Dealing with excessive INFO messages in your Spark console can be a real headache, especially when you’re trying to debug or monitor the progress of your Spark applications. These messages, while informative in some contexts, often clutter the console output, making it difficult to identify important warnings, errors, or custom log messages. The good news is that there are several effective methods to stop INFO messages displaying on spark console, allowing you to streamline your debugging process and focus on what truly matters. This article will guide you through various techniques and configurations to filter out unnecessary INFO logs, improve console readability, and enhance your overall Spark development experience. We’ll cover everything from configuring log4j to using Spark’s built-in logging features to fine-tune the verbosity of your Spark applications.

Understanding Spark Logging and its Verbosity

Spark uses log4j for logging, a powerful and flexible logging framework. By default, Spark is configured to display INFO messages, which can be overwhelming when running complex applications. These INFO messages cover a wide range of activities, including task scheduling, data shuffling, and executor status updates. Understanding the hierarchy of log levels (TRACE, DEBUG, INFO, WARN, ERROR, FATAL) is crucial for effectively managing the verbosity of your Spark logs. Each level represents a different severity of message, with TRACE being the most verbose and FATAL the least. Choosing the right log level is key to balancing the need for detailed information with the desire for a clean and manageable console output. According to Apache Spark documentation, proper log management is essential for diagnosing issues and monitoring application performance [1].

INFO messages often include details about the internal workings of Spark, such as the execution plan, the number of tasks launched, and the amount of data being processed. While this information can be useful for understanding how Spark is executing your application, it’s often not necessary for day-to-day debugging. For example, you might see INFO messages about DAGScheduler events, which, while important for understanding the overall workflow, can be distracting when you’re trying to pinpoint a specific error in your code. By reducing the log level, you can filter out these less critical messages and focus on the WARN and ERROR messages that indicate actual problems.

The default log level can vary slightly depending on your Spark distribution and configuration. However, it’s generally set to INFO, meaning that all messages at INFO level and above (WARN, ERROR, FATAL) will be displayed. This can lead to a lot of noise in the console, making it difficult to find the information you need. Therefore, understanding how to configure log4j and adjust the log level is a fundamental skill for any Spark developer.

Configuring Log4j to Reduce INFO Messages

The primary method for controlling Spark’s logging behavior is through the log4j configuration file. Spark typically looks for a log4j.properties or log4j.xml file in the conf directory of your Spark installation. If this file doesn’t exist, Spark will use a default configuration that includes INFO messages. To stop INFO messages displaying on spark console, you need to modify this file to change the root logger’s level or configure specific loggers for different components of Spark. This approach offers fine-grained control over what gets logged, allowing you to suppress INFO messages from specific classes or packages while still retaining them for others. Remember to restart your Spark application after making changes to the log4j configuration file for the changes to take effect.

To modify the log4j configuration, you can set the root logger’s level to WARN or ERROR. This will suppress all INFO messages from the entire Spark application. For example, in log4j.properties, you would change the line log4j.rootCategory=INFO, console to log4j.rootCategory=WARN, console. Alternatively, you can configure specific loggers to suppress INFO messages from certain components. For example, if you want to suppress INFO messages from the org.apache.spark.scheduler package, you can add the line log4j.logger.org.apache.spark.scheduler=WARN to your log4j.properties file. This allows you to selectively filter out INFO messages from noisy components while still retaining them for others. According to a study on Spark performance optimization, reducing log verbosity can significantly improve application execution time, especially in resource-constrained environments [2]. This is because writing logs to disk can be a significant overhead, especially when dealing with large datasets.

Featured Snippet Paragraph: To effectively suppress INFO messages in Spark, modify your log4j.properties file located in Spark’s conf directory. Change the root logger’s level from INFO to WARN by editing the line log4j.rootCategory=INFO, console to log4j.rootCategory=WARN, console. This will globally suppress INFO messages, allowing you to focus on more critical warnings and errors. Remember to restart your Spark application for the changes to take effect.

• Modify the log4j.properties file.
• Change the root logger level to WARN or ERROR.
• Configure specific loggers for fine-grained control.

Programmatically Controlling Log Levels in Spark

While modifying the log4j.properties file is the most common way to control logging, Spark also provides programmatic ways to adjust log levels at runtime. This can be particularly useful when you want to dynamically change the logging verbosity of your application based on certain conditions or events. You can use the Logger class from the org.apache.log4j package to get a logger instance and then set its level programmatically. This allows you to temporarily increase or decrease the logging verbosity of specific components without having to restart your entire Spark application. This approach provides greater flexibility and control over your Spark logging behavior.

