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Curated SQL Posts

The Importance of Exploratory Testing

Thuy covers why exploratory testing is important:

Exploratory Testing is a software testing method that testers use to explore, find, and test features, bugs, or issues in an application freely and without the need for a prior testing plan. In exploratory testing, the tester will focus on freely working with the app as a real user and trying to find bugs and issues without following a specific test scenario.

Exploratory testing is a type of testing in which test cases are not created beforehand, but testers can test the system quickly. They can jot down ideas about what needs to be checked before performing the test. The focus of exploratory testing focuses more on testing as a “thinking” activity that explores new cases that do not follow the mainstream activity.

It’s amazing (and dismaying) how many bugs you can find simply by clicking around. The tricky part about exploratory testing is not actually finding bugs, but keeping track of your actions so that a developer knows how to fix the bugs you’ll inevitably find.

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Forms and Filters in Streamlit

I have a new video:

In this video, I extend the Streamlit app that we’ve been working on even more. We’ll convert a set of drop-down lists into a form, change the behavior of these drop-down lists, and add date picker logic.

Click through for the video, the code to date, and links to additional resources. I’m pretty happy so far with this series, and we’re about to kick it up to another level with the next video.

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A Comparison of Backup Types between SQL Server and Oracle

Sergey Gigoyan compares two sets of backups:

It is a well-known fact that backing up databases is one of the most common tasks for any database administrator (DBA), regardless of the relational database management system (RDBMS) used. However, the definition, naming, and structure of backup types can differ for each RDBMS. Therefore, if a DBA switches from one RDBMS to another, dealing with backup types can sometimes be very confusing. For instance, switching to Oracle databases after working with SQL Server databases for a long time can cause some difficulties. DBAs can work through these challenges by understanding the differences in database backup types.

Click through for the comparison. This a good reminder that, just because two things have the same name, doesn’t mean they do the same thing.

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Using Query Store on Azure Database for PostgreSQL

Marisa Mathews covers an episode of Data Exposed with Anna Hoffman and Grant Fritchey:

Capturing query metrics in PostgreSQL can be a challenge. Add in running that PostgreSQL database on Azure and things get even more challenging. However, Microsoft has a great addition to PostgreSQL when running in Azure Database, Query Store. This session will show the great query information available within the Query Store and how to query it.

Click through for a link to the video and additional links.

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Testing Postgres Configuration Parameter Performance Changes

Semab Tariq follows up on a prior blog post:

In a previous blog post, we configured an EC2 instance and installed PostgreSQL on it. After the initial setup, we ran pgbench to measure the initial TPS (transactions per second). Then, we tuned PostgreSQL and reran pgbench to see how much we could improve the TPS. To tune PostgreSQL, we adjusted various parameters based on the system’s available resources. In this blog, we will identify which of those parameters contributed the most to the performance improvements.

If you want to read the initial blog post, you can find it here: Key PostgreSQL Configuration Parameters for Enhanced Performance.

We will use the same instance size on AWS EC2 as before, which is t2.large.

I suppose it shouldn’t matter very much if you’re running a performance test over a significant timespan, though it was an interesting choice to use a burstable VM type for the test.

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Finding Long-Running Queries in Azure SQL DB

Kunal Rathi has a script for us:

Identifying and optimizing long-running queries is crucial for maintaining the performance and efficiency of your Azure SQL Database. Slow queries can lead to resource bottlenecks, affecting the overall user experience and system stability. In this post.we will see how to find long running queries in Azure SQL database. Whether you’re a database administrator or a developer, these insights will enable you to ensure your database runs smoothly and efficiently.

Click through for the script. It’ll work not only for Azure SQL DB, but also SQL Server and Azure SQL Managed Instance as well.

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Telegraf Performance Optimization

Riya shares a few tips on making Telegraf stream data more efficiently:

As businesses grow and their infrastructures become more complex, monitoring becomes a critical component of maintaining system health and performance. Telegraf, an open-source server agent for collecting and sending metrics and events from databases, systems, and IoT sensors, is widely used for this purpose. However, handling high volumes of metrics can strain resources and degrade performance. This blog will explore strategies for optimizing Telegraf’s performance when dealing with high-volume metrics.

Click through for an architectural overview and five things you can do to optimize performance.

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Calling a REST Endpoint from Azure SQL Database

Meagan Longoria makes a call:

External REST endpoint invocation in Azure SQL DB went GA in August 2023. Whereas before, we might have needed an intermediate technology to make a REST call to an Azure service, we can now use an Azure SQL Database to call a REST endpoint directly.

One use case for this would be to retrieve a file from blob storage. I explain how to set this up below.

Read on to learn more about the process, as well as a few ideas on when you might use it.

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A Glossary of Microsoft Fabric Terms

Reza Rad talks terminology:

There are a lot of similar terminologies and words when working with Fabric. We have Data Lake, Delta Lake, OneLake, and Lakehouse, and the list continues. I found it confusing for some to understand the differences between them. Although explaining these terms and their differences requires many different blog articles, having one place for a quick definition for each can be helpful. Hence, this Glossary.

I will keep this updated as we have added new features, tools, and workloads, and I welcome all your input regarding what keywords, terms and terminologies you would like to see added here.

Click through for the video, as well as a list of terms and brief definitions.

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The Challenges of Index Cleanup

Aaron Bertrand talks index maintenance of a different sort:

I’m not the first person to write about cleaning up unused or redundant indexes. You can read many articles about how non-clustered indexes are expensive to store and maintain, that they can occupy valuable space in memory, and that they can negatively impact the performance of the write portion of any SQL Server workload. There are great scripts out there already – like sp_BlitzIndex – that can help identify index issues without any grunt work. So, nothing new in this area.

But reinforcement can be beneficial.

Aaron raises some good points and explains why simply deleting any index with 0 reads is not necessarily a good idea. All bets are off, however, when you see 45 indexes on the same table with the same dta_ prefix.

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