SQL Database resource limits and resource governance
This article provides an overview of the SQL Database resource limits for a SQL Database server that manages single databases and elastic pools. It provides information on what happens when those resource limits are hit or exceeded, and describes the resource governance mechanisms used to enforce these limits.
For Managed Instance limits, see SQL Database resource limits for managed instances.
Maximum resource limits
|Databases per server||5000|
|Default number of servers per subscription in any region||20|
|Max number of servers per subscription in any region||200|
|DTU / eDTU quota per server||54,000|
|vCore quota per server/instance||540|
|Max pools per server||Limited by number of DTUs or vCores. For example, if each pool is 1000 DTUs, then a server can support 54 pools.|
To obtain more DTU/eDTU quota, vCore quota, or more servers than the default amount, a new support request can be submitted in the Azure portal for the subscription with issue type “Quota”. The DTU/eDTU quota and database limit per server constrains the number of elastic pools per server.
As the number of databases approaches the limit per SQL Database server, the following can occur:
- Increasing latency in running queries against the master database. This includes views of resource utilization statistics such as sys.resource_stats.
- Increasing latency in management operations and rendering portal viewpoints that involve enumerating databases in the server.
What happens when database resource limits are reached
Compute (DTUs and eDTUs / vCores)
When database compute utilization (measured by DTUs and eDTUs, or vCores) becomes high, query latency increases, and queries can even time out. Under these conditions, queries may be queued by the service and are provided resources for execution as resources become free. When encountering high compute utilization, mitigation options include:
- Increasing the compute size of the database or elastic pool to provide the database with more compute resources. See Scale single database resources and Scale elastic pool resources.
- Optimizing queries to reduce the resource utilization of each query. For more information, see Query Tuning/Hinting.
When database space used reaches the max size limit, database inserts and updates that increase the data size fail and clients receive an error message. SELECT and DELETE statements continue to succeed.
When encountering high space utilization, mitigation options include:
- Increasing the max size of the database or elastic pool, or adding more storage. See Scale single database resources and Scale elastic pool resources.
- If the database is in an elastic pool, then alternatively the database can be moved outside of the pool so that its storage space is not shared with other databases.
- Shrink a database to reclaim unused space. For more information, see Manage file space in Azure SQL Database
Sessions and workers (requests)
The maximum number of sessions and workers are determined by the service tier and compute size (DTUs/eDTUs or vCores. New requests are rejected when session or worker limits are reached, and clients receive an error message. While the number of connections available can be controlled by the application, the number of concurrent workers is often harder to estimate and control. This is especially true during peak load periods when database resource limits are reached and workers pile up due to longer running queries, large blocking chains, or excessive query parallelism.
When encountering high session or worker utilization, mitigation options include:
- Increasing the service tier or compute size of the database or elastic pool. See Scale single database resources and Scale elastic pool resources.
- Optimizing queries to reduce the resource utilization of each query if the cause of increased worker utilization is due to contention for compute resources. For more information, see Query Tuning/Hinting.
To enforce resource limits, Azure SQL Database uses a resource governance implementation that is based on SQL Server Resource Governor, modified and extended to run a SQL Server database service in Azure. On each SQL Server instance in the service, there are multiple resource pools and workload groups, with resource limits set at both pool and group levels to provide a balanced Database-as-a-Service. User workload and internal workloads are classified into separate resource pools and workload groups. User workload on the primary and readable secondary replicas, including geo-replicas, is classified into the
SloSharedPool1 resource pool and
UserPrimaryGroup.DBId[N] workload group, where
N stands for the database ID value. In addition, there are multiple resource pools and workload groups for various internal workloads.
In addition to using Resource Governor to govern resources within the SQL Server process, Azure SQL Database also uses Windows Job Objects for process level resource governance, and Windows File Server Resource Manager (FSRM) for storage quota management.
Azure SQL Database resource governance is hierarchical in nature. From top to bottom, limits are enforced at the OS level and at the storage volume level using operating system resource governance mechanisms and Resource Governor, then at the resource pool level using Resource Governor, and then at the workload group level using Resource Governor. Resource governance limits in effect for the current database or elastic pool are surfaced in the sys.dm_user_db_resource_governance view.
Data IO governance
Data IO governance is a process in Azure SQL Database used to limit both read and write physical IO against data files of a database. IOPS limits are set for each service level to minimize the "noisy neighbor" effect, to provide resource allocation fairness in the multi-tenant service, and to stay within the capabilities of the underlying hardware and storage.
For single databases, workload group limits are applied to all storage IO against the database, while resource pool limits apply to all storage IO against all databases on the same SQL Server instance, including the
tempdb database. For elastic pools, workload group limits apply to each database in the pool, whereas resource pool limit applies to the entire elastic pool, including the
tempdb database, which is shared among all databases in the pool. In general, resource pool limits may not be achievable by the workload against a database (either single or pooled), because workload group limits are lower than resource pool limits and limit IOPS/throughput sooner. However, pool limits may be reached by the combined workload against multiple databases on the same SQL Server instance.
