Click through for a demo of how to embed tweets into your RMarkdown documentation.
In this module you will learn how to use the Timeline Storyteller. The Timeline Storyteller is a great way to tell a story about your data. It gives you the ability to create multiple representations of your data and then pull them together by creating multiple scenes.
This is a flashy visual and I think Devin’s set is an excellent example of where you might want to use it.
What I’ve done here is use the cpus and memory switches to limit that container to a maximum of 2 CPUs and 2GB of RAM. There are other options available, more info is available here.
Simple, eh? But it does show something interesting.
I’m running Docker on my Windows 10 machine, using Linux containers. The way this works is by spinning up a Hyper-V Linux VM to run the containers (you can read more about this here).
The estimated costs for the two queries are very close to each other: 74.6842 and 74.6839 optimizer units. However, we saw earlier that the tipping point for an adaptive join on this query can vary between 22680 and 80388.3 rows. This inconsistency means that we can find a query that performs worse with adaptive joins enabled.
Click through to see the queries Joe is using. Based on this, I’d guess that this is probably a knife-edge problem: most of the time, adaptive join processing is better, but if you hit the wrong query, it’s worse.
By contrast, if you have a staging table that flows 10 million rows a day (meaning 10 million leave and 10 million new ones enter), you’ll overflow an int column in less than a year. It’s worth thinking about data sizes before deciding on the type of a surrogate key. Bigint is the safest, and if you think you’ll need it, go with it. But there is that storage overhead.