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使用maven工程构建ES Java API的测试项目，其用到的依赖如下：
org.elasticsearch
elasticsearch
2.3.0
com.fasterxml.jackson.core
jackson-databind
2.7.0
org.dom4j
dom4j
2.0.0
org.projectlombok
lombok
1.16.10
使用junit进行测试，其使用的全局变量与setUp函数如下：
private TransportClient client;
private String index = "bigdata"; // 要操作的索引库为"bigdata"
private String type = "product"; // 要操作的类型为"product"
@Before
public void setup() throws UnknownHostException {
// 连接的是ES集群，所以需要添加集群名称，否则无法创建客户端
Settings settings = Settings.builder().put("cluster.name", "bigdata-08-28").build();
client = TransportClient.builder().settings(settings).build();
TransportAddress ta1 = new InetSocketTransportAddress(InetAddress.getByName("uplooking01"), 9300);
TransportAddress ta2 = new InetSocketTransportAddress(InetAddress.getByName("uplooking02"), 9300);
TransportAddress ta3 = new InetSocketTransportAddress(InetAddress.getByName("uplooking03"), 9300);
client.addTransportAddresses(ta1, ta2, ta3);
/*settings = client.settings();
Map asMap = settings.getAsMap();
for(Map.Entry setting : asMap.entrySet()) {
System.out.println(setting.getKey() + "::" + setting.getValue());
}*/
} /**
* 注意：往es中添加数据有4种方式
* 1.JSON
* 2.Map
* 3.Java Bean
* 4.XContentBuilder
*
* 1.JSON方式
*/
@Test
public void testAddJSON() {
String source = "{\"name\":\"sqoop\", \"author\": \"apache\", \"version\": \"1.4.6\"}";
IndexResponse response = client.prepareIndex(index, type, "4").setSource(source).get();
System.out.println(response.isCreated());
}/**
* 添加数据：
* 2.Map方式
*/
@Test
public void testAddMap() {
Map source = new HashMap();
source.put("name", "flume");
source.put("author", "Cloudera");
source.put("version", "1.8.0");
IndexResponse response = client.prepareIndex(index, type, "5").setSource(source).get();
System.out.println(response.isCreated());
} /**
* 添加数据：
* 3.Java Bean方式
*
* 如果不将对象转换为json字符串，则会报下面的异常：
* The number of object passed must be even but was [1]
*/
@Test
public void testAddObj() throws JsonProcessingException {
Product product = new Product("kafka", "linkedIn", "0.10.0.1", "kafka.apache.org");
ObjectMapper objectMapper = new ObjectMapper();
String json = objectMapper.writeValueAsString(product);
System.out.println(json);
IndexResponse response = client.prepareIndex(index, type, "6").setSource(json).get();
System.out.println(response.isCreated());
}/**
* 添加数据：
* 4.XContentBuilder方式
*/
@Test
public void testAddXContentBuilder() throws IOException {
XContentBuilder source = XContentFactory.jsonBuilder();
source.startObject()
.field("name", "redis")
.field("author", "redis")
.field("version", "3.2.0")
.field("url", "redis.cn")
.endObject();
IndexResponse response = client.prepareIndex(index, type, "7").setSource(source).get();
System.out.println(response.isCreated());
}/**
* 查询具体的索引信息
*/
@Test
public void testGet() {
GetResponse response = client.prepareGet(index, type, "6").get();
Map map = response.getSource();
/*for(Map.Entry me : map.entrySet()) {
System.out.println(me.getKey() + "=" + me.getValue());
}*/
// lambda表达式，jdk 1.8之后
map.forEach((k, v) -> System.out.println(k + "=" + v));
// map.keySet().forEach(key -> System.out.println(key + "xxx"));
} /**
* 局部更新操作与curl的操作是一致的
* curl -XPOST http://uplooking01:9200/bigdata/product/AWA184kojrSrzszxL-Zs/_update -d' {"doc":{"name":"sqoop", "author":"apache"}}'
*
* 做全局更新的时候，也不用prepareUpdate，而直接使用prepareIndex
*/
@Test
public void testUpdate() throws Exception {
/*String source = "{\"doc\":{\"url\": \"http://flume.apache.org\"}}";
UpdateResponse response = client.prepareUpdate(index, type, "4").setSource(source.getBytes()).get();*/
// 使用下面这种方式也是可以的
String source = "{\"url\": \"http://flume.apache.org\"}";
UpdateResponse response = client.prepareUpdate(index, type, "4").setDoc(source.getBytes()).get();
System.out.println(response.getVersion());
}/**
* 删除操作
*/
@Test
public void testDelete() {
DeleteResponse response = client.prepareDelete(index, type, "5").get();
