The Write-DbaDbTableData cmdlet is pretty neat because it can create automatically the destination table if it doesn’t exists, truncate the table if it exists (or append, your choice), keep the identity values and nulls if necessary and everything is done via a bulk insert with a configurable batch size.
Click through for the script. It’s not a replacement for a real ETL process but if you just need something fast, it will do the job.
A while back I learned that it’s possible to create temporary stored procedures in SQL Server.
I never put that knowledge into practice however because I struggled to think of a good use case for when a temporary stored procedure would be preferable to a permanent stored procedure.
Not long ago I encountered a scenario where using a temporary stored procedure was the perfect solution to my problem.
Those scenarios are rare but Bert did hit one of them.
READ COMMITTED: One step up (and the default for SQL Server). A query in the current transaction can’t read data modified by someone else that hasn’t yet committed. No dirty reads. BUT….data could be changed by others between statements in the current transaction, so the data may not look the same twice. READ COMMITTED uses shared locks to prevent dirty reads, but that’s about all you get. You still get non-repeatable reads and phantom reads here (more on phantom reads below).
Click through for the full list.
Yes, a really nice new shiny feature where we have the ability to suspend and resume the encryption scan for TDE – Transparent Data Encryption which is available in SQL Server 2019. (Tested against the latest version CTP 2.4)
Arun shows the syntax you can use to suspend and resume should you need to do that.
Jonathan was working with a client recently who experienced a CLR assembly failure after an AG failover and needed to figure out why. They’d been testing their AG disaster recovery strategy and ran into an unexpected problem with their application which relies heavily on SQLCLR and an UNSAFE assembly that calls a web service from inside SQL Server. When they failed over their AG to their DR server, the CLR assembly failed with the following error:
An error occurred in the Microsoft .NET Framework while trying to load assembly id 65546. The server may be running out of resources, or the assembly may not be trusted with PERMISSION_SET = EXTERNAL_ACCESS or UNSAFE. Run the query again, or check documentation to see how to solve the assembly trust issues. For more information about this error: System.IO.FileLoadException: Could not load file or assembly ‘sqlclr_assemblyname, Version=126.96.36.199, Culture=neutral, PublicKeyToken=fa39443c11b12591’ or one of its dependencies. Exception from HRESULT: 0x80FC80F1
Read on to see the root cause and what you can do to correct it.
I wanted to show you two situations with two different kinds of spools, and how they differ with the amount of work they do.
I’ll also show you how you can tell the difference between the two.
Click through to learn more about why Erik’s queries generate spools and what that means for you.
Spark session is a unified entry point of a spark application from Spark 2.0. It provides a way to interact with various spark’s functionality with a lesser number of constructs. Instead of having a spark context, hive context, SQL context, now all of it is encapsulated in a Spark session.
Read on to learn more about SparkSession and how you can use it.
In this blog, we’ll move one step forward to get an understanding of the Dual streaming model to see what abstractions does KSQL use to process the data.
All the data that we are working on with KSQL is produced to Kafka topics by some client. This client can be any Application, Kafka connectors etc., which produces continuous never-ending data to the topics.
KSQL does not directly interact with these topics, it rather introduces a couple of abstractions in between to process the data, which are known as Streams and Tables.
Read on to learn what these are and why it’s useful to think in these terms.
The ISO/IEC 9075:2016 standard (aka SQL:2016) introduces support for Row Pattern Recognition (RPR) in SQL. Similar to using regular expressions to identify patterns in a string, RPR allows you to use regular expressions to identify patterns in a sequence of rows.
To me, it’s the next step in the evolution of window functions. If you think that window functions are profound and useful, RPR is really going to bake your noodle.
RPR has limitless practical applications, including identifying patterns in stock market activity, handling time series, fraud detection, material handling, shipping applications, DNA sequencing, gaps and islands, top N per group, and many others.
I’ve voted it up and recommend you do so too. This is a great way to think of streams of data sitting in a database. If you have business use cases where this could help, adding those as comments would be great too.
First things first: what is a semi-additive calculation? Any calculation can be either additive, non-additive or semi-additive. An additive measure uses SUM to aggregate over any attribute. The sales amount is a perfect example of an additive measure. Indeed, the sales amount for all customers is the sum of the individual sales for each customer; at the same time, the amount over a year is the sum of the amounts for each month.
A non-additive measure does not use SUM over any dimension. Distinct count is the simplest example: the distinct count of products sold over a month is not the sum of the distinct counts of individual days. The same happens with any other dimension: a distinct count of products sold in a country is not the sum of the distinct counts of the products sold in each city in the country.
Semi-additive calculations are the hardest ones: a semi-additive measure uses SUM to aggregate over some dimensions and a different aggregation over other dimensions – a typical example being time.
Semi-additive measures are probably the trickiest of the three, as you can easily work with additive measures and you know you won’t be able to do much with non-additive measures.