Updating table variables sql 2016


As an example, we can transform a JSON array in the @orders variable into a set of rows and insert them into a standard table: Four columns in the result set that is returned by OPENJSON are defined in the WITH clause.OPENJSON will try to find the properties Number, Date, Customer, and Quantity in each JSON object and convert their values into columns in the result set.These are not considered ‘user created’ even though our user query was the cause of them being auto-generated.(“User created” means someone ran a CREATE STATISTICS command.) SQL Server can now use the statistic on Gender and the statistic on First Name Id for future queries that run. First Name By Year table has a clustered primary key, and here is the statistic that was created along with that index: If columns are important enough to index, SQL Server assumes that it’s also important to estimate how many rows would be returned by that index when you query it.I’ve been asked a lot of questions about updating statistics in SQL Server over the years. Here’s a rundown of all the practical questions that I tend to get about how to maintain these in SQL Server.I don’t dig into the internals of statistics and optimization in this post. ⇒ Be a proactive: If you have millions of rows in some of your tables, you can get burned by doing no statistics maintenance at all if query performance stats to get slow and out of date statistics are found to be the cause.If you want to create an index on some JSON property that is frequently used in queries, you can create a non-persisted computed column that references the value and creates a standard index on that column.In the following example, we will optimize queries that filter rows using the $.

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Statistics are small, lightweight objects that describe the distribution of data in a SQL Server table. First Name By Year and you run this query: SQL Server needs to estimate how many rows will come back for First Name Id=74846. Statistics are lightweight little pieces of information that SQL Server keeps on tables and indexes to help the optimizer do a good job. First Name By Year table was freshly created when we ran our query, it would have no column statistics.Also, OPENJSON can be used to combine relational and JSON data in the same query.If we assume that the JSON array shown in the previous example is stored in the Orders column, the following query can combine the columns and JSON fields: OPENJSON will open an array in each cell and return one row for each JSON object (i.e. CROSS APPLY OPENJSON syntax is used to “join” rows in the table with the child inner table that will be materialized from a JSON array in the JSON cell.I’m also not talking about statistics for memory optimized tables in this article. Back when I read philosophy, I found Aristotle a bit annoying because he talked so much about “moderation”. You shouldn’t run statistics maintenance against a database at the same time you’re checking for corruption, rebuilding indexes, or running other IO intensive processes.

If you have multiple SQL Servers using shared storage, that maintenance may hit the storage at the same time. ⇒ The moderate approach: One widely used free script is Ola Hallengren’s SQL Server Index and Statistics Maintenance script.

Full text index is applicable on arrays of numbers and simple string values.



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  • Improving temp table and table variable performance using. profil de paulette60

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    Mar 20, 2016 Improving temp table and table variable performance using memory optimization. and SQL Server 2016. table variables or large temp tables in SQL.…