Expression Engine

Exportizer Pro/Exportizer Enterprise expression engine allows you to execute Pascal-like expressions, which can be embedded in dynamic elements during the data exporting. You can use them in several situations:

• in the field mappings, instead of the source fields;
• in the field mappings, as totals expressions;
• in the field mappings, to calculate dynamic target file names for exporting contents of the fields to individual files;
• when exporting database data to HTML using HTML templates.

The expressions can consist of functions (nesting of any depth), arithmetical and logical operators, and literals (numeric, string, or boolean).

An expression returns one value. Available types of returning values:

• Integer
• Numeric (floating-point number)
• String
• DateTime
• Boolean

HTML template can be filled with dynamic data in more than one step. For example, if your HTML document consists of several tables, which must be filled with database data from different datasets, you can fill them only in several export steps (usually, one dataset per step). That's why database related and some other functions have a StepNo parameter. When exporting a dataset to HTML using HTML template, it is also possible to specify an export step number (it is 1 by default). During the export procedure, only those expressions will be calculated where the StepNo parameter of expression function(s) is not present or matches with a step number, specified in export options. For example, the database_field_val(2, 'payment_sum')/100 expression will be calculated only in the second export step.

Combining different StepNo parameters in one expression (for example, database_field_val(1, 'total_sum') + database_field_val(2, 'payment_sum')) is possible, but such expression should be written so that functions with smaller StepNo will be calculated earlier.

Examples of Valid Expressions

 12 + abs(sqrt(5) - 2)/3 {Arithmetic expression}

 25 - 5 >= 14 {Logical expression}

 'File name: ' + dataset_field_val(1, 'FileName') {String expression}

 date + 4 {Date expression. Adds four days to current date. It is valid because dates are numbers internally.}

 iif(length(Target_File_Name(1)) > 25, 'yes', 'no') {Complex expression}

Examples of Invalid Expressions

 12 / target_file_name(1) {Illegal use of string function in arithmetic expression (type mismatch)}

 concat(cos(dataset_field_val(1, 'FuncParam')), ' is larger') {Illegal use of floating point number as an argument of string concatenation. There must be explicit type casting using to_string function.}

 5 <> False {Illegal comparison of a numeric and boolean constants}

 power(4) {Missing a second argument for power function}

 iif(length(Target_File_Name(1) > 25, 'yes', 'no') {Missing right parenthesis for length function}

Operators

Functions

Use functions to calculate values. Each function returns one value. Function parameters can be constants or expressions (including recursive function calls), unless otherwise noted in function descriptions.

Database Related

General

dataset_field_val(
  StepNo Integer,
  FieldName String | FieldIndex Integer
): <FieldType>

dataset_field_hex_val(
  StepNo Integer,
  FieldName String | FieldIndex Integer
): String

dataset_field_is_null(
  StepNo Integer,
  FieldName String | FieldIndex Integer
): Boolean

dataset_nvl(
  StepNo Integer,
  FieldName String | FieldIndex Integer,
  SubstVal <FieldType>
): <FieldType>

dataset_field_exists(
  StepNo Integer,
  FieldName String
): Boolean

dataset_param_val(
  StepNo Integer,
  ParamName String
): <ParamType>

query_res(
  StepNo Integer,
  SqlText String
): <FieldType>

dbnull: Unknown

Statistical (aggregate)

These functions perform calculation on current dataset data. Unlike other functions, statistical functions accumulate data of each record of dataset field, therefore they need more resources and their using (especially for conditional functions) may slow down processing for large datasets.

It is strongly recommended to use these functions only inside or after loop(s) through dataset rows, otherwise they will return inadequate values.

Standard (similar to aggregate functions in SQL)

dataset_row_count(
  StepNo Integer,
  GroupLevel Integer
): Integer

dataset_min(
  StepNo Integer,
  GroupLevel Integer,
  FieldName String
): Numeric

dataset_max(
  StepNo Integer,
  GroupLevel Integer,
  FieldName String
): Numeric

dataset_sum(
  StepNo Integer,
  GroupLevel Integer,
  FieldName String
): Numeric

dataset_avg(
  StepNo Integer,
  GroupLevel Integer,
  FieldName String
): Numeric

dataset_count(
  StepNo Integer,
  GroupLevel Integer,
  FieldName String
): Integer

Conditional

These functions work like standard statistical functions, but with one important difference: they check Condition for each row, and calculate only if it evaluates to True. It is important to write the Condition parameter in these functions as String constant (i.e. enclosed in single quotes) or a string deterministic expression (see below).

