Querying with user defined functions - Amazon Athena
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Querying with user defined functions

User Defined Functions (UDF) in Amazon Athena allow you to create custom functions to process records or groups of records. A UDF accepts parameters, performs work, and then returns a result.

To use a UDF in Athena, you write a USING EXTERNAL FUNCTION clause before a SELECT statement in a SQL query. The SELECT statement references the UDF and defines the variables that are passed to the UDF when the query runs. The SQL query invokes a Lambda function using the Java runtime when it calls the UDF. UDFs are defined within the Lambda function as methods in a Java deployment package. Multiple UDFs can be defined in the same Java deployment package for a Lambda function. You also specify the name of the Lambda function in the USING EXTERNAL FUNCTION clause.

You have two options for deploying a Lambda function for Athena UDFs. You can deploy the function directly using Lambda, or you can use the Amazon Serverless Application Repository. To find existing Lambda functions for UDFs, you can search the public Amazon Serverless Application Repository or your private repository and then deploy to Lambda. You can also create or modify Java source code, package it into a JAR file, and deploy it using Lambda or the Amazon Serverless Application Repository. For example Java source code and packages to get you started, see Creating and deploying a UDF using Lambda. For more information about Lambda, see Amazon Lambda Developer Guide. For more information about Amazon Serverless Application Repository, see the Amazon Serverless Application Repository Developer Guide.

For an example that uses UDFs with Athena to translate and analyze text, see the Amazon Machine Learning Blog article Translate and analyze text using SQL functions with Amazon Athena, Amazon Translate, and Amazon Comprehend.

For an example of using UDFs to extend geospatial queries in Amazon Athena, see Extend geospatial queries in Amazon Athena with UDFs and Amazon Lambda in the Amazon Big Data Blog.

Considerations and limitations

  • Built-in Athena functions – Built-in functions in Athena are designed to be highly performant. We recommend that you use built-in functions over UDFs when possible. For more information about built-in functions, see Functions in Amazon Athena.

  • Scalar UDFs only – Athena only supports scalar UDFs, which process one row at a time and return a single column value. Athena passes a batch of rows, potentially in parallel, to the UDF each time it invokes Lambda. When designing UDFs and queries, be mindful of the potential impact to network traffic of this processing.

  • UDF handler functions use abbreviated format – Use abbreviated format (not full format), for your UDF functions (for example, package.Class instead of package.Class::method).

  • UDF methods must be lowercase – UDF methods must be in lowercase; camel case is not permitted.

  • UDF methods require parameters – UDF methods must have at least one input parameter. Attempting to invoke a UDF defined without input parameters causes a runtime exception. UDFs are meant to perform functions against data records, but a UDF without arguments takes in no data, so an exception occurs.

  • Java runtime support – Currently, Athena UDFs support the Java 8 and Java 11 runtimes for Lambda. For more information, see Building Lambda functions with Java in the Amazon Lambda Developer Guide.

  • IAM permissions – To run and create UDF query statements in Athena, the IAM principal running the query must be allowed to perform actions in addition to Athena functions. For more information, see Example IAM permissions policies to allow Amazon Athena User Defined Functions (UDF).

  • Lambda quotas – Lambda quotas apply to UDFs. For more information, see Lambda quotas in the Amazon Lambda Developer Guide.

  • Row-level filtering – Lake Formation row-level filtering is not supported for UDFs.

  • Views – You cannot use views with UDFs.

  • Known issues – For the most up-to-date list of known issues, see Limitations and issues in the awslabs/aws-athena-query-federation section of GitHub.

UDF query syntax

The USING EXTERNAL FUNCTION clause specifies a UDF or multiple UDFs that can be referenced by a subsequent SELECT statement in the query. You need the method name for the UDF and the name of the Lambda function that hosts the UDF. In place of the Lambda function name, you can use the Lambda ARN. In cross-account scenarios, the Lambda ARN is required.


USING EXTERNAL FUNCTION UDF_name(variable1 data_type[, variable2 data_type][,...]) RETURNS data_type LAMBDA 'lambda_function_name_or_ARN' [, EXTERNAL FUNCTION UDF_name2(variable1 data_type[, variable2 data_type][,...]) RETURNS data_type LAMBDA 'lambda_function_name_or_ARN'[,...]] SELECT [...] UDF_name(expression) [, UDF_name2(expression)] [...]


USING EXTERNAL FUNCTION UDF_name(variable1 data_type[, variable2 data_type][,...])

UDF_name specifies the name of the UDF, which must correspond to a Java method within the referenced Lambda function. Each variable data_type specifies a named variable and its corresponding data type that the UDF accepts as input. The data_type must be one of the supported Athena data types listed in the following table and map to the corresponding Java data type.

Athena data type Java data type


java.time.LocalDateTime (UTC)


java.time.LocalDate (UTC)
























java.util.Map<String, Object>

RETURNS data_type

data_type specifies the SQL data type that the UDF returns as output. Athena data types listed in the table above are supported. For the DECIMAL data type, use the syntax RETURNS DECIMAL(precision, scale) where precision and scale are integers.

