Real-time Data & Modern UXs: The Power and the Peril When Things Go Wrong
Tags: automation, microservices, ai, kubernetes
Published: 2024-06-10 16:53:08

Imagine a world where user experiences adapt to you in real time. Personalized recommendations appear before you even think of them, updates happen instantaneously, and interactions flow seamlessly. This captivating world is powered by real-time data, the lifeblood of modern applications.

But this power comes at a cost. The intricate [architecture behind real-time services](https://thenewstack.io/how-to-build-a-scalable-platform-architecture-for-real-time-data/) can make troubleshooting issues a nightmare. Organizations that rely on real-time data to deliver products and services face a critical challenge: ensuring data is delivered fresh and on time. Missing data or delays can cripple the user experience and demand resolutions within minutes, if not seconds.

This article delves into the world of real-time data challenges. We’ll explore the business settings where real-time data is king, highlighting the potential consequences of issues. Then I will introduce a novel approach that injects automation into the troubleshooting process, saving valuable time and resources, but most importantly mitigating the business impact when problems arise.

## Lags & Missing Data: The Hidden Disruptors Across Industries
Lags and missing data can be silent assassins, causing unseen disruptions that ripple through various industries. Let’s dig into the specific ways these issues can impact different business sectors.

![Disruptions in real-time data can cause business impact](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/9kah80qzxct5dcqneblm.jpeg)

### Financial markets

- **Trading:** In high-frequency trading, even milliseconds of delay can mean the difference between a profitable and losing trade. Real-time data on market movements is crucial for making informed trading decisions.
- **Fraud detection:** Real-time monitoring of transactions allows financial institutions to identify and prevent fraudulent activity as it happens. Delays in data can give fraudsters a window of opportunity.
- **Risk management:** Real-time data on market volatility, creditworthiness, and other factors helps businesses assess and manage risk effectively. Delays can lead to inaccurate risk assessments and potentially large losses.

### Supply chain management

- **Inventory management:** Real-time data on inventory levels helps businesses avoid stockouts and optimize inventory costs. Delays can lead to overstocking or understocking, impacting customer satisfaction and profitability.
- **Logistics and transportation:** Real-time tracking of shipments allows companies to optimize delivery routes, improve efficiency, and provide accurate delivery estimates to customers. Delays can disrupt logistics and lead to dissatisfied customers.
- **Demand forecasting:** Real-time data on customer behavior and sales trends allows businesses to forecast demand accurately. Delays can lead to inaccurate forecasts and production issues.

### Customer service

- **Live chat and phone support:** Real-time access to customer data allows support agents to personalize interactions and resolve issues quickly. Delays can lead to frustration and longer resolution times.
- **Social media monitoring:** Real-time tracking of customer sentiment on social media allows businesses to address concerns and build brand reputation. Delays can lead to negative feedback spreading before it’s addressed.
- **Personalization:** Real-time data on customer preferences allows businesses to personalize website experiences, product recommendations, and marketing campaigns. Delays can limit the effectiveness of these efforts.

###Manufacturing

- **Machine monitoring:** Real-time monitoring of machine performance allows for predictive maintenance, preventing costly downtime. Delays can lead to unexpected breakdowns and production delays.
- **Quality control:** Real-time data on product quality allows for immediate identification and correction of defects. Delays can lead to defective products reaching customers.
- **Process optimization:** Real-time data on production processes allows for continuous improvement and optimization. Delays can limit the ability to identify and address inefficiencies.

### Other examples

- **Online gaming:** Real-time data is crucial for smooth gameplay and a fair playing field. Delays can lead to lag, disconnects, and frustration for players.
- **Healthcare:** Real-time monitoring of vital signs and patient data allows for faster diagnosis and treatment. Delays can have serious consequences for patient care.
- **Energy management:** Real-time data on energy consumption allows businesses and utilities to optimize energy use and reduce costs. Delays can lead to inefficient energy usage and higher costs.
- **Cybersecurity:** Real-time data is the backbone of modern cybersecurity, enabling rapid threat detection, effective incident response, and accurate security analytics. However, delays in the ability to see and understand this data can create critical gaps in your defenses. From attackers having more time to exploit vulnerabilities to outdated security controls and hindered automated responses, data lags can significantly compromise your ability to effectively combat cyber threats.

