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These tools do more than just display data; they help in quickly reading, analyzing, and making informed decisions, which is critically important for both developers and businesses. A well-designed dashboard can become a key element in forecasting trends and anticipating outcomes, impacting the efficiency and motivation of the team. Hello! My name is Lisa, and I have worked as a product designer in large IT companies and small startups. Among my experience was the creation of internal products that help monitor metrics and the state of service operations. Thanks to this, I have accumulated significant experience in dashboard development, and today I want to share this experience with you. Today, I want to inspire you to create useful and effective dashboards by sharing my approach and emphasizing the importance of UI design in this process. The sense of dashboard I want to start with three fundamental principles that should guide the creation of dashboards. These are precisely what you, as a product manager or product designer, should advocate for: One of the less obvious but critically important tasks in dashboard creation is the ability . Visualization should not only display numbers but also convey context, show cause-and-effect relationships, and identify trends. This requires a deep understanding of how data correlates with real business processes and user behavior. Dashboard developers should aim not only to present data but also to explain it, creating an intuitive and engaging narrative structure. to tell a story with data Often in the dashboard development process, you may encounter the issue of “expectation overload” — when users want to see too many features and data. It’s important to be able to manage these expectations, explaining limitations and focusing on the most important aspects that truly add value. This might require communication and persuasion skills, as well as the ability to and the impact of various features on the final product. objectively assess priorities Technical and organizational obstacles often become a serious challenge. For instance, integrating data from various sources can be a complex task due to format incompatibilities or API limitations. In such cases, it’s crucial to to find solutions that ensure smooth integration and reliable system performance. It’s also important to consider organizational aspects, such as training the team to use the new dashboard and implementing it into work processes. closely collaborate with developers Goals Every successful dashboard begins with a clear understanding of its goals and audience. At my current job, before starting the development of a dashboard, we conduct . This could be, for example, monitoring key performance indicators for executives or providing technical data for the development team. an analysis of the objectives it needs to achieve and the audience it is intended for To illustrate, in one of our projects, the goal was to create a dashboard for monitoring server operations. The audience consisted of technical specialists who required detailed, real-time information about the system’s status. The goals and audience defined the structure and design of the dashboard, making it maximally informative and user-friendly for the end users. Moreover, an effective dashboard helps reduce risks for the company by providing For instance, thanks to the instant display of server downtime data, the team can quickly respond to issues, minimizing downtime and potential losses. quick access to critically important information. Equally important is the impact of the dashboard on the team’s motivation and awareness. When the team sees a dashboard tailored specifically for them with up-to-date metrics every day, it enhances their engagement and provides a clear understanding of the current state of the project. This creates a sense of control and confidence in the decisions being made, which in turn contributes to an overall increase in work efficiency. Users Before beginning the design of a dashboard, it’s critically important to understand . In my practice, this starts with a deep dive into the world of the end-users — developers, project managers, data analysts, and so on. I conduct interviews, analyze workflows, and gather feedback to understand their daily tasks and challenges. who the primary users will be and what tasks they will be addressing For example, might need real-time system performance monitoring, while may require an overview of task completion timelines and resources. By identifying these key usage scenarios, I can ensure that the dashboard will not only be functional but also maximally useful for specific users. developers project managers Next, I link the functionality of the dashboard to the overall It’s not just about presenting data; it’s about how these data can help achieve specific objectives. For instance, if a business goal is to increase efficiency, then the dashboard should highlight metrics that track progress and identify bottlenecks. business goals of the company. In one of the projects, the goal was to improve customer service quality. For this, we developed a dashboard that displayed key indicators in real-time related to the processing of customer inquiries, allowing the team to respond promptly to issues and improve service quality. Thus, understanding both user needs and business goals not only displays data but actively contributes to achieving the organization’s ultimate goals. Strategy When we start defining the key functions and priorities of a dashboard, it’s important to find a balance between what users want and what is truly needed to achieve business goals. In my experience, I begin by creating a list of features that could be useful and then prioritize them based on their impact on users and business objectives. For instance, for a dashboard designed for monitoring application performance, key features might include displaying system response time, code errors, and server load levels. This is not just about having “nice-to-have” features; it’s about creating a “must-have” list that is guided by strategic goals and the real needs of the user. In developing a dashboard, I always think about how it will fit into the broader context of the product. 👾How will it interact with other features? 📎 How will it help the user better understand and use the product as a whole? For example, if a dashboard is designed for a CRM system, it should not only provide data but also simplify interactions with customers and improve processes for working with them. In this way, we make the dashboard . a valuable addition, not just an isolated tool Design. Prototyping. Iterations. I start with basic sketches that allow for a quick assessment of ideas and structure. These sketches are more than just drawings; they help me and my team visualize the dashboard concept and initiate a dialogue about how it should function. Once the concept is approved, I move on to creating more detailed prototypes. I develop prototypes that not only resemble the final product but also . This is a critically important step as it allows for testing with real users and gathering feedback that will influence subsequent design iterations. give an insight into user interaction When designing the dashboard interface, my main goal is to create an intuitive and effective user experience. Here are a few key principles I always keep in mind: 🎧 Clarity and Noise Reduction It’s important that users can quickly understand what is displayed on the dashboard. I aim to avoid information overload, focusing on key metrics and data necessary for the specific audience. 🧭 Consistency and Predictability I use consistent design elements and layouts so that users can intuitively understand how to interact with the dashboard. This includes uniformity in colors, fonts, and layouts. 💠 Interactivity and Feedback The dashboard should be informative as well as interactive. I include elements such as filters and dropdown menus that allow users to customize data display according to their preferences. 🍟 Accessibility and User-Friendliness I believe that design should be accessible to all users. This includes considering people with different abilities and preferences, as well as ensuring that the dashboard is user-friendly on various devices. Data Visualization and Information Architecture One of the key aspects of creating effective dashboards is the proper selection of data . Each data type requires its own approach to presentation to ensure the information is understandable and easily digestible. visualization methods For example, line charts are ideal for time series as they clearly illustrate the dynamics of changes. Bar or pie charts work well for comparing values across categories. Heat maps can be useful in visualizing complex datasets, allowing for quick identification of patterns and anomalies. It’s important to remember that visualization should be not only informative but also . I use colors and shapes to make data more accessible and comprehensible, while avoiding overloading with visual information. appealing Additionally, I focus on and ensuring a consistent flow of information, which aids users in easily navigating and finding the data they need. For instance, in a dashboard designed for sales analysis, I might group data related to customer demographics, sales figures, and product performance in distinct but interconnected sections. This way, a sales manager can quickly locate specific insights, such as the best-selling products in a particular demographic segment. grouping related data Another example is a financial dashboard where I categorize data into revenue, expenses, and profit margins. By doing so, financial analysts can effortlessly switch between different financial aspects without losing track of the overall financial health of the company. These thoughtful groupings and layouts not only improve the usability of the dashboard but also enhance the decision-making process by presenting a coherent narrative of the data. Testing and Iteration To identify usability issues and understand how well the dashboard meets users’ needs and expectations, we showcase our developments to real users. Based on the feedback collected, the process of iterations begins. The key is to be the design in response to the data received. Iterations can affect both minor details of the interface and larger aspects of structure and functionality. open to changes and ready to adapt In one of our recent projects on creating a dashboard for server performance monitoring, user testing became a pivotal moment. After the initial development of the dashboard, we conducted a series of tests with our development team, who were the primary users of the product. During these sessions, I observed how they interacted with the dashboard, . A key issue was that the developers had trouble interpreting the performance time series. The charts were overloaded with data, making it difficult to quickly analyze the situation. noting any difficulties or misunderstandings Based on this initial testing, we began the process of iterative improvements. Our first step was to simplify the charts by highlighting the most critical metrics and reducing visual clutter. We also added the ability to filter data by time intervals, allowing developers to focus more easily on specific periods. After implementing these changes, we conducted , this time with a broader group of users, including project managers. The feedback was significantly more positive: the charts became clearer, and the data filtering feature was particularly appreciated for its usefulness. a second round of testing Product Integration The next key stage is the integration of the dashboard into the overall product. In my project of creating a dashboard for server monitoring, one of the main tasks was to ensure its . This required close collaboration with the development team to ensure that the dashboard accurately collects and displays data in real-time. seamless integration with the existing system We utilized APIs to connect the dashboard to various data sources, ensuring high performance and reliability. Work was also done to optimize data loading so that the dashboard could respond quickly to user queries, even when processing large volumes of information. It’s crucial that the dashboard not only meets current needs but can also . To this end, we developed a modular architecture that allows for the easy addition of new features and the integration of additional data sources. Ensuring continuous support and updates for the dashboard was also an important part of the process, guaranteeing its stability and the relevance of its data. adapt to future changes and expansions of the system Creating dashboards is an integral part of product design. They not only provide valuable information for decision-making but also significantly enhance user convenience and efficiency. My experience has shown that a well-designed dashboard can have a substantial impact on business, improving processes and aiding in the achievement of goals. Tips and Tricks for Product Designers Start by figuring out what your users and . It’s like being a detective, but instead of solving crimes, you’re solving design puzzles. business really need Regularly . Be ready to iterate based on the feedback test the dashboard with real users Aim for in data visualization. Avoid information overload and focus on the key metrics. simplicity Develop dashboards with and potential data or process changes in mind. future scaling Postscript Alternative Ways to Create Dashboards: No-Code and Low-Code Solutions In the modern world of product design, there is an increasing number of . These no-code and low-code platforms offer convenient means for quick and efficient dashboard development, making them accessible not just for professional developers but also for business analysts, marketers, and other specialists. tools that allow for dashboard creation without deep programming knowledge Platforms: is  a powerful platform for creating interactive dashboards, particularly valuable for monitoring and analyzing time-series data. Ideal for real-time system monitoring, network analysis, and IoT applications, Grafana supports multiple data sources, including Prometheus, MySQL, PostgreSQL, and many others. This tool is highly flexible in visualization customization, allowing for detailed and adjustable dashboards. However, it may require deeper technical knowledge for setup and use compared to other no-code/low-code solutions. Grafana is one of the most popular platforms for data visualization. It offers robust tools for creating a variety of dashboards with the ability to connect to different data sources. Tableau makes it easy to integrate data from various sources and create interactive dashboards. It is ideal for integration with other Microsoft products, such as Excel and Azure. Microsoft Power BI is a free solution from Google that allows for creating customizable dashboards. Particularly well-suited for working with data from Google Analytics, Google Ads, and other Google services. Google Data Studio : This tool provides flexible options for dashboard creation and business analytics. It enables not just data visualization but also in-depth analysis. Looker Using no-code and low-code platforms has its advantages, including . However, one should consider their limitations in terms of customization and integration with some complex systems. Therefore, it’s important to assess the needs of the project and choose a tool that best meets these requirements. development speed, ease of use, and flexibility in design alteration

