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88 Stories To Learn About Usa

After this long digression

94 Stories To Learn About Government

THE CURIOUS CASE OF BENJAMIN BUTTON

AN ANGRY KING

Crypto Is Persona Non Grata on the Hill. What to Do? Cryptocurrency (crypto) has become in the capital due to the FTX collapse.  Choice samples from among innumerable current vilifications: persona non grata David Z. Norris, CoinDesk’s chief insights columnist, . FTX’s Collapse Was a Crime, Not an Accident, 11/30/22 And Casey Michael at : CNN, 12/1/22, Crypto is out of control. FTX is just the latest example “... FTX’s stupendous collapse shines a light on just how symbiotic the entire crypto industry now is with the world of offshore finance – and how open the entire industry is to fraudsters and scammers, with offshore havens racing to offer crooks the kinds of allowances, and lack of regulation, they need to thrive.” All fair. But the critics are missing the forest for the trees, potentially to America’s great detriment.  Blockchain, the invaluable technology underlying crypto, is not crypto. We are now very much at risk of having the next Congress throw out the baby with the bathwater. If America is going to be relevant to the future of blockchain, our political leaders and policymakers must get crystal clear on the distinction between blockchain and crypto. Currently, they are confused. They do not grasp the essential nature of the blockchain. We are thus poised for a policy disaster that will set America back, maybe forever, on one of the world’s potentially most valuable emerging technologies. What layer one blockchains are currently domiciled in the US? For our , “Layer 1 refers to the base infrastructure of a blockchain.” non-geek readers Ethereum isn’t based here. Avalanche isn’t. Cosmos isn’t. Apart from RChain Cooperative, a Washington State Cooperative (of which the lead co-author was the principal founder), . no significant layer one blockchain has been brave enough to domicile itself in the US Why not? Legislative and regulatory risk. In the game played on the world’s stage, it isn’t SBF who is playing Slim Shady to crypto’s gangster rap. It’s the US Congress. Congress still does not grasp the Internet (TCP/IP, Web1) and the Worldwide Web (HTTP; Web2). It needs to understand that Web3, which will be built, if at all, on the blockchain, is all about fault tolerance. The Real Value of the Technology The decentralized nature of these layer-one networks is a side effect of their fault tolerance. That has profound implications for the US position in global network services. America is fumbling its leadership in the blockchain space – blockchain as a computing platform, not as a “cryptocurrency.” This could well cost its leadership, its dominance … and the attendant prosperity in this sector. And, without exaggeration, this is an issue of national security. What is important about Bitcoin is not its use as an alternate currency nor as a purported store of value. What is important about Bitcoin is its . resilience The nature of its (admittedly brutally inefficient) consensus mechanism means that if 10 nodes go down, 100 more, running anywhere in the world, can take their place. That means that it is resilient (fault tolerant) not only against governments and gangsters but against failures that will stem from imminent environmental disasters. This feature makes it a crucial step in a process leading to a fault-tolerant global computational infrastructure that we can use to coordinate as various existential disasters come at us. What is important about Ethereum is not its use as “programmable money.” What is important about Ethereum is its improvement of the genetic blueprint of Bitcoin. It took the software architecture used in every Internet-facing API — from Google to the Guardian — and showed how to combine it with Bitcoin’s consensus innovation to make a resilient and defensible – if not scalable – compute infrastructure. Every Internet-facing API has for more than a decade been protecting their APIs from denial of service attacks . by issuing tokens They just weren’t cryptographically significant tokens. Combining tokenized network security with a decentralized consensus creates the blueprint for a defensible, resilient — though not scalable — compute infrastructure. Were it to be made scalable, it constitutes a design pattern for a vastly more valuable Web offering, and a real global compute infrastructure we can use to coordinate. At the architectural level, it only takes a few modifications to get a better fit for the goal of a scalable and resilient coordination infrastructure. Networks like RChain improve Ethereum’s genetic blueprint using a much more energy-efficient consensus and a mathematically rigorous concurrent execution mechanism. Astonishingly, this same mechanism expands smart contract capability to provide a query language for on-chain data. In this way, not only do we get near limitless scaling, but we get a blockchain so that Web3 is not a step backward, but a step forward with an expansion of searchable Internet resources. searchable The leadership of this “Superweb” is what America is at risk of forfeiting to and Singapore. , Zug, Switzerland Blockchains > Crypto The critics are right that speculation-driven crypto belongs in a museum, relegated to a dust-collecting corner of the Internet. That way performance-oriented technological developments, like RChain, can get the oxygen they deserve, and the US can regain the technological, economic, national security, and even moral high ground. If Bill Gates and John Doerr are correct, we are facing threats requiring an unprecedented degree of social coordination on a global scale. And now, just at the moment we need an exponential lift in our capacity to coordinate a concurrency-enabled blockchain, comes along, giving us a blueprint for a resilient, defensible, scalable, searchable global computational infrastructure. It would be a national tragedy if, distracted by the hoarding impulses typically associated with thrift stores and unregulated speculation markets, we were to waste this opportunity. We know what will happen if Congress fumbles this opportunity. It’s looking increasingly likely it will. Can Congressional leadership see the need for legislation inhibiting the collusion opportunities inherent in the current generation of the crypto sector while unleashing the imprisoned lightning of a concurrency-enabled blockchain? Leaders who can grasp this will have the power both to build back better and make America great again. Greg Meredith and Ralph Benko were recently recognized by HackerNoon as “2022 contributor of the year” on the topic of scaling. Lucius Gregory (Greg) Meredith, the founder of RTech, is a mathematician, the discoverer of the rho-calculus, a co-inventor of OSLF (Operational Semantics in Logic Form), and the inventor of the ToGL approach to graph theory. Ralph Benko, an advisor to RTech and other enterprises, is co-author of several critically acclaimed books, including , and is a former White House official. Redefining the Future of the Economy:governance blocks and economic architecture Photo from Unsplash by Andy Feliciotti Lucius Gregory Meredith and Ralph Benko

