flink-udf

Files (10)

• SKILL.md
• evals.json · evals/
• confluent-cloud-setup.md · references/
• dependencies-confluent-cloud.md · references/
• dependencies-local.md · references/
• local-docker-setup.md · references/
• ptf-java-confluent-cloud.md · references/
• ptf-java-local.md · references/
• udf-udtf-java-confluent-cloud.md · references/
• udf-udtf-java-local.md · references/

SKILL.md (first lines)

# Flink User-Defined Functions (UDFs)

Build and deploy custom functions in Java for Apache Flink to extend SQL and Table API capabilities with custom logic.

## Function Types

Before proceeding, identify which type of function the user needs:

- **Scalar UDF**: Maps input values to a single output value (e.g., custom hash, string manipulation, calculations)
- **User-Defined Table Function (UDTF)**: Maps input to multiple output rows (e.g., split strings, explode arrays)
- **Process Table Function (PTF)**: Advanced stateful processing with N-to-M semantics, managed state, and timers (e.g., windowing, deduplication, state machines)

## Gather Requirements

Ask the user these questions to determine the implementation path (if not already clear from context):

1. **Deployment target**: Confluent Cloud or local Docker?
2. **Infrastructure**: Deploy new infrastructure (Kafka + Flink) or use existing?
3. **Invocation method**: Flink SQL or Table API?

## Route to Implementation Guide

Based on the answers above, read the appropriate reference file:

### Confluent Cloud Deployment

- **Scalar UDF or UDTF** → Read `references/udf-udtf-java-confluent-cloud.md`
- **Process Table Function (PTF)** → Read `references/ptf-java-confluent-cloud.md`

If infrastructure setup is needed, also read `references/confluent-cloud-setup.md` first.

### Local Docker Deployment

- **Scalar UDF or UDTF** → Read `references/udf-udtf-java-local.md`
- **Process Table Function (PTF)** → Read `references/ptf-java-local.md`

If infrastructure setup is needed, also read `references/local-docker-setup.md` first.

## Implementation Workflow

After reading the appropriate reference:

1. **Set up infrastructure** (if needed)
2. **Generate boilerplate code** for the function
3. **Implement the business logic**
4. **Build and package** the JAR
5. **Confirm the deployment plan with the user.** Before any resource-modifying call (`confluent flink artifact create`, `docker cp` into a running container, `CREATE FUNCTION`, etc.), present the plan and wait for explicit approval. Show:
   - Artifact name and JAR path
   - Function name to register
   - Target environment (Confluent Cloud env + compute pool ID, or local Docker container name)
   - The exact commands and SQL that will run
   Do not proceed to steps 6–7 until the user confirms.
6. **Deploy the artifact**
7. **Register the function** in Flink
8. **Test the function** with sample data
9. **Provide usage examples** (SQL or Table API)

Keep code scaffolding concise and focused on the user's specific requirements. Avoid over-engineering.

From confluentinc/agent-skills, Apache-2.0.

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