Creating the Perfect Dashboard: Visualizing Your Metrics

নিখুঁত ড্যাশবোর্ড তৈরি করা: আপনার মেট্রিক্স ভিজ্যুয়ালাইজ করা

Erstellen des perfekten Dashboards: Visualisierung Ihrer Metriken

Creando el panel perfecto: visualizando tus métricas

Créer le tableau de bord parfait : visualiser vos métriques

परफेक्ट डैशबोर्ड बनाना: अपने मेट्रिक्स को विज़ुअलाइज़ करना

完璧なダッシュボードの作成: 指標の視覚化

완벽한 대시보드 만들기: 측정항목 시각화

Criando o painel perfeito: visualizando suas métricas

Создание идеальной информационной панели: визуализация показателей

Mükemmel Kontrol Panelini Oluşturma: Metriklerinizi Görselleştirme

Tạo Trang tổng quan hoàn hảo: Trực quan hóa các chỉ số của bạn

创建完美的仪表板：可视化您的指标

Learn about the key performance metrics that mobile teams should track in their mobile apps so they can deliver better user experiences. In order to build the best experiences, engineers need the best data available. Mobile makes this challenge more complex, with variables that include device types, different operating systems, and connectivities, to name just a few. With a growing number of tools that all offer varying levels of visibility into your mobile app's health, performance, and stability, it can be difficult to know exactly which metrics you should be tracking. In this post, we’ll outline the most important performance metrics you need to track and how they help you get to the root cause of issues more quickly so you can build better mobile experiences. 1. Startup time Mobile users typically check their apps on the go and are accustomed to instantaneous results. Therefore, they won’t sit around and wait for the app to load. For example, if the Uber app takes too long to load, the user will probably switch to the Lyft app instead. In addition, users who had a poor experience in the Uber app and a decent experience in the Lyft app are much more likely to become a loyal Lyft user. Now, not only have you lost revenue from that user’s session, but your cost per acquisition and churn increased, not to mention the LTV that particular customer could have brought the company. Therefore, is essential to its success. ensuring your app’s startup time meets user expectations However, if the mobile team only has access to the , they probably miss key changes and respond reactively rather than proactively. average startup time For example, let’s say a company is launching its app into a new market, and the overall feedback is negative. The average startup time shows that the app is a few milliseconds longer than it was before the launch, but it doesn’t indicate that it became dramatically slower during the launch. So there must be another problem, right? Unfortunately, because the percentage of users in the new market is only a fraction of the overall user base, it makes sense that even an abysmal startup time in the new market would only minimally impact the total user base’s startup time. Instead, the team needs to be able to segment the data to better understand how startup time impacts the business. For example, how are high-value users suffering from slow startups? Are users in a new market experiencing worse performance? Are certain devices experiencing slower startups? This data puts your company back in control of the situation and omits the guesswork in time-sensitive scenarios that impact your revenue. To learn more, check out our eBook on . how to improve mobile app startup time 2. Crash rate are a surefire way to anger customers and for a good reason! It’s essentially the equivalent of a customer walking into a brick-and-mortar store where the staff kicks them out the door halfway through their shopping. Crashes This is a major issue for the company’s brand for two reasons: It as customers feel their time is disrespected. damages the reputation of your brand It hurts your revenue as customers can’t complete their immediate transaction, and you lose the lifetime value of that customer if they decide to switch to a competitor. Here are just a few examples from various industries where a crash directly translates to lost revenue: If an e-commerce app crashes during checkout, the customer won’t be able to make their purchase and likely won’t be back. E-commerce Apps: : If a POS system crashes during a live event, none of those live customers will be able to make a purchase or enter the venue. POS Systems : If a smart device, such as a toothbrush, crashes during the setup process, it’s very likely the customer will return the product. Smart Device Apps However, while tracking the average crash rate is a good start, it’s . still not enough to understand how crashes impact your business For example, if the current crash rate is just 0.5%, you might not see the need to drill deeper. However, what if all of the crashes that occur happen at the checkout screen? That tiny percentage could be cheating the business out of significant revenue. So, in addition to looking at the major metric numbers, it’s also important to have data that shows patterns in the crash rate. Specifically, how are various high-value areas in your app performing? Which devices tend to experience the most crashes? How is the app performing for high-value users? Are there any regions that experience particularly poor crash rates? And if so, should the app be fixed or removed from those regions? By segmenting crash details, your team can better prioritize fixing issues. 3. ANR rate are typically described as freezes or glitches. Application Not Responding (ANR) errors Essentially, if the main thread is blocked, the applications cannot run effectively. Therefore, the user cannot proceed, which can have a major impact on your business. For example, a retailer we worked with had an issue with ANRs. This issue was causing startup times to increase by almost 60%, translating to an estimated revenue loss of $6.5 million per year. , the engineering team was able to quickly address the issue and reclaim that lost revenue. With the right data Beyond revenue, ANRs can also in the Google Play Store and make it less visible to new customers. negatively impact an app’s rankings To effectively track ANRs, you can look at the stack trace and see how users responded to the issue. From there, the mobile team can prioritize what to fix first based on how many people leave at various thresholds, where high-value users are most affected, and which device types and screens are most affected by ANRs. To learn more, check out . this post on investigating Android crashes like ANRs 4. Region metrics Tracking region metrics is also very important as users in different regions have different devices and different connectivities. This can have a major impact on the business for several reasons. First, regions differ in their importance to a business’s bottom line. For example, perhaps only a portion of your users are in Singapore, but they might make up a large portion of your total annual revenue. Therefore, checking region metrics on a granular level will spotlight opportunities to improve the app, particularly for high-value users. Tracking segmented region metrics is also essential when launching into a new region. For example, suppose you expand into Australia, yet that geographic area only makes up 5% of all users on launch. In that case, it won’t impact median/average metrics enough to allow the team to effectively track performance. It’s also important to take into account cultural differences, and with a tool that provides region-specific metrics, the team can test specific aspects of the app just for that region. For example, an e-commerce checkout screen that converts well in the United States may not convert as well in Dubai. In addition, granular region metrics make it easy to roll out new features slowly. For example, the team can roll out a new feature in a smaller region and see how it performs. If it performs well, roll it out to more and more regions with higher-value users. This data can guide your team in answering critical questions like: Do we need to build a new app for this region? How is this new launch performing compared to launches in other regions? Which regions are the most problematic? And should we stop serving them altogether? ​​How are various critical areas of the app performing in one region versus another? 