Will the Real Slim Shady Please Stand Up?

With the downfall of FTX, cryptocurrency is getting another black eye. The valuations shrink and those who bought in the last few years are looking at huge losses. Sam Bankman-Fried is persona-non-grata now. The FTX implosion is a warning to all that the world now runs on Internet time AND that includes the money world. Remember it is not just cryptocurrency tokens that are digital money. Most money everywhere is digital. cryptocurrency Sending money via our smartphones is convenient, but is it in our best interest? Already some countries are nearly free of physical money with most transactions being done digitally. There is an extreme disconnect in some of these countries between the assets stored digitally as savings and checking accounts and the actual physical currency available. In New Zealand, there is only enough physical money to cover about 5% of the money in citizen accounts. If 6% of New Zealand citizens withdraw their money due to some panic, the system would be in crisis and perhaps even collapse. One could be forgiven for seeing today’s digital improvements as far superior. Ironically in the Information Age, information is more controlled than ever before. The digital revolution now allows for more control of the distribution of information rather than facilitating a wide distribution of useful knowledge. Digital money of all kinds has made things more opaque for average people. During the Great Recession, the run on the banks was significant. Wealth was fleeing American financial institutions very quickly in the summer of 2008. The flight of “digital cash” nearly crashed the financial system. The reasons to move back to Glass-Steagall and other regulations born from the Great Depression seem quite justified when one sees some of the numbers, but I digress. The Great Recession of 2008 was a Great Warning. It was a warning that things can move dangerously fast in digital finance, not just cryptocurrency. FTX should be the final proof anyone needs that cryptocurrencies are some kind of “safe haven” from things like the Great Recession or the Great Depression. Cryptocurrencies are an evolving tool in an ever more complicated financial world, but they are no silver bullet. A possible positive outcome from the crash of FTX could be a slowing of the movement to eliminate analog cash. There is no question that analog money can be a throttle on finance. Physical transactions must take longer than digital ones. In that light though, analog cash can be a stabilizing factor because things just take longer to move through the systems. Analog cash also forces transparency on the financial markets. Transparency is the great gift of analog cash. The elimination of cash has made things predictably more opaque. Digital cash ESPECIALLY digital cryptocurrencies hide terrible things behind the scenes and then collapses quickly. The collapse of FTX illustrates demonstrates the speed. In the late 20th century, when the savings and loan crisis roiled American financial systems, I learned of it because I saw a line outside of Tahoe Savings and Loan in Rancho Cordova, CA. There was a physical manifestation to the savings and loan crisis. Even before it really started showing itself in the financial markets, I saw lines begin to form at some local savings and loan institutions. A cryptocurrency exchange has no physicality. Insiders probably started queueing up early to get their money out of FTX, but no one saw it. Regular accounts on the exchange had no idea what was happening. No one was watching traffic to FTX. There were no lines out of the FTX offices.  It just went, Poof! Analog cash does act as a throttle on how quickly things can get really bad. We on the street get to know a bit more about what is truly going on in the financial markets when there is more physical cash. I fear a completely opaque system operated by greedy robber barons. Analog cash prevents COMPLETE opacity. Regular people should preserve that as much as possible. Let me be clear here. I have some cryptocurrencies. I definitely think there is a place for them in this world. Mostly, I decided to park some money in the crypto space to wait and see. Depending on how this epic battle between the super-rich libertarians and the all-powerful nation-states works out, things like Bitcoin, Ethereum, and Dogecoin may still have their place as cash alternatives. Cash is king, but cash is a friend of the regular people, too. Do not forget this. If a person split their pandemic stimulus check between cash put in a cookie jar and crypto, I hazard a guess the cookie jar would be worth more than any crypto holdings accumulated. Though, if they were smart they still have their crypto in a hardware wallet. Despite FTX, I continue to HODL cryptocurrencies, even if cash is still king to me. I am proud of the fact is just as valid today as when I wrote it well over a year ago. If you bought then, you were not on FTX when it imploded. the book I wrote on cryptocurrency that book If you found my advice on cryptocurrency durable and useful - please vote for me in the Noonies → https://www.noonies.tech/2022/web3/2022-hackernoon-contributor-of-the-year-cryptocurrency

The FTX Fiasco Demonstrates the Utility of Analog Currency...Again

Astounding Stories of Super-Science June 1931: VOL. VI, NO. 3 - The Man From 2071

