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The "UserDefinedFunctions" page has been changed by KasperSorensen: https://wiki.apache.org/metamodel/UserDefinedFunctions Comment: Added page about UDFs New page: = User Defined Functions (UDFs) = Apache MetaModel allows for extending the query language using User Defined Functions. In fact, the functions that are built into Apache MetaModel are also "just" built-in UDFs. Functions in MetaModel come in two forms: * Aggregate functions, which implement the org.apache.metamodel.query.AggregateFunction interface. * Scalar functions, which implement the org.apache.metamodel.query.ScalarFunction interface. == Scalar functions == A scalar function is a function that provides a result on each row of the dataset that it is applied to. For instance, the built-in function TO_NUMBER will convert each value to a java.lang.Number instead of some other data type. Normally you can apply the Scalar function TO_NUMBER like this: {{{#!java Query q = dataContext.query().from(table).select(FunctionType.TO_NUMBER, "id").toQuery(); }}} If you dig into FunctionType.TO_NUMBER you will find out that it is simply an object that implements ScalarFunction. Let's imagine you wanted to implement instead now a hash code function. We can implement it like this: {{{#!java public class HashCodeFunction implements ScalarFunction { @Override public ColumnType getExpectedColumnType(ColumnType type) { return ColumnType.INTEGER; } @Override public String getFunctionName() { return "HASH_CODE"; } @Override public Object evaluate(Row row, SelectItem operandItem) { Object value = row.getValue(operandItem); return value == null ? null : value.hashCode(); } } }}} As you can see the implementation part here is pretty easy. We need only to provide a name, a data type and implement the evaluate(...) method. Now to apply the function to our query: {{{#!java Query q = dataContext.query().from(table).select(new HashCodeFunction(), "id").toQuery(); }}} == Aggregate functions == Aggregate functions are used to make calculations which span multiple rows of the dataset. Typically used on a complete dataset or in combination with a GROUP BY condition. In a similar way to scalar functions, you can also implement your own aggregate functions. Let's say we wanted to implement a DISTINCT_COUNT function (ie. a count of distinct/unique values), we could do it like this: {{{#!java public class DistinctCountFunction implements AggregateFunction { @Override public String getFunctionName() { return "DISTINCT_COUNT"; } @Override public ColumnType getExpectedColumnType(ColumnType type) { return ColumnType.INTEGER; } @Override public Object evaluate(Object... values) { AggregateBuilder<?> aggregateBuilder = createAggregateBuilder(); for (Object value : values) { aggregateBuilder.add(value); } return aggregateBuilder.getAggregate(); } @Override public AggregateBuilder<?> createAggregateBuilder() { return new AggregateBuilder<Integer>() { private Set<Object> uniqueSet = new HashSet<>(); @Override public void add(Object o) { uniqueSet.add(o); } @Override public Integer getAggregate() { return uniqueSet.size(); } }; } } }}} (you may choose to extend DefaultAggregateFunction which will save you the effort of implementing evaluate(...)) And again you can apply your function in a query like this: {{{#!java Query q = dataContext.query().from(table).select(new DistinctCountFunction(), "type").toQuery(); }}}