参照网页:单机搭建elasticsearch和mongodb的river
三个步骤:
一,搭建单机replicSet
二,安装mongodb-river插件
三,创建meta,验证使用
第一步,搭建单机mongodb的replSet
1,配置/etc/mongodb.conf
增加两个配置:
replSet=rs0 #这里是指定replSet的名字
oplogSize=100 #这里是指定oplog表数据大小(太大了不支持)
启动mongodb:bin/mongod --fork --logpath /data/db/mongodb.log -f /etc/mongodb.conf
2,初始化replicSet
root# bin/mongo
>rs.initiate( {"_id" : "rs0", "version" : 1, "members" : [ { "_id" : 0, "host" : "127.0.0.1:27017" } ]})
3,搭建好replicSet之后,退出mongo shell重新登录,提示符会变成:
rs0:PRIMARY>
第二步, 安装mongodb-river插件
插件项目:https://github.com/richardwilly98/elasticsearch-river-mongodb
安装插件命令:
bin/plugin --install com.github.richardwilly98.elasticsearch/elasticsearch-river-mongodb/2.0.0
完毕后启动elasticsearch,正常会显示如下提示信息:
root# bin/elasticsearch ...
[2014-03-14 19:28:34,179][INFO ][plugins] [Super Rabbit] loaded [mongodb-river], sites [river-mongodb]
[2014-03-14 19:28:41,032][INFO ][org.elasticsearch.river.mongodb.MongoDBRiver] Starting river mongodb_test
[2014-03-14 19:28:41,087][INFO ][org.elasticsearch.river.mongodb.MongoDBRiver] MongoDB River Plugin - version[2.0.0] - hash[a0c23f1] - time[2014-02-23T20:40:05Z]
[2014-03-14 19:28:41,087][INFO ][org.elasticsearch.river.mongodb.MongoDBRiver] starting mongodb stream. options: secondaryreadpreference [false], drop_collection [false], include_collection [], throttlesize [], gridfs [false], filter [null], db [test], collection [page], script [null], indexing to [test]/[page]
[2014-03-14 19:28:41,303][INFO ][org.elasticsearch.river.mongodb.MongoDBRiver] MongoDB version - 2.2.7
第三步,创建meta信息
1,创建mongodb连接
root# curl -XPUT "localhost:9200/_river/mongodb_mytest/_meta" -d '
> {
> "type": "mongodb",
> "mongodb": {
> "host": "localhost",
> "port": "27017",
> "db": "testdb",
> "collection": "testcollection"
> },
> "index": {
> "name": "testdbindex",
> "type": "testcollection"} }'
{"_index":"_river","_type":"mongodb_mytest","_id":"_meta","_version":1,"created":true}'
返回created为true,表示创建成功,也可通过curl "http://localhost:9200/_river/mongodb_mytest/_meta"查看
主要分为三个部分:
type:river的类型,也就是“mongodb”
mongodb:mongodb的连接信息
index:elastisearch中用于接收mongodb数据的索引index和“type”。
其中mongodb_mytest为${es.river.name},每个索引名称都不一样,如果重复插入会导致索引被覆盖的问题。
2,往mongodb插入数据
rs0:PRIMARY> db.testcollection.save({name:"stone"})
3,自定义查询
root# curl -XGET 'http://localhost:9200/testdbindex/_search?q=name:stone'
{"took":2,"timed_out":false,"_shards":{"total":5,"successful":5,"failed":0},"hits":{"total":1,"max_score":0.30685282,"hits":[{"_index":"testdbindex","_type":"testcollection","_id":"5322eb23fdfc233ffcfa02bb","_score":0.30685282, "_source" : {"_id":"5322eb23fdfc233ffcfa02bb","name":"stone"}}]}}
一个问题(我这边测试不存在这个问题,创建meta后之前mongodb中已存在的数据也会被索引,不过还是把原作者的解决方案放在下面吧)
"在river建立之后的数据变动会体现在elasticsearh里,但是river建立前的数据变动因为没有在oplog表里,不能被同步。解决方案是,遍历一次需要导出的表,重新插入到另外一个表里,然后将river指定到这个新表,这样新表的变动就可以全部体现在oplog里了。"
遍历mongodb的表可以通过cursor来实现:
var myCursor = db.oldcollection.find( { }, {html:0} );
myCursor.forEach(function(myDoc) {db.newcollection.save(myDoc); });
附:mongodb&mongodb-river(elasticsearch)部署
elasticsearch使用示例如下:(index索引 对应 database数据库,type类型 对应 table数据表)
1,查询单个索引条目
curl -XGET 'http://localhost:9200/testdbindex/testcollection/532a45ad94af83f0122292cf'
{"_index":"testdbindex","_type":"testcollection","_id":"532a45ad94af83f0122292cf","_version":1,"found":true, "_source" : {"_id":"532a45ad94af83f0122292cf","name":"stone"}} 2,查询多个索引条目
curl 'localhost:9200/testdbindex/testcollection/_mget' -d '{
