Elasticsearch：在 Java 客户端中使用 scroll 来遍历搜索结果 - Elastic Stack 8.x
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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。如果你对搜索结果分页不是很熟悉的话，请参考我之前的文章 “Elasticsearch：运用 scroll 接口对大量数据实现更好的分页”。
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"
]
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"
]
很显然，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。
运用 Java client APIs 来实现分页
接下来，我们来设计 Java 应用来对搜索结果进行分页。为了方便大家对代码的理解，我把最终的项目上传到 github：https://github.com/liu-xiao-guo/elasticsearchjava-scroll
首先我们创建一个叫做 Twitter 的 class：
Twitter.java
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。我们可以参考之前的文章 “Elasticsearch：在 Java 客户端中使用 truststore 来创建 HTTPS 连接”。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。
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