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Updates groupBy doc: 

* changes sample dimension names to be more meaningful
* fixes timestamps in example output
* adds formatting to names appearing in paragraphs for easier reading
This commit is contained in:
Igal Levy 2015-04-21 17:30:37 -07:00
parent 6b0ba7602b
commit 1216512e49
1 changed files with 25 additions and 21 deletions
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docs/content/GroupByQuery.md
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@ -10,36 +10,40 @@ An example groupBy query object is shown below:
"queryType": "groupBy", "queryType": "groupBy",
"dataSource": "sample_datasource", "dataSource": "sample_datasource",
"granularity": "day", "granularity": "day",
"dimensions": ["dim1", "dim2"], "dimensions": ["country", "device"],
"limitSpec": { "type": "default", "limit": 5000, "columns": ["dim1", "metric1"] }, "limitSpec": { "type": "default", "limit": 5000, "columns": ["country", "data_transfer"] },
"filter": { "filter": {
"type": "and", "type": "and",
"fields": [ "fields": [
{ "type": "selector", "dimension": "sample_dimension1", "value": "sample_value1" }, { "type": "selector", "dimension": "carrier", "value": "AT&T" },
{ "type": "or", { "type": "or",
"fields": [ "fields": [
{ "type": "selector", "dimension": "sample_dimension2", "value": "sample_value2" }, { "type": "selector", "dimension": "make", "value": "Apple" },
{ "type": "selector", "dimension": "sample_dimension3", "value": "sample_value3" } { "type": "selector", "dimension": "make", "value": "Samsung" }
] ]
} }
] ]
}, },
"aggregations": [ "aggregations": [
{ "type": "longSum", "name": "sample_name1", "fieldName": "sample_fieldName1" }, { "type": "longSum", "name": "total_usage", "fieldName": "user_count" },
{ "type": "doubleSum", "name": "sample_name2", "fieldName": "sample_fieldName2" } { "type": "doubleSum", "name": "data_transfer", "fieldName": "data_transfer" }
], ],
"postAggregations": [ "postAggregations": [
{ "type": "arithmetic", { "type": "arithmetic",
"name": "sample_divide", "name": "avg_usage",
"fn": "/", "fn": "/",
"fields": [ "fields": [
{ "type": "fieldAccess", "name": "sample_name1", "fieldName": "sample_fieldName1" }, { "type": "fieldAccess", "fieldName": "data_transfer" },
{ "type": "fieldAccess", "name": "sample_name2", "fieldName": "sample_fieldName2" } { "type": "fieldAccess", "fieldName": "total_usage" }
] ]
} }
], ],
"intervals": [ "2012-01-01T00:00:00.000/2012-01-03T00:00:00.000" ], "intervals": [ "2012-01-01T00:00:00.000/2012-01-03T00:00:00.000" ],
"having": { "type": "greaterThan", "aggregation": "sample_name1", "value": 0 } "having": {
"type": "greaterThan",
"aggregation": "total_usage",
"value": 100
}
} }
``` ```
@ -59,7 +63,7 @@ There are 11 main parts to a groupBy query:
|intervals|A JSON Object representing ISO-8601 Intervals. This defines the time ranges to run the query over.|yes| |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| |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: 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 `country` dimension, m is the cardinality of the `device` dimension, each day between 2012-01-01 and 2012-01-03, from the `sample_datasource` table. Each data point contains the (long) sum of `total_usage` if the value of the data point is greater than 100, the (double) sum of `data_transfer` and the (double) result of `total_usage` divided by `data_transfer` for the filter set for a particular grouping of `country` and `device`. The output looks like this:
```json ```json
[ [
@ -67,22 +71,22 @@ To pull it all together, the above query would return *n\*m* data points, up to
"version" : "v1", "version" : "v1",
"timestamp" : "2012-01-01T00:00:00.000Z", "timestamp" : "2012-01-01T00:00:00.000Z",
"event" : { "event" : {
"dim1" : <some_dim_value_one>, "country" : <some_dim_value_one>,
"dim2" : <some_dim_value_two>, "device" : <some_dim_value_two>,
"sample_name1" : <some_sample_name_value_one>, "total_usage" : <some_value_one>,
"sample_name2" :<some_sample_name_value_two>, "data_transfer" :<some_value_two>,
"sample_divide" : <some_sample_divide_value> "avg_usage" : <some_avg_usage_value>
} }
}, },
{ {
"version" : "v1", "version" : "v1",
"timestamp" : "2012-01-01T00:00:00.000Z", "timestamp" : "2012-01-01T00:00:12.000Z",
"event" : { "event" : {
"dim1" : <some_other_dim_value_one>, "dim1" : <some_other_dim_value_one>,
"dim2" : <some_other_dim_value_two>, "dim2" : <some_other_dim_value_two>,
"sample_name1" : <some_other_sample_name_value_one>, "sample_name1" : <some_other_value_one>,
"sample_name2" :<some_other_sample_name_value_two>, "sample_name2" :<some_other_value_two>,
"sample_divide" : <some_other_sample_divide_value> "avg_usage" : <some_other_avg_usage_value>
} }
}, },
... ...