Version 5.1


A signal is a recorded event related to one or more documents in a collection. Signals can record any kind of event that is useful to your organization. Click signals are the most common type of signals as this is the most common action a user takes with an item. In addition, other signal types can be defined, such as "addToCart", "purchase", and so on.

Using a sufficiently large collection of signals, Fusion can automatically generate recommendations such as these:

  • Based on the user’s search query, which items are most likely to interest them?

  • Based on the user’s similarity to other users, which additional items are likely to interest them?

Signals are indexed in a secondary collection which is linked to the primary collection by the naming convention <primarycollectionname>_signals. So, if your main collection is named products, the associated signals collection is named products_signals. The signals collection is created automatically when signals are enabled for the primary collection. Signals are enabled by default whenever a new collection is created.

Signals are indexed just like ordinary documents. The signals collection can be searched like any other collection, for example by using the Query Workbench with the signals collection selected.

App Insights provides visualizations and reports with which to analyze your signals. App Insights mainly uses raw signals, but also uses some aggregated signals. Currently only the signal types Request, Response and Click are supported within the App Insights dashboards.

See signals types and structure for more information.


Aggregations compile Signals into a set of summaries that you can use to enrich the search experience through recommendations and boosting.

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.

Aggregations are created automatically whenever you enable signals or recommendations. This topic explains how to create or modify aggregations individually. You can do this using the Fusion UI or the Jobs API. For more information, see Creating Aggregations.

signals boosting

Rollup SQL

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 <collection>_aggr collection.

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, halfLife, ref_time, and weight_d.

The basic rollup SQL can be grouped by DOC_ID, QUERY, USER_ID, and 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

Time-range filtering

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

SQL functions

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.

Weight aggregated values using a time-decay function

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 UserDefinedAggregateFunction (UDAF).

Full function signature

This is the UDAF signature of the default time_decay function:

time_decay(count: Long,
           timestamp: Timestamp,
           halfLife: String (calendar interval),
           ref_time: Timestamp,
           weight_d: Double)

One small difference between the prior and current behavior is worth mentioning in passing:

  • Prior to Release 4.0, the decay_sum aggregator function used the difference between the aggregationTime (the time at which the aggregation job is run) and the event time to calculate exponentially decayed numerical values.

  • In Release 4.0, time_decay is a similar function. In time_decay, ref_time is used instead of aggregationTime. You can set aggregationTime to some other time than the run time of the aggregation job.

In practice, you will probably want to use aggregationTime as ref_time.

Abbreviated function signature and default values

Your function call can also use this abbreviated UDAF signature, that omits halfLife, ref_time, and weight_d:

time_decay(count: Long,
           timestamp: Timestamp)

In this case, Fusion fills in these values for the omitted parameters: halfLife = 30 days, ref_time = NOW, and weight_d = 0.1.

Users indexing custom signals with weight_d specified should ensure that the "default" value matches the weight_d parameter used by time_decay
Matching legacy aggregation

To match the results of legacy aggregation, either use the abbreviated function signature or supply these values for the mentioned parameters: halfLife = 30 days, ref_time = NOW, and weight_d = 0.1.


Parameters for time_decay are:

Parameter Description


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 simply pass count_i, which is the event count field used by Fusion signals, as shown in the SQL aggregation examples.


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, 30 days or 10 minutes. The interval prefix is optional. Fusion treats 30 days as equivalent to interval 30 days.


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 (NOW). The reference time is not present in the data; Fusion determines the reference time at runtime. Fusion automatically attaches a ref_time column to every row before executing the SQL.


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 weight_d values, this parameter should be set to match the "neutral" value there.

You can use SQL to compute weight_d; see Use Different Weights Based on Signal Types for an example.

Sample calculation of the age of a signal

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.

The cold start problem

The "cold start" problem means it is hard to personalize the search experience when insufficient signals have been aggregated. For example, it is hard to offer recommendations to users who have never visited before, or for queries that have never been issued before, or for items that have been recently introduced into the system.

Fusion provides solutions for this problem using its query pipelines. A query pipeline that includes stages for blocking, boosting, or recommending based on signals can also include stages that provide fallbacks. In the case where there is not enough data to provide specialized blocking, boosting, or recommendations, the pipeline can return a simpler set of search results using Solr’s normal relevancy calculation.

A common solution to the cold start problem is to sort or boost on a certain field to provide pseudo-recommendations when more specific recommendations are not available. For example, you can sort on the sales_rank field to recommend the most popular products, or boost on the date_added field to recommend the newest items.

Indexing or importing Signals

Normally, signals are indexed as streaming data during the natural activity of users. See Indexing Signals for details.

However, you can also import existing signals, in Parquet format, using Spark shell. See Import Signals for instructions.