SQL Timestamp Incremental Load Issue
An incremental load is implemented based on timestamp, using a view to update a fact table. • The fact table contains multiple KPIs coming from different source tables. • Two main KPI tables are included directly in the view and drive the incremental logic (their timestamps change). • Additional KPIs come from other source tables, which are LEFT JOINed to the view. During an incremental run: • Main source tables are updated → timestamp changes • Other source tables are NOT updated → timestamp unchanged • Because of the LEFT JOIN, missing values from these tables are returned as NULL • These NULLs overwrite existing values in the fact table Example Fact table (before load) id app_number score 1 333 5 Source tables • source1, source2 → timestamps updated (drive incremental load) • source3 → timestamp unchanged Stored procedure logic MERGE fact_table tgt USING ( SELECT app_number, ISNULL(score, 0) AS score FROM vw_main_kpis v LEFT JOIN source3 s3 ON v.app_number = s3.app_number ) src ON tgt.app_number = src.app_number WHEN MATCHED THEN UPDATE SET tgt.score = src.score; Result (incorrect) id app_number score 1 333 0 Existing data is lost.
SQL Server 2025 VECTOR functions accepting JSON array strings
Hello! Playing with new VECTOR type and functions, I can read following https://learn.microsoft.com/en-us/sql/t-sql/functions/vector-distance-transact-sql?view=sql-server-ver17 for vector parameters: An expression that evaluates to vector data type. To me this means that any expression (including character strings as JSON-arrays) can be used as parameter. Since INSERT statement accepts to convert a JSON-array string to a VECTOR, I would expect that these function also accept this conversion. However, it appears that we are forced to cast the JSON-array to VECTOR. Any chance to improve this? Here some T-SQL example: declare v1 as VECTOR(3) = '[-1,1,0]' declare S1 as VARCHAR(50) = '[-1,1,0]' drop table tab1; create table tab1 (pkey int not null primary key, emb vector(3)); insert into tab1 values ( 101, v1 ); insert into tab1 values ( 102, S1 ); select * from tab1 order by pkey; select vector_distance('cosine',emb,@v1) from tab1; select vector_distance('cosine',emb,@s1) from tab1; -- fails Seb
SQL Server 2005 (compatibility level 90)
Hello, I’m testing the behavior described in the SQL Server documentation for **compatibility level 90** regarding the special attributes `xsi:nil` and `xsi:type`: > “The special attributes `xsi:nil` and `xsi:type` can't be queried or modified by data manipulation language statements. This means that `/e/@xsi:nil` fails while `/e/@*` ignores the `xsi:nil` and `xsi:type` attributes. However, `/e` returns the `xsi:nil` and `xsi:type` attributes for consistency with `SELECT xmlCol`, even if `xsi:nil = "false"`. ” But on **SQL Server 2005**, I can successfully query `@xsi:nil` and it returns the expected value. I’m trying to reproduce the documented “`/e/@xsi:nil` fails” behavior, but I can’t. ### Environment - Product: **Microsoft SQL Server 2005** - Database compatibility level: **90** --- ## ✅ Repro script ```sql IF EXISTS (SELECT * FROM sys.xml_schema_collections WHERE name = 'MyTestSchema') DROP XML SCHEMA COLLECTION MyTestSchema; GO CREATE XML SCHEMA COLLECTION MyTestSchema AS N' <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"> <xsd:element name="root"> <xsd:complexType> <xsd:sequence> <xsd:element name="element" nillable="true" type="xsd:string" /> </xsd:sequence> </xsd:complexType> </xsd:element> </xsd:schema>'; GO DECLARE @xmlData XML(MyTestSchema) = N' <root xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <element xsi:nil="true" /> </root>'; ;WITH XMLNAMESPACES ('http://www.w3.org/2001/XMLSchema-instance' as xsi) SELECT @xmlData.query('<result> { /root/element/@xsi:nil } </result>') AS Typed_Result; ``` ### Actual result `Typed_Result` contains `xsi:nil="true"` under `<result>...`. ### Expected result (based on docs) I expected `/root/element/@xsi:nil` to fail, or not return `xsi:nil`. --- ## Questions 1. In the documentation, does “data manipulation language statements” mean only **XML DML** (i.e., `.modify()`), not XQuery used in `SELECT` with `.query()` / `.value()`? 2. Does the “`/e/@xsi:nil` fails” behavior apply only when the XML is stored in a **table column**, not when using an **XML variable**? 3. Is the behavior different between **typed XML** (with an XML schema collection) vs **untyped XML**? 4. Can someone provide a minimal reproduction in SQL Server 2005 where `/e/@xsi:nil` fails as described? Thank you. ---Compat level 90: XML string-to-datetime UDF
