误 curl localhost:9200 #Elastic默认在9200端口运行，这是拜访服务的命令拜访胜利后果如下,返回一个JSON对象。{ "name" : "qhnoMSW", "cluster_name" : "elasticsearch", "cluster_uuid" : "7lC0QcTZSxGXO5aknY8clw", "version" :{ "number" :"5.5.1", "build_hash" : "19c13d0", "build_date" : "2017-07-18T20:44:24.823Z", "build_snapshot" : false, "lucene_version" : "6.6.0" }, "tagline" : "You Know, for Search"}默认的，Elastic只容许本机拜访，若要近程拜访，须要批改以批改 Elastic 装置目录的config/elasticsearch.yml文件(config目录和bin目录同级。去掉network.host的正文，将它的值改成0.0.0.0（设成0.0.0.0让任何人都能够拜访。线上服务不要这样设置，要设成具体的IP），而后重新启动 Elastic。批改后果如下： ...

近程字典ES-ik的字典加载反对近程扩大字典，并且能够实现热加载，基于此能够实现自定义字典的近实时加载，从而做到对分词后果的灵便管制。 配置与原理ES-ik是ES的一个分词插件，放在在elasticsearch\plugins上面，在ES-ik的config\ik\IKAnalyzer.cfg.xml中配置了分词依赖的相干字典信息，包含“用户自定义扩大字典”、“用户自定义扩大进行词字典”、“近程扩大字典”、“近程扩大进行词字典”，如图： 近程扩大字典是配置一个近程的url获取，将url返回的字典数据加载到ES-ik分词依赖的内存字典中，实现字典数据的非本地化治理。如图： 对于近程扩大字典，ES-ik的依赖包中的实现如下，会每隔1分钟检测下近程扩大字典是否有更新，如果有更新，那么就会将最新的数据加载进来。 Monitor.java中的要害代码如下，会依据Last-Modified字段或者ETag字段的变动来判断字典是否有更新。 表结构设计 功能设计导入字典单词将行业单词，比方设施的机型、品牌、型号等作为字典批量导入，这样就能够对行业单词依照字典进行精确的分词。 增加扩大字典单词从治理后盾零碎操作增加字典单词将字典数据保留到ik_ext_dict表中，同时更新ik_ext_dict表的更新工夫。例如：新的机型、品牌、型号等数据， 增加停用字典单词将停用词数据保留到ik_ext_stop_dict表中，同时更新ik_ext_stop_dict表的更新工夫，例如：一些不须要在全文检索过程中参加分词要被过滤掉的单词。 近程字典接口实现“近程扩大字典”的数据加载，服务端提供接口，首先从redis的ext_dict中获取，如果ext_dict中有数据，间接返回，如果没有，从ik_ext_dict表中加载数据并返回，同时将数据set到redis中ext_dict缓存中。代码如下： public void remoteExtDic(HttpServletRequest request, HttpServletResponse response) { try { OutputStream out = response.getOutputStream(); String modifyTime = request.getHeader("If-Modified-Since"); String remoteDicModifyTime = ikDicService.getRemoteDicModifyTime(); response.setHeader("ETag", "extDic"); response.setHeader("Last-Modified", remoteDicModifyTime); response.setContentType(contentType); if(!remoteDicModifyTime.equals(modifyTime)){ String remoteDicText = ikDicService.getRemoteDicText(); out.write(remoteDicText.getBytes(charsetName)); } out.flush(); out.close(); } catch (Exception e) { logger.error("近程扩大词字典接口异样", e); }}“近程扩大停用词字典”的数据加载，服务端提供接口，从redis中的ext_stop_dict获取，如果没有，从ik_ext_stop_dict表中加载数据并返回，同时将数据set到redis中的ext_stop_dict缓存中。 public void remoteStopDic(HttpServletRequest request, HttpServletResponse response) { try { OutputStream out = response.getOutputStream(); String modifyTime = request.getHeader("If-Modified-Since"); String remoteStopDicModifyTime = ikDicService.getRemoteStopDicModifyTime(); response.setHeader("ETag", "stopDic"); response.setHeader("Last-Modified", remoteStopDicModifyTime); response.setContentType(contentType); if(!remoteStopDicModifyTime.equals(modifyTime)){ String remoteStopDicText = ikDicService.getRemoteStopDicText(); out.write(remoteStopDicText.getBytes(charsetName)); } out.flush(); out.close(); } catch (Exception e) { logger.error("近程扩大停用词字典接口异样", e); }}索引版本治理ES对索引字段的从新分词须要重建索引实现，在白天零碎正在运行的时候如果重建索引就会影响到线上零碎的应用。 ...