The Beautiful and Damned: Book I, Chapter I - ANTHONY PATCH 

Maybe you have noticed that there is a lot of fake Binance, Ethereum, Ripple and Bitcoin Cash streams on Youtube lately. The crazy part is that those live streams are on a big accounts with thousands of subscribers. The scammer or group of scammers most likely from Russia started a new scam that is targeting YouTubers. They simply write you an offer to promote their platform on your youtube channel in the form of a preroll video. They want you to download and try out their platform to see how it is and if you are interested. After clicking on the shady link it will take you to their website that is made in a 1:1 ratio with another website from a real company. After downloading and running the program nothing will happen, but in the background, there is a program running. What is the program doing? Screenshot of your PC screen. Text document with all information about your PC. Text document with all passwords saved in your browser + copies your session ID. Download cookies from your browser Gets remote access to your computer without you noticing. All that repeating every 5 seconds. If they get access to your YouTube account they will just turn to rename the account to Binance or Binance announcements, hide all old videos, change profile picture and run a streaming loop with a fake giveaway in the description to scam people of money. This happened to a lot of YouTubers lately. According to unofficial information, it could be over 50+ YouTubers with big fanbases. Because of scammers are able to copy your browser session and cookies they are able to bypass -Step . Two Verification Conclusion Scammers will always look for new tricks and ways to scam people. Sometimes they use people’s naivety or carelessness. If you are a YouTuber be careful and don’t fall for such a cheap trick. It’s not always Binance, sometimes it’s Ethereum or Ripple, but in 90% of cases, it’s Binance. On top of everything, YouTubers can lose their sensitive personal data if they hold them on their PC. One Czech YouTuber lost his ID to a scammer to find selling on the dark web 3 days later. Among accounts that have been scammed are for example that have over 300K subscribers, his account was broadcasting Binance fake giveaway for 3 days before his account was restored, but not everyone is lucky and some can’t restore their accounts. Roth Wellden Be careful, not only YouTubers but everyone because scammers are trying their best. REAL STORIES BEHIND YouTube hacks In this article, we will focus on a real examples of hacked YouTubers. If you want to read the previous article on how the scammers were able to steal those accounts click here. Since my last article, a lot of you asked if this is truly real if those accounts are stolen from the real creators and the answer is YES… According to my observation in most of the cases, we can prove that there was a real person behind that account before. GhostBlock - 589 000 (Article updated to add this story 10.01) GhostBlock hacked! GhostBlock is a famous YouTuber with a huge community and close to 60M views in total. He is doing animated Minecraft videos. Today (10.01) he was hacked and hackers instantly turned ON Binance fake giveaway to scam his community. There are even from his fans that are confused about what’s happening and who’s Binance CZ. videos coming Man…your channel got hacked… What heppend to your chanell😢 WHAT HAPPENED TO YOUR CHANNEL?!! What Happen to GhostBlock? Those are just few examples of comments by his confused community. Youtube needs to act fast and precise to stop hackers from taking an advantage of such a huge community. 2. MarcoStyle - 355 000 subscribers Account restored Using Marco as an example we can truly unveil YouTube’s ignorance and slow approach towards creators that are operating on their platform. Marco got his account hacked and it took 2 weeks to restore it. YouTube acknowledges the fact that his account was stolen since day one, but nothing happened to the hacker! He was freely using the account, streaming and scamming Marco’s community that he was building for years. On top of that, they kept the Marcos verified badge ON! Doing scammer a favor to look legit. Marco’s community was scammed of $15,000 in Ripple during the fake giveaway. Eventually, Marcos got his account back after 2 weeks, but the fact that YouTube did nothing to protect his community during the hack is disgusting. Marco’s story was covered in . Forbes 3. RodiL - 327 000 subscribers Rodil is Portugesse gamer, YouTuber and streamer. Three of His accounts were hacked over one week ago. Something that he was building for so long is lost now. He is desperately trying to reach youtube, but it seems like they are ignoring him and his community. Meanwhile the hacker aka. is happily broadcasting scammy giveaway. Brad Garlinghouse [Ripple CEO] This is from RodiL’s twitter Guys, I got robbed! RT THIS POST PLEASE I lost access to my 3 channels, "Festinha do Rodil", "RodiL" and "Rodilzera". When I go to any of these channels, ask me to create a channel from 0, with a new name. That is, they unlinked my channels to another email 4. Roth Wallden - 307 000 subscribers Account restored The story was almost the same as in Marco’s case. Roth Wallden was hacked while downloading software from the scammy email. In his case, the account was held by the hackers for 3 days. According to him, YouTube has acknowledged the hack since day one but did nothing to stop the hacker streaming fake giveaway. Finally, thanks to his constant pressure on YouTube, big follower base and connections he was able to recover his account after 3 days. Not only that his YouTube was stolen it was available on the black market for 0.45 BTC 5. Dollar Selmouni 179 000 Subscribers is a Hispanic musician with an official badge on YouTube. His account is still being held by hackers despite that it was hacked over 1 month ago! Broadcasting fake Ripple giveaways. Right now it seems like the hackers are not using this account to broadcast giveaways, but more likely it’s already on the black market waiting for the “new owner”. How is it possible? That verified musician by YouTube wasn’t able to recover his account? Dollar Selmouni 6. BinNhiTV 165 000 subscribers Group of people mainly kids from Philipines making songs for kids with over 42 000 000 of total views were hacked 5 months ago. The hacker didn’t even bother to delete the old videos. There is a big problem with YouTube that they are ignoring most of the hacks and by doing so they expose innocent communities to hackers. 7. GratakosYT - 128 000 subscribers GratakosYT was doing games clips on youtube, but his account was lost to the scammers. is constantly reporting his hacked account trying to reach YouTube, but with no result so far. Is this bad staff? or just pure ignorance from YouTube? not caring about creators nor their community. It’s clear that the account is hacked yet they are not banning the stream in order to protect the Grakos community. He was forced to create and start all over again. GrakatosYT community the second channel OTHER EXAMPLES OF HACKED ACCOUNTS Thai gaming channel Greek Basketball team with over 10 000 000 total views Malaysian music channel Russian YouTuber Katyusha YouTube artist Savannah YouTube educational channel There are so many more examples, but it’s simply impossible to highlight them all. We can clearly see that YouTube is doing close to 0 to stop those hackers. Creators with a lot of subscribers might have a chance to recover their account thanks to publicity, however, when someone has less than 100K subscribers it seems like YouTube doesn’t even care. For many of those smaller YouTubers its years and years of work and thanks to YouTube, it seems like they lost it all. YouTube’s approach is weird and unprofessional allowing the scammers to stream their fake giveaways even when they acknowledge that the account was stolen. It looks like they are working with the hackers, not against them. Let’s hope that thanks to bigger publicity YouTube will fix this mess soon. Thank you for reading Benjamin from DAO Makers

