Fusion uses SQL to produce these signal summaries. SQL is a familiar query language that is well suited to data aggregation. Fusion’s SQL Aggregation Engine is powerful and flexible.
You can perform a SQL aggregation on a signals collection for a datasource (or on some other collection) through the Fusion UI or using a Fusion API. For more information, see Create and Run a SQL Aggregation Job.
Most aggregation jobs run with the catch-up flag set to
true, which means that Fusion only computes aggregations for new signals that have arrived since the last time the job was run, and up to and including
ref_time, which is usually the run time of the current job. Fusion must "roll up" the newly aggregated rows into any existing aggregated rows in the
If you are using SQL to do aggregation but have not supplied a custom rollup SQL, Fusion generates a basic rollup SQL script automatically by consulting the schema of the aggregated documents. If your rollup logic is complex, you can provide a custom rollup SQL script.
Fusion’s basic rollup is a SUM of numeric types (long, integer, double, and float) and can support
time_decay for the weight field. If you are not using
time_decay in your weight calculations, then the weight is calculated using
weight_d. If you do include
time_decay with your weight calculations, then the weight is calculated as a combination of timestamp,
The basic rollup SQL can be grouped by
FILTERS. The last GROUP BY field in the main SQL is used so it will ultimately group any newly aggregated rows with existing rows.
This is an example of a rollup query:
SELECT query_s, doc_id_s, time_decay(1, timestamp_tdt, "30 days", ref_time, weight_d) AS weight_d , SUM(aggr_count_i) AS aggr_count_i FROM `commerce_signals_aggr` GROUP BY query_s, doc_id_s
When Fusion rolls up new data into an aggregation, time-range filtering lets you ensure that Fusion does not aggregate the same data over and over again.
Fusion applies a time-range filter when loading rows from Solr, before executing the aggregation SQL statement. In other words, the SQL executes over rows that are already filtered by the appropriate time range for the aggregation job.
Notice that the examples Perform the Default SQL Aggregation and Use Different Weights Based on Signal Types do not include a time-range filter. Fusion computes the time-range filter automatically as follows:
If the catch-up flag is set to
true, Fusion uses the last time the job was run and
ref_time(which you typically set to the current time). This is equivalent to the WHERE clause
WHERE time > last_run_time AND time <= ref_time.
If the catch-up flag is not set to
true, Fusion uses a filter with
ref_time(and no start time). This is equivalent to the WHERE clause
WHERE time <= ref_time.
The built-in time logic should suffice for most use cases. You can set the time range filter to TO and specify a WHERE clause filter to achieve more complex time based filtering.
Time range in Fusion is equivalent to date range in Solr.
Example values for time range:
[* TO NOW]- all past events
[2000-11-01 TO 2020-12-01]– specify by exact date
[* TO 2020-12-01]– from the first data point to the end of the day specified
[2000 TO 2020]- from the start of a year to the end of another year
A Spark SQL aggregation query can use any of the functions provided by Spark SQL. Use these functions to perform complex aggregations or to enrich aggregation results.
Fusion automatically uses a default
time_decay function to compute and apply appropriate weights to aggregation groups during aggregation. Larger weights are assigned to more recent events. This reduces the impact of less-recent signals. Intuitively, older signals (and the user behavior they represent) should count less than newer signals.
If the default
time_decay function does not meet your needs, you can modify it. The
time_decay function is implemented as a User Defined Aggregate Function (UDAF).
This is the UDAF signature of the default
time_decay(count: Long, timestamp: Timestamp, halfLife: String (calendar interval), ref_time: Timestamp, weight_d: Double)
Your function call can also use this abbreviated UDAF signature, that omits
time_decay(count: Long, timestamp: Timestamp)
In this case, Fusion fills in these values for the omitted parameters:
Users indexing custom signals with
To match the results of legacy aggregation, either use the abbreviated function signature or supply these values for the mentioned parameters:
Number of occurrences of the event. Typically, the increment is 1, though there is no reason it could not be some other number. In most cases, you pass
The date-and-time for the event. This time is the beginning of the interval used to calculate the time-based decay factor.
Half life for the exponential decay that Fusion calculates. It is some interval of time, for example,
Reference time used to compute the age of an event for the time-decay computation. It is usually the time when the aggregation job runs (
Initial weight for an event if not specified in the signal, prior to the decay calculation. This value is typically not present in the signal data. If some signals do contain
You can use SQL to compute
This is an example of how Fusion calculates the age of a signal:
Imagine a SQL aggregation job that runs at
Tuesday, July 11, 2017 1:00:00 AM (1499734800).
For a signal with the timestamp
Tuesday, July 11, 2017 12:00:00 AM (1499731200), the age of the signal in relation to the reference time is 1 hour.