[toc] 实际007-elasticsearch查问之2-Request Body与DSL查问 1. 查问语句通过HTTP Request Body发送给ESPOST movies/_search?ignore_unavailable=true{ "profile": "true", "query": { "match_all": {} }}2. 分页：from+sizePOST movies/_search?ignore_unavailable=true{ "profile": "true", "from": 0, "size": 2, "query": { "match_all": {} }}3. 排序：sortPOST movies/_search?ignore_unavailable=true{ "profile": "true", "from": 0, "size": 5, "sort": [ { "year": "desc" }, { "title.keyword": "desc" } ], "query": { "match_all": {} }}4. source字段过滤:只返回须要的列POST movies/_search?ignore_unavailable=true{ "profile": "true", "_source": [ "id", "title", "year" ], "from": 0, "size": 5, "sort": [ { "year": "desc" }, { "title.keyword": "desc" } ], "query": { "match_all": {} }}反对通配符*： _"source": [ "name*", "desc\*" ]5. script_fields脚本字段：自加工后果POST movies/_search{ "from": 0, "size": 5, "sort": [ { "year": { "order": "desc" } }, { "title.keyword": { "order": "desc" } } ], "script_fields": { "year_and_title": { "script": { "lang": "painless", "source": "doc['year'].value + '->' + doc['title.keyword'].value" } } }, "query": { "match_all": {} }}title=When We First Met; year=2018; ...

[toc] 实际006-elasticsearch查问之1-URI Search查问 查问基于movielens数据索引。一、URI Search详解1.1. URI Search介绍GET movies/_search?q=2012&df=title&sort=year:desc&from=0&size=10&timeout=1s{ "profile": "true"}q: 指定查问语句。应用 Query String Syntaxdf(default field): 默认字段，不指定时，会对所有字段进行查问。sort: 排序from/size: 用于分页profile: 能够查看查问是如何被执行的后果： { "took" : 1, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 2, "relation" : "eq" }, "max_score" : null, "hits" : [ { "_index" : "movies", "_type" : "_doc", "_id" : "105254", "_score" : null, "_source" : { "id" : "105254", "genre" : [ "Adventure", "Comedy" ], "title" : "Crystal Fairy & the Magical Cactus and 2012", "@version" : "1", "year" : 2013 }, "sort" : [ 2013 ] }, { "_index" : "movies", "_type" : "_doc", "_id" : "72378", "_score" : null, "_source" : { "id" : "72378", "genre" : [ "Action", "Drama", "Sci-Fi", "Thriller" ], "title" : "2012", "@version" : "1", "year" : 2009 }, "sort" : [ 2009 ] } ] }, "profile" : { "shards" : [ { "id" : "[q3LSyHNoTSeAj6pqYovXjg][movies][0]", "searches" : [ { "query" : [ { "type" : "TermQuery", "description" : "title:2012", "time_in_nanos" : 46388, "breakdown" : { "set_min_competitive_score_count" : 0, "match_count" : 0, "shallow_advance_count" : 0, "set_min_competitive_score" : 0, "next_doc" : 1112, "match" : 0, "next_doc_count" : 2, "score_count" : 0, "compute_max_score_count" : 0, "compute_max_score" : 0, "advance" : 954, "advance_count" : 1, "score" : 0, "build_scorer_count" : 4, "create_weight" : 7640, "shallow_advance" : 0, "create_weight_count" : 1, "build_scorer" : 36674 } } ], "rewrite_time" : 9815, "collector" : [ { "name" : "CancellableCollector", "reason" : "search_cancelled", "time_in_nanos" : 151352, "children" : [ { "name" : "SimpleFieldCollector", "reason" : "search_top_hits", "time_in_nanos" : 141275 } ] } ] } ], "aggregations" : [ ] } ] }}1.2. 字段查问：查问title里有2012的记录GET movies/_search?q=2012&df=title{ "profile": "true"}后果 ...

