This content originally appeared on DEV Community and was authored by Aaron Rose
Introduction
You have done everything right. Your team has modernized to Java 17 LTS, your builds are clean, your Flink job compiles without warnings, and you have successfully pushed it to Amazon Managed Service for Apache Flink. You sit back, expecting smooth sailing, only to watch your job crash with this cryptic message:
has been compiled by a more recent version of the Java Runtime (class file version 61.0),
this version of the Java Runtime only recognizes class file versions up to 55.0
If you are staring at this error right now, you are not alone - and you are definitely not doing anything wrong.
Problem
The error message above is Java's way of saying "I cannot run code that was compiled with a newer version than me." In this case, your Flink job was compiled with Java 17 (class file version 61.0), but the runtime environment only supports up to Java 11 (class file version 55.0).
This creates a frustrating disconnect: you have invested time modernizing your development environment to Java 17, but your production deployments are failing because of a runtime mismatch you had no visibility into during development.
Clarifying the Issue
Here is what is actually happening behind the scenes. The problem is not with Apache Flink itself - Flink 1.18 and later versions fully support Java 17. The real culprit is AWS Managed Service for Apache Flink, which still runs its managed containers on Amazon Corretto 11, not Java 17.
This means:
- Your local development environment: Java 17 (works)
- Your build pipeline: Java 17 (works)
- Your compiled bytecode: Java 17 (class file version 61.0) (works)
- AWS Managed Flink runtime: Java 11 (fails)
The mismatch only surfaces when your code hits the managed runtime, making this a particularly sneaky issue that can slip through your entire CI/CD pipeline undetected.
Why It Matters
This is not just a minor inconvenience - it represents a significant friction point for modern Java development:
Organizational Java Strategy: Many companies have standardized on Java 17 LTS as their target platform. Having to maintain dual Java versions just for Flink deployments complicates toolchain management and developer workflows.
Dependency Management: Modern libraries increasingly assume Java 17 as a baseline. Spring Boot 3.x, for example, requires Java 17+. If your Flink jobs use these libraries, you are forced to choose between staying current with your stack or using managed Flink.
Development Velocity: Teams typically discover this issue only after building, deploying, and failing in production - a costly learning experience that can derail sprint commitments and release timelines.
Technical Debt: Without a clear resolution strategy, teams often choose suboptimal workarounds like downgrading their entire build pipeline or abandoning managed services in favor of self-hosted infrastructure.
Key Terms
Before diving into solutions, let us clarify the key components at play:
Apache Flink: An open-source stream processing framework for real-time data pipelines and analytics.
Amazon Managed Service for Apache Flink: AWS fully managed Flink service that handles infrastructure provisioning, scaling, and maintenance, but gives you limited control over the underlying runtime environment.
Amazon Corretto: AWS no-cost, multiplatform distribution of OpenJDK. Currently, Managed Flink uses Corretto 11.
Java Class File Versions: Each Java version produces bytecode with a specific class file version number:
- Java 8: version 52
- Java 11: version 55
- Java 17: version 61
- Java 21: version 65
The JVM can only execute bytecode compiled for its version or earlier - it cannot run "future" bytecode.
Steps at a Glance
Here is the high-level approach to resolve this compatibility issue:
- Verify the runtime environment to confirm Java 11 is indeed running your Flink jobs
- Configure Maven for cross-compilation to target Java 11 bytecode while using Java 17 toolchain
- Configure Gradle for cross-compilation (if using Gradle instead of Maven)
- Add build-time API safeguards to prevent accidental use of Java 17-specific APIs
- Deploy and validate that your jobs run successfully on the managed runtime
- Consider alternative approaches if you absolutely need Java 17 runtime features
This approach lets you maintain Java 17 in your development environment while ensuring compatibility with AWS Managed Flink Java 11 runtime.
Detailed Steps
Step 1: Verify the Runtime Environment
First, confirm that AWS Managed Flink is indeed using Java 11. You can check this by examining your job logs or by adding a simple diagnostic snippet to your Flink job:
public class FlinkRuntimeCheck {
public static void main(String[] args) {
System.out.println("Java Home: " + System.getProperty("java.home"));
System.out.println("Java Version: " + System.getProperty("java.version"));
System.out.println("Java Vendor: " + System.getProperty("java.vendor"));
}
}
You should see output indicating Amazon Corretto 11 as the runtime environment.
