Elasticsearch 的使用

一、简介 

1.Shard(分片)
数据分散集群的架构模式,Elasticsearch 将一个 Index(索引)中的数据切为多个 Shard(分片),分布在不同服务器节点上。
默认每个索引会分配5个主分片和1个副本分片,可根据需要调整主分片和副本分片的数量。

2.Replica(副本)
主从架构模式,每个Shard(分片)创建多个备份——Replica副本,保证数据不丢失。

1.主分片和副本分片数量的调整
PUT /my-index/_settings
{
  "number_of_shards": 3,
  "number_of_replicas": 2
}

2.新建索引时设置分片
PUT /my-index
{
  "settings": {
    "number_of_shards": 3,
    "number_of_replicas": 2
  }
}

1.1、数据类型 

1.1.1、常见数据类型 

字符串型:text(分词)、keyword(不分词)

数值型:long、integer、short、byte、double、float、half_float、scaled_float

日期类型:date

布尔类型:boolean

二进制类型:binary

范围类型:integer_range、float_range、long_range、double_range、date_range

1.1.2、复杂数据类型 

数组类型:array

对象类型:object

嵌套类型:nested object

1.1.3、特殊数据类型 

地理位置数据类型:geo_point(点)、geo_shape(形状) 

记录IP地址ip 

实现自动补全completion 

记录分词数:token_count 

记录字符串hash值murmur3 

多字段特性multi-fields 

1.2、工作流程 

1.2.1、路由 

ES采用 hash 路由算法,对 document 的 id 标识进行计算,产生 shard 序号,通过序号可立即确定shard。

1.2.2、写入流程 

1.A节点接到请求,计算路由,转发"对应节点"。
2."对应节点"处理完数据后,数据同步到副本节点。
3.A节点收到"对应节点"的响应,将结果返回给调用者。

1.2.3、读取流程 

1.协调节点接到请求,计算路由,用round-robin算法,在对应的primary shard及其所有replica中随机选择一个发送请求。
3.协调节点收到"对应节点"的响应,将结果返回给调用者。

二、工作原理

2.1、到排序索引 

到排序索引

2.2、分词器 

ES内置分词器:standard analyzer、simple analyzer、whitespace analyzer、language analyzer

对于document中的不同字段类型,ES采用不同的分词器进行处理,如date类型不会分词要完全匹配,text类型会分词。

2.2.1、常用的中文分词器:IK分词器 

7.6.0版本的IK:https://github.com/medcl/elasticsearch-analysis-ik/releases
解压缩放到YOUR_ES_ROOT/plugins/ik/目录下,重启Elasticsearch即可。 

1、IK分词器的两种分词模式(一般用 ik_max_word) 

ik_max_word:会将文本做最细粒度的拆分,比如会将“中华人民共和国国歌”拆分为“中华人民共和国,中华人民,中华,华人,人民共和国,人民,人,民,共和国,共和,和,国国,国歌”等等,会穷尽各种可能的组合。
ik_smart:只做最粗粒度的拆分,比如会将“中华人民共和国国歌”拆分为“中华人民共和国,国歌”。

PUT /my_index 
{
  "mappings": {
	  "properties": {
		"text": {
		  "type": "text",
		  "analyzer": "ik_max_word"
		}
	  }
  }
}

2、配置文件 

IK的配置文件存在于YOUR_ES_ROOT/plugins/ik/config目录下

main.dic: IK原生内置的中文词库,总共有27万多条,只要是这些单词,都会被分在一起;
quantifier.dic: 放了一些单位相关的词;
suffix.dic: 放了一些后缀;
surname.dic: 中国的姓氏;
stopword.dic: 英文停用词。 

2.3、数据同步机制 

one、all、quorum(默认),可在请求时带上consistency参数表明采用哪种模式。

one 模式 
有一个primary shard是active活跃可用,操作算成功。

all 模式 
必须所有的primary shard和replica shard都是活跃的,操作算成功。

quorum 模式 
确保大多数shard可用,不满足条件时,会默认等1分钟,超间就报timeout错,可在写时加timeout
PUT /index/type/id?timeout=30

2.4、数据持久化策略 

1.数据先写入 in-memory buffer(应用内存)中,同时写入 translog 日志文件(日志内存每5秒刷到磁盘)。
2.每隔1秒,ES会执行一次 refresh 操作:将buffer中的数据refresh到filesystem cache的(os cache系统内存)中的segment file中(可被检索到)。
3 每隔30分钟将内存数据flush到磁盘,或者translog大到一定程度时,会触发 flush 操作。
可设置index的index.translog.durability参数,使每次写入一条数据,都写入buffer,同时fsync写入磁盘上的translog文件。

