Elasticsearch Java API - 客户端连接(TransportClient，PreBuiltXPackTransportClient)（一）
本节介绍以下 CRUD API：
单文档 APIs
- Index API
- Get API
- Delete API
- Delete By Query API
- Update API
多文档 APIs
- Multi Get API
- Bulk API
- Using Bulk Processor
Multi Get API
Bulk API
注意:所有的单文档的CRUD API，index参数只能接受单一的索引库名称，或者是一个指向单一索引库的alias。
Index API
Index API 允许我们存储一个JSON格式的文档，使数据可以被搜索。文档通过index、type、id唯一确定。我们可以自己提供一个id，或者也使用Index API 为我们自动生成一个。
这里有几种不同的方式来产生JSON格式的文档(document)：
- 手动方式，使用原生的byte[]或者String
- 使用Map方式，会自动转换成与之等价的JSON
- 使用第三方库来序列化beans，如Jackson
- 使用内置的帮助类 XContentFactory.jsonBuilder()
手动方式
数据格式
String json = "{" +
"\"user\":\"kimchy\"," +
"\"postDate\":\"2013-01-30\"," +
"\"message\":\"trying out Elasticsearch\"" +
"}";
实例
/**
* 手动生成JSON
*/
@Test
public void CreateJSON(){
String json = "{" +
"\"user\":\"fendo\"," +
"\"postDate\":\"2013-01-30\"," +
"\"message\":\"Hell word\"" +
"}";
IndexResponse response = client.prepareIndex("fendo", "fendodate")
.setSource(json)
.get();
System.out.println(response.getResult());
}
Map方式
Map是key:value数据类型，可以代表json结构.
Map json = new HashMap();
json.put("user","kimchy");
json.put("postDate",new Date());
json.put("message","trying out Elasticsearch");
实例
/**
* 使用集合
*/
@Test
public void CreateList(){
Map json = new HashMap();
json.put("user","kimchy");
json.put("postDate","2013-01-30");
json.put("message","trying out Elasticsearch");
IndexResponse response = client.prepareIndex("fendo", "fendodate")
.setSource(json)
.get();
System.out.println(response.getResult());
}
序列化方式
ElasticSearch已经使用了jackson，可以直接使用它把javabean转为json.
import com.fasterxml.jackson.databind.*;
// instance a json mapper
ObjectMapper mapper = new ObjectMapper(); // create once, reuse
// generate json
byte[] json = mapper.writeValueAsBytes(yourbeaninstance);
实例
/**
* 使用JACKSON序列化
* @throws Exception
*/
@Test
public void CreateJACKSON() throws Exception{
CsdnBlog csdn=new CsdnBlog();
csdn.setAuthor("fendo");
csdn.setContent("这是JAVA书籍");
csdn.setTag("C");
csdn.setView("100");
csdn.setTitile("编程");
csdn.setDate(new Date().toString());
// instance a json mapper
ObjectMapper mapper = new ObjectMapper(); // create once, reuse
// generate json
byte[] json = mapper.writeValueAsBytes(csdn);
IndexResponse response = client.prepareIndex("fendo", "fendodate")
.setSource(json)
.get();
System.out.println(response.getResult());
}
XContentBuilder帮助类方式
ElasticSearch提供了一个内置的帮助类XContentBuilder来产生JSON文档
// Index name
String _index = response.getIndex();
// Type name
String _type = response.getType();
// Document ID (generated or not)
String _id = response.getId();
// Version (if it's the first time you index this document, you will get: 1)
long _version = response.getVersion();
// status has stored current instance statement.
