mirror of https://github.com/apache/druid.git
119 lines
3.0 KiB
Markdown
119 lines
3.0 KiB
Markdown
---
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layout: default
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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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<pre>
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<code>
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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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{
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[type]() “selector”,
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[dimension]() “sample\_dimension1”,
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[value]() “sample\_value1”
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},
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{
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[type]() “or”,
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[fields]() [
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{
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[type]() “selector”,
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[dimension]() “sample\_dimension2”,
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[value]() “sample\_value2”
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},
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{
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[type]() “selector”,
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[dimension]() “sample\_dimension3”,
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[value]() “sample\_value3”
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}
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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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{
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[type]() “longSum”,
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[name]() “sample\_name1”,
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[fieldName]() “sample\_fieldName1”
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},
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{
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[type]() “doubleSum”,
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[name]() “sample\_name2”,
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[fieldName]() “sample\_fieldName2”
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}
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],
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[postAggregations]() [
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{
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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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{
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[type]() “fieldAccess”,
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[name]() “sample\_name1”,
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[fieldName]() “sample\_fieldName1”
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},
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{
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[type]() “fieldAccess”,
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[name]() “sample\_name2”,
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[fieldName]() “sample\_fieldName2”
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}
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]
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}
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],
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[intervals]() [
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“2012-01-01T00:00:00.000/2012-01-03T00:00:00.000”
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]
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}
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</pre>
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</code>
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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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<pre>
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<code>
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[
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{
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[timestamp]() “2012-01-01T00:00:00.000Z”,
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[result]() {
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[sample\_name1]() <some_value>,
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[sample\_name2]() <some_value>,
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[sample\_divide]() <some_value>
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}
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},
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{
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[timestamp]() “2012-01-02T00:00:00.000Z”,
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[result]() {
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[sample\_name1]() <some_value>,
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[sample\_name2]() <some_value>,
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[sample\_divide]() <some_value>
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}
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}
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]
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</pre>
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</code>
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