Introduction

The ADB001 – Azure Databricks Resource Limit Exceeded error typically occurs when you exceed the resource limits assigned to your Azure Databricks workspace. This could involve compute limits, storage limits, or concurrent job limits. Resource limits are usually tied to your Azure subscription quotas or Databricks workspace configuration.

🚨 Common Causes:

• Exceeding the maximum number of concurrent clusters.
• Hitting VM instance quotas for your subscription.
• Storage limits exceeded in Azure Data Lake or Blob Storage.
• Concurrent job limits reached.

This guide explains the causes of ADB001, how to troubleshoot it, and how to resolve the issue.

Common Causes and Fixes for ADB001

1. Exceeded VM Quota for Compute Resources

Symptoms:

• Error: “ADB001: Resource limit exceeded.”
• Cannot create new clusters.
• Clusters fail to start or remain in a pending state.

Causes:

• VM quota limits for your Azure region are reached.
• Insufficient quota for the specific VM family (e.g., Standard_DS3_v2).
• Multiple running clusters consuming the available quota.

Fix:
✅ Check VM Quota Limits in Azure Portal:

1. Go to Azure Portal → Subscriptions → Usage + Quotas.
2. Look for Compute (VM) quotas for your region.
3. Identify if you have exceeded the limit for any VM type.

✅ Request a Quota Increase:

1. In Azure Portal, select Help + Support → New Support Request.
2. Choose Quota → Compute-VM (Core).
3. Request an increase for the relevant VM family and region.

✅ Optimize Cluster Usage:

• Use job clusters instead of all-purpose clusters for scheduled jobs.
• Terminate idle clusters to free up resources.

2. Concurrent Job Limit Reached

Symptoms:

• Error: “ADB001: Resource limit exceeded.”
• Cannot start new jobs in Databricks.
• Scheduled jobs fail to launch.

Causes:

• The maximum number of concurrent jobs allowed by your Databricks plan has been reached.
• Multiple jobs running simultaneously consume all available resources.

Fix:
✅ Check the number of running jobs:

• Go to Databricks UI → Jobs and review active jobs.
• Terminate unnecessary jobs to free up capacity.

✅ Upgrade to a higher-tier plan:

• Standard plans have lower concurrent job limits compared to Premium and Enterprise.

✅ Use Cluster Pools:

• Cluster pools reduce startup time and manage resources efficiently.

{
  "cluster_pool_id": "<your-cluster-pool-id>"
}

3. Storage Quota Exceeded

Symptoms:

• Error: “ADB001: Resource limit exceeded.”
• Cannot write data to Azure Data Lake, Blob Storage, or Delta Lake.
• Data ingestion pipelines fail with storage errors.

Causes:

• The storage account has reached its capacity limit.
• Delta transaction logs have grown too large, consuming storage.

Fix:
✅ Check Azure Storage Quotas:

1. Go to Azure Portal → Storage Accounts → Metrics.
2. Review the total used storage and available capacity.

✅ Optimize Delta Tables:

• Run VACUUM to remove old files.

VACUUM delta.`/mnt/delta/table/` RETAIN 168 HOURS;

• OPTIMIZE Delta tables to reduce small files.

OPTIMIZE delta.`/mnt/delta/table/`;

✅ Request a storage capacity increase if needed.

4. Network or Resource Group Limits

Symptoms:

• Error: “ADB001: Resource limit exceeded.”
• Cluster creation or job execution fails intermittently.
• Networking operations (e.g., connecting to external storage) fail.

Causes:

• Network resource limits (e.g., VNet or Private Endpoint capacity) have been reached.
• Resource group limits restrict the number of active resources.

Fix:
✅ Check Resource Group and Network Limits:

• Go to Azure Portal → Resource Groups and check active resources.
• Review VNet usage and Private Endpoint allocations.

✅ Increase VNet or Private Endpoint limits if necessary.

Step-by-Step Troubleshooting Guide

1. Identify the Cause of the ADB001 Error

1. Go to Azure Portal → Subscriptions → Usage + Quotas.
2. Review Compute, Storage, and Networking quotas.
3. In Databricks, check for active clusters and jobs.

2. Optimize Cluster and Job Usage

• Use job clusters for scheduled tasks.
• Enable cluster auto-termination to avoid resource waste.

{
  "auto_termination_minutes": 30
}

3. Request Quota Increases in Azure

• For VM cores, storage, or network resources, submit a quota increase request.

4. Monitor and Automate Resource Management

• Use Azure Monitor and Databricks Cluster Policies to manage resource usage proactively.

Best Practices to Avoid ADB001 Errors

✅ Monitor Resource Quotas Regularly

• Use Azure Monitor to track usage and set alerts for nearing quotas.

✅ Optimize Cluster Usage

• Use autoscaling clusters and cluster pools to manage resources efficiently.

✅ Request Quota Increases in Advance

• For large-scale projects, plan ahead and increase quotas before hitting limits.

✅ Use Delta Lake Optimization Techniques

• Regularly run VACUUM and OPTIMIZE to reduce storage usage.

Conclusion

The ADB001 – Azure Databricks Resource Limit Exceeded error typically arises from exceeding compute, storage, or job concurrency limits. By identifying the resource causing the limit, optimizing cluster and job usage, and requesting quota increases, you can resolve this error and ensure uninterrupted operations in Azure Databricks.