The best elasticsearch highlevel java rest api-----bboss
Elasticsearch Sliced Scroll分页检索案例分享
我们在文章《Elasticsearch Scroll分页检索案例分享》中介绍了elasticsearch scroll的基本用法，本文介绍Elasticsearch Sliced Scroll分页检索功能。
1.准备工作
参考文档《高性能elasticsearch ORM开发库使用介绍》导入和配置es客户端
2.定义Sliced Scroll检索dsl
创建配置文件-在resources目录下定义文件scroll.xml
esmapper/scroll.xml
文件内容包含Sliced Scroll检索dsl语句-scrollSliceQuery
<property name="scrollSliceQuery">
<![CDATA[
{
"slice": {
"id": $id,
"max": $max
},
"size":$size,
"query": {
"term" : {
"gc.jvmGcOldCount" : 3
}
}
}
]]>
</property>3.串行方式执行slice检索
/**
* 串行方式执行slice scroll操作
*/
@Test
public void testSliceScroll() {
ClientInterface clientUtil = ElasticSearchHelper.getConfigRestClientUtil("esmapper/scroll.xml");
List<String> scrollIds = new ArrayList<>();
long starttime = System.currentTimeMillis();
//scroll slice分页检索
int max = 6;
long realTotalSize = 0;
for (int i = 0; i < max; i++) {
Map params = new HashMap();
params.put("id", i);
params.put("max", max);//最多6个slice，不能大于share数
params.put("size", 100);//每页100条记录
ESDatas<Map> sliceResponse = clientUtil.searchList("agentstat-*/_search?scroll=1m",
"scrollSliceQuery", params,Map.class);
List<Map> sliceDatas = sliceResponse.getDatas();
realTotalSize = realTotalSize + sliceDatas.size();
long totalSize = sliceResponse.getTotalSize();
String scrollId = sliceResponse.getScrollId();
if (scrollId != null)
scrollIds.add(scrollId);
System.out.println("totalSize:" + totalSize);
System.out.println("scrollId:" + scrollId);
if (sliceDatas != null && sliceDatas.size() >= 100) {//每页100条记录，迭代scrollid，遍历scroll分页结果
do {
sliceResponse = clientUtil.searchScroll("1m", scrollId, Map.class);
String sliceScrollId = sliceResponse.getScrollId();
if (sliceScrollId != null)
scrollIds.add(sliceScrollId);
sliceDatas = sliceResponse.getDatas();
if (sliceDatas == null || sliceDatas.size() < 100) {
break;
}
realTotalSize = realTotalSize + sliceDatas.size();
} while (true);
}
}
//打印处理耗时和实际检索到的数据
long endtime = System.currentTimeMillis();
System.out.println("耗时："+(endtime - starttime)+",realTotalSize："+realTotalSize);
//查询存在es服务器上的scroll上下文信息
String scrolls = clientUtil.executeHttp("_nodes/stats/indices/search", ClientUtil.HTTP_GET);
System.out.println(scrolls);
//处理完毕后清除scroll上下文信息
if(scrollIds.size() > 0) {
scrolls = clientUtil.deleteScrolls(scrollIds);
System.out.println(scrolls);
}
//清理完毕后查看scroll上下文信息
scrolls = clientUtil.executeHttp("_nodes/stats/indices/search", ClientUtil.HTTP_GET);
System.out.println(scrolls);
}4.并行方式执行slice检索
//用来存放实际slice检索总记录数
long realTotalSize ;
//辅助方法，用来累计每次scroll获取到的记录数
synchronized void incrementSize(int size){
this.realTotalSize = this.realTotalSize + size;
}
/**
* 并行方式执行slice scroll操作
*/
@Test
public void testParralSliceScroll() {
final ClientInterface clientUtil = ElasticSearchHelper.getConfigRestClientUtil("esmapper/scroll.xml");
final List<String> scrollIds = new ArrayList<>();
long starttime = System.currentTimeMillis();
//scroll slice分页检索
final int max = 6;
final CountDownLatch countDownLatch = new CountDownLatch(max);//线程任务完成计数器，每个线程对应一个sclice,每运行完一个slice任务,countDownLatch计数减去1
<span class="hljs-keyword">for</span> (<span class="hljs-keyword">int</span> j = <span class="hljs-number">0</span>; j < max; j++) {<span class="hljs-comment">//启动max个线程，并行处理每个slice任务</span>
<span class="hljs-keyword">final</span> <span class="hljs-keyword">int</span> i = j;
Thread sliceThread = <span class="hljs-keyword">new</span> Thread(<span class="hljs-keyword">new</span> Runnable() {<span class="hljs-comment">//多线程并行执行scroll操作做，每个线程对应一个sclice</span>
<span class="hljs-meta">@Override</span>
