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Chapter 6. Authoring

6.1. Decision Tables in Spreadsheets
6.1.1. When to Use Decision Tables
6.1.2. Overview
6.1.3. How Decision Tables Work
6.1.4. Spreadsheet Syntax
6.1.5. Creating and integrating Spreadsheet based Decision Tables
6.1.6. Managing Business Rules in Decision Tables
6.1.7. Rule Templates
6.2. Templates
6.2.1. The Rule Template File
6.2.2. Expanding a Template
6.2.3. Example

Decision tables are a "precise yet compact" (ref. Wikipedia) way of representing conditional logic, and are well suited to business level rules.

Drools supports managing rules in a spreadsheet format. Supported formats are Excel (XLS), and CSV, which means that a variety of spreadsheet programs (such as Microsoft Excel, OpenOffice.org Calc amongst others) can be utilized. It is expected that web based decision table editors will be included in a near future release.

Decision tables are an old concept (in software terms) but have proven useful over the years. Very briefly speaking, in Drools decision tables are a way to generate rules driven from the data entered into a spreadsheet. All the usual features of a spreadsheet for data capture and manipulation can be taken advantage of.

The key point to keep in mind is that in a decision table each row is a rule, and each column in that row is either a condition or action for that rule.

The spreadsheet looks for the RuleTable keyword to indicate the start of a rule table (both the starting row and column). Other keywords are also used to define other package level attributes (covered later). It is important to keep the keywords in one column. By convention the second column ("B") is used for this, but it can be any column (convention is to leave a margin on the left for notes). In the following diagram, C is actually the column where it starts. Everything to the left of this is ignored.

If we expand the hidden sections, it starts to make more sense how it works; note the keywords in column C.

Now the hidden magic which makes it work can be seen. The RuleSet keyword indicates the name to be used in the rule package that will encompass all the rules. This name is optional, using a default, but it must have the RuleSet keyword in the cell immediately to the right.

The other keywords visible in Column C are Import and Sequential which will be covered later. The RuleTable keyword is important as it indicates that a chunk of rules will follow, based on some rule templates. After the RuleTable keyword there is a name, used to prefix the names of the generated rules. The row numbers are appended to guarantee unique rule names. The column of RuleTable indicates the column in which the rules start; columns to the left are ignored.

Referring to row 14 (the row immediately after RuleTable), the keywords CONDITION and ACTION indicate that the data in the columns below are for either the LHS or the RHS parts of a rule. There are other attributes on the rule which can also be optionally set this way.

Row 15 contains declarations of ObjectTypes. The content in this row is optional, but if this option is not in use, the row must be left blank; however this option is usually found to be quite useful. When using this row, the values in the cells below (row 16) become constraints on that object type. In the above case, it generates Person(age=="42") and Cheese(type=="stilton"), where 42 and "stilton" come from row 18. In the above example, the "==" is implicit; if just a field name is given the translator assumes that it is to generate an exact match.

Row 16 contains the rule templates themselves. They can use the "$param" placeholder to indicate where data from the cells below should be interpolated. (For multiple insertions, use "$1", "$2", etc., indicating parameters from a comma-separated list in a cell below.) Row 17 is ignored; it may contain textual descriptions of the column's purpose.

Rows 18 and 19 show data, which will be combined (interpolated) with the templates in row 15, to generate rules. If a cell contains no data, then its template is ignored. (This would mean that some condition or action does not apply for that rule row.) Rule rows are read until there is a blank row. Multiple RuleTables can exsist in a sheet. Row 20 contains another keyword, and a value. The row positions of keywords like this do not matter (most people put them at the top) but their column should be the same one where the RuleTable or RuleSet keywords should appear. In our case column C has been chosen to be significant, but any other column could be used instead.

In the above example, rules would be rendered like the following (as it uses the "ObjectType" row):

//row 18
rule "Cheese_fans_18"
    list.add("Old man stilton");

Entries in a Rule Set area may define DRL constructs (except rules), and specify rule attributes. While entries for constructs may be used repeatedly, each rule attribute may be given at most once, and it applies to all rules unless it is overruled by the same attribute being defined within the Rule Table area.