Hello, I’m testing a behavior described in SQL Server documentation for **database compatibility level 90**. The docs state that a user-defined function that converts an XML constant string value to a SQL Server date/time type is marked as **deterministic**. On **SQL Server 2005**, I’m seeing the opposite: the function is marked as **non-deterministic** (`IsDeterministic = 0`). I’m trying to understand whether I’m missing a requirement/constraint or whether this is a doc mismatch / version-specific behavior. ### Environment - Product: **Microsoft SQL Server 2005** - Database compatibility level: **90** --- ## ✅ Repro script ```sql IF OBJECT_ID('dbo.fn_ParamXmlToDatetime', 'FN') IS NOT NULL DROP FUNCTION dbo.fn_ParamXmlToDatetime; GO CREATE FUNCTION dbo.fn_ParamXmlToDatetime (@xml XML) RETURNS DATETIME WITH SCHEMABINDING AS BEGIN DECLARE @y DATETIME; -- Convert an XML value to DATETIME SET @y = CONVERT(DATETIME, @xml.value('(/r)[1]', 'datetime')); RETURN @y; END GO SELECT OBJECTPROPERTY(OBJECT_ID('dbo.fn_ParamXmlToDatetime'), 'IsDeterministic') AS IsDeterministic, OBJECTPROPERTY(OBJECT_ID('dbo.fn_ParamXmlToDatetime'), 'IsPrecise') AS IsPrecise; GO ``` ### Actual result `IsDeterministic = 0` (non-deterministic) ### Expected result (based on docs) `IsDeterministic = 1` (deterministic) for this pattern under compat level 90. --- ## Questions 1. Are there additional conditions required for SQL Server to mark this UDF as deterministic (for example, specific XQuery usage, avoiding `CONVERT`, using `CAST`, using `datetime2` doesn’t exist in 2005, etc.)? 2. Does the determinism rule apply only when converting from an **XML literal constant** inside the function, rather than an XML parameter value? 3. Is this behavior different for **typed XML** (XML schema collections) vs **untyped XML**? 4. Is this a known difference/bug in SQL Server 2005 where the UDF is functionally deterministic but still reported as non-deterministic by `OBJECTPROPERTY`? Thank you for any clarification. ---2025 Year in Review: What’s new across SQL Server, Azure SQL and SQL database in Fabric
What a year 2025 has been for SQL! ICYMI and are looking for some hype, might I recommend you start with this blog from Priya Sathy, the product leader for all of SQL at Microsoft: One consistent SQL: The launchpad from legacy to innovation. In this blog post, Priya explains how we have developed and continue to develop one consistent SQL which “unifies your data estate, bringing platform consistency, performance at scale, advanced security, and AI-ready tools together in one seamless experience and creates one home for your SQL workloads in the era of AI.” For the FIFTH(!!) year in a row (my heart is warm with the number, I love SQL and #SQLfamily, and time is flying), I am sharing my annual Year in Review blog with all the SQL Server, Azure SQL and SQL database in Fabric news this year. Of course, you can catch weekly episodes related to what’s new and diving deeper on the Azure SQL YouTube channel at aka.ms/AzureSQLYT. This year, in addition to Data Exposed (52 new episodes and over 70K views!). We saw many new series related to areas like GitHub Copilot, SSMS, VS Code, and Azure SQL Managed Instance land in the channel, in addition to Data Exposed. Microsoft Ignite announcements Of course, if you’re looking for the latest announcements from Microsoft Ignite, Bob Ward and I compiled this slide of highlights. Comprehensive list of 2025 updates You can read this blog (or use AI to reference it later) to get all the updates and references from the year (so much happened at Ignite but before it too!). Here’s all the updates from the year: SQL Server, Arc-enabled SQL Server, and SQL Server on Azure VMs Generally Available SQL Server 2025 is Now Generally Available Backup/Restore capabilities in SQL Server 2025 SQL Server 2025: Deeply Integrated and Feature-rich on Linux Resource Governor for Standard Edition Reimagining Data Excellence: SQL Server 2025 Accelerated by Pure Storage Security Update for SQL Server 2022 RTM CU21 Cumulative Update #22 for SQL Server 2022 RTM