System.out.println(response.getVersion());
}/**
* 批量操作
*/
@Test
public void testBulk() {
IndexRequestBuilder indexRequestBuilder = client.prepareIndex(index, type, "8")
.setSource("{\"name\":\"elasticsearch\", \"url\":\"http://www.elastic.co\"}");
UpdateRequestBuilder updateRequestBuilder = client.prepareUpdate(index, type, "1").setDoc("{\"url\":\"http://hadoop.apache.org\"}");
BulkRequestBuilder bulk = client.prepareBulk();
BulkResponse bulkResponse = bulk.add(indexRequestBuilder).add(updateRequestBuilder).get();
Iterator it = bulkResponse.iterator();
while(it.hasNext()) {
BulkItemResponse response = it.next();
System.out.println(response.getId() + "<--->" + response.getVersion());
}
} /**
* 获取索引记录数
*/
@Test
public void testCount() {
CountResponse response = client.prepareCount(index).get();
System.out.println("索引记录数：" + response.getCount());
}基于junit进行测试，其用到的setUp函数和showResult函数如下：
全局变量与setUp：
private TransportClient client;
private String index = "bigdata";
private String type = "product";
private String[] indics = {"bigdata", "bank"};
@Before
public void setUp() throws UnknownHostException {
Settings settings = Settings.builder().put("cluster.name", "bigdata-08-28").build();
client = TransportClient.builder().settings(settings).build();
TransportAddress ta1 = new InetSocketTransportAddress(InetAddress.getByName("uplooking01"), 9300);
TransportAddress ta2 = new InetSocketTransportAddress(InetAddress.getByName("uplooking02"), 9300);
TransportAddress ta3 = new InetSocketTransportAddress(InetAddress.getByName("uplooking03"), 9300);
client.addTransportAddresses(ta1, ta2, ta3);
}showResult：
/**
* 格式化输出查询结果
* @param response
*/
private void showResult(SearchResponse response) {
SearchHits searchHits = response.getHits();
float maxScore = searchHits.getMaxScore(); // 查询结果中的最大文档得分
System.out.println("maxScore: " + maxScore);
long totalHits = searchHits.getTotalHits(); // 查询结果记录条数
System.out.println("totalHits: " + totalHits);
SearchHit[] hits = searchHits.getHits(); // 查询结果
System.out.println("当前返回结果记录条数：" + hits.length);
for (SearchHit hit : hits) {
long version = hit.version();
String id = hit.getId();
String index = hit.getIndex();
String type = hit.getType();
float score = hit.getScore();
System.out.println("===================================================");
String source = hit.getSourceAsString();
System.out.println("version: " + version);
System.out.println("id: " + id);
System.out.println("index: " + index);
System.out.println("type: " + type);
System.out.println("score: " + score);
System.out.println("source: " + source);
}
}查询类型有如下4种：
query and fetch(速度最快)(返回N倍数据量)
query then fetch（默认的搜索方式）
DFS query and fetch
DFS query then fetch(可以更精确控制搜索打分和排名。)查看API的注释如下：
/**
* Same as {@link #QUERY_THEN_FETCH}, except for an initial scatter phase which goes and computes the distributed
* term frequencies for more accurate scoring.
*/
DFS_QUERY_THEN_FETCH((byte) 0),
/**
* The query is executed against all shards, but only enough information is returned (not the document content).
* The results are then sorted and ranked, and based on it, only the relevant shards are asked for the actual
* document content. The return number of hits is exactly as specified in size, since they are the only ones that
* are fetched. This is very handy when the index has a lot of shards (not replicas, shard id groups).
*/
QUERY_THEN_FETCH((byte) 1),
/**
* Same as {@link #QUERY_AND_FETCH}, except for an initial scatter phase which goes and computes the distributed
* term frequencies for more accurate scoring.
*/
DFS_QUERY_AND_FETCH((byte) 2),
/**
* The most naive (and possibly fastest) implementation is to simply execute the query on all relevant shards
* and return the results. Each shard returns size results. Since each shard already returns size hits, this
* type actually returns size times number of shards results back to the caller.