YouTube Fake Crypto Giveaways Explained - Behind The Account Hacks

We all have this one friend — we will call him “Fred” — Fred got into the “industry” sometime in 2017 and rode many “coins” to the top. Now in the depths of the bear in 2018, Fred likes to joke that he has the most “steady hands” in the industry. Fred never dumps. He is a true believer and watches them go up… and down and maybe up again (but does he dump then?) So the point of this article isn’t necessarily about , who jokes to give him all the allocations because he will never dump them. Fred loves his coins so much he says he is “married to them.” But more so this is about the constant villainization of dumpers. Being the CEO of a company that is issuing a token (and obviously wants to maintain a strong Utility), I get to hear it from all sides. Literally I can talk to entities A,B,C,D and E and they all will, like clockwork, accuse the others in the environment of being “dumpers.” Steady Hands Fred When we were in Shanghai meeting with people and funds, all they could do is talk about the “expert dumpers in Beijing” and when we were in Beijing — they told us to “be careful of Shanghai funds…” Here are a few examples that I have heard specifically in the last month as we continue to secure funding for Unification. Don’t give an allocation to XYZ Syndicate because they are all retail and they will dump. If XYZ Fund is involved I wont invest because it is well known that they take 90% discount with no lock and then pummel the market. Airdrops are the worst because all the token receivers will dump immediately. On his last project, “Joe Adviser” took 3% of the total token supply, sold it all and crashed the price so all of the core team and investors got shafted — don’t work with him. I heard they gave John McAfee 10% of the token supply with no lock in exchange for a few tweets — and everybody knows this — not good optics there. I heard that So-and-So’s Group got a 2 Million dollar allocation — that is too much so I don’t want to be involved. ABC Fund are responsible for dumping all their coins when it hit the market for WXY Coin and crashing the price so don’t give them an allocation because they will do the same to you. Ok — the reality is that not everybody is a dumper. One of our very close mentors and adviser told me frankly that he is still holding (for good or bad) all of the advisory tokens he got from other projects and that he feels that it is a net gain and dedication to the project to keep them all the way through. But he is a rare one in my experience. So this brings up the maybe or perhaps practical question of what is the obligation of the investor not to dump? Is it like tipping in the USA where its not included in the bill, but is culturally a “de facto mandatory” and you are more or less persona non-grata if you don’t leave one? I mean if tokens are unlocked and able to be transferred then they are the property of the private key holder and they have no “responsibility” to the community in any sort of black and white terms? Right? philosophical So whose responsibility is it? Well, as far as I am concerned as a project founder, it is the responsibility of the community because well — we are all believers in the true Utility of a token. So WHO IS THE DUMPER? In my experience, more often than not — “funds” are going to dump you as hard as they can unless they have a good reason not to (exception is USA funds that are more VC style — they get married to the coin). Why do these glorious “funds” actually become the bad guy in these situations? So let me take that back, I don’t want to put all “funds” in the same bucket as the word can mean many different things. Fund Type 1— Some guy that bought ETH in early 2017 or got lucky on a ICON/VERGE/TRON flip and institutionalized with his buddies to feel more legit and now they are a “fund.” Typically there is no LP’s and they are just playing with founders’ capital. These are more likely “whale pods” calling themselves funds and in my experience these guys typically are low-volume dumpers. EtherBro Fund. They are “involved” in the industry — go to conferences, get to know the founders, and generally looking for “wins” not only in money but in net happiness of community building. They have made good money and want to make more, but on the same note don’t really have any need to take immediate profit and sometimes will sell just enough to cover their downside and leave the rest on the table. Fund Type 2 — This “fund” usually started with some guy that is in BTC. Maybe he bought a bunch and held, maybe he was a miner or had a fund from the old days. They have already been through a few market swings and are still here and likely view themselves as a of living deity of sorts. BTCBro Fund. OG status These “funds” usually have gob-smacks of coins and have hired in professional money goblins to manage the day-to-day and make sure that they are profitable despite all of the founders wild ideas and whims. Even though they may seem large and varied, typically still they take no LP’s (but they all plan to LOL) and are just an extended carnival of the founders mind. Projects can be fortunate to get involved in these and experience will vary — but despite the friendly face at the top, rest assured that they have an ex-Wall Street guy the rich guy hired in to make sure they have profitable quarters. There are many stories of these “funds” mercilessly dumping the bottom out of a project that they helped kickstart. They seem to feel entitled to do this because they feel they are the “signal-giver” in the market and that the project would have never been anything without them in the first place. Fund Type 3 — This is probably 70% of the funds we meet and are a variant of the EtherBroFund who never liquidated. They basically have no money to invest and will want to take you on “roadshows” and “incubate you.” More information on the BrokeAssFund in the BrokeAss Fund. How to ICO in 2019 article. Fund Type 4 — These are typically associated with the real-life VC industry in the USA with the main players having