三、使用 

2.1、语法规则 

2.2、ES的 DSL 语法

1、创建索引(HTTP请求)
shopping:索引名称

# 1.创建索引(等于创建数据库,PUT请求)
http://127.0.0.1:9200/shopping

# 2.获取索引(GET请求)
http://127.0.0.1:9200/shopping

# 3.删除索引(DELETE请求)
http://127.0.0.1:9200/shopping

2、文档数据的创建

# 1.往索引里新增数据(不自定义ID:POST,传JSON)
http://127.0.0.1:9200/shopping/_doc/

# 2.往索引里新增数据(自定义ID:POST、PUT,传JSON)
http://127.0.0.1:9200/shopping/_doc/123
http://127.0.0.1:9200/shopping/_create/123

3.修改

# 1.全量修改(PUT、POST)
http://127.0.0.1:9200/shopping/_doc/123
{
    "name":"haige",
    "age",123
}
# 2.局部修改(POST)
http://127.0.0.1:9200/shopping/_update/123
{
    "doc" :{
         "name":"haige",
    }
}

4、主键查询 & 全查询

# 查询主键单数据(GET)
http://127.0.0.1:9200/shopping/_doc/123
# 查询全部数据(GET)
http://127.0.0.1:9200/shopping/_search

5.多条件查询,范围查询

http://127.0.0.1:9200/shopping/_search
{
    "query":{
        "bool" :{
            "should" :[
                {
                    "match" :{
                        "name":"测试"
                    }
                }
            ],
            "filter" :{
                "range":{
                    "age":{
                        "gt" : 20
                    }
                }
            }
        }
    }
}

6.分页查询、排序(且只显示name字段)

http://127.0.0.1:9200/shopping/_search
{
    "query":{
        "match":{
            "name":"哈喽"
        }
    },
    "from":0,
    "size":2,
    "_source" : ["name"],
    "sort" : {
        "age" : {
            "order" : "desc"
        }
    }
}

2.3、org.elasticsearch.client 客户端 

2.3.1、引入依赖 

<dependency>
    <groupId>org.elasticsearch.client</groupId>
	<artifactId>elasticsearch-rest-high-level-client</artifactId>
	<version>7.5.0</version>
	<exclusions>
		<exclusion>
			<groupId>org.elasticsearch</groupId>
			<artifactId>elasticsearch</artifactId>
		</exclusion>
		<exclusion>
			<groupId>org.elasticsearch.client</groupId>
			<artifactId>elasticsearch-rest-client</artifactId>
		</exclusion>
	</exclusions>
</dependency>

<dependency>
	<groupId>org.elasticsearch.client</groupId>
	<artifactId>elasticsearch-rest-client</artifactId>
	<version>7.5.0</version>
</dependency>
<dependency>
	<groupId>org.elasticsearch</groupId>
	<artifactId>elasticsearch</artifactId>
	<version>7.5.0</version>
</dependency>

 2.3.2、SearchRequest 、SearchSourceBuilder 、QueryBuilder 、SearchResponse 、SearchHit组件常用设置

public static void testRequest()throws Exception{
	// 创建请求对象,设置查询多个文档库,也可指定单个文档库。
	SearchRequest request = new SearchRequest("index01","index02","index03");
	// 也可通过 indices 方法指定文档库中
	request.indices("posts01","posts02", "posts03");
	// 设置指定查询的路由分片
	request.routing("routing");
	// 指定优先去某个分片上去查询(默认的是随机先去某个分片)
	request.preference("_local");
	// 设置缓存
	request.requestCache();
	// 取出查询语句
	request.toString();
}
 
public static void testSource()throws Exception{
	//创建源
	SearchSourceBuilder source= new SearchSourceBuilder();
	// 第几页
	source.from(0);
	// 每页多少条数据(默认是10条)
	source.size(100);
	// 设置排序规则
	source.sort(new ScoreSortBuilder().order(SortOrder.DESC));
	source.sort(new FieldSortBuilder("id").order(SortOrder.ASC));
	//获取的字段(列)和不需要获取的列
	String[] includeFields = new String[]{"birthday","name"};
	String[] excludeFields = new String[]{"age","address"};
	source.fetchSource(includeFields,excludeFields);
	// 设置超时时间
	source.timeout(new TimeValue(60, TimeUnit.SECONDS));
	source.highlighter();// 高亮
	source.aggregation(AggregationBuilders.terms("by_company"));// 聚合
	//分词查询
	source.profile(true);
	source.query();
}
 
