It’s how you start to package “legacy” ASP.NET apps in Docker images, so you can run them in containers on Windows 10 and Windows Server 2016. Once you’ve packaged your app into a container image you have:
a central artifact which dev and ops teams can work with, which helps you transition to DevOps;
an app that runs the same on your laptop, on the server, on Azure, on AWS, which helps you move to the cloud;
an app platform which supports distributed systems, which helps you break down the monolith into microservices.
This is part one of a series, but if you read through this post, you’ll end up with a fully-functional app.
To simplify things initially, we’ll forget about hidden schedulers and assume hard CPU affinity. That gives us an execution environment that looks like this:
Each CPU is physically tied to a scheduler.
Therefore, out of all the workers in the system, there is a subset of workers that will only run on that CPU.
Workers occasionally hand over control of their CPU to a different worker in their scheduler.
At any given moment, each CPU is expected to be running a worker that does something of interest to the middle or upper layers of SQL Server.
Some of this useful work will be done on behalf of the worker’s scheduler siblings.
However, a (hopefully) tiny percentage of a worker’s time is spent within the act of scheduling.
As usual, this is worth the read.
The broker serves several purposes:
- Know who the producers are and who the consumers are. This way, the producers don’t care who exactly consumes a message and aren’t responsible for the message after they hand it off.
- Buffer for performance. If the consumers are a little slow at the moment but don’t usually get overwhelmed, that’s okay—messages can sit with the broker until the consumer is ready to fetch.
- Let us scale out more easily. Need to add more producers? That’s fine—tell the broker who they are. Need to add consumers? Same thing.
- What about when a consumer goes down? That’s the same as problem #2: hold their messages until they’re ready again.
So brokers add a bit of complexity, but they solve some important problems. The nice part about a broker is that it doesn’t need to know anything about the messages, only who is supposed to receive it.
This is an introduction to the product and part one of an eight-part series.
Stephen Few‘s definition of Dashboard: A dashboard is a visual display of the most important information needed to achieve one or more objectives; consolidated and arranged on a single screen so the information can be monitored at a glance.
A Report on the other hand is any informational work. This information can be at any format. Table, Chart, text, number or anything else.
Reza then ties it back to Power BI, showing how to take advantage of both of these concepts.
R’s ggplot2 package is a well-known tool for producing beautiful static data visualizations that you can include in a printed report. But what if you want to include a ggplot2 graphic on a webpage and provide the ability for the user to interact with the data? The ggiraph package by David Gohel (available for installation via CRAN). WIth ggiraph, you can take an existing ggplot2 bar chart, scatterplot, boxplot, map, or many other types of chart and add one or both of the following iteractions:
Display a tooltip of your choice (e.g. data values or labels) when the cursor hovers over sections of the chart
I like it.
What exactly is Azure Active Directory B2C?
Cloud identity service with support for social accounts and app-specific (local) accounts
For enterprises and ISVs building consumer facing web, mobile & native apps
Builds on Azure Active Directory – a global identity service serving hundreds of millions of users and billions of sign-ins per day (same directory system used by Microsoft online properties – Office 365, XBox Live and so on)
Worldwide, highly-available, geo-redundant service – globally distributed directory across all of Microsoft Azure’s datacenters
I am a big fan of OAuth and making it easy for line-of-business developers to deal with authentication (lest they get harebrained ideas like rolling their own encryption algorithms).
Great for parsing unstructured data
Utilize stop words to remove commonly used filler words like a, the, an, etc…
- You can use the default stop word that are provided and add your own that you would like to remove from the visual.
The size of the words in the visual tell you how frequently the word is used.
Cf. yesterday’s word cloud example. I’m not sure how truly valuable word clouds are for visualization purposes, but at least they’re fun to peruse.
From the perspective of the disk access, this is where you will definitely win at least a couple of times with the amount of the disk access while processing the information, amount of memory that you will need to store and process (think hashing and sorting for the late materialisation phases), and you will pay less for the occupied storage.
Another noticeable thing was that the memory grants for the Indexed Views query was smaller compared to the query that was processing the original columnstore table FactOnlineSales.
Clustered indexes are currently not available as an option; we’ll see if that changes in the next version of SQL Server.
As you can see by looking at the available Power BI options, there are a number of options to choose from. If you select the top item PowerBI activities, then everything gets selected. After doing that click outside of the menu for the menu to go away. Select a date and time range of your choosing, select specific users if you wish, then click on the Search button. Depending on how big your date range is, this may take some time to load. Once you see the results, you have the ability to filter as well.
Another day, another two dozen new Power BI features… This one’s a good one.
Every week, someone on Reddit posts a “word cloud” on all of the NFL team’s subreddits. These word clouds show the most used words on that subreddit for the week (the larger the word, the more it was used). These word plots are always really fascinating to me, so I wanted to try to make some for myself. In this tutorial, we’ll be making the following word cloud from my board game stats twitter feed, @BGGStats
Looks like the implementation is fairly straightforward, so check it out.