traditional institutional investing experience. If you are lucky enough to get one of these to invest, then give them all your allocation because they are all looking for the next BTC and will HODL it until it snows in Miami. ResponsibleAmerican Fund. Fund Type 5— Usually means Korean or more typically Big Chinese Fund. For much of ICO 1.0, Asia Funds were the main “signal” in the market and typically EtherBros and others would just blindly follow what the Asia funds did — Why? Asia Fund. Well — two reasons. a — Asian funds were the early employers or implementations of “market makers.” These would be entities that would use capital to buy up coins if they went below issuance price so it would keep the price from going to the floor. They typically didn’t need to do this forever — as it combined with reason B to create a healthy environment. b — Asian funds would actively push/hype the projects to the Asian retail markets who would then come into the project and bring an influx of new investment and interest. Now this certainly was not limited to Asian funds and markets, but certainly they were clearly coming with the professional and structured approach to things — rather than western funds who sorta just followed. There is one prominent (famous actually) US-based investor with his bespoke “fund” which told a project, “get me two more china fund logos on your website and I will invest.” Basically for the ICO 1.0 run, there was a certain confidence that Chinese players were the adults in the room and were serious about making money so it was generally a safe bet to follow them if you could get in early enough. I think the difference in early ICO 1.0 is the Asia funds did work and “made sure” that their projects would succeed instead of western funds who were betting on the team to figure it out. Sound pretty daunting? So what is the best course of action for a project? By the above description, it seems that most “funds” seem fairly bleak. So we need to look at the actual value a “fund” could provide. A — They provide a “signal” — No logic to it of course, it is a herd mentality — It works. B — They provide exchange “intros.” Totally unneeded in late 2018, the industry is small. I can walk up to at a conference and everybody and their dog is providing If you have a halfway decent token and volume, all the tier2 exchanges will list you for next to nothing and there are 100 ways to start the application for tier1 exchanges who will judge you according to their own criteria. CZ “exchange intro services.” C — They actually “do the work.” A three letter China Fund got a great reputation as the fund to follow in ICO 1.0 because they weren't just investing — they were actually making sure that projects did well, by doing the marketing in-house and managing the Asia retail hype. So for a time it was a good party if you heard they were on a project. This was fun while it lasted and eventually fell apart and their reputation petered out. D — They buy your token. Ok this one is good as long as terms are happy. Personally I have spoken with some well regarded funds who basically want me to give them a dump truck of UND for free (massive discount), and then they would roadshow us and resell the tokens themselves at a premium. Effectively profit taking from the beginning which is ok I guess if it works. We politely declined their offer. All of these things are ok and worked great in the Bull run that happened because of the massive influx of retail investors to sell to. These retail investors are gone now and none of these “tricks” are working anymore. Funds are dried up, they pushed all their remaining liquid assets into various ICO’s in Spring and Summer with no retail to take the torch and founding teams who were in it for the pump and not the long term technology. These investments never went liquid and only the ones with deep pockets remained and they sit quiet on the sidelines or “incubate” projects close to them looking to be the new signal-giver. So the point is that as of right now, the market has no compass. Things that worked in the past are not working — the reason being is that they worked not because of the genius of the strategy, but because retail investors had FOMO’ed in and were taking the dump. Smart forward-looking players will recognize this. Things like: — Top Ranking on So-and-So’s Spreadsheet — 5 China funds’ logos on the website — Random PHD with 8 years of blockchain experience on the team — They have signed a partnership agreement with <Insert big company name> (reality is they usually have a buddy that owns a local franchise) — They have a 50-million user base from their other company they are bringing over to use their payment token. (Stuff like this is usually debunked with any amount of real diligence — except if you are MachineZone and doing your own Reverse ICO.) — MASTERNODES! (did you LOL?) So we are at a point where we as an industry need new signals so that we know what projects are “good” and I think that we, collectively, are beginning to grow up and “funds” are not necessarily holding any more authority than before. I don’t want to make this a political article, but I can use a recent political example to make a very stinging point. Remember when Donald Trump said he was going to run for president? Well, the “republican establishment” was like, “umm no — you don’t listen to anybody and going to get us all in jail.” (In this example, this “establishment” is the “Funds”), but the establishment (funds) had already alienated their parties base (retail investors) so along comes DJT who just says a bunch of stuff and the hometown voter bases start to follow him. Guess what? The establishment had to follow just as funds There is a whole lot wrong with the above example — but stepping back and looking at it logically — it is all true. will follow any project that gets the attention of retail. The new signals aren’t going to come from the establishment or existing funds — they have already burned their bridges. Rather we need to be looking for projects that touch the retail investor or common person