说明
在明确了ES的基本概念和使用方法后,我们来学习如何使用ES的Java API.
本文假设你已经对ES的基本概念已经有了一个比较全面的认识。
客户端
你可以用Java客户端做很多事情:
- 执行标准的index,get,delete,update,search等操作。
- 在正在运行的集群上执行管理任务。
但是,通过官方文档可以得知,现在存在至少三种Java客户端。
- Transport Client
- Java High Level REST Client
- Java Low Level Rest Client
造成这种混乱的原因是:
-
长久以来,ES并没有官方的Java客户端,并且Java自身是可以简单支持ES的API的,于是就先做成了TransportClient。但是TransportClient的缺点是显而易见的,它没有使用RESTful风格的接口,而是二进制的方式传输数据。
-
之后ES官方推出了Java Low Level REST Client,它支持RESTful,用起来也不错。但是缺点也很明显,因为TransportClient的使用者把代码迁移到Low Level REST Client的工作量比较大。官方文档专门为迁移代码出了一堆文档来提供参考。
-
现在ES官方推出Java High Level REST Client,它是基于Java Low Level REST Client的封装,并且API接收参数和返回值和TransportClient是一样的,使得代码迁移变得容易并且支持了RESTful的风格,兼容了这两种客户端的优点。当然缺点是存在的,就是版本的问题。ES的小版本更新非常频繁,在最理想的情况下,客户端的版本要和ES的版本一致(至少主版本号一致),次版本号不一致的话,基本操作也许可以,但是新API就不支持了。
-
强烈建议ES5及其以后的版本使用Java High Level REST Client。笔者这里使用的是ES5.6.3,下面的文章将基于JDK1.8+Spring Boot+ES5.6.3 Java High Level REST Client+Maven进行示例。
stackoverflow上的问答:
https://stackoverflow.com/questions/47031840/elasticsearchhow-to-choose-java-client/47036028#47036028
详细说明:
https://www.elastic.co/blog/the-elasticsearch-java-high-level-rest-client-is-out
参考资料:
https://www.elastic.co/guide/en/elasticsearch/client/java-rest/5.6/java-rest-high.html
Java High Level REST Client 介绍
Java High Level REST Client 是基于Java Low Level REST Client的,每个方法都可以是同步或者异步的。同步方法返回响应对象,而异步方法名以“async”结尾,并需要传入一个监听参数,来确保提醒是否有错误发生。
Java High Level REST Client需要Java1.8版本和ES。并且ES的版本要和客户端版本一致。和TransportClient接收的参数和返回值是一样的。
以下实践均是基于5.6.3的ES集群和Java High Level REST Client的。
Maven 依赖
<dependency>
<groupId>org.elasticsearch.client</groupId> <artifactId>elasticsearch-rest-high-level-client</artifactId> <version>5.6.3</version> </dependency>
初始化
High Level REST Client的初始化是依赖Low Level客户端的
Index API
类似HTTP请求,Index API包括index request和index response
Index request的构造
构造一条index request的例子:
IndexRequest request = new IndexRequest(
"posts",
注意到这里是使用的String 类型。
另一种构造的方法:
Map<String, Object> jsonMap = new HashMap<>(); jsonMap.put("user", "kimchy"); jsonMap.put("postDate", new Date()); jsonMap.put("message", "trying out Elasticsearch"); IndexRequest indexRequest = new IndexRequest("posts", "doc", "1") .source(jsonMap);
除了String和Map ,XContentBuilder 类型也是可以的:
XContentBuilder builder = XContentFactory.jsonBuilder();
builder.startObject();
{
builder.field("user", "kimchy");
builder.field("postDate", new Date()); builder.field("message", "trying out Elasticsearch"); } builder.endObject(); IndexRequest indexRequest = new IndexRequest("posts", "doc", "1") .source(builder);
更直接一点的,在实例化index request对象时,可以直接给出键值对:
IndexRequest indexRequest = new IndexRequest("posts", "doc", "1") .source("user", "kimchy", "postDate", new Date(), "message", "trying out Elasticsearch");
index response的获取
同步执行
IndexResponse indexResponse = client.index(request);
异步执行
client.indexAsync(request, new ActionListener<IndexResponse>() {
@Override
public void onResponse(IndexResponse indexResponse) { } @Override public void onFailure(Exception e) { } });
需要注意的是,异步执行的方法名以Async结尾,并且多了一个Listener参数,并且需要重写回调方法。
在kibana控制台查询得到数据:
{
"_index": "posts",
"_type": "doc",
