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Feb 2022: Update on crypto markets, stock markets and global macroeconomics. When the bulls are away, the bears will play. If you have been watching the markets in the past few weeks, well… hopefully you have not been looking too closely. In the past, many experts have highlighted some main points about crypto markets and this particular time of year. Some characteristics of crypto markets are similar to stocks. However, there are large differences with crypto, as the market is truly worldwide and open 24/7. First, we have the eternal battle between the bears and the bulls. An actual bear attacks with a downward strike of his claws; in stock and crypto markets, bears want the markets to go down. This is because markets tend to go down swiftly, especially in times of fear, and bears can make more money, more quickly, when markets go swiftly down. A literal bull attacks with an upward strike of his horns; in stock and crypto markets, bulls want the markets to go up. Markets often tend to behave irrationally, but it is frequently said that “markets take the elevator down, and the stairs back up." A downturn in prices can drop further and faster when there is panic in the markets. Markets are driven by fear and greed, and fear tends to be more contagious than a simple pandemic, as you can catch it online as well. When the markets are panicking, it can be hard to keep your cool and think rationally, but that is exactly what the bulls and value investors must do to win. Value investors believe that crypto and blockchain are the future of banking, insurance, technology, and financial systems. Despite the distracting media static, doom and gloom headlines, and other bad news, value investors must keep faith in what they believe and where they think the world will be. "If in doubt, zoom on out" is also great advice. Financial adviser since 1992, Jeremy Britton, says, “if in doubt, zoom right out”. Hourly and daily price fluctuations can cause panic and uncertainty, so it is important to look at price growth over 1, 3, or 5 years. The charts show Bitcoin is up 249% over the last four years, whilst the diversified Bostoncoin crypto portfolio is up over 800% in the same period. What else is driving the markets? Aside from the eternal struggle of bulls and bears, over the past several years, it is noted that seasons and holidays play a role, particularly in crypto prices. Western nations may celebrate Easter, Christmas, Thanksgiving, and similar traditions, but westerners must remember that over two-thirds of the world may not celebrate these dates. Many Asian countries celebrate Chinese New Year instead ( ). 2022 is the year of the Water Tiger, which last occurred in 1962, thanks for asking In many Asian nations, Chinese New Year is a time to spend with family. Millions of factories and businesses close, and hundreds of millions of people take time off work and travel to be with family. Production slows to a snail’s pace, and many investors sell off their holdings to spend funds on travel, gifts, food, or just because there is no work. If you have been around crypto markets for a few years, you will know that crypto investors in Asian nations tend to sell off towards Dec-Jan and then the market slowly recovers leading back into March-April ( ). the date of Chinese New Year is linked to the moon, so it is not the same day every year In the West, many investors are watching the US Fed as it struggles with the outcome of its own cash-printing silliness. There were many people who could not work during pandemic shutdowns, and small businesses, in particular, did require some increased funding. The problem was that under-regulated Fed QE policies saw some very large businesses receiving massive bail-outs, even as their profits rose. The system was not perfect and did not seem fair to all. Anyone who has completed Economics 101 knows that giving boatloads of free cash to anyone and everyone would result in hyperinflation and rising asset prices. Perhaps the boffins working in Central Banks may have skipped class that day? A few weeks ago, the US government was claiming that higher prices, inflation, and supply-chain shortages would be “transitory”. They are no longer saying this, as the realisation of hyperinflation sets in. The only way to prevent record inflation is to increase interest rates, which would likely devastate both homeowners and business owners. Higher interest rates can also trigger a drop in stocks, bonds, and yes, even crypto, as investors are forced to sell down assets to repay debt. On the surface, one can understand why market panic may be driving prices down, even though western rate rises are currently just a rumour. Interestingly, Lloyds of London and other . Draw your own conclusions, but it seems that many banks may be assuming very low interest rates for a decade or more. UK banks are now offering fixed-rate mortgages of 1.66% for ten years Is there any good news? The good news includes: 1)      Bears eventually go back to hibernating, particularly when the seasons change and other news comes out. 2)      Interest rate hikes may be lower and slower than first feared. 3)      The COVID pandemic may be having less of an impact on economies in 2022, as vaccine rates increase, less-deadly variants emerge, and herd immunity increases. 4)      Crypto’s brief forays into lower-price territory can mean some bargains for value investors, especially the brave bulls who have the opportunity to invest before the recovery is fully known. 5)      Tried and tested habits such as dollar-cost averaging (DCA), diversifying, and “buying the dip” still work well, provided you choose quality investments. What is the smart money doing? There is nothing much the bulls can do to reverse the impetus when the bears want to drive prices down. What the ‘zoomers’ (smart money investors who zoom out on charts) can do is take advantage of lower prices when there is market panic, or incidences of Fear, Uncertainty, and Doubt (FUD). This month, directors of BostonTrading.co took advantage of lower prices to add additional holdings to some of their existing investments. In a month where market leaders Bitcoin and Ethereum were down by 30-50%, the Bostoncoin fund bought more of each asset, just as a smart person would stock up on toilet paper or tinned food when the prices are suddenly slashed. Whether you believe the near future will include an idyllic Metaverse or a zombie apocalypse, people will probably need BTC and ETH just as much as we need toilet paper and tinned corn. Crypto winners this month include •        CRO, even after a drop, still up 220% for the year •        SAND, the promise of gaming and metaverse, up 420% •        SECRET, a wise buy that is now up 476% for the year •        LPT, defying trends to grow by 725% in 12 months •        XYO, still going gangbusters at 8 580% increase for the year In other news… Directors double-down during the dip When Bitcoin was first created, the founder(s) never held any tokens for themselves just because they knew how. If anyone wanted to get Bitcoin, the person had to either add power and value to the network by mining or buying BTC from the open market. There were no special privileges for a select few. From all accounts, it seemed that Satoshi Nakamoto despised the elites and their "enrichment for the 1%" agenda. Some (dodgy) coin issuers engage in unfair market practices by holding back a large chunk of coins for founders, developers, marketers, and influencers. The scammers release 20-50% of the coins to the market, and when the price increases, they can dump their own “free” coins for a profit and get rich whilst they crash the market. This has happened time and time again with . Let the buyer beware, especially of celebrities who are not experts, and always do your homework. worthless meme coins and tokens pushed by celebrities The founding team of BostonCoin and BostonTrading.co is comprised of all investors, who put in their own funds before the coin was launched to the public. There were never any preferential tokens held back from the open market, and none were awarded to anyone for any reason. This is known as a “fair launch” or “fair issue”, where, just like Bitcoin, everyone is invested on an equal opportunity basis. During the recent price drop, the Bostoncoin team invested more into the portfolio whilst the price was down. This is a show of confidence, and the team is proud to be long-term value investors. “It is important to believe in what crypto stands for. We avoid the scammers, FUD-sters, and nay-sayers. News media changes every day, but long-term, value investing always beats the market fluff and hype. To those who HODL for the long-term, and to those who double-down on the dips, we salute you,” said CFO Jeremy Britton in his latest newsletter. Cathie Wood’s flagship ARK Innovation ETF is now trading at around a 56% discount from its peak last year, and some investors may be wondering whether to take notice of her clarion call to buy innovation which is on “sale.” Speaking last Tuesday at Ark Investment Management’s virtual Big Ideas Summit 2022, following a big drawdown from her flagship ETF, Wood declared, “Innovation is on sale and it will be really important to investors to get to move towards the right side of change, given the amount of disruption that we do expect.” Nine of the fifty ARK Innovation ETF’s holdings have grown revenues at over 100% per annum, including those that have grown revenues at a rate of over 50% brings the total number to 21, just under half of their 50 stocks. Considering the average returns in traditional stocks, such as the ASX200 (3.7% for the past twelve months) and the SP500 (18.8% for the year), crypto markets, with their innovation and double or triple-digit returns, can no longer be ignored.