directly. The blockchain circle jerk as we know it is dry of juice and if you want to know who is going to win — look to the project that regular people are talking about. The one that people who can maybe invest 1 ETH into a project are getting excited about. The one that shows use cases that bridge over to the “real world” outside of blockchain. The ones that would be well suited and just as comfortable at CES or SXSW as they would at Consensys. So back to the point that every project founder wants to know. How do we “avoid the dump?” Nobody knows for sure —But I can tell you some “best practices” that we have been seeing from the A quality projects providing a true value to the community. The run the raise like a “product launch” with a timeline — beginning, middle and end. Meaning that investors can see that if milestones are achieved that they will see liquidity on a specific date. This, and by setting a stage where there is a limited time to contribute (instead of open ended) allows for clear yes/no answers which have a much higher chance of yes then giving them the option to defer. No Bonus for anybody — Bonuses encourage dumping and dumping lower and lower because if a token is issued at 5 cents and some guy got it for 2 cents — he will basically keep dumping all the way down to 2 cents if all of the other factors show him no confidence or he just needs to be liquid for something else. What can often happen here as well is these “bonus getters” will dump the price to their real paid amount and destroy confidence in the token and then will do a rebuy at that level leaving all of the weak hands rekt and any sort of immediate confidence in the token gone. Strategic lock — working with an A+ market team — they have formulas that tell them exactly how to do their magic and they need to know EXACTLY how many coins will be in circulation. Investors don’t like locks? That is their problem — A powerful team will always allocate for the worst possible scenario — hence the need to lock. Market Makers — Point blank if you are a founder of a team and not planning on employing the best market makers you can find and budgeting for exactly what they need, then you need to go refund all your tokens right now and go back to running your eCom store with your brother from the coffee shop. Get on a proper exchange and yes it is not easy — We all know what it takes to get on a Tier1 exchange- and the reality is that a token being listed on a Tier1 vs Tier2 is the difference between being David Beckham or being the backup Goalie for the local Sunday football league. Plan for it and do it. We aren’t here to fuck spiders. Early lockout from Decentralized Exchanges (DEX’s) — DEX’s are bad for this game— There I said it. We have seen so many tokens ruined because they get shifted around on DEX’s for months before they get a proper exchange listing. What happens is this allows semi-retail buyers to relieve some FOMO to buy and let them get the token in a little drip. This may not crash the price, it may even make it go up a little over issuing price — but what it does is makes it so that your token has lost any air of exclusivity and “hard to get-ness” so when it hits an exchange then nobody really cares. This goes in line with the law of exponential growth — if five people want a token and each one buys it on five different days then the noise is minimal. However if we force these five people to all buy it on the same day, then a moon landing is imminent. Practically there are two ways to do this, one is to write the token contract in a way to lockout DEX trading — you can have it as a one time “turn-on” variable. The other is to just not actually issue tokens until the day/hours before the token is listed (preferred). Intelligent Allocations — I have a personal rule that I do not give private sale allocations to anybody who have I not shaken their hand or had a substantial phone conversation with, and that came well recommended. I have committed full/double time to UND for the next 3 years and know exactly the growth path I am going to take to get there. Anybody who holds our token is investing in a token that is useful in the long run and can look past short speculative gains (which we do not want to encourage but sadly seems to be prevalent in the industry) By looking a person in the eyes and shaking their hand and telling them my story — — they can then share that mutual vision and likely understand that UND is the future backbone of data liquidity that will be around for multiple generations. our story Run it like a real business — I think by this point we have more or less shaken out the early entrants who were just in the right place and right time but never had any plans or knowledge on how to actually run a business. Well, businesses have strategy. Time your announcements, engage with new influencers around the time of new token releases so that there is fresh retail interest to offset the sell pressure. Make partnerships and prosper. If you can’t do this and instead just spend your time and investors monies popping bottles and doing Ketamine at conferences, then your brothers eCom store awaits. And most importantly — Spend your money from the token sale to do good things — Let’s be honest, no startup out of Silicon Valley in the current environment would ever get 10/20/30+ million USD to build out some idea. If you are building out a tech-thingy, guess what? You don’t need 10 million dollars to do that. You don’t. So what is all this money for? It certainty isn’t for you to buy a ski cabin (erm…). Use it for press, roadshows, real conferences, talent — make a business. Push your coin to mainstream. This is how we win. Your investors have trusted you by investing in the token you have issued — put it to use to for the ecosystem development and not to celebrate the fact that you were able to raise so much money. Neyma Jahan is the Founder of — You can follow him on twitter Unification.com @neyma Please note: this article is the authors own opinion and should not be considered associated or endorsed by Unification. Good luck.