[toc] Search API概览1. Search API分两种URI Search: 在URI中应用查问参数Request Body Search： 应用ES提供的，基于JSON格局的更加齐备的DSL: Query Domain Specific Language 查问畛域特定语言2. 指定查问的索引语法范畴/_search集群上的所有index/index1/_searchindex1上查问/index1,index2/_searchindex1和index2/index*/_search通配符匹配：index结尾的索引名相干的索引上查问3. 查问数据筹备：ES中的usersindex里的数据一览_index_type_id▲_scorenameagegenderbirthusers_doc11niewj36male1985-01-01users_doc31lifubo33male users_doc41weibinbin32male users_doc21nie26male1995-02-024. URI查问4.1 curl形式在命令行应用"q", 指定查问字符串； "query string syntax", KV键值对： curl -XGET --user username:password "http://localhost:9200/users/_search?q=name:nie"后果： { "took": 4, "timed_out": false, "_shards": { "total": 1, "successful": 1, "skipped": 0, "failed": 0 }, "hits": { "total": { "value": 1, "relation": "eq" }, "max_score": 0.87546873, "hits": [ { "_index": "users", "_type": "_doc", "_id": "2", "_score": 0.87546873, "_source": { "name": "nie", "age": 26, "gender": "male", "birth": "1995-02-02" } } ] }}4.2 在kibana DevTools里：GET /users/_search?q=name:nie后果： ...

[toc] 一、Index Template简介1. 什么是index模板？帮忙你设定mappings和settings; 并依照肯定的规定，主动匹配到新建的索引上；2. 对于index模板的几个问题Q1: 已新建了索引A应用了模板M,批改了M后会影响A吗？A: 不会! 模板仅仅在一个索引被创立时，才产生作用。批改模板不会影响曾经创立的索引。Q2: 可否设定应用多个index模板?A: 能够! 设定多个索引模板，这些模板会被merge在一起。Q3: 设定多个index模板，起作用是按什么程序？A: 你能够指定order的数值，管制merging的过程。order大的无限起作用!二、 index template案例2.1 两个模板定义和创立索引的作用优先级2.1.1 模板1设定所有索引创立时->分片数1，正本数1# 1. 所有索引：分片数1，正本数1PUT _template/my_template{ "index_patterns": ["*"], "order": 10, // order大的优先 "version": 1, "settings": { "number_of_shards": 1, "number_of_replicas": 1 }}2.1.2 模板2设定test结尾的索引设置主分片1，正本数2; 敞开date辨认，开启数字辨认date辨认(date_detection)就是：字符串的日期，辨认为date(默认是开启的)数字辨认(numeric_detection)就是：字符串的数字，辨认为数字类型(默认是敞开的);#2.test结尾的索引设置主分片1，正本数2; 敞开date辨认，开启数字辨认PUT _template/my_template_test{ "index_patterns": ["test*"], "order": 1, "settings":{ "number_of_shards": 1, "number_of_replicas": 2 }, "mappings":{ "date_detection": false, "numeric_detection": true }}2.1.3 创立test结尾的索引，验证PUT test_template_index/_doc/1{ "someNumber": "1", "somDate": "2019/01/01"}看看作用成果： GET test_template_index/_mapping看看后果： { "test_template_index" : { "mappings" : { "date_detection" : false, "numeric_detection" : true, "properties" : { "somDate" : { "type" : "text", "fields" : { "keyword" : { "type" : "keyword", "ignore_above" : 256 } } }, "someNumber" : { "type" : "long" } } } }}可见模板2起作用了： somDate被辨认为text而非date; someNumber被辨认为long而非text了； ...

