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Developing Applications On Managed Service For Apache Flink

AWSOfficialPublished by AWS

  • Skill
  • Tool integration
  • Cloud and DevOps
  • Apache-2.0
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From the author

MANDATORY for Flink or Amazon Managed Service for Apache Flink (MSF) questions. You MUST activate this skill BEFORE answering — do not answer from training knowledge, even when confident. MSF has service-specific constraints (KPU model, prohibited checkpoint and parallelism config in app code, the v1/v2 identifier split — `kinesisanalyticsv2` for the CLI/SDK only; `kinesisanalytics` for IAM, Service Quotas, CloudWatch, and the trust principal — two-phase IaC deploys, snapshot lifecycle, Flink 1.x→2.x migration) that override generic Flink knowledge. Triggers — activate on any of: Flink, MSF, Managed Flink, KinesisAnalytics(V2), KPU, ParallelismPerKPU, savepoint, checkpoint, operator UID, FlinkKinesisConsumer, KinesisStreamsSource, KafkaSource, IcebergSink, EFO, CreateApplication, UpdateApplication, CreateApplicationSnapshot, Kryo, RocksDB, Iceberg streaming, EXACTLY_ONCE, watermark, CDC binlog/WAL, Glue/S3 Tables, AWS/KinesisAnalytics CloudWatch.
— the description by AWS

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides domain-specific knowledge for building, deploying, and maintaining Apache Flink applications on Amazon Managed Service for Apache Flink (MSF). It covers service-specific constraints, including the KPU resource model, configuration requirements, and operational lifecycle management. It emphasizes the use of service-level configurations over application-level settings for checkpoints and parallelism, and provides guidance on navigating the v1/v2 API identifier split for AWS CLI and SDK operations.

What it does

  • Provides patterns for KPU-based resource configuration and scaling.
  • Outlines service-specific constraints for checkpointing and parallelism.
  • Details the lifecycle management for starting, stopping, and updating applications.
  • Offers guidance on connector integration for Kinesis, Kafka, and Iceberg.
  • Explains the v1/v2 identifier differences for IAM, CloudWatch, and SDK usage.
  • Provides troubleshooting steps for application restart loops and state management.

When to use it

  • When developing or migrating Apache Flink applications for Amazon Managed Service for Apache Flink.
  • When configuring KPU resources or troubleshooting application scaling behavior.
  • When setting up IAM roles, trust policies, or CloudWatch logging for MSF.
  • When performing version upgrades or migrating from Flink 1.x to 2.x.
  • When implementing connectors for Kinesis, Kafka, or Iceberg within an MSF environment.

Advantages

  • Ensures adherence to MSF-specific service constraints and operational patterns.
  • Prevents common configuration errors related to the v1/v2 API identifier split.
  • Provides structured workflows for application lifecycle and deployment tasks.
  • Offers clear guidance on avoiding anti-patterns in job graph design and resource management.

Usage examples

“How do I correctly configure checkpoints for an application running on Managed Service for Apache Flink?”
The agent will advise against hardcoding checkpoint configurations in the application code and instead direct the user to manage these settings via the MSF service-level configuration.
“I am getting a ConcurrentModificationException when updating my Flink application. How do I fix this?”
The agent will explain the need to fetch the current application version ID or use a conditional token before issuing an update command, providing the necessary CLI syntax.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/aws/developing-applications-on-managed-service-for-apache-flink/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip developing-applications-on-managed-service-for-apache-flink.zip -d .agents/skills

Documentation by AWS

Overview

Domain expertise for Apache Flink applications on Amazon Managed Service for Apache Flink (MSF). Covers development, KPU resource management, connectors, state management, monitoring, IaC deployment, and version migration.

Execute commands using available tools from the AWS MCP server when connected — it provides sandboxed execution, audit logging, and observability. When the MCP server is not available, fall back to the AWS CLI or shell as needed.

General Guidance

Before starting, ensure you have a clear understanding of the user persona, use case, and requirements:

STOP: Determine the users background and use case before proceeding:

  • Are they new to Flink? New to Managed Service for Apache Flink?

  • Are they familiar with Java development?

  • Is the use case complex with lots of business logic? Or simple and declarative?

These will inform how to organize the project, and whether to use Flink Table API or DataStream API. In general, assume the DataStream API.

