User-defined functions (UDFs) in Azure Cosmos DB

The SQL API provides support for user-defined functions (UDFs). With scalar UDFs, you can pass in zero or many arguments and return a single argument result. The API checks each argument for being legal JSON values.

UDF use cases

The API extends the SQL syntax to support custom application logic using UDFs. You can register UDFs with the SQL API, and reference them in SQL queries. Unlike stored procedures and triggers, UDFs are read-only.

Using UDFs, you can extend Azure Cosmos DB's query language. UDFs are a great way to express complex business logic in a query's projection.

However, we recommending avoiding UDFs when:

  • An equivalent system function already exists in Azure Cosmos DB. System functions will always use fewer RU's than the equivalent UDF.
  • The UDF is the only filter in the WHERE clause of your query. UDF's do not utilize the index so evaluating the UDF will require loading documents. Combining additional filter predicates that use the index, in combination with a UDF, in the WHERE clause will reduce the number of documents processed by the UDF.

If you must use the same UDF multiple times in a query, you should reference the UDF in a subquery, allowing you to use a JOIN expression to evaluate the UDF once but reference it many times.


The following example registers a UDF under an item container in the Cosmos database. The example creates a UDF whose name is REGEX_MATCH. It accepts two JSON string values, input and pattern, and checks if the first matches the pattern specified in the second using JavaScript's string.match() function.

       UserDefinedFunction regexMatchUdf = new UserDefinedFunction
           Id = "REGEX_MATCH",
           Body = @"function (input, pattern) {
                      return input.match(pattern) !== null;

       UserDefinedFunction createdUdf = client.CreateUserDefinedFunctionAsync(
           UriFactory.CreateDocumentCollectionUri("myDatabase", "families"),

Now, use this UDF in a query projection. You must qualify UDFs with the case-sensitive prefix udf. when calling them from within queries.

    SELECT udf.REGEX_MATCH(, ".*eattle")
    FROM Families

The results are:

        "$1": true
        "$1": false

You can use the UDF qualified with the udf. prefix inside a filter, as in the following example:

    FROM Families
    WHERE udf.REGEX_MATCH(, ".*eattle")

The results are:

        "id": "AndersenFamily",
        "city": "Seattle"

In essence, UDFs are valid scalar expressions that you can use in both projections and filters.

To expand on the power of UDFs, look at another example with conditional logic:

       UserDefinedFunction seaLevelUdf = new UserDefinedFunction()
           Id = "SEALEVEL",
           Body = @"function(city) {
                   switch (city) {
                       case 'Seattle':
                           return 520;
                       case 'NY':
                           return 410;
                       case 'Chicago':
                           return 673;
                           return -1;

            UserDefinedFunction createdUdf = await client.CreateUserDefinedFunctionAsync(
                UriFactory.CreateDocumentCollectionUri("myDatabase", "families"),

The following example exercises the UDF:

    SELECT, udf.SEALEVEL( AS seaLevel
    FROM Families f

The results are:

        "city": "Seattle",
        "seaLevel": 520
        "city": "NY",
        "seaLevel": 410

If the properties referred to by the UDF parameters aren't available in the JSON value, the parameter is considered as undefined and the UDF invocation is skipped. Similarly, if the result of the UDF is undefined, it's not included in the result.

As the preceding examples show, UDFs integrate the power of JavaScript language with the SQL API. UDFs provide a rich programmable interface to do complex procedural, conditional logic with the help of built-in JavaScript runtime capabilities. The SQL API provides the arguments to the UDFs for each source item at the current WHERE or SELECT clause stage of processing. The result is seamlessly incorporated in the overall execution pipeline. In summary, UDFs are great tools to do complex business logic as part of queries.

Next steps