Elastic Stack--17--Scroll 查询

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Scroll 案例

Elastic Stack–16–ES三种分页策略

• 如果你搜索不经常更改的文档，则使用标准查询的分页效果非常好； 否则，使用实时数据执行分页会返回不可预测的结果。 为了绕过这个问题，Elasticsearch 在查询中提供了一个额外的参数：scroll。

准备数据

在今天的练习中，为了说明问题的方便，我们使用如下的数据来进行练习：

POST _bulk
{ "index" : { "_index" : "twitter", "_id": 1} }
{"user":"双榆树-张三","message":"今儿天气不错啊，出去转转去","uid":2,"age":20,"city":"北京","province":"北京","country":"中国","address":"中国北京市海淀区","location":{"lat":"39.970718","lon":"116.325747"}}
{ "index" : { "_index" : "twitter", "_id": 2 }}
{"user":"东城区-老刘","message":"出发，下一站云南！","uid":3,"age":30,"city":"北京","province":"北京","country":"中国","address":"中国北京市东城区台基厂三条3号","location":{"lat":"39.904313","lon":"116.412754"}}
{ "index" : { "_index" : "twitter", "_id": 3} }
{"user":"东城区-李四","message":"happy birthday!","uid":4,"age":30,"city":"北京","province":"北京","country":"中国","address":"中国北京市东城区","location":{"lat":"39.893801","lon":"116.408986"}}
{ "index" : { "_index" : "twitter", "_id": 4} }
{"user":"朝阳区-老贾","message":"123,gogogo","uid":5,"age":35,"city":"北京","province":"北京","country":"中国","address":"中国北京市朝阳区建国门","location":{"lat":"39.718256","lon":"116.367910"}}
{ "index" : { "_index" : "twitter", "_id": 5} }
{"user":"朝阳区-老王","message":"Happy BirthDay My Friend!","uid":6,"age":50,"city":"北京","province":"北京","country":"中国","address":"中国北京市朝阳区国贸","location":{"lat":"39.918256","lon":"116.467910"}}
{ "index" : { "_index" : "twitter", "_id": 6} }
{"user":"虹桥-老吴","message":"好友来了都今天我生日，好友来了,什么 birthday happy 就成!","uid":7,"age":90,"city":"上海","province":"上海","country":"中国","address":"中国上海市闵行区","location":{"lat":"31.175927","lon":"121.383328"}}

• 在上面，我们写入6个文档到 Elasticsearch 中。在练习中，我将设置每页的文档数为 2。我们可以进行如下的搜索：

GET twitter/_search
{"query": {"bool": {"must": [{"match": {"city": "北京"}}],"filter": [{"range": {"age": {"gte": 0,"lte": 100}}}]}},"size": 2
}

上面的搜索显示搜索结果中的前两个：

{"took": 0,"timed_out": false,"_shards": {"total": 1,"successful": 1,"skipped": 0,"failed": 0},"hits": {"total": {"value": 5,"relation": "eq"},"max_score": 0.48232412,"hits": [{"_index": "twitter","_id": "1","_score": 0.48232412,"_source": {"user": "双榆树-张三","message": "今儿天气不错啊，出去转转去","uid": 2,"age": 20,"city": "北京","province": "北京","country": "中国","address": "中国北京市海淀区"}},{"_index": "twitter","_id": "2","_score": 0.48232412,"_source": {"user": "东城区-老刘","message": "出发，下一站云南！","uid": 3,"age": 30,"city": "北京","province": "北京","country": "中国","address": "中国北京市东城区台基厂三条3号"}}]}
}

scroll 参数

• 从上面的显示结果中，我们可以看出来，它共有5个文档是满足搜索的条件的。按照每页 2 个文档，我们共有 3页。那么我们该如何对搜索结果进行分页呢？我们可以使用 scroll 参数：

GET twitter/_search?scroll=2m
{"query": {"bool": {"must": [{"match": {"city": "北京"}}],"filter": [{"range": {"age": {"gte": 0,"lte": 100}}}]}},"size": 2
}