Example Workflow for New Applications
1. User asks to build a Flink application
2. Confirm user's goals and use case
3. READ [best-practices.md](references/best-practices.md)
4. READ [dependency-management.md](references/dependency-management.md)
5. READ relevant connector guides (e.g. [kinesis-connector-guide.md](references/kinesis-connector-guide.md))
6. Generate code following the loaded guidance
7. Validate against best practices
8. READ environment-setup.md via [environment-setup.md](references/environment-setup.md)
9. Compile and test locally
Example Workflow for General Questions
1. User asks about real time delivery of data to Iceberg
2. Confirm user's goals and use case
3. READ [best-practices.md](references/best-practices.md)
4. READ [iceberg-connector-guide.md](references/iceberg-connector-guide.md)
5. READ other reference files as needed
6. Answer question with loaded guidance

Reference Files

  • You MUST use this skill and its reference files to answer any question on these topics.

  • Do NOT answer from training knowledge or by searching general AWS documentation when the question concerns Apache Flink, Managed Service for Apache Flink, KPU sizing, Flink monitoring, deployment, migration, real-time analytics, or Iceberg/LakeHouse streaming with Flink

    • You MUST load the relevant reference files below before taking other steps.

    • The reference files contain MSF-specific details (thresholds, statistics, namespaces, constraints) that differ from generic Flink guidance and are required for correct responses.

GoalReferenceWhen to Load
Best practicesbest-practices.mdAlways before writing code
Maven dependenciesdependency-management.mdNew project or adding connectors
Local dev environmentenvironment-setup.mdDocker-based local development
MSF architecturemsf-overview.mdKPU model and service constraints
MSF constraints and patternsmsf-constraints-and-patterns.mdMSF vs self-managed Flink, service-level vs application-level configuration separation, MSF-specific resource/network/storage limits, common MSF patterns
Quotas, ENI planning, MSF vs EMR, source/sink choicefoundation-operations.mdCapacity planning, service selection, architecture design, CLI/IAM/CloudWatch identifier disambiguation
IAM execution role, trust policy, action prefix, service principalfoundation-operations.mdWriting IAM policies for MSF — covers the kinesisanalytics: (no v2) action prefix, kinesisanalytics.amazonaws.com (no v2) trust principal, and the v2/non-v2 disconnect that is the most common source of permission and AssumeRole failures
Flink 2.x migrationflink-2x-migration.mdVersion upgrades, state compatibility
KPU sizingresource-optimization.mdRight-sizing, performance diagnosis, scaling
Scaling decisions on running appsscaling-decisions.mdIn-flight scaling matrix, cost/memory impact of scale changes, autoscaling behavior, anti-patterns
Cost estimationpricing-calculator.mdBudget planning, sizing-to-cost mapping, optimization levers
Application lifecycle opsapplication-lifecycle.mdStart/stop, deploy code, rollback, snapshot lifecycle, runtime properties, delete
Restart loop diagnosisfirst-fault-isolation.mdCrashing/restarting apps, finding original failure vs loop sustainers, Flink Dashboard live diagnosis
Checkpoint tuningcheckpoint-tuning.mdCheckpoint impact on KPU memory and CPU, frequency vs network bandwidth trade-offs, checkpoint duration exceeding interval, OOM/GC during checkpoints
Job graph designjob-graph-architecture.mdPerformance issues, splitting jobs
Job graph anti-patternsjob-graph-anti-patterns.mdData skew detection and mitigation, monolith job anti-pattern, high fan-out anti-pattern, removing multiple shuffles, when to split a large application
Monitoring and alarmsmonitoring-and-metrics.mdCloudWatch dashboards, alarms, metrics
Logginglogging-configuration.mdLog4j2, CloudWatch Logs setup
Kinesis connectorskinesis-connector-guide.mdKinesis source and sink builders, polling configuration and throttling (READER_EMPTY_RECORDS_FETCH_INTERVAL, SHARD_GET_RECORDS_MAX, ReadProvisionedThroughputExceeded, LimitExceededException), legacy connector migration
Kinesis Enhanced Fan-Out (EFO)kinesis-efo-guide.mdWhen to use EFO vs polling, EFO source configuration, consumer lifecycle (JOB_MANAGED vs SELF_MANAGED), parallelism vs shard count, IAM permissions, troubleshooting
Iceberg integration (write APIs, distribution modes, partitioning)iceberg-connector-guide.mdIceberg write APIs (append, upsert, dynamic), distribution modes (NONE/HASH/RANGE), CoW vs MoR, read patterns, partitioning, DDL. Does NOT contain catalog choice or maintenance approaches — for those, load iceberg-tuning-and-operations.md.
Iceberg tuning, operations, catalog choice, maintenanceiceberg-tuning-and-operations.mdProvides maintenance approaches for S3 Tables, Glue + Glue auto-compaction, and Glue + Flink embedded maintenance with JDBC lock for catalog-choice questions; small files problem and mitigations; Flink TableMaintenance API, post-commit maintenance, lock factories; IcebergSink monitoring, anti-patterns.
CDC connectorscdc-connector-guide.mdMySQL, PostgreSQL, Oracle, SQL Server, MongoDB CDC
IaC and deploymentiac-and-deployment.mdCloudFormation, CDK, Terraform, two-phase deployment
Serializationserialization-guide.mdPOJO, Avro, Kryo guidance
State managementstate-management.mdTTL, state types, migration safety

