SQL Hyperscale performance troubleshooting diagnostics

Άρθρο
08/16/2024

To troubleshoot performance problems in a Hyperscale database, general performance tuning methodologies on the Azure SQL Database compute node is the starting point of a performance investigation. However, given the distributed architecture of Hyperscale, additional diagnostic data might need to be considered. This article describes Hyperscale-specific diagnostic data.

Log rate throttling waits

Every Azure SQL Database service objective has log generation rate limits enforced via log rate governance. In Hyperscale, the log governance limit is set to 105 MB/sec, regardless of the service level. This value is exposed in the primary_max_log_rate column in sys.dm_user_db_resource_governance.

However, there are times when the log generation rate on the primary compute replica has to be throttled to maintain recoverability SLAs. This throttling happens when a page server or another compute replica is significantly behind applying new log records from the log service. If no page servers or replicas are behind, the throttling mechanism allows log generation rate to reach 100 MB/s. This is the effective maximum log generation rate in all Hyperscale service objectives. Log generation rate of 150 MB/s is available as an opt-in preview feature. For more information and to opt-in to 150 MB/s, see Blog: November 2024 Hyperscale enhancements.

The following wait types (in sys.dm_os_wait_stats) describe the reasons why log rate can be throttled on the primary compute replica:

Wait Type	Description
RBIO_RG_STORAGE	Occurs when a Hyperscale database primary compute node log generation rate is being throttled due to delayed log consumption by one or more page servers.
RBIO_RG_DESTAGE	Occurs when a Hyperscale database compute node log generation rate is being throttled due to delayed log consumption by the long-term log storage.
RBIO_RG_REPLICA	Occurs when a Hyperscale database compute node log generation rate is being throttled due to delayed log consumption by one or more readable secondary replicas.
RBIO_RG_GEOREPLICA	Occurs when a Hyperscale database compute node log generation rate is being throttled due to delayed log consumption by the Geo-secondary replica.
RBIO_RG_LOCALDESTAGE	Occurs when a Hyperscale database compute node log generation rate is being throttled due to delayed log consumption by the log service.

Page server reads

The compute replicas do not cache a full copy of the database locally. The data local to the compute replica is stored in the buffer pool (in memory) and in the local resilient buffer pool extension (RBPEX) cache that is a partial (non-covering) cache of data pages. This local RBPEX cache is sized proportionally to the compute size. RBPEX is similar to the buffer pool in that it has the most frequently accessed data. Each page server, on the other hand, has a covering RBPEX cache for the portion of the database it maintains.

When a read is issued on a compute replica, if the data doesn't exist in the buffer pool or local RBPEX cache, a getPage(pageId, LSN) function call is issued, and the page is fetched from the corresponding page server. Reads from page servers are remote reads and are thus slower than reads from the local RBPEX. When troubleshooting IO-related performance problems, we need to be able to tell how many IOs were done via relatively slower remote page server reads.

Several dynamic managed views (DMVs) and extended events have columns and fields that specify the number of remote reads from a page server, which can be compared against the total reads. Query store also captures remote reads as part of the query run time stats.

Columns to report page server reads are available in execution DMVs and catalog views, such as:
Page server reads are added to the following extended events:
- sql_statement_completed
- sp_statement_completed
- sql_batch_completed
- rpc_completed
- scan_stopped
- query_store_begin_persist_runtime_stat
- query-store_execution_runtime_info
ActualPageServerReads/ActualPageServerReadAheads are added to query plan XML for actual plans. For example:

Note

To view these attributes in the query plan properties window, SSMS 18.3 or later is required.

Virtual file stats and IO accounting

In Azure SQL Database, the sys.dm_io_virtual_file_stats() DMF is the primary way to monitor SQL Database IO. IO characteristics in Hyperscale are different due to its distributed architecture. In this section, we focus on IO (reads and writes) to data files as seen in this DMF. In Hyperscale, each data file visible in this DMF corresponds to a remote page server. The RBPEX cache mentioned here is a local SSD-based cache, that is a non-covering cache on the compute replica.