Crypto is Down But Not Out

The crypto market is notoriously volatile. Just recently we experienced a major drop in prices due to the news about , and after that we had the news regarding . So what should you do as a crypto project when the market is down in order to strengthen your brand? Should you continue to pump out press news or should you wait for the market to recover? Evergrande’s debt crisis China’s ban of bitcoin To retain strong brand positioning during the inevitable market downturns and to be best prepared for the eventual uptick, you need to pursue several strategies. All of them hinge on keeping your team and your community informed, engaged, and focused on the end goal. In moments of crisis, your community members are your safe haven and buoy to keep you safe from the market’s tidal waves. You can help them by defueling the FUD and reminding them to stay focused and not to panic. Here are a few more things you can do to strengthen your project. Time for product development While talk of the next great crypto depression is flying around, it’s probably not the best time to announce your long-awaited news to the press. Instead, during this time, invest your and your team’s efforts into product development. Now is the time to work out the kinks in your wallet, to supercharge your plans for a smart contract solution, or to add new security features to your platform. The period during a down market is a great time to roll up your sleeves and focus on some intensive in-house production work as opposed to external, overt communication. Working intensively through the market dip also means that when the market goes back up and it’s time to ramp up external communications, you have even more great news to share. So document your product development meticulously and have your announcements at the ready for after the crisis is over. Then you can enjoy maximum impact and well-deserved media attention, as crypto media will likely be looking for positive stories to tell after a prolonged period of pessimism and tanking valuations. Direct communication inwards A down market means you should put your media outreaches on ice, yet there is another audience you should be speaking to even more often: your team. As you pursue company progression and product development, you need to kick your internal communications into higher gear. This involves more internal company memos and speeches, maintaining transparency, checking in regularly with the team members, and boosting morale. Your employees are getting the same news and market analyses as everyone else in the space, so it’s natural for them to feel at least a little bit disheartened about the current low. It’s your job to keep the team members’ and the community’s spirits high and remind them why they’re a part of your project in the first place. Remind them of the vision, give them strength and encouragement to hold on, and reassure them not to let any FUD detract them from their mission. Make sure you communicate the project’s goals clearly and demonstrate how each step contributes to achieving them. Draw up a roadmap clearly depicting how your project will not only survive but thrive in the future. Such a programmatic document helps you plan your activities better. It also brings the team closer because it presents a unified vision of the different groups’ and departments’ efforts coming together. This sense of belonging is always crucial, but especially key during a market downturn. Communicate genuine WAGMI messaging with the community Product development and maintaining morale internally are all well and good, but there is one more activity during a market low that you cannot put a price on: Huddling your community together and letting them know that “we are going to make it” (WAGMI). The investors, developers, and believers all need your encouragement right now. They are probably rattled by the market reports, high on FUD, and consider abandoning ship. You need to speak to them directly and keep them strong and focused. First and foremost, make yourself available to speak directly with your community. Genuine conversations on crypto podcasts or scheduled AMA sessions and the immediacy of hearing your voice or seeing you in a livestream can make a world of difference to them. Social media appearances should be part of your regular communications strategy anyway, but they become especially important during times when you have to keep your community strong. Help the community with thought leadership and commentary Sharing your expertise and advice especially during a down market, is a wonderful way to show your tribe and the crypto community as a whole that you are here to help the ecosystem. A down market is a genuine opportunity to speak candidly and openly about current challenges and to chart a path forward. By sharing your expert thoughts, you have a chance not only to bring your community together but also to extend your knowledge and experience to other folks in the industry in need of encouragement and guidance. Even under normal circumstances, readers appreciate a well-researched and insightful thought leadership piece that expands their horizons and provides them with a fresh perspective on the crypto space. A market dip naturally increases their appetite for content that educates them on how to handle the situation properly. It is also a prime opportunity to broaden your audience and increase your brand recognition while also helping out journalists who may need a comment or two from an OG such as yourself. People will remember that your deep analyses and insightful observations got them through the hard times and will continue keeping an eye on your project when the sun is shining again. Beat the down market and set sail for growth Thanks to the above steps, your crypto project can maintain its top-of-mind brand positioning despite the inevitable market fluctuations. By investing internally and giving back to your community, while also sharing your knowledge and expertise with the whole crypto space, you strengthen your own brand and help people suppress FUD. With these actions you benefit the whole crypto community, and folks will remember you for it. A down market causes widespread distress, but it also presents an extraordinary opportunity for crypto projects – and crypto leaders – to set themselves apart. After all, crypto is about community, trust, and boldly chasing the horizon. Fluctuating market conditions cannot change these fundamental truths. If you remain faithful to a blockchain future, no price drop will take you off course, and you will emerge stronger and wiser on the other side.