5. Session duration Another key metric to keep an eye on is session duration, as it signals how long users are using your app. If the average session duration changes dramatically in a week, it’s a pretty good hint that there is an issue with the app. For example, if you have a gaming app and notice the average session duration dropped from 15 minutes to 5 minutes, there’s a good chance that users had a poor experience. With this clue, you can ask questions like: Did we ship a bad release? Are users less engaged with the app? Is there a corresponding change in another metric that can help explain the change in session duration? Tracking session duration also allows the team to investigate patterns among the affected individual sessions and discover the root cause of issues. By analyzing session duration in parallel with other metrics, mobile engineers can draw a clearer picture of which issues are most intrusive to users. For example, an e-commerce store might have an OOM issue on the main scrolling feed of products that correlates strongly with a shorter session duration, whereas slow network calls on another screen have little to no correlation with the session duration. Therefore, tracking session duration is a great way to prioritize which issues should be fixed first as it directly reveals reduced user engagement. 6. Churn/retention rate It costs a lot more to acquire a new customer than it does to keep a current customer happy, and a leading cause of churn in mobile app users is a poor user experience. Downloading a new app only takes a few seconds, so if an app’s experience interrupts the user in a way that takes more than a few seconds of their time, don’t expect them to stick around. Therefore, track not only the overall churn and retention rate but also the churn and retention rates of segments of the user base (by devices, connectivities, regions, etc.). This will illuminate various opportunities to improve and prioritize retention. For example, you may find that while the churn rate is very high in one particular region, that region has few high-value users. Therefore, you may decide to invest engineering resources somewhere else where they will translate to larger business outcomes. Tracking churn and retention is also a great way to understand how users respond to new releases and various experiments. If there is a correlation between high churn and a new feature update, that’s a strong indicator that the team needs to roll back that feature update. Detailed data will allow the team to track the churn of various segments (such as specific regions or devices) to which the update is rolled out. Without segmented data, it will be difficult to see the impact that various feature updates have on small testing groups. Therefore, only once the feature is rolled out to a large group of users (and presumably caused many monthly active users to leave) will it become apparent that the feature rollout was premature. 7. User termination rate While some user terminations are nothing more than a user decluttering their phone, many terminations occur because the app has frozen, and the user has no choice but to swipe up and terminate the session. Of course, a user who is forced to terminate their session will likely be disgruntled and choose a competitor’s app instead. To prevent this, monitoring the average user termination rate is an excellent indicator of potential issues that could lead to churn, including: Failed ad loads are causing freezes Slow or broken user flows Failing purchases Server outages causing login errors Excessive media loading that causes slowdowns In addition to providing an overview of the average user termination rates, mobile teams need to know the source of every poor user experience. Therefore, one of the key features we built into Embrace is the ability to see . which screens cause frustrated users to abandon your app Therefore, rather than wasting valuable time and losing sales while engineers guess where the terminations are occurring, the mobile team is immediately directed to the issue so that they can fix the problem as efficiently as possible. 8. Timing of key user actions There are likely a few user actions within your app that absolutely must work 100% of the time. For example, if an office offers keyless entry, that feature must work every time. Otherwise, people may not be able to enter the office without calling additional support. Therefore, select a few key user actions and add them to the list of performance metrics the mobile team is tracking. In many cases, customer reviews don’t say where they experienced an issue in the app, so tracking specific user actions is a great way to catch issues that otherwise aren’t immediately apparent. This also helps you answer questions like: How many users experience issues in these critical areas of the app? Is there a direct correlation between issues in specific areas of the app and churn? How long do users try before giving up and abandoning the app? For example, one of our customers noticed that about 1% of all purchase attempts resulted in purchase failures. However, both of the associated network calls were resolving successfully, so there were no obvious errors to inspect. Therefore, they began tracking the exact moment a customer would make a purchase and found that the two network calls were happening out of order in 1% of purchase attempts, which resulted in a failed purchase. Though customers had been complaining about this issue, the mobile team couldn’t pinpoint the root cause without knowing the timing, outcome, and order of all events within the affected sessions. High-fidelity user experience data was vital in helping them reclaim 1% of their total sales. For a company doing $10 million in annual sales, that’s $100,000 that would otherwise be lost each year! 9. Memory usage It’s important to track your app’s memory consumption to identify any potential memory leaks. The efficiency of your app’s memory usage directly relates to how responsive your user experience is. This is why monitoring and optimizing memory usage plays a crucial role in delivering a seamless user experience. You can avoid issues with your app’s memory usage by: Optimizing your codebase and pinpointing areas where memory is unintentionally retained causes an increase in memory usage. Tracking your app’s memory consumption on a regular basis, noting any changes after new releases. Carefully monitoring memory-intensive operations like loading large images or processing large files and creating for abnormal spikes or sustained high memory usage. alerts A fine-tuned strategy regarding your app’s memory consumption creates responsive, stable, and effective apps that your users will love. 10. Connectivity Mobile apps operate in diverse environments with varying , including 3G, 4G, 5G, Wi-Fi, and sometimes limited or unstable connectivity. network conditions This is why it’s important to recognize the challenges posed by these varying conditions to create great user experiences. Indicators of poor network performance include: Latency and slow response times. Bandwidth usage. Inefficient API calls. Device-specific offline capabilities. For example, is an agricultural app that allows farmers and agronomists to document their findings while they’re in the field with their peers and colleagues. Farm Dog The app would crash often when network response times were exceptionally slow and when devices couldn’t determine whether they were connected. They noticed that Google Maps response times between 18-22 seconds when they should be only a few seconds. Without the proper tooling, they would need to use convoluted workarounds to solve issues using a proxy to simulate a bad network connection. However, with higher-fidelity data, they can see the exact conditions users have in the field, including: Network calls across device types, app versions, Wi-Fi, and cellular. Insight into 4xx and 5xx error trends across common domains to identify problematic pathways. The broken endpoints that prevent your users from starting up the app, loading key content, or completing crucial transactions. The duration of every network call from the client side reveals hidden points of latency. Armed with this information, the Farm Dog team simulated the issues their users faced, easily identified the problematic conditions, and fixed them. Set your mobile team up for success If you care about mobile experiences, then you need the data that enables your team to see all of the metrics mentioned above. Embrace helps you build better mobile experiences by giving you just that data, making your engineers more efficient and less bogged down by tedious work. Learn more about and download report to learn about key app frustrations according to users. Embrace The State of Mobile Experience Author: Colin Contreary Also published . here

