Update resource-limits-logical-server.md

Author: pmasl

articles/azure-sql/database/resource-limits-logical-server.md

@@ -90,6 +90,8 @@ Sessions, workers, and requests are defined as follows:
 - A request is the logical representation of a query or batch. A request is issued by a client connected to a session. Over time, multiple requests may be issued on the same session.
 - A worker thread, also known as a worker or thread, is a logical representation of an operating system thread. A request may have many workers when executed with a parallel query execution plan, or a single worker when executed with a serial (single threaded) execution plan. Workers are also required to support activities outside of requests: for example, a worker is required to process a login request as a session connects.
 
+For more information about these concepts, see the [Thread and Task Architecture Guide](/sql/relational-databases/thread-and-task-architecture-guide.md).
+
 The maximum numbers of sessions and workers are determined by the service tier and compute size. 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.
 
 > [!NOTE]
@@ -109,7 +111,6 @@ Find worker and session limits for Azure SQL Database by service tier and comput
 - [Resource limits for single databases using the DTU purchasing model](resource-limits-dtu-single-databases.md)
 - [Resources limits for elastic pools using the DTU purchasing model](resource-limits-dtu-elastic-pools.md)
 
-
 Learn more about troubleshooting specific errors for session or worker limits in [Resource governance errors](troubleshoot-common-errors-issues.md#resource-governance-errors).
 
 ### Memory
@@ -156,21 +157,21 @@ In addition to using Resource Governor to govern resources within the database e
 
 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 reported in the [sys.dm_user_db_resource_governance](/sql/relational-databases/system-dynamic-management-views/sys-dm-user-db-resource-governor-azure-sql-database) view.
 
-### Data IO governance
+### Data I/O 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 a multi-tenant service, and to stay within the capabilities of the underlying hardware and storage.
+Data I/O governance is a process in Azure SQL Database used to limit both read and write physical I/O 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 a 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. For elastic pools, workload group limits apply to each database in the pool. Additionally, the resource pool limit additionally applies to the cumulative IO of the elastic pool. Tempdb IO is subject to workload group limits, with the exception of Basic, Standard, and General Purpose service tier, where higher tempdb IO limits apply. 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 in the same pool.
+For single databases, workload group limits are applied to all storage I/O against the database. For elastic pools, workload group limits apply to each database in the pool. Additionally, the resource pool limit additionally applies to the cumulative I/O of the elastic pool. Tempdb I/O is subject to workload group limits, with the exception of Basic, Standard, and General Purpose service tier, where higher tempdb I/O limits apply. 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 in the same pool.
 
-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 won't 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.
+For example, if a query generates 1000 IOPS without any I/O resource governance, but the workload group maximum IOPS limit is set to 900 IOPS, the query won't be able to generate more than 900 IOPS. However, if the resource pool maximum IOPS limit is set to 1500 IOPS, and the total I/O from all workload groups associated with the resource pool exceeds 1500 IOPS, then the I/O of the same query may be reduced below the workgroup limit of 900 IOPS.
 
-The IOPS and throughput max values returned by the [sys.dm_user_db_resource_governance](/sql/relational-databases/system-dynamic-management-views/sys-dm-user-db-resource-governor-azure-sql-database) view act as limits/caps, not as guarantees. Further, resource governance doesn't 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 Database 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.
+The IOPS and throughput max values returned by the [sys.dm_user_db_resource_governance](/sql/relational-databases/system-dynamic-management-views/sys-dm-user-db-resource-governor-azure-sql-database) view act as limits/caps, not as guarantees. Further, resource governance doesn't guarantee any specific storage latency. The best achievable latency, IOPS, and throughput for a given user workload depend not only on I/O resource governance limits, but also on the mix of I/O sizes used, and on the capabilities of the underlying storage. SQL Database uses I/Os that vary in size between 512 KB and 4 MB. For the purposes of enforcing IOPS limits, every I/O 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 I/Os, to align with Azure Storage I/O 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 doesn't have enough data files to cumulatively provide this number of IOPS, or if data isn't distributed evenly across files, or if the performance tier of underlying blobs limits IOPS/throughput below the resource governance limits. Similarly, with small log IOs generated by frequent transaction commits, the `primary_max_log_rate` value may not be achievable by a workload due to the IOPS limit on the underlying Azure Storage blob. For databases using Azure Premium Storage, Azure SQL Database uses sufficiently large storage blobs to obtain needed IOPS/throughput, regardless of database size. For larger databases, multiple data files are created to increase total IOPS/throughput capacity.
 
 Resource utilization values such as `avg_data_io_percent` and `avg_log_write_percent`, reported in the [sys.dm_db_resource_stats](/sql/relational-databases/system-dynamic-management-views/sys-dm-db-resource-stats-azure-sql-database),  [sys.resource_stats](/sql/relational-databases/system-catalog-views/sys-resource-stats-azure-sql-database), and [sys.elastic_pool_resource_stats](/sql/relational-databases/system-catalog-views/sys-elastic-pool-resource-stats-azure-sql-database) views, are calculated as percentages of maximum resource governance limits. Therefore, when factors other than resource governance limit IOPS/throughput, it's possible to see IOPS/throughput flattening out and latencies increasing as the workload increases, even though reported resource utilization remains below 100%.
 
-To determine read and write IOPS, throughput, and latency per database file, use the [sys.dm_io_virtual_file_stats()](/sql/relational-databases/system-dynamic-management-views/sys-dm-io-virtual-file-stats-transact-sql) function. This function surfaces all IO against the database, including background IO that isn't accounted towards `avg_data_io_percent`, but uses IOPS and throughput of the underlying storage, and can impact observed storage latency. The function reports additional latency that may be introduced by IO resource governance for reads and writes, in the `io_stall_queued_read_ms` and `io_stall_queued_write_ms` columns respectively.
+To determine read and write IOPS, throughput, and latency per database file, use the [sys.dm_io_virtual_file_stats()](/sql/relational-databases/system-dynamic-management-views/sys-dm-io-virtual-file-stats-transact-sql) function. This function surfaces all I/O against the database, including background I/O that isn't accounted towards `avg_data_io_percent`, but uses IOPS and throughput of the underlying storage, and can impact observed storage latency. The function reports additional latency that may be introduced by I/O resource governance for reads and writes, in the `io_stall_queued_read_ms` and `io_stall_queued_write_ms` columns respectively.
 
 ### Transaction log rate governance
 
@@ -202,7 +203,7 @@ When encountering a log rate limit that is hampering desired scalability, consid
 
 ### Storage space governance
 
-In Premium and Business Critical service tiers, customer data including *data files*, *transaction log files*, and *tempdb files* is stored on the local SSD storage of the machine hosting the database or elastic pool. Local SSD storage provides high IOPS and throughput, and low IO latency. In addition to customer data, local storage is used for the operating system, management software, monitoring data and logs, and other files necessary for system operation.
+In Premium and Business Critical service tiers, customer data including *data files*, *transaction log files*, and *tempdb files* is stored on the local SSD storage of the machine hosting the database or elastic pool. Local SSD storage provides high IOPS and throughput, and low I/O latency. In addition to customer data, local storage is used for the operating system, management software, monitoring data and logs, and other files necessary for system operation.
 
 The size of local storage is finite and depends on hardware capabilities, which determine the **maximum local storage** limit, or local storage set aside for customer data. This limit is set to maximize customer data storage, while ensuring safe and reliable system operation. To find the **maximum local storage** value for each service objective, see resource limits documentation for [single databases](resource-limits-vcore-single-databases.md) and [elastic pools](resource-limits-vcore-elastic-pools.md).