The “Unsupported Class File Major Version” error in Apache Spark typically occurs when there is a mismatch between the Java version used to compile the code and the Java version used to run the code. This can happen when the version of Java used to build the dependencies is newer than the version of Java being used to execute the code in your Spark job.
Understanding the Error
Java class files have a version number that indicates the version of Java used to compile them. If the runtime JVM version is older than the version used to compile the class, it won’t be able to read the class file, resulting in the “Unsupported Class File Major Version” error.
Steps to Fix the Error
1. Check Java Version
First, verify the Java version used in your environment and the version used to compile your code. You can check the Java version using the following command:
java -version
2. Update Java Version
Ensure that you are using a compatible Java version. If necessary, update your Java environment to match the version required by your dependencies. For instance, if your class files are compiled with Java 11 and your current environment uses Java 8, you need to update to Java 11.
3. Recompile with Appropriate Version
If updating the Java version is not feasible, you can recompile your code and dependencies with a version compatible with your runtime environment. Use the appropriate Java compiler (javac) with the correct target version specified:
javac -target 1.8 -source 1.8 MyClass.java
4. Configure Spark to Use Correct Java Version
Ensure that Apache Spark is also configured to use the correct Java version. This may require setting the `JAVA_HOME` environment variable:
export JAVA_HOME=/path/to/your/java
5. Update Spark and Dependencies
Sometimes, upgrading Spark and its related dependencies to the latest version can solve compatibility issues. Check your build file (e.g., `pom.xml` for Maven or `build.sbt` for SBT) and ensure all dependencies are compatible with each other.
Examples for Deployment Types
Running PySpark Locally
If you are running PySpark locally and encounter this error, configure the correct JAVA_HOME as follows:
export JAVA_HOME=</path/to/your/java>
Using Spark in a Cluster
For cluster environments, make sure that all nodes are running compatible Java versions and set the JAVA_HOME environment variable accordingly in each node.
Example: PySpark with Correct JAVA_HOME Configuration
from pyspark.sql import SparkSession
# Set the JAVA_HOME environment variable
import os
os.environ["JAVA_HOME"] = "/path/to/your/java"
# Create Spark Session
spark = SparkSession.builder.appName("example").getOrCreate()
# Sample DataFrame operation
data = [("James", "Smith"), ("Anna", "Rose"), ("Robert", "Williams")]
columns = ["First Name", "Last Name"]
df = spark.createDataFrame(data, columns)
df.show()
+----------+-----------+
|First Name| Last Name|
+----------+-----------+
| James| Smith|
| Anna| Rose|
| Robert| Williams|
+----------+-----------+
Conclusion
By ensuring that your Java version is compatible across your development and runtime environments, and by appropriately configuring your system and Spark settings, you can avoid the “Unsupported Class File Major Version” error. If you still face issues, consider recompiling your code with a compatible Java version or updating your dependencies to align with your runtime Java environment.