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Troubleshoot out of memory errors with Azure SQL Database and Fabric SQL database

  • Article

Applies to: Azure SQL Database SQL database in Fabric

You might see error messages when the SQL database engine has failed to allocate sufficient memory to run the query. This can be caused by various reasons including the limits of selected service objective, aggregate workload memory demands, and memory demands by the query.

For more information on the memory resource limit for Azure SQL Database, see Resource management in Azure SQL Database. Fabric SQL database shares many features with Azure SQL Database, for more information on performance monitoring, see Fabric SQL database performance monitoring.

For more on troubleshooting out of memory issues in SQL Server, see MSSQLSERVER_701.

Try the following avenues of investigation in response to:

  • Error code 701 with error message "There is insufficient system memory in resource pool '%ls' to run this query."
  • Error code 802 with error message "There is insufficient memory available in the buffer pool."

View out of memory events

If you encounter out of memory errors, review sys.dm_os_out_of_memory_events. This view includes information on the predicted out of memory cause, determined by a heuristic algorithm and is provided with a finite degree of confidence.

SELECT * FROM sys.dm_os_out_of_memory_events ORDER BY event_time DESC;

Investigate memory allocation

If out of memory errors persist in Azure SQL Database, consider at least temporarily increasing the service level objective of the database in the Azure portal.

If out of memory errors persist, use the following queries to look for unusually high query memory grants that might contribute to an insufficient memory condition. Run the following example queries in the database that experienced the error (not in the master database of the Azure SQL logical server).

Use DMV to view out of memory events

The sys.dm_os_out_of_memory_events allows visibility to the events and causes of out of memory (OOM) events in Azure SQL Database. The summarized_oom_snapshot extended event is a part of the existing system_health event session to simplify detection. For more information, see sys.dm_os_out_of_memory_events and Blog: A new way to troubleshoot out-of-memory errors in the database engine.

Use DMVs to view memory clerks

Start with a broad investigation, if the out of memory error occurred recently, by viewing the allocation of memory to memory clerks. Memory clerks are internal to the database engine for this Azure SQL Database. The top memory clerks in terms of pages allocated might be informative to what type of query or feature of SQL Server is consuming the most memory.

SELECT [type], [name], pages_kb, virtual_memory_committed_kb
FROM sys.dm_os_memory_clerks
WHERE memory_node_id <> 64 -- ignore Dedicated Admin Connection (DAC) node
ORDER BY pages_kb DESC;
GO
SELECT [type], [name], pages_kb, virtual_memory_committed_kb
FROM sys.dm_os_memory_clerks
WHERE memory_node_id <> 64 -- ignore Dedicated Admin Connection (DAC) node
ORDER BY virtual_memory_committed_kb DESC;
  • Some common memory clerks, such as MEMORYCLERK_SQLQERESERVATIONS, are best resolved by identifying queries with large memory grants and improving their performance with better indexing and index tuning.
  • While OBJECTSTORE_LOCK_MANAGER is unrelated to memory grants, it is expected to be high when queries claim many locks, for example, because of disabled lock escalation or very large transactions.
  • Some clerks are expected to be the highest utilization: MEMORYCLERK_SQLBUFFERPOOL is almost always the top clerk, while CACHESTORE_COLUMNSTOREOBJECTPOOL will be high when columnstore indexes are used. Highest utilization by these clerks is expected.

For more information about memory clerk types, see sys.dm_os_memory_clerks.

Use DMVs to investigate active queries

In most cases, the query that failed is not the cause of this error.

The following sample query for Azure SQL Database returns important information on transactions that are currently holding or waiting for memory grants. Target the top queries identified for examination and performance tuning, and evaluate whether or not they are executing as intended. Consider the timing of memory-intensive reporting queries or maintenance operations.

--Active requests with memory grants
SELECT
--Session data 
  s.[session_id], s.open_transaction_count
--Memory usage
, r.granted_query_memory, mg.grant_time, mg.requested_memory_kb, mg.granted_memory_kb
, mg.required_memory_kb, mg.used_memory_kb, mg.max_used_memory_kb     
--Query 
, query_text = t.text, input_buffer = ib.event_info, query_plan_xml = qp.query_plan
, request_row_count = r.row_count, session_row_count = s.row_count
--Session history and status
, s.last_request_start_time, s.last_request_end_time, s.reads, s.writes, s.logical_reads
, session_status = s.[status], request_status = r.status
--Session connection information
, s.host_name, s.program_name, s.login_name, s.client_interface_name, s.is_user_process
FROM sys.dm_exec_sessions s 
LEFT OUTER JOIN sys.dm_exec_requests AS r 
    ON r.[session_id] = s.[session_id]
LEFT OUTER JOIN sys.dm_exec_query_memory_grants AS mg 
    ON mg.[session_id] = s.[session_id]
OUTER APPLY sys.dm_exec_sql_text (r.[sql_handle]) AS t
OUTER APPLY sys.dm_exec_input_buffer(s.[session_id], NULL) AS ib 
OUTER APPLY sys.dm_exec_query_plan (r.[plan_handle]) AS qp 
WHERE mg.granted_memory_kb > 0
ORDER BY mg.granted_memory_kb desc, mg.requested_memory_kb desc;

You might decide to use the KILL statement to stop a currently executing query that is holding or waiting for a large memory grant. Use this statement carefully, especially when critical processes are running. For more information, see KILL (Transact-SQL).