*/
QUERY_AND_FETCH((byte) 3),关于DFS的说明：
DFS是什么缩写？
这个D可能是Distributed，F可能是frequency的缩写，至于S可能是Scatter的缩写，整个单词可能是分布式词频率和
文档频率散发的缩写。
初始化散发是一个什么样的过程？
从es的官方网站我们可以发现，初始化散发其实就是在进行真正的查询之前，先把各个分片的词频率和文档频率收集一
下，然后进行词搜索的时候，各分片依据全局的词频率和文档频率进行搜索和排名。显然如果使用
DFS_QUERY_THEN_FETCH这种查询方式，效率是最低的，因为一个搜索，可能要请求3次分片。但，使用DFS方法，搜索
精度应该是最高的。总结：
总结一下，从性能考虑QUERY_AND_FETCH是最快的，DFS_QUERY_THEN_FETCH是最慢的。从搜索的准确度来说，DFS要
比非DFS的准确度更高。/**
* 1.精确查询
* termQuery
* term就是一个字段
*/
@Test
public void testSearch2() {
SearchRequestBuilder searchQuery = client.prepareSearch(indics) // 在prepareSearch()的参数为索引库列表，意为要从哪些索引库中进行查询
.setSearchType(SearchType.DEFAULT) // 设置查询类型，有QUERY_AND_FETCH QUERY_THEN_FETCH DFS_QUERY_AND_FETCH DFS_QUERY_THEN_FETCH
.setQuery(QueryBuilders.termQuery("author", "apache"))// 设置相应的query，用于检索，termQuery的参数说明：name是doc中的具体的field，value就是要找的具体的值
;
// 如果上面不加查询条件，则会查询所有
SearchResponse response = searchQuery.get();
showResult(response);
}/**
* 2.模糊查询
* prefixQuery
*/
@Test
public void testSearch3() {
SearchResponse response = client.prepareSearch(indics).setSearchType(SearchType.QUERY_THEN_FETCH)
.setQuery(QueryBuilders.prefixQuery("name", "h"))
.get();
showResult(response);
}/**
* 3.分页查询
* 查询索引库bank中
* 年龄在(25, 35]之间的数据信息
*
* 分页算法：
* 查询的第几页，每一页显示几条
* 每页显示10条记录
*
* 查询第4页的内容
* setFrom(30=(4-1)*size)
* setSize(10)
* 所以第N页的起始位置：(N - 1) * pageSize
*/
@Test
public void testSearch4() {
// 注意QUERY_THEN_FETCH和注意QUERY_AND_FETCH返回的记录数不一样，前者默认10条，后者是50条（5个分片）
SearchResponse response = client.prepareSearch(indics).setSearchType(SearchType.DFS_QUERY_THEN_FETCH)
.setQuery(QueryBuilders.rangeQuery("age").gt(25).lte(35))
// 下面setFrom和setSize用于设置查询结果进行分页
.setFrom(0)
.setSize(5)
.get();
showResult(response);
}/**
* 4.高亮显示查询
* 获取数据，
* 查询apache，不仅在author拥有，也可以在url，在name中也可能拥有
* author or url --->booleanQuery中的should操作
* 如果是and的类型--->booleanQuery中的must操作
* 如果是not的类型--->booleanQuery中的mustNot操作
* 使用的match操作，其实就是使用要查询的keyword和对应字段进行完整匹配，是否相等，相等返回
*/
@Test
public void testSearch5() {
SearchResponse response = client.prepareSearch(indics).setSearchType(SearchType.DEFAULT)
// .setQuery(QueryBuilders.multiMatchQuery("apache", "author", "url"))
// .setQuery(QueryBuilders.regexpQuery("url", ".*apache.*"))
// .setQuery(QueryBuilders.termQuery("author", "apache"))
.setQuery(QueryBuilders.boolQuery()
.should(QueryBuilders.regexpQuery("url", ".*apache.*"))
.should(QueryBuilders.termQuery("author", "apache")))
// 设置高亮显示--->设置相应的前置标签和后置标签
.setHighlighterPreTags("")
.setHighlighterPostTags("")
// 哪个字段要求高亮显示
.addHighlightedField("author")
.addHighlightedField("url")
.get();
SearchHits searchHits = response.getHits();
float maxScore = searchHits.getMaxScore(); // 查询结果中的最大文档得分
System.out.println("maxScore: " + maxScore);
long totalHits = searchHits.getTotalHits(); // 查询结果记录条数
System.out.println("totalHits: " + totalHits);
SearchHit[] hits = searchHits.getHits(); // 查询结果
System.out.println("当前返回结果记录条数：" + hits.length);
for(SearchHit hit : hits) {
System.out.println("========================================================");
Map highlightFields = hit.getHighlightFields();
for(Map.Entry me : highlightFields.entrySet()) {
System.out.println("--------------------------------------");
String key = me.getKey();
HighlightField highlightField = me.getValue();
String name = highlightField.getName();
System.out.println("key: " + key + ", name: " + name);
Text[] texts = highlightField.fragments();
String value = "";
for(Text text : texts) {
// System.out.println("text: " + text.toString());
value += text.toString();
}
System.out.println("value: " + value);
}
}
} /**
* 5.排序查询
* 对结果集进行排序
* balance（收入）由高到低
*/
@Test
public void testSearch6() {
// 注意QUERY_THEN_FETCH和注意QUERY_AND_FETCH返回的记录数不一样，前者默认10条，后者是50条（5个分片）
SearchResponse response = client.prepareSearch(indics).setSearchType(SearchType.DFS_QUERY_THEN_FETCH)
.setQuery(QueryBuilders.rangeQuery("age").gt(25).lte(35))
.addSort("balance", SortOrder.DESC)
// 下面setFrom和setSize用于设置查询结果进行分页
.setFrom(0)
.setSize(5)
.get();
showResult(response);
}/**
* 6.聚合查询：计算平均值
*/
@Test
public void testSearch7() {
indics = new String[]{"bank"};
// 注意QUERY_THEN_FETCH和注意QUERY_AND_FETCH返回的记录数不一样，前者默认10条，后者是50条（5个分片）
SearchResponse response = client.prepareSearch(indics).setSearchType(SearchType.DFS_QUERY_THEN_FETCH)
.setQuery(QueryBuilders.rangeQuery("age").gt(25).lte(35))
/*
select avg(age) as avg_name from person;
那么这里的avg("balance")--->就是返回结果avg_name这个别名
*/
.addAggregation(AggregationBuilders.avg("avg_balance").field("balance"))
.addAggregation(AggregationBuilders.max("max").field("balance"))
.get();
// System.out.println(response);
/*
response中包含的Aggregations
"aggregations" : {
"max" : {
"value" : 49741.0
},
"avg_balance" : {
"value" : 25142.137373737372
}
}
则一个aggregation为：
{
"value" : 49741.0
}
*/
Aggregations aggregations = response.getAggregations();
List aggregationList = aggregations.asList();
for(Aggregation aggregation : aggregationList) {
System.out.println("========================================");
String name = aggregation.getName();
// Map map = aggregation.getMetaData();
System.out.println("name: " + name);
// System.out.println(map);
Object obj = aggregation.getProperty("value");
System.out.println(obj);
}
/*Aggregation avgBalance = aggregations.get("avg_balance");
Object obj = avgBalance.getProperty("value");
System.out.println(obj);*/
} 如果我们的数据包含中文，而在查询时希望可以支持对中文进行分词搜索，那么ES本身依赖于Lucene的分词对中文就不佳了，这时就可以考虑使用其它分词方法，如这里要说明的IK中文分词，其集成到ES的步骤如下：
1)下载地址：
https://github.com/medcl/elasticsearch-analysis-ik
2)使用maven对源代码进行编译(mvn clean install -DskipTests)（package）
3)把编译后的target/releases下的zip文件拷贝到 ES_HOME/plugins/analysis-ik目录下面，然后解压
4)把下载的ik插件中的conf/ik目录拷贝到ES_HOME/config下
5)修改ES_HOME/config/elasticsearch.yml文件，添加index.analysis.analyzer.default.type: ik
(把IK设置为默认分词器,这一步是可选的)
6)重启es服务
7)测试分词效果需要说明的是，数据需要重新插入，并使用ik分词，即需要重新构建期望使用中文分词IK的索引库。
测试代码如下：
package cn.xpleaf.bigdata.elasticsearch;
import org.elasticsearch.action.search.SearchRequestBuilder;
import org.elasticsearch.action.search.SearchResponse;
import org.elasticsearch.action.search.SearchType;
import org.elasticsearch.client.transport.TransportClient;
import org.elasticsearch.common.settings.Settings;
import org.elasticsearch.common.text.Text;
import org.elasticsearch.common.transport.InetSocketTransportAddress;
import org.elasticsearch.common.transport.TransportAddress;
import org.elasticsearch.index.query.QueryBuilders;
import org.elasticsearch.search.SearchHit;