How to Keep Your Brand Afloat when the Crypto Market is Down 

5 Effective Methods for Passing Parameters to Apache APISIX

If you had to pick a key determinant for your satisfaction on a web page, speed would undoubtedly be in the top two of all your arguments. You’re not alone either, more than half of web users worldwide require a website’s load time to be under three seconds or they lose interest. Beyond load times, a page’s responsiveness also contributes to the perception of speed for users. However, due to server round trip times, network glitches, and sometimes the processing power of the machine, response times can be wonky. But nobody wants to listen to technical jargon, they just need their technology to work. To fix this issue, front-end developers turned to optimistic updates. You know when you like an image on Instagram, and the heart appears even when your network is down? Those are optimistic updates. Typically, the User interface (UI) waits until the server confirms an action before reflecting an update, however, optimistic updates bypass that sequence. In one line, Optimistic updates is a tool used to enhance user experience by initiating UI changes without backend validation. This article looks to dive deeper into optimistic updates. How do they work? Why use them? What use cases render them ineffective? Why do we use optimistic updates? Why embrace this tool, you might ask? The advantages are persuasive. As mentioned earlier in this article, the tool updates the UI instantly, creating a smoother and more responsive user experience, eliminating delays, and keeping users engaged. It also takes away the wait time for server confirmation making it possible for faster interaction, and improving the user's satisfaction. Optimistic updates can be applied in various functions like common actions; liking a post, updating a profile picture, or adding an item to a cart. Further, offline editing allows users to make changes without access to the internet, and later sync when they're back online. An example of this is Google Docs. How optimistic updates work Once an action is implemented, the tool identifies it and triggers an update. Next, it updates the local state, modifying the local UI structure as if the change were successful. A request is sent to the server to communicate the desired change, which in turn sends back a response of either Success or Failure. User initiates action (clicks like) -> UI updates immediately (instantly reflects 1Like) -> Server communication (request is sent to the server notifying it of the action) -> Server response (server processes the request and sends a response, success or failure). If it's successful then the optimistic update is validated, if it's a failure then the optimistic update is reverted (the like indicator is removed.). Learn more here When not to use optimistic updates While Optimistic Update is a good tool, it's only ideal for frequent, low-risk action where the server validation is simple and quick. Optimistic updates require careful implementation. It's not to be used in transactions where failed updates could have significant backfires (e.g. financial transactions). Avoid using them when maintaining strict data integrity across multiple users. Optimistic updates are valuable tools in a developer toolkit. By understanding their functions and importance, you have an advantage in using them to create responsive user experiences that leave your users satisfied. If you had to pick a key determinant for your satisfaction on a web page, speed would undoubtedly be in the top two of all your arguments. You’re not alone either, more than half of web users worldwide require a website’s load time to be under three seconds or they lose interest. Beyond load times, a page’s responsiveness also contributes to the perception of speed for users. However, due to server round trip times, network glitches, and sometimes the processing power of the machine, response times can be wonky. But nobody wants to listen to technical jargon, they just need their technology to work. To fix this issue, front-end developers turned to optimistic updates. You know when you like an image on Instagram, and the heart appears even when your network is down? Those are optimistic updates. Typically, the User interface (UI) waits until the server confirms an action before reflecting an update, however, optimistic updates bypass that sequence. front-end developers User interface In one line, Optimistic updates is a tool used to enhance user experience by initiating UI changes without backend validation. This article looks to dive deeper into optimistic updates. How do they work? Why use them? What use cases render them ineffective? Why do we use optimistic updates? Why embrace this tool, you might ask? The advantages are persuasive. As mentioned earlier in this article, the tool updates the UI instantly, creating a smoother and more responsive user experience, eliminating delays, and keeping users engaged. It also takes away the wait time for server confirmation making it possible for faster interaction, and improving the user's satisfaction. Optimistic updates can be applied in various functions like common actions; liking a post, updating a profile picture, or adding an item to a cart. Further, offline editing allows users to make changes without access to the internet, and later sync when they're back online. An example of this is Google Docs. How optimistic updates work Once an action is implemented, the tool identifies it and triggers an update. Next, it updates the local state, modifying the local UI structure as if the change were successful. A request is sent to the server to communicate the desired change, which in turn sends back a response of either Success or Failure. User initiates action (clicks like) -> UI updates immediately (instantly reflects 1Like) -> Server communication (request is sent to the server notifying it of the action) -> Server response (server processes the request and sends a response, success or failure). If it's successful then the optimistic update is validated, if it's a failure then the optimistic update is reverted (the like indicator is removed.). Learn more here here When not to use optimistic updates While Optimistic Update is a good tool, it's only ideal for frequent, low-risk action where the server validation is simple and quick. Optimistic updates require careful implementation. It's not to be used in transactions where failed updates could have significant backfires (e.g. financial transactions). Avoid using them when maintaining strict data integrity across multiple users. Optimistic updates are valuable tools in a developer toolkit. By understanding their functions and importance, you have an advantage in using them to create responsive user experiences that leave your users satisfied.