<span class="hljs-function"><span class="hljs-keyword">public</span> <span class="hljs-keyword">void</span> <span class="hljs-title">run</span><span class="hljs-params">()</span> </span>{
Map params = <span class="hljs-keyword">new</span> HashMap();
params.put(<span class="hljs-string">"id"</span>, i);
params.put(<span class="hljs-string">"max"</span>, max);<span class="hljs-comment">//最多6个slice，不能大于share数</span>
params.put(<span class="hljs-string">"size"</span>, <span class="hljs-number">100</span>);<span class="hljs-comment">//每页100条记录</span>
ESDatas<Map> sliceResponse = clientUtil.searchList(<span class="hljs-string">"agentstat-*/_search?scroll=1m"</span>,
<span class="hljs-string">"scrollSliceQuery"</span>, params,Map.class);
List<Map> sliceDatas = sliceResponse.getDatas();
incrementSize( sliceDatas.size());<span class="hljs-comment">//统计实际处理的文档数量</span>
<span class="hljs-keyword">long</span> totalSize = sliceResponse.getTotalSize();
String scrollId = sliceResponse.getScrollId();
<span class="hljs-keyword">if</span> (scrollId != <span class="hljs-keyword">null</span>)
scrollIds.add(scrollId);
System.out.println(<span class="hljs-string">"totalSize:"</span> + totalSize);
System.out.println(<span class="hljs-string">"scrollId:"</span> + scrollId);
<span class="hljs-keyword">if</span> (sliceDatas != <span class="hljs-keyword">null</span> && sliceDatas.size() >= <span class="hljs-number">100</span>) {<span class="hljs-comment">//每页100条记录，迭代scrollid，遍历scroll分页结果</span>
<span class="hljs-keyword">do</span> {
sliceResponse = clientUtil.searchScroll(<span class="hljs-string">"1m"</span>, scrollId, Map.class);
String sliceScrollId = sliceResponse.getScrollId();
<span class="hljs-keyword">if</span> (sliceScrollId != <span class="hljs-keyword">null</span>)
scrollIds.add(sliceScrollId);
sliceDatas = sliceResponse.getDatas();
<span class="hljs-keyword">if</span> (sliceDatas == <span class="hljs-keyword">null</span> || sliceDatas.size() < <span class="hljs-number">100</span>) {
<span class="hljs-keyword">break</span>;
}
incrementSize( sliceDatas.size());<span class="hljs-comment">//统计实际处理的文档数量</span>
} <span class="hljs-keyword">while</span> (<span class="hljs-keyword">true</span>);
}
countDownLatch.countDown();<span class="hljs-comment">//slice检索完毕后计数器减1</span>
}
});
sliceThread.start();<span class="hljs-comment">//启动线程</span>
}
<span class="hljs-keyword">try</span> {
countDownLatch.await();<span class="hljs-comment">//等待所有的线程执行完毕,计数器变成0</span>
} <span class="hljs-keyword">catch</span> (InterruptedException e) {
e.printStackTrace();
}
<span class="hljs-comment">//打印处理耗时和实际检索到的数据</span>
<span class="hljs-keyword">long</span> endtime = System.currentTimeMillis();
System.out.println(<span class="hljs-string">"耗时："</span>+(endtime - starttime)+<span class="hljs-string">",realTotalSize："</span>+realTotalSize);
<span class="hljs-comment">//查询存在es服务器上的scroll上下文信息</span>
String scrolls = clientUtil.executeHttp(<span class="hljs-string">"_nodes/stats/indices/search"</span>, ClientUtil.HTTP_GET);
// System.out.println(scrolls);
//处理完毕后清除scroll上下文信息
if(scrollIds.size() > 0) {
scrolls = clientUtil.deleteScrolls(scrollIds);
// System.out.println(scrolls);
}
//清理完毕后查看scroll上下文信息
scrolls = clientUtil.executeHttp("_nodes/stats/indices/search", ClientUtil.HTTP_GET);
// System.out.println(scrolls);
}
通过串行运行和并行运行结果比较，并行处理的性能要好很多，实际检索到的文档数量等价一致。
5.参考文档
https://www.elastic.co/guide/en/elasticsearch/reference/6.2/search-request-scroll.html
6.开发交流
elasticsearch技术交流群：166471282
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