public static void testBuilder()throws Exception{
	//全匹配(查出全部)
	MatchAllQueryBuilder matchAllQuery = QueryBuilders.matchAllQuery();
	//匹配查询
	MatchQueryBuilder matchQuery = QueryBuilders.matchQuery("","").analyzer("");
	//匹配文本查询
	MatchPhraseQueryBuilder matchPhraseQuery = QueryBuilders.matchPhraseQuery("","");
	//匹配文本前缀查询
	MatchPhrasePrefixQueryBuilder matchPhrasePrefixQuery = QueryBuilders.matchPhrasePrefixQuery("","");
	//判断莫子是否有值(String)
	ExistsQueryBuilder existsQuery = QueryBuilders.existsQuery("");
	//前缀查询
	PrefixQueryBuilder prefixQuery = QueryBuilders.prefixQuery("","");
	//精确查询
	TermQueryBuilder termQuery = QueryBuilders.termQuery("","");
	//范围查询
	RangeQueryBuilder rangeQuery = QueryBuilders.rangeQuery("birthday").from("2016-01-01 00:00:00");
	QueryStringQueryBuilder queryBuilder009 = QueryBuilders.queryStringQuery("");
	QueryBuilders.disMaxQuery();
 
	HighlightBuilder highlightBuilder = new HighlightBuilder();
	HighlightBuilder.Field highlightTitle =
			new HighlightBuilder.Field("title");
	highlightTitle.highlighterType("unified");
	highlightBuilder.field(highlightTitle);
	HighlightBuilder.Field highlightUser = new HighlightBuilder.Field("user");
	highlightBuilder.field(highlightUser);
 
	// 组合器
	BoolQueryBuilder builder = QueryBuilders.boolQuery();
	//过滤
	builder.filter();
	//且
	builder.must();
	//非
	builder.mustNot();
	//或
	builder.should();
}

public static void testResponse()throws Exception {
	RestHighLevelClient client = new RestHighLevelClient(
			RestClient.builder(new HttpHost("127.0.0.1", 9200, "http")));
	SearchRequest searchRequest = new SearchRequest("user");
	// 同步
	SearchResponse response = client.search(searchRequest, RequestOptions.DEFAULT);
	RestStatus status = response.status();
	TimeValue took = response.getTook();
	Boolean terminatedEarly = response.isTerminatedEarly();
	boolean timedOut = response.isTimedOut();
	int totalShards = response.getTotalShards();
	int successfulShards = response.getSuccessfulShards();
	int failedShards = response.getFailedShards();
	for (ShardSearchFailure failure : response.getShardFailures()) {
		// failures should be handled here
	}
	// 异步
	ActionListener<SearchResponse> listener = new ActionListener<SearchResponse>() {
		@Override
		public void onResponse(SearchResponse searchResponse) {
		}
		@Override
		public void onFailure(Exception e) {
		}
	};
	client.searchAsync(searchRequest, RequestOptions.DEFAULT, listener);
}


public static void testHits()throws Exception {
	RestHighLevelClient client = new RestHighLevelClient(
			RestClient.builder(new HttpHost("127.0.0.1", 9200, "http")));
	SearchRequest searchRequest = new SearchRequest("user");
	// 同步
	SearchResponse response = client.search(searchRequest, RequestOptions.DEFAULT);
 
	SearchHits hits = response.getHits();
	TotalHits totalHits = hits.getTotalHits();
	//总数
	long numHits = totalHits.value;
	//
	TotalHits.Relation relation = totalHits.relation;
	float maxScore = hits.getMaxScore();
	SearchHit[] searchHits = hits.getHits();
	for (SearchHit hit : searchHits) {
		String index = hit.getIndex();
		String id = hit.getId();
		float score = hit.getScore();
		String sourceAsString = hit.getSourceAsString();
		Map<String, Object> sourceAsMap = hit.getSourceAsMap();
		String documentTitle = (String) sourceAsMap.get("title");
		List<Object> users = (List<Object>) sourceAsMap.get("user");
		Map<String, Object> innerObject =
				(Map<String, Object>) sourceAsMap.get("innerObject");
	}
	// 高亮获取
	for (SearchHit hit : response.getHits()) {
		Map<String, HighlightField> highlightFields = hit.getHighlightFields();
		HighlightField highlight = highlightFields.get("title");
		Text[] fragments = highlight.fragments();
		String fragmentString = fragments[0].string();
	}
	// 获取聚合结果
	Aggregations aggregations = response.getAggregations();
	Terms byCompanyAggregation = aggregations.get("by_company");
	Terms.Bucket elasticBucket = byCompanyAggregation.getBucketByKey("Elastic");
	Avg averageAge = elasticBucket.getAggregations().get("average_age");
	double avg = averageAge.getValue();
 
	// 获取大量聚合结果
	Map<String, Aggregation> aggregationMap = aggregations.getAsMap();
	Terms companyAggregation = (Terms) aggregationMap.get("by_company");
	List<Aggregation> aggregationList = aggregations.asList();
	for (Aggregation agg : aggregations) {
		String type = agg.getType();
		   if (type.equals(TermsAggregationBuilder.NAME)) {
			 Terms.Bucket elasticBucket2 = ((Terms) agg).getBucketByKey("Elastic");
			 long numberOfDocs = elasticBucket2.getDocCount();
		   }
		}
}