10 Mobile App Performance Metrics You Need To Start Tracking

10টি মোবাইল অ্যাপ পারফরম্যান্স মেট্রিকস যা আপনাকে ট্র্যাকিং শুরু করতে হবে

10 Leistungskennzahlen für mobile Apps, die Sie benötigen, um mit dem Tracking zu beginnen

Diez métricas de rendimiento de aplicaciones móviles que necesita para comenzar a realizar un seguimiento

10 mesures de performances des applications mobiles dont vous avez besoin pour commencer le suivi

10 मोबाइल ऐप प्रदर्शन मेट्रिक्स जिनकी आपको ट्रैकिंग शुरू करने के लिए आवश्यकता है

追跡を開始する必要がある 10 のモバイル アプリのパフォーマンス指標

추적을 시작하는 데 필요한 10가지 모바일 앱 성능 지표

10 métricas de desempenho de aplicativos móveis que você precisa para começar a monitorar

10 показателей производительности мобильных приложений, которые нужно отслеживать

Takibe Başlamak İçin İhtiyacınız Olan 10 Mobil Uygulama Performans Metriği

10 số liệu hiệu suất ứng dụng di động bạn cần để bắt đầu theo dõi

您需要开始跟踪的 10 个移动应用性能指标

The rapid upsurge in cryptocurrency segment activity might overheat the market. It could also bring unexpected pressure to Web3 infrastructure. In December 2023, blockchain-as-a-service teams started adjusting their offerings to help dApps meet increased traffic. DeFis Are Back in Business: TVL Up by 50% in Two Months, Solana, Cardano Lead the Surge In October-December, 2023, the global ecosystem of decentralized finances (DeFis) impressed traders with a massive comeback. Some critical metrics of the DeFi scene are targeting pre-FTX collapse levels. To start with, aggregated Total Value Locked (TVL) across all mainstream smart contracts platforms - Ethereum, BNB Chain, Tron, Solana, Cardano, Optimism, Arbitrum, etc. - soared by 50% in less than two months. In mid-October, this metric was attempting to conquer $40 billion. Two months later, it almost reached $60 billion, as per data. DefiLlama High-performance smart contracts platform Solana (SOL) seems to benefit the most: its TVL increased by a whopping 153% amidst the euphoria around upcoming potential retroactive airdrops on the protocol. The frenzy was triggered by the airdrop of Lido competitor Jito (JTO) which unexpectedly distributed four-digit bonuses to the participants. Cardano blockchain registers an 186% TVL upsurge in the corresponding period: depositors locked over $520 million in its dApps which is a record-breaking level for the entire history of Cardano as a programmable blockchain. The largest DeFi platform, Ethereum, slightly “outperforms” the segment with its 57% upsurge in TVL. Its largest DeFis, Lido, Maker, Aave, and Uniswap, registered a double-digit TVL upsurge in November-December. Simply put, that can’t be explained by a coincidence or “dead cat bounce”: the bullish market in DeFi is gaining steam. All crypto segment actors - traders, investors, infrastructure providers - should be prepared to get the maximum of opportunities it is set to unlock. Stress Tests for the RPC Endpoints’ Reliability Typically, such events are accompanied by infrastructure overload issues. Back in the 2020-2021 bullish market, centralized crypto exchanges - even Tier 1 platforms like Coinbase, Kraken, and Binance - went offline when Bitcoin price surged past crucial levels - $10,000, $15,000, and so on. Even fully decentralized services struggle with increased traffic. Amidst the 2020 DeFi craze, Ethereum-based apps worked much slower than usual, let alone $10-$50 gas for conversion. During the ongoing market rebound, Bitcoin transaction confirmation time surged by 70-100%, as per Blockchain.com data. Remote procedure call (RPC) endpoints, “points of entry” for dApps connected to the blockchain, are under pressure in such periods. In January 2023, amidst the collapse of the cryptocurrency market, all public RPC endpoints by Solana Foundation went offline. Users were forced to migrate to private RPC node providers, i.e., services that are running RPC nodes (actually, blockchain full nodes) themselves, and connect dApps to them via API. The point is that the stability and performance of RPC endpoints are of lethal importance for the functionality of DeFis. Periods of market euphoria only amplify this dependency. Lifting Rate Limit Barriers on Shared Infrastructure: Case of Private RPC Node Provider Increasing the accessibility limits for decentralized applications using shared infrastructure is one of the ways RPC node providers can help clients handle increased traffic. When it comes to premium service packages, all providers guarantee the best possible rate limit (number of requests can be processed per second), but such options can be rather costly for early-stage dApps. As such, top-tier RPC node providers are raising rate limits for the tariffs with limited throughput. For instance, , one of the most popular and innovative Web3 infrastructure providers in DeFi, increased its rate limit for shared nodes from 60 to 200 requests per second, i.e., by over 230%. GetBlock What does this actually mean for dApps? With the “RPS boost”, they can send 230% more requests to blockchains without delay. Say, its infrastructure won’t notice even a 3x increase in traffic acting just like previously and paying the same price per request. To prevent the endpoints from being attacked by spammers, GetBlock introduces this offering only for paid shared node plans. Users of a free plan with 40,000 requests per 24 hours can upgrade their dashboards. Also, for maximum safety, GetBlock only increases rate limits for those users who migrated toward API tokens, a new authorization model. New customers receive these tokens automatically while the existing ones are invited to claim them in their accounts. According to internal research, with this offer activated, becomes the most resource-effective provider when it comes to shared nodes with 200 requests per second bandwidth. GetBlock Wrapping up, the rate limits increase initiative seems to be a reasonable way to help dApps to kickstart new users on-boarding in Q4, 2023.