"_id": "1", "_version": 1, "found": true, "_source": { "user": "kimchy", "postDate": "2017-11-01T05:48:26.648Z", "message": "trying out Elasticsearch" } }
index request中的数据已经成功入库。
index response的返回值操作
client.index()方法返回值类型为IndexResponse,我们可以用它来进行如下操作:
String index = indexResponse.getIndex();
对version冲突的判断:
IndexRequest request = new IndexRequest("posts", "doc", "1") .source("field", "value") .version(1); try { IndexResponse response = client.index(request); } catch(ElasticsearchException e) { if (e.status() == RestStatus.CONFLICT) { } }
对index动作的判断:
IndexRequest request = new IndexRequest("posts", "doc", "1") .source("field", "value") .opType(DocWriteRequest.OpType.CREATE);
GET API
GET request
GetRequest getRequest = new GetRequest(
"posts",
GET response
同步方法:
GetResponse getResponse = client.get(getRequest);
异步方法:
client.getAsync(request, new ActionListener<GetResponse>() {
@Override
public void onResponse(GetResponse getResponse) { } @Override public void onFailure(Exception e) { } });
对返回对象的操作:
String index = getResponse.getIndex();
String type = getResponse.getType();
String id = getResponse.getId();
if (getResponse.isExists()) { long version = getResponse.getVersion(); String sourceAsString = getResponse.getSourceAsString(); Map<String, Object> sourceAsMap = getResponse.getSourceAsMap(); byte[] sourceAsBytes = getResponse.getSourceAsBytes(); } else {
异常处理:
GetRequest request = new GetRequest("does_not_exist", "doc", "1"); try { GetResponse getResponse = client.get(request); } catch (ElasticsearchException e) { if (e.status() == RestStatus.NOT_FOUND) { } if (e.status() == RestStatus.CONFLICT) { } }
DELETE API
与Index API和 GET API及其相似
DELETE request
DeleteRequest request = new DeleteRequest(
"posts",
"doc",
"1");
DELETE response
同步:
DeleteResponse deleteResponse = client.delete(request);
异步:
client.deleteAsync(request, new ActionListener<DeleteResponse>() {
@Override
public void onResponse(DeleteResponse deleteResponse) { } @Override public void onFailure(Exception e) { } });
Update API
update request
UpdateRequest updateRequest = new UpdateRequest(
"posts",
"doc",
"1");
update脚本:
在之前我们介绍了如何使用简单的脚本来更新数据
POST /posts/doc/1/_update?pretty
{
"script" : "ctx._source.age += 5"
}
也可以写成:
POST /posts/doc/1/_update?pretty
{
"script" : {
"lang":"painless",
"source":"ctx._source.age += 5" } }
对应代码:
UpdateRequest updateRequest = new UpdateRequest("posts", "doc", "1"); Map<String, Object> parameters = new HashMap<>(); parameters.put("age", 4); Script inline = new Script(ScriptType.INLINE, "painless", "ctx._source.age += params.age", parameters); updateRequest.script(inline); try { UpdateResponse updateResponse = client.update(updateRequest); } catch (IOException e) {
使用部分文档更新
- String
String jsonString = "{" +
"\"updated\":\"2017-01-02\"," +
"\"reason\":\"easy update\"" + "}"; updateRequest.doc(jsonString, XContentType.JSON); try { client.update(updateRequest); } catch (IOException e) {
2.Map
Map<String, Object> jsonMap = new HashMap<>(); jsonMap.put("updated", new Date()); jsonMap.put("reason", "dailys update"); UpdateRequest updateRequest = new UpdateRequest("posts", "doc", "1").doc(jsonMap); try { client.update(updateRequest); } catch (IOException e) {
