A Comprehensive Guide to Databricks Log Types and Access

Introduction

In the world of big data, managing logs is important for performance. It helps to fix problems and ensures everything runs smoothly. Databricks is a strong cloud platform that uses Apache Spark. It offers different logging options. These help monitor the health and performance of their applications. You look at driver and executor logs, cluster events, or job details. Data engineers and data scientists must know how to access and analyze these logs.

This post will help you understand the various types of logs in Databricks. You will learn how to access them through the user interface, the Spark UI, and the REST API. Additionally, we will provide tips for connecting with outside logging tools. We will also discuss common issues you may find in these logs and best practices for managing them effectively. By the end of this article, you’ll know how to use logging features in Databricks. These features will help improve your data processing tasks. You will also learn how to solve problems more efficiently.

Types of Logs in Databricks

1. Driver Logs: Logs related to the Spark driver, including job execution status and errors.
2. Executor Logs: Logs generated by Spark executors running tasks on worker nodes.
3. Cluster Event Logs: Logs containing cluster lifecycle events such as startup, scaling, and termination.
4. Job and Notebook Logs: Logs generated during job and notebook execution.
5. Event Logs: System-level logs that track cluster and workspace activities.

1. Accessing Logs from the Databricks UI

Driver and Executor Logs

1. Go to the Clusters Page:
   • Navigate to the Databricks workspace.
   • Click on Compute and select the cluster of interest.
2. View Driver Logs:
   • Under the cluster, go to the Driver Logs tab.
   • Download or view logs directly to check issues related to the driver.
3. View Executor Logs:
   • In the Spark UI for the cluster, go to the Executors tab.
   • Locate the log links for each executor and click to view stderr, stdout, or log files.

Job Logs

1. Navigate to Jobs:
   • Go to Workflows > Jobs and select the job of interest.
2. Inspect Job Run Logs:
   • Open a specific job run.
   • Review logs under the Run Output section or expand individual task logs for detailed execution steps.

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2. Using the Spark UI

1. Access the Spark UI:
   • From the cluster details page, click on the Spark UI link.
2. View Logs in Executors Tab:
   • Navigate to the Executors tab to see logs specific to individual executors.
3. Check Stages and Tasks:
   • Use the Stages and Tasks tabs to track job execution at a granular level.
   • Look for failed tasks or long-running stages.

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3. Downloading Logs

Cluster Logs

1. Go to the Cluster Logs Location:
   • Logs are stored in the cluster’s driver and executor directories.
   • If the cluster is configured to write logs to DBFS (Databricks File System), find them at:javascript dbfs:/cluster-logs/<cluster-id>/
2. Download Logs:
   • Use the Databricks UI or CLI to download logs for offline analysis:bash databricks fs cp dbfs:/cluster-logs/<cluster-id>/local/path --recursive

Notebook Logs

1. In the Databricks notebook:
   • Use the notebook sidebar to view the Job Output logs.
   • Errors or warnings during execution are highlighted.

4. Accessing Logs via Databricks REST API

You can programmatically fetch logs using the Databricks REST API.

Example: Fetch Cluster Event Logs

1. Get Cluster ID:
   • Use the API to list clusters:bash curl -X GET -H "Authorization: Bearer <your-token>" https://<databricks-instance>/api/2.0/clusters/list
2. Retrieve Logs:
   • Fetch cluster event logs using:bash curl -X GET -H "Authorization: Bearer <your-token>" \ https://<databricks-instance>/api/2.0/clusters/events \ -d '{"cluster_id": "<cluster-id>"}'

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5. External Logging with Monitoring Tools

Push Logs to a Centralized System

1. Configure Log Delivery:
   • Send Databricks logs to cloud storage (e.g., S3, Azure Blob, GCS).
   • Use the Cluster Logging feature in the Databricks cluster configuration.
2. Integrate with Log Analytics Tools:
   • Forward logs to tools like Datadog, Splunk, or CloudWatch for real-time analysis and alerts.

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6. Common Issues in Logs

1. Driver Out of Memory:
   • Look for “OutOfMemoryError” in driver logs.
2. Executor Failures:
   • Check executor logs for “Task not serializable” or “Lost executor” errors.
3. Slow Queries:
   • Use the Spark UI’s query plan to identify bottlenecks in stages or tasks.
4. Cluster Termination Errors:
   • Review cluster event logs to diagnose failures during startup or termination.

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Best Practices for Log Management

1. Enable Log Delivery:
   • Configure clusters to automatically deliver logs to cloud storage for long-term retention and analysis.
2. Use Structured Logging:
   • Ensure application logs are structured (e.g., JSON format) for better parsing and searching.
3. Monitor Regularly:
   • Set up alerts for critical log events, such as repeated task failures or memory issues.

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By leveraging these tools and practices, you can efficiently read Databricks logs. You can also manage them. This helps in analyzing the logs to maintain the health of your workloads and clusters.