Month: February 2024

In this article, we will analyze how composite index can help us with better query response time from DB. In SQL, a composite index is an index built on multiple columns within a table, instead of just one. 

If you go through the first part of this article SQL Query Performance Tuning in MySQL - Part 1, you would have a better understanding of setup.

Vector search looks for similar vectors or data points in a dataset based on their vector representations. However, pure vector search is rarely sufficient in real-world scenarios. Vectors usually come with metadata, and users often need to apply one or more filters to this metadata. That makes filtered vector search come into play.

Filtered vector search is becoming increasingly vital for intricate retrieval scenarios. You can apply a filtering mechanism to filter out the undesired vectors beyond the top-k/range of multi-dimensional embeddings.

The SQL database is always in one of the modes: online, offline, suspect, storing, recovery pending, and emergency. When the SQL database recovery fails, or the database becomes damaged or corrupted, it moves to suspect mode. When the database is marked as SUSPECT mode, the database is unavailable for user access. You can recover the database from the suspected state using different commands in SSMS. In this article, we’ll cover what causes the database to go to suspect mode and its recovery methods. Also, we’ll outline an advanced MS SQL repair tool to help you quickly restore the database from suspect mode without data loss.

Reasons for SQL Server Marked As” Suspect Mode”

The SQL Server database suspect mode indicates the recovery process has started but failed to finish. The database states may become suspect due to several reasons. Some of them are below:  

For many enterprises, an efficient legacy system integration can be a big challenge. And if you’re one of them, this blog is specifically curated for you. We’ve covered everything related to it — from the hurdles you might face to steps you need to take and how we can lend a hand in integrating those legacy systems seamlessly.

So, let’s get started!

The purpose of this use case is to explain how to define different RAML data types, define the business-related status code for request payload validation, define the single unit test case for multiple field validation with dynamic request payload, and how to use the parameterized test suite. 

With a parameterized test suite, we can write a reusable unit test case. It can help to write and test multiple scenarios based on different inputs with a single test case. This can be more useful when we are writing a test case to validate the request and response payload.

For business leaders and IT pros, Amazon Bedrock is a game-changer. It uses the capabilities of generative AI to make a big impact. Let’s look at a simple example to understand how powerful it can be.

Picture this: You are a business owner or an IT professional navigating the ever-changing landscape of technology. In your pursuit of innovative solutions that can propel your operations to new heights, you stumble upon a momentous change – Amazon Bedrock. This isn’t just another tool; it’s a revolutionary force poised to transform industries through the lens of generative AI.

Anyone is capable of having their caps lock key on at any given time without realizing so. Users can easily spot unwanted caps lock when typing in most inputs, but when using a password input, the problem isn’t so obvious. That leads to the user’s password being incorrect, which is an annoyance. Ideally developers could let the user know their caps lock key is activated.

To detect if a user has their keyboard’s caps lock turn on, we’ll employ KeyboardEvent‘s getModifierState method:

document.querySelector('input[type=password]').addEventListener('keyup', function (keyboardEvent) {
    const capsLockOn = keyboardEvent.getModifierState('CapsLock');
    if (capsLockOn) {
        // Warn the user that their caps lock is on?
    }
});

I’d never seen getModifierState used before, so I explored the W3C documentation to discover other useful values:

dictionary EventModifierInit : UIEventInit {
  boolean ctrlKey = false;
  boolean shiftKey = false;
  boolean altKey = false;
  boolean metaKey = false;

  boolean modifierAltGraph = false;
  boolean modifierCapsLock = false;
  boolean modifierFn = false;
  boolean modifierFnLock = false;
  boolean modifierHyper = false;
  boolean modifierNumLock = false;
  boolean modifierScrollLock = false;
  boolean modifierSuper = false;
  boolean modifierSymbol = false;
  boolean modifierSymbolLock = false;
};

getModifierState provides a wealth of insight as to the user’s keyboard during key-centric events. I wish I had known about getModifier earlier in my career!

The post Detect Caps Lock with JavaScript appeared first on David Walsh Blog.