DeFi Is Back: Metrics Rocketing and RPC Node Providers Address Traffic Challenges

DeFi ফিরে এসেছে: মেট্রিক্স রকেটিং এবং RPC নোড প্রদানকারীরা ট্র্যাফিক চ্যালেঞ্জের সমাধান করে

DeFi ist zurück: Metriken schießen in die Höhe und RPC-Knotenanbieter bewältigen Traffic-Herausforderungen

DeFi está de vuelta: las métricas se disparan y los proveedores de nodos RPC abordan los desafíos del tráfico

DeFi est de retour : les fournisseurs de métriques et de nœuds RPC résolvent les problèmes de trafic

DeFi वापस आ गया है: मेट्रिक्स रॉकेटिंग और RPC नोड प्रदाता ट्रैफ़िक चुनौतियों का समाधान करते हैं

DeFi が帰ってきた: メトリクスロケットと RPC ノードプロバイダーがトラフィックの課題に対処

DeFi가 돌아왔습니다: 메트릭스 로켓팅 및 RPC 노드 공급자가 트래픽 문제를 해결합니다.

DeFi está de volta: Metrics Rocketing e provedores de nós RPC enfrentam desafios de tráfego

DeFi возвращается: рост показателей и поставщики узлов RPC решают проблемы с трафиком

DeFi Geri Döndü: Metrikler Hızla Yükseliyor ve RPC Düğüm Sağlayıcıları Trafik Sorunlarını Ele Alıyor

DeFi đã trở lại: Các nhà cung cấp nút RPC và tên lửa đo lường giải quyết các thách thức về lưu lượng truy cập