Entries must be given in a vertically stacked sequence of cell pairs. The first one contains a keyword and the one to its right the value, as shown in the table below. This sequence of cell pairs may be interrupted by blank rows or even a Rule Table, as long as the column marked by RuleSet is upheld as the one containing the keyword.

For defining rule attributes that apply to all rules in the generated DRL file you can use any of the entries in the following table. Notice, however, that the proper keyword must be used. Also, each of these attributes may be used only once.

All Rule Tables begin with a cell containing "RuleTable", optionally followed by a string within the same cell. The string is used as the initial part of the name for all rules derived from this Rule Table, with the row number appended for distinction. (This automatic naming can be overridden by using a NAME column.) All other cells defining rules of this Rule Table are below and to the right of this cell.

The next row defines the column type, with each column resulting in a part of the condition or the consequence, or providing some rule attribute, the rule name or a comment. The table below shows which column headers are available; additional columns may be used according to the table showing rule attribute entries given in the preceding section. Note that each attribute column may be used at most once. For a column header, either use the keyword or any other word beginning with the letter given in the "Initial" column of these tables.

Given a column headed CONDITION, the cells in successive lines result in a conditional element.

  • Text in the first cell below CONDITION develops into a pattern for the rule condition, with the snippet in the next line becoming a constraint. If the cell is merged with one or more neighbours, a single pattern with multiple constraints is formed: all constraints are combined into a parenthesized list and appended to the text in this cell. The cell may be left blank, which means that the code snippet in the next row must result in a valid conditional element on its own.

    To include a pattern without constraints, you can write the pattern in front of the text for another pattern.

    The pattern may be written with or without an empty pair of parentheses. A "from" clause may be appended to the pattern.

    If the pattern ends with "eval", code snippets are supposed to produce boolean expressions for inclusion into a pair of parentheses after "eval".

  • Text in the second cell below CONDITION is processed in two steps.

    1. The code snippet in this cell is modified by interpolating values from cells farther down in the column. If you want to create a constraint consisting of a comparison using "==" with the value from the cells below, the field selector alone is sufficient. Any other comparison operator must be specified as the last item within the snippet, and the value from the cells below is appended. For all other constraint forms, you must mark the position for including the contents of a cell with the symbol $param. Multiple insertions are possible by using the symbols $1, $2, etc., and a comma-separated list of values in the cells below.

      A text according to the pattern forall(delimiter){snippet} is expanded by repeating the snippet once for each of the values of the comma-separated list of values in each of the cells below, inserting the value in place of the symbol $ and by joining these expansions by the given delimiter. Note that the forall construct may be surrounded by other text.

    2. If the cell in the preceding row is not empty, the completed code snippet is added to the conditional element from that cell. A pair of parentheses is provided automatically, as well as a separating comma if multiple constraints are added to a pattern in a merged cell.

      If the cell above is empty, the interpolated result is used as is.

  • Text in the third cell below CONDITION is for documentation only. It should be used to indicate the column's purpose to a human reader.

  • From the fourth row on, non-blank entries provide data for interpolation as described above. A blank cell results in the omission of the conditional element or constaint for this rule.

Given a column headed ACTION, the cells in successive lines result in an action statement.

  • Text in the first cell below ACTION is optional. If present, it is interpreted as an object reference.

  • Text in the second cell below ACTION is processed in two steps.

    1. The code snippet in this cell is modified by interpolating values from cells farther down in the column. For a singular insertion, mark the position for including the contents of a cell with the symbol $param. Multiple insertions are possible by using the symbols $1, $2, etc., and a comma-separated list of values in the cells below.

      A method call without interpolation can be achieved by a text without any marker symbols. In this case, use any non-blank entry in a row below to include the statement.

      The forall construct is available here, too.

    2. If the first cell is not empty, its text, followed by a period, the text in the second cell and a terminating semicolon are stringed together, resulting in a method call which is added as an action statement for the consequence.

      If the cell above is empty, the interpolated result is used as is.

  • Text in the third cell below ACTION is for documentation only. It should be used to indicate the column's purpose to a human reader.

  • From the fourth row on, non-blank entries provide data for interpolation as described above. A blank cell results in the omission of the action statement for this rule.


Using $1 instead of $param works in most cases, but it will fail if the replacement text contains a comma: then, only the part preceding the first comma is inserted. Use this "abbreviation" judiciously.