Who Dumped Your “token” and How to Avoid it next time.

Real-Time Event Monitoring with Redpanda, OpenTelemetry, and Grafana

Regardless of the industry they operate in or the number of people they employ, businesses with mature observability practices can respond to incidents faster — and save time and money in the process, according to the second annual . Grafana Labs Observability Survey Organizations are making observability a critical part of their software development lifecycles as they grapple with the complexity of modern applications. However, this year’s survey makes it clear that not everyone is at the same stage of adoption, with teams that are further behind struggling to keep up with the always-on demands of today’s users. More than 300 observability practitioners participated in the survey. They shared their thoughts on a wide range of topics, including the varying degrees of maturity, the sheer volume of tools in use, the lingering concerns about expenses, and the ubiquity of open-source tools in this still-emerging market. Key Takeaways From the Grafana Labs Observability Survey You can to learn what respondents had to say about these topics and more, but for a quick assessment of the state of observability, here are the five key takeaways from the survey. read the full survey Observability Practices Are Still Maturing We gauged where respondents were in their journeys by asking how they approached observability.  A little over half say their organization has taken a proactive approach, which points to the growth in this space. However, organizations are still more likely to be reactive than systematic, which means they often hear about problems from their customers rather than have systems in place to address issues before their users ever find out. (Check out our for more on these designations.) Observability Journey Maturity Model And the different approaches can result in very different outcomes, with . 65% of those with a systematic approach having saved time or money through centralized observability, compared to just 35% of those who take a reactive approach Tool and Data Sprawl Remains a Major Hurdle More than two-thirds of teams use at least four observability technologies, with . In addition, half of Grafana users say they have at least six different data sources configured and actively in use in Grafana. respondents collectively citing more than 60 technologies currently in use This can translate to considerable complexity and overhead, and the bigger the company, the more tools and data sources they have. It’s no wonder that among teams that have centralized observability, 79% say it has saved them time or money as a result. Open Source Is the De Facto Standard We cater to a vibrant open-source community at Grafana Labs. And while that likely skews the results in our survey, there’s no denying the impact of OSS. Eight of the 10 most popular technologies cited in our survey are open source, while an . overwhelming majority of respondents are investing in Prometheus (89%) or OpenTelemetry (85%) Interestingly, there’s considerable overlap between the and communities. Almost 40% of respondents use both in their operations, and more than 50% increased their usage of each project over the past year. Prometheus OpenTelemetry AI Could Play a Big Role — Eventually AI is getting tons of attention these days, but its role in observability is largely aspirational today. Still, observability practitioners remain excited about its potential to accelerate incident response and simplify observability adoption. respondents say they would like to see, with more than three-quarters calling it out. Other top wish-list items for AI include predictive analytics and dashboard generation. Anomaly detection is the most cited AI-enabled feature Cost Emerges as a Top Concern , while other related topics — cardinality, unpredictable bills, and vendor lock-in — are also cited. More than half of respondents say cost is their biggest concern about observability

Navigating the Observability Landscape: Highlights From Grafana Labs’ 2024 Survey

পর্যবেক্ষণযোগ্য ল্যান্ডস্কেপ নেভিগেট করা: গ্রাফানা ল্যাবসের 2024 সমীক্ষা থেকে হাইলাইট

Navigieren in der Observability-Landschaft: Highlights aus der Umfrage 2024 von Grafana Labs

Navegando por el panorama de la observabilidad: aspectos destacados de la encuesta de 2024 de Grafana Labs

Naviguer dans le paysage de l'observabilité : points saillants de l'enquête 2024 de Grafana Labs

ऑब्जर्वेबिलिटी लैंडस्केप को नेविगेट करना: ग्राफाना लैब्स के 2024 सर्वेक्षण की मुख्य विशेषताएं

可観測性の展望をナビゲートする: Grafana Labs の 2024 年調査のハイライト

관측 가능성 환경 탐색: Grafana Labs의 2024년 설문조사 하이라이트

Navegando no cenário de observabilidade: destaques da pesquisa de 2024 do Grafana Labs

Навигация по ландшафту наблюдаемости: основные моменты исследования Grafana Labs за 2024 год

Gözlemlenebilirlik Ortamında Gezinme: Grafana Labs'ın 2024 Araştırmasından Önemli Noktalar

Điều hướng bối cảnh khả năng quan sát: Những điểm nổi bật từ Khảo sát năm 2024 của Grafana Labs