2.3.3、 增删改

//单条增
public static void addDocment()throws Exception{
	RestHighLevelClient client = new RestHighLevelClient(
			RestClient.builder(
					new HttpHost("127.0.0.1", 9200, "http")));

	//Map提供供文档源
	Map<String, Object> jsonMap = new HashMap<>();
	jsonMap.put("name", "小红");
	jsonMap.put("sex", "女");
	jsonMap.put("age", 22);
	jsonMap.put("birthDay", new Date());
	jsonMap.put("message", "测试");
	IndexRequest indexRequest1 = new IndexRequest("user2", "doc", "5")
			.source(jsonMap);
	// 同步执行
	IndexResponse indexResponse1 =client.index(indexRequest1,RequestOptions.DEFAULT);
	client.close();


	//XContentBuilder提供供文档源
	XContentBuilder builder = XContentFactory.jsonBuilder();
	builder.startObject();
	{
		builder.field("name", "South");
		builder.timeField("birthDay", new Date());
		builder.field("message", "第二个小demo");
	}
	builder.endObject();
	IndexRequest indexRequest2 = new IndexRequest("user", "doc", "2")
			.source(builder);
	// 同步执行
	IndexResponse indexResponse2 =client.index(indexRequest2,RequestOptions.DEFAULT);
	String index = indexResponse1.getIndex();
	String type = indexResponse1.getType();
	String id = indexResponse1.getId();
	long version = indexResponse1.getVersion();
	RestStatus restStatus = indexResponse1.status();
	DocWriteResponse.Result result = indexResponse1.getResult();
	ReplicationResponse.ShardInfo shardInfo = indexResponse1.getShardInfo();
	client.close();
}

//删
public void deleteTest()throws Exception{
	RestHighLevelClient client = new RestHighLevelClient(RestClient.builder(
			new HttpHost("127.0.0.1", 9200, "http")));
	DeleteRequest request = new DeleteRequest("posts","1");
	DeleteResponse deleteResponse = client.delete(request, RequestOptions.DEFAULT);
}

//单个改
public static void updateDocment()throws Exception{
	RestHighLevelClient client = new RestHighLevelClient(
			RestClient.builder(
					new HttpHost("127.0.0.1", 9200, "http")));
	Map<String, Object> jsonMap = new HashMap<>();
	jsonMap.put("name", "JunSouth");
	UpdateRequest updateRequest = new UpdateRequest("user","doc","6").doc(jsonMap);
	UpdateResponse updateResponse  =client.update(updateRequest,RequestOptions.DEFAULT);
	String index = updateResponse.getIndex();
	String type = updateResponse.getType();
	String id = updateResponse.getId();
	long version = updateResponse.getVersion();
	System.out.println("index:"+index+"  type:"+type+"   id:"+id+"   version:"+version);
	if(updateResponse.getResult() == DocWriteResponse.Result.CREATED) {
		System.out.println("文档已创建");
	}else if(updateResponse.getResult() == DocWriteResponse.Result.UPDATED) {
		System.out.println("文档已更新");
	}else if(updateResponse.getResult() == DocWriteResponse.Result.DELETED) {
		System.out.println("文档已删除");
	}else if(updateResponse.getResult() == DocWriteResponse.Result.NOOP) {
		System.out.println("文档不受更新的影响");
	}
	client.close();
}
 
 
//批量操作
public static void bulkDocment()throws Exception{
	RestHighLevelClient client = new RestHighLevelClient(
			RestClient.builder(
					new HttpHost("127.0.0.1", 9200, "http")));
	BulkRequest bulkRequest = new BulkRequest();
	bulkRequest.add(new IndexRequest("user","doc","5")
			.source(XContentType.JSON,"name", "test")); // 将第一个 IndexRequest 添加到批量请求中
	bulkRequest.add(new IndexRequest("user","doc","6")
			.source(XContentType.JSON,"name","test")); // 第二个
	BulkResponse bulkResponse = client.bulk(bulkRequest,RequestOptions.DEFAULT);
	boolean falgs = bulkResponse.hasFailures();    // true 表示至少有一个操作失败
	System.out.println("falgs: "+falgs);
	for (BulkItemResponse bulkItemResponse : bulkResponse) { // 遍历所有的操作结果
		DocWriteResponse itemResponse = bulkItemResponse.getResponse(); // 获取操作结果的响应,可以是 IndexResponse,UpdateResponse or DeleteResponse,它们都可以惭怍是 DocWriteResponse 实例。
		if (bulkItemResponse.getOpType() == DocWriteRequest.OpType.INDEX || bulkItemResponse.getOpType() == DocWriteRequest.OpType.CREATE) {
			IndexResponse indexResponse = (IndexResponse) itemResponse;
			System.out.println("index 操作后的响应结果");
		}else if(bulkItemResponse.getOpType() == DocWriteRequest.OpType.UPDATE) {
			UpdateResponse updateResponse = (UpdateResponse) itemResponse;
			System.out.println("update 操作后的响应结果");
		}else if(bulkItemResponse.getOpType() == DocWriteRequest.OpType.DELETE) {
			DeleteResponse deleteResponse = (DeleteResponse) itemResponse;
			System.out.println("delete 操作后的响应结果");
		}
	}
	for (BulkItemResponse bulkItemResponse : bulkResponse) {
		if (bulkItemResponse.isFailed()) {                                      // 检测给定的操作是否失败
			BulkItemResponse.Failure failure = bulkItemResponse.getFailure();
			System.out.println("获取失败信息: "+failure);
		}
	}
	client.close();
}