Use Query Store to investigate past query memory usage

While the previous sample query reports only live query results, the following query uses the Query Store to return information on past query execution. This can be helpful in investigating an out of memory error that occurred in the past.

The following sample query for Azure SQL Database return important information on query executions recorded by the Query Store. Target the top queries identified for examination and performance tuning, and evaluate whether or not they are executing as intended. Note the time filter on qsp.last_execution_time to restrict results to recent history. You can adjust the TOP clause to produce more or fewer results depending on your environment.

SELECT TOP 10 PERCENT --limit results
  a.plan_id, query_id, plan_group_id, query_sql_text
, query_plan = TRY_CAST(query_plan as XML)
, avg_query_max_used_memory
, min_query_max_used_memory
, max_query_max_used_memory
, last_query_max_used_memory 
, last_execution_time  
, query_count_executions
    FROM (
    SELECT 
      qsp.plan_id, qsp.query_id, qsp.plan_group_id, qsp.query_plan, qsqt.query_sql_text
    , last_execution_time = MAX(qsp.last_execution_time)
    , query_count_executions = SUM(qsrs.count_executions) 
    , avg_query_max_used_memory = AVG(qsrs.avg_query_max_used_memory)
    , min_query_max_used_memory = MIN(qsrs.min_query_max_used_memory)
    , max_query_max_used_memory = MAX(qsrs.max_query_max_used_memory)
    , last_query_max_used_memory = MAX(qsrs_latest.last_query_max_used_memory) --only from latest result
    FROM sys.query_store_plan AS qsp 
    INNER JOIN sys.query_store_query AS qsq
        ON qsp.query_id = qsq.query_id
    INNER JOIN sys.query_store_query_text AS qsqt
        ON qsq.query_text_id = qsqt.query_text_id 
    INNER JOIN sys.query_store_runtime_stats AS qsrs
        ON qsp.plan_id = qsrs.plan_id 
    INNER JOIN (SELECT plan_id
            , last_query_max_used_memory 
            , rownum = ROW_NUMBER() OVER (PARTITION BY plan_id ORDER BY last_execution_time DESC)
            FROM sys.query_store_runtime_stats qsrs) AS qsrs_latest
        ON qsrs_latest.plan_id = qsp.plan_id
        AND qsrs_latest.rownum = 1 --use latest last_query_max_used_memory per plan_id
    WHERE DATEADD(hour, -24, sysdatetime()) < qsp.last_execution_time --past 24 hours only
    AND qsrs_latest.last_query_max_used_memory > 0
    GROUP BY qsp.plan_id, qsp.query_id, qsp.plan_group_id, qsp.query_plan, qsqt.query_sql_text
    ) AS a
ORDER BY max_query_max_used_memory DESC, avg_query_max_used_memory DESC;

Extended events

In addition to the previous information, it can be helpful to capture a trace of the activities on the server to thoroughly investigate an out of memory issue in Azure SQL Database.

There are two ways to capture traces in SQL Server; Extended Events (XEvents) and Profiler Traces. However, SQL Server Profiler is deprecated trace technology not supported for Azure SQL Database. Extended Events is the newer tracing technology that allows more versatility and less impact to the observed system, and its interface is integrated into SQL Server Management Studio (SSMS). For more information on querying extended events in Azure SQL Database, see Extended events in Azure SQL Database.

Refer to the document that explains how to use the Extended Events New Session Wizard in SSMS. For Azure SQL databases however, SSMS provides an Extended Events subfolder under each database in Object Explorer. Use an Extended Events session to capture these useful events, and identify the queries generating them:

  • Category Errors:

    • error_reported
    • exchange_spill
    • hash_spill_details

  • Category Execution:

    • excessive_non_grant_memory_used

  • Category Memory:

    • query_memory_grant_blocking
    • query_memory_grant_usage

  • summarized_oom_snapshot

    The capture of memory grant blocks, memory grant spills, or excessive memory grants could be potential clue to a query suddenly taking on more memory than it had in the past, and a potential explanation for an emergent out of memory error in an existing workload. The summarized_oom_snapshot extended event is a part of the existing system_health event session to simplify detection. For more information, see Blog: A new way to troubleshoot out-of-memory errors in the database engine.

In-memory OLTP out of memory

You might encounter Error code 41805: There is insufficient memory in the resource pool '%ls' to run this operation if using In-Memory OLTP. Reduce the amount of data in memory-optimized tables and memory-optimized table-valued parameters, or scale up the database to a higher service objective to have more memory. For more information on out of memory issues with SQL Server In-Memory OLTP, see Resolve Out Of Memory issues.

Get Azure SQL Database support

If out of memory errors persist in Azure SQL Database, file an Azure support request by selecting Get Support on the Azure Support site.

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