在上面，2m 代表2分钟之内有效。它返回的结果为：

{"_scroll_id": "FGluY2x1ZGVfY29udGV4dF91dWlkDXF1ZXJ5QW5kRmV0Y2gBFi1rOUlBMFdGU2tLSS0yTlMyUkdRdUEAAAAAAAFeHBZReU4zSnhXVlR5eW5WQW5Yb09RSHNR","took": 0,"timed_out": false,"_shards": {"total": 1,"successful": 1,"skipped": 0,"failed": 0},"hits": {"total": {"value": 5,"relation": "eq"},"max_score": 0.48232412,"hits": [{"_index": "twitter","_id": "1","_score": 0.48232412,"_source": {"user": "双榆树-张三","message": "今儿天气不错啊，出去转转去","uid": 2,"age": 20,"city": "北京","province": "北京","country": "中国","address": "中国北京市海淀区"}},{"_index": "twitter","_id": "2","_score": 0.48232412,"_source": {"user": "东城区-老刘","message": "出发，下一站云南！","uid": 3,"age": 30,"city": "北京","province": "北京","country": "中国","address": "中国北京市东城区台基厂三条3号"}}]}
}

显然，它返回了第一个页的两个结果，但是它同时返回了一个 _scroll_id。我们可以运用这个 _scroll_id 来返回第二页的搜索结果：

GET _search/scroll
{"scroll": "2m","scroll_id": "FGluY2x1ZGVfY29udGV4dF91dWlkDXF1ZXJ5QW5kRmV0Y2gBFi1rOUlBMFdGU2tLSS0yTlMyUkdRdUEAAAAAAAFeHBZReU4zSnhXVlR5eW5WQW5Yb09RSHNR"
}

上面的返回结果为：

{"_scroll_id": "FGluY2x1ZGVfY29udGV4dF91dWlkDXF1ZXJ5QW5kRmV0Y2gBFi1rOUlBMFdGU2tLSS0yTlMyUkdRdUEAAAAAAAFeHBZReU4zSnhXVlR5eW5WQW5Yb09RSHNR","took": 1,"timed_out": false,"_shards": {"total": 1,"successful": 1,"skipped": 0,"failed": 0},"hits": {"total": {"value": 5,"relation": "eq"},"max_score": 0.48232412,"hits": [{"_index": "twitter","_id": "3","_score": 0.48232412,"_source": {"user": "东城区-李四","message": "happy birthday!","uid": 4,"age": 30,"city": "北京","province": "北京","country": "中国","address": "中国北京市东城区"}},{"_index": "twitter","_id": "4","_score": 0.48232412,"_source": {"user": "朝阳区-老贾","message": "123,gogogo","uid": 5,"age": 35,"city": "北京","province": "北京","country": "中国","address": "中国北京市朝阳区建国门"}}]}
}

我们可以运用返回的 _scroll_id 再接着返回接下来的搜索结果，直到我们的 hits 里的数组里没有数据为止。

清理上下文

• 如果完成此过程，则需要清理上下文，因为上下文在超时之前仍会占用计算资源。 如下面的屏幕快照所示，您可以使用 scroll_id 参数在DELETE API 中指定一个或多个上下文：

DELTE_search/scroll
{"scroll_id":"DXF1ZXJ5QW5kRmV0Y2gBAAAAAAAAHC8WWUdCVlRMUllRb3UzMkdqb0IxVnZNUQ=="
}

Java client APIs 来实现分页

案例1

• 首先我们创建一个叫做 Twitter 的 class：

public class Twitter {private String user;private long uid;private String province;private String message;private String country;private String city;private long age;private String address;public Twitter() {}public Twitter(String user, long uid, String province, String message,String country, String city, long age, String address) {this.user = user;this.uid = uid;this.province = province;this.message = message;this.country = country;this.city = city;this.age = age;this.address = address;}public String getUser() {return user;}public long getUid() {return uid;}public String getProvince() {return province;}public String getMessage() {return message;}public String getCountry() {return country;}public String getCity() {return city;}public long getAge() {return age;}public String getAddress() {return address;}public void setUser(String user) {this.user = user;}public void setUid(long uid) {this.uid = uid;}public void setProvince(String province) {this.province = province;}public void setMessage(String message) {this.message = message;}public void setCountry(String country) {this.country = country;}public void setCity(String city) {this.city = city;}public void setAge(long age) {this.age = age;}public void setAddress(String address) {this.address = address;}
}