探索可观测性领域：Grafana Labs 2024 年调查要点

Prometheus and Grafana are two big names in the open-source world of observability. Both are widely liked and used, with vibrant, opinionated communities, and they routinely build on top of each other. So, how do Prometheus and Grafan stack up against each other? In this blog, we'll compare them and examine - How their offerings overlap and differ How they perform against each other on a variety of criteria How they’re commonly used - together and separately, and why Introduction to Prometheus and Grafana Prometheus is a monitoring solution. An open-source project, it was started by SoundCloud in 2012 and has since gained immense popularity and traction. One reason for its widespread adoption is its seamless integration with Kubernetes. Prometheus is the de facto monitoring standard for a Kubernetes environment. Prometheus Prometheus offering At its core, Prometheus is a time-series DB that uses a pull mode to fetch metrics from instrumented jobs. With its multidimensional data model and flexible query language, Prometheus allows devs to easily get, store, and work with metrics data. : Prometheus discovers and scrapes metrics from predefined targets, typically service endpoints or infra components. Data Collection : Prometheus has a time-series DB that allows for highly efficient storage and querying of metrics data. Data Storage : PromQL (Prometheus Query Language) is used to retrieve and analyze metrics. It's a flexible query language allowing for precise slicing, dicing, and aggregation of data, ideal for deep performance analysis. Querying with PromQL : Prometheus comes with a built-in visualization interface, but it is basic and primarily intended for ad-hoc querying. For a richer, more robust visualization experience, Prometheus recommends using Grafana. Visualization Prometheus Expression browser: Image Source In contrast, the Grafana visualization of Prometheus data is much richer Image Source Grafana Grafana started as a visualization tool. However, over the years, Grafana has evolved into a full-stack observability platform. It not only helps users visualize data but also assists in collecting and aggregating it. Grafana can be used not just for metrics but also for other observability data (logs and traces). See the image below for the difference between Prometheus and Grafana offerings. In summary, the primary difference is that Prometheus is primarily a monitoring solution, while Grafana is a more comprehensive, full-stack solution that can be used across metrics, traces, and logs. Prometheus vs. Grafana: Detailed assessment Now that we understand what each of Prometheus and Grafana offers, let us compare them across the following criteria Core observability functions (Data collection, processing & storage) Scalability Querying Alerting Visualization (Visualization, UI/ UX, collaboration) Others (Documentation, ease of deployment, integrations, and pricing) Summary assessment Features Prometheus Grafana Breadth of solution ✓(Only metrics) ✓✓ ( across metrics, logs, traces) Data collection/ instrumentation ✓✓ (metrics) ✓✓ (also has logs/ traces; metrics agent similar to Prometheus) Data Storage ✓ (purpose-built for metrics;) ✓✓ (across metrics, logs, traces; metrics DB built on top of Prometheus) Scalability ✓ ✓✓(Mimir more scalable) Alerting ✓✓ (built-in AlertManager) ✓ (slightly less performant) Querying ✓✓ (PromQL) ✓✓ (Built on PromQL) Visualization & User Flows Visualization ✓ ✓✓ UI & UX ✓ ✓✓ Collaboration ✗ ✓✓ Other Documentation ✓✓ ✓✓ Easy Deployment ✓✓ ✓ Integration with other tools ✓✓ ✓✓ Free Plan ✓✓ (open-source) ✓✓ (open-source, plus paid cloud version) - Best-in-class ✓✓ - Good enough ✓ - Poor ✗ Detailed assessment Data collection/ instrumentation The main difference today is that Prometheus supports data collection for just metrics, while Grafana agent can be used for collection & forwarding of traces and logs as well. Note that for metrics data collection, Prometheus (Prom agent) in 2021, to make the solution more scalable. The Prom agent was inspired by the Grafana agent and mainly from it. introduced an agent mode takes the code related to metrics functionality In summary, the Grafana agent trumps for a few reasons - Allows you to collect & forward as well traces and logs You can send data to OTel systems as well (not just Prometheus-based ones) Allows more control over the agent’s components with Grafana’s rich UI debugging capabilities Prometheus agent is preferred in situations where teams are only focused on metrics data or are in the process of switching between standard Prometheus to prom agent. Data Storage Prometheus shines within metrics data storage with its efficient time-series database, optimized for the retention and querying of time-stamped metrics. Its unique storage model ensures that older data is compacted and can be efficiently queried over long periods. Grafana now has data storage back-ends across metrics, traces, and logs.  Loki for log aggregation and storage, Tempo for distributed traces, and  Mimir for metrics. For metrics itself, should you use Grafana Mimir or Prometheus? Note that Grafana Mimir builds  on Prometheus and many pieces of it have Prometheus code so there is some overlap :) In general, Prometheus is more widely used/ popular. That said, is a more modern metrics solution that addresses many of the challenges with Prometheus (like multi-tenancy, longer retention, and faster queries). See here for a Mimir more detailed comparison They’re also compatible with each other, so if you have a Prometheus agent, you could just set it to send data to a Mimir cluster so they’re . compatible with each other Scalability When it comes to scalability, Prometheus adopts a pull-based, single-tenant model which, while straightforward, poses challenges as systems grow. To handle vast amounts of data, Prometheus typically requires sharding and federation, adding some complexity. Grafana Mimir, on the other hand, is built for scalability and high performance. It has a distributed multi-tenant model that allows you to scale horizontally seamlessly, and a dedicated long-term storage solution, to store and process vast amounts of data. Grafana wins on scalability here. Querying Functional query language, PromQL, is both robust and expressive, allowing users to extract intricate details from their metrics. Alerts in Prometheus are defined using the same query language, ensuring precision. Grafana can leverage PromQL as well. In keeping with the theme of both companies building on top of each other, Grafana has also built its own . Prometheus query builder, which improves on PromQL Alerting Prometheus has a separate component called the Prometheus Alert Manager, that allows you to create and manage any alerts based on Prometheus data. It’s widely used, proven and well-liked. Historically, Grafana alerting was limited to data on the dashboards. However, with Grafana’s evolution into full-stack, Grafana alerting has become more comprehensