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Month: May 2024

Dangling Images with Oracle 23ai Free Edition

Published 2024-05-10 by Kevin Feasel

Kellyn Gorman runs into an issue:

When I tried to connect via SQLPlus as SYSDBA, I received an EXTPROC error. It pointed clearly to the listener.ora file, which I discovered a path listed still to ora23c for the extproc, corrected it, started the Listener, but to no avail- an ORA-12547 error, realizing I had a make file issue on the binaries for Oracle.

I contacted Geral Venzl, who was very gracious and after some quick research, he came back that his folks said everything was fine with the images, so I thanked him and dug into the issue deeper.  I quickly discovered this problem could happen to others, so decided I better document here for anyone who does happen upon it.

Click through for the high-level explanation and a bit more detail on dangling images.

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Multi-Master Architecture in PostgreSQL

Published 2024-05-10 by Kevin Feasel

Semab Tariq describes a scale-out technique for Postgres:

Multi-master architecture has gained significant traction in the world of database management, offering a solution to traditional limitations in scalability, fault tolerance, and high availability. By allowing multiple nodes to operate as master, this architecture promises a more flexible and robust database system. However, along with these benefits come certain challenges, including data consistency, resource demands, and conflict resolution.

In this blog, we will explore what multi-master architecture is, delve into its key advantages, and discuss the potential drawbacks that come with its implementation. Also in our upcoming blogs, we will see how you can setup your first multi-master architecture with a tool called PGD (Postgres Distributed) by EnterpriseDB (EDB).

Read on to learn how it works, as well as some of the pros and cons of using it.

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Counting NA Values in R

Published 2024-05-09 by Kevin Feasel

Steven Sanderson counts what doesn’t exist:

Welcome back, R enthusiasts! Today, we’re going to explore a fundamental task in data analysis: counting the number of missing (NA) values in each column of a dataset. This might seem straightforward, but there are different ways to achieve this using different packages and methods in R.

Let’s dive right in and compare how to accomplish this task using base R, dplyr, and data.table. Each method has its own strengths and can cater to different preferences and data handling scenarios.

Read on for 3 1/2 separate methods.

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Classification with Random Forest

Published 2024-05-09 by Kevin Feasel

I have a new video:

In this video, I cover a powerful ensemble method for classification: random forests. We get an idea of how this differs from CART, learn the best possible metaphor for random forests, and dig into random search for hyperparameter optimization.

Click through to see the video in all its glory.

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Adding the Current Date and Time to a PySpark Data Frame

Published 2024-05-09 by Kevin Feasel

Gilbert Quevauvilliers wants to know what time it is:

How to add current DateTime to existing PySpark data frame in a Fabric Notebook

In the blog post below, I am going to describe how to add the current Date Time to your existing Spark data frame.

This is really useful when I am inserting data into a Fabric Lakehouse table, and I want to know when the data got inserted.

Read on for the answer.

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Building Workers in Azure Data Factory

Published 2024-05-09 by Kevin Feasel

Martin Schoombee continues a series on orchestration in Azure Data Factory:

We’re finally ready to dive into the Data Factory components that form part of the framework, and we’re going to work our way from the bottom up. To paraphrase the previous blog post, worker pipelines perform the actual work of either moving data (from source to staging) or executing a stored procedure that will load a dimension/fact table.

Although worker pipelines can contain any number of tasks you may need, my worker pipelines that move data from a source system into the staging area follow a similar pattern with at least the following activities:

Click through for that list, as well as more information.

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Editing the JSON of a Microsoft Fabric Pipeline

Published 2024-05-09 by Kevin Feasel

Dennes Torres makes a change:

A Fabric Pipeline uses JSON as source code. They are also saved in repositories as JSON.

We first idea we get is editing the pipeline in JSON format. We can copy the JSON and create new pipelines with small variations, making changes directly on the JSON.

However, at first sight we get disappointed, because the pipeline doesn’t allow the JSON to be edited. We have the option to view the JSON, but nothing else.

Read on to see how to tell the Fabric pipeline who’s boss.

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Avoid OPENJSON in Cursor Declarations

Published 2024-05-09 by Kevin Feasel

Joe Obbish has a public service announcement:

I found a few stored procedures with single plan cache entries over 500 MB of memory each according to sys.dm_exec_cached_plans while optimizing the plan cache in production. The stored procedures were relatively simple and both execution plans were less than 1 MB when saved as sqlplan files. I was confused, offended, and confused.

Read on to learn how this happened, see an example of it in action, and how you can avoid it in your own environment.

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Discriminated Unions in F# and C#

Published 2024-05-08 by Kevin Feasel

The NDepend blog talks discriminated unions:

In recent years, there has been a notable surge in inquiries from the C# community regarding language-level support for C# Discriminated Unions. What individuals truly desire is the ability to express complex data structures more elegantly and effectively.

This blog post offers a thorough introduction to simulating Discriminated Unions (DUs) in C# programs, highlighting their usefulness.

Discriminated Unions, also known as “or” types (because you select one from the set of options), are really powerful concepts in functional programming. And this article gives you a way to simulate the approach in C#, at least until C# catches up to F# in this aspect.

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Table Audits with Temporal Tables

Published 2024-05-08 by Kevin Feasel

Erik Darling is keeping an eye on you:

Sort of recently, a client really wanted a way to figure out if support staff was manipulating data in a way that they shouldn’t have. Straight away: this method will not track if someone is inserting data, but inserting data wasn’t the problem. Data changing or disappearing was.

The upside of this solution is that not only will it detect who made the change, but also what data was updated and deleted.

Read on to see how it works. I’ve used temporal tables for this type of scenario, and they’re fine for stable table designs.

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