LAMBDA 'lambda_function'

lambda_function specifies the name of the Lambda function to be invoked when running the UDF.

SELECT [...] UDF_name(expression) [...]

The SELECT query that passes values to the UDF and returns a result. UDF_name specifies the UDF to use, followed by an expression that is evaluated to pass values. Values that are passed and returned must match the corresponding data types specified for the UDF in the USING EXTERNAL FUNCTION clause.


For example queries based on the AthenaUDFHandler.java code on GitHub, see the GitHub Amazon Athena UDF connector page.

Creating and deploying a UDF using Lambda

To create a custom UDF, you create a new Java class by extending the UserDefinedFunctionHandler class. The source code for the UserDefinedFunctionHandler.java in the SDK is available on GitHub in the awslabs/aws-athena-query-federation/athena-federation-sdk repository, along with example UDF implementations that you can examine and modify to create a custom UDF.

The steps in this section demonstrate writing and building a custom UDF Jar file using Apache Maven from the command line and a deploy.

Clone the SDK and prepare your development environment

Before you begin, make sure that git is installed on your system using sudo yum install git -y.

To install the Amazon query federation SDK
  • Enter the following at the command line to clone the SDK repository. This repository includes the SDK, examples and a suite of data source connectors. For more information about data source connectors, see Using Amazon Athena Federated Query.

    git clone https://github.com/awslabs/aws-athena-query-federation.git
To install prerequisites for this procedure

If you are working on a development machine that already has Apache Maven, the Amazon CLI, and the Amazon Serverless Application Model build tool installed, you can skip this step.

  1. From the root of the aws-athena-query-federation directory that you created when you cloned, run the prepare_dev_env.sh script that prepares your development environment.

  2. Update your shell to source new variables created by the installation process or restart your terminal session.

    source ~/.profile

    If you skip this step, you will get errors later about the Amazon CLI or Amazon SAM build tool not being able to publish your Lambda function.

Create your Maven project

Run the following command to create your Maven project. Replace groupId with the unique ID of your organization, and replace my-athena-udf with the name of your application For more information, see How do I make my first Maven project? in Apache Maven documentation.

mvn -B archetype:generate \ -DarchetypeGroupId=org.apache.maven.archetypes \ -DgroupId=groupId \ -DartifactId=my-athena-udfs

Add dependencies and plugins to your Maven project

Add the following configurations to your Maven project pom.xml file. For an example, see the pom.xml file in GitHub.

<properties> <aws-athena-federation-sdk.version>2022.47.1</aws-athena-federation-sdk.version> </properties> <dependencies> <dependency> <groupId>com.amazonaws</groupId> <artifactId>aws-athena-federation-sdk</artifactId> <version>${aws-athena-federation-sdk.version}</version> </dependency> </dependencies> <build> <plugins> <plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-shade-plugin</artifactId> <version>3.2.1</version> <configuration> <createDependencyReducedPom>false</createDependencyReducedPom> <filters> <filter> <artifact>*:*</artifact> <excludes> <exclude>META-INF/*.SF</exclude> <exclude>META-INF/*.DSA</exclude> <exclude>META-INF/*.RSA</exclude> </excludes> </filter> </filters> </configuration> <executions> <execution> <phase>package</phase> <goals> <goal>shade</goal> </goals> </execution> </executions> </plugin> </plugins> </build>

Write Java code for the UDFs

Create a new class by extending UserDefinedFunctionHandler.java. Write your UDFs inside the class.

In the following example, two Java methods for UDFs, compress() and decompress(), are created inside the class MyUserDefinedFunctions.

