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Category: Misc Languages

Migrating Oracle Exadata Workloads to Azure

Kellyn Pot’vin-Gorman shows the process of moving from an Exadata system to Oracle on Azure:

An Exadata is an engineered system-  database nodes, secondary cell nodes, (also referred to as storage nodes/cell disks), InfiniBand for fast network connectivity between the nodes, specialized cache, along with software features such as Real Application Clusters, (RAC), hybrid columnar compression, (HCC), storage indexes, (indexes in memory) offloading technology that has logic built into it to move object scans and other intensive workloads to cell nodes from the primary database nodes.  There are considerable other features, but understanding that Exadata is an ENGINEERED system, not a hardware solution is important and its both a blessing and a curse for those databases supported by one.  The database engineer must understand both Exadata architecture and software along with database administration.  There is an added tier of performance knowledge, monitoring and patching that is involved, including knowledge of the Cell CLI, the command line interface for the cell nodes.  I could go on for hours on more details, but let’s get down to what is required when I am working on a project to migrate an Exadata to Azure.

Click through for the process.

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Strongly Type Table-Valued Parameters

Jonathan Kehayias shows the benefits of using the MaxLength parameter when calling a table-valued parameter from .NET code:

We can see that the MaxLength for the string columns is set at -1, meaning they are being passed over TDS to SQL Server as LOBs (Large Objects) or essentially as MAX datatyped columns, and this can impact performance in a negative manner. If we change the .NET DataTable definition to be strongly-typed to the schema definition of the user-defined table type as follows and look at the MaxLength of the same column using a debug break:

This can be important, especially if you make a lot of calls or use fairly large TVP sizes.

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Installing .NET Core on a Raspberry Pi 4

Hasan Savran continues a series on Microsoft + Pi:

I have been writing about Azure IOT Hub and Raspberry Pi 4. So far, I bought a Raspberry Pi 4. I registered it as Azure IOT Edge device. Now, I am ready to write some code in Raspberry Pi. In this post, I will show you how install .NET Core 3.1 to Raspberry Pi so we can write some code to generate some data and push this data to Azure IOT Hub.

     First, you need to go to the .NET Core homepage to get the latest version’s url. Following page lists all .NET Core version, 3.1 was the latest when I was writing this blog. Pick the latest one from this list.

Another route might be to install Docker on your Pi.

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sqlcmd and Complex Passwords

Randolph West hits one of my bugbears with respect to the Windows command shell:

Using accepted good practice, the password and script were escaped with double quotes. (note that instancepassword and database are the replacement values in question):

sqlcmd -S instance -U maintenanceUser -P "password" -Q "dbcc checkdb ('database') with DATA_PURITY, NO_INFOMSGS;"

Unfortunately, one of the passwords started with a double quotation mark which led to the command failing for one specific Express Edition instance.

Read on to see the mess as well as a way to extricate yourself from the mess.

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Functional Java

Rishi Khandelwal lays out imperative versus functional Java with several examples:

As a java developer, you must have confused, whether should I move to the functional programming paradigm? What are the benefits it provide to us? People are talking about it everywhere. So let’s give it a try once and then you can decide whether you should go to the functional paradigm or not.

We will see the functional programming features one by one with the code examples and will compare it with the imperative way of java programming.

The snide part of me says “Hey, look, Java’s almost caught up to C# 3.0!” But that’s pushing it a little far. I think these functional pieces improve the language similarly to how they did C#, but if I were a regular Java developer, I’d probably look to Scala or Kotlin instead (says the guy who won’t shut up already about F#).

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Making an Executable JAR from sbt

Sakshi Gawande walks us through two problems you might hit when building Scala projects into JARs:

Now, before going to see the solution we first understand why this problem occurs. When you uses Simple Build Tool Command ‘sbt package’, it creates a jar file that includes the class files from your source code and also the content from your src/main/resources folder.

But there are mainly two things which is important to execute jar file, are not included

Read on for these two things.

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Web Scraping with F#

Jamie Dixon walks us through scraping a webpage using F#:

I need to go through all 8 pages of the grid and download the .pdfs that are associated with the “View Report” link. The challenge in this particular site is that they didn’t do any url parameters so there is no way to go through the grid via the uri. Looking at the page source, they are using ASP.NET and in typical enterprise-derpy manner, named their table “GridView1”

The way to get to the next page is to press on the “Next” link defined like this:

They over-achieved in the bloated View State for a simple page category though.

#Sigh

The code is straightforward and available as a Gist in the post.

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Dealing with NULLs in Java with SQL Server 2019

Niels Berglund covers changes in SQL Server Machine Learning Services around Java code execution:

In the null values post mentioned above, I mentioned that there are differences between SQL Server and Java in how they handle null. So, when we call into Java from SQL Server, we may want to treat null values the same way as we do in SQL Server.

I wrote about this in the SQL Server 2019 Extensibility Framework & Java – Null Values post mentioned above. However, that post was written before SQL Server 2019 CTP 2.5. In CTP 2.5 Microsoft introduced the Java SDK, and certain things changed. Amongst the things that changed is the way we handle nulls when we receive datasets from SQL Server in our Java code.

Read on to learn how it works today.

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A New Notebook Tool: Polynote

Jeremy Smith, et al, announce a new product:

We are pleased to announce the open-source launch of Polynote: a new, polyglot notebook with first-class Scala support, Apache Spark integration, multi-language interoperability including Scala, Python, and SQL, as-you-type autocomplete, and more.

Polynote provides data scientists and machine learning researchers with a notebook environment that allows them the freedom to seamlessly integrate our JVM-based ML platform — which makes heavy use of Scala — with the Python ecosystem’s popular machine learning and visualization libraries. It has seen substantial adoption among Netflix’s personalization and recommendation teams, and it is now being integrated with the rest of our research platform.

There are some nice pieces to it, especially around language interop.

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Scala Views

Girish Bharti takes us through a performance-tuning technique in Scala:

We all know the power of lazy variables in Scala programming. If you are developing the application with huge data then you must have worked with the Scala collections. Some mostly used collections are List, Seq, Vector, etc. Similarly, you must be aware of the power of Streams. The streams are a very powerful tool for handling the infinite flow of data and streams are powerful because of there lazy transformations. As we know most of the Scala collections are strict so applying an operation on immutable collections creates a new collection. The size of the collection can be huge in the big data world. So, what if you have to apply a lot of transformations to the collection? Is there a way to handle collections in a lazy way? What if you can find a way to apply operations on your usual collections lazily? In this blog, we will be talking about the Scala views and how to use them.

Read the whole thing.

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