A Fine Slice Of SQL Server
Sameer Nigam explains the differences between convolutional and recurrent neural networks:
CNN and RNN are amongst most important algorithm of Neural Network family, also they differ in their network process and solving problems.
So talking about their differences:
CNN are used to solve classification and regression problems and RNN are used to solve sequence information.
Read on to see what Sameer means by this.
Comments closedJoe Obbish takes us through a fun concurrency problem:
Very little has been written about exchange operators with a partitioning type of demand, so I forgive you for not hearing of it before today. There is a brief explanation available here, an example of using demand partitioning to improve some query plans involving partitioned tables, and a Stack Exchange answer for someone comparing round robin and demand partitioning. You have the honor of reading perhaps the fourth blog post about the subject.
Read on for an in-depth look at the problem.
Comments closedMarc Lelijveld shows how you can create custom tooltips per user in Power BI:
From an end user perspective, it is very user friendly to see that the report is adjusted to your personal preferences. Similar is the case for page navigation. If I am the country manager for Germany, I would prefer the report to be adjusted to my area.
With row level security you can filter the report down to only the specified area you want. But in this case, we want clarity over all countries. So, we do not want to filter the dataset itself, but only customize the end user interaction. For this setup, we will still use row level security, but in a different setup.
Click through to see how to do this.
Comments closedEd Pollack has started a series on columnstore indexing:
By storing data grouped by columns, like values can be grouped together and therefore compress very effectively. This compression will often reduce the size of a table by 10x and offers significant improvements over standard SQL Server compression.
For example, if a table with a billion rows has an ID lookup column that has 100 distinct values, then on average each value will be repeated 10 million times. Compressing sequences of the same value is easy and results in a tiny storage footprint.
Just like standard compression, when columnstore data is read into memory, it remains compressed. It is not decompressed until runtime when needed. As a result, less memory is used when processing analytic queries. This allows more data to fit in memory at one time, and the more operations that can be performed in memory, the faster queries can execute.
In scenarios where it makes sense, I absolutely love clustered columnstore indexes.
Comments closedHugo Kornelis points out another issue when checking arrow width when reading execution plans:
The issue I will talk about today is caused when a node is missing, due to a post-optimization rewrite. I myself have so far only seen this in execution plans where a bitmap filter (created by a Bitmap operator, by a Batch Hash Table Build operator, or by a Hash Match operator with a BitmapCreator property) was pushed into an Index Scan, Clustered Index Scan, or Columnstore Index Scan operator. So that’s what I’ll focus on here. But do keep in mind that there may be other situations where a post-optimization rewrite removes a node.
To understand the root cause of this (and, credit where credit is due, I only understood this myself after getting it explained by Paul White, whose explanation I am now paraphrasing here), we need to dig deeper into the internals of the optimizer.
This is where you put on the internals mining cap and watch out for falling rocks.
Comments closedJeffrey Hicks builds out a quick Powershell script to test network performance:
I hope you’ve been trying your hand at the scripting challenges being posted on the Iron Scripter web site. The challenges are designed for individuals to do on their own to build up their PowerShell scripting skills. A few weeks ago, a challenge was posted to create a network monitoring tool using PowerShell and the Write-Progress cmdlet. I thought I’d share my notes on the challenge and some of the code I came up with.
It’s an interesting challenge and solution.
Comments closedJosh Darnell dealt with an issue with Availability Groups:
I had a 2-node availability group (AG) + fileshare witness system experience an unexpected failover recently.
The synchronous secondary was being patched, and when it came back up from a reboot, the current primary unexpectedly failed over. We weren’t done with all the patching on the secondary, so this caused a short outage, and we had to fail back to the original primary to finish the patching (which is of course another short interruption in availability).
The root cause was interesting enough that I decided to share the story here, and provide some general advice and debugging tips along the way.
Click through to understand why this happened and how you might be able to avoid the pain Josh experienced.
Comments closedKundan Kumarr explains two techniques for creating DataFrames in Apache Spark:
There are two different ways to create a Dataframe in Spark. First, using toDF() and second is using createDataFrame(). In this blog we will see how we can create Dataframe using these two methods and what’s the exact difference between them.
Read on for the explanation and examples.
Comments closedSteve Belcher explains how to configure a custom package repository in your environment:
One of the strengths of the R language is the thousands of third-party packages that have been made publicly available via CRAN, the Comprehensive R Archive Network. R includes several functions that make it easy to download and install these packages. However, in many enterprise environments, access to the Internet is limited or non-existent. In such environments, it is useful to create a local package repository that users can access from within the corporate firewall.
Your local repository may contain source packages, binary packages, or both. If at least some of your users will be working on Windows systems, you should include Windows binaries in your repository. Windows binaries are R-version-specific; if you are running R 3.3.3, you need Windows binaries built under R 3.3. These versioned binaries are available from CRAN and other public repositories. If at least some of your users will be working on Linux systems, you must include source packages in your repository.
There are some tools which help out with this, so read the whole thing.
Comments closedHasan Savran takes us through Jupyter notebooks in Cosmos DB:
Jupyter Notebooks are in everywhere in these days. You can write chunk of code and run it on a web application without worrying about compiler is a great feeling. C# has been little bit late to the party, but we started to see C# Notebooks lately too. Azure Cosmos DB announced their version if C# Notebook this week.
You can reach all notebook functionalities under the Data Explorer link, There are bunch of sample notebooks you will see under the Notebook link.
There are some limitations here, like needing to use the SQL API, but it’s an interesting approach to data access in Cosmos DB.
Comments closed