2.3.4、查

//查询某索引下全部数据
public static void searchAll()throws Exception{
        RestHighLevelClient client = new RestHighLevelClient(
                RestClient.builder(new HttpHost("127.0.0.1", 9200, "http")));
        SearchRequest searchRequest = new SearchRequest("user");  // 设置搜索的 index 。
        QueryBuilder queryBuilder = QueryBuilders.matchAllQuery();
        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
        searchSourceBuilder.query(queryBuilder); //设置搜索,可以是任何类型的 QueryBuilder.
        searchRequest.source(searchSourceBuilder);
        SearchResponse searchResponse = client.search(searchRequest,RequestOptions.DEFAULT);
        SearchHits hits = searchResponse.getHits();
        float maxScore = hits.getMaxScore();
        for (SearchHit hit : hits.getHits()) {
            System.out.println("hit: "+hit);
            String sourceAsString = hit.getSourceAsString();
            Map<String, Object> sourceAsMap = hit.getSourceAsMap();
            String name = (String) sourceAsMap.get("name");
            System.out.println("name: "+name);
        }
        client.close();
 
        //匹配查询器
       QueryBuilder matchQueryBuilder = QueryBuilders.matchQuery("user", "kimchy")
                                                .fuzziness(Fuzziness.AUTO)
                                                .prefixLength(3)
                                                .maxExpansions(10);
        searchSourceBuilder.query(matchQueryBuilder);
 
        //高亮
        HighlightBuilder highlightBuilder = new HighlightBuilder();
        HighlightBuilder.Field highlightTitle = new HighlightBuilder.Field("name"); // title 字段高亮
        highlightTitle.highlighterType("unified");  // 配置高亮类型
        highlightBuilder.field(highlightTitle);  // 添加到 builder
        HighlightBuilder.Field highlightUser = new HighlightBuilder.Field("user");
        highlightBuilder.field(highlightUser);
        searchSourceBuilder.highlighter(highlightBuilder);
}

//普通条件查询
public static void search01()throws Exception{
        RestHighLevelClient client = new RestHighLevelClient(
                RestClient.builder(new HttpHost("127.0.0.1", 9200, "http")));
        SearchRequest searchRequest = new SearchRequest("user");  // 设置搜索的 index 。
        // 查询器
        QueryBuilder queryBuilder01 = QueryBuilders.termQuery("name", "test"); //完全匹配
        QueryBuilder queryBuilder02 =QueryBuilders.fuzzyQuery("name", "t");    //模糊查询
        QueryBuilder queryBuilder03 =QueryBuilders.prefixQuery("name", "小"); //前缀查询
        QueryBuilder queryBuilder04 =QueryBuilders.matchQuery("name", "小");    //匹配查询
        WildcardQueryBuilder queryBuilder = QueryBuilders.wildcardQuery("name","*jack*");//搜索名字中含有jack文档(name中只要包含jack即可)
 
        // 搜索器(排序、分页...)。
        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
        searchSourceBuilder.query(queryBuilder04);   // 设置搜索条件
        searchSourceBuilder.from(0); // 起始 index
        searchSourceBuilder.size(5); // 大小 size
      //  searchSourceBuilder.timeout(new TimeValue(60, TimeUnit.SECONDS)); // 设置搜索的超时时间
      //  searchSourceBuilder.sort(new ScoreSortBuilder().order(SortOrder.DESC)); // 根据分数 _score 降序排列 (默认行为)
      //  searchSourceBuilder.sort(new FieldSortBuilder("_uid").order(SortOrder.ASC));  // 根据 id 降序排列
        searchRequest.source(searchSourceBuilder); // 将 SearchSourceBuilder  添加到 SeachRequest 中。
        SearchResponse searchResponse = client.search(searchRequest,RequestOptions.DEFAULT);
        SearchHits hits = searchResponse.getHits();
        float maxScore = hits.getMaxScore();
        for (SearchHit hit : hits.getHits()) {
            String sourceAsString = hit.getSourceAsString();
            Map<String, Object> sourceAsMap = hit.getSourceAsMap();
            String name = (String) sourceAsMap.get("name");
            System.out.println("hit: "+hit);
            System.out.println("name: "+name);
        }
        client.close();
}
 