Additional Resources

Files

references/environment-setup.md

Overview

This guide provides setup instructions for Apache Flink development targeting Amazon Managed Service for Apache Flink deployment. The setup ensures compatibility with Managed Service for Apache Flink requirements while enabling efficient local development and testing workflows using Docker containers that mirror the Managed Service for Apache Flink environment. For new applications, default to Flink 2.2. For existing applications, use the user's current Flink version.

Prerequisites

Before starting, ensure you have:

  • Administrative access to your development machine

  • Stable internet connection for downloading dependencies

  • At least 16GB RAM and 30GB free disk space (for Docker containers)

  • Docker Desktop installed and running

  • Java 11 (for Flink 1.20) or Java 17 (for Flink 2.2) installed

  • Basic familiarity with command-line operations and Docker concepts

Core Development Environment

1. Docker Desktop Installation

Docker is required for running Flink, Kafka, LocalStack, and other infrastructure locally for testing. Ensure Docker is installed.

Verification:

docker --version
docker-compose --version
docker run hello-world
2. Java Development Kit (JDK) for Container Development

While Flink runs in containers, JDK is still needed for compilation and IDE support. Flink 1.20 requires Java 11. Flink 2.2 requires Java 17 (Java 21 also supported).

Verification:

java -version
javac -version
echo $JAVA_HOME
Docker Compose Configuration

Create a comprehensive Docker Compose setup that mirrors the Managed Service for Apache Flink environment. Adjust the Flink image tag to match your target version:

  • Flink 1.20: flink:1.20-java11

  • Flink 2.2: flink:2.2-java17

Create docker-compose.yml in your project root:

version: '3.8'

# Set FLINK_IMAGE_TAG in your environment or .env file:
#   Flink 1.20: FLINK_IMAGE_TAG=1.20-java11
#   Flink 2.2:  FLINK_IMAGE_TAG=2.2-java17

services:
  # Flink JobManager
  jobmanager:
    image: flink:${FLINK_IMAGE_TAG:-2.2-java17}
    hostname: jobmanager
    container_name: flink-jobmanager
    ports:
      - "8081:8081"
    command: jobmanager
    environment:
      - |
        FLINK_PROPERTIES=
        jobmanager.rpc.address: jobmanager
        jobmanager.memory.process.size: 1600m
        jobmanager.execution.failover-strategy: region
      - RUNTIME_ENVIRONMENT=local
    volumes:
      - ./flink-jobs:/opt/flink/jobs
      - flink-checkpoints:/tmp/flink-checkpoints
      - flink-savepoints:/tmp/flink-savepoints
    networks:
      - flink-network

  # Flink TaskManager
  taskmanager:
    image: flink:${FLINK_IMAGE_TAG:-2.2-java17}
    depends_on:
      - jobmanager
    command: taskmanager
    scale: 2
    environment:
      - |
        FLINK_PROPERTIES=
        jobmanager.rpc.address: jobmanager
        taskmanager.numberOfTaskSlots: 2
        parallelism.default: 2        
        taskmanager.memory.process.size: 1728m
        taskmanager.memory.managed.fraction: 0.4
        state.backend: rocksdb
        state.checkpoints.dir: file:///tmp/flink-checkpoints
        state.savepoints.dir: file:///tmp/flink-savepoints
        execution.checkpointing.interval: 60000
        execution.checkpointing.mode: EXACTLY_ONCE
        restart-strategy: exponential-delay
        restart-strategy.exponential-delay.initial-backoff: 10s
        restart-strategy.exponential-delay.max-backoff: 2min
        restart-strategy.exponential-delay.backoff-multiplier: 2.0
      - RUNTIME_ENVIRONMENT=local
    volumes:
      - ./flink-jobs:/opt/flink/jobs
      - flink-checkpoints:/tmp/flink-checkpoints
      - flink-savepoints:/tmp/flink-savepoints
    networks:
      - flink-network