[toc] 一、Elasticsearch analizer组成1. 组成三大件1.1 Character Filter(字符过滤器)用于原始文本过滤，比方原文本为html的文本，须要去掉html标签： html_strip1.2 Tokenizer(分词器)按某种规定(比方空格) 对输出(Character Filter解决完的文本)进行切分1.3 Token Filter(分词过滤器)对Tokenizer切分后的准term进行二次加工，比方大写->小写，stop word过滤(跑去in、the等)二、Analyzer测试分词2.1 指定analyzer测试分词2.1.1 standard analyzerTokenizer: Standard Tokenize 基于unicode文本宰割，适于大多数语言Token Filter: Lower Case Token Filter/Stop Token Filter(默认禁用) LowerCase Token Filter: 过滤后，变小写-->所以standard默认分词后的搜寻匹配是小写Stop Token Filter(默认禁用) -->停用词：分词后索引里会抛弃的GET _analyze{ "analyzer": "standard", "text": "#!#For example, UUU you can see 27 accounts in ID (Idaho)."}2.1.2 standard后果可见全小写数字还在没有stop word(默认敞开的){ "tokens" : [ { "token" : "for", "start_offset" : 3, "end_offset" : 6, "type" : "<ALPHANUM>", "position" : 0 }, { "token" : "example", "start_offset" : 7, "end_offset" : 14, "type" : "<ALPHANUM>", "position" : 1 }, { "token" : "uuu", "start_offset" : 16, "end_offset" : 19, "type" : "<ALPHANUM>", "position" : 2 }, { "token" : "you", "start_offset" : 20, "end_offset" : 23, "type" : "<ALPHANUM>", "position" : 3 }, { "token" : "can", "start_offset" : 24, "end_offset" : 27, "type" : "<ALPHANUM>", "position" : 4 }, { "token" : "see", "start_offset" : 28, "end_offset" : 31, "type" : "<ALPHANUM>", "position" : 5 }, { "token" : "27", "start_offset" : 32, "end_offset" : 34, "type" : "<NUM>", "position" : 6 }, { "token" : "accounts", "start_offset" : 35, "end_offset" : 43, "type" : "<ALPHANUM>", "position" : 7 }, { "token" : "in", "start_offset" : 44, "end_offset" : 46, "type" : "<ALPHANUM>", "position" : 8 }, { "token" : "id", "start_offset" : 47, "end_offset" : 49, "type" : "<ALPHANUM>", "position" : 9 }, { "token" : "idaho", "start_offset" : 51, "end_offset" : 56, "type" : "<ALPHANUM>", "position" : 10 } ]}2.2 其余analyzerstandardstop stopword剔除simplewhitespace 只用空白符宰割，不剔除keyword 残缺文本，不分词2.3 指定Tokenizer和Token Filter测试分词2.3.1 应用standard雷同的Tokenizer和Filter后面一节说：standard analyzer应用的Tokenizer是standard Tokenizer 应用的filter是lowercase, 咱们通过应用tokenizer和filter来替换analyzer试试： ...

[toc] 一. 数据筹备当初有上面几个index：books，my_index, users 1.books索引内容：_index_type_id▲_scorebookIdbookNameauthor books_doc111Thinking in JavaBruce Eckel books_doc212中国通史吕思勉 books_doc313中国通史吕思勉 books_docNCvOY4ABRxSL2QPxNNHf14中国通史4吕思勉 books_docNSvOY4ABRxSL2QPxPdEy14中国通史4吕思勉 books_docNivOY4ABRxSL2QPxQdEo14中国通史4吕思勉 books_docNyvOY4ABRxSL2QPxRNHC14中国通史4吕思勉 books_docOCvOY4ABRxSL2QPxSdHA14中国通史4吕思勉 books_docOSvOY4ABRxSL2QPxTNHb14中国通史4吕思勉 books_docOivOY4ABRxSL2QPxYNGs14鲁迅散文鲁迅 2. my_index索引内容：_index_type_id_scorefirstNamelastNamemy_index_doc11niewjmy_index_doc21 3.users索引内容_index_type_id_scorenameagegenderbirthusers_doc11niewj36male1985-01-01users_doc21nie26female1995-02-02users_doc31lifubo33male users_doc41weibinbin32male 二、bulk、mget、msearch的API测试demo1. bulk执行多个索引的index/create/update/delete1.1 测试执行需要users中的gender改为"male";my_index中减少"张大千"应用index；"李太白,杜甫"的两条记录应用createbooks中删除bookId=4的记录1.2 执行脚本POST _bulk{"delete":{"_index":"books","_id":"NSvOY4ABRxSL2QPxPdEy"}}{"delete":{"_index":"books","_id":"NivOY4ABRxSL2QPxQdEo"}}{"delete":{"_index":"books","_id":"NyvOY4ABRxSL2QPxRNHC"}}{"delete":{"_index":"books","_id":"OCvOY4ABRxSL2QPxSdHA"}}{"delete":{"_index":"books","_id":"OSvOY4ABRxSL2QPxTNHb"}}{"delete":{"_index":"books","_id":"OivOY4ABRxSL2QPxYNGs"}}{"delete":{"_index":"books","_id":"NCvOY4ABRxSL2QPxNNHf"}}{"index":{"_index":"my_index","_id":"3"}}{"firstName":"张","lastName":"大千"}{"create":{"_index":"my_index","_id":"4"}}{"firstName":"李","lastName":"太白"}{"create":{"_index":"my_index","_id":"5"}}{"firstName":"杜","lastName":"甫"}{"update":{"_index":"users","_id":"2"}}{"doc":{"gender":"male"}}1.3 留神delete不能间接指定：bookId:41.4 执行后果：{ "took" : 68, "errors" : false, "items" : [ { "delete" : { "_index" : "books", "_type" : "_doc", "_id" : "NSvOY4ABRxSL2QPxPdEy", "_version" : 2, "result" : "deleted", "_shards" : { "total" : 2, "successful" : 1, "failed" : 0 }, "_seq_no" : 12, "_primary_term" : 1, "status" : 200 } }, { "delete" : { "_index" : "books", "_type" : "_doc", "_id" : "NivOY4ABRxSL2QPxQdEo", "_version" : 2, "result" : "deleted", "_shards" : { "total" : 2, "successful" : 1, "failed" : 0 }, "_seq_no" : 13, "_primary_term" : 1, "status" : 200 } }, { "delete" : { "_index" : "books", "_type" : "_doc", "_id" : "NyvOY4ABRxSL2QPxRNHC", "_version" : 2, "result" : "deleted", "_shards" : { "total" : 2, "successful" : 1, "failed" : 0 }, "_seq_no" : 14, "_primary_term" : 1, "status" : 200 } }, { "delete" : { "_index" : "books", "_type" : "_doc", "_id" : "OCvOY4ABRxSL2QPxSdHA", "_version" : 2, "result" : "deleted", "_shards" : { "total" : 2, "successful" : 1, "failed" : 0 }, "_seq_no" : 15, "_primary_term" : 1, "status" : 200 } }, { "delete" : { "_index" : "books", "_type" : "_doc", "_id" : "OSvOY4ABRxSL2QPxTNHb", "_version" : 2, "result" : "deleted", "_shards" : { "total" : 2, "successful" : 1, "failed" : 0 }, "_seq_no" : 16, "_primary_term" : 1, "status" : 200 } }, { "delete" : { "_index" : "books", "_type" : "_doc", "_id" : "OivOY4ABRxSL2QPxYNGs", "_version" : 2, "result" : "deleted", "_shards" : { "total" : 2, "successful" : 1, "failed" : 0 }, "_seq_no" : 17, "_primary_term" : 1, "status" : 200 } }, { "delete" : { "_index" : "books", "_type" : "_doc", "_id" : "NCvOY4ABRxSL2QPxNNHf", "_version" : 3, "result" : "deleted", "_shards" : { "total" : 2, "successful" : 1, "failed" : 0 }, "_seq_no" : 18, "_primary_term" : 1, "status" : 200 } }, { "index" : { "_index" : "my_index", "_type" : "_doc", "_id" : "3", "_version" : 1, "result" : "created", "_shards" : { "total" : 2, "successful" : 1, "failed" : 0 }, "_seq_no" : 3, "_primary_term" : 1, "status" : 201 } }, { "create" : { "_index" : "my_index", "_type" : "_doc", "_id" : "4", "_version" : 1, "result" : "created", "_shards" : { "total" : 2, "successful" : 1, "failed" : 0 }, "_seq_no" : 4, "_primary_term" : 1, "status" : 201 } }, { "create" : { "_index" : "my_index", "_type" : "_doc", "_id" : "5", "_version" : 1, "result" : "created", "_shards" : { "total" : 2, "successful" : 1, "failed" : 0 }, "_seq_no" : 5, "_primary_term" : 1, "status" : 201 } }, { "update" : { "_index" : "users", "_type" : "_doc", "_id" : "2", "_version" : 10, "result" : "updated", "_shards" : { "total" : 2, "successful" : 1, "failed" : 0 }, "_seq_no" : 15, "_primary_term" : 4, "status" : 200 } } ]}1.5 执行完后果： users_index_type_id_scorefirstNamelastNamemy_index_doc11niewjmy_index_doc21 my_index_doc31张大千my_index_doc41李太白my_index_doc51杜甫1.6 执行完后果： my_index_index_type_id_scorenameagegenderbirthusers_doc11niewj36male1985-01-01users_doc31lifubo33male users_doc41weibinbin32male users_doc21nie26male1995-02-021.7 执行完后果： books_index_type_id▲_scorebookIdbookNameauthor books_doc111Thinking in JavaBruce Eckelbooks_doc212中国通史吕思勉books_doc313中国通史吕思勉2. mget:get多个索引中的文档2.1 mget的脚本GET _mget{"docs":[ {"_index":"users","_id":"1"}, {"_index":"books","_id":"1"}, {"_index":"my_index","_id":"1"} ]}2.2 mget后果：{ "docs" : [ { "_index" : "users", "_type" : "_doc", "_id" : "1", "_version" : 1, "_seq_no" : 12, "_primary_term" : 4, "found" : true, "_source" : { "name" : "niewj", "age" : 36, "gender" : "male", "birth" : "1985-01-01" } }, { "_index" : "books", "_type" : "_doc", "_id" : "1", "_version" : 2, "_seq_no" : 1, "_primary_term" : 1, "found" : true, "_source" : { "bookId" : "1", "bookName" : "Thinking in Java", "author" : "Bruce Eckel" } }, { "_index" : "my_index", "_type" : "_doc", "_id" : "1", "_version" : 1, "_seq_no" : 0, "_primary_term" : 1, "found" : true, "_source" : { "firstName" : "nie", "lastName" : "wj" } } ]}3.msearch批量查问多个索引3.1 查问多个索引的多个条件（缩小IO次数）POST _msearch{"index":"movies"}{"query":{"match_all":{}},"from":0,"size":3}{"index":"users"}{"query":{"match_phrase":{"name.keyword":"nie"}}}{"index":"books"}{"query":{"match":{"bookName":"中国通史"}}}3.2 查问后果{ "took" : 0, "responses" : [ { "took" : 0, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 9743, "relation" : "eq" }, "max_score" : 1.0, "hits" : [ { "_index" : "movies", "_type" : "_doc", "_id" : "982", "_score" : 1.0, "_source" : { "id" : "982", "genre" : [ "Drama" ], "title" : "Picnic", "@version" : "1", "year" : 1955 } }, { "_index" : "movies", "_type" : "_doc", "_id" : "984", "_score" : 1.0, "_source" : { "id" : "984", "genre" : [ "Comedy", "Drama" ], "title" : "Pompatus of Love, The", "@version" : "1", "year" : 1996 } }, { "_index" : "movies", "_type" : "_doc", "_id" : "986", "_score" : 1.0, "_source" : { "id" : "986", "genre" : [ "Adventure", "Children" ], "title" : "Fly Away Home", "@version" : "1", "year" : 1996 } } ] }, "status" : 200 }, { "took" : 0, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 1, "relation" : "eq" }, "max_score" : 0.87546873, "hits" : [ { "_index" : "users", "_type" : "_doc", "_id" : "2", "_score" : 0.87546873, "_source" : { "name" : "nie", "age" : 26, "gender" : "male", "birth" : "1995-02-02" } } ] }, "status" : 200 }, { "took" : 0, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 2, "relation" : "eq" }, "max_score" : 1.8126037, "hits" : [ { "_index" : "books", "_type" : "_doc", "_id" : "2", "_score" : 1.8126037, "_source" : { "bookId" : "2", "bookName" : "中国通史", "author" : "吕思勉" } }, { "_index" : "books", "_type" : "_doc", "_id" : "3", "_score" : 1.8126037, "_source" : { "bookId" : "3", "bookName" : "中国通史", "author" : "吕思勉" } } ] }, "status" : 200 } ]}每个索引的查问都返回了本人的后果

