Error Code 49919 – Overview

Error Code 49919 in Azure SQL Database occurs when an operation on a database or elastic pool cannot proceed due to resource limitations, typically caused by exceeding CPU, memory, or other resource thresholds for the current pricing tier.

Summary Table

Background

Error Code 49919 typically occurs when operations on the Azure SQL Database encounter resource constraints, often related to high CPU, memory, or I/O usage. This can happen if your current pricing tier has limited capacity for the requested operation.

Error Explanation

The error message for Error Code 49919 usually reads:

Error 49919: The operation failed because it exceeded the current resource limits.

This message indicates that the requested operation requires more resources than are currently available in the pricing tier or elastic pool configuration.

Common Causes

1. High CPU or Memory Usage: Operations consuming excessive CPU, memory, or I/O resources.
2. Pricing Tier Limitations: Each Azure SQL Database pricing tier has set resource limits.
3. Locked Resources: Long-running transactions can lock resources, blocking other operations and triggering resource constraints.

Steps to Troubleshoot and Resolve Error Code 49919

Step 1: Check Current Resource Usage

• Purpose: To identify if CPU, memory, or I/O usage is nearing the tier limits.
• Example Query:

SELECT avg_cpu_percent, avg_data_io_percent, avg_log_write_percent
FROM sys.dm_db_resource_stats
ORDER BY end_time DESC;

• This query shows recent resource usage, helping pinpoint any resource spikes.

Step 2: Optimize Resource-Intensive Queries

• Purpose: Identify and optimize high CPU or I/O-consuming queries.
• Example Query:

SELECT TOP 10 query_text.text AS [Query Text],
       stats.total_worker_time AS [Total CPU Time],
       stats.total_logical_reads AS [Total Reads]
FROM sys.dm_exec_query_stats AS stats
CROSS APPLY sys.dm_exec_sql_text(stats.sql_handle) AS query_text
ORDER BY stats.total_worker_time DESC;

• Focus on the top queries consuming the most CPU and optimize them by adding indexes or simplifying the logic.

Step 3: Scale Up Database or Server Resources

• Purpose: To increase available resources by moving to a higher pricing tier.
• Steps:
  • In the Azure Portal, navigate to SQL Server > Settings > Compute + Storage.
  • Choose a higher SKU or pricing tier.
  • Confirm changes to apply increased capacity.
• Example command with Azure CLI:

az sql db update --resource-group <ResourceGroup> --server <ServerName> --name <DatabaseName> --service-objective <DesiredTier>

Step 4: Implement Retry Logic

• Purpose: To handle transient errors by retrying the operation after a delay.
• Example Code (C#):csharpCopy code

var retryPolicy = Policy
    .Handle<SqlException>(ex => ex.Number == 49919)
    .WaitAndRetry(3, retryAttempt => TimeSpan.FromSeconds(2 * retryAttempt));

retryPolicy.Execute(() => ExecuteDatabaseOperation());

Step 5: Review Elastic Pool Configuration (if applicable)

• Purpose: Ensure the elastic pool has sufficient resources for the operation.
• Steps:
  • In Azure Portal, go to SQL Database server > Elastic Pools.
  • Check the DTU or vCore limits and current usage.
  • Adjust database allocations within the pool if necessary.

Step 6: Monitor and Manage Long-Running Transactions

• Purpose: To identify any transactions holding resources for extended periods.
• Example Query:

SELECT session_id, blocking_session_id, wait_type, wait_time, resource_description
FROM sys.dm_exec_requests
WHERE blocking_session_id <> 0;

• Investigate and, if necessary, kill long-running or blocked transactions.

Step 7: Redistribute Workload Across Multiple Servers

• Purpose: Balance resource consumption across servers to reduce load.
• Steps:
  • Create a new SQL server in Azure Portal.
  • Move or add databases to the new server to balance resources.

Workarounds

1. Scale Up Temporarily: Use a higher pricing tier briefly to handle peak demand.
2. Distribute Databases: Move databases to different servers or pools.
3. Adjust Configuration: Modify database settings to fit within available resources.

Solutions

1. Increase Server Capacity: Scale up the pricing tier or add vCores.
2. Optimize Elastic Pool Configurations: Balance resource allocation within pools.
3. Distribute Workloads: Use multiple servers to prevent resource constraints.
4. Monitor Resources: Regularly track resource usage with Azure Monitor to avoid hitting limits.

Example Scenario

Suppose you encounter Error Code 49919 during a bulk data import operation:

1. Run a Resource Usage Query:

SELECT avg_cpu_percent, avg_data_io_percent FROM sys.dm_db_resource_stats;

• This shows that CPU usage is near the limit, confirming a resource constraint.

By following these steps, you can effectively address and resolve Error Code 49919 in Azure SQL Database, ensuring the smooth operation of database transactions and resource management.