The parameters of these functions must be constants or deterministic expressions (i.e. return the same result any time they are called with a specific set of input values). The Condition parameter can use also nondeterministic expressions, but, as noted above, it should be written as string literal or string deterministic expression.

dataset_row_count_ex(
  StepNo Integer,
  GroupLevel Integer,
  Condition String
): Integer

dataset_min_ex(
  StepNo Integer,
  GroupLevel Integer,
  FieldName String,
  Condition String
): Numeric

dataset_max_ex(
  StepNo Integer,
  GroupLevel Integer,
  FieldName String,
  Condition String
): Numeric

dataset_sum_ex(
  StepNo Integer,
  GroupLevel Integer,
  FieldName String,
  Condition String
): Numeric

dataset_avg_ex(
  StepNo Integer,
  GroupLevel Integer,
  FieldName String,
  Condition String
): Numeric

dataset_count_ex(
  StepNo Integer,
  GroupLevel Integer,
  FieldName String,
  Condition String
): Integer

It is not recommended to use nested calls of statistical functions (i.e. when Condition contains statistical functions calls), because the application cannot guarantee the correct result of such calls.
But if you do this, try to avoid cyclic field references in these functions because this may cause unexpected results of expressions. For example, using FieldName inside Condition is a cyclic reference and should be avoided.

Examples of correct using of conditional statistical functions:

dataset_sum_ex(1, 0, 'PaymentSum', 'dataset_field_val(1, ''CustNo'') > 1000')

(this expression will sum data from PaymentSum field, when value of numeric field CustNo is larger then 1000)

dataset_count_ex(1, 0, 'CustNo', 'dataset_field_val(1, ''Paid'')')

(this expression will return count of values from field CustNo, when value of boolean field Paid is True)

dataset_sum_ex(1, 0, 'BillSum', 'dataset_sum_ex(1, 0, ''OldBillSum'', ''dataset_field_val(1, ''''CustNo'''') > 0'') > 0')

(this function will return sum of values from field BillSum, when conditional sum of field OldBillSum is larger than 0)

Examples of incorrect using of conditional statistical functions:

dataset_count_ex(1, 0, 'CustNo', 'yes')

(error: not boolean constant cannot be used as a condition)

dataset_sum_ex(1, 0, 'BillSum', 'dataset_sum_ex(1, 0, ''OldBillSum'', ''dataset_sum_ex(1, 0, ''''BillSum'''', True) > 0'') > 0')

(error: cyclic reference for field BillSum)

Mathematical

abs(
  x Numeric
): Numeric

ceil(
  x Numeric
): Numeric

frac(
  x Numeric
): Numeric

int(
  x Numeric
): Numeric

max(
  x Numeric,
  y Numeric
): Numeric

min(
  x Numeric,
  y Numeric
): Numeric

round(
  x Numeric
): Integer

sqrt(
  x Numeric
): Numeric

power(
  x Numeric,
  y Numeric
): Numeric

exp(
  x Numeric
): Numeric

ln(
  x Numeric
): Numeric

cos(
  x Numeric
): Numeric

sin(
  x Numeric
): Numeric

tan(
  x Numeric
): Numeric

atan(
  x Numeric
): Numeric

Date and Time

date: DateTime

time: DateTime

date_time: DateTime

add_date_time(
  x DateTime,
  y Numeric
): DateTime

compare_date_time(
  Date1 DateTime,
  Date2 DateTime
): Integer

date_time_diff(
  Date1 DateTime,
  Date2 DateTime
): Numeric

format_date_time(
  x DateTime,
  Mask String
): String

HTML Related (work in export to HTML context)

doc_title: String

File Related

target_file_name(
  StepNo Integer
): String

file_created(
  FileName String
): DateTime

file_last_modified(
  FileName String
): DateTime

file_last_accessed(
  FileName String
): DateTime

file_size(
  FileName String
): Numeric

file_version(
  FileName String
): String

extract_file_ext(
  FileName String
): String

extract_file_name(
  FileName String
): String

extract_file_dir(
  FileName String
): String

extract_file_path(
  FileName String
): String

extract_file_text(
  FileName String
): String

file_exists(
  FileName String
): Boolean

dir_exists(
  DirectoryName String
): Boolean

Miscellaneous

to_number(
  <AnyType> String
): Numeric

to_string(
  <AnyType> String
): String

parse(
  x <AnyType>
): <AnyType>

format_float(
  x Numeric,
  Mask String
): String

lpad(
  Str String,
  Count Numeric,
  Char String
): String

rpad(
  Str String,
  Count Numeric,
  Char String
): String

iif(
  Condition Boolean,
  x <AnyType>,
  y <AnyType>
): <AnyType>

char(
  x Integer
): String

upper(
  Str String,
): String

lower(
  Str String,
): String

capitalize(
  Str String,
): String

pretty(
  Str String,
): String

length(
  Str String,
): Integer

pos(
  SubStr String,
  Str String
): Integer

substr(
  Str String,
  Index Integer,
  Count Integer
): String

substr_count(
  Str String,
  SubStr String
): Integer

trim(
  Str String,
): String

trim_left(
  Str String,
): String

trim_right(
  Str String,
): String

string_replace(
  Str String,
  SubStr String,
  NewSubStr String
): String

concat(
  Str1 String,
  Str2 String
): String

ordinal_number(
  x Integer,
  Language String,
  Case String,
  Gender String
): String

quantitative_numeral(
  x Integer,
  Language String,
  Case String,
  Gender String
): String

number_to_words(
  x Numeric,
  Language String,
  Options String
): String

rgb(
  Red Integer,
  Green Integer,
  Blue Integer
): String

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