Given a column headed METADATA, the cells in successive lines result in a metadata annotation for the generated rules.

  • Text in the first cell below METADATA is ignored.

  • Text in the second cell below METADATA is subject to interpolation, as described above, using values from the cells in the rule rows. The metadata marker character @ is prefixed automatically, and thus it should not be included in the text for this cell.

  • Text in the third cell below METADATA is for documentation only. It should be used to indicate the column's purpose to a human reader.

  • From the fourth row on, non-blank entries provide data for interpolation as described above. A blank cell results in the omission of the metadata annotation for this rule.

The various interpolations are illustrated in the following example.

The next example demonstrates the joint effect of a cell defining the pattern type and the code snippet below it.

This spreadsheet section shows how the Person type declaration spans 2 columns, and thus both constraints will appear as Person(age == ..., type == ...). Since only the field names are present in the snippet, they imply an equality test.

In the following example the marker symbol $param is used.

The result of this column is the pattern Person(age == "42")). You may have noticed that the marker and the operator "==" are redundant.

The next example illustrates that a trailing insertion marker can be omitted.

Here, appending the value from the cell is implied, resulting in Person(age < "42")).

You can provide the definition of a binding variable, as in the example below. .

Here, the result is c: Cheese(type == "stilton"). Note that the quotes are provided automatically. Actually, anything can be placed in the object type row. Apart from the definition of a binding variable, it could alse be an additional pattern that is to be inserted literally.

A simple construction of an action statement with the insertion of a single value is shown below.

The cell below the ACTION header is left blank. Using this style, anything can be placed in the consequence, not just a sinle method call. (The same technique is applicable within a CONDITION column as well.)

Below is a comprehensive example, showing the use of various column headers. It is not an error to have no value below a column header (as in the NO-LOOP column): here, the attribute will not be applied in any of the rules.

And, finally, here is an example of Import, Variables and Functions.

Multiple package names within the same cell must be separated by a comma. Also, the pairs of type and variable names must be comma-separated. Functions, however, must be written as they appear in a DRL file. This should appear in the same column as the "RuleSet" keyword; it could be above, between or below all the rule rows.


It may be more convenient to use Import, Variables, Functions and Queries repeatedly rather than packing several definitions into a single cell.

Related to decision tables (but not necessarily requiring a spreadsheet) are "Rule Templates" (in the drools-templates module). These use any tabular data source as a source of rule data - populating a template to generate many rules. This can allow both for more flexible spreadsheets, but also rules in existing databases for instance (at the cost of developing the template up front to generate the rules).

With Rule Templates the data is separated from the rule and there are no restrictions on which part of the rule is data-driven. So whilst you can do everything you could do in decision tables you can also do the following:

As an example, a more classic decision table is shown, but without any hidden rows for the rule meta data (so the spreadsheet only contains the raw data to generate the rules).

See the ExampleCheese.xls in the examples download for the above spreadsheet.

If this was a regular decision table there would be hidden rows before row 1 and between rows 1 and 2 containing rule metadata. With rule templates the data is completely separate from the rules. This has two handy consequences - you can apply multiple rule templates to the same data and your data is not tied to your rules at all. So what does the template look like?

1  template header
2  age
3  type
4  log
6  package org.drools.examples.templates;
8  global java.util.List list;
10 template "cheesefans"
12 rule "Cheese fans_@{row.rowNumber}"
13 when
14    Person(age == @{age})
15    Cheese(type == "@{type}")
16 then
17    list.add("@{log}");
18 end
20 end template

Annotations to the preceding program listing:

The rule templates rely on MVEL to do substitution using the syntax @{token_name}. There is currently one built-in expression, @{row.rowNumber} which gives a unique number for each row of data and enables you to generate unique rule names. For each row of data a rule will be generated with the values in the data substituted for the tokens in the template. With the example data above the following rule file would be generated:

package org.drools.examples.templates;

global java.util.List list;

rule "Cheese fans_1"
  Person(age == 42)
  Cheese(type == "stilton")
  list.add("Old man stilton");

rule "Cheese fans_2"
  Person(age == 21)
  Cheese(type == "cheddar")
  list.add("Young man cheddar");

The code to run this is simple:

DecisionTableConfiguration dtableconfiguration =
dtableconfiguration.setInputType( DecisionTableInputType.XLS );
KnowledgeBuilder kbuilder = KnowledgeBuilderFactory.newKnowledgeBuilder();
kbuilder.add( ResourceFactory.newClassPathResource( getSpreadsheetName(),
                                                    getClass() ),
              dtableconfiguration );

If you discover that you have a group of rules following the same arrangement of patterns, constraints and actions on the RHS, differing only in constants or names for objects or fields, you might think of employing Drool's rule template feature for generating the actual rules. You would write a rule template file, containing the textual skeleton of your rule and use the Drools template compiler in combination with a collection of objects providing the actual values for the "flesh" of the rules for their instantiation.

The mechanism is very similar to what a macro processor does. The major advantage proffered by template expansion is that it's nicely integrated in the overall handling of Knowledge Resources.

A rule template file begins with a header defining the placeholders, or formal template parameters for the strings that are to be inserted during instantiation. After the first line, which invariably contains template header, you should write a number of lines, each of which contains a single parameter name.

The template header is followed by the text that is to be replicated and interpolated with the actual parameters. It may begin with a package statement, followed by some additional lines. These may be sectioned into one or more templates, each of them between a pair of matching template and end template statements. The template takes an argument, which puts a name to the template. The name can be a simple unquoted name or an arbitrary string enclosed in double quotes. The template text between these lines may contain one or more rules, constituting the "raw material" for the expansion.

The resulting text will begin with the package line and the header text following it, if present. Then, each template text will be expanded individually, yielding one set of rules for each of the actual parameter sets. Therefore, the structure of the template sections affect the order of the generated rules, since the generator iterates over the sections and then over the set of actual parameters.

Any interpolation takes place between a pair of template and end template statements, when this template is expanded. The template text is scanned for occurrences of parameter expansions written according to:


The name between '@{' and '}' should be one of the parameter names defined in the template header. The substitution is effected anywhere, even within string literals.

An important parameter is available without having to be included in the data source providing the actual values. The parameter substitution @{row.rowNumber} expands to the integers 0, 1, 2, etc., providing a unique distinction for the instantiation derived from a parameter set. You would use this as part of each rule name, because, without this precaution, there would be duplicate rule names. (You are, of course, free to use your own identification included as an extra parameter.)

To expand a template, you must prepare a data source. This can be a spreadsheet, as explained in the previous section. Here, we'll concentrate on expansion driven by Java objects. There are two straightforward ways of supplying values for a fixed set of names: Java objects, in the JavaBeans style, and Maps. Both of them can be arranged in a Collection, whose elements will be processed during the expansion, resulting in an instantiation for each element.

You may use a Java object that provides getter methods corresponding to all of the parameter names of your template file. If, for instance, you have defined a header

template header

the following Java class could be used:

public class ParamSet {
    public ParamSet( String t, int l, boolean w ) {
    public String  getType(){...}
    public int     getLimit(){...}
    public boolean isWord(){...}

Although interpolation is pure text manipulation, the actual values supplied may be of any type, just as long as this type provides a reasonable toString() method. (For simple types, the eponymous static method of the related class from java.lang is used.)

Assuming that we have created a Collection<ParamSet> for a template file template.drl, we can now proceed to request its expansion.

Collection<ParamSet> paramSets = new ArrayList<ParamSet>();
// populate paramSets
paramSets.add( new ParamSet( "Foo", 42, true ) );
paramSets.add( new ParamSet( "Bar", 13, false ) );
ObjectDataCompiler converter = new ObjectDataCompiler();
InputStream templateStream =
    this.getClass().getResourceAsStream( "template.drl" );
String drl = converter.compile( objs, templateStream );
The resulting string contains the expanded rules text. You could write it to a file and proceed as usual, but it's also possible to feed this to a KnowledgeBuilder and continue with the resulting Knowledge Packages.