As we’ve seen, the consequences of lags and missing data can be far-reaching. From lost profits in financial markets to frustrated customers and operational inefficiencies, these issues pose a significant threat to business success. Having the capability to identify the root cause, impact and remediate issues with precision and speed is an imperative to mitigate the business impact.

> _Causely  automatically captures cause and effect relationships based on real-time, dynamic data across the entire application environment. [Request a demo](https://www.causely.io/demo/?utm_source=dev.to&utm_medium=referral&utm_campaign=real-time) to see it in action._

## The Delicate Dance: A Web of Services and Hidden Culprits
Modern user experiences that leverage real-time data rely on complex chains of interdependent services – a delicate dance of microservices, databases, messaging platforms, and virtualized compute infrastructure. A malfunction in any one element can create a ripple effect, impacting the freshness and availability of data for users. This translates to frustrating delays, lags, or even complete UX failures.

![microservices environments are complex](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/45fuozu8g4t1fqucq8g5.png)

Let’s delve into the hidden culprits behind these issues and see how seemingly minor bottlenecks can snowball into major UX problems:

## Slowdown Domino with Degraded Microservice

- **Scenario:** A microservice responsible for product recommendations experiences high latency due to increased user traffic and internal performance degradation (e.g., memory leak, code inefficiency).
- **Impact 1:** The overloaded and degraded microservice takes significantly longer to process requests and respond to the database.
- **Impact 2:** The database, waiting for the slow microservice response, experiences delays in retrieving product information.
- **Impact 3:** Due to the degradation, the microservice might also have issues sending messages efficiently to the message queue. These messages contain updates on product availability, user preferences, or other relevant data for generating recommendations.
- **Impact 4:** Messages pile up in the queue due to slow processing by the microservice, causing delays in delivering updates to other microservices responsible for presenting information to the user.
- **Impact 5:** The cache, not receiving timely updates from the slow microservice and the message queue, relies on potentially outdated data.
- **User Impact:** Users experience significant delays in seeing product recommendations. The recommendations themselves might be inaccurate or irrelevant due to outdated data in the cache, hindering the user experience and potentially leading to missed sales opportunities. Additionally, users might see inconsistencies between product information displayed on different pages (due to some parts relying on the cache and others waiting for updates from the slow microservice).

## Message Queue Backup

- **Scenario:** A sudden spike in user activity overwhelms the message queue handling communication between microservices.
- **Impact 1:** Messages pile up in the queue, causing delays in communication between microservices.
- **Impact 2:** Downstream microservices waiting for messages experience delays in processing user actions.
- **Impact 3:** The cache, not receiving updates from slow microservices, might provide outdated information.
- **User Impact:** Users experience lags in various functionalities – for example, slow loading times for product pages, delayed updates in shopping carts, or sluggish responsiveness when performing actions.

## Cache Miss Cascade
- **Scenario:** A cache experiences a high rate of cache misses due to frequently changing data (e.g., real-time stock availability).
- **Impact 1:** The microservice needs to constantly retrieve data from the database, increasing the load on the database server.
- **Impact 2:** The database, overloaded with requests from the cache, experiences performance degradation.
- **Impact 3:** The slow database response times further contribute to cache misses, creating a feedback loop.
- **User Impact:** Users experience frequent delays as the system struggles to retrieve data for every request, leading to a sluggish and unresponsive user experience.

