Product

Fusion 5.10

    Use this job when you want to aggregate your data in some way.

    id - stringrequired

    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]?

    sparkConfig - array[object]

    Spark configuration settings.

    object attributes:{key required : {
     display name: Parameter Name
     type: string
    }    value : {
     display name: Parameter Value
     type: string
    }    }

    inputCollection - stringrequired

    Collection containing signals to be aggregated.

    outputCollection - string

    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

    rows - integer

    Number of rows to read from the source collection per request.

    Default: 10000

    sql - stringrequired

    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

    rollupSql - string

    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

    readOptions - array[object]

    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
    }    }

    sourceCatchup - boolean

    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

    sourceRemove - boolean

    If checked, remove signals from source collection once aggregation job has finished running.

    Default: false

    aggregationTime - string

    Timestamp to use for the aggregation results. Defaults to NOW.

    referenceTime - string

    Timestamp to use for computing decays and to determine the value of NOW.

    skipCheckEnabled - boolean

    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

    skipJobIfSignalsEmpty - boolean

    Skip Job run if signals collection is empty

    parameters - array[object]

    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
    }    }

    signalTypes - array[string]

    The signal types. If not set then any signal type is selected

    selectQuery - string

    The query to select the desired input documents.

    >= 1 characters

    Default: *:*

    timeRange - string

    The time range to select signals on.

    >= 1 characters

    useNaturalKey - boolean

    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

    optimizeSegments - integer

    If set to a value above 0, the aggregator job will optimize the resulting Solr collection into this many segments

    exclusiveMinimum: false

    Default: 0

    dataFormat - stringrequired

    Spark-compatible format that contains training data (like 'solr', 'parquet', 'orc' etc)

    >= 1 characters

    Default: solr

    sparkSQL - string

    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

    sparkPartitions - integer

    Spark will re-partition the input to have this number of partitions. Increase for greater parallelism

    Default: 200

    type - stringrequired

    Default: aggregation

    Allowed values: aggregation