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PQL SET()

The Set() call associates or assigns a value to a record in a specified field. If the value we are assigning is in a Mutex, Int, Decimal, or Timestamp field, the current value is overwritten. If the value we are assigning is in a Set or Time field, all previous values will remain.

While using “Set” in PQL is a convenient way to get familiar with FeatureBase, it’s almost always better to use a dedicated ingest tool. See ingesters for details.

Call Definition 

Set(UINT_OR_STRING, FIELD=FIELD_VALUE, TIMESTAMP)

Mandatory Arguments

  • UINT_OR_STRING : the record to write to – UINT or unsigned int for a non-keyed index and string for a keyed index.
  • FIELD : the name of the field that contains the value we want to associate with the record
  • FIELD_VALUE : the value we want to associate with the record

Optional Arguments

  • TIMESTAMP : when FIELD is a Time field, this is an optional arguement and is used to associate this relationship with a specific time. Otherwise, it should not be provided.

Returns

  • boolean
    • true indicates that the value was update or added.
    • false indicates that the value was not updated or added – in some cases this means the value already existed or was alread equal to the desired value.

Examples

Example 1

Customer 3 bought from brand5 - update the index accordinly.

Data Pre-Query 

Index: customer (non keyed index)

 _id | age (Int) | has_purchased (Set)
-----+-----------+---------------------
 0   |    23     | ["brand1","brand2"]
 1   |    31     | ["brand1","brand3"]
 2   |    28     | ["brand1","brand3"]
 3   |    19     | []  
 4   |    25     | ["brand1","brand4"]
 5   |    40     | ["brand4"]

Query 

[customer]Set(3, has_purchased=brand5)

Tabular Response 

 result
--------
 true

Data Post-Query 

 _id | age (Int) | has_purchased (Set)
-----+-----------+---------------------
 0   |    23     | ["brand1","brand2"]
 1   |    31     | ["brand1","brand3"]
 2   |    28     | ["brand1","brand3"]
 3   |    19     | ["brand5"]
 4   |    25     | ["brand1","brand4"]
 5   |    40     | ["brand4"]


Example 1

Update the age of customer 3 to 20.

Data Pre-Query 

Index: customer (non keyed index)

 _id | age (Int) | has_purchased (Set)
-----+-----------+---------------------
 0   |    23     | ["brand1","brand2"]
 1   |    31     | ["brand1","brand3"]
 2   |    28     | ["brand1","brand3"]
 3   |    19     | []
 4   |    25     | ["brand1","brand4"]
 5   |    40     | ["brand4"]

Query 

[customer]Set(3, age=20)

Tabular Response 

 result
--------
 true

Data Post-Query 

Index: customer (non keyed index)

 _id | age (Int) | has_purchased (Set)
-----+-----------+---------------------
 0   |    23     | ["brand1","brand2"]
 1   |    31     | ["brand1","brand3"]
 2   |    28     | ["brand1","brand3"]
 3   |    20     | []
 4   |    25     | ["brand1","brand4"]
 5   |    40     | ["brand4"]