ElasticSearch的基本用法与集群搭建
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一、简介
ElasticSearch和Solr都是基于Lucene的搜索引擎，不过ElasticSearch天生支持分布式，而Solr是4.0版本后的SolrCloud才是分布式版本，Solr的分布式支持需要ZooKeeper的支持。
这里有一个详细的ElasticSearch和Solr的对比：http://solr-vs-elasticsearch.com/
二、基本用法
Elasticsearch集群可以包含多个索引（indices），每一个索引可以包含多个类型（types），每一个类型包含多个文档（documents），然后每个文档包含多个字段（Fields），这种面向文档型的储存，也算是NoSQL的一种吧。
ES比传统关系型数据库，对一些概念上的理解：
Relational DB -> Databases -> Tables -> Rows -> Columns
Elasticsearch -> Indices -> Types -> Documents -> Fields从创建一个Client到添加、删除、查询等基本用法：
1、创建Client
public ElasticSearchService(String ipAddress, int port) {
client = new TransportClient()
.addTransportAddress(new InetSocketTransportAddress(ipAddress,
port));
}
这里是一个TransportClient。
ES下两种客户端对比：
TransportClient：轻量级的Client，使用Netty线程池，Socket连接到ES集群。本身不加入到集群，只作为请求的处理。
Node Client：客户端节点本身也是ES节点，加入到集群，和其他ElasticSearch节点一样。频繁的开启和关闭这类Node Clients会在集群中产生“噪音”。
2、创建/删除Index和Type信息
// 创建索引
public void createIndex() {
client.admin().indices().create(new CreateIndexRequest(IndexName))
.actionGet();
}
// 清除所有索引
public void deleteIndex() {
IndicesExistsResponse indicesExistsResponse = client.admin().indices()
.exists(new IndicesExistsRequest(new String[] { IndexName }))
.actionGet();
if (indicesExistsResponse.isExists()) {
client.admin().indices().delete(new DeleteIndexRequest(IndexName))
.actionGet();
}
}
// 删除Index下的某个Type
public void deleteType(){
client.prepareDelete().setIndex(IndexName).setType(TypeName).execute().actionGet();
}
// 定义索引的映射类型
public void defineIndexTypeMapping() {
try {
XContentBuilder mapBuilder = XContentFactory.jsonBuilder();
mapBuilder.startObject()
.startObject(TypeName)
.startObject("properties")
.startObject(IDFieldName).field("type", "long").field("store", "yes").endObject()
.startObject(SeqNumFieldName).field("type", "long").field("store", "yes").endObject()
.startObject(IMSIFieldName).field("type", "string").field("index", "not_analyzed").field("store", "yes").endObject()
.startObject(IMEIFieldName).field("type", "string").field("index", "not_analyzed").field("store", "yes").endObject()
.startObject(DeviceIDFieldName).field("type", "string").field("index", "not_analyzed").field("store", "yes").endObject()
.startObject(OwnAreaFieldName).field("type", "string").field("index", "not_analyzed").field("store", "yes").endObject()
.startObject(TeleOperFieldName).field("type", "string").field("index", "not_analyzed").field("store", "yes").endObject()
.startObject(TimeFieldName).field("type", "date").field("store", "yes").endObject()
.endObject()
.endObject()
.endObject();
PutMappingRequest putMappingRequest = Requests
.putMappingRequest(IndexName).type(TypeName)
.source(mapBuilder);
client.admin().indices().putMapping(putMappingRequest).actionGet();
} catch (IOException e) {
log.error(e.toString());
}
}
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")。
详情参考：https://www.elastic.co/guide/en/elasticsearch/guide/current/mapping-intro.html
3、索引数据
// 批量索引数据
public void indexHotSpotDataList(List<Hotspotdata> dataList) {
if (dataList != null) {
int size = dataList.size();
if (size > 0) {
BulkRequestBuilder bulkRequest = client.prepareBulk();
for (int i = 0; i < size; ++i) {
Hotspotdata data = dataList.get(i);
String jsonSource = getIndexDataFromHotspotData(data);
if (jsonSource != null) {
bulkRequest.add(client
.prepareIndex(IndexName, TypeName,
data.getId().toString())
.setRefresh(true).setSource(jsonSource));
}
}
BulkResponse bulkResponse = bulkRequest.execute().actionGet();
if (bulkResponse.hasFailures()) {
Iterator<BulkItemResponse> iter = bulkResponse.iterator();
while (iter.hasNext()) {
BulkItemResponse itemResponse = iter.next();
if (itemResponse.isFailed()) {