DeFi 回归：Metrics Rockets 和 RPC 节点提供商解决流量挑战

Distributed systems contain a lot of moving parts, and it is critical to monitor telemetry data such as metrics, logs and traces to gain visibility and allow teams to determine the root cause of an issue. The goal of many observability initiatives is to increase availability and performance. Grafana Labs makes one of the most widely used open source observability stacks (Grafana for visualization, Loki for logs, Mimir for metrics, Tempo for traces, Alertmanager for alerts), and sells and . Grafana Cloud Grafana Enterprise Grafana Mimir is an AGPLv3 licensed open source software project that, when coupled with MinIO, provides scalable, long-term storage for Prometheus metrics. Mimir was built using a microservices-based architecture that is horizontally scalable. Each microservice is referred to as a component, and Mimir runs as a single binary made up of these components. Most components are stateless and do not require any data to be persisted between restarts. When you combine Mimir and MinIO you produce an infrastructure that is particularly well suited to meet the needs of enterprise cloud-native observability with: MinIO’s combination of scalability and high-performance puts every workload, no matter how demanding, within reach. MinIO is capable of tremendous performance - a achieved 325 GiB/s (349 GB/s) on GETs and 165 GiB/s (177 GB/s) on PUTs with just 32 nodes of off-the-shelf NVMe SSDs. Performance: recent benchmark MinIO knows no limit as it scales horizontally through server pools. Each server pool is an independent group of nodes with their own compute, network and storage resources. In multi-tenant configurations, each tenant is a cluster of server pools in a single namespace, fully isolated from the other tenants’ server pools. Capacity can easily be added to an existing system by pointing MinIO at a new server pool and MinIO automatically prepares it for and places it in service. Scale: If you’d rather put Mimir to use than spend hours fiddling with object storage, then you can’t find a more straightforward solution than MinIO. MinIO only serves objects - it is all we do and we’re obsessive about being the best. Other products combine object and file storage, which results in multiple storage layers that introduce latency to Mimir’s query response times and create a more complex architecture with greater chance of failure.. Simplicity: MinIO, born in the cloud, runs anywhere on any combination of hardware and software. A rich set of integrations means that MinIO transparently plugs into existing security and management tools and services to centralize identity management, encryption key management, and more. MinIO provides S3 API compatible object storage on baremetal or any version of Kubernetes - including GKE, EKS, AKS, Red Hat OpenShift, VMware Tanzu - and efficiently synchronizes data using . Multi-Cloud: active-active replication Some of the core strengths of include: Grafana Mimir Grafana Mimir’s extensive documentation, tutorials, and deployment tooling make it quick to get started. Using its monolithic mode, you can get Grafana Mimir up and running with just one binary and no additional dependencies. Once deployed, the best-practice dashboards, alerts, and playbooks packaged with Grafana Mimir make it easy to monitor the health of the system. Easy to install and maintain: You can run Grafana Mimir’s horizontally-scalable architecture across multiple machines, resulting in the ability to process orders of magnitude more time series than a single Prometheus instance. Internal testing shows that Grafana Mimir handles up to 1 billion active time series. Massive scalability: Grafana Mimir enables you to run queries that aggregate series from multiple Prometheus instances, giving you a global view of your systems. Its query engine extensively parallelizes query execution, so that even the highest-cardinality queries complete with blazing speed. Global view of metrics: Grafana Mimir uses object storage for long-term data storage, allowing it to take advantage of this ubiquitous, cost-effective, high-durability technology. It is compatible with multiple object store implementations, including AWS S3, Google Cloud Storage, Azure Blob Storage, OpenStack Swift, as well as any S3-compatible object storage. Cheap, durable metric storage: Grafana Mimir replicates incoming metrics, ensuring that no data is lost in the event of machine failure. Its horizontally scalable architecture also means that it can be restarted, upgraded, or downgraded with zero downtime, which means no interruptions to metrics ingestion or querying. High availability: Grafana Mimir’s multi-tenant architecture enables you to isolate data and queries from independent teams or business units, making it possible for these groups to share the same cluster. Advanced limits and quality-of-service controls ensure that capacity is shared fairly among tenants. Natively multi-tenant: to be the most scalable, most performant open source time series database available. Mimir easily scales to 1 billion metrics and beyond, with blazing fast query performance that is up to 40x faster than Cortex, the TSDB Mimir was built to replace. Cortex has been a CNCF project since 2018 and is widely used to store Prometheus metrics. When creating Mimir, Grafana Labs laid the groundwork for enterprise-ready observability with AGPLv3 licensing, access controls, and improved performance, scalability and availability. Mimir was developed Grafana Labs has a goal for Mimir: To be the best scalable time series database regardless of metrics format. Enterprises should be able to consume Prometheus metrics (and other metrics as other vendors collaborate) without modifying existing code. Now that we’ve learned what Mimir is, let’s run through an introductory tutorial. Grafana Mimir and MinIO Tutorial This tutorial draws on an existing tutorial, to show how easy it is to get started with Mimir using Docker. Play with Grafana Mimir Create a copy of the Grafana Mimir repository using the Git command line: git clone https://github.com/grafana/mimir.git Navigate to the tutorial directory: cd mimir/docs/sources/tutorials/play-with-grafana-mimir/ Start MinIO, Mimir, Prometheus, Grafana and NGINX docker compose up This will bring up the following: Grafana Mimir - three instances of Mimir for high availability. Multi-tenancy is enabled (tenant ID is demo). Prometheus - scrapes Mimir metrics, then writes them back to Mimir so they’re available MinIO - S3-compatible software defined persistent storage for blocks, rules and alerts Grafana - includes pre-installed datasource to query Mimir and pre-installed dashboards for monitoring Mimir Load Balancer - NGINX-based load balancer that exposes Mimir instances The following ports are used: Grafana: http://localhost:9000 Grafana Mimir: . http://localhost:9009 The components of our tutorial go together like this: If you want to dig deeper into any configurations used in this tutorial, please see the YAML files saved to ~/mimir/docs/sources/tutorials/play-with-grafana-mimir/config/ To access Grafana, launch a browser and open . You’ll use Grafana to view dashboards that display the status of the Mimir cluster. The dashboards query Mimir for the metrics they display. From the menu on the top left, click Dashboards, then Browse to see the dashboards that have been preloaded for the tutorial. These dashboards are from the Grafana Mimir mixin, which packages together Grafana Labs’ best practice dashboards, recording rules and alerts for monitoring Mimir. http://localhost:9000 It typically takes 3-5 minutes after we launch our tutorial containers for metrics to be displayed in Grafana dashboards. We’re also running Mimir without an ingress gateway, query-scheduler or memcached, so the related dashboards will be empty. At this early stage of learning Mimir, start by browsing the dashboards for writes, reads, queries and object store. For example, the object store dashboard shows operations that have taken place since we brought Mimir up. Configure a Recording Rule Recording rules are a mechanism that precomputes frequently needed or computationally costly expressions and saves the result as a new set of time series. Follow these instructions to configure a recording rule in Mimir using Grafana. This recording rule will display the number of Mimir instances that are up and reachable to be scraped. Once the rule is created, it will be available for querying and inclusion in dashboards. sum:up Open the Alerting menu from the left toolbar and click “New alert rule”: Enter the following to configure the recording rule: Choose Mimir or Loki recording rule Configure the following: Rule name = sum:up Choose Mimir in the Select data source field Namespace = example-namespace Group = example-group Query Expression = sum(up) Choose Save and Exit in the upper right corner. To verify that your new recording rule runs correctly, open Explore from the left hand menu: In the Metric dropdown, choose , then click Run query from the top right, then click on the Inspector button. Below, click Data to see a list of times and query results. The result should be “3”, indicating that the three local instances of Mimir are operational. sum:up Configure an Alert Rule Alerting rules built on Mimir follow the same format as those built on Prometheus and Loki. Grafana evaluates the expression and, if necessary, fires an alert using Alertmanager. We dug into this pretty deeply in an earlier blog post, . PromQL Multi-Cloud Monitoring and Alerting with Prometheus and Grafana We’re going to create an alert that fires when the number of Mimir instances drops below three. In the left hand menu, hover over Alerting and then click New alert rule. Choose Mimir or Loki alert Configure the following: Rule name = MimirNotRunning Choose in the Select data source field Mimir Namespace = example-namespace Group = example-group Query Expression = up == 0 Choose Save and Exit in the upper right corner. Navigate to the Alerting page and you will see our Mimir recording rule and alert rule. Note that there’s a nice, big, comforting green Normal status shown next to the alert because all of our Mimir containers are still running. We’ll simulate an error condition by terminating one of the three Mimir instances (make sure that you are in the directory : ~/mimir/docs/sources/tutorials/play-with-grafana-mimir docker compose kill mimir-3 As we abruptly terminated a Mimir instance, there will be a brief period where Grafana shows an error while querying rules. This will automatically resolve as soon as Mimir’s internal health checks detect the terminated instance as unhealthy. In about one minute, the alert will shortly indicate a yellow Pending state. After another minute, the alert will turn to the red Firing state: If we had configured Alertmanager with notification channels, alerts would be firing off to the appropriate mechanism and contact. Please see for instructions. Multi-Cloud Monitoring and Alerting with Prometheus and Grafana Before we bring our terminated Mimir instance back up, return to the Explore page in Grafana and query our recording rule. We can see that Mimir continued to record metrics even though a Mimir instance was down. sum:up Finally, bring the Mimir instance back up: docker compose start mimir-3 Return to the Alerting page and notice that our alert status is back to Normal. Conclusion In this tutorial, you learned how to run Grafana Mimir and MinIO in a high-availability configuration. We consumed Prometheus metrics from Mimir itself, then queried and visualized them in Grafana. We also configured a recording rule and an alert and verified that the alert fired as expected when the condition was met. You can also configure Mimir and Grafana to scrape Prometheus metrics from MinIO and fire alerts via AlertManager. Mimir stores data in object storage for persistence, allowing it to take advantage of ubiquitous, cost-effective and high-durability MinIO. Give Grafana Mimir a go! If you have questions, please join our or send us an email at hello@min.io. Slack channel Also published . here

Your Guide to Running Grafana Mimir with MinIO for Persistent Metrics Storage

Su guía para ejecutar Grafana Mimir con MinIO para el almacenamiento de métricas persistentes

Votre guide pour exécuter Grafana Mimir avec MinIO pour le stockage de métriques persistantes

परसिस्टेंट मेट्रिक्स स्टोरेज के लिए मिनियो के साथ ग्राफाना मिमिर चलाने के लिए आपकी मार्गदर्शिका

永続的なメトリクス ストレージのために MinIO を使用して Grafana Mimir を実行するためのガイド

Seu guia para executar o Grafana Mimir com MinIO para armazenamento de métricas persistentes