// 聚合查询
public static void search02()throws Exception{
        RestHighLevelClient client = new RestHighLevelClient(
                RestClient.builder(new HttpHost("127.0.0.1", 9200, "http")));
        SearchRequest searchRequest = new SearchRequest("user2");
        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
        // 根据 sex 字段分组
        TermsAggregationBuilder aggregation = AggregationBuilders.terms("my_sex")
                .field("sex.keyword");
        aggregation.subAggregation(AggregationBuilders.avg("avg_age")
                .field("age")); // age(统计的字段)需是数值型
        aggregation.subAggregation(AggregationBuilders.max("max_age")
                .field("age"));
        aggregation.subAggregation(AggregationBuilders.min("min_age")
                .field("age"));
        searchSourceBuilder.aggregation(aggregation);
        searchRequest.source(searchSourceBuilder);
        SearchResponse searchResponse = client.search(searchRequest, RequestOptions.DEFAULT);
        Aggregations aggregations = searchResponse.getAggregations();
        Terms sexTerms = aggregations.get("my_sex");
 
        //获取每组的信息
        for (Terms.Bucket bucket : sexTerms.getBuckets()) {
            System.out.println("分组的字段名: " + bucket.getKeyAsString());
            System.out.println("每组数量: " + bucket.getDocCount());
        }
        //求平均
        Terms.Bucket elasticBucket1 = sexTerms.getBucketByKey("女");
        Avg averageAge1 = elasticBucket1.getAggregations().get("avg_age");
        double avg1 = averageAge1.getValue();
        System.out.println("女性平均年龄:"+avg1);
        Terms.Bucket elasticBucket2 = sexTerms.getBucketByKey("男");
        Avg averageAge2 = elasticBucket2.getAggregations().get("avg_age");
        double avg2 = averageAge2.getValue();
        System.out.println("男性平均年龄:"+avg2);
        //求最大最小
        Terms.Bucket elasticBucket3 = sexTerms.getBucketByKey("女");
        Max maxAge3 = elasticBucket3.getAggregations().get("max_age");
        double maxAge = maxAge3.getValue();
        System.out.println("女性最大年龄:"+maxAge);
        Terms.Bucket elasticBucket4 = sexTerms.getBucketByKey("女");
        Min maxAge4 = elasticBucket4.getAggregations().get("min_age");
        double minAge = maxAge4.getValue();
        System.out.println("女性最大年龄:"+minAge);
        client.close();
}
 
// 多查询
public static void multiSearch()throws Exception{
	MultiSearchRequest multiSearchRequest = new MultiSearchRequest();  
	// 查两个张索引
	SearchRequest firstSearchRequest = new SearchRequest("user");   
	SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
	searchSourceBuilder.query(QueryBuilders.matchQuery("name", "大黑"));
	firstSearchRequest.source(searchSourceBuilder);
	multiSearchRequest.add(firstSearchRequest);
	SearchRequest secondSearchRequest = new SearchRequest("car");  
	searchSourceBuilder = new SearchSourceBuilder();
	searchSourceBuilder.query(QueryBuilders.matchQuery("weight", "3T"));
	secondSearchRequest.source(searchSourceBuilder);
	multiSearchRequest.add(secondSearchRequest);
	// 取值1
	MultiSearchResponse multiSearchResponse = client.msearch(multiSearchRequest,RequestOptions.DEFAULT);
	MultiSearchResponse.Item firstResponse = multiSearchResponse.getResponses()[0];                                 
	SearchResponse firstSearchResponse = firstResponse.getResponse();        
	for (SearchHit hit : firstSearchResponse.getHits()) {
		Map<String, Object> sourceAsMap = hit.getSourceAsMap();
				String name = (String) sourceAsMap.get("name");
	}
	MultiSearchResponse.Item secondResponse = response.getResponses()[1];  
	SearchResponse secondSearchResponse = secondResponse.getResponse();
	for (SearchHit hit : secondSearchResponse.getHits()) {
		Map<String, Object> sourceAsMap = hit.getSourceAsMap();
		String name = (String) sourceAsMap.get("weight");
	}
 
	// 取值2
	for (MultiSearchResponse.Item item : multiSearchResponse.getResponses()) {
		SearchResponse response = item.getResponse();
			for (SearchHit hit : response.getHits()) {
			String index=hit.getIndex();
			//根据不同索引名作不同的处理。
			if(index.equals("user")){
				Map<String, Object> sourceAsMap = hit.getSourceAsMap();
				String name = (String) sourceAsMap.get("name");
			}else if(index.equals("car")){
				Map<String, Object> sourceAsMap = hit.getSourceAsMap();
				String name = (String) sourceAsMap.get("weight");
			}
		}
	}