[toc]一. 查看集群衰弱、节点、分片1.查看集群衰弱度GET _cluster/health { "cluster_name" : "elasticsearch", "status" : "yellow", "timed_out" : false, "number_of_nodes" : 1, "number_of_data_nodes" : 1, "active_primary_shards" : 8, "active_shards" : 8, "relocating_shards" : 0, "initializing_shards" : 0, "unassigned_shards" : 3, "delayed_unassigned_shards" : 0, "number_of_pending_tasks" : 0, "number_of_in_flight_fetch" : 0, "task_max_waiting_in_queue_millis" : 0, "active_shards_percent_as_number" : 72.72727272727273}2.查看节点信息GET _cat/nodes 127.0.0.1 21 95 3 0.00 0.03 0.05 dilm * niewj3. 查看分片信息GET _cat/shards .security-7 0 p STARTED 42 80.1kb 127.0.0.1 niewj.apm-agent-configuration 0 p STARTED 0 283b 127.0.0.1 niewjusers 0 p STARTED 4 5.3kb 127.0.0.1 niewjusers 0 r UNASSIGNED movies 0 p STARTED 9743 1.3mb 127.0.0.1 niewjmovies 0 r UNASSIGNED .kibana_1 0 p STARTED 88 101.9kb 127.0.0.1 niewjkibana_sample_data_flights 0 p STARTED 13059 6.3mb 127.0.0.1 niewj.kibana_task_manager_1 0 p STARTED 2 12.5kb 127.0.0.1 niewjmy_index 0 p STARTED 2 10kb 127.0.0.1 niewjmy_index 0 r UNASSIGNED 二. index、create、update文档4. index一个文档4.1 第一次indexPUT books/_doc/1{ "bookId":"1", "bookName":"Thinking in Java", "price": 99.99}后果： ...

大家好，我是咔咔 不期速成，日拱一卒 ElasticSearch致力于搜寻的同时，也提供了聚合实时剖析数据的性能，聚合能够实现把简单的数据进行一系列计算后得出咱们想要的数据。 尽管聚合的性能与搜寻齐全不同，但应用的数据结构是完全相同的，因而聚合的执行速度很快，也就是说在一次申请中对雷同数据能够同时进行搜寻+过滤、剖析。 在ElasticSearch中聚合共分为四大类： Bucket Aggregation：分桶类型，一些列满足特定条件的文档汇合Metric Aggregation：指标剖析类型，对数据进行数学运算，例如求最大、小值Pipeline Aggregation：管道剖析类型，曾经聚合的后果进行二次聚合Matix Aggregation：矩阵剖析类型，反对对多个字段操作并提供一个后果矩阵先从简开始，看一下Bucket、Metric这两种类型，Bucket实现的后果就是MySQL中group关键字的应用，Metric则是MySQL中max、min函数的应用。 一、Buckert Aggregation介绍 通过上图可得悉将数据分为了三个桶，第一个桶统计的是身高小于300，第二个桶统计的是身高大于600，第三个桶统计的是身高在300到600之间的，在这个案例中就是依据不同的身高分到不同的桶中。 应用聚合剖析机制还能够依照年龄、地理位置、性别、薪资范畴、订单增长状况、工作岗位散布等。只有有肯定共同点的数据都可应用聚合进行归档解决。 常见的Bucket分桶策略 terms：依照term来分桶，如果是text类型则会依照分词后的后果进行分桶range：指定数值的范畴来设定分桶规定data range：指定日期的范畴来设定分桶规定histogram：固定的距离来来设定分桶规定data histogram：针对日期的直方图或柱状图Terms依据目的地进行分桶 post /kibana_sample_data_flights/_search{ "size":0, "aggs":{ "destcountry_term":{ "terms": { "field": "DestCountry" }}}, "profile":"true"}从返回后果中看到依据目的地将航班信息进行了归类解决，同时也会发现在ElasticSearch中如果不手动定义size值都会默认只返回10条后果 "aggregations" : { "destcountry_term" : { "doc_count_error_upper_bound" : 0, "sum_other_doc_count" : 3187, "buckets" : [ { "key" : "IT", "doc_count" : 2371 }, { "key" : "US", "doc_count" : 1987 }, { "key" : "CN", "doc_count" : 1096 }, { "key" : "CA", "doc_count" : 944 }, { "key" : "JP", "doc_count" : 774 }, { "key" : "RU", "doc_count" : 739 }, { "key" : "CH", "doc_count" : 691 }, { "key" : "GB", "doc_count" : 449 }, { "key" : "AU", "doc_count" : 416 }, { "key" : "PL", "doc_count" : 405 } ]}}Range想要查问平均价格在300以下、300~600之间、大于600的案例 ...