Optimistic Updates in Frontend Applications

Every tech company in the world has a software development team, and right now, 70% of them are considering how to use AI tools in the development process itself. Should they integrate AI co-pilots, outsource parts of the work to code generators, or develop bespoke tools in-house? One of the key determinants in this process is, simply - whether the tool is leading to positive results. For this, you need to run a proof of concept, while measuring team performance over time. The problem is that the old technologies developers use to track performance are 2D in their insights. New technologies are revolutionizing how engineers assess their team’s success. The old guard of performance trackers are known as management systems or engineering management platforms (EMPs) - think Jira or Azure DevOps Boards. But a new iteration is emerging with one crucial additional layer: intelligence. Software Engineering Intelligence Platforms (SEIPs) are like EMPs with an added element of intelligent data gathering and analysis. They track performance not just based on task completion. Instead, they leverage multiple data sources, create unique metrics using aggregated data, carry out complex analyses, and have predictive capabilities. It is these 3D platforms that are most suited to closely measuring the success or failure of AI adoption in teams at the forefront of technological advancements. This is how SEIPs work. 1. Layered Data Points - The What EMPs are limited in their insights because of the narrow data they focus on: essentially, they can tell you about the status of a task, and the time it took to complete it. As well as these basic engineering metrics, SEIPs integrate several other layers of data. They capture all the engineers’ work across platforms: what’s happening on Jira, their activity on GitHub, what’s being recorded on DevOps tools such as Jenkins, and more. SEIPs then translate these signals into metrics to give an otherwise impossible level of insight into what’s happening within your team. If you were to assess the success of an AI product by just looking at the rate of task completion, you might reach a skewed conclusion. For example, more tasks may have been completed within the time frame. But by connecting the SEIP to other tools like Jenkins or GitHub, you may find that your Change Failure Rate (CFR) - code changes that lead to failed deployments - has simultaneously increased, creating a new vulnerability. Furthermore, unlike EMPs, SEIPs will integrate contextual data that helps to understand the value of people’s work, such as project costs and engineer salaries. That allows managers to measure the ROI and business outcomes of a specific tool you’re experimenting with. 2. Measuring the Invisible - The Where Without a SEIP, measuring the *impact *of AI deployment is extremely hard, as all you have is a black box. Today, most engineering teams’ performance is measured in ticket completion: such as how many tickets were completed in a sprint. But the productivity of AI tools can’t just be measured in tickets completed - it’s more nuanced than EMPs allow for. With SEIPs, you can quantify more than whether a team was able to complete a task, and with what speed. They can also measure the impact and quality of work, or the less “visible” elements of an engineer’s work. SEIPs look at metrics such as DORA Metrics, Cycle Time, and how much-unplanned work is being generated. This can show if work is improving, if more code is being deployed than before, and if it includes fewer failures. SEIPs can also shed light on correlations between metrics that EMPs can’t. SEIPs can also measure developer experience (DX) - an often overlooked but critical metric relating to developer well-being. This can be done by looking at specific metrics, such as deployment frequency - if this grows, it could suggest increased enthusiasm and engagement within the team. 3. Analytical Metrics - The Why EMPs can tell you that a task was completed in 5 days, rather than the benchmark of 2 days, but they can’t show you why velocity slowed down. SEIPs allow managers to zoom in on the ‘why’ metrics or causal factors: were there any bottlenecks? Did certain internal events over the past week slow down operations across the board? For example, a slowed-down sprint time after AI integration might not be due to an engineering team working more slowly, but because the code review time is way longer than the benchmark. Perhaps, the slower sprint time was due to the code reviewer being overwhelmed by the amount of new code because the AI tool increased developer output so much that they couldn’t keep up. That tells you both that the AI was successful in part and that changes are needed internally (you need more code reviewers) to make full adoption efficient. SEIPs have another important advantage - they can provide recommendations on how to improve the situation based on that same granular data. So if your AI deployment is being hindered by a specific bottleneck, the SEIP could provide recommendations to remove it and take fuller advantage of the AI tool’s benefits. 4. Internal Benchmarks - The Who It doesn’t always make sense to only compare your engineers’ work to benchmarks that are the industry standard - which is something that often occurs with EMPs. If you’re assessing the effectiveness of a new AI tool you need to make sure it’s making your team better than they were before the deployment. SEIPs gather historical data from your team in order to create internal benchmarks of what constitutes a satisfactory commit count or cycle time. It does this by reviewing previous performance and identifying in what circumstances the team performs best - all of which can provide better conditions for predicting the AI deployment’s real impact. Your AI deployment could have its greatest potential in specific areas where you’re already meeting industry benchmarks. But if you’re already hitting the industry average, you may not focus on tracking impact in that area. A SEIP could show you how the AI is making that process 200 or 300% more efficient. 5. Predictive Capabilities - The When If you want to go one step further, SEIPs also offer predictive features that can greatly speed up the proof of concept process. During a sprint, by collecting a series of metrics and comparing them to past performance and results, a SEIP can predict whether you will be able to complete the sprint by the given deadline. An EMP will tell you you’re late only after you’re already late. The benefit here for testing out new products is that it can give you far quicker insights into your experimentations. It could allow you to test multiple AI tools at once, or to cycle more quickly between AI tools to find the most effective one in a reduced amount of time. Most tech companies are moving towards adopting AI, but the current software they’re using to monitor performance simply isn’t inquisitive enough to give managers a real view of what’s working and what’s not. A new generation of SEIPs will help bridge that knowledge gap. Every tech company in the world has a software development team, and right now, 70% of them are considering how to use AI tools in the development process itself. Should they integrate AI co-pilots, outsource parts of the work to code generators, or develop bespoke tools in-house? 70% of them One of the key determinants in this process is, simply - whether the tool is leading to positive results. For this, you need to run a proof of concept, while measuring team performance over time. The problem is that the old technologies developers use to track performance are 2D in their insights. New technologies are revolutionizing how engineers assess their team’s success. The old guard of performance trackers are known as management systems or engineering management platforms (EMPs) - think Jira or Azure DevOps Boards. But a new iteration is emerging with one crucial additional layer: intelligence . intelligence Software Engineering Intelligence Platforms (SEIPs) are like EMPs with an added element of intelligent data gathering and analysis. They track performance not just based on task completion. Instead, they leverage multiple data sources, create unique metrics using aggregated data, carry out complex analyses, and have predictive capabilities. Software Engineering Intelligence Platforms (SEIPs) It is these 3D platforms that are most suited to closely measuring the success or failure of AI adoption in teams at the forefront of technological advancements. This is how SEIPs work. 1. Layered Data Points - The What 1. Layered Data Points - The What EMPs are limited in their insights because of the narrow data they focus on: essentially, they can tell you about the status of a task, and the time it took to complete it. As well as these basic engineering metrics, SEIPs integrate several other layers of data. They capture all the engineers’ work across platforms: what’s happening on Jira, their activity on GitHub, what’s being recorded on DevOps tools such as Jenkins, and more. SEIPs then translate these signals into metrics to give an otherwise impossible level of insight into what’s happening within your team. If you were to assess the success of an AI product by just looking at the rate of task completion, you might reach a skewed conclusion. For example, more tasks may have been completed within the time frame. But by connecting the SEIP to other tools like Jenkins or GitHub, you may find that your Change Failure Rate (CFR) - code changes that lead to failed deployments - has simultaneously increased, creating a new vulnerability. Furthermore, unlike EMPs, SEIPs will integrate contextual data that helps to understand the value of people’s work, such as project costs and engineer salaries. That allows managers to measure the ROI and business outcomes of a specific tool you’re experimenting with. 2. Measuring the Invisible - The Where 2. Measuring the Invisible - The Where Without a SEIP, measuring the *impact *of AI deployment is extremely hard, as all you have is a black box. Today, most engineering teams’ performance is measured in ticket completion: such as how many tickets were completed in a sprint. But the productivity of AI tools can’t just be measured in tickets completed - it’s more nuanced than EMPs allow for. With SEIPs, you can quantify more than whether a team was able to complete a task, and with what speed. They can also measure the impact and quality of work, or the less “visible” elements of an engineer’s work. SEIPs look at metrics such as DORA Metrics, Cycle Time, and how much-unplanned work is being generated. This can show if work is improving, if more code is being deployed than before, and if it includes fewer failures. SEIPs can also shed light on correlations between metrics that EMPs can’t. SEIPs can also measure developer experience (DX) - an often overlooked but critical metric relating to developer well-being. This can be done by looking at specific metrics, such as deployment frequency - if this grows, it could suggest increased enthusiasm and engagement within the team. 3. Analytical Metrics - The Why 3. Analytical Metrics - The Why EMPs can tell you that a task was completed in 5 days, rather than the benchmark of 2 days, but they can’t show you why velocity slowed down. why SEIPs allow managers to zoom in on the ‘why’ metrics or causal factors: were there any bottlenecks? Did certain internal events over the past week slow down operations across the board? For example, a slowed-down sprint time after AI integration might not be due to an engineering team working more slowly, but because the code review time is way longer than the benchmark. Perhaps, the slower sprint time was due to the code reviewer being overwhelmed by the amount of new code because the AI tool increased developer output so much that they couldn’t keep up. That tells you both that the AI was successful in part and that changes are needed internally (you need more code reviewers) to make full adoption efficient. SEIPs have another important advantage - they can provide recommendations on how to improve the situation based on that same granular data. So if your AI deployment is being hindered by a specific bottleneck, the SEIP could provide recommendations to remove it and take fuller advantage of the AI tool’s benefits. 4. Internal Benchmarks - The Who 4. Internal Benchmarks - The Who It doesn’t always make sense to only compare your engineers’ work to benchmarks that are the industry standard - which is something that often occurs with EMPs. If you’re assessing the effectiveness of a new AI tool you need to make sure it’s making your team better than they were before the deployment. SEIPs gather historical data from your team in order to create internal benchmarks of what constitutes a satisfactory commit count or cycle time. It does this by reviewing previous performance and identifying in what circumstances the team performs best - all of which can provide better conditions for predicting the AI deployment’s real impact. internal Your AI deployment could have its greatest potential in specific areas where you’re already meeting industry benchmarks. But if you’re already hitting the industry average, you may not focus on tracking impact in that area. A SEIP could show you how the AI is making that process 200 or 300% more efficient. 5. Predictive Capabilities - The When 5. Predictive Capabilities - The When If you want to go one step further, SEIPs also offer predictive features that can greatly speed up the proof of concept process. During a sprint, by collecting a series of metrics and comparing them to past performance and results, a SEIP can predict whether you will be able to complete the sprint by the given deadline. An EMP will tell you you’re late only after you’re already late. The benefit here for testing out new products is that it can give you far quicker insights into your experimentations. It could allow you to test multiple AI tools at once, or to cycle more quickly between AI tools to find the most effective one in a reduced amount of time. Most tech companies are moving towards adopting AI, but the current software they’re using to monitor performance simply isn’t inquisitive enough to give managers a real view of what’s working and what’s not. A new generation of SEIPs will help bridge that knowledge gap.