Hướng dẫn chạy Grafana Mimir với MinIO để lưu trữ số liệu liên tục

使用 MinIO 运行 Grafana Mimir 以实现持久指标存储的指南

Assuming you already have a product that has a great product-market fit, meaning customers have an inherent demand for your product without any marketing effort, there are still a variety of approaches to take to achieve growth. Is a massive big marketing campaign always the best way to grow? How about shaping or creating a product that drives growth? To do this, you need to develop a deep understanding of your customer base and what drives their behaviour. One way to do this is to analyze product metrics, such as usage frequency or customer retention rates. This article will guide you through some unique approaches used by product-led companies as examples. By understanding what drives customer behaviour, you can then shape your product to better meet their needs and ultimately drive growth. 1. PRODUCT METRICS AND GROWTH METRICS Product Metrics A product serves as a solution to customers' problems, acting like a black box. When new customers interact with the product, it processes them into active users, profit, support requests, and more. Product metrics show how the product converts new users into other metrics, such as: Product awareness and product adoption (click for more information) here , which how the product converts new users into active users Retention displays Lifetime value (LTV), which measures the profit generated by new users for the entire duration of their service use Purchase conversion, which indicates how the product converts new users into "higher value" customers (i.e., customers willing to pay for the product or generate revenue) Growth Metrics On the other hand, If product metrics describe the product itself, then growth metrics describe the business built around it. Growth metrics are derived from product metrics and the number of new users, which are the primary levers influencing overall business growth. Here are some examples of growth metrics: or daily active audience (new users * retention) DAU : There are multiple ways to create a profit Profit Increase the number of new users to generate more revenue. This can be achieved through multiple marketing techniques such as growth hacking, word of mouth, or creating an exclusive community (such as Clubhouse). However, this strategy could lead to massive failure if the company fails to retain new users. While this may increase revenue in the short term, it could also result in more forced churn. Raise the price of the product to increase revenue. For example, Lyft increased its prices significantly in 2017 in an attempt to increase profits and become more competitive with Uber. However, the price increases were met with backlash from Lyft riders, and many riders began to switch to Uber. As a result, Lyft's ridership declined, and the company's profits began to fall. In 2019, Lyft went public, but its IPO was a disappointment, and its stock price fell sharply. Lyft has continued to struggle financially, and it is now facing increasing competition from other ride-sharing companies, such as Uber and Didi. My preferred approach, in this case, is to focus on acquiring users through product development while also considering their LTV lifetime value Profit can be calculated = (number of new users * LTV) Number of new subscribers (new users * subscription conversion rate) In short, I believe creating a product that fuels growth and paying attention to the connection between product metrics and growth metrics are both effective techniques that many successful product-led companies use. 2. LEVERAGE PRODUCT METRICS FOR GROWTH Let's look at two examples of product-led companies that drive sustainable growth and eventually become profitable by leveraging product metrics such as retention and LTV (lifetime value). CASE STUDY 1: Zilch - Europe's Fastest Unicorn (Became a Unicorn Within 9 Months of Full Launch) : The big names in the industry, such as Klarna and Clearpay, entered the market within the last five years to offer customers an alternative to credit cards. For example, with Klarna's payment method, you can purchase anything on Amazon and split the transaction into four instalments over time. Story (Buy Now Pay Later) industry by optimizing payment points and Ad monetization. Zilch has disrupted the BNPL Zilch offers a similar alternative to borrowing money through the following: A virtual card that allows customers to make debit or credit purchases (pay in four instalments over time). It is flexible and can be used anywhere and anytime, reducing the lengthy process of integrating each merchant as it is with Klarna. This creates a great market fit. Defined well product-market-fit: Shaping the product to higher retention and higher conversion rate. Zilch offers in-app ads to unlock offers. Customers can go to the app and unlock any merchant they want to purchase from and get a 0% interest rate when using a credit product. by providing the benefit to the customer: Zilch turns the ad money paid by the merchant into savings on the interest/fee that the customer has to pay. Higher retention in the phase of educating customers: It is also a smart move to save on 3DS costs when customers choose to unlock the offer in the app and verify their authorization to the transaction (2-3% of payment cost). Knowing the cost and leverage by product feature CASE STUDY 2: turns to profit in July 2023 Uber : Uber is an on-demand transportation technology app that allows customers to easily book the most convenient and fastest mode of transportation. Since its launch in 2009, Uber has disrupted the traditional taxi industry. Its strategy is to hack growth by acquiring as many drivers and customers as possible to adopt the product in the market, and then slowly tightening the rules to optimize the customer journey and experience. Story The downside of this strategy is that for a long time, Uber burned through a huge amount of funds from big names to fuel their growth. As a result, they captured a large market share but struggled to become profitable. However, Uber turned a profit in July 2023.
There are several good approaches to consider: Cut costs by 20% during COVID. Run as lean as possible: Uber sold off non-strategic businesses, such as its self-driving unit, which held a $400M stake in Careem and a $392M stake in Zomato. Focus on what's most important: Leave markets where you're not the #1 or #2 player (e.g., abandon food delivery in Italy) Only play games you can win: Uber leaned into UberOne, a Prime-like membership that includes: But most importantly, create a new product that significantly increases LTV: : In-app popup notifications and direct discounts on trips to encourage action. Awareness : A free trial for the first 1-3 months (depending on the market). Adoption Retention: Over 60% of customers continue to engage (and pay) the following month after their first purchase, resulting in a 12M member increase (2x YoY). This results in lower margins but significantly higher retention and, therefore, a much longer LTV. In the long run, this approach is more profitable and even turns a profit 2-3 years earlier than predicted. In summary, while a big marketing campaign may seem like the obvious solution to drive growth, it is not always the most effective method. Instead, it is important to focus on to achieve higher retention rates and a longer lifetime value for your customers. creating or shaping your product However, it is also worth considering other value metrics that can come into play, such as customer satisfaction, brand loyalty, and market share. By taking a more holistic approach to your business strategy, you can ensure long-term success and sustainability in a constantly evolving market.

