Databricks

Introduction DELTA002 – Delta Table Version Mismatch is an error in Delta Lake that indicates a version conflict between the transaction log and the data being read or written. This…

Introduction DELTA001 (Delta table corruption) is a critical error that occurs when Delta Lake transaction logs become inconsistent or corrupted, preventing access to the Delta table. This issue can disrupt…

Introduction The SQL004 error – “Missing partition key in a query” typically occurs when querying a partitioned table in Databricks or other SQL-based systems without specifying the partition key in…

Introduction The SQL003 – Table Not Found error in Databricks indicates that the specified table does not exist or is inaccessible in the catalog, schema, or database you are querying.…

Introduction SQL002 – Query Execution Timeout is a common error in Databricks SQL and Spark SQL, indicating that the query execution exceeded the configured timeout. This can occur when a…

Introduction The SQL001 – SQL query syntax error in Databricks indicates an issue in the SQL query syntax. This error typically appears when there are typos, unsupported SQL syntax, missing…

Introduction The SQL002 – Query Execution Timeout error in Databricks occurs when a SQL query takes too long to execute and exceeds the configured timeout threshold. This can happen due…

Introduction The SQL001 – SQL query syntax error in Databricks occurs when a SQL statement contains incorrect syntax, unsupported commands, or incompatible references. This error typically appears when running SQL…

Introduction The SPARK004 – Shuffle Read Failure (Insufficient Disk Space) error occurs when Spark runs out of disk space while performing shuffle operations. This can lead to job failures, slow…

Introduction The SPARK001 – Job Execution Timeout error occurs in Databricks when a query or job takes too long to execute, exceeding the cluster’s configured timeout. This issue can result…

Introduction The SPARK003 – Job Execution Failed (bad Spark config) error occurs in Databricks when a job fails due to incorrect Spark configurations, resource limitations, or incompatible cluster settings. 🚨…

Introduction The SPARK005 – Too Many Open Files error in Databricks indicates that the number of open file descriptors has exceeded the system’s limit. This issue can slow down workloads,…

Introduction The SPARK002 – Out of Memory (OOM) error in Databricks occurs when a Spark executor or driver runs out of memory while processing data. This can cause job failures,…

Introduction The AUTOSCALE001 error in Databricks indicates that autoscaling failed due to insufficient cloud capacity. This happens when Databricks tries to add worker nodes to an autoscaling cluster, but AWS,…

Introduction Databricks leverages Spot Instances (AWS) and Preemptible VMs (Azure/GCP) to reduce costs, but these instances can be terminated unexpectedly due to availability issues. Worker node failures (CLUSTER004 errors) disrupt…

Introduction The CLUSTER003 – Driver node unavailable (possible memory crash) error in Databricks indicates that the driver node has run out of memory (OOM) or become unresponsive due to excessive…

Introduction The CLUSTER002 – Cluster Termination Failed error in Databricks occurs when a cluster fails to shut down due to misconfigurations, resource constraints, or cloud provider-related issues. 🚨 Common symptoms…

Introduction If your Databricks cluster fails to start with the error “CLUSTER001 – Cluster creation failed (capacity or config issue)”, it typically indicates insufficient resources, misconfigured settings, or cloud provider…

Here are 30 common error codes in Databricks, along with their possible causes and solutions: 1. DBFS001 – Databricks File System (DBFS) Access Denied 2. DBFS002 – DBFS Mount Failure…