![]() Wait stats are another source of information that helps to troubleshoot performance in the Database Engine. Use sys.sp_xtp_control_query_exec_stats to enable data collection for natively compiled stored procedures. Query Store does not collect data for natively compiled stored procedures by default. Query Store collects plans for DML Statements such as SELECT, INSERT, UPDATE, DELETE, MERGE, and BULK INSERT. Plan forcing can resolve a query performance regression caused by a plan change in a very short period of time. Plan forcing in Query Store is provided by using a mechanism similar to the USE PLAN query hint, but it does not require any change in user applications. Since the Query Store retains multiple execution plans per query, it can enforce policies to direct the Query Processor to use a specific execution plan for a query. As a result, query performance regressions caused by execution plan changes can be non-trivial and time consuming to resolve. Plans also get evicted from the plan cache due to memory pressure. The procedure cache (where cached query plans are stored) only stores the latest execution plan. Information in the Query StoreĮxecution plans for any specific query in SQL Server typically evolve over time due to a number of different reasons such as statistics changes, schema changes, creation/deletion of indexes, etc. For information on shaping query plans without changing application code, see Query Store hints.įor information on enabling Query Store and keeping it adjusted to your workload, refer to Best Practice with the Query Store.For information on discovering actionable information and tune performance with the Query Store, see Tune performance with the Query Store.For information about operating the Query Store in Azure SQL Database, see Operating the Query Store in Azure SQL Database.You can configure query store using the ALTER DATABASE SET option. It separates data by time windows so you can see database usage patterns and understand when query plan changes happened on the server. Query Store automatically captures a history of queries, plans, and runtime statistics, and retains these for your review. The Query Store simplifies performance troubleshooting by helping you quickly find performance differences caused by query plan changes. The Query Store feature provides you with insight on query plan choice and performance for SQL Server, Azure SQL Database, Azure SQL Managed Instance, and Azure Synapse Analytics. GENERATED ALWAYS AS STR_TO_DATE(JSON_VALUE(content, '$.bilan.victimes.*.preview.SQL Server 2016 (13.x) and later Azure SQL Database Azure SQL Managed Instance Azure Synapse Analytics (dedicated SQL pool only) ![]() I've read about virtual columns ( ) and also improving performance for JSON object ( ) but I can't really figure out if I should create a virtual column per key ? And, how can I create a virtual column with a transform ? In above case, I would create something like : ALTER TABLE bilan_json I am supposed to retrieve multiples values from the JSON, sometimes in a single query. This is working fine, but since there is a lot of records, and JSON could be very long, it takes an awful bunch of time to display the result. WHERE STR_TO_DATE(JSON_VALUE(content, '$.bilan.victimes.*.preview.TAGSAU'),'%e/%c/%Y >= '' JSON_VALUE(content, '$.bilan.victimes.*.preview.DSA') AS DSA Now, I am using queries to lookup information in the JSON like: SELECT and sometimes it is only '$.bilan.victimes' (without further subkeys) $.bilan.victimes.*.preview.TAGSAU (I use a star, since sometimes, it's '1', '2', etc. To make it simple, the hierarchy is always the same, but sometimes part of the "tree" is missing or slightly different. ![]() Unfortunately, the content is not always the same. I have a Mysql8 DB which contains JSON data.
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