import org.elasticsearch.search.SearchHits;
import org.elasticsearch.search.aggregations.Aggregation;
import org.elasticsearch.search.aggregations.AggregationBuilders;
import org.elasticsearch.search.aggregations.Aggregations;
import org.elasticsearch.search.highlight.HighlightField;
import org.elasticsearch.search.sort.SortOrder;
import org.junit.After;
import org.junit.Before;
import org.junit.Test;
import java.net.InetAddress;
import java.net.UnknownHostException;
import java.util.List;
import java.util.Map;
/**
* 使用Java API来操作es集群
* Transport
* 代表了一个集群
* 我们客户端和集群通信是使用TransportClient
*
* 使用prepareSearch来完成全文检索之
* 中文分词
*/
public class ElasticSearchTest3 {
private TransportClient client;
private String index = "bigdata";
private String type = "product";
private String[] indics = {"chinese"};
@Before
public void setUp() throws UnknownHostException {
Settings settings = Settings.builder().put("cluster.name", "bigdata-08-28").build();
client = TransportClient.builder().settings(settings).build();
TransportAddress ta1 = new InetSocketTransportAddress(InetAddress.getByName("uplooking01"), 9300);
TransportAddress ta2 = new InetSocketTransportAddress(InetAddress.getByName("uplooking02"), 9300);
TransportAddress ta3 = new InetSocketTransportAddress(InetAddress.getByName("uplooking03"), 9300);
client.addTransportAddresses(ta1, ta2, ta3);
}
/**
* 中文分词的操作
* 1.查询以"中"开头的数据，有两条
* 2.查询以“中国”开头的数据，有0条
* 3.查询包含“烂”的数据，有1条
* 4.查询包含“烂摊子”的数据，有0条
* 分词：
* 为什么我们搜索China is the greatest country~
* 中文：中国最牛逼
*
* ×××
* 中华
* 人民
* 共和国
* 中华人民
* 人民共和国
* 华人
* 共和
* 特殊的中文分词法：
* 庖丁解牛
* IK分词法
* 搜狗分词法
*/
@Test
public void testSearch2() {
SearchResponse response = client.prepareSearch(indics) // 在prepareSearch()的参数为索引库列表，意为要从哪些索引库中进行查询
.setSearchType(SearchType.DEFAULT) // 设置查询类型，有QUERY_AND_FETCH QUERY_THEN_FETCH DFS_QUERY_AND_FETCH DFS_QUERY_THEN_FETCH
//.setQuery(QueryBuilders.prefixQuery("content", "烂摊子"))// 设置相应的query，用于检索，termQuery的参数说明：name是doc中的具体的field，value就是要找的具体的值
// .setQuery(QueryBuilders.regexpQuery("content", ".*烂摊子.*"))
.setQuery(QueryBuilders.prefixQuery("content", "中国"))
.get();
showResult(response);
}
/**
* 格式化输出查询结果
* @param response
*/
private void showResult(SearchResponse response) {
SearchHits searchHits = response.getHits();
float maxScore = searchHits.getMaxScore(); // 查询结果中的最大文档得分
System.out.println("maxScore: " + maxScore);
long totalHits = searchHits.getTotalHits(); // 查询结果记录条数
System.out.println("totalHits: " + totalHits);
SearchHit[] hits = searchHits.getHits(); // 查询结果
System.out.println("当前返回结果记录条数：" + hits.length);
for (SearchHit hit : hits) {
long version = hit.version();
String id = hit.getId();
String index = hit.getIndex();
String type = hit.getType();
float score = hit.getScore();
System.out.println("===================================================");
String source = hit.getSourceAsString();
System.out.println("version: " + version);
System.out.println("id: " + id);
System.out.println("index: " + index);
System.out.println("type: " + type);
System.out.println("score: " + score);
System.out.println("source: " + source);
}
}
@After
public void cleanUp() {
client.close();
}
}
相关测试代码已上传到GitHub：https://github.com/xpleaf/elasticsearch-study