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flink-optimizer

Analyze Flink runtime metrics, stream DAGs, and YAML configurations to generate optimization recommendations.

31|4|Updated Aug 24, 2021
Search Tags:#optimization#checkpoints#rest-api#flink#runtime-metrics#dag-analysis#yaml-analysis
One-click install
npx skills add https://github.com/razorpay/trino-gateway --skill flink-optimizer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: flink-optimizer
Source: https://github.com/razorpay/trino-gateway/tree/main/.agents/skills/flink-optimizer
Command: npx skills add https://github.com/razorpay/trino-gateway --skill flink-optimizer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, pyyaml, and includes scripts (resource) components.

What problem does it solve?

Flink job optimization by evaluating runtime metrics, stream DAGs, and YAML configurations to identify bottlenecks and misconfigurations that degrade throughput and reliability.

Core Features & Use Cases

  • Analyze runtime metrics from Flink REST API to detect backpressure, skew, and parallelism issues.
  • Inspect YAML jobspec for wiring, parallelism, and checkpoint configuration to validate DAG correctness.
  • Generate actionable recommendations with exact file paths and configuration changes to fix issues in production clusters.

Quick Start

Run the Flink optimizer against a live cluster and a jobspec YAML to receive an optimization report with prioritized fixes.

Frequently Asked Questions about flink-optimizer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I optimize Apache Flink jobs using runtime metrics?▼

To optimize Apache Flink jobs, analyze runtime metrics from the Flink REST API to detect backpressure, skew, and parallelism issues. Evaluating these metrics identifies bottlenecks that degrade throughput and reliability in production clusters.

How do I validate my Flink stream DAG and YAML jobspec configuration?▼

Validate your Flink stream DAG by inspecting your YAML jobspec for wiring, parallelism, and checkpoint configuration. This process checks DAG correctness and identifies misconfigurations that may cause missing streams or processing failures.

Do I need access to the Flink REST API to analyze checkpoint tuning?▼

Yes, analyzing checkpoint tuning requires access to the Flink REST API. The optimizer evaluates live cluster metrics and YAML job specifications to provide actionable recommendations for improving throughput and reliability.

What is the best way to fix backpressure and missing streams in Flink?▼

The best way to fix backpressure and missing streams in Flink is to evaluate runtime metrics and stream DAGs together. This generates actionable recommendations with exact file paths and configuration changes to resolve bottlenecks.

Can I generate actionable configuration recommendations for my Flink cluster?▼

Yes, you can generate actionable configuration recommendations for your Flink cluster. The optimizer outputs a prioritized report with exact file paths and configuration changes to apply directly to production environments.

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