mirror of https://github.com/apache/druid.git
73 lines
2.9 KiB
Markdown
73 lines
2.9 KiB
Markdown
---
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layout: doc_page
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---
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Timeseries queries
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==================
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These types of queries take a timeseries query object and return an array of JSON objects where each object represents a value asked for by the timeseries query.
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An example timeseries query object is shown below:
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```json
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{
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"queryType": "timeseries",
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"dataSource": "sample_datasource",
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"granularity": "day",
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"filter": {
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"type": "and",
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"fields": [
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{ "type": "selector", "dimension": "sample_dimension1", "value": "sample_value1" },
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{ "type": "or",
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"fields": [
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{ "type": "selector", "dimension": "sample_dimension2", "value": "sample_value2" },
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{ "type": "selector", "dimension": "sample_dimension3", "value": "sample_value3" }
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]
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}
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]
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},
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"aggregations": [
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{ "type": "longSum", "name": "sample_name1", "fieldName": "sample_fieldName1" },
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{ "type": "doubleSum", "name": "sample_name2", "fieldName": "sample_fieldName2" }
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],
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"postAggregations": [
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{ "type": "arithmetic",
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"name": "sample_divide",
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"fn": "/",
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"fields": [
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{ "type": "fieldAccess", "name": "sample_name1", "fieldName": "sample_fieldName1" },
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{ "type": "fieldAccess", "name": "sample_name2", "fieldName": "sample_fieldName2" }
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]
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}
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],
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"intervals": [ "2012-01-01T00:00:00.000/2012-01-03T00:00:00.000" ]
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}
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```
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There are 7 main parts to a timeseries query:
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|property|description|required?|
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|--------|-----------|---------|
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|queryType|This String should always be "timeseries"; this is the first thing Druid looks at to figure out how to interpret the query|yes|
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|dataSource|A String defining the data source to query, very similar to a table in a relational database|yes|
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|granularity|Defines the granularity of the query. See [Granularities](Granularities.html)|yes|
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|filter|See [Filters](Filters.html)|no|
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|aggregations|See [Aggregations](Aggregations.html)|yes|
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|postAggregations|See [Post Aggregations](Post-aggregations.html)|no|
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|intervals|A JSON Object representing ISO-8601 Intervals. This defines the time ranges to run the query over.|yes|
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|context|An additional JSON Object which can be used to specify certain flags.|no|
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To pull it all together, the above query would return 2 data points, one for each day between 2012-01-01 and 2012-01-03, from the "sample\_datasource" table. Each data point would be the (long) sum of sample\_fieldName1, the (double) sum of sample\_fieldName2 and the (double) the result of sample\_fieldName1 divided by sample\_fieldName2 for the filter set. The output looks like this:
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```json
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[
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{
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"timestamp": "2012-01-01T00:00:00.000Z",
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"result": { "sample_name1": <some_value>, "sample_name2": <some_value>, "sample_divide": <some_value> }
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},
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{
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"timestamp": "2012-01-02T00:00:00.000Z",
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"result": { "sample_name1": <some_value>, "sample_name2": <some_value>, "sample_divide": <some_value> }
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}
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]
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```
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