RestStatus status = response.status();
实例
/**
* 使用ElasticSearch 帮助类
* @throws IOException
*/
@Test
public void CreateXContentBuilder() throws IOException{
XContentBuilder builder = XContentFactory.jsonBuilder()
.startObject()
.field("user", "ccse")
.field("postDate", new Date())
.field("message", "this is Elasticsearch")
.endObject();
IndexResponse response = client.prepareIndex("fendo", "fendodata").setSource(builder).get();
System.out.println("创建成功!");
}
综合实例
import java.io.IOException;
import java.net.InetAddress;
import java.net.UnknownHostException;
import java.util.Date;
import java.util.HashMap;
import java.util.Map;
import org.elasticsearch.action.index.IndexResponse;
import org.elasticsearch.client.transport.TransportClient;
import org.elasticsearch.common.settings.Settings;
import org.elasticsearch.common.transport.InetSocketTransportAddress;
import org.elasticsearch.common.xcontent.XContentBuilder;
import org.elasticsearch.common.xcontent.XContentFactory;
import org.elasticsearch.transport.client.PreBuiltTransportClient;
import org.junit.Before;
import org.junit.Test;
import com.fasterxml.jackson.core.JsonProcessingException;
import com.fasterxml.jackson.databind.ObjectMapper;
public class CreateIndex {
private TransportClient client;
@Before
public void getClient() throws Exception{
//设置集群名称
Settings settings = Settings.builder().put("cluster.name", "my-application").build();// 集群名
//创建client
client = new PreBuiltTransportClient(settings)
.addTransportAddress(new InetSocketTransportAddress(InetAddress.getByName("127.0.0.1"), 9300));
}
/**
* 手动生成JSON
*/
@Test
public void CreateJSON(){
String json = "{" +
"\"user\":\"fendo\"," +
"\"postDate\":\"2013-01-30\"," +
"\"message\":\"Hell word\"" +
"}";
IndexResponse response = client.prepareIndex("fendo", "fendodate")
.setSource(json)
.get();
System.out.println(response.getResult());
}
/**
* 使用集合
*/
@Test
public void CreateList(){
Map json = new HashMap();
json.put("user","kimchy");
json.put("postDate","2013-01-30");
json.put("message","trying out Elasticsearch");
IndexResponse response = client.prepareIndex("fendo", "fendodate")
.setSource(json)
.get();
System.out.println(response.getResult());
}
/**
* 使用JACKSON序列化
* @throws Exception
*/
@Test
public void CreateJACKSON() throws Exception{
CsdnBlog csdn=new CsdnBlog();
csdn.setAuthor("fendo");
csdn.setContent("这是JAVA书籍");
csdn.setTag("C");
csdn.setView("100");
csdn.setTitile("编程");
csdn.setDate(new Date().toString());
// instance a json mapper
ObjectMapper mapper = new ObjectMapper(); // create once, reuse
// generate json
byte[] json = mapper.writeValueAsBytes(csdn);
IndexResponse response = client.prepareIndex("fendo", "fendodate")
.setSource(json)
.get();
System.out.println(response.getResult());
}
/**
* 使用ElasticSearch 帮助类
* @throws IOException
*/
@Test
public void CreateXContentBuilder() throws IOException{
XContentBuilder builder = XContentFactory.jsonBuilder()
.startObject()
.field("user", "ccse")
.field("postDate", new Date())
.field("message", "this is Elasticsearch")
.endObject();
IndexResponse response = client.prepareIndex("fendo", "fendodata").setSource(builder).get();
System.out.println("创建成功!");
}
}
你还可以通过startArray(string)和endArray()方法添加数组。.field()方法可以接受多种对象类型。你可以给它传递数字、日期、甚至其他XContentBuilder对象。
Get API
根据id查看文档：
GetResponse response = client.prepareGet("twitter", "tweet", "1").get();
更多请查看 rest get API 文档