KnowledgeBase kBase = KnowledgeBaseFactory.newKnowledgeBase();
KnowledgeBuilder kBuilder = KnowledgeBuilderFactory.newKnowledgeBuilder();
Reader rdr = new StringReader( drl );
kBuilder.add( ResourceFactory.newReaderResource( rdr ), ResourceType.DRL );
if( kBuilder.hasErrors() ){
    // ...
    throw new IllegalStateException( "DRL errors" );
kBase.addKnowledgePackages( kBuilder.getKnowledgePackages() );

The following example illustrates template expansion. It is based on simple objects of class Item containing a couple of integer fields and an enum field of type ItemCode.

public class Item {
    // ...
    public Item( String n, int p, int w, ItemCode c ){...}
    public String   getName() {...}
    public int      getWeight() {...}
    public int      getPrice() {...}
    public ItemCode getCode() {...}
public enum ItemCode {

The rule template contains a single rule. Notice that the field name for the range test is a parameter, which enables us to instantiate the template for different fields.

template header

package range;
template "inRange"
rule "is in range @{row.rowNumber}"
    Item( $name : name, $v : @{field} >= @{lower} && <= @{upper}, $code : code @{codes} )
    System.out.println( "Item " + $name + " @{field} in range: " + $v + " code: " + $code );
end template

The next code snippet is from the application, where several parameter sets have to be set up. First, there is class ParamSet, for storing a set of actual parameters.

public class ParamSet {
    private EnumSet<ItemCode> codeSet;
    public ParamSet( String f, int l, int u, EnumSet<ItemCode> cs ){...}
    public String getField() { return field; }
    public int getLower() { return lower; }
    public int getUpper() { return upper; }
    public String getCodes(){
        StringBuilder sb = new StringBuilder();
        String conn = "";
        for( ItemCode ic: codeSet ){
             sb.append( conn ).append( " == ItemCode." ).append( ic );
             conn = " ||";
        return sb.toString();

Note that the method getCodes() does returns the EnumSet<ItemCode> field value as a String value representing a multiple restriction, i.e., a test for one out of a list of values.

The task of expanding a template, passing the resulting DRL text to a Knowledge Builder and adding the resulting Knowledge Packages to a Knowledge Base is generic. The utility class Expander takes care of this, using a Knowledge Base, the InputStream with the rule template and the collection of parameter sets.

public class Expander {
    public void expand( KnowledgeBase kBase, InputStream is, Collection<?> act )
        throws Exception {
        ObjectDataCompiler converter = new ObjectDataCompiler();
        String drl = converter.compile( act, is );
        KnowledgeBuilder kBuilder = KnowledgeBuilderFactory.newKnowledgeBuilder();
        Reader rdr = new StringReader( drl );
        kBuilder.add( ResourceFactory.newReaderResource( rdr ), ResourceType.DRL );
        if( kBuilder.hasErrors() ){
            for( KnowledgeBuilderError err: kBuilder.getErrors() ){
                System.err.println( err.toString() );
            throw new IllegalStateException( "DRL errors" );
        kBase.addKnowledgePackages( kBuilder.getKnowledgePackages() );

We are now all set to prepare the Knowledge Base with some generated rules. First, we define several parameter sets, constructed as ParamSet objects, and add them to a List, which is passed to the expand method shown above. Then we launch a stateful session, insert a few Item, and watch what happens.

Collection<ParamSet> cfl = new ArrayList<ParamSet>();
cfl.add( new ParamSet( "weight",  10,  99, EnumSet.of( ItemCode.LOCK, ItemCode.STOCK ) ) );
cfl.add( new ParamSet( "price",   10,  50, EnumSet.of( ItemCode.BARREL ) ) );
KnowledgeBase kBase = KnowledgeBaseFactory.newKnowledgeBase();
Expander ex = new Expander();
InputStream dis = new FileInputStream( new File( "rangeTemp.drl" ) );
ex.expand( kBase, dis, cfl );
StatefulKnowledgeSession session = kBase.newStatefulKnowledgeSession();
session.insert( new Item( "A", 130,  42, ItemCode.LOCK ) );
session.insert( new Item( "B",  44, 100, ItemCode.STOCK ) );
session.insert( new Item( "C", 123, 180, ItemCode.BARREL ) );
session.insert( new Item( "D",  85,   9, ItemCode.LOCK ) );

Notice that the two resulting rules deal with different fields, one with an item's weight, the other one with its price. - Below is the output.

Item E price in range: 25 code: BARREL
Item A weight in range: 42 code: LOCK