Backup/Restore enhancements in SQL Server 2025 Unified configuration and governance Expanding Azure Arc for Hybrid and Multicloud Management US Government Virginia region support I/O Analysis for SQL Server on Azure VMs NVIDIA Nemotron RAG Integration Preview Azure Arc resource discovery in Azure Migrate Multicloud connector support for Google Cloud Migrations Generally Available SQL Server migration in Azure Arc Azure Database Migration Service Hub Experience SQL Server Migration Assistant (SSMA) v10.3, including Db2 SKU recommendation (preview) Database Migration Service: PowerShell, Azure CLI, and Python SDK SQL Server Migration Assistant (SSMA) v10.4, including SQL Server 2025 support, Oracle conversion Copilot Schema migration support in Azure Database Migration Service Preview Azure Arc resource discovery in Azure Migrate Azure SQL Managed Instance Generally Available Next-gen General Purpose Service Tier Improved connectivity types in Azure SQL Managed Instance Improved resiliency with zone redundancy for general purpose, improved log rate for business critical Apply reservation discount for zone redundant Business Critical databases Free offer Windows principals use to simplify migrations Data exfiltration improvements Preview Windows Authentication for Cloud-Native Identities New update policy for Azure SQL Managed Instance Azure SQL Database Generally Available LTR Backup Immutability Free Azure SQL Database Offer updates Move to Hyperscale while preserving existing geo-replication or failover group settings Improve redirect connection type to require only port 1433 and promote to default Bigint support in DATEADD for extended range calculations Restart your database from the Azure portal Replication lag metric Enhanced server audit and server audit action groups Read-access geo-zone redundant storage (RA-GZRS) as a backup storage type for non-Hyperscale Improved cutover experience to Hyperscale SLA-compliant availability metric Use database shrink to reduced allocated space for Hyperscale databases Identify causes of auto-resuming serverless workloads Preview Multiple geo-replicas for Azure SQL Hyperscale Backup immutability for Azure SQL Database LTR backups Updates across SQL Server, Azure SQL and Fabric SQL database Generally Available Regex Support and fuzzy-string matching Geo-replication and Transparent Data Encryption key management Optimized locking v2 Azure SQL hub in the Azure portal UNISTR intrinsic function and ANSI SQL concatenation operator (||) New vector data type JSON index JSON data type and aggregates Preview Stream data to Azure Event Hubs with Change Event Streaming (Azure SQL DB Public Preview/Fabric SQL Private Preview) DiskANN vector indexing SQL database in Microsoft Fabric and Mirroring Generally Available Fabric Databases SQL database in Fabric Unlocking Enterprise ready SQL database in Microsoft Fabric: ALM improvements, Backup customizations and retention, Copilot enhancements & more update details Mirroring for SQL Server Mirroring for Azure SQL Managed Instance in Microsoft Fabric Connect to your SQL database in Fabric using Python Notebook Updates to database development tools for SQL database in Fabric Using Fast Copy for data ingestion Copilot for SQL analytics endpoint Any updates across Microsoft Fabric that apply to the SQL analytics endpoint are generally supported in mirrored databases and Fabric SQL databases via the SQL analytics endpoint. This includes many exciting areas, like Data Agents. See the Fabric blog to get inspired Preview Data virtualization support Workspace level Private Link support (Private Preview) Customer-managed keys in Fabric SQL Database Auditing for Fabric SQL Database Fabric CLI: Create a SQL database in Fabric SQL database workload in Fabric with Terraform Spark Connector for SQL databases Tools and developer Blog to Read: How the Microsoft SQL team is investing in SQL tools and experiences SQL Server Management Studio (SSMS) 22.1 GitHub Copilot Walkthrough (Preview): Guided onboarding from the Copilot badge. Copilot right-click actions (Preview): Document, Explain, Fix, and Optimize. Bring your own model (BYOM) support in Copilot (Preview). Copilot performance: improved response time after the first prompt in a thread. Fixes: addressed Copilot “Run ValidateGeneratedTSQL” loop and other stability issues. SQL Server