If you’ve implemented a cache, message broker, or any other data use case that prioritizes speed, chances are you’ve used Redis. Redis has been the most popular in-memory data store for the past decade and for good reason; it’s built to handle these types of use cases. However, if you are operating a Redis instance, you should be aware of the most common points of failure, most of which are a result of its single-threaded design.

If your Redis instance completely fails, or just becomes temporarily unavailable, data loss is likely to occur, as new data can’t be written during these periods. If you're using Redis as a cache, the result will be poor user performance and potentially a temporary outage. However, if you’re using Redis as a primary datastore, then you could suffer partial data loss. Even worse, you could end up losing your entire dataset if the Redis issue affects its ability to take proper snapshots, or if the snapshots get corrupted.

Imagine the challenge of rapidly aggregating and processing large volumes of data from multiple point-of-sale (POS) systems for real-time analysis. In such scenarios, where speed is critical, the combination of Kafka and ClickHouse emerges as a formidable solution. Kafka excels in handling high-throughput data streams, while ClickHouse distinguishes itself with its lightning-fast data processing capabilities. Together, they form a powerful duo, enabling the construction of top-level analytical dashboards that provide timely and comprehensive insights. This article explores how Kafka and ClickHouse can be integrated to transform vast data streams into valuable, real-time analytics.

This diagram depicts the initial, straightforward approach: data flows directly from POS systems to ClickHouse for storage and analysis. While seemingly effective, this somewhat naive solution may not scale well or handle the complexities of real-time processing demands, setting the stage for a more robust solution involving Kafka.

One of the HTML elements that frequently comes into collision with CSS is the img element. As we learned in Request Metrics’ Fixing Cumulative Layout Shift Problems on DavidWalshBlog article, providing image dimensions within the image tag will help to improve your website’s score. But in a world where responsive design is king, we need CSS and HTML to work together.

Most responsive design style adjustments are done via max-width values, but when you provide a height value to your image, you can get a distorted image. The goal should always be a display images in relative dimensions. So how do we ensure the height attribute doesn’t conflict with max-width values?

The answer is as easy as height: auto!

/* assuming any media query */
img {
  /* Ensure the image doesn't go offscreen */
  max-width: 500px;
  /* Ensure the image height is responsive regardless of HTML attribute */
  height: auto;
}

The dance to please users and search engines is always a fun balance. CSS and HTML were never meant to conflict but in some cases they do. Use this code to optimize for both users and search engines!

The post How to Override width and height HTML attributes with CSS appeared first on David Walsh Blog.

Want to improve the look of your Instagram profile and make it more professional? Instagram Story Highlight Icons can help you do this.  Instagram Story Highlight Icons are used...

The post 24 Best Instagram Story Highlight Icons for 2024 appeared first on Onextrapixel.

Digital transformation is the goal of each business in the retail industry today. It is the tool used by various businesses across the world to understand and modify their business models. A digital transformation is a strategic approach through which businesses access a wider market. It is a process by which the company integrates new technologies into its operations. Different departments within the company can rely on the technology for data analytics, especially with the growing customer base. As a result, digital transformation gives companies a better avenue through which they can engage their customers to understand and meet their needs effectively.

Digital transformation is relevant in the current business environments due to the changing customer needs. Organizations are experiencing a very competitive business environment in which new technologies are giving customers convenience. Customers’ expectations, therefore, already reflect the changing technical landscape. Online shopping, tracking orders online, and personalized advertisements that suit customers’ preferences are some of the elements affecting customer experience. In the retail sector, changing customer expectations in the wake of new technology influences a company’s competitive advantage. Markedly, companies that invest massive resources in digital transformations have an advantage compared to those still relying on traditional business models.

Columnar storage is a commonly used storage technique. Often, it implies high performance and has basically become a standard configuration for today’s analytical databases.

The basic principle of columnar storage is reducing the amount of data retrieved from the hard disk.  A data table can have a lot of columns, but the computation may use only a very small number of them. With columnar storage, useless columns do not need to be retrieved, while with row-wise storage, all columns need to be scanned. When the retrieved columns only take up a very small part of the total, columnar storage has a big advantage in terms of IO time, and computation seems to get much faster.