这个和上面的 twitter 文档相对应。

我们接下来连接到 Elasticsearch 集群。一旦连接到 Elasticsearch 后，我们可以设计如下的代码来对搜索的结果进行分页：

ElasticsearchJava.java

        final String INDEX_NAME = "twitter";SearchRequest searchRequest = new SearchRequest.Builder().index(INDEX_NAME).query( q -> q.bool(b -> b.must(must->must.match(m ->m.field("city").query("北京"))).filter(f -> f.range(r -> r.field("age").gte(JsonData.of(0)).lte(JsonData.of(100)))))).size(2).scroll(Time.of(t -> t.time("2m"))).build();SearchResponse<Twitter> response = client.search(searchRequest, Twitter.class);do {System.out.println("size: " + response.hits().hits().size());for (Hit<Twitter> hit : response.hits().hits()) {System.out.println("hit: " + hit.index() + ": " + hit.id());}final SearchResponse<Twitter> old_response = response;System.out.println("scrollId: " + old_response.scrollId());response = client.scroll(s -> s.scrollId(old_response.scrollId()).scroll(Time.of(t -> t.time("2m"))),Twitter.class);System.out.println("=================================");} while (response.hits().hits().size() != 0); // 0 hits mark the end of the scroll and the while loop.

我们运行上面的代码后，我们可以看到如下的搜索结果：

size: 2
hit: twitter: 1
hit: twitter: 2
scrollId: FGluY2x1ZGVfY29udGV4dF91dWlkDXF1ZXJ5QW5kRmV0Y2gBFi1rOUlBMFdGU2tLSS0yTlMyUkdRdUEAAAAAAAFAnxZReU4zSnhXVlR5eW5WQW5Yb09RSHNR
=================================
size: 2
hit: twitter: 3
hit: twitter: 4
scrollId: FGluY2x1ZGVfY29udGV4dF91dWlkDXF1ZXJ5QW5kRmV0Y2gBFi1rOUlBMFdGU2tLSS0yTlMyUkdRdUEAAAAAAAFAnxZReU4zSnhXVlR5eW5WQW5Yb09RSHNR
=================================
size: 1
hit: twitter: 5
scrollId: FGluY2x1ZGVfY29udGV4dF91dWlkDXF1ZXJ5QW5kRmV0Y2gBFi1rOUlBMFdGU2tLSS0yTlMyUkdRdUEAAAAAAAFAnxZReU4zSnhXVlR5eW5WQW5Yb09RSHNR
=================================

从上面的搜索结果中，我们可以看出来它有三个页。共有5个文档被搜索到了。

案例2

scroll滚动查询

/*** 滚动查询数据* @param indexName* @param utime*/
public List<String> scrollSearchAll(String indexName, String utime) throws IOException{BoolQueryBuilder boolQueryBuilder = QueryBuilders.boolQuery();boolQueryBuilder.must(QueryBuilders.rangeQuery("utime").lt(utime).gt("946656000"));//946656000为2000-01-01 00:00:00//builderSearchSourceBuilder builder = new SearchSourceBuilder().query(boolQueryBuilder).size(500);// 构建SearchRequestSearchRequest searchRequest = new SearchRequest();searchRequest.indices(indexName);searchRequest.source(builder);Scroll scroll = new Scroll(new TimeValue(600000));searchRequest.scroll(scroll);SearchResponse searchResponse = restHighLevelClient.search(searchRequest);String scrollId = searchResponse.getScrollId();SearchHit[] hits = searchResponse.getHits().getHits();List<String> resultSearchHit = new ArrayList<>();while (ArrayUtils.isNotEmpty(hits)) {for (SearchHit hit : hits) {log.info("准备删除的数据hit:{}", hit);resultSearchHit.add(hit.getId());}// 再次发送请求,并使用上次搜索结果的ScrollIdSearchScrollRequest searchScrollRequest = new SearchScrollRequest(scrollId);searchScrollRequest.scroll(scroll);SearchResponse searchScrollResponse = restHighLevelClient.searchScroll(searchScrollRequest);scrollId = searchScrollResponse.getScrollId();hits = searchScrollResponse.getHits().getHits();}// 及时清除es快照，释放资源ClearScrollRequest clearScrollRequest = new ClearScrollRequest();clearScrollRequest.addScrollId(scrollId);restHighLevelClient.clearScroll(clearScrollRequest);return resultSearchHit;
}