Local RBPEX cache usage

Local RBPEX cache exists on the compute replica, on local SSD storage. Thus, IO against this cache is faster than IO against remote page servers. Currently, sys.dm_io_virtual_file_stats() in a Hyperscale database has a special row reporting the IO against the local RBPEX cache on the compute replica. This row has the value of 0 for both database_id and file_id columns. For example, the query below returns RBPEX usage statistics since database startup.

select * from sys.dm_io_virtual_file_stats(0,NULL);

A ratio of reads done on RBPEX to aggregated reads done on all other data files provides RBPEX cache hit ratio. The counter RBPEX cache hit ratio is also exposed in the performance counters DMV sys.dm_os_performance_counters.

Data reads

When reads are issued by the SQL Server database engine on a compute replica, they may be served either by the local RBPEX cache, or by remote page servers, or by a combination of the two if reading multiple pages.
When the compute replica reads some pages from a specific file, for example file_id 1, if this data resides solely on the local RBPEX cache, all IO for this read is accounted against file_id 0 (RBPEX). If some part of that data is in the local RBPEX cache, and some part is on a remote page server, then IO is accounted towards file_id 0 for the part served from RBPEX, and the part served from the remote page server is accounted towards file_id 1.
When a compute replica requests a page at a particular LSN from a page server, if the page server has not caught up to the LSN requested, the read on the compute replica waits until the page server catches up before the page is returned to the compute replica. For any read from a page server on the compute replica, you see the PAGEIOLATCH_* wait type if it is waiting on that IO. In Hyperscale, this wait time includes both the time to catch up the requested page on the page server to the LSN required, and the time needed to transfer the page from the page server to the compute replica.
Large reads such as read-ahead are often done using "Scatter-Gather" Reads. This allows reads of up to 4 MB of pages at a time, considered a single read in the SQL Server database engine. However, when data being read is in RBPEX, these reads are accounted as multiple individual 8-KB reads, since the buffer pool and RBPEX always use 8-KB pages. As the result, the number of read IOs seen against RBPEX may be larger than the actual number of IOs performed by the engine.

Data writes

The primary compute replica does not write directly to page servers. Instead, log records from the log service are replayed on corresponding page servers.
Writes that happen on the compute replica are predominantly writes to the local RBPEX (file_id 0). For writes on logical files that are larger than 8 KB, in other words those done using Gather-write, each write operation is translated into multiple 8-KB individual writes to RBPEX since the buffer pool and RBPEX always use 8-KB pages. As the result, the number of write IOs seen against RBPEX may be larger than the actual number of IOs performed by the engine.
Non-RBPEX files, or data files other than file_id 0 that correspond to page servers, also show writes. In the Hyperscale service tier, these writes are simulated, because the compute replicas never write directly to page servers. Write IOPS and throughput are accounted as they occur on the compute replica, but latency for data files other than file_id 0 does not reflect the actual latency of page server writes.

Log writes

On the primary compute, a log write is accounted for in file_id 2 of sys.dm_io_virtual_file_stats. A log write on primary compute is a write to the log Landing Zone.
Log records are not hardened on the secondary replica on a commit. In Hyperscale, log is applied by the log service to the secondary replicas asynchronously. Because log writes don't actually occur on secondary replicas, any accounting of log IOs on the secondary replicas is for tracking purposes only.

Data IO in resource utilization statistics

In a non-Hyperscale database, combined read and write IOPS against data files, relative to the resource governance data IOPS limit, are reported in sys.dm_db_resource_stats and sys.resource_stats views, in the avg_data_io_percent column. The same value is reported in the Azure portal as Data IO Percentage.

In a Hyperscale database, this column reports on data IOPS utilization relative to the limit for local storage on compute replica only, specifically IO against RBPEX and tempdb. A 100% value in this column indicates that resource governance is limiting local storage IOPS. If this is correlated with a performance problem, tune the workload to generate less IO, or increase database service objective to increase the resource governance Max Data IOPS limit. For resource governance of RBPEX reads and writes, the system counts individual 8-KB IOs, rather than larger IOs that may be issued by the SQL Server database engine.

Data IO against remote page servers is not reported in resource utilization views or in the portal, but is reported in the sys.dm_io_virtual_file_stats() DMF, as noted earlier.

Additional resources

For vCore resource limits for a Hyperscale single database see Hyperscale service tier vCore Limits
For monitoring Azure SQL Databases, enable database watcher
For Azure SQL Database performance tuning, see Query performance in Azure SQL Database
For performance tuning using Query Store, see Performance monitoring using Query store
For DMV monitoring scripts, see Monitoring performance Azure SQL Database using dynamic management views

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