What Are SEIPs? The New Way Engineering Leaders Measure Successful AI Adoption

Data Security and Its Aspect Data is a very important aspect of any system, organization, and business. It plays a crucial role in user behavior, and sentiments and drives the businesses. In day-to-day life, as we come online, we start playing with data. These data are collected from various means like social media, cookies, user profiles, user login, browsing, etc. It is imperative for any system to critically deal with any kind of data. If compromised, it brings harm to users, and organizations and, in turn, leads to loss of money, time, and trust. It is of the utmost importance for any system to deal with data in a very careful way. Usually, when we design a system, security is the last thing considered. In this article, I would like to discuss various different classifications of data and its handling. Before we deal with handling the data, we should understand its classification and accordingly design our system. Data Classification Data can be categorized into various types based on sensitivity; it is classified into high, medium, and low. A high sensitive data is highly confidential and restricted like financial data. Medium sensitive data is sensitive data but does not contain any confidential information like emails without any confidentiality. A low sensitive data is publicly available data like website data and social media posts and is unrestricted. Oftentimes, data when looked at individually, can look like a medium but when combined with different data, becomes restricted or confidential like PII (Personal Identifiable Data), for e.g., an address independently is public data but when combined with user information is sensitive data. Data classification plays a crucial role in understanding the level of criticality a system is dealing with. Requirement Analysis In order to design a system better, we gather the requirements. Based on the requirements, the system is designed and developed. During the requirement phase, one should outline security requirements along with functional and non-functional requirements. Oftentimes, we focus more on functional requirements as these are the core of any system. We can derive the security requirements based on functional and non-functional requirements. Once we narrow down the functional and non-functional requirements, we can outline the type of data the system will deal with at various layers say transport layer, data layer, service layer, downstream, etc. Once all the dependent systems are identified, one will get a good idea of the type of data flow through and into the system. It is highly recommended to outline all the possible security callouts in the design phase. A few of these could be: Authentication
Authorization
Encryption at REST
Encryption at Transport
Application level encryption
Logging preservation time
Logging of non-critical data Best Practices Design Once we have outlined the security requirements, we can implement various security practices into the system. These can be: Security code reviews where the review will look from the security perspective and best practices.
Proper authentication and authorization mechanisms should be in place.
PEN testing before release and at an interval can be incorporated into best practices.
Proper alarms should be configured.
Alarms in case of any malicious or high burst/volume of data are requested.
Gating mechanism to ensure DDoS protection.
Application-level encryption using custom-managed keys.
Encryption at REST using custom-managed keys.
Key rotation at a certain interval.
Gating mechanism to ensure access to critical data is protected.
Policies at storage to ensure critical data egress for qualified consumers.
Data loss prevention mechanism in case of any compromise. System Design Once the requirements are in place, we move to high-level and low-level system design. This acts as a blueprint for the system and acts as a basis for requirement conversion. One should carefully look into various aspects of the system and use proper infrastructure to ensure security. In case the system is currently not dealing with any critical data, but in the future, due to some changes, the system starts to interact with critical data, the documents should be updated as such and re-reviewed to ensure all security practices are followed to uphold the high security and avoid any incidents. Data Security and Its Aspect Data is a very important aspect of any system, organization, and business. It plays a crucial role in user behavior, and sentiments and drives the businesses. In day-to-day life, as we come online, we start playing with data. These data are collected from various means like social media, cookies, user profiles, user login, browsing, etc. It is imperative for any system to critically deal with any kind of data. If compromised, it brings harm to users, and organizations and, in turn, leads to loss of money, time, and trust. It is of the utmost importance for any system to deal with data in a very careful way. Usually, when we design a system, security is the last thing considered. In this article, I would like to discuss various different classifications of data and its handling. Before we deal with handling the data, we should understand its classification and accordingly design our system. Data Classification Data can be categorized into various types based on sensitivity; it is classified into high, medium, and low. A high sensitive data is highly confidential and restricted like financial data. Medium sensitive data is sensitive data but does not contain any confidential information like emails without any confidentiality. A low sensitive data is publicly available data like website data and social media posts and is unrestricted. Oftentimes, data when looked at individually, can look like a medium but when combined with different data, becomes restricted or confidential like PII (Personal Identifiable Data), for e.g., an address independently is public data but when combined with user information is sensitive data. Data classification plays a crucial role in understanding the level of criticality a system is dealing with. Requirement Analysis In order to design a system better, we gather the requirements. Based on the requirements, the system is designed and developed. During the requirement phase, one should outline security requirements along with functional and non-functional requirements. Oftentimes, we focus more on functional requirements as these are the core of any system. We can derive the security requirements based on functional and non-functional requirements. Once we narrow down the functional and non-functional requirements, we can outline the type of data the system will deal with at various layers say transport layer, data layer, service layer, downstream, etc. Once all the dependent systems are identified, one will get a good idea of the type of data flow through and into the system. It is highly recommended to outline all the possible security callouts in the design phase. A few of these could be: Authentication Authorization Encryption at REST Encryption at Transport Application level encryption Logging preservation time Logging of non-critical data Authentication Authorization Encryption at REST Encryption at Transport Application level encryption Logging preservation time Logging of non-critical data Best Practices Design Once we have outlined the security requirements, we can implement various security practices into the system. These can be: Security code reviews where the review will look from the security perspective and best practices. Proper authentication and authorization mechanisms should be in place. PEN testing before release and at an interval can be incorporated into best practices. Proper alarms should be configured. Alarms in case of any malicious or high burst/volume of data are requested. Gating mechanism to ensure DDoS protection. Application-level encryption using custom-managed keys. Encryption at REST using custom-managed keys. Key rotation at a certain interval. Gating mechanism to ensure access to critical data is protected. Policies at storage to ensure critical data egress for qualified consumers. Data loss prevention mechanism in case of any compromise. Security code reviews where the review will look from the security perspective and best practices. Proper authentication and authorization mechanisms should be in place. PEN testing before release and at an interval can be incorporated into best practices. Proper alarms should be configured. Alarms in case of any malicious or high burst/volume of data are requested. Gating mechanism to ensure DDoS protection. Application-level encryption using custom-managed keys. Encryption at REST using custom-managed keys. Key rotation at a certain interval. Gating mechanism to ensure access to critical data is protected. Policies at storage to ensure critical data egress for qualified consumers. Data loss prevention mechanism in case of any compromise. System Design Once the requirements are in place, we move to high-level and low-level system design. This acts as a blueprint for the system and acts as a basis for requirement conversion. One should carefully look into various aspects of the system and use proper infrastructure to ensure security. In case the system is currently not dealing with any critical data, but in the future, due to some changes, the system starts to interact with critical data, the documents should be updated as such and re-reviewed to ensure all security practices are followed to uphold the high security and avoid any incidents.