//滚动查询
public static void scrollSerach()throws Exception{
        System.out.print("11111111111111111");
        RestHighLevelClient client = new RestHighLevelClient(
                RestClient.builder(new HttpHost("127.0.0.1", 9200, "http")));
        SearchRequest searchRequest = new SearchRequest("user");  // 设置搜索的 index 。
        QueryBuilder queryBuilder = QueryBuilders.matchAllQuery();
        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
        searchSourceBuilder.query(queryBuilder); //设置搜索,可以是任何类型的 QueryBuilder.
        //设置每次查询数量
        searchSourceBuilder.size(3);
        //设置滚动等待时间
        final Scroll scroll = new Scroll(TimeValue.timeValueMinutes(1));
        searchRequest.scroll(scroll);
        searchRequest.source(searchSourceBuilder);
        //第一次获取查询结果
        SearchResponse searchResponse = client.search(searchRequest, RequestOptions.DEFAULT);
        String scrollId = searchResponse.getScrollId();
        SearchHit[] searchHits = searchResponse.getHits().getHits();
 
        for (SearchHit hit : searchHits) {
            Map<String, Object> sourceAsMap = hit.getSourceAsMap();
            System.out.print("第一次获取查询结果,此处可做一些操作。");
            String name = (String) sourceAsMap.get("name");
            System.out.println("name: "+name);
        }
        //遍历剩余结果
        while (searchHits != null && searchHits.length > 0) {
            SearchScrollRequest scrollRequest = new SearchScrollRequest(scrollId);
            scrollRequest.scroll(scroll);
            searchResponse = client.scroll(scrollRequest, RequestOptions.DEFAULT);
            scrollId = searchResponse.getScrollId();
            searchHits = searchResponse.getHits().getHits();
            for (SearchHit hit : searchHits) {
                Map<String, Object> sourceAsMap = hit.getSourceAsMap();
                System.out.print("遍历剩余结果,此处可做一些操作。");
                String name = (String) sourceAsMap.get("name");
                System.out.println("name: "+name);
            }
        }
        // 清除游标
        ClearScrollRequest clearScrollRequest = new ClearScrollRequest();
        clearScrollRequest.addScrollId(scrollId);
        ClearScrollResponse clearScrollResponse = client.clearScroll(clearScrollRequest, RequestOptions.DEFAULT);
        boolean succeeded = clearScrollResponse.isSucceeded();
        client.close();
    }
}

四、性能调优 

4.1、生产部署

1.ES对于CPU的要求比较低,对内存磁盘要求较高。一般64G内存,8~16核CPU,SSD固态硬盘即可。

2.ES内存主要两部分—os cache、jvm heap,ES官方建议,ES默认jvm heap分配2G内存,可通过jvm.options配置文件设置。50%内存给jvm heap,50%的内存给os cache。os cache的内存会被Lucene用光,来缓存segment file。

3.不对任何分词field聚合操作,就不使用fielddata(用jvm heap),可给os cache更多内存。更多的内存留给了lucene用os cache提升索引读写性能。

4.给ES的heap内存最好不要超过32G,当heap内存小于32G时,JVM才会用一种compressed oops技术来压缩对象指针(object pointer),解决object pointer耗费过大空间的问题。

5.禁止swapping,因为swapping会导致GC过程从毫秒级变成分钟级,在GC的时候需要将内存从磁盘swapping到内存里,特别耗时,这会导致es节点响应请求变得很慢,甚至导致ES node跟cluster失联。

4.1.2、ES目录

ES升级时,目录会被覆盖掉,导致之前plugin、log、data、config信息丢失,可通过elasticsearch.yml改变目录位置:
path.logs: /var/log/elasticsearch
path.data: /var/data/elasticsearch
path.plugins: /var/plugin/elasticsearch 

4.2、写入优化

1.bulk批量写入
尽量采用bulk方式,每次批量写个几百条。

2.多线程写入
用多线程并发的将数据bulk写入集群中,可减少每次磁盘fsync的次数和开销。

3.增加refresh间隔
默认的refresh间隔是1s,可调大index.refresh_interval参数至30s,每隔30s才会创建一个segment file。

4.禁止refresh和replia
如果要一次加载大批量的数据进ES,可先禁止refresh和replia复制,
将index.refresh_interval设为-1,将index.number_of_replicas设为0,此时就没refresh和replica机制了,写入速度会非常快。

5.减少副本数量
ES默认副本为3个,这样提高集群的可用性,增加搜索的并发数,也会影响写入索引的效率。

5.禁止swapping
将swapping内存页交换禁止,因为swapping会导致大量磁盘IO,性能很差。

6.增加filesystem cache大小
filesystem cache被用来执行更多的IO操作,给filesystem cache更多内存,ES的写入性能会好很多。

