Improvements to the Type System

This section provides information on improvements to the Alteryx® type system.

General Improvements in Typecasting

Mismatched data types

Where there are mismatches between inputs and the expected input data type, the following values are generated for the mismatches:

State values and custom data types are converted to string values, if they are mismatched.

Three-value logic for null values

The Alteryx Server execution engine has been augmented to use three-value logic for null values. 

When values are compared, the result can be true or false in most cases.

If a null value was compared to a null value in the Alteryx Server:

• In Release 3.0 and earlier, this evaluated to true.
• In Release 3.1 and later, this evaluates to an unknown (null) value.

This change aligns the behavior of the execution engine with that of SQL and Hadoop Pig.

Improved handling of null values

Assume that the column nuller contains null values and that you have the following transform:

derive value:(nuller >= 0)

Prior to Release 3.1, the above transform generated a column of true values.

In Release 3.1 and later, the transform generates a column of null values.

More consistent evaluation of null values in ternaries

In the following example, a_null_expression always evaluates to a null value.

derive value: (a_null_expression ? 'a' : 'b')

In Release 3.0, this expression generated b for all inputs on the Alteryx Server execution engine and a null value on Hadoop Pig.

In Release 3.1 and later, this expression generates a null value for all inputs on both execution engines.

For example, you have the following dataset: 

You test each row for the presence of the string can't:

derive value: if(find(MyStringCol, 'can\'t',true,0) > -1, true, false) as:'MyFindResults'

The above transform results in the following:

In this case, the value of false is not written to the other columns, since the find function returns a null value. This null value, in turn, nullifies the entire expression, resulting in a null value written in the new column.

You can use the following to locate the null values:

derive value:isnull(MyFindResults) as:'nullInMyFindResults'

Datetime changes

Raw date and time values must be properly formatted

Before this release, you could create a transform like the following:

derive value:date(2016,2,15)

This transform generated a column of map values, like the following:

{"year":"2016","month":"2","date":"15"}

Beginning this release, the above command is invalid, as the date values must be properly formatted prior to display. The following works:

derive value:dateformat(date(2016,2,15),'yyyy-MM-dd')

This transform generates a column of Datetime values in the following format:

2016-02-15

Time:

Before this release:

derive value:time(11,34,58)

Prior release output:

{"hours":"11","minutes":"34","seconds":"58"}

This release:

derive value:dateformat(time(11,34,58), 'HH-mm-ss')

This release's output:

11-34-58

• See DATEFORMAT Function.
• See UNIXTIMEFORMAT Function.

Date formatting functions supports 12-hour time only if AM/PM indicator is included

Beginning in this release, the unixtimeformat and dateformat functions requires an AM/PM indicator (a) if the date formatting string uses a 12-hour time indicator (h or hh).

Valid for earlier releases:

derive value: unixtimeformat(myDate, 'yyyy-MM-dd hh:mm:ss') as:'myUnixDate'

Valid for this release and later:

derive value: unixtimeformat(myDate, 'yyyy-MM-dd hh:mm:ss a') as:'myUnixDate'

These references in recipes fail to validate in this release or later and must be fixed. 

• See DATEFORMAT Function.
• See Unixtimeformat Function.

Un-inferrable formats from dateformat and unixtimeformat functions are written as strings

If a formatting string is not a datetime format recognized by the Designer Cloud Powered by Trifacta platform, the output is generated as a string value.

This change was made to provide clarity to some ambiguous conditions. 

Colon as a delimiter for date values is no longer supported

Beginning in this release, the colon (:) is no longer supported as a delimiter for date values. It is still supported for time values.

When data such as the above is imported, it may not be initially recognized by the Designer Cloud application as Datetime type.

To fix, you might apply the following transform:

replace col:myDateValue with:'-' on:`-` global:true

The new column values are more likely to be inferred as Datetime values. If not, you can choose the appropriate Datetime format from the data type drop-down for the column. See Data Grid Panel.