Designing For Security: What You Need to Know

Mission-critical applications require . The goal of high availability is to provide users with consistent access to services or resources, minimizing the chances of interruption. is a specific mechanism used to achieve high availability. It involves automatically detecting the failure of a system component (like a server, network, or database) and immediately switching operations to a standby component without human intervention. This increases resiliency. high availability Automatic failover MariaDB MaxScale is a database proxy that includes features for high availability. In this article, I'll show you how you can try it out with an application implemented in Java and Svelte. online store simulator Architecture The following diagram shows the architecture of the demo application: A web application developed with JavaScript and the Svelte framework makes HTTP requests to a Java backend. The backend answers with that the frontend uses to update the user interface on the browser. server-sent events The backend is implemented with and connects to a database cluster using (reactive). The backend logic is, in short, a simulation of reads and writes to an online store database. The simulation is parameterized, and the user can adjust: Spring Boot MariaDB R2DBC How many reads to the database per minute. Product visits per minute: How many writes to the database per minute. Orders per minute: Write amplification. Products per order: How many seconds until a request to the database is considered failed. Timeout in milliseconds: The database cluster is front-ended by a called MaxScale. This proxy makes the cluster look like a single logical database to the Java backend. MaxScale also performs read/write splitting (sending writes to the primary MariaDB server and reads to replicas), as well as load-balancing of reads among replica servers using a configurable algorithm. Data is automatically replicated from the primary to the replica database servers. database proxy Building the Docker images from source I have prepared custom Docker images for every component in the simulator. You can either build the images from the source (optional) or use the already built and published images from Docker Hub. If you decide to build the images yourself, you can find the source code on GitHub: Custom images for easy deployment of replicated MariaDB topologies with MaxScale. These images are suitable only for demo applications. Use the for production deployments. : MariaDB deployments DO NOT USE THESE IN PRODUCTION! official MariaDB Docker images The backend application that connects to the database cluster. : Backend application The frontend application that makes simulation configuration requests to the backend and receives events to show the simulation result. : Frontend application Each repository has Dockerfiles that you can use to build your own Docker images. For example, to build the backend application image, run: docker build --tag alejandrodu/online-store-simulator-java-backend . Running the simulation All the services can be started using the following Docker Compose file ( ): docker-compose.yml version: "3.9"
services:
  server-1:
    container_name: server-1
    image: alejandrodu/mariadb
    ports:
      - "3306:3306"
    environment:
      - MARIADB_CREATE_DATABASE=demo
      - MARIADB_CREATE_USER=user:Password123!
      - MARIADB_CREATE_REPLICATION_USER=replication_user:ReplicationPassword123!
      - MARIADB_CREATE_MAXSCALE_USER=maxscale_user:MaxScalePassword123!

  server-2:
    container_name: server-2
    image: alejandrodu/mariadb
    ports:
      - "3307:3306"
    environment:
      - MARIADB_REPLICATE_FROM=replication_user:ReplicationPassword123!@server-1:3306

  server-3:
    container_name: server-3
    image: alejandrodu/mariadb
    ports:
      - "3308:3306"
    environment:
      - MARIADB_REPLICATE_FROM=replication_user:ReplicationPassword123!@server-1:3306

  maxscale:
    container_name: maxscale
    image: alejandrodu/mariadb-maxscale
    command: --admin_host 0.0.0.0 --admin_secure_gui false
    ports:
      - "4000:4000"
      - "8989:8989"
      - "27017:27017"
    environment:
      - MAXSCALE_USER=maxscale_user:MaxScalePassword123!
      - MARIADB_HOST_1=server-1 3306
      - MARIADB_HOST_2=server-2 3306
      - MARIADB_HOST_3=server-3 3306
    healthcheck:
      test: ["CMD", "maxctrl", "list", "servers"]
      interval: 5s
      timeout: 10s
      retries: 5