log.error(itemResponse.getFailureMessage());
}
}
}
}
}
}
// 索引数据
public boolean indexHotspotData(Hotspotdata data) {
String jsonSource = getIndexDataFromHotspotData(data);
if (jsonSource != null) {
IndexRequestBuilder requestBuilder = client.prepareIndex(IndexName,
TypeName).setRefresh(true);
requestBuilder.setSource(jsonSource)
.execute().actionGet();
return true;
}
return false;
}
// 得到索引字符串
public String getIndexDataFromHotspotData(Hotspotdata data) {
String jsonString = null;
if (data != null) {
try {
XContentBuilder jsonBuilder = XContentFactory.jsonBuilder();
jsonBuilder.startObject().field(IDFieldName, data.getId())
.field(SeqNumFieldName, data.getSeqNum())
.field(IMSIFieldName, data.getImsi())
.field(IMEIFieldName, data.getImei())
.field(DeviceIDFieldName, data.getDeviceID())
.field(OwnAreaFieldName, data.getOwnArea())
.field(TeleOperFieldName, data.getTeleOper())
.field(TimeFieldName, data.getCollectTime())
.endObject();
jsonString = jsonBuilder.string();
} catch (IOException e) {
log.equals(e);
}
}
return jsonString;
}
ES支持批量和单个数据索引。
4、查询获取数据
// 获取少量数据100个
private List<Integer> getSearchData(QueryBuilder queryBuilder) {
List<Integer> ids = new ArrayList<>();
SearchResponse searchResponse = client.prepareSearch(IndexName)
.setTypes(TypeName).setQuery(queryBuilder).setSize(100)
.execute().actionGet();
SearchHits searchHits = searchResponse.getHits();
for (SearchHit searchHit : searchHits) {
Integer id = (Integer) searchHit.getSource().get("id");
ids.add(id);
}
return ids;
}
// 获取大量数据
private List<Integer> getSearchDataByScrolls(QueryBuilder queryBuilder) {
List<Integer> ids = new ArrayList<>();
// 一次获取100000数据
SearchResponse scrollResp = client.prepareSearch(IndexName)
.setSearchType(SearchType.SCAN).setScroll(new TimeValue(60000))
.setQuery(queryBuilder).setSize(100000).execute().actionGet();
while (true) {
for (SearchHit searchHit : scrollResp.getHits().getHits()) {
Integer id = (Integer) searchHit.getSource().get(IDFieldName);
ids.add(id);
}
scrollResp = client.prepareSearchScroll(scrollResp.getScrollId())
.setScroll(new TimeValue(600000)).execute().actionGet();
if (scrollResp.getHits().getHits().length == 0) {
break;
}
}
return ids;
}
这里的QueryBuilder是一个查询条件，ES支持分页查询获取数据，也可以一次性获取大量数据，需要使用Scroll Search。
5、聚合（Aggregation Facet）查询
// 得到某段时间内设备列表上每个设备的数据分布情况<设备ID，数量>
public Map<String, String> getDeviceDistributedInfo(String startTime,
String endTime, List<String> deviceList) {
Map<String, String> resultsMap = new HashMap<>();
QueryBuilder deviceQueryBuilder = getDeviceQueryBuilder(deviceList);
QueryBuilder rangeBuilder = getDateRangeQueryBuilder(startTime, endTime);
QueryBuilder queryBuilder = QueryBuilders.boolQuery()
.must(deviceQueryBuilder).must(rangeBuilder);
TermsBuilder termsBuilder = AggregationBuilders.terms("DeviceIDAgg").size(Integer.MAX_VALUE)
.field(DeviceIDFieldName);
SearchResponse searchResponse = client.prepareSearch(IndexName)
.setQuery(queryBuilder).addAggregation(termsBuilder)
.execute().actionGet();
Terms terms = searchResponse.getAggregations().get("DeviceIDAgg");
if (terms != null) {
for (Terms.Bucket entry : terms.getBuckets()) {
resultsMap.put(entry.getKey(),
String.valueOf(entry.getDocCount()));
}
}
return resultsMap;
}
Aggregation查询可以查询类似统计分析这样的功能：如某个月的数据分布情况，某类数据的最大、最小、总和、平均值等。
详情参考：https://www.elastic.co/guide/en/elasticsearch/client/java-api/current/java-aggs.html
三、集群配置
配置文件elasticsearch.yml
集群名和节点名：
#cluster.name: elasticsearch
#node.name: "Franz Kafka"
是否参与master选举和是否存储数据
#node.master: true
#node.data: true
分片数和副本数
#index.number_of_shards: 5
#index.number_of_replicas: 1
master选举最少的节点数，这个一定要设置为整个集群节点个数的一半加1，即N/2+1
#discovery.zen.minimum_master_nodes: 1
discovery ping的超时时间，拥塞网络，网络状态不佳的情况下设置高一点