3.XContentBuilder
try {
XContentBuilder builder = XContentFactory.jsonBuilder();
builder.startObject();
{
builder.field("updated", new Date()); System.out.println(new Date()); builder.field("reason", "daily update"); } builder.endObject(); UpdateRequest request = new UpdateRequest("posts", "doc", "1") .doc(builder); client.update(request); } catch (IOException e) {
4.键值对
try {
UpdateRequest request = new UpdateRequest("posts", "doc", "1") .doc("updated", new Date(), "reason", "daily updatesss"); client.update(request); } catch (IOException e) {
upsert
如果文档不存在,可以使用upsert来生成这个文档。
String jsonString = "{\"created\":\"2017-01-01\"}";
request.upsert(jsonString, XContentType.JSON);
同样地,upsert可以接Map,Xcontent,键值对参数。
update response
同样地,update response可以是同步的,也可以是异步的
同步执行:
UpdateResponse updateResponse = client.update(request);
异步执行:
client.updateAsync(request, new ActionListener<UpdateResponse>() {
@Override
public void onResponse(UpdateResponse updateResponse) { } @Override public void onFailure(Exception e) { } });
与其他response类似,update response返回对象可以进行各种判断操作,这里不再赘述。
Bulk API
Bulk request
之前的文档说明过,bulk接口是批量index/update/delete操作
在API中,只需要一个bulk request就可以完成一批请求。
BulkRequest request = new BulkRequest();
request.add(new IndexRequest("posts", "doc", "1") .source(XContentType.JSON,"field", "foo")); request.add(new IndexRequest("posts", "doc", "2") .source(XContentType.JSON,"field", "bar")); request.add(new IndexRequest("posts", "doc", "3") .source(XContentType.JSON,"field", "baz"));
- 注意,Bulk API只接受JSON和SMILE格式.其他格式的数据将会报错。
- 不同类型的request可以写在同一个bulk request里。
BulkRequest request = new BulkRequest();
request.add(new DeleteRequest("posts", "doc", "3")); request.add(new UpdateRequest("posts", "doc", "2") .doc(XContentType.JSON,"other", "test")); request.add(new IndexRequest("posts", "doc", "4") .source(XContentType.JSON,"field", "baz"));
bulk response
同步执行:
BulkResponse bulkResponse = client.bulk(request);
异步执行:
client.bulkAsync(request, new ActionListener<BulkResponse>() {
@Override
public void onResponse(BulkResponse bulkResponse) { } @Override public void onFailure(Exception e) { } });
对response的处理与其他类型的response十分类似,在这不再赘述。
bulk processor
BulkProcessor 简化bulk API的使用,并且使整个批量操作透明化。
BulkProcessor 的执行需要三部分组成:
- RestHighLevelClient :执行bulk请求并拿到响应对象。
- BulkProcessor.Listener:在执行bulk request之前、之后和当bulk response发生错误时调用。
- ThreadPool:bulk request在这个线程池中执行操作,这使得每个请求不会被挡住,在其他请求正在执行时,也可以接收新的请求。
示例代码:
Settings settings = Settings.EMPTY;
ThreadPool threadPool = new ThreadPool(settings);
Search API
Search request
Search API提供了对文档的查询和聚合的查询。
它的基本形式:
SearchRequest searchRequest = new SearchRequest();
SearchRequest searchRequest = new SearchRequest("posts");
使用SearchSourceBuilder
大多数的查询控制都可以使用SearchSourceBuilder实现。
举一个简单例子:
SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();
配置好searchSourceBuilder后,将它传入searchRequest里:
SearchRequest searchRequest = new SearchRequest();
searchRequest.source(sourceBuilder);
建立查询
在上面的例子,我们注意到,sourceBuilder构造查询条件时,使用QueryBuilders对象.