  # Kafka (for streaming data sources)
  zookeeper:
    image: confluentinc/cp-zookeeper:7.4.0
    hostname: zookeeper
    container_name: zookeeper
    ports:
      - "2181:2181"
    environment:
      ZOOKEEPER_CLIENT_PORT: 2181
      ZOOKEEPER_TICK_TIME: 2000
    networks:
      - flink-network

  kafka:
    image: confluentinc/cp-kafka:7.4.0
    hostname: kafka
    container_name: kafka
    depends_on:
      - zookeeper
    ports:
      - "9092:9092"
      - "9101:9101"
    environment:
      KAFKA_BROKER_ID: 1
      KAFKA_ZOOKEEPER_CONNECT: 'zookeeper:2181'
      KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: PLAINTEXT:PLAINTEXT,PLAINTEXT_HOST:PLAINTEXT
      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://kafka:29092,PLAINTEXT_HOST://localhost:9092
      KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1
      KAFKA_TRANSACTION_STATE_LOG_MIN_ISR: 1
      KAFKA_TRANSACTION_STATE_LOG_REPLICATION_FACTOR: 1
      KAFKA_GROUP_INITIAL_REBALANCE_DELAY_MS: 0
      KAFKA_JMX_PORT: 9101
      KAFKA_JMX_HOSTNAME: localhost
    networks:
      - flink-network

  # LocalStack (for local AWS service emulation: Kinesis, S3, DynamoDB, etc.)
  localstack:
    image: localstack/localstack:3.5.0
    container_name: localstack
    ports:
      - "4566:4566"
    environment:
      SERVICES: kinesis,s3,dynamodb,cloudwatch
      DEFAULT_REGION: us-east-1
    networks:
      - flink-network

volumes:
  flink-checkpoints:
  flink-savepoints:

networks:
  flink-network:
    driver: bridge
Starting the Development Environment

1. Create Project Structure:

# Create Managed Service for Apache Flink project directory
mkdir my-msf-app
cd my-msf-app

# Create required directories
mkdir -p flink-jobs src/main/java src/test/java

# Copy docker-compose.yml to project root

2. Start Docker Environment:

# Start all services
docker-compose up -d

# Verify services are running
docker-compose ps

# Check taskmanager logs for submitted Flink jobs 
docker-compose logs taskmanager

# Check Flink Web UI
open http://localhost:8081

# Check Kafka
docker-compose exec kafka kafka-topics --bootstrap-server localhost:9092 --list

Build Tools and Dependencies

Build the JAR in local mode to include Managed Service for Apache Flink provided dependencies:

# For Flink 1.20 (Java 11):
mvn clean package -Plocal -q

# For Flink 2.2 (Java 17):
# Set JAVA_HOME to your Java 17 installation path before building
export JAVA_HOME=<path-to-java-17>
mvn clean package -Plocal -q

cp target/my-flink-app-1.0.jar flink-jobs/

docker exec flink-jobmanager flink run /opt/flink/jobs/my-flink-app-1.0.jar

See dependency-management.md for examples of dependencies and pom.xml requried for Managed Service for Apache Flink.

AWS CLI Managed Service for Apache Flink Commands:

# List Managed Service for Apache Flink applications
aws kinesisanalyticsv2 list-applications

# Describe Managed Service for Apache Flink application
aws kinesisanalyticsv2 describe-application --application-name my-app

# Create savepoint
aws kinesisanalyticsv2 create-application-snapshot --application-name my-app --snapshot-name my-snapshot

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an MCP server

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Attribution and licence

Published by
AWS
Publisher
AWS (Official)
Source
aws/agent-toolkit-for-aws · plugins/aws-data-analytics/skills/developing-applications-on-managed-service-for-apache-flink · commit 0a218f8
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • NOTICE (added by AutoGineer from NOTICE)
  • ATTRIBUTION.md (written by AutoGineer)
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