  java-backend:
    container_name: java-backend
    image: alejandrodu/online-store-simulator-java-backend
    ports:
      - "8080:8080"
    environment:
    - spring.r2dbc.url=r2dbc:mariadb://maxscale:4000/demo
    - spring.r2dbc.username=user
    - spring.r2dbc.password=Password123!
    - spring.liquibase.url=jdbc:mariadb://maxscale:4000/demo
    - spring.liquibase.user=user
    - spring.liquibase.password=Password123!
    depends_on:
      maxscale:
        condition: service_healthy

  svelte-frontend:
    container_name: svelte-fronted
    image: alejandrodu/online-store-simulator-svelte-frontend
    ports:
      - "5173:80"
    environment:
      - BACKEND_URL=http://java-backend:8080 Move to the directory in which the Docker Compose file is, and start the services in detached mode as follows: docker compose up -d Configuring MaxScale Before you start the simulation, configure MaxScale for transaction replay. Also, timeouts should be adjusted to make the simulation more interesting. Navigate to and log into the UI using: http://localhost:8989/ Username: admin Password: mariadb You'll see a dashboard with the MariaDB cluster state. There's a primary server ( ), and two replicas ( and ). Replication is already configured from (primary) to and (replicas). All servers should be up and running. server-1 server-2 server-3 server-1 server-2 server-3 Click on and then on the icon to enable parameter editing. Set the following parameters: mdb_monitor pencil ( ): This enables automatic failover. When a MariaDB server is down, MaxScale selects a replica server and reconfigures it as the new primary so that writes can continue to happen. auto_failover true ( ): This enables the automatic rejoin of recovered servers. When a failed server is up again, MaxScale detects it and configures it as an available replica server. auto_rejoin true ( ): Sets the number of monitor (a component in MaxScale that checks server status) iterations required for a server to be down in order to activate the failover process. We set a value of to make sure the failover starts immediately after failure. failcount 1 1 ( ): Connection timeout for monitor connections. We set a low value (one second) to quickly activate failover for this demo. backend_connect_timeout 1000 ( ): Read timeout for monitor connections. backend_read_timeout 1000 ( ): Write timeout for monitor connections. backend_write_timeout 1000 ( ): Primary failure timeout. master_failure_timeout 1000 ( ): How often the servers are monitored. monitor_interval 1000 ⚠️ These values are appropriate for this demo but very likely not the best for production environments! WARNING: Once the parameters are set, click on and . Done Editing Confirm You also need to enable transaction replay, which automatically re-executes failed in-flight transactions on servers that went down just after a SQL statement was routed. This is a useful feature for software developers since it prevents the need for coding failure cases and transaction retry. On the main menu, click on and then on any of the links in the list of servers. Edit the parameters as follows: Dashboard query_router_service ( ): Activates automatic retry of failed transactions. transaction_replay true ( ): Same as the previous when a deadlock occurs. transaction_replay_retry_on_deadlock true ( ): Same as the previous when a checksum mismatch occurs. transaction_replay_retry_on_mismatch true Once the parameters are set, click on and . Done Editing Confirm Starting the simulation With everything configured, you can start the simulation. Navigate to and configure the following parameters (names are, I hope, self-explanatory): http://localhost:5173/ Product visits per minute: 6000 Orders per minute: 60 Timeout in milliseconds: 8000 But before you start the simulation, you need to create the products for the online store. Click on | . Leave the default values and click on . You should see the UI updating as products are created in the database. Data Create products... Create Now, you can finally click on and see the simulation in action. Start Simulating a server failure At this point, the primary server is handling writes (orders). What happens if you stop that server? In the command line run: docker stop server-1 Depending on multiple factors, you might get some "disappointed visitors" or even a few "missed opportunities" in the simulator. Or maybe you don't get any at all! Product visits (reads) and orders (writes) continue to happen, thanks to MaxScale. Without automatic failover, you have to reconfigure everything manually, which results in more offline time, many disappointed visitors, and missed opportunities! Start the failed server: docker start server-1 Go to the MaxScale ( ) and check that is now a functioning replica. Dashboard http://localhost:8989/ server-1 You can perform a manual to make the primary server again. Click on and then mouse hover over the section. Click on the icon and select . Click and check again in the that the new primary server is . switchover server-1 mdb_monitor MASTER pencil server-1 Swap Dashboard server-1 Conclusion Automatic failover is only one of the components in highly available systems. You can use a database proxy like MaxScale to set up automatic failover, as well as other components such as load-balancing, query routing, transaction retry, topology isolation, and more. Check out the documentation at . https://mariadb.com/kb/en/maxscale/ Also published . here

Achieving High Availability with MariaDB MaxScale

I blogged about in the past, but I skipped an important method that's worthy of a post of its own: peek. This blog post delves into the practicalities of using to debug Java streams, complete with samples and common pitfalls. Java Stream debugging peek() code Understanding Java Streams Java Streams represent a significant shift in how Java developers work with collections and data processing, introducing a functional approach to handling sequences of elements. Streams facilitate declarative processing of collections, enabling operations such as filter, map, reduce, and more in a fluent style. This not only makes the code more readable but also more concise compared to traditional iterative approaches. A Simple Stream Example To illustrate, consider the task of filtering a list of names to only include those that start with the letter "J" and then transform each name into uppercase. Using the traditional approach, this might involve a loop and some if statements. However, with streams, this can be accomplished in a few lines: List<String> names = Arrays.asList("John", "Jacob", "Edward", "Emily");
// Convert list to stream
List<String> filteredNames = names.stream()       
                  // Filter names that start with "J"
                  .filter(name -> name.startsWith("J"))  
                  // Convert each name to uppercase
                  .map(String::toUpperCase)              
                  // Collect results into a new list
                  .collect(Collectors.toList());         
System.out.println(filteredNames); Output: [JOHN, JACOB] This example demonstrates the power of Java streams: by chaining operations together, we can achieve complex data transformations and filtering with minimal, readable code. It showcases the declarative nature of streams, where we describe what we want to achieve rather than detailing the steps to get there. What is the Method? peek() At its core, is a method provided by the interface, allowing developers a glance into the elements of a stream without disrupting the flow of its operations. The signature of is as follows: peek() Stream peek() Stream<T> peek(Consumer<? super T> action) It accepts a functional interface, which means it performs an action on each element of the stream without altering them. The most common use case for is logging the elements of a stream to understand the state of data at various points in the stream pipeline. To understand peek, let’s look at a sample similar to the previous one: Consumer peek() List<String> collected = Stream.of("apple", "banana", "cherry")
                               .filter(s -> s.startsWith("a"))
                               .collect(Collectors.toList());
System.out.println(collected); This code filters a list of strings, keeping only the ones that start with "a.” While it's straightforward, understanding what happens during the filter operation is not visible. Debugging with peek() Now, let's incorporate to gain visibility into the stream: peek() List<String> collected = Stream.of("apple", "banana", "cherry")
                               .peek(System.out::println) // Logs all elements
                               .filter(s -> s.startsWith("a"))
                               .peek(System.out::println) // Logs filtered elements
                               .collect(Collectors.toList());
System.out.println(collected); By adding both before and after the operation, we can see which elements are processed and how the filter impacts the stream. This visibility is invaluable for debugging, especially when the logic within the stream operations becomes complex. peek() filter We can't step over stream operations with the debugger, but provides a glance into the code that is normally obscured from us. peek() Uncovering Common Bugs with peek() Filtering Issues Consider a scenario where a filter condition is not working as expected: List<String> collected = Stream.of("apple", "banana", "cherry", "Avocado")
                               .filter(s -> s.startsWith("a"))
                               .collect(Collectors.toList());
System.out.println(collected); Expected output might be , but let's say we also wanted "Avocado" due to a misunderstanding of the method's behavior. Since "Avocado" is spelled with an upper case "A" this code will return false: . Using , we can observe the elements that pass the filter: ["apple"] startsWith Avocado".startsWith("a") peek() List<String> debugged = Stream.of("apple", "banana", "cherry", "Avocado")
                              .peek(System.out::println)
                              .filter(s -> s.startsWith("a"))
                              .peek(System.out::println)
                              .collect(Collectors.toList());
System.out.println(debugged); Large Data Sets In scenarios involving large datasets, directly printing every element in the stream to the console for debugging can quickly become impractical. It can clutter the console and make it hard to spot the relevant information. Instead, we can use in a more sophisticated way to selectively collect and analyze data without causing side effects that could alter the behavior of the stream. peek() Consider a scenario where we're processing a large dataset of transactions, and we want to debug issues related to transactions exceeding a certain threshold: class Transaction {
    private String id;
    private double amount;