For example, if a query generates 1000 IOPS without any IO resource governance, but the workload group maximum IOPS limit is set to 900 IOPS, the query will not be able to generate more than 900 IOPS. However, if the resource pool maximum IOPS limit is set to 1500 IOPS, and the total IO from all workload groups associated with the resource pool exceeds 1500 IOPS, then the IO of the same query may be reduced below the workgroup limit of 900 IOPS.
The IOPS and throughput min/max values returned by the sys.dm_user_db_resource_governance view act as limits/caps, not as guarantees. Further, resource governance does not guarantee any specific storage latency. The best achievable latency, IOPS, and throughput for a given user workload depend not only on IO resource governance limits, but also on the mix of IO sizes used, and on the capabilities of the underlying storage. SQL Server uses IOs that vary in size between 512 KB and 4 MB. For the purposes of enforcing IOPS limits, every IO is accounted regardless of its size, with the exception of databases with data files in Azure Storage. In that case, IOs larger than 256 KB are accounted as multiple 256 KB IOs, to align with Azure Storage IO accounting.
For Basic, Standard, and General Purpose databases, which use data files in Azure Storage, the
primary_group_max_io value may not be achievable if a database does not have enough data files to cumulatively provide this number of IOPS, or if data is not distributed evenly across files, or if the performance tier of underlying blobs limits IOPS/throughput below the resource governance limit. Similarly, with small log IOs generated by frequent transaction commit, the
primary_max_log_rate value may not be achievable by a workload due to the IOPS limit on the underlying Azure storage blob.
Resource utilization values such as
avg_log_write_percent, reported in the sys.dm_db_resource_stats, sys.resource_stats, and sys.elastic_pool_resource_stats views, are calculated as percentages of maximum resource governance limits. Therefore, when factors other than resource governance limit IOPS/throughput, it is possible to see IOPS/throughput flattening out and latencies increasing as the workload increases, even though reported resource utilization remains below 100%. To see read and write IOPS, throughput, and latency per database file, use the sys.dm_io_virtual_file_stats() function. This function surfaces all IO against the database, including background IO that is not accounted towards
avg_data_io_percent, but uses IOPS and throughput of the underlying storage, and can impact observed storage latency.
Transaction log rate governance
Transaction log rate governance is a process in Azure SQL Database used to limit high ingestion rates for workloads such as bulk insert, SELECT INTO, and index builds. These limits are tracked and enforced at the subsecond level to the rate of log record generation, limiting throughput regardless of how many IOs may be issued against data files. Transaction log generation rates currently scale linearly up to a point that is hardware-dependent, with the maximum log rate allowed being 96 MB/s with the vCore purchasing model.
The actual physical IOs to transaction log files are not governed or limited.
Log rates are set such that they can be achieved and sustained in a variety of scenarios, while the overall system can maintain its functionality with minimized impact to the user load. Log rate governance ensures that transaction log backups stay within published recoverability SLAs. This governance also prevents an excessive backlog on secondary replicas.
As log records are generated, each operation is evaluated and assessed for whether it should be delayed in order to maintain a maximum desired log rate (MB/s per second). The delays are not added when the log records are flushed to storage, rather log rate governance is applied during log rate generation itself.
The actual log generation rates imposed at run time may also be influenced by feedback mechanisms, temporarily reducing the allowable log rates so the system can stabilize. Log file space management, avoiding running into out of log space conditions and Availability Group replication mechanisms can temporarily decrease the overall system limits.
Log rate governor traffic shaping is surfaced via the following wait types (exposed in the sys.dm_db_wait_stats DMV):
|INSTANCE_LOG_RATE_GOVERNOR||Instance level limiting|
|HADR_THROTTLE_LOG_RATE_SEND_RECV_QUEUE_SIZE||Feedback control, availability group physical replication in Premium/Business Critical not keeping up|
|HADR_THROTTLE_LOG_RATE_LOG_SIZE||Feedback control, limiting rates to avoid an out of log space condition|
When encountering a log rate limit that is hampering desired scalability, consider the following options:
- Scale up to a higher service level in order to get the maximum 96 MB/s log rate.
- If data being loaded is transient, such as staging data in an ETL process, it can be loaded into tempdb (which is minimally logged).
- For analytic scenarios, load into a clustered columnstore covered table. This reduces the required log rate due to compression. This technique does increase CPU utilization and is only applicable to data sets that benefit from clustered columnstore indexes.
- For information about general Azure limits, see Azure subscription and service limits, quotas, and constraints.
- For information about DTUs and eDTUs, see DTUs and eDTUs.
- For information about tempdb size limits, see TempDB in Azure SQL Database.