在所有ES查询中,它存在于所有ES支持的查询类型中。
使用它的构造体来创建:
MatchQueryBuilder matchQueryBuilder = new MatchQueryBuilder("user", "kimchy");
这里的代码相当于:
"query": { "match": { "user": "kimchy" } }
相关设置:
matchQueryBuilder.fuzziness(Fuzziness.AUTO);
QueryBuilder还可以使用 QueryBuilders工具类来创造,编程体验比较顺畅:
QueryBuilder matchQueryBuilder = QueryBuilders.matchQuery("user", "kimchy")
.fuzziness(Fuzziness.AUTO)
.prefixLength(3)
.maxExpansions(10);
无论QueryBuilder对象是如何创建的,都要将它传入SearchSourceBuilder里面:
searchSourceBuilder.query(matchQueryBuilder);
在之前导入的account数据中,使用match的示例代码:
GET /bank/_search?pretty
{
"query": {
"match": {
"firstname": "Virginia"
}
}
}
JAVA:
@Test
public void test2(){ RestClient lowLevelRestClient = RestClient.builder( new HttpHost("172.16.73.50", 9200, "http")).build(); RestHighLevelClient client = new RestHighLevelClient(lowLevelRestClient); SearchRequest searchRequest = new SearchRequest("bank"); searchRequest.types("account"); SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder(); MatchQueryBuilder mqb = QueryBuilders.matchQuery("firstname", "Virginia"); searchSourceBuilder.query(mqb); searchRequest.source(searchSourceBuilder); try { SearchResponse searchResponse = client.search(searchRequest); System.out.println(searchResponse.toString()); } catch (IOException e) { e.printStackTrace(); } }
排序
SearchSourceBuilder可以添加一种或多种SortBuilder。
有四种特殊的排序实现:
- field
- score
- GeoDistance
- scriptSortBuilder
sourceBuilder.sort(new ScoreSortBuilder().order(SortOrder.DESC));
过滤
默认情况下,searchRequest返回文档内容,与REST API一样,这里你可以重写search行为。例如,你可以完全关闭"_source"检索。
sourceBuilder.fetchSource(false);
该方法还接受一个或多个通配符模式的数组,以更细粒度地控制包含或排除哪些字段。
String[] includeFields = new String[] {"title", "user", "innerObject.*"}; String[] excludeFields = new String[] {"_type"}; sourceBuilder.fetchSource(includeFields, excludeFields);
聚合请求
通过配置适当的 AggregationBuilder ,再将它传入SearchSourceBuilder里,就可以完成聚合请求了。
之前的文档里面,我们通过下面这条命令,导入了一千条account信息:
curl -H "Content-Type: application/json" -XPOST 'localhost:9200/bank/account/_bulk?pretty&refresh' --data-binary "@accounts.json"
随后,我们介绍了如何通过聚合请求进行分组:
GET /bank/_search?pretty
{
"size": 0,
"aggs": {
"group_by_state": {
"terms": {
"field": "state.keyword" } } } }
我们将这一千条数据根据state字段分组,得到响应:
{
"took": 2,
"timed_out": false,
"_shards": { "total": 5, "successful": 5, "skipped": 0, "failed": 0 }, "hits": { "total": 999, "max_score": 0, "hits": [] }, "aggregations": { "group_by_state": { "doc_count_error_upper_bound": 20, "sum_other_doc_count": 770, "buckets": [ { "key": "ID", "doc_count": 27 }, { "key": "TX", "doc_count": 27 }, { "key": "AL", "doc_count": 25 }, { "key": "MD", "doc_count": 25 }, { "key": "TN", "doc_count": 23 }, { "key": "MA", "doc_count": 21 }, { "key": "NC", "doc_count": 21 }, { "key": "ND", "doc_count": 21 }, { "key": "MO", "doc_count": 20 }, { "key": "AK", "doc_count": 19 } ] } } }
Java实现:
@Test
public void test2(){ RestClient lowLevelRestClient = RestClient.builder( new HttpHost("172.16.73.50", 9200, "http")).build(); RestHighLevelClient client = new RestHighLevelClient(lowLevelRestClient); SearchRequest searchRequest = new SearchRequest("bank"); searchRequest.types("account"); TermsAggregationBuilder aggregation = AggregationBuilders.terms("group_by_state") .field("state.keyword"); SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder(); searchSourceBuilder.aggregation(aggregation); searchSourceBuilder.size(0); searchRequest.source(searchSourceBuilder); try { SearchResponse searchResponse = client.search(searchRequest); System.out.println(searchResponse.toString()); } catch (IOException e) { e.printStackTrace(); } }
输出:
{"took":4,"timed_out":false,"_shards":{"total":5,"successful":5,"skipped":0,"failed":0},"hits":{"total":999,"max_score":0.0,"hits":[]},"aggregations":{"sterms#group_by_state":{"doc_count_error_upper_bound":20,"sum_other_doc_count":770,"buckets":[{"key":"ID","doc_count":27},{"key":"TX","doc_count":27},{"key":"AL","doc_count":25},{"key":"MD","doc_count":25},{"key":"TN","doc_count":23},{"key":"MA","doc_count":21},{"key":"NC","doc_count":21},{"key":"ND","doc_count":21},{"key":"MO","doc_count":20},{"key":"AK","doc_count":19}]}}}
同步执行
SearchResponse searchResponse = client.search(searchRequest);
异步执行
client.searchAsync(searchRequest, new ActionListener<SearchResponse>() {
@Override
public void onResponse(SearchResponse searchResponse) { } @Override public void onFailure(Exception e) { } });
Search response
Search response返回对象与其在API里的一样,返回一些元数据和文档数据。
首先,返回对象里的数据十分重要,因为这是查询的返回结果、使用分片情况、文档数据,HTTP状态码等
RestStatus status = searchResponse.status();
TimeValue took = searchResponse.getTook();
Boolean terminatedEarly = searchResponse.isTerminatedEarly();
boolean timedOut = searchResponse.isTimedOut();
其次,返回对象里面包含关于分片的信息和分片失败的处理:
int totalShards = searchResponse.getTotalShards();
int successfulShards = searchResponse.getSuccessfulShards();
int failedShards = searchResponse.getFailedShards();
for (ShardSearchFailure failure : searchResponse.getShardFailures()) {
取回searchHit
为了取回文档数据,我们要从search response的返回对象里先得到searchHit对象。
SearchHits hits = searchResponse.getHits();
取回文档数据:
@Test
public void test2(){ RestClient lowLevelRestClient = RestClient.builder( new HttpHost("172.16.73.50", 9200, "http")).build(); RestHighLevelClient client = new RestHighLevelClient(lowLevelRestClient); SearchRequest searchRequest = new SearchRequest("bank"); searchRequest.types("account"); SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder(); searchRequest.source(searchSourceBuilder); try { SearchResponse searchResponse = client.search(searchRequest); SearchHits searchHits = searchResponse.getHits(); SearchHit[] searchHit = searchHits.getHits(); for (SearchHit hit : searchHit) { System.out.println(hit.getSourceAsString()); } } catch (IOException e) { e.printStackTrace(); } }
根据需要,还可以转换成其他数据类型:
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");
取回聚合数据
聚合数据可以通过SearchResponse返回对象,取到它的根节点,然后再根据名称取到聚合数据。
GET /bank/_search?pretty
{
"size": 0,
"aggs": {
"group_by_state": {
"terms": {
"field": "state.keyword" } } } }
响应:
{
"took": 2,
"timed_out": false,
"_shards": { "total": 5, "successful": 5, "skipped": 0, "failed": 0 }, "hits": { "total": 999, "max_score": 0, "hits": [] }, "aggregations": { "group_by_state": { "doc_count_error_upper_bound": 20, "sum_other_doc_count": 770, "buckets": [ { "key": "ID", "doc_count": 27 }, { "key": "TX", "doc_count": 27 }, { "key": "AL", "doc_count": 25 }, { "key": "MD", "doc_count": 25 }, { "key": "TN", "doc_count": 23 }, { "key": "MA", "doc_count": 21 }, { "key": "NC", "doc_count": 21 }, { "key": "ND", "doc_count": 21 }, { "key": "MO", "doc_count": 20 }, { "key": "AK", "doc_count": 19 } ] } } }
Java实现:
@Test
public void test2(){ RestClient lowLevelRestClient = RestClient.builder( new HttpHost("172.16.73.50", 9200, "http")).build(); RestHighLevelClient client = new RestHighLevelClient(lowLevelRestClient); SearchRequest searchRequest = new SearchRequest("bank"); searchRequest.types("account"); TermsAggregationBuilder aggregation = AggregationBuilders.terms("group_by_state") .field("state.keyword"); SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder(); searchSourceBuilder.aggregation(aggregation); searchSourceBuilder.size(0); searchRequest.source(searchSourceBuilder); try { SearchResponse searchResponse = client.search(searchRequest); Aggregations aggs = searchResponse.getAggregations(); Terms byStateAggs = aggs.get("group_by_state"); Terms.Bucket b = byStateAggs.getBucketByKey("ID");
Search Scroll API
search scroll API是用于处理search request里面的大量数据的。
- 使用ES做分页查询有两种方法。一是配置search request的from,size参数。二是使用scroll API。搜索结果建议使用scroll API,查询效率高。