7.使用自动生成的id
如果手动给es document设置一个id,es每次都去确认id是否存在。用自动生成的id,那es就可跳过这个步骤,写入性能会更好。

8.提升硬件
给filesystem cache更多的内存、用SSD替代机械硬盘、避免用NAS等网络存储、用RAID 0来提升磁盘并行读写效率等。

9.索引缓冲 index buffer
写入并发量高,可通过indices.memory.index_buffer_size参数,将index buffer调大一些。

10.尽量避免用 nested、parent/child 的字段
nested query 慢,parent/child query 更慢。在 mapping 设计阶段用大宽表设计或用比较 smart 的数据结构。

4.3、查询优化

1.慢查询日志
elasticsearch.yml中,可通过设置参数配置慢查询阈值:
PUT  /_template/{TEMPLATE_NAME}
{
  "template":"{INDEX_PATTERN}",
  "settings" : {
    "index.indexing.slowlog.level": "INFO",
    "index.indexing.slowlog.threshold.index.warn": "10s",
    "index.indexing.slowlog.threshold.index.info": "5s",
    "index.indexing.slowlog.threshold.index.debug": "2s",
    "index.indexing.slowlog.threshold.index.trace": "500ms",
    "index.indexing.slowlog.source": "1000",
    "index.search.slowlog.level": "INFO",
    "index.search.slowlog.threshold.query.warn": "10s",
    "index.search.slowlog.threshold.query.info": "5s",
    "index.search.slowlog.threshold.query.debug": "2s",
    "index.search.slowlog.threshold.query.trace": "500ms",
    "index.search.slowlog.threshold.fetch.warn": "1s",
    "index.search.slowlog.threshold.fetch.info": "800ms",
    "index.search.slowlog.threshold.fetch.debug": "500ms",
    "index.search.slowlog.threshold.fetch.trace": "200ms"
  },
  "version"  : 1
}
PUT {INDEX_PAATERN}/_settings
{
    "index.indexing.slowlog.level": "INFO",
    "index.indexing.slowlog.threshold.index.warn": "10s",
    "index.indexing.slowlog.threshold.index.info": "5s",
    "index.indexing.slowlog.threshold.index.debug": "2s",
    "index.indexing.slowlog.threshold.index.trace": "500ms",
    "index.indexing.slowlog.source": "1000",
    "index.search.slowlog.level": "INFO",
    "index.search.slowlog.threshold.query.warn": "10s",
    "index.search.slowlog.threshold.query.info": "5s",
    "index.search.slowlog.threshold.query.debug": "2s",
    "index.search.slowlog.threshold.query.trace": "500ms",
    "index.search.slowlog.threshold.fetch.warn": "1s",
    "index.search.slowlog.threshold.fetch.info": "800ms",
    "index.search.slowlog.threshold.fetch.debug": "500ms",
    "index.search.slowlog.threshold.fetch.trace": "200ms"
}
在日志目录下的慢查询日志
{CLUSTER_NAME}_index_indexing_slowlog.log
{CLUSTER_NAME}_index_search_slowlog.log

2.all、_source 字段的使用
_all 字段包含了所有的索引字段,便做全文检索,无需求,可禁用;
_source 存储原始的 document 内容,可设置 includes、excludes 属性来定义放入 _source 的字段。

3.合理的配置使用 index 属性
index 属性:analyzed、not_analyzed,根据业务需求来控制字段是否分词或不分词。

4.用过滤器(Filter)替代查询(Query)
Query:查询会计算相关性分数
Filter:查询只做匹配「是」或「否」,结果可以缓存。

5.不要返回过大的结果集

6.避免超大的document

7.避免稀疏数据
Lucene的内核结构,跟稠密的数据配合起来性能会更好。
   每个document的field为空过多,就是稀疏数据。

4.4、分页

4.4.1、from + size:普通分页 

1.每个分片会查询打分排名在前面的 from+size 条数据。
2.协同节点收集每个分配的前 from+size 条数据(n*(from+size)),在总的n*(from+size)数据中排序,将其中 from 到 from+size 的数据返给客户。
优化:若文档 id 有序,以文档 id 作为分页的偏移量,先把id查出,在id结果集里取出数据。

4.4.2、滚动翻页(Search Scroll):

游标滚动式查询

4.4.3、流式翻页(Search After 仅支持向后翻页)

用上页中的一组排序值检索下页数据,搜索的查询和排序参数须保持不变。
PIT(Point In Time):存储索引数据状态的轻量级视图。
1.获取索引的pit 
2.根据pit首次查询 
3.根据search_after和pit进行翻页查询 

本图文内容来源于网友网络收集整理提供,作为学习参考使用,版权属于原作者。
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