Flow From Power BI

Chris Webb uses Microsoft Flow’s REST API to pass messages via Power BI:

So basically, in this case I’ve used Flow to create a web service without writing a single line of code. I can see a lot of potential uses for this and I suspect I’ll be blogging about Flow a lot in the future. A word of warning though: do not try to use this as a way of updating a data source. As I mentioned last time, when you run your query you’ll find Power Query/Power BI calls the web service twice. For example, I created a Flow similar to the one above that used the Insert Row step to take text sent to a Request trigger and add it to a table in an Excel workbook, and of course every time I refreshed my query I got two identical rows in my Excel table.

Read the whole thing.

Building Your 2016 VM

Kevin Feasel

2016-11-14

Tools

James Serra looks at putting together a VM for 2016:

With Windows Server 2016 just been released, now is the perfect time to build an Azure VM with SQL Server 2016 on Windows Server 2016.  In a matter of minutes you can be playing and learning both platforms.  Below I will document the steps I took to build the VM along with the additional software I installed.  This is a fully-loaded VM that I use for demo’s and to build small projects:

(Software updates as of 11/4/2016)

There’s a lot of good software here.  And Java.

Considerations For Backups

Kevin Hill goes back to basics on backups:

Backing up SQL Server databases has been documented in thousands of books, Microsoft documentation and blog posts since the product was released in the 90s.

I’m not about to try and tell you HOW to backup your SQL databases. Rather, I want to take you through the basic considerations of deciding what to back up, when, how often, etc. in clear language that non-SQL folks can work through.

Understanding how to take and tune backups is important; this is more fundamental advice.

Making Cortana Work

Rob Sewell shows how to integrate Cortana with dbareports via Power BI:

When you start using Cortana to query your data you will find that at times it is very frustrating. My wife was in fits of giggles listening to me trying to record the video below as Cortana refused to understand that I was saying “servers” and repeatedly searched Bing for “service” Whilst you can negate the effect by using the alternate names for the Q and A settings it is still a bit hit and miss at times.

This is an interesting post.

External File Formats

I look at file formats in Polybase:

Delimited text is exactly as it sounds:  you can use a comma, tab, pipe, tilde, or any other delimiter (including multi-character delimiters).  So let’s go through the options here.  First, FORMAT_TYPE must be DELIMITEDTEXT.  From there, we have a few FORMAT_OPTIONS.  I mentioned FIELD_TERMINATOR, which is how we separate the values in a record.  We can also use STRING_DELIMITER if there are quotes or other markers around our string values.

DATE_FORMAT makes it easier for Polybase to understand how dates are formatted in your file.  The MSDN document gives you hints on how to use specific date formats, but you can’t define a custom format today, or even use multiple date formats.

It feels like there’s a new Hadoop file format every day.

Temporal Tables For Error Recovery

Dimitri Furman looks at using temporal tables to recover from user errors:

Have you ever got that sinking feeling after hitting the Execute button in SSMS, thinking “I should not have done that”? DML statements with missing WHERE clause, DROP statements accidentally targeting slightly mistyped (but existing) tables or databases, RESTORE statements overwriting databases with new data that haven’t been backed up, are all examples of actions prompting an “Oops…” (or worse) shortly thereafter. “Oops recovery” is the term that became popular to describe the process of fixing the consequences.

For most of these scenarios, the usual, and often the only, recovery mechanism is to restore the database from backup to a point in time just before the “oops”, known as point-in-time recovery (PITR). Even though PITR remains the most general and the most effective recovery mechanism, it does have some drawbacks and limitations: the recovery process requires a full database restore, taking the time proportional to the size of the database; a sequence of restores may be needed if multiple “oops” transactions have occurred; in the general case, there will be difficulties reconciling recovered data with data modified after the “oops” point in time, etc. Nevertheless, PITR remains the most widely applicable recovery method for SQL Server databases, both on-premises and in the cloud.

The solution entails a non-trivial amount of effort, but it is interesting.

CHECKDB Snapshots

Kevin Feasel

2016-11-11

DBCC

Arun Sirpal shows that the CHECKDB snapshot is no longer hidden:

I am in the middle of some research regarding CHECKDB and learnt something new. From SQL Server 2014 onwards the database snapshot that is used to get to a transactionally-consistent point-in-time is no longer hidden.

Arun includes a sample to prove it.

Sparklyr On HDInsight

Kevin Feasel

2016-11-11

Cloud, Hadoop, R

Ali Zaidi has a walkthrough on using sparklyr on HDInsight:

The majority of Spark is written in Scala (~80% of Spark core), which is a functional programming language. Functional programming languages emphasize functional purity (the output only depends on the inputs) and strive to avoid side-effects. One important component of most functional programming languages is their lazy evaluation. While it might seem odd that we would appreciate laziness from our computing tools, lazy evaluation is an effective way of ensuring computations are evaluated in the most efficient manner possible.

Lazy evaluation allows Spark SQL to highly optimize the queries. When a user submits a query to Spark SQL, Spark composes the components of the SQL query into a logical plan. The logical plan is basically a recipe Spark SQL creates in order to evaluate the desired query. Spark SQL then submits the logical plan to its highly optimized engine called Catalyst, which optimizes this plan into a physical plan of action that is executed inside Spark computation engine (a series of coordinating JVMs).

Read on for more description and code.

Performance Of IN

Daniel Janik looks at how the IN clause behaves differently based on the number of items in the list:

As you can see the second query is much slower and the extra value in the IN caused late filtering. This is a limitation on some types of operators such as this clustered index scan.

There isn’t just a limitation of 15 input values. There’s also one at 64. On the 65th input value the list will be converted to a constant scan which is then sorted and joined. Most interestingly enough is that the list in my demo query is already sorted ascending.

Read the whole thing.

Bulk Administration

Kenneth Fisher discusses the bulk administration right:

So as with all permissions we only grant them if there is an actual need right? And the best practice of least privilege says that if someone has to be able to do a bulk load on a table then we should grant the bulk load to that one table right? There’s the rub. Bulk admin permissions are at the instance level and are not granular in any way. Ie you can’t grant it specifically to a single database or table. It’s all or nothing.

Read on for Kenneth’s thoughts.

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