#discovery.zen.ping.timeout: 3s
注意，分布式系统整个集群节点个数N要为奇数个！！
如何避免ElasticSearch发生脑裂（brain split）：http://blog.trifork.com/2013/10/24/how-to-avoid-the-split-brain-problem-in-elasticsearch/
即使集群节点个数为奇数，minimum_master_nodes为整个集群节点个数一半加1，也难以避免脑裂的发生，详情看讨论：https://github.com/elastic/elasticsearch/issues/2488
四、Elasticsearch插件
1、elasticsearch-head是一个elasticsearch的集群管理工具：./elasticsearch-1.7.1/bin/plugin -install mobz/elasticsearch-head
2、elasticsearch-sql：使用SQL语法查询elasticsearch：./bin/plugin -u https://github.com/NLPchina/elasticsearch-sql/releases/download/1.3.5/elasticsearch-sql-1.3.5.zip --install sql
github地址：https://github.com/NLPchina/elasticsearch-sql
3、elasticsearch-bigdesk是elasticsearch的一个集群监控工具，可以通过它来查看ES集群的各种状态。
安装：./bin/plugin -install lukas-vlcek/bigdesk
访问：http://192.103.101.203:9200/_plugin/bigdesk/，
4、elasticsearch-servicewrapper插件是ElasticSearch的服务化插件，
在https://github.com/elasticsearch/elasticsearch-servicewrapper下载该插件后，解压缩，将service目录拷贝到elasticsearch目录的bin目录下。
而后，可以通过执行以下语句安装、启动、停止ElasticSearch：
sh elasticsearch install
sh elasticsearch start
sh elasticsearch stop
参考：
https://www.elastic.co/guide/en/elasticsearch/client/java-api/current/index.html
https://www.elastic.co/guide/en/elasticsearch/reference/current/index.html
http://stackoverflow.com/questions/10213009/solr-vs-elasticsearch
http://www.cnblogs.com/luxiaoxun/archive/2015/10/11/4869509.html
解决bigdesk环境搭建问题：
e:\\elasticsearch-2.3.4\\bin>plugin.bat install lukas-vlcek/bigdesk/2.5.0
-> Installing lukas-vlcek/bigdesk/2.5.0...
Trying https://download.elastic.co/lukas-vlcek/bigdesk/bigdesk-2.5.0.zip ...
Trying https://search.maven.org/remotecontent?filepath=lukas-vlcek/bigdesk/2.5.0/bigdesk-2.5.0.zip ...
Trying https://oss.sonatype.org/service/local/repositories/releases/content/luka
s-vlcek/bigdesk/2.5.0/bigdesk-2.5.0.zip ...
Trying https://github.com/lukas-vlcek/bigdesk/archive/2.5.0.zip ...
Trying https://github.com/lukas-vlcek/bigdesk/archive/master.zip ...
Downloading ....................................................................
................................................................................
................................................................................
......................DONE
Verifying https://github.com/lukas-vlcek/bigdesk/archive/master.zip checksums if available ...
NOTE: Unable to verify checksum for downloaded plugin (unable to find .sha1 or .md5 file to verify)
ERROR: Could not find plugin descriptor \'plugin-descriptor.properties\' in plugin zip
解决办法：https://github.com/lukas-vlcek/bigdesk/issues/86
I have modified bigdesk code to be compatible with elasticsearch 2.x https://github.com/nishantsaini/bigdesk Hope this helps
e:\\elasticsearch-2.3.4\\bin>plugin.bat install nishantsaini/bigdesk
-> Installing nishantsaini/bigdesk...
Trying https://github.com/nishantsaini/bigdesk/archive/master.zip ...
Downloading ....................................................................
................................................................................
................................................................................
......................DONE
Verifying https://github.com/nishantsaini/bigdesk/archive/master.zip checksums if available ...
NOTE: Unable to verify checksum for downloaded plugin (unable to find .sha1 or .md5 file to verify)
Installed bigdesk into e:\\elasticsearch-2.3.4\\plugins\\bigdesk
e:\\elasticsearch-2.3.4\\bin>
e:\\elasticsearch-2.3.4\\bin>
e:\\elasticsearch-2.3.4\\bin>plugin.bat list
Installed plugins in e:\\elasticsearch-2.3.4\\plugins:
- bigdesk
- head
- ik
查看效果：
http://localhost:9200/_plugin/bigdesk/
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