When working with Apache Spark, it becomes essential to understand how to manage dependencies and external libraries effectively. Spark applications can depend on third-party libraries or custom-built jars that need to be available on the classpath for the driver, executors, or both. This comprehensive guide will discuss the various methods and best practices for adding jars to the Spark Submit classpath.
Understanding Spark Submit
Before diving into the specifics of adding jars to the classpath, it’s important to understand what Spark Submit is. Spark Submit is the command-line interface that allows users to submit batch jobs to a Spark cluster. It provides a range of options to configure the Spark application’s resources, dependencies, and runtime behavior.
Adding Muliple Jars Using –jars Option
To add multiple jars to the classpath when using Spark Submit, you can use the –jars option. This option allows you to specify a comma-separated list of local or remote jars that should be included in the classpath of the Spark application. The simplest method to add a jar to your Spark job is through the --jars option in the spark-submit command. This option takes a comma-separated list of paths to jars that you want to include.
For example:
spark-submit \
--class com.example.MySparkApp \
--master local[4] \
--jars /path/to/mylib.jar,/path/to/anotherlib.jar \
my-spark-app_2.12-1.0.0.jar
This command would add mylib.jar and anotherlib.jar to the classpath of your Spark application.
Packaging Jars with sbt-assembly
For Scala-based Spark applications, one common approach to managing dependencies is by creating an uber-jar (also known as a fat jar) that contains all the necessary dependencies. This can be done using the sbt-assembly plugin for Scala’s build tool sbt.
To include the sbt-assembly plugin, add the following to your project’s plugins.sbt:
addSbtPlugin("com.eed3si9n" % "sbt-assembly" % "X.Y.Z")
Replace X.Y.Z with the latest version of the plugin.
Then, run sbt assembly to generate the uber-jar. Your build.sbt should define the main class if your jar will be executable:
mainClass in assembly := Some("com.example.MySparkApp")
After successfully building the uber-jar, you can submit your Spark job without specifying the --jars because all dependencies are included within the uber-jar.
Using –packages to Fetch from Maven Coordinates
Spark Submit also allows you to fetch jars from Maven repositories using the --packages option. This can be especially useful if you’d like to include libraries hosted on public repositories such as Maven Central or others.
Here’s an example:
spark-submit \
--class com.example.MySparkApp \
--master local[4] \
--packages "org.apache.commons:commons-math3:3.6.1" \
my-spark-app_2.12-1.0.0.jar
This will download Apache Commons Math library version 3.6.1 and include it in the classpath.
Adding Jars for Executors
When you’re running a Spark application in a distributed mode, you need to ensure that executors have access to the necessary jars. The --jars option discussed earlier includes jars for both the driver and executors. However, if you need to include jars only for executors, you can use the spark.executor.extraClassPath configuration property.
For example, when you are submitting a Spark job using spark-submit, you would include:
spark-submit \
--class com.example.MySparkApp \
--master spark://master:7077 \
--conf spark.executor.extraClassPath=/path/to/executorslib.jar \
my-spark-app_2.12-1.0.0.jar
Note that this property requires a file path that’s accessible on each Spark executor node.
Adding Jars for the Driver
In some cases, you might need to include jars exclusively for the driver, especially when you have a dependency that is only required for job submission or initialization. You can achieve this using the spark.driver.extraClassPath configuration property.
When running spark-submit, you could add:
spark-submit \
--class com.example.MySparkApp \
--master spark://master:7077 \
--conf spark.driver.extraClassPath=/path/to/driverslib.jar \
my-spark-app_2.12-1.0.0.jar
This inclusion targets only the driver’s classpath without affecting the executors.
Distributing Large Dependent Files
If you have large dependent files that need to be shared across nodes, like non-jar files or datasets, you can use the --files argument with spark-submit. These files will be copied to each executor’s working directory.
For example:
spark-submit \
--class com.example.MySparkApp \
--master local[4] \
--files /path/to/data.csv \
my-spark-app_2.12-1.0.0.jar
After submission, data.csv will be available to the application, and you can access it using SparkContext’s getSparkFiles method.
Best Practices
Here are some best practices to consider when adding jars to your Spark classpath:
- Minimize the number of dependencies to avoid excessive overhead.
- Use uber-jars for simplicity when you have multiple dependencies.
- If using the
--jarsoption with distributed storage like HDFS or S3, ensure that the path is accessible from all nodes. - Try to rely on built-in Spark libraries where possible, to avoid conflicts.
- If the application has dependencies with conflicting versions, consider using shading.
- Regularly update dependencies to reduce the risk of security vulnerabilities.
Maintaining an efficient classpath approach will help ensure your Spark jobs run smoothly without class-loading issues or unnecessary overhead.|,
Remember to consider the scope and lifecycle of your dependencies—some might be needed only at runtime and not during the build phase, for example. Correctly managing these distinctions can streamline your Spark application’s deployment process and optimize its execution.
Conclusion
Managing the classpath for Spark applications can be complex, but understanding the various options available for adding jars to Spark Submit is crucial. Whether you are working with local clusters or complex distributed environments, knowing how to properly include external libraries will greatly contribute to the success of your Spark jobs. Always consider the application’s needs, along with the best practices mentioned, to create clean and efficient Spark submissions.