转载请标明原处：http://blog.csdn.net/hu948162999/article/details/50563110
本来这块业务 是放到SolrCloud上去的 ， 然后 採用solr的facet统计查询，
详细代码參考之前写的文章：http://blog.csdn.net/hu948162999/article/details/50162643
近期遇到SolrCloud 遇到一些问题。。查询db时间过长，SolrCloud的长连接CloudSolrServer老timeout。索引的效率也不够满
意。为了稳定。临时先还原solr单机版本号（上线时，被运维打回来了）。搜索日志就用elasticsearch实时去处理。
大概流程：
基于日志系统ELK 的原型下。參考ELK处理nginx日志文章：http://blog.csdn.net/hu948162999/article/details/50502875
还是用logstash正则去解析搜索日志。搜索日志採用log4j生成。logstash检測到传递给elasticsearch。
log4j:
log4j.appender.E.layout.ConversionPattern= %d|%m%n
logstash配置
新增logstash_search.conf:
input {
file {
type => "searchword"
path => ["/home/work/log/hotword/data"]
}
}
filter {
grok {
match => [
"message", "%{TIMESTAMP_ISO8601:timestamp}|{%{GREEDYDATA:kvs}}"
]
}
kv {
source => "kvs"
field_split => ","
value_split => "="
trimkey => " "
}
date {
match => ["timestamp" , "YYYY-MM-dd HH:mm:ss,SSS"]
}
}
output {
elasticsearch {
hosts => ["host1:9200", "host2:9200", "host3:9200", "host4:9200"]
index => "searchword-%{+YYYY.MM.dd}"
}
}这里要注意聚合操作的时候。
Logstash 自带有一个优化好的模板。其默认的mapping，string类型都是analyzer。
也就是说，默认分
词是採用单字分词的。
改动默认的logstash mapping模板。參考 http://udn.yyuap.com/doc/logstash-best-practice-cn/output/elasticsearch.html
结构例如以下：
启动logstash：
nohup bin/logstash -f conf/logstash_search.conf &
运行搜索測试。
能够立即在elasticsearch的插件上看到该搜索行为日志的数据索引。这就是elk的实时性了。
elasticsearch java端
參考指定mapping和聚合查询代码：
Client client=esobj.getClient();
SearchResponse response = client.prepareSearch("searchword*").setTypes("searchword").addAggregation(AggregationBuilders.terms("hotword").field("keyword")).execute().actionGet();
Terms terms = response.getAggregations().get("hotword");
/**
* 初始化索引
* @param client
* @param indexName
* @param indexType
* @param cols
* @return 初始化成功,返回true。否则返回false
* @throws Exception
*/
public static boolean initIndexMapping(Client client, String indexName, String indexType, List<ColumnInfo> cols) throws Exception {
if(StringUtil.isEmpty(indexName) || StringUtil.isEmpty(indexType)) {
return false;
}
indexName = indexName.toLowerCase();
indexType = indexType.toLowerCase();
//推断索引库是否存在
if(indicesExists(client, indexName)) {
OpenIndexRequestBuilder openIndexBuilder = new OpenIndexRequestBuilder(client.admin().indices(), OpenIndexAction.INSTANCE);
openIndexBuilder.setIndices(indexName).execute().actionGet();
}else{
//不存在则新建索引库
client.admin().indices().prepareCreate(indexName).execute().actionGet();
}
TypesExistsRequest ter = new TypesExistsRequest(new String[]{indexName.toLowerCase()}, indexType);
boolean typeExists = client.admin().indices().typesExists(ter).actionGet().isExists();
//假设 存在 返回!不能覆盖mapping
if(typeExists) {
return true;
}
//定义索引字段属性
XContentBuilder mapping = jsonBuilder().startObject().startObject(indexType).startObject("properties");
for (ColumnInfo col : cols) {
String colName = col.getName().toLowerCase().trim();
String colType = col.getType().toLowerCase().trim();
if("string".equals(colType)) {
mapping.startObject(colName).field("type", colType).field("store", ""+col.isStore()).field("indexAnalyzer", col.getIndexAnalyzer()).field("searchAnalyzer", col.getSearchAnalyzer()).field("include_in_all", col.isStore()).field("boost", col.getBoost()).endObject();
}else if("long".equals(colType)) {
mapping.startObject(colName).field("type", colType).field("index", "not_analyzed").field("include_in_all", false).endObject();
}else if("date".equals(colType)) {
mapping.startObject(colName).field("type", colType).field("format", "yyyy-MM-dd HH:mm:ss||yyyy-MM-dd").field("index", "not_analyzed").field("include_in_all", false).endObject();
}else {
mapping.startObject(colName).field("type", "string").field("index", "not_analyzed").endObject();
}
}
mapping.endObject().endObject().endObject();
PutMappingRequest mappingRequest = Requests.putMappingRequest(indexName).type(indexType).source(mapping);
PutMappingResponse response = client.admin().indices().putMapping(mappingRequest).actionGet();
return response.isAcknowledged();
}