Empowering Growth through Product Metrics

পণ্য মেট্রিক্স মাধ্যমে বৃদ্ধি ক্ষমতায়ন

Förderung des Wachstums durch Produktkennzahlen

Impulsando el crecimiento a través de métricas de productos

Stimuler la croissance grâce aux métriques de produit

उत्पाद मेट्रिक्स के माध्यम से विकास को सशक्त बनाना

製品指標を通じて成長を促進する

제품 지표를 통한 성장 강화

Capacitando o crescimento por meio de métricas de produto

Ürün Metrikleri Yoluyla Büyümeyi Güçlendirmek

Thúc đẩy tăng trưởng thông qua các chỉ số sản phẩm

通过产品指标促进增长

Hello everyone! For several years now, I have been writing various "assistant" telegram bots for myself in Python that handle various small routine tasks for me - notifying me about something, checking service uptime, forwarding interesting content from telegram channels and chats, and so forth. This is convenient because the phone is always at hand, and being able to fix something on the server without even opening my laptop brings me particular pleasure. In general, I have accumulated a lot of different small project templates that I want to share with HackerNoon readers. I'll say right away that the examples may be niche in terms of their application "as is", but I will mark those places where, by changing a few lines of code to your own, you will be able to reuse most of the developments for your projects. I completed this specific project a few days ago, and it has already brought me a lot of benefits. I work at a Web3 infrastructure provider chainstack.com, dealing with a service for indexing data from smart contracts on EVM blockchains. And the quality of the service being developed critically depends on how "well" the nodes from which the service retrieves data online are functioning. I spent many hours trying to use ready-made tools that our infrastructure division uses, such as Grafana, BetterUptime, and others, but as I have little interest in the system's internals, with the main focus for me being the metrics at the entrance and the exit, I decided to write my own bot, which would do the following: At my request, it would go to the service, check the metrics, and send me a brief report on the current situation. At my other request, it would send me graphs of what has been happening over the last X hours. In case of a special situation, it would send me a notification that something is happening at that moment. In this article, I will focus on the first part, that is, receiving metrics on request. We will need a new virtual environment for work. cd ~ 
virtualenv -p python3.8 up_env  # crete a virtualenv
source ~/up_env/bin/activate  # activate the virtualenl Install dependencies: pip install python-telegram-bot
pip install "python-telegram-bot[job-queue]" --pre
pip install --upgrade python-telegram-bot==13.6.0  # the code was written before version 20, so here the version is explicitly specified

pip install numpy # needed for the median value function
pip install web3 # needed for requests to nodes (replace with what you need) File with functions (you can implement it with classes, but since the example is short, I did not plan to divide it into modules, but a multi-threading library requires functions to be moved to a separate file). Import dependencies: functions.py import numpy as np
import multiprocessing

from web3 import Web3 #  add those libraries needed for your task Describing a function for checking the state. In my case, it involved looping through pre-selected public nodes, retrieving their last block, taking the median value to filter out any deviations, and then, checking our own node against this median. Service state checking function (you can replace it with your own): # Helper function that checks a single node
def get_last_block_once(rpc):
    try:
        w3 = Web3(Web3.HTTPProvider(rpc))
        block_number = w3.eth.block_number
        if isinstance(block_number, int):
            return block_number
        else:
            return None
    except Exception as e:
        print(f'{rpc} - {repr(e)}')
        return None


# Main function to check the status of the service that will be called
def check_service():
    # pre-prepared list of reference nodes
    # for any network, it can be found on the website https://chainlist.org/
    list_of_public_nodes = [
        'https://polygon.llamarpc.com',
        'https://polygon.rpc.blxrbdn.com',
        'https://polygon.blockpi.network/v1/rpc/public',
        'https://polygon-mainnet.public.blastapi.io',
        'https://rpc-mainnet.matic.quiknode.pro',
        'https://polygon-bor.publicnode.com',
        'https://poly-rpc.gateway.pokt.network',
        'https://rpc.ankr.com/polygon',
        'https://polygon-rpc.com'
    ]
    
    # parallel processing of requests to all nodes
    with multiprocessing.Pool(processes=len(list_of_public_nodes)) as pool:
        results = pool.map(get_last_block_once, list_of_public_nodes)
        last_blocks = [b for b in results if b is not None and isinstance(b, int)]

    # define the maximum and median value of the current block
    med_val = int(np.median(last_blocks))
    max_val = int(np.max(last_blocks))
    # determine the number of nodes with the maximum and median value
    med_support = np.sum([1 for x in last_blocks if x == med_val])
    max_support = np.sum([1 for x in last_blocks if x == max_val])

    return max_val, max_support, med_val, med_support The next important file of the bot is . We import libraries and functions from the file above and set the necessary constants: uptime_bot.py import telegram
from telegram.ext import Updater, CommandHandler, Filters

from functions import get_last_block_once, check_service

# Here one can to set a limited circle of bot users, 
# listing the usernames of the users

ALLOWED_USERS = ['your_telegram_account', 'someone_else']
# The address of the node that I am monitoring (also a public node in this case)
OBJECT_OF_CHECKING = 'https://polygon-mainnet.chainstacklabs.com'
# Threshold for highlighting critical lag
THRESHOLD = 5 Next, let's describe a function that will be called when the command is issued from the bot's UI. def start(update, context):
    """Send a message when the command /start is issued."""
  
    try:
        # Get the user
        user = update.effective_user

        # Filter out bots
        if user.is_bot:
            return

        # Check if the user is allowed
        username = str(user.username)
        if username not in ALLOWED_USERS:
            return
    except Exception as e:
        print(f'{repr(e)}')
        return

    # Call the main function to check the network status
    max_val, max_support, med_val, med_support = check_service()
    # Call the function to check the status of the specified node
    last_block = get_last_block_once(OBJECT_OF_CHECKING)

    # Create the message to send to Telegram
    message = ""

    # Information about the state of the nodes in the public network (median, maximum, and number of nodes)
    message += f"Public median block number {med_val} (on {med_support}) RPCs\n"
    message += f"Public maximum block number +{max_val - med_val} (on {max_support}) PRCs\n"

     # Compare with the threshold
    if last_block is not None:
        out_text = str(last_block - med_val) if last_block - med_val < 0 else '+' + str(last_block - med_val)

        if abs(last_block - med_val) > THRESHOLD:
            message += f"The node block number shift ⚠️<b>{out_text}</b>⚠️"
        else:
            message += f"The node block number shift {out_text}"
    else: # Exception processing if a node has not responded
        message += f"The node has ⚠️<b>not responded</b>⚠️"

    # Send the message to the user
    context.bot.send_message(chat_id=user.id, text=message, parse_mode="HTML") Now, all that's left is to add the part where the bot is initialized, and the handler function is connected: token = "xxx"  # Bot token obtained from BotFather

# set up the bot
bot = telegram.Bot(token=token)
updater = Updater(token=token, use_context=True)
dispatcher = updater.dispatcher

# bind the handler function
dispatcher.add_handler(CommandHandler("start", start, filters=Filters.chat_type.private))

# run the bot
updater.start_polling() Finally, you can run the code on a cheap VPS server using: source ~/up_env/bin/activate
python uptime_bot.py After configuring the systemd unit file. As a result, the bot's work will look like this. If everything is fine: And if the lag becomes too large, then as follows: In the following articles, I will describe how to implement the two remaining tasks: Retrieve graphs on request showing the events that occurred over the last X hours. Receive an alert indicating that something is currently happening and requires action. The project's source code is available in the GitHub . If you found this tutorial helpful, feel free to give it a star on GitHub, I would appreciate it🙂 repository

How to Create a Telegram Bot to Monitor Your Service Uptime in Python (Part 1: Instant Metrics)

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पायथन में अपनी सेवा के अपटाइम की निगरानी के लिए टेलीग्राम बॉट कैसे बनाएं (भाग 1: त्वरित मेट्रिक्स)

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Cách tạo Bot Telegram để theo dõi thời gian hoạt động của dịch vụ của bạn bằng Python (Phần 1: Số liệu tức thì)

如何使用 Python 创建 Telegram 机器人来监控服务正常运行时间（第 1 部分：即时指标）

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