你好，先谢谢你，我对弹性搜索很新，我对查询有疑问，我有一个有很多字段的媒体列表：
{
“title”：“xxx”
“userId”：“1”
“image”：“zzz.jpg”
“city”：“paris”
.....
}
我需要获取城市巴黎的所有媒体，并为每个用户推送前x个图像的分数，以便不会从同一个用户获取结果。例如：
让我们说我们想要为每个用户推送前2张图片的分数：
user1 image1
user1 image2
user1 image3
user1 image4
user2 image1
user2 image2
user2 image3
user2 image4
user2 image5
user2 image6
user3 image1
user3 image2
user3 image3
........
预期产出：
user1 image1
user1 image2
user2 image1
user2 image2
user3 image1
user3 image2
user1 image3
user1 image4
user2 image3
user2 image4
user2 image5
user2 image6
user3 image3
答案 0 :(得分：0)
您可以尝试以下查询：
GET _search
{
"query": {
"query_string": {
"query": "*"
}
},
"size": 0,
"aggs": {
"@user_id": {
"terms": {
"field": "user_id",
"size": 0,
"order": {
"_term": "asc"
}
},
"aggs": {
"@image": {
"terms": {
"field": "image",
"size": 2,
"order": {
"_term": "asc"
}
},
"aggs": {
"@source": {
"top_hits": {
"size": 1
}
}
}
}
}
}
}
}
＆＃34; size＆＃34;：@image aggs中的2允许您指定图像的数量。
输出如下：
{
"took": 2,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"failed": 0
},
"hits": {
"total": 16,
"max_score": 0,
"hits": []
},
"aggregations": {
"@user_id": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "user1",
"doc_count": 4,
"@image": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 2,
"buckets": [
{
"key": "image1",
"doc_count": 1,
"@source": {
"hits": {
"total": 1,
"max_score": 1,
"hits": [
{
"_index": "test",
"_type": "test",
"_id": "AVQVaph2LY9bI6YPTNWp",
"_score": 1,
"_source": {
"user_id": "user1",
"image": "image1"
}
}
]
}
}
},
{
"key": "image2",
"doc_count": 1,
"@source": {
"hits": {
"total": 1,
"max_score": 1,
"hits": [
{
"_index": "test",
"_type": "test",
"_id": "AVQVareVLY9bI6YPTPCg",
"_score": 1,
"_source": {
"user_id": "user1",
"image": "image2"
}
}
]
}
}
}
]
}
},
{
"key": "user2",
"doc_count": 4,
"@image": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 2,
"buckets": [
{
"key": "image1",
"doc_count": 1,
"@source": {
"hits": {
"total": 1,
"max_score": 1,
"hits": [
{
"_index": "test",
"_type": "test",
"_id": "AVQVau9JLY9bI6YPTSPx",
"_score": 1,
"_source": {
"user_id": "user2",
"image": "image1"
}
}
]
}
}
},
{
"key": "image2",
"doc_count": 1,
"@source": {
"hits": {
"total": 1,
"max_score": 1,
"hits": [
{
"_index": "test",
"_type": "test",
"_id": "AVQVavh1LY9bI6YPTSP0",
"_score": 1,
"_source": {
"user_id": "user2",
"image": "image2"
}
}
]
}
}
}
]
}
},
{
"key": "user3",
"doc_count": 4,
"@image": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 2,
"buckets": [
{
"key": "image1",
"doc_count": 1,
"@source": {
"hits": {
"total": 1,
"max_score": 1,
"hits": [
{
"_index": "test",
"_type": "test",
"_id": "AVQVayTFLY9bI6YPTVZA",
"_score": 1,
"_source": {
"user_id": "user3",
"image": "image1"
}
}
]
}
}
},
{
"key": "image2",
"doc_count": 1,
"@source": {
"hits": {
"total": 1,
"max_score": 1,
"hits": [
{
"_index": "test",
"_type": "test",
"_id": "AVQVazP4LY9bI6YPTWnK",
"_score": 1,
"_source": {
"user_id": "user3",
"image": "image2"
}
}
]
}
}
}
]
}
},
{
"key": "user4",
"doc_count": 4,
"@image": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 2,
"buckets": [
{
"key": "image1",
"doc_count": 1,
"@source": {
"hits": {
"total": 1,
"max_score": 1,
"hits": [
{
"_index": "test",
"_type": "test",
"_id": "AVQVa16pLY9bI6YPTbgL",
"_score": 1,
"_source": {
"user_id": "user4",
"image": "image1"
}
}
]
}
}
},
{
"key": "image2",
"doc_count": 1,
"@source": {
"hits": {
"total": 1,
"max_score": 1,
"hits": [
{
"_index": "test",
"_type": "test",
"_id": "AVQVa2a3LY9bI6YPTcuX",
"_score": 1,
"_source": {
"user_id": "user4",
"image": "image2"
}
}
]
}
}
}
]
}
}
]
}
}
}