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Day: September 22, 2020

The Session Window in Flink

Kundan Kumarr continues a series on windows in Apache Flink:

In the real world, all the work that we do online- Visiting a website, Clicking around the website, do online transactions, and so on are in sessions. We might just go to an e-commerce website like amazon, looking for products, clicking around for a bit, and then stop. All is done within a session. There is a use case where these websites may want to track pages that we visited in a single session. For that, it needs to group all clicks together which are streaming in, based on a session. These streaming use cases can be implemented easily by Flink Session window.

The Session windows assigner groups elements by sessions of activity. Session windows do not overlap and do not have a fixed start and end time. The number of entities within a session window is not fixed. Because it is a user who defines typically how long the session would be. A session window closes when it does not receive elements for a certain period of time, i.e., when a gap of inactivity occurred. For example, once we have been idle on the amazon website let say for 1 minute that is the end of the previous session and if go back to the site after 1 sec it will start a new session. The way it would determine the session is the pause between one click and another click.

Click through for a depiction and an example.

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The Problem with VM Backups of SQL Server

Sean Gallardy turns a problem on its head:

Now let’s get to the main point, which is how long the VM stays paused or stunned – remember, this is a “small” or “short” amount of time, one might even say “trivial”. When it is kept this short to where it’s “trivial” as in less than a second then all is good and you most likely won’t notice it except in very high workloads… but we should be running with VSS integration and not VM level so it’s still incorrect, but hey. When this time is not short of trivial then GOOD things start to happen, most notably that high availability kicks in.

I appreciate the framing of this post, as the failover wasn’t a problem; it merely exposes the actual problem.

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Space Savings from Separate Date and Time Columns in Power BI

Shabnam Watson runs an experiment:

As you may have already heard, one of the easiest ways to reduce a Power BI model (dataset) size is by splitting DateTime columns into separate Date and Time columns but the question is how much space reduction can you achieve by doing so. As I show in this blog post, the reduction can be significant and up to % 80 or % 90 depending on the number and cardinality of the datetime columns.

That’s a lot of savings.

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A Postgres Version of WhoIsActive

Josh Simar is on a mission:

 while ago, I asked in the twitterverse if there was a Postgres equivalent for the great sp_WhoIsActive script. While I didn’t get a flat-out no (which I wasn’t expecting) I didn’t get anyone pointing me in the direction of something pre-done and did get some advice that I should take it on.

Well it took quite a while and it’s still nowhere near as robust as the MSSQL version but as a first stab I have created the pg_WhoIsActive function.

While doing it I basically said that I want a 1 to 1 equivalent as much as possible but to get it out quick I had one major rule for the POC.

Check it out.

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T-SQL Tuesday 130 Roundup: Automate Your Stress Away

Elizabeth Noble recaps T-SQL Tuesday #130:

I’m really grateful for all the bloggers that took part this month. Especially since automation has been a topic discussed before. However, it’s hard for me to get too much of my day to day work automated. And I was really looking forward to these topics so that I could learn new tasks I could automate myself. With that said, let’s see all the wonderful ideas people contributed this month. And if you’re like me, you’re going to want to put some of this automation in place as soon as possible.

Click through for 24 entries.

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