Jobs

These articles can help you with your Databricks jobs.

• Distinguish active and dead jobs
• Spark job fails with Driver is temporarily unavailable
• How to delete all jobs using the REST API
• Identify less used jobs
• Job cluster limits on notebook output
• Job fails, but Apache Spark tasks finish
• Job fails due to job rate limit
• Create table in overwrite mode fails when interrupted
• Apache Spark Jobs hang due to non-deterministic custom UDF
• Apache Spark job fails with Failed to parse byte string
• Apache Spark UI shows wrong number of jobs
• Apache Spark job fails with a Connection pool shut down error
• Job fails with atypical errors message
• Apache Spark job fails with maxResultSize exception
• Databricks job fails because library is not installed
• Jobs failing on Databricks Runtime 5.5 LTS with an SQLAlchemy package error
• Job failure due to Azure Data Lake Storage (ADLS) CREATE limits
• How to ensure idempotency for jobs
• Monitor running jobs with a Job Run dashboard
• Streaming job has degraded performance
• Task deserialization time is high