mirror of https://github.com/apache/druid.git
90 lines
3.7 KiB
Markdown
90 lines
3.7 KiB
Markdown
---
|
|
layout: doc_page
|
|
---
|
|
These types of queries take a groupBy query object and return an array of JSON objects where each object represents a grouping asked for by the query.
|
|
|
|
An example groupBy query object is shown below:
|
|
|
|
``` json
|
|
{
|
|
"queryType": "groupBy",
|
|
"dataSource": "sample_datasource",
|
|
"granularity": "day",
|
|
"dimensions": ["dim1", "dim2"],
|
|
"limitSpec": { "type": "default", "limit": 5000, "columns": ["dim1", "metric1"] },
|
|
"filter": {
|
|
"type": "and",
|
|
"fields": [
|
|
{ "type": "selector", "dimension": "sample_dimension1", "value": "sample_value1" },
|
|
{ "type": "or",
|
|
"fields": [
|
|
{ "type": "selector", "dimension": "sample_dimension2", "value": "sample_value2" },
|
|
{ "type": "selector", "dimension": "sample_dimension3", "value": "sample_value3" }
|
|
]
|
|
}
|
|
]
|
|
},
|
|
"aggregations": [
|
|
{ "type": "longSum", "name": "sample_name1", "fieldName": "sample_fieldName1" },
|
|
{ "type": "doubleSum", "name": "sample_name2", "fieldName": "sample_fieldName2" }
|
|
],
|
|
"postAggregations": [
|
|
{ "type": "arithmetic",
|
|
"name": "sample_divide",
|
|
"fn": "/",
|
|
"fields": [
|
|
{ "type": "fieldAccess", "name": "sample_name1", "fieldName": "sample_fieldName1" },
|
|
{ "type": "fieldAccess", "name": "sample_name2", "fieldName": "sample_fieldName2" }
|
|
]
|
|
}
|
|
],
|
|
"intervals": [ "2012-01-01T00:00:00.000/2012-01-03T00:00:00.000" ],
|
|
"having": { "type": "greaterThan", "aggregation": "sample_name1", "value": 0 }
|
|
}
|
|
```
|
|
|
|
There are 9 main parts to a groupBy query:
|
|
|
|
|property|description|required?|
|
|
|--------|-----------|---------|
|
|
|queryType|This String should always be "groupBy"; this is the first thing Druid looks at to figure out how to interpret the query|yes|
|
|
|dataSource|A String defining the data source to query, very similar to a table in a relational database|yes|
|
|
|dimensions|A JSON list of dimensions to do the groupBy over|yes|
|
|
|orderBy|See [OrderBy](OrderBy.html).|no|
|
|
|having|See [Having](Having.html).|no|
|
|
|granularity|Defines the granularity of the query. See [Granularities](Granularities.html)|yes|
|
|
|filter|See [Filters](Filters.html)|no|
|
|
|aggregations|See [Aggregations](Aggregations.html)|yes|
|
|
|postAggregations|See [Post Aggregations](Post-Aggregations.html)|no|
|
|
|intervals|A JSON Object representing ISO-8601 Intervals. This defines the time ranges to run the query over.|yes|
|
|
|context|An additional JSON Object which can be used to specify certain flags.|no|
|
|
|
|
To pull it all together, the above query would return *n\*m* data points, up to a maximum of 5000 points, where n is the cardinality of the "dim1" dimension, m is the cardinality of the "dim2" dimension, each day between 2012-01-01 and 2012-01-03, from the "sample_datasource" table. Each data point contains the (long) sum of sample_fieldName1 if the value of the data point is greater than 0, the (double) sum of sample_fieldName2 and the (double) the result of sample_fieldName1 divided by sample_fieldName2 for the filter set for a particular grouping of "dim1" and "dim2". The output looks like this:
|
|
|
|
```json
|
|
[
|
|
{
|
|
"version" : "v1",
|
|
"timestamp" : "2012-01-01T00:00:00.000Z",
|
|
"event" : {
|
|
"dim1" : <some_dim_value_one>,
|
|
"dim2" : <some_dim_value_two>,
|
|
"sample_name1" : <some_sample_name_value_one>,
|
|
"sample_name2" :<some_sample_name_value_two>,
|
|
"sample_divide" : <some_sample_divide_value>
|
|
}
|
|
},
|
|
{
|
|
"version" : "v1",
|
|
"timestamp" : "2012-01-01T00:00:00.000Z",
|
|
"event" : {
|
|
"dim1" : <some_other_dim_value_one>,
|
|
"dim2" : <some_other_dim_value_two>,
|
|
"sample_name1" : <some_other_sample_name_value_one>,
|
|
"sample_name2" :<some_other_sample_name_value_two>,
|
|
"sample_divide" : <some_other_sample_divide_value>
|
|
}
|
|
},
|
|
...
|
|
]
|
|
``` |