*package *com.mycompany.athena.udfs; public class MyUserDefinedFunctions extends UserDefinedFunctionHandler { private static final String SOURCE_TYPE = "MyCompany"; public MyUserDefinedFunctions() { super(SOURCE_TYPE); } /** * Compresses a valid UTF-8 String using the zlib compression library. * Encodes bytes with Base64 encoding scheme. * * @param input the String to be compressed * @return the compressed String */ public String compress(String input) { byte[] inputBytes = input.getBytes(StandardCharsets.UTF_8); // create compressor Deflater compressor = new Deflater(); compressor.setInput(inputBytes); compressor.finish(); // compress bytes to output stream byte[] buffer = new byte[4096]; ByteArrayOutputStream byteArrayOutputStream = new ByteArrayOutputStream(inputBytes.length); while (!compressor.finished()) { int bytes = compressor.deflate(buffer); byteArrayOutputStream.write(buffer, 0, bytes); } try { byteArrayOutputStream.close(); } catch (IOException e) { throw new RuntimeException("Failed to close ByteArrayOutputStream", e); } // return encoded string byte[] compressedBytes = byteArrayOutputStream.toByteArray(); return Base64.getEncoder().encodeToString(compressedBytes); } /** * Decompresses a valid String that has been compressed using the zlib compression library. * Decodes bytes with Base64 decoding scheme. * * @param input the String to be decompressed * @return the decompressed String */ public String decompress(String input) { byte[] inputBytes = Base64.getDecoder().decode((input)); // create decompressor Inflater decompressor = new Inflater(); decompressor.setInput(inputBytes, 0, inputBytes.length); // decompress bytes to output stream byte[] buffer = new byte[4096]; ByteArrayOutputStream byteArrayOutputStream = new ByteArrayOutputStream(inputBytes.length); try { while (!decompressor.finished()) { int bytes = decompressor.inflate(buffer); if (bytes == 0 && decompressor.needsInput()) { throw new DataFormatException("Input is truncated"); } byteArrayOutputStream.write(buffer, 0, bytes); } } catch (DataFormatException e) { throw new RuntimeException("Failed to decompress string", e); } try { byteArrayOutputStream.close(); } catch (IOException e) { throw new RuntimeException("Failed to close ByteArrayOutputStream", e); } // return decoded string byte[] decompressedBytes = byteArrayOutputStream.toByteArray(); return new String(decompressedBytes, StandardCharsets.UTF_8); } }

Build the JAR file

Run mvn clean install to build your project. After it successfully builds, a JAR file is created in the target folder of your project named artifactId-version.jar, where artifactId is the name you provided in the Maven project, for example, my-athena-udfs.

Deploy the JAR to Amazon Lambda

You have two options to deploy your code to Lambda:

  • Deploy Using Amazon Serverless Application Repository (Recommended)

  • Create a Lambda Function from the JAR file

Option 1: Deploying to the Amazon Serverless Application Repository

When you deploy your JAR file to the Amazon Serverless Application Repository, you create an Amazon SAM template YAML file that represents the architecture of your application. You then specify this YAML file and an Amazon S3 bucket where artifacts for your application are uploaded and made available to the Amazon Serverless Application Repository. The procedure below uses the publish.sh script located in the athena-query-federation/tools directory of the Athena Query Federation SDK that you cloned earlier.

For more information and requirements, see Publishing applications in the Amazon Serverless Application Repository Developer Guide, Amazon SAM template concepts in the Amazon Serverless Application Model Developer Guide, and Publishing serverless applications using the Amazon SAM CLI.

The following example demonstrates parameters in a YAML file. Add similar parameters to your YAML file and save it in your project directory. See athena-udf.yaml in GitHub for a full example.

Transform: 'AWS::Serverless-2016-10-31' Metadata: 'AWS::ServerlessRepo::Application': Name: MyApplicationName Description: 'The description I write for my application' Author: 'Author Name' Labels: - athena-federation SemanticVersion: 1.0.0 Parameters: LambdaFunctionName: Description: 'The name of the Lambda function that will contain your UDFs.' Type: String LambdaTimeout: Description: 'Maximum Lambda invocation runtime in seconds. (min 1 - 900 max)' Default: 900 Type: Number LambdaMemory: Description: 'Lambda memory in MB (min 128 - 3008 max).' Default: 3008 Type: Number Resources: ConnectorConfig: Type: 'AWS::Serverless::Function' Properties: FunctionName: !Ref LambdaFunctionName Handler: "full.path.to.your.handler. For example, com.amazonaws.athena.connectors.udfs.MyUDFHandler" CodeUri: "Relative path to your JAR file. For example, ./target/athena-udfs-1.0.jar" Description: "My description of the UDFs that this Lambda function enables." Runtime: java8 Timeout: !Ref LambdaTimeout MemorySize: !Ref LambdaMemory

Copy the publish.sh script to the project directory where you saved your YAML file, and run the following command:

./publish.sh MyS3Location MyYamlFile

For example, if your bucket location is s3://DOC-EXAMPLE-BUCKET/mysarapps/athenaudf and your YAML file was saved as my-athena-udfs.yaml:

./publish.sh DOC-EXAMPLE-BUCKET/mysarapps/athenaudf my-athena-udfs
To create a Lambda function
  1. Open the Lambda console at https://console.amazonaws.cn/lambda/, choose Create function, and then choose Browse serverless app repository

  2. Choose Private applications, find your application in the list, or search for it using key words, and select it.

  3. Review and provide application details, and then choose Deploy.

    You can now use the method names defined in your Lambda function JAR file as UDFs in Athena.

Option 2: Creating a Lambda function directly

You can also create a Lambda function directly using the console or Amazon CLI. The following example demonstrates using the Lambda create-function CLI command.

aws lambda create-function \ --function-name MyLambdaFunctionName \ --runtime java8 \ --role arn:aws:iam::1234567890123:role/my_lambda_role \ --handler com.mycompany.athena.udfs.MyUserDefinedFunctions \ --timeout 900 \ --zip-file fileb://./target/my-athena-udfs-1.0-SNAPSHOT.jar