为了使用scroll,按照下面给出的步骤执行:
初始化search scroll上下文
带有scroll参数的search请求必须被执行,来初始化scroll session。ES能检测到scroll参数的存在,保证搜索上下文在相应的时间间隔里存活
SearchRequest searchRequest = new SearchRequest("account");
取回所有相关文档
第二步,得到的scroll id 和新的scroll间隔要设置到 SearchScrollRequest里,再调用searchScroll方法。
ES会返回一批带有新scroll id的查询结果。以此类推,新的scroll id可以用于子查询,来得到另一批新数据。这个过程应该在一个循环内,直到没有数据返回为止,这意味着scroll消耗殆尽,所有匹配上的数据都已经取回。
SearchScrollRequest scrollRequest = new SearchScrollRequest(scrollId);
清理 scroll 上下文
使用Clear scroll API来检测到最后一个scroll id 来释放scroll上下文.虽然在scroll过期时,这个清理行为会最终自动触发,但是最好的实践是当scroll session结束时,马上释放它。
可选参数
scrollRequest.scroll(TimeValue.timeValueSeconds(60L));
如果在scrollRequest不设置的话,会以searchRequest.scroll()设置的为准。
同步执行
SearchResponse searchResponse = client.searchScroll(scrollRequest);
异步执行
client.searchScrollAsync(scrollRequest, new ActionListener<SearchResponse>() {
@Override
public void onResponse(SearchResponse searchResponse) { } @Override public void onFailure(Exception e) { } });
- 需要注意的是,search scroll API的请求响应返回值也是一个searchResponse对象。
完整示例
@Test
public void test3(){ RestClient lowLevelRestClient = RestClient.builder( new HttpHost("172.16.73.50", 9200, "http")).build(); RestHighLevelClient client = new RestHighLevelClient(lowLevelRestClient); SearchRequest searchRequest = new SearchRequest("bank"); SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder(); MatchAllQueryBuilder mqb = QueryBuilders.matchAllQuery(); searchSourceBuilder.query(mqb); searchSourceBuilder.size(10); searchRequest.source(searchSourceBuilder); searchRequest.scroll(TimeValue.timeValueMinutes(1L)); try { SearchResponse searchResponse = client.search(searchRequest); String scrollId = searchResponse.getScrollId(); SearchHit[] hits = searchResponse.getHits().getHits(); System.out.println("first scroll:"); for (SearchHit searchHit : hits) { System.out.println(searchHit.getSourceAsString()); } Scroll scroll = new Scroll(TimeValue.timeValueMinutes(1L)); System.out.println("loop scroll:"); while(hits != null && hits.length>0){ SearchScrollRequest scrollRequest = new SearchScrollRequest(scrollId); scrollRequest.scroll(scroll); searchResponse = client.searchScroll(scrollRequest); scrollId = searchResponse.getScrollId(); hits = searchResponse.getHits().getHits(); for (SearchHit searchHit : hits) { System.out.println(searchHit.getSourceAsString()); } } ClearScrollRequest clearScrollRequest = new ClearScrollRequest(); clearScrollRequest.addScrollId(scrollId); ClearScrollResponse clearScrollResponse = client.clearScroll(clearScrollRequest); boolean succeeded = clearScrollResponse.isSucceeded(); System.out.println("cleared:"+succeeded); } catch (IOException e) {
Info API
Info API 提供一些关于集群、节点相关的信息查询。
request
MainResponse response = client.info();
response
ClusterName clusterName = response.getClusterName();
String clusterUuid = response.getClusterUuid();
String nodeName = response.getNodeName();
Version version = response.getVersion();
Build build = response.getBuild();
@Test
public void test4(){ RestClient lowLevelRestClient = RestClient.builder( new HttpHost("172.16.73.50", 9200, "http")).build(); RestHighLevelClient client = new RestHighLevelClient(lowLevelRestClient); try { MainResponse response = client.info(); ClusterName clusterName = response.getClusterName(); String clusterUuid = response.getClusterUuid(); String nodeName = response.getNodeName(); Version version = response.getVersion(); Build build = response.getBuild(); System.out.println("cluster name:"+clusterName); System.out.println("cluster uuid:"+clusterUuid); System.out.println("node name:"+nodeName); System.out.println("node version:"+version); System.out.println("node name:"+nodeName); System.out.println("build info:"+build); } catch (IOException e) {
总结
关于Elasticsearch 的 Java High Level REST Client API的基本用法大概就是这些,一些进阶技巧、概念要随时查阅官方文档。