elasticSearch Java Api使用

1 创建 client 客户端

private final static String host = "127.0.0.1";

//创建客户端
public static Client getClient() {
try {
Client client = TransportClient.builder().build()
.addTransportAddress(new InetSocketTransportAddress(InetAddress.getByName(host), 9300));
return client;
} catch (Exception ex) {
System.out.println(ex);
}
return null;
}

2.根据index，type查询数据 ，由于Elasticsearch是天生异步的，执行execute后会在等到查询结果之前就返回caller block，所以我们可以简单的使用actionGet()方法来阻塞程序直到查询结构返回。

public static void forGetResponse(String getIndex, String getType, String getId) {

//GetResponse getResponse = getClient().prepareGet(getIndex, getType, getId).execute().actionGet();

GetResponse getResponse = getClient().prepareGet(getIndex, getType, getId).get(); Map<String, Object> responMap = getResponse.getSourceAsMap();

//转换成json的格式
System.out.println("getResponse返回结果为:" + Json2Str(responMap));

}

3. 插入一条数据到ES，由于设置了主键,如果id存在就做更新的操作，不存在就插入类似mysql的upsert操作，另外在用spark对ES的写入的时候因为有native的接口 直接Data Frame进行写入，所以此upsert是可以设置的如：conf.set（"es.write.operation","upsert"）

Client cli = getClient();
IndexResponse response = cli.prepareIndex("account", "stu", String.valueOf(students.getSid()))//参数说明： 索引，类型 ，_id)
.setSource(Json2Str(students))//setSource可以传以上map string  byte[] 几种方式
.get();
boolean created = response.isCreated();

4.

这里自定义了某个Type的索引映射（Mapping），默认ES会自动处理数据类型的映射：针对整型映射为long，浮点数为double，字符串映射为string，时间为date，true或false为boolean。注意：针对字符串，ES默认会做“analyzed”处理，即先做分词、去掉stop words等处理再index。如果你需要把一个字符串做为整体被索引到，需要把这个字段这样设置：field("index", "not_analyzed")。 例如：中华人民共和国

//搜索 select name from table where name=zhangsan and age >12 and age <18

SearchResponse searchResponse = cli.prepareSearch("account")//可以同时搜索多个索引prepareSearch("index","index2")
.setTypes("stu", "info")//可以同时搜索多个类型
.setSearchType(SearchType.DFS_QUERY_THEN_FETCH)
.setQuery(QueryBuilders.termQuery("sex", "male"))                 // Query
.setPostFilter(QueryBuilders.rangeQuery("age").from(10).to(20))     // Filter
.setFrom(0)
.setSize(10)
.setExplain(true)
.execute()
.actionGet();

封装方法：查询response返回的结果集

public static void forSearchResponse(SearchResponse response) {
//SearchHit类代表了一个满足查询条件的document，获得SearchHit后可以通过便利来输出每一个hit的信息
for (SearchHit searchHit : response.getHits()) {
//获取高亮显示的数据，含有富文本
/*            Map<String, HighlightField> highlightResult = searchHit.getHighlightFields();
HighlightField highlightField = highlightResult.get("name");
Text[] titleText = highlightField.fragments();
String higtName = "";
for (Text text : titleText) {
higtName += text;
}*/
Map<String, Object> source = searchHit.getSource();
if (!source.isEmpty()) {
Iterator<Map.Entry<String, Object>> it = source.entrySet().iterator();
while (it.hasNext()) {
Map.Entry<String, Object> entry = it.next();
System.out.println("hists结果为：" + entry.getKey() + "----" + entry.getValue());
}
}
}
}

5.简单的聚合操作

public static void forAggreationSearch() {SearchRequestBuilder srb = getClient().prepareSearch("account");srb.setTypes("stu");TermsBuilder ageTermsBuilder = AggregationBuilders.terms("gradeAge").field("age");TermsBuilder sexTermsBuilder = AggregationBuilders.terms("gradeSex").field("sex");sexTermsBuilder.subAggregation(ageTermsBuilder);srb.addAggregation(sexTermsBuilder);SearchResponse response = srb.execute().actionGet();Map<String, Aggregation> aggMap = response.getAggregations().asMap();StringTerms sexTerms = (StringTerms) aggMap.get("gradeSex");Iterator<Bucket> sexBuckets = sexTerms.getBuckets().iterator();while (sexBuckets.hasNext()) {Bucket sexBucket = sexBuckets.next();System.out.println("性别:" + sexBucket.getKey() + " 人数为：" + sexBucket.getDocCount());StringTerms ageTerms = (StringTerms) sexBucket.getAggregations().asMap().get("gradeAge");Iterator<Bucket> ageBuckets = ageTerms.getBuckets().iterator();while (ageBuckets.hasNext()) {Bucket ageBucket = ageBuckets.next();System.out.println("性别：" + sexBucket.getKey() + "  " + ageBucket.getKey() + "岁" + "，人数为：" + ageBucket.getDocCount());}}}