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AWSKnown publisheraws/agent-toolkit-for-aws/plugins/aws-data-analytics/skills/developing-applications-on-managed-service-for-apache-flink/SKILL.md

developing-applications-on-managed-service-for-apache-flink

Skill2.9k starsChanged 5 months agoApache-2.0View on GitHub

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.

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 3 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈3

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

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

    line 28
  2. Distinctive:

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

    line 30
  3. Distinctive:

    Do NOT answer from training knowledge or by searching general AWS documentation when the question concerns Apache Flink, Managed Service …

    line 66

≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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 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

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