Question
What are the differences between executing a Spark/Scala JAR using spark-submit versus using java -jar?
spark-submit --class com.example.YourApp your-spark-app.jar
Answer
When running a Spark application, you have two prevalent options for executing your JAR file: using the "spark-submit" command or the Java "-jar" option. Each has its use cases, advantages, and configurations that suit different scenarios in Spark job deployments.
spark-submit --class com.example.YourApp --master spark://masterURL:7077 your-spark-app.jar
Causes
- The two commands process the application differently, which can affect configuration, dependencies, and application resource management.
- "spark-submit" integrates seamlessly with Spark's infrastructure, allowing for automatic resource allocation and execution on a cluster.
- Using "java -jar" runs the JAR in a standalone Java environment, which may not fully utilize Spark's capabilities.
Solutions
- Use "spark-submit" for optimal integration with Spark, allowing for better resource management and configuration options.
- Utilize different parameters with "spark-submit" to specify the application class, master URL, and other runtime settings, improving flexibility and scalability.
- Reserve "java -jar" for smaller-scale applications or testing where Spark's full capabilities are not required.
Common Mistakes
Mistake: Using the wrong configuration options that only apply to spark-submit.
Solution: Ensure that you are using spark-submit with appropriate flags to configure the master, executor memory, and other settings.
Mistake: Not specifying the main class when using spark-submit, leading to a runtime error.
Solution: Always include the --class parameter followed by the main class name when using spark-submit.
Mistake: Assuming that java -jar provides the same capabilities as spark-submit.
Solution: Understand that spark-submit manages Spark-specific parameters and optimizes execution for the Spark environment.
Helpers
- Spark submit
- Execute Spark JAR
- Scala JAR execution
- Java -jar Spark
- Spark application deployment
- Spark job submission