    // Constructor, getters, and setters omitted for brevity
}

List<Transaction> transactions = // Imagine a large list of transactions

// A placeholder for debugging information
List<Transaction> highValueTransactions = new ArrayList<>();

List<Transaction> processedTransactions = transactions.stream()
    // Filter transactions above a threshold
    .filter(t -> t.getAmount() > 5000) 
    .peek(t -> {
        if (t.getAmount() > 10000) {
            // Collect only high-value transactions for debugging
            highValueTransactions.add(t);
        }
     })
     .collect(Collectors.toList());

// Now, we can analyze high-value transactions separately, without overloading the console
System.out.println("High-value transactions count: " + 
       highValueTransactions.size()); In this approach, is used to inspect elements within the stream conditionally. High-value transactions that meet a specific criterion (e.g., amount > 10,000) are collected into a separate list for further analysis. This technique allows for targeted debugging without printing every element to the console, thereby avoiding performance degradation and clutter. peek() Addressing Side Effects While streams shouldn't have side effects. In fact, such side effects would break the stream debugger in IntelliJ, which I discussed in the past. It's crucial to note that while collecting data for debugging within avoids cluttering the console, it does introduce a side effect to the stream operation, which goes against the recommended use of streams. Streams are designed to be side-effect-free to ensure predictability and reliability, especially in parallel operations. peek() Therefore, while the above example demonstrates a practical use of for debugging, it's important to use such techniques judiciously. Ideally, this debugging strategy should be temporary and removed once the debugging session is completed to maintain the integrity of the stream's functional paradigm. peek() Limitations and Pitfalls While is undeniably a useful tool for debugging Java streams, it comes with its own set of limitations and pitfalls that developers should be aware of. Understanding these can help avoid common traps and ensure that is used effectively and appropriately. peek() peek() Potential for Misuse in Production Code One of the primary risks associated with is its potential for misuse in production code. Because is intended for debugging purposes, using it to alter state or perform operations that affect the outcome of the stream can lead to unpredictable behavior. This is especially true in parallel stream operations, where the order of element processing is not guaranteed. Misusing in such contexts can introduce hard-to-find bugs and undermine the declarative nature of stream processing. peek() peek() peek() Performance Overhead Another consideration is the performance impact of using . While it might seem innocuous, can introduce a significant overhead, particularly in large or complex streams. This is because every action within is executed for each element in the stream, potentially slowing down the entire pipeline. When used excessively or with complex operations, can degrade performance, making it crucial to use this method judiciously and remove any calls from production code after debugging is complete. peek() peek() peek() peek() peek() Side Effects and Functional Purity As highlighted in the enhanced debugging example, can be used to collect data for debugging purposes, but this introduces side effects to what should ideally be a side-effect-free operation. The functional programming paradigm, which streams are a part of, emphasizes purity and immutability. Operations should not alter state outside their scope. By using to modify external state (even for debugging), you're temporarily stepping away from these principles. While this can be acceptable for short-term debugging, it's important to ensure that such uses of do not find their way into production code, as they can compromise the predictability and reliability of your application. peek() peek() peek() The Right Tool for the Job Finally, it's essential to recognize that is not always the right tool for every debugging scenario. In some cases, other techniques, such as logging within the operations themselves, using breakpoints and inspecting variables in an IDE, or writing unit tests to assert the behavior of stream operations, might be more appropriate and effective. Developers should consider as one tool in a broader debugging toolkit, employing it when it makes sense and opting for other strategies when they offer a clearer or more efficient path to identifying and resolving issues. peek() peek() Navigating the Pitfalls To navigate these pitfalls effectively: Reserve strictly for temporary debugging purposes. If you have a linter as part of your CI tools it might make sense to add a rule that blocks code from invoking . peek() peek() Always remove calls from your code before committing it to your codebase, especially for production deployments. peek() Be mindful of performance implications and the potential introduction of side effects. Consider alternative debugging techniques that might be more suited to your specific needs or the particular issue you're investigating. By understanding and respecting these limitations and pitfalls, developers can leverage to enhance their debugging practices without falling into common traps or inadvertently introducing problems into their codebases. peek() Final Thoughts The method offers a simple yet effective way to gain insights into Java stream operations, making it a valuable tool for debugging complex stream pipelines. By understanding how to use effectively, developers can avoid common pitfalls and ensure their stream operations perform as intended. As with any powerful tool, the key is to use it wisely and in moderation. peek() peek() The true value of is in debugging massive data sets, these elements are very hard to analyze even with dedicated tools. By using we can dig into said data set and understand the source of the issue programmatically. peek() peek() Also published . here

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