Where can Spark application logs be found?

Spark application logs provide valuable insights into the execution of your Spark jobs and help in debugging issues. The location of these logs depends on how your Spark application is run, the environment it's executed in, and the configuration settings.

Places to Find Spark Application Logs:

1. Spark UI (Web UI):

   • Spark provides a built-in web UI that allows you to access logs related to the running application. By default, the Spark UI can be accessed at http://<driver-node>:4040.
   • You can view logs for each job, stage, and task in the application through the UI.

2. Driver and Executor Logs in Local Mode:

   • If you're running Spark in local mode, logs are typically displayed on the console where you started the application.
   • The Spark driver and executor logs can also be found in the working directory where the Spark job was executed.

3. YARN Logs (for YARN cluster mode):

   • In YARN cluster mode, application logs are stored in the YARN resource manager logs. You can access the logs using the yarn logs command.

     yarn logs -applicationId <application_id>
     

   • Alternatively, you can access logs from the YARN ResourceManager UI at http://<resourcemanager-host>:8088.

4. Mesos Logs (for Mesos cluster mode):

   • In Mesos mode, logs are available through the Mesos UI, or you can use the mesos logs command to fetch them from the Mesos master.

5. Kubernetes Logs (for Kubernetes mode):

   • If you're running Spark on Kubernetes, logs can be accessed using the kubectl command.

     kubectl logs <pod_name> -c <container_name>
     

6. Log Files on Disk:

   • If you're using Spark on a cluster manager (like YARN, Mesos, or Kubernetes), logs may also be stored in the configured log directory. The log directory location is typically configured in the Spark configuration file (spark-defaults.conf).
   • You can set the log directory by modifying the spark.eventLog.dir property:

     spark.eventLog.dir=/path/to/logs
     

7. Cloud-Based Spark (e.g., Databricks, EMR):

   • For cloud-based platforms like Databricks or Amazon EMR, Spark logs are typically accessible through the platform’s web interface. In Databricks, logs can be found in the "Driver Logs" and "Cluster Logs" sections.
   • In AWS EMR, logs can be accessed via the EMR console or S3 bucket if configured to store logs in S3.

Nuances to Consider:

• Log Level Configuration: You can adjust the verbosity of Spark logs by configuring the log level. This can be done in the log4j.properties file or programmatically using the SparkConf object.

  log4j.rootCategory=INFO, console
  log4j.logger.org.apache.spark=DEBUG
  

• Log Retention: Depending on the environment, logs may be retained for different periods. In cluster modes like YARN, you can set log retention policies.

Code Example to Set Log Level:

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("Log Example").getOrCreate()

# Set the log level to DEBUG
spark.sparkContext.setLogLevel("DEBUG")

# Run some Spark operations
df = spark.read.csv("path/to/csv")
df.show()

spark.stop()

Tecyfy Takeaway:
Spark logs are accessible through multiple sources depending on the cluster manager and execution mode. The Spark UI, YARN, Mesos, Kubernetes, and cloud platforms like Databricks and EMR provide various ways to view logs. Be sure to set appropriate log levels for more detailed troubleshooting and ensure proper log retention policies are in place for ongoing monitoring.