SQL Aggregation Jobs
A Spark SQL aggregation job where user-defined parameters are injected into a built-in SQL template at runtime.
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A Spark SQL aggregation job where user-defined parameters are injected into a built-in SQL template at runtime.
Use this job when you want to aggregate your data in some way.
The ID for this Spark job. Used in the API to reference this job. Allowed characters: a-z, A-Z, dash (-) and underscore (_). Maximum length: 63 characters.
<= 63 characters
Match pattern: [a-zA-Z][_\-a-zA-Z0-9]*[a-zA-Z0-9]?
Spark configuration settings.
object attributes:{key
required : {
display name: Parameter Name
type: string
}value
: {
display name: Parameter Value
type: string
}}
Collection containing signals to be aggregated.
The collection to write the aggregates to on output. This property is required if the selected output / rollup pipeline requires it (the default pipeline does). A special value of '-' disables the output.
>= 1 characters
Number of rows to read from the source collection per request.
Default: 10000
Use SQL to perform the aggregation. You do not need to include a time range filter in the WHERE clause as it gets applied automatically before executing the SQL statement.
>= 1 characters
Use SQL to perform a rollup of previously aggregated docs. If left blank, the aggregation framework will supply a default SQL query to rollup aggregated metrics.
>= 1 characters
Additional configuration settings to fine-tune how input records are read for this aggregation.
object attributes:{key
required : {
display name: Parameter Name
type: string
}value
: {
display name: Parameter Value
type: string
}}
If checked, only aggregate new signals created since the last time the job was successfully run. If there is a record of such previous run then this overrides the starting time of time range set in 'timeRange' property. If unchecked, then all matching signals are aggregated and any previously aggregated docs are deleted to avoid double counting.
Default: true
If checked, remove signals from source collection once aggregation job has finished running.
Default: false
Timestamp to use for the aggregation results. Defaults to NOW.
Timestamp to use for computing decays and to determine the value of NOW.
If the catch-up flag is enabled and this field is checked, the job framework will execute a fast Solr query to determine if this run can be skipped.
Default: true
Skip Job run if signals collection is empty
Other aggregation parameters (e.g. timestamp field etc..).
object attributes:{key
required : {
display name: Parameter Name
type: string
}value
: {
display name: Parameter Value
type: string
}}
The signal types. If not set then any signal type is selected
The query to select the desired input documents.
>= 1 characters
Default: *:*
The time range to select signals on.
>= 1 characters
Use a natural key provided in the raw signals data for aggregation, rather than relying on Solr UUIDs. Migrated aggregations jobs from Fusion 4 will need this set to false.
Default: true
If set to a value above 0, the aggregator job will optimize the resulting Solr collection into this many segments
exclusiveMinimum: false
Default: 0
Spark-compatible format that contains training data (like 'solr', 'parquet', 'orc' etc)
>= 1 characters
Default: solr
Use this field to create a Spark SQL query for filtering your input data. The input data will be registered as spark_input
Default: SELECT * from spark_input
Spark will re-partition the input to have this number of partitions. Increase for greater parallelism
Default: 200
Default: aggregation
Allowed values: aggregation