Management Studio (SSMS) 22 Support for SQL Server 2025 Modern connection dialog as default + Fabric browsing on the Browse tab. Windows Arm64 support (initial) for core scenarios (connect + query). GitHub Copilot in SSMS (Preview) is available via the AI Assistance workload in the VS Installer. T-SQL/UX improvements: open execution plan in new tab, JSON viewer, results grid zooms. New index support: create JSON and Vector indexes from Object Explorer SQL Server Management Studio (SSMS) 21 Installation and automatic updates via Visual Studio Installer. Workloads/components model: smaller footprint + customizable install. Git integration is available via the Code tools workload. Modern connection dialog experience (Preview). New customization options (e.g., vertical tabs, tab coloring, results in grid NULL styling). Always Encrypted Assessment in the Always Encrypted Wizard. Migration assistance via the Hybrid and Migration workload. mssql-python Driver ODBC: Microsoft ODBC Driver 18.5.2.1 for SQL Server OLE DB: Microsoft OLE DB Driver 19.4.1 for SQL Server JDBC (latest train): Microsoft JDBC Driver for SQL Server 13.2.1 Also updated in 2025: supported JDBC branches received multiple servicing updates (including Oct 13, 2025, security fixes). See the same JDBC release notes for the full list. .NET: Microsoft.Data.SqlClient 6.0.2 Related - some notes on drivers released/updated in 2025 (recap): MSSQL extension for VS Code 1.37.0 GitHub Copilot integration : Ask/Agent modes, slash commands, onboarding. Edit Data : interactive grid for editing table data (requires mssql.enableExperimentalFeatures: true). Data-tier Application dialog : deploy/extract .dacpac and import/export .bacpac (requires mssql.enableExperimentalFeatures: true). Publish SQL Project dialog : deploy .sqlproj to an existing DB or a local SQL dev container. Added “What’s New” panel + improved query results grid stability/accessibility. MSSQL extension for VS Code 1.36.0 Fabric connectivity : browse Fabric workspaces and connect to SQL DBs / SQL analytics endpoints. SQL database in Fabric provisioning : create Fabric SQL databases from Deployments. GitHub Copilot slash commands : connection, schema exploration, query tasks. Schema Compare extensibility: new run command for external extensions/SQL Projects (incl. Update Project from Database support). Query results in performance/reliability improvements (incremental streaming, fewer freezes, better settings handling). SqlPackage 170.0.94 release notes (April 2025) Vector: support for vector data type in Azure SQL Database target platform (import/export/extract/deploy/build). SQL projects: default compatibility level for Azure SQL Database and SQL database in Fabric set to 170. Parquet: expanded supported types (including json, xml, and vector) + bcp fallback for unsupported types. Extract: unpack a .dacpac to a folder via /Action:Extract. Platform: Remove .NET 6 support; .NET Framework build updated to 4.7.2. SqlPackage 170.1.61 release notes (July 2025) Data virtualization (Azure SQL DB): added support for data virtualization objects in import/export/extract/publish. Deployment: new publishing properties /p:IgnorePreDeployScript and /p:IgnorePostDeployScript. Permissions: support for ALTER ANY EXTERNAL MIRROR (Azure SQL DB + SQL database in Fabric) for exporting mirrored tables. SQL Server 2025 permissions: support for CREATE ANY EXTERNAL MODEL, ALTER ANY EXTERNAL MODEL, and ALTER ANY INFORMATION PROTECTION. Fixes: improved Fabric compatibility (e.g., avoid deploying unsupported server objects; fixes for Fabric extraction scripting). SqlPackage 170.2.70 release notes (October 2025) External models: support for external models in Azure SQL Database and SQL Server 2025. AI functions: support for AI_GENERATE_CHUNKS and AI_GENERATE_EMBEDDINGS. JSON: support for JSON indexes + functions JSON_ARRAYAGG, JSON_OBJECTAGG, JSON_QUERY. Vector: vector indexes + VECTOR_SEARCH and expanded vector support for SQL Server 2025. Regex: support for REGEXP_LIKE. Microsoft.Build.Sql 1.0.0 (SQL database projects SDK) Breaking: .NET 8 SDK required for dotnet build (Visual Studio build unchanged). Globalization support. Improved SDK/Templates docs (more detailed README + release notes links). Code analyzer template defaults DevelopmentDependency. Build validation: check for duplicate build items. Microsoft.Build.Sql 2.0.0 (SQL database projects SDK) Added SQL Server 2025 target platform (Sql170DatabaseSchemaProvider). Updated DacFx version to 170.2.70. .NET SDK targets imported by default (includes newer .NET build features/fixes; avoids full rebuilds with no changes Azure Data Studio retirement announcement (retirement February 28, 2026) Anna’s Pick of the Month Year It’s hard to pick a highlight representative of the whole year, so I’ll take the cheesy way out: people. I get to work with great people working on a great set of products for great people (like you) solving real world problems for people. So, thank YOU and you’re my pick of the year 🧀 Until next time… That’s it for now! We release new episodes on Thursdays and new #MVPTuesday episodes on the last Tuesday of every month at aka.ms/azuresqlyt. The team has been producing a lot more video content outside of Data Exposed, which you can find at that link too! Having trouble keeping up? Be sure to follow us on twitter to get the latest updates on everything, @AzureSQL. And if you lose this blog, just remember aka.ms/newsupdate2025 We hope to see you next YEAR, on Data Exposed! --Anna and Marisa
SQL Sever 2025 request with SQL connection
Hello, I installed SQL Server 2025 on a new VM, and I’m using a C# project to initialize my database based on a model (creating tables, foreign keys, default values, etc.). The process is extremely slow on SQL Server 2025: on my SQL Server 2022 environment, it takes about 30 minutes, but my first test on 2025 ran for over 4 hours. I’m connecting to SQL using a SQL authentication login (I tried both SA and a newly created account — same issue). I then tested using a Windows Authentication login, and surprisingly, the performance issue disappeared. Are there any known issues related to SQL authentication in SQL Server 2025?Introducing "Backups on Secondary" for SQL Server Always On Availability Groups with SQL Server 2025
We’re excited to announce a major enhancement for SQL Server Always On Availability Groups Backups on Secondary, in SQL Server 2025. Until SQL Server 2022, you could only perform COPY_ONLY full backups and transaction log backups on a secondary replica of an Always On Availability Group. This enhancement in SQL Server 2025 allows you to offload all types of backups—full, differential, and transaction logs - to a secondary replica, significantly improving performance, resource utilization, and operational flexibility. What Is "Backups on Secondary"? Traditionally, backups in an Always On Availability Group were typically performed on the primary replica, which could lead to resource contention and performance degradation for mission-critical workloads. With this update in SQL Server 2025 to the Backups on Secondary feature, you can now configure your environment to perform all backup operations on a designated secondary replica. This includes: Full backups Differential backups Transaction log backups Key Benefits 🔄 Reduced Load on Primary Replica By offloading backup operations to a secondary replica, you free up CPU, memory, and I/O resources on the primary replica, ensuring that your production workloads run more smoothly and efficiently. 🧩 Flexible Backup Strategies You can now design more flexible and resilient backup strategies by leveraging secondary replicas in different geographic locations or data centers. 🛡️ Enhanced High Availability and Disaster Recovery In the event of a failover, backup operations can continue seamlessly on another secondary replica, ensuring continuous data protection. How It Works The feature is easy to configure using T-SQL or SQL Server Management Studio (SSMS). There are two steps to make backups run on secondary: Configure backups to run on secondary replica via the AUTOMATED_BACKUP_PREFERENCE and BACKUP_PRIORITY parameters – refer to Configure backups on secondary replicas of an Always On availability group for prerequisites and detailed steps. Run the T-SQL BACKUP command using one of the tools such as SSMS or SQL Agent jobs or Maintenance plans. These are the same steps that have been currently in place for SQL Server 2022 and for prior versions. In the prior versions this configuration only allowed COPY_ONLY and transaction log backups. Now, starting with SQL Server 2025, with the same configuration in place, you can perform FULL, DIFFERENTIAL and T-LOG backups on a secondary replica and truly offload the backup overhead on to the secondary replica. You can also query system views like sys.dm_hadr_backup_is_preferred_replica to programmatically determine the preferred replica for backups. Final Thoughts The Backups on Secondary feature is a game-changer for organizations looking to optimize their SQL Server environments for performance, availability, and cost. Whether you're managing a large-scale enterprise deployment or a hybrid cloud setup, this feature gives you the flexibility and control you need to build a more efficient and resilient data platform. This feature is available in SQL Server 2025 CTP 2.0. You can download it and try it out today.Whats new in the Backup/Restore area in SQL Server 2025
Over the past several months, we’ve heard from countless customers who are eager for more robust options to protect, compress, and safeguard their SQL Server data. Since introducing these features in public preview, organizations of all sizes have validated their value in real-world workloads and provided invaluable feedback. Following are three “hidden gems” (as one of our customers called them), in the Backup/Restore area we announced with SQL Server 2025. Backups on Secondary for Always On Availability Groups, ZSTD Compression, and Immutable Backups for Ransomware Protection. These advancements are now ready for production use, built around the needs and requests of the SQL Server customers. 1. Backups on Secondary for SQL Server Always On Availability Groups Previously in preview, this feature now reaches GA, allowing you to offload backup operations to secondary replicas in Always On Availability Groups. This enhancement optimizes resource utilization and minimizes overhead on primary replicas, ensuring better performance for mission-critical workloads. What’s New in GA: Improved reliability and support for production environments. Key Benefits: Comprehensive support: Full, differential, and transaction log backups are now fully supported on secondary replicas—not just COPY_ONLY and transaction logs. Reduced impact on primary replica performance. Simplified high-availability strategies. Learn more from the original announcement: Introducing Backups on Secondary for SQL Server Always On Availability Groups. 2. ZSTD Compression in SQL Server 2025 The GA of ZSTD compression brings modern, efficient data compression to SQL Server. ZSTD compression introduces industry-leading performance and efficiency, letting you save storage and speed up workloads. What’s New in GA: Full production support for ZSTD across key workloads. Key Benefits: Faster compression and decompression compared to legacy algorithms. Lower storage footprint without sacrificing performance. Choose your algorithm: ZSTD is now a standard option right alongside MS_XPRESS for row, page, and backup compression. Tunable compression levels: Administrators can select from LOW, MEDIUM, or HIGH to balance resource use and savings. Ideal for large-scale data environments. Explore the preview details: ZSTD Compression in SQL Server 2025. 3. Backups to immutable storage: A Powerful Shield Against Ransomware Your backups are now safer than ever. Thanks to native support for immutability with Azure Blob Storage, backup files can be rendered tamper-proof—protecting them from ransomware or even accidental deletion. Key Benefits: Strong defense against ransomware and malicious tampering. Compliance with regulatory requirements for data integrity. Peace of mind for critical backup strategies. Read the detailed use-case and how-to: Immutability: A Powerful Shield Against Ransomware in SQL Environments. These features collectively empower organizations to: Optimize performance and resource utilization. Reduce operational costs through efficient compression. Strengthen security posture against evolving threats. Get Started Today These features are available to all SQL Server 2025 customers. Ready to elevate your data protection, efficiency, and compliance posture? Access the official SQL Server 2025 documentation for step-by-step guides via visit Microsoft Learn. Review upgrade guidance and best practices. Explore real-world configurations and FAQs. Upgrade now and unlock the next level of resilience, efficiency, and security for your SQL Server workloads!
ABORT_QUERY_EXECUTION query hint - public preview
We are pleased to announce the public preview of a new query hint, ABORT_QUERY_EXECUTION. The hint is intended to be used as a Query Store hint to let administrators block future execution of known problematic queries, for example non-essential queries causing high resource consumption and affecting application workloads. The hint is now available in Azure SQL Database for all databases without restrictions. The hint will later be available in Azure SQL Managed Instance with the always-up-to-date update policy, as well as in a future version of SQL Server. For more information, see Block future execution of problematic queries in documentation. Update 2025-10-06: The ABORT_QUERY_EXECUTION hint is now generally available. Frequently Asked Questions Is this supported by Microsoft Support during public preview? Yes, just like other query hints. How do I use this? Use Query Store catalog views or the Query Store UI in SSMS to find the query ID of the query you want to block and execute sys.sp_query_store_set_hints specifying that query ID as a parameter. For example: EXEC sys.sp_query_store_set_hints @query_id = 17, @query_hints = N'OPTION (USE HINT (''ABORT_QUERY_EXECUTION''))'; What happens when a query with this hint is executed? This hint is intended to be used as a Query Store hint but can be specified directly as well. In either case, the query fails immediately with error 8778, severity 16: Query execution has been aborted because the ABORT_QUERY_EXECUTION hint was specified. How do I unblock a query? Remove the hint by executing sys.sp_query_store_clear_hints with the query ID value of the query you want to unblock passed via the @query_id parameter. Can I block a query that is not in Query Store? No. At least one execution of the query must be recorded in Query Store. That query execution does not have to be successful. This means that a query that started executing but was canceled or timed out can be blocked too. When I add the hint, does it abort any currently executing queries? No. The hint only aborts future query executions. You can use KILL to abort currently executing queries. What permissions are required to use this? As with all other Query Store hints, the ALTER permission on the database is required to set and clear the hint. Can I block all queries matching a query hash? Not directly. As with all other Query Store hints, you must use a query ID to set and clear a hint. However, you can create automation that will periodically find all new query IDs matching a given query hash and block them. Can I find all blocked queries in Query Store? Yes, by executing the following query: SELECT qsh.query_id, q.query_hash, qt.query_sql_text FROM sys.query_store_query_hints AS qsh INNER JOIN sys.query_store_query AS q ON qsh.query_id = q.query_id INNER JOIN sys.query_store_query_text AS qt ON q.query_text_id = qt.query_text_id WHERE UPPER(qsh.query_hint_text) LIKE '%ABORT[_]QUERY[_]EXECUTION%' Where do I send feedback about this hint? The preferred feedback channel is via https://aka.ms/sqlfeedback. Feedback sent that way is public and can be voted and commented on by other SQL community members. You can also leave comments on this blog post or email us at intelligentqp@microsoft.com.Stream data in near real time from SQL to Azure Event Hubs - Public preview
If near-real time integration is something you are looking to implement and you were looking for a simpler way to get the data out of SQL, keep reading. SQL is making it easier to integrate and Change Event Streaming is a feature continuing this trend. Modern applications and analytics platforms increasingly rely on event-driven architectures and real-time data pipelines. As the businesses speed up, real time decisioning is becoming especially important. Traditionally, capturing changes from a relational database requires complex ETL jobs, periodic polling, or third-party tools. These approaches often consume significant cycles of the data source, introduce operational overhead, and pose challenges with scalability, especially if you need one data source to feed into multiple destinations. In this context, we are happy to release Change Event Streaming ("CES") feature into Public Preview for Azure SQL Database. This feature enables you to stream row-level changes - inserts, updates, and deletes - from your database directly to Azure Event Hubs in near real time. Change Event Streaming addresses the above challenges by: Reducing latency: Changes are streamed (pushed by SQL) as they happen. This is in contrast with traditional CDC (change data capture) or CT (change tracking) based approaches, where an external component needs to poll SQL at regular