## Kubernetes Lag

- **Scenario:** A resource bottleneck occurs within the Kubernetes cluster, limiting the processing power available to microservices.
- **Impact 1:** Microservices experience slow response times due to limited resources.
- **Impact 2:** Delays in microservice communication and processing cascade throughout the service chain.
- **Impact 3:** The cache might become stale due to slow updates, and message queues could experience delays.
- **User Impact:** Users experience lags across various functionalities, from slow page loads and unresponsive buttons to delayed updates in real-time data like stock levels or live chat messages.

Even with advanced monitoring tools, pinpointing the root cause of these and other issues can be a [time-consuming detective hunt](https://www.causely.io/blog/devops-may-have-cheated-death-but-do-we-all-need-to-work-for-the-king-of-the-underworld/?utm_source=dev.to&utm_medium=referral&utm_campaign=real-time). The triage & troubleshooting process often requires a team effort, bringing together experts from various disciplines. Together, they sift through massive amounts of observability data – traces, metrics, logs, and the results of diagnostic tests – to piece together the evidence and draw the right conclusions so they can accurately determine the cause and effect. The speed and accuracy of the process is very much determined by the skills of the available resources when issues arise

Only when the root cause is understood can the responsible team make informed decisions to resolve the problem and restore reliable service.

## Transforming Incident Response: Automation of the Triage & Troubleshooting Process
Traditional methods of incident response, often relying on manual triage and troubleshooting, can be slow, inefficient, and prone to human error. This is where automation comes in, particularly with the advancements in Artificial Intelligence (AI). Specifically, a subfield of AI called [Causal AI](https://ssir.org/articles/entry/the_case_for_causal_ai) presents a revolutionary approach to transforming incident response.


![what troubleshooting looks like before and after causal AI](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/iqpq24pvmfkmsts741yg.jpeg)
 

Causal AI goes beyond correlation, directly revealing cause-and-effect relationships between incidents and their root causes. In an environment where services rely on real-time data and fast resolution is critical, Causal AI offers significant benefits:

- **Automated Triage:** Causal AI analyzes alerts and events to prioritize incidents based on severity and impact. It can also pinpoint the responsible teams, freeing resources from chasing false positives.
- **Machine Speed Root Cause Identification:** By analyzing causal relationships, Causal AI quickly identifies the root cause, enabling quicker remediation and minimizing damage.
- **Smarter Decisions:** A clear understanding of the causal chain empowers teams to make informed decisions for efficient incident resolution.

[Causely](https://www.causely.io/?utm_source=dev.to&utm_medium=referral&utm_campaign=real-time) is leading the way in applying Causal AI to incident response for modern cloud-native applications. Causely’s technology utilizes [causal reasoning](https://www.causely.io/blog/bridging-the-gap-between-observability-and-automation-with-causal-reasoning/?utm_source=dev.to&utm_medium=referral&utm_campaign=real-time) to automate triage and troubleshooting, significantly reducing resolution times and mitigating business impact. Additionally, Causal AI streamlines post-incident analysis by automatically documenting the causal chain.

Beyond reactive incident response, Causal AI offers proactive capabilities that focus on measures to reduce the probability of future incidents and service disruptions, through improved hygiene, predictions and “what if” analysis.

[The solution](https://www.causely.io/platform/?utm_source=dev.to&utm_medium=referral&utm_campaign=real-time) is built for the modern world that incorporates real-time data, applications that communicate synchronously and asynchronously, and leverage modern cloud building blocks (databases, caching, messaging & streaming platforms and Kubernetes).

This is just the beginning of the transformative impact Causal AI is having on incident response. As the technology evolves, we can expect even more advancements that will further streamline and strengthen organizations’ ability to continuously assure the reliability of applications.

If you would like to learn more about Causal AI and its applications in the world of real-time data and cloud-native applications, don’t hesitate to reach out.

You may also want to check out [this article by Endre Sara](https://www.causely.io/blog/eating-our-own-dog-food-causelys-journey-with-opentelemetry-causal-ai/?utm_source=dev.to&utm_medium=referral&utm_campaign=real-time) which explains how Causely is using Causely to manage its own SaaS service, which is built around a real-time data architecture.