"ids":["532a40f51d82291684692d1d","532a45ad94af83f0122292cf"]
}' 3,搜索指定域(类似关系型数据库列字段)
curl -XGET 'http://localhost:9200/testdbindex/testcollection/532a40f51d82291684692d1d?fields=title' 4,搜索
curl -XGET 'http://localhost:9200/testdbindex/testcollection/_search' -d '{
"query":{
"term" : {"name":"penjin"}
}
}' 5,在所有type类型里面搜索name=stone
curl -XGET 'http://localhost:9200/testdbindex/_search?q=name:stone' 6,在指定type为testcollection里面搜索
curl -XGET 'http://localhost:9200/testdbindex/testcollection/_search?q=name:stone' 7
查找count数目
curl -XGET 'http://localhost:9200/testdbindex/testcollection/_count?q=name:stone'
curl -XGET 'http://localhost:9200/testdbindex/_count?q=name:stone' curl -XGET 'http://localhost:9200/testdbindex/blogs/_count' -d '
{
"query" : {
"term" : { "name" : "stone" }
}
}' 8,复杂查询
/**
* 1,指定查询起始及数目
* 2,指定排序
* 3,查询指定域
* 4,查询条件
*/
curl -XGET 'http://localhost:9200/testdbindex/blogs/_search' -d '
{
"from" : 0, "size" : 10,
"sort" : [
{ "name" : "desc" }
],
"fields" : ["name"],
"query" : {
"term" : { "name" : "stone" }
}
}'
/**
* 依赖分词
*/
curl -XGET 'http://localhost:9200/testdbindex/blogs/_search' -d '
{
"query" : {
"match" : {
_all : "stone"
}
}
}'
/**
* 类似数据库like语句
*/
curl -XGET 'http://localhost:9200/testdbindex/blogs/_search' -d '
{
"query" : {
"fuzzy_like_this" : {
"fields" : ["name"],
"like_text" : "ston",
"max_query_terms" : 12
}
}
}' 9,更多高级查询参照elasticsearch官方页面
如果索引数据多了,elasticsearch的data目录会很大,如果不得不清理磁盘的话,删除索引即可。一般情况需要扩容磁盘。
root# curl -XDELETE 'http://localhost:9200/testdbindex'
root# curl -XDELETE 'http://localhost:9200/_river' (这行不需要)
{"acknowledged":true}
java语言使用jar包查询等操作也很方便(依赖elasticsearch.jar与lucene-core.jar包,es的安装包解压后lib目录下有)
package com.ciaos; import java.util.Iterator;
import java.util.Map.Entry; import org.elasticsearch.action.search.SearchResponse;
import org.elasticsearch.action.search.SearchType;
import org.elasticsearch.client.transport.TransportClient;
import org.elasticsearch.common.transport.InetSocketTransportAddress;
import org.elasticsearch.common.unit.TimeValue;
import org.elasticsearch.index.query.QueryBuilder;
import org.elasticsearch.index.query.QueryBuilders;
import org.elasticsearch.search.SearchHit; public class EsDemo { private static TransportClient client = null; public static void GetConnection(){
client = new TransportClient().addTransportAddress(new InetSocketTransportAddress(
"127.0.0.1", 9300));
} public static void searchIndex() { QueryBuilder qb = QueryBuilders.termQuery("name", "stone"); SearchResponse scrollResp = client.prepareSearch("testdbindex")
.setSearchType(SearchType.SCAN)
.setScroll(new TimeValue(60000))
.setQuery(qb.buildAsBytes())
.setSize(100).execute().actionGet();
while (true) {
scrollResp = client.prepareSearchScroll(scrollResp.getScrollId()).setScroll(new TimeValue(600000)).execute().actionGet();
boolean hitsRead = false;
for (SearchHit hit : scrollResp.getHits()) {
hitsRead = true;
Iterator<Entry<String, Object>> rpItor = hit.getSource().entrySet().iterator();
while (rpItor.hasNext()) {
Entry<String, Object> rpEnt = rpItor.next();
System.out.println(rpEnt.getKey() + " : " + rpEnt.getValue());
}
}
if (!hitsRead) {
break;
}
}
} public static void main(String[] args) {
// TODO Auto-generated method stub
GetConnection();
searchIndex(); client.close();
}
}
运行结果如下:
_id : 532a49e294af83f0122292d3
name : stone
_id : 532a45ad94af83f0122292cf
name : stone