Elasticsearch高手进阶篇(38)
深入聚合数据分析_实战hitogram按价格区间统计电视销量和销售额
- histogram：类似于terms，也是进行bucket分组操作，接收一个field，按照这个field的值的各个范围区间，进行bucket分组操作
"histogram":{
"field": "price",
"interval": 2000
},
-
interval：2000，划分范围，0-2000，2000-4000，4000-6000，6000-8000，8000-10000，buckets去根据price的值，比如2500，看落在哪个区间内，比如2000-4000，此时就会将这条数据放入2000-4000对应的那个bucket中
-
bucket划分的方法，terms，将field值相同的数据划分到一个bucket中
- bucket有了之后，一样的，去对每个bucket执行avg，count，sum，max，min，等各种metric操作，聚合分析
GET /waws_tvs/sales/_search
{
"size" : 0,
"aggs":{
"price":{
"histogram":{
"field": "price",
"interval": 2000
},
"aggs":{
"revenue": {
"sum": {
"field" : "price"
}
}
}
}
}
}
{
"took": 17,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 8,
"max_score": 0,
"hits": []
},
"aggregations": {
"price": {
"buckets": [
{
"key": 0,
"doc_count": 3,
"revenue": {
"value": 3700
}
},
{
"key": 2000,
"doc_count": 4,
"revenue": {
"value": 9500
}
},
{
"key": 4000,
"doc_count": 0,
"revenue": {
"value": 0
}
},
{
"key": 6000,
"doc_count": 0,
"revenue": {
"value": 0
}
},
{
"key": 8000,
"doc_count": 1,
"revenue": {
"value": 8000
}
}
]
}
}
}
Elasticsearch高手进阶篇(39)
深入聚合数据分析_实战date hitogram之统计每月电视销量
date hitogram
- bucket，分组操作，histogram，按照某个值指定的interval，划分一个一个的bucket
-
date histogram
- 按照我们指定的某个date类型的日期field，以及日期interval，按照一定的日期间隔，去划分bucket
date interval = 1m，
- 2017-01-01~2017-01-31，就是一个bucket
- 2017-02-01~2017-02-28，就是一个bucket
然后会去扫描每个数据的date field，判断date落在哪个bucket中，就将其放入那个bucket
- 2017-01-05，就将其放入2017-01-01~2017-01-31，就是一个bucket
- min_doc_count：即使某个日期interval，2017-01-01~2017-01-31中，一条数据都没有，那么这个区间也是要返回的，不然默认是会过滤掉这个区间的
- extended_bounds，min，max：划分bucket的时候，会限定在这个起始日期，和截止日期内
GET /waws_tvs/sales/_search
{
"size" : 0,
"aggs": {
"sales": {
"date_histogram": {
"field": "sold_date",
"interval": "month",
"format": "yyyy-MM-dd",
"min_doc_count" : 0,
"extended_bounds" : {
"min" : "2016-01-01",
"max" : "2017-12-31"
}
}
}
}
}
{
"took": 8,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 8,
"max_score": 0,
"hits": []
},
"aggregations": {
"sales": {
"buckets": [
{
"key_as_string": "2016-01-01",
"key": 1451606400000,
"doc_count": 0
},
{
"key_as_string": "2016-02-01",
"key": 1454284800000,
"doc_count": 0
},
{
"key_as_string": "2016-03-01",
"key": 1456790400000,
"doc_count": 0
},
{
"key_as_string": "2016-04-01",
"key": 1459468800000,
"doc_count": 0
},
{
"key_as_string": "2016-05-01",
"key": 1462060800000,
"doc_count": 1
},
{
"key_as_string": "2016-06-01",
"key": 1464739200000,
"doc_count": 0
},
{
"key_as_string": "2016-07-01",
"key": 1467331200000,
"doc_count": 1
},
{
"key_as_string": "2016-08-01",
"key": 1470009600000,
"doc_count": 1
},
{
"key_as_string": "2016-09-01",
"key": 1472688000000,
"doc_count": 0
},
{
"key_as_string": "2016-10-01",
"key": 1475280000000,
"doc_count": 1
},
{
"key_as_string": "2016-11-01",
"key": 1477958400000,
"doc_count": 2
},
{
"key_as_string": "2016-12-01",
"key": 1480550400000,
"doc_count": 0
},
{
"key_as_string": "2017-01-01",
"key": 1483228800000,
"doc_count": 1
},
{
"key_as_string": "2017-02-01",
"key": 1485907200000,
"doc_count": 1
},
{
"key_as_string": "2017-03-01",
"key": 1488326400000,
"doc_count": 0
},
{
"key_as_string": "2017-04-01",
"key": 1491004800000,
"doc_count": 0
},
{
"key_as_string": "2017-05-01",
"key": 1493596800000,
"doc_count": 0
},
{
"key_as_string": "2017-06-01",
"key": 1496275200000,
"doc_count": 0
},
{
"key_as_string": "2017-07-01",
"key": 1498867200000,
"doc_count": 0
},
{
"key_as_string": "2017-08-01",
"key": 1501545600000,
"doc_count": 0
},
{
"key_as_string": "2017-09-01",
"key": 1504224000000,
"doc_count": 0
},
{
"key_as_string": "2017-10-01",
"key": 1506816000000,
"doc_count": 0
},
{
"key_as_string": "2017-11-01",
"key": 1509494400000,
"doc_count": 0
},
{
"key_as_string": "2017-12-01",
"key": 1512086400000,
"doc_count": 0
}
]
}
}
}