For example, you can use the following code snippet to set the log level of a specific logger to WARN: scala import org.apache.log4j.{Level, Logger} val logger = Logger.getLogger(“org.apache.spark.scheduler”) logger.setLevel(Level.WARN) This code snippet retrieves the logger for the org.apache.spark.scheduler package and sets its level to WARN, effectively suppressing all INFO messages from that component. You can adapt this code to set the log level of any logger in your Spark application. Remember that programmatic log level changes are typically temporary and will be reset when the application restarts. Therefore, if you want to make permanent changes to the logging configuration, you should modify the log4j.properties file. Using the Spark UI, developers can also monitor the logging configurations and dynamically adjust them for real-time debugging and optimization [3]. Understanding these programmatic options provides another layer of control over your Spark logging.

Here’s how to programmatically change the log level:

1. Import the necessary Logger and Level classes.
2. Get the logger instance for the desired component.
3. Set the log level to WARN or ERROR.

Best Practices for Spark Logging Management

Effectively managing Spark logging requires a combination of configuration and programmatic techniques. It’s important to choose the right log level for your specific needs and to balance the need for detailed information with the desire for a clean and manageable console output. In addition to configuring log4j and programmatically adjusting log levels, there are several other best practices that can help you optimize your Spark logging. These include using custom log messages to provide context-specific information, filtering out unnecessary logs based on specific criteria, and regularly reviewing your logging configuration to ensure that it’s still appropriate for your application. Following these best practices will help you improve your debugging efficiency and reduce the overhead associated with excessive logging. Efficient log management can also improve the performance of your Spark applications, especially when dealing with large datasets. Proper configuration ensures that only essential information is recorded.

Consider the following points for better log management:

• Use custom log messages for context-specific information.
• Filter unnecessary logs based on specific criteria.
• Regularly review your logging configuration.

For example, instead of simply logging a generic “Task completed” message, you could include information about the task ID, the input data size, and the execution time. This provides valuable context that can help you diagnose performance issues or identify bottlenecks. You can also filter out logs based on specific criteria, such as the thread ID or the application ID. This can be useful when you’re running multiple Spark applications on the same cluster and you want to isolate the logs for a specific application. Finally, it’s important to regularly review your logging configuration to ensure that it’s still appropriate for your application. As your application evolves, the types of information you need to log may change, so it’s important to adjust your logging configuration accordingly. By following these best practices, you can ensure that your Spark logging is efficient, effective, and tailored to your specific needs.

How do I permanently disable INFO messages in Spark?

To permanently disable INFO messages, modify the log4j.properties file in your Spark conf directory. Change the root logger level from INFO to WARN or ERROR.

Can I disable INFO messages for a specific Spark component?

Yes, you can configure specific loggers in the log4j.properties file to suppress INFO messages from certain components by setting their log level to WARN or ERROR.

Does reducing log verbosity improve Spark application performance?

Yes, reducing log verbosity can improve performance by reducing the overhead associated with writing logs to disk.

What if I don't have a log4j.properties file?

If you don't have a log4j.properties file, Spark will use a default configuration. You can create a new log4j.properties file in the conf directory to customize the logging behavior.

Question & Answer :
I’d like to stop various messages that are coming on spark shell.

I tried to edit the log4j.properties file in order to stop these message.

Here are the contents of log4j.properties

# Define the root logger with appender file log4j.rootCategory=WARN, console log4j.appender.console=org.apache.log4j.ConsoleAppender log4j.appender.console.target=System.err log4j.appender.console.layout=org.apache.log4j.PatternLayout log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n # Settings to quiet third party logs that are too verbose log4j.logger.org.eclipse.jetty=WARN log4j.logger.org.eclipse.jetty.util.component.AbstractLifeCycle=ERROR log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO 

But messages are still getting displayed on the console.

Here are some example messages

15/01/05 15:11:45 INFO SparkEnv: Registering BlockManagerMaster 15/01/05 15:11:45 INFO DiskBlockManager: Created local directory at /tmp/spark-local-20150105151145-b1ba 15/01/05 15:11:45 INFO MemoryStore: MemoryStore started with capacity 0.0 B. 15/01/05 15:11:45 INFO ConnectionManager: Bound socket to port 44728 with id = ConnectionManagerId(192.168.100.85,44728) 15/01/05 15:11:45 INFO BlockManagerMaster: Trying to register BlockManager 15/01/05 15:11:45 INFO BlockManagerMasterActor$BlockManagerInfo: Registering block manager 192.168.100.85:44728 with 0.0 B RAM 15/01/05 15:11:45 INFO BlockManagerMaster: Registered BlockManager 15/01/05 15:11:45 INFO HttpServer: Starting HTTP Server 15/01/05 15:11:45 INFO HttpBroadcast: Broadcast server star 

How do I stop these?

Edit your conf/log4j.properties file and change the following line:

log4j.rootCategory=INFO, console 

to

log4j.rootCategory=ERROR, console 

Another approach would be to :

Start spark-shell and type in the following:

import org.apache.log4j.Logger import org.apache.log4j.Level Logger.getLogger("org").setLevel(Level.OFF) Logger.getLogger("akka").setLevel(Level.OFF) 

You won’t see any logs after that.

Other options for Level include: all, debug, error, fatal, info, off, trace, trace_int, warn

Details about each can be found in the documentation.