Step 2: Configure Maven for Cross-Compilation
If you are using Maven, configure the compiler plugin to use Java 17 toolchain while targeting Java 11 bytecode:
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.11.0</version>
<configuration>
<release>11</release>
</configuration>
</plugin>
The release flag is crucial - it ensures that:
- Your code compiles against the Java 11 API surface
- The resulting bytecode is compatible with Java 11 runtimes
- You cannot accidentally use Java 17-specific language features
Step 3: Configure Gradle for Cross-Compilation
For Gradle projects, you can achieve the same result using Java toolchains:
java {
toolchain {
languageVersion = JavaLanguageVersion.of(17)
}
}
tasks.withType(JavaCompile).configureEach {
options.release = 11
}
This configuration tells Gradle to:
- Use Java 17 for compilation (giving you access to modern build tools)
- Target Java 11 for bytecode compatibility
- Restrict API usage to Java 11-compatible methods
Step 4: Add Build-Time API Safeguards
To prevent accidental use of Java 17 APIs in your code, add the Animal Sniffer Maven plugin:
<plugin>
<groupId>org.codehaus.mojo</groupId>
<artifactId>animal-sniffer-maven-plugin</artifactId>
<version>1.23</version>
<configuration>
<signature>
<groupId>org.codehaus.mojo.signature</groupId>
<artifactId>java11</artifactId>
<version>1.0</version>
</signature>
</configuration>
<executions>
<execution>
<goals>
<goal>check</goal>
</goals>
</execution>
</executions>
</plugin>
This plugin will fail your build if you accidentally use APIs that were not available in Java 11, such as:
- String.isBlank() (added in Java 11, so this is safe)
- String.repeat() (added in Java 11, so this is safe)
- Records (Java 14+, would be flagged)
- Pattern matching for switch (Java 17+, would be flagged)
Step 5: Deploy and Validate
With your build configuration updated:
- Clean and rebuild your Flink job:
mvn clean packageorgradle clean build - Deploy the new JAR to AWS Managed Flink
- Monitor the job startup logs for successful initialization
- Verify that your job processes data correctly
If successful, you should no longer see class file version compatibility errors.
Step 6: Alternative Approaches
If you absolutely need Java 17 runtime features (like records, pattern matching, or newer API methods), you have two main alternatives:
Self-Managed Flink: Run Flink on your own infrastructure (EC2, EKS, or ECS) using Amazon Corretto 17. This gives you full control over the runtime but requires managing infrastructure, scaling, and maintenance yourself.
Hybrid Approach: Use AWS Managed Flink for stable, production workloads while running experimental or feature-rich jobs on self-managed clusters. This lets you evaluate which jobs truly benefit from Java 17 features.
Conclusion
The "class file version 61.0" error is not a bug in your code or a problem with Apache Flink - it is simply AWS Managed Service for Apache Flink running Java 11 while your build pipeline has moved to Java 17.
The solution is straightforward: configure your build tools to compile with Java 17 toolchains while targeting Java 11 bytecode compatibility. This approach lets you maintain consistency with your organization Java 17 adoption while ensuring your Flink jobs run successfully on AWS managed infrastructure.
The key takeaway is understanding the distinction between your build environment (where you want modern tooling) and your runtime environment (which AWS controls). By bridging this gap with cross-compilation, you can have the best of both worlds: modern development experiences with reliable managed deployments.
Until AWS updates Managed Flink to support Java 17 runtimes natively, this cross-compilation approach provides a clean, sustainable solution that keeps your team productive and your Flink jobs running smoothly.
Aaron Rose is a software engineer and technology writer at tech-reader.blog and the author of The Rose Theory series on math and physics.
This content originally appeared on DEV Community and was authored by Aaron Rose
Aaron Rose | Sciencx (2025-08-20T20:31:54+00:00) Running Managed Apache Flink with Java 17: Why Your Job Fails (and How to Fix It). Retrieved from https://www.scien.cx/2025/08/20/running-managed-apache-flink-with-java-17-why-your-job-fails-and-how-to-fix-it/
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