配置线程
operationThreaded 设置为 true 是在不同的线程里执行此次操作
下面的例子是operationThreaded 设置为 false ：
GetResponse response = client.prepareGet("twitter", "tweet", "1")
.setOperationThreaded(false)
.get();
Delete API
根据ID删除：
DeleteResponse response = client.prepareDelete("twitter", "tweet", "1").get();
更多请查看 delete API 文档
配置线程
operationThreaded 设置为 true 是在不同的线程里执行此次操作
下面的例子是operationThreaded 设置为 false ：
GetResponse response = client.prepareGet("twitter", "tweet", "1")
.setOperationThreaded(false)
.get();
DeleteResponse response = client.prepareDelete("twitter", "tweet", "1")
.setOperationThreaded(false)
.get();
Delete By Query API
通过查询条件删除
BulkByScrollResponse response =
DeleteByQueryAction.INSTANCE.newRequestBuilder(client)
.filter(QueryBuilders.matchQuery("gender", "male")) //查询条件
.source("persons") //index(索引名)
.get(); //执行
long deleted = response.getDeleted(); //删除文档的数量
如果需要执行的时间比较长，可以使用异步的方式处理,结果在回调里面获取
DeleteByQueryAction.INSTANCE.newRequestBuilder(client)
.filter(QueryBuilders.matchQuery("gender", "male")) //查询
.source("persons") //index(索引名)
.execute(new ActionListener() { //回调监听
@Override
public void onResponse(BulkByScrollResponse response) {
long deleted = response.getDeleted(); //删除文档的数量
}
@Override
public void onFailure(Exception e) {
// Handle the exception
}
});
Update API
有两种方式更新索引：
- 创建
UpdateRequest,通过client发送； - 使用
prepareUpdate()方法；
使用UpdateRequest
UpdateRequest updateRequest = new UpdateRequest();
updateRequest.index("index");
updateRequest.type("type");
updateRequest.id("1");
updateRequest.doc(jsonBuilder()
.startObject()
.field("gender", "male")
.endObject());
client.update(updateRequest).get();
使用 prepareUpdate() 方法
这里官方的示例有问题，new Script（）参数错误，所以一下代码是我自己写的（2017/11/10）
client.prepareUpdate("ttl", "doc", "1")
.setScript(new Script("ctx._source.gender = \"male\"" ,ScriptService.ScriptType.INLINE, null, null))//脚本可以是本地文件存储的，如果使用文件存储的脚本，需要设置 ScriptService.ScriptType.FILE
.get();
client.prepareUpdate("ttl", "doc", "1")
.setDoc(jsonBuilder() //合并到现有文档
.startObject()
.field("gender", "male")
.endObject())
.get();
Update by script
使用脚本更新文档
UpdateRequest updateRequest = new UpdateRequest("ttl", "doc", "1")
.script(new Script("ctx._source.gender = \"male\""));
client.update(updateRequest).get();
Update by merging documents
合并文档
UpdateRequest updateRequest = new UpdateRequest("index", "type", "1")
.doc(jsonBuilder()
.startObject()
.field("gender", "male")
.endObject());
client.update(updateRequest).get();
Upsert
更新插入,如果存在文档就更新，如果不存在就插入
IndexRequest indexRequest = new IndexRequest("index", "type", "1")
.source(jsonBuilder()
.startObject()
.field("name", "Joe Smith")
.field("gender", "male")
.endObject());
UpdateRequest updateRequest = new UpdateRequest("index", "type", "1")
.doc(jsonBuilder()
.startObject()
.field("gender", "male")
.endObject())
.upsert(indexRequest); //如果不存在此文档 ，就增加 `indexRequest`
client.update(updateRequest).get();
如果 index/type/1 存在，类似下面的文档：
{
"name" : "Joe Dalton",
"gender": "male"
}
如果不存在，会插入新的文档：
{
"name" : "Joe Smith",
"gender": "male"
}
Multi Get API
一次获取多个文档
MultiGetResponse multiGetItemResponses = client.prepareMultiGet()
.add("twitter", "tweet", "1") //一个id的方式
.add("twitter", "tweet", "2", "3", "4") //多个id的方式
.add("another", "type", "foo") //可以从另外一个索引获取
.get();
for (MultiGetItemResponse itemResponse : multiGetItemResponses) { //迭代返回值
GetResponse response = itemResponse.getResponse();
if (response.isExists()) { //判断是否存在
