Elasticsearch Sliced Scroll分页检索案例分享
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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
for (int j = 0; j < max; j++) {//启动max个线程，并行处理每个slice任务
final int i = j;
Thread sliceThread = new Thread(new Runnable() {//多线程并行执行scroll操作做，每个线程对应一个sclice
@Override
public void run() {
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();
incrementSize( sliceDatas.size());//统计实际处理的文档数量
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;
}
incrementSize( sliceDatas.size());//统计实际处理的文档数量
} while (true);
}
countDownLatch.countDown();//slice检索完毕后计数器减1
}
});
sliceThread.start();//启动线程
}
try {
countDownLatch.await();//等待所有的线程执行完毕,计数器变成0
} catch (InterruptedException e) {
e.printStackTrace();
}
//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);
}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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