Developing Applications On Managed Service For Apache Flink
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- Tool integration
- Cloud and DevOps
- Apache-2.0
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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
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Documentation by AWS
Managed Service for Apache Flink
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 locallyExample 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 guidanceReference 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.
| Goal | Reference | When to Load |
|---|---|---|
| Best practices | best-practices.md | Always before writing code |
| Maven dependencies | dependency-management.md | New project or adding connectors |
| Local dev environment | environment-setup.md | Docker-based local development |
| MSF architecture | msf-overview.md | KPU model and service constraints |
| MSF constraints and patterns | msf-constraints-and-patterns.md | MSF 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 choice | foundation-operations.md | Capacity planning, service selection, architecture design, CLI/IAM/CloudWatch identifier disambiguation |
| IAM execution role, trust policy, action prefix, service principal | foundation-operations.md | Writing 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 migration | flink-2x-migration.md | Version upgrades, state compatibility |
| KPU sizing | resource-optimization.md | Right-sizing, performance diagnosis, scaling |
| Scaling decisions on running apps | scaling-decisions.md | In-flight scaling matrix, cost/memory impact of scale changes, autoscaling behavior, anti-patterns |
| Cost estimation | pricing-calculator.md | Budget planning, sizing-to-cost mapping, optimization levers |
| Application lifecycle ops | application-lifecycle.md | Start/stop, deploy code, rollback, snapshot lifecycle, runtime properties, delete |
| Restart loop diagnosis | first-fault-isolation.md | Crashing/restarting apps, finding original failure vs loop sustainers, Flink Dashboard live diagnosis |
| Checkpoint tuning | checkpoint-tuning.md | Checkpoint impact on KPU memory and CPU, frequency vs network bandwidth trade-offs, checkpoint duration exceeding interval, OOM/GC during checkpoints |
| Job graph design | job-graph-architecture.md | Performance issues, splitting jobs |
| Job graph anti-patterns | job-graph-anti-patterns.md | Data skew detection and mitigation, monolith job anti-pattern, high fan-out anti-pattern, removing multiple shuffles, when to split a large application |
| Monitoring and alarms | monitoring-and-metrics.md | CloudWatch dashboards, alarms, metrics |
| Logging | logging-configuration.md | Log4j2, CloudWatch Logs setup |
| Kinesis connectors | kinesis-connector-guide.md | Kinesis 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.md | When 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.md | Iceberg 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, maintenance | iceberg-tuning-and-operations.md | Provides 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 connectors | cdc-connector-guide.md | MySQL, PostgreSQL, Oracle, SQL Server, MongoDB CDC |
| IaC and deployment | iac-and-deployment.md | CloudFormation, CDK, Terraform, two-phase deployment |
| Serialization | serialization-guide.md | POJO, Avro, Kryo guidance |
| State management | state-management.md | TTL, state types, migration safety |
Additional Resources
Files
- ATTRIBUTION.mdAdded by AutoGineer998 B
- LICENSELicence file added by AutoGineer9.9 KiB
- NOTICELicence file added by AutoGineer67 B
- SKILL.md8.8 KiB
- references/application-lifecycle.md20.7 KiB
- references/best-practices.md11.6 KiB
- references/cdc-connector-guide.md47.2 KiB
- references/checkpoint-tuning.md6.4 KiB
- references/dependency-management.md13.4 KiB
- references/environment-setup.md7.0 KiB
- references/first-fault-isolation.md14.4 KiB
- references/flink-2x-migration.md34.1 KiB
- references/foundation-operations.md12.1 KiB
- references/iac-and-deployment.md17.1 KiB
- references/iceberg-connector-guide.md17.1 KiB
- references/iceberg-tuning-and-operations.md31.1 KiB
- references/job-graph-anti-patterns.md11.8 KiB
- references/job-graph-architecture.md8.9 KiB
- references/kinesis-connector-guide.md9.3 KiB
- references/kinesis-efo-guide.md7.3 KiB
- references/logging-configuration.md9.9 KiB
- references/monitoring-and-metrics.md34.3 KiB
- references/msf-constraints-and-patterns.md8.8 KiB
- references/msf-overview.md6.3 KiB
- references/pricing-calculator.md6.2 KiB
- references/resource-optimization.md9.9 KiB
- references/scaling-decisions.md5.4 KiB
- references/serialization-guide.md9.2 KiB
- references/state-management.md4.5 KiB
references/environment-setup.md
Managed Service for Apache Flink Development Environment Setup
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-world2. 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 $JAVA_HOMEDocker-Based Flink Development Environment
Docker Compose Configuration
Create a comprehensive Docker Compose setup that mirrors the Managed Service for Apache Flink environment. Adjust the Flink image tag to match your target version:
Flink 1.20:
flink:1.20-java11Flink 2.2:
flink:2.2-java17
Create docker-compose.yml in your project root:
version: '3.8'
# Set FLINK_IMAGE_TAG in your environment or .env file:
# Flink 1.20: FLINK_IMAGE_TAG=1.20-java11
# Flink 2.2: FLINK_IMAGE_TAG=2.2-java17
services:
# Flink JobManager
jobmanager:
image: flink:${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: bridgeStarting 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 root2. 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 --listBuild Tools and Dependencies
Maven Project Template for Managed Service for Apache Flink
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.jarSee dependency-management.md for examples of dependencies and pom.xml requried for Managed Service for Apache Flink.
Managed Service for Apache Flink-Specific Tools
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-snapshotRequirements and compatibility
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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)
- Synced
- Also distributed in
- aws/agent-toolkit-for-aws
AutoGineer is not affiliated with or endorsed by AWS or AWS.
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