String json = response.getSourceAsString(); //_source 字段
}
}
更多请浏览REST multi get 文档
Bulk API
Bulk API，批量插入：
import static org.elasticsearch.common.xcontent.XContentFactory.*;
BulkRequestBuilder bulkRequest = client.prepareBulk();
// either use client#prepare, or use Requests# to directly build index/delete requests
bulkRequest.add(client.prepareIndex("twitter", "tweet", "1")
.setSource(jsonBuilder()
.startObject()
.field("user", "kimchy")
.field("postDate", new Date())
.field("message", "trying out Elasticsearch")
.endObject()
)
);
bulkRequest.add(client.prepareIndex("twitter", "tweet", "2")
.setSource(jsonBuilder()
.startObject()
.field("user", "kimchy")
.field("postDate", new Date())
.field("message", "another post")
.endObject()
)
);
BulkResponse bulkResponse = bulkRequest.get();
if (bulkResponse.hasFailures()) {
// process failures by iterating through each bulk response item
//处理失败
}
使用 Bulk Processor
BulkProcessor 提供了一个简单的接口，在给定的大小数量上定时批量自动请求
创建BulkProcessor实例
首先创建BulkProcessor实例
import org.elasticsearch.action.bulk.BackoffPolicy;
import org.elasticsearch.action.bulk.BulkProcessor;
import org.elasticsearch.common.unit.ByteSizeUnit;
import org.elasticsearch.common.unit.ByteSizeValue;
import org.elasticsearch.common.unit.TimeValue;
BulkProcessor bulkProcessor = BulkProcessor.builder(
client, //增加elasticsearch客户端
new BulkProcessor.Listener() {
@Override
public void beforeBulk(long executionId,
BulkRequest request) { ... } //调用bulk之前执行 ，例如你可以通过request.numberOfActions()方法知道numberOfActions
@Override
public void afterBulk(long executionId,
BulkRequest request,
BulkResponse response) { ... } //调用bulk之后执行 ，例如你可以通过request.hasFailures()方法知道是否执行失败
@Override
public void afterBulk(long executionId,
BulkRequest request,
Throwable failure) { ... } //调用失败抛 Throwable
})
.setBulkActions(10000) //每次10000请求
.setBulkSize(new ByteSizeValue(5, ByteSizeUnit.MB)) //拆成5mb一块
.setFlushInterval(TimeValue.timeValueSeconds(5)) //无论请求数量多少，每5秒钟请求一次。
.setConcurrentRequests(1) //设置并发请求的数量。值为0意味着只允许执行一个请求。值为1意味着允许1并发请求。
.setBackoffPolicy(
BackoffPolicy.exponentialBackoff(TimeValue.timeValueMillis(100), 3))//设置自定义重复请求机制，最开始等待100毫秒，之后成倍更加，重试3次，当一次或多次重复请求失败后因为计算资源不够抛出 EsRejectedExecutionException 异常，可以通过BackoffPolicy.noBackoff()方法关闭重试机制
.build();
BulkProcessor 默认设置
- bulkActions 1000
- bulkSize 5mb
- 不设置flushInterval
- concurrentRequests 为 1 ，异步执行
- backoffPolicy 重试 8次，等待50毫秒
增加requests
然后增加requests到BulkProcessor
bulkProcessor.add(new IndexRequest("twitter", "tweet", "1").source(/* your doc here */));
bulkProcessor.add(new DeleteRequest("twitter", "tweet", "2"));
关闭 Bulk Processor
当所有文档都处理完成，使用awaitClose 或 close 方法关闭BulkProcessor:
bulkProcessor.awaitClose(10, TimeUnit.MINUTES);
或
bulkProcessor.close();
在测试中使用Bulk Processor
如果你在测试种使用Bulk Processor可以执行同步方法
BulkProcessor bulkProcessor = BulkProcessor.builder(client, new BulkProcessor.Listener() { /* Listener methods */ })
.setBulkActions(10000)
.setConcurrentRequests(0)
.build();
// Add your requests
bulkProcessor.add(/* Your requests */);
// Flush any remaining requests
bulkProcessor.flush();
// Or close the bulkProcessor if you don't need it anymore
bulkProcessor.close();
// Refresh your indices
client.admin().indices().prepareRefresh().get();
// Now you can start searching!
client.prepareSearch().get();
所有实例 已经上传到Git
更多请浏览 spring-boot-starter-es 开源项目
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