intervals. Traditional approaches allow you to increase polling frequency, but it gets difficult to find a sweet spot between minimal latency and minimal overhead due to too frequent polls. Simplifying architecture: No need for Change Data Capture (CDC), Change Tracking, custom polling or external connectors - SQL streams directly to configured destination. This means simpler security profile (fewer authentication points), fewer failure points, easier monitoring, lower skill bar to deploy and run the service. No need to worry about cleanup jobs, etc. SQL keeps track of which changes are successfully received by the destination, handles the retry logic and releases log truncation point. Finally, with CES you have fewer components to procure and get approved for production use. Decoupling: The integration is done on the database level. This eliminates the problem of dual writes - the changes are streamed at transaction boundaries, once your source of truth (the database) has saved the changes. You do not need to modify your app workloads to get the data streamed - you tap right onto the data layer - this is useful if your apps are dated and do not possess real-time integration capabilities. In case of some 3rd party apps, you may not even have an option to do anything other than database level integration, and CES makes it simpler. Also, the publishing database does not concern itself with the final destination for the data - Stream the data once to the common message bus, and you can consume it by multiple downstream systems, irrespective of their number or capacity - the (number of) consumers does not affect publishing load on the SQL side. Serving consumers is handled by the message bus, Azure Event Hubs, which is purpose built for high throughput data transfers. onceptually visualizing data flow from SQL Server, with an arrow towards Azure Event Hubs, from where a number of arrows point to different final destinations. Key Scenarios for CES Event-driven microservices: They need to exchange data, typically thru a common message bus. With CES, you can have automated data publishing from each of the microservices. This allows you to trigger business processes immediately when data changes. Real-time analytics: Stream operational data into platforms like Fabric Real Time Intelligence or Azure Stream Analytics for quick insights. Breaking down the monoliths: Typical monolithic systems with complex schemas, sitting on top of a single database can be broken down one piece at a time: create a new component (typically a microservice), set up the streaming from the relevant tables on the monolith database and tap into the stream by the new components. You can then test run the components, validate the results against the original monolith, and cutover when you build the confidence that the new component is stable. Cache and search index updates: Keep distributed caches and search indexes in sync without custom triggers. Data lake ingestion: Capture changes continuously into storage for incremental processing. Data availability: This is not a scenario per se, but the amount of data you can tap into for business process mining or intelligence in general goes up whenever you plug another database into the message bus. E.g. You plug in your eCommerce system to the message bus to integrate with Shipping providers, and consequently, the same data stream is immediately available for any other systems to tap into. How It Works CES uses transaction log-based capture to stream changes with minimal impact on your workload. Events are published in a structured JSON format following the CloudEvents standard, including operation type, primary key, and before/after values. You can configure CES to target Azure Event Hubs via AMQP or Kafka protocols. For details on configuration, message format, and FAQs, see the official documentation: Feature Overview CES: Frequently Asked Questions Get Started Public preview CES is available today in public preview for Azure SQL Database and as a preview feature in SQL Server 2025. Private preview CES is also available as a private preview for Azure SQL Managed Instance and Fabric SQL database: you can request to join the private preview by signing up here: https://aka.ms/sql-ces-signup We encourage you to try the feature out and start building real-time integrations on top of your existing data. We welcome your feedback—please share your experience through Azure Feedback portal or support channels. The comments below on this blog post will also be monitored, if you want to engage with us. Finally, CES team can be reached via email: sqlcesfeedback [at] microsoft [dot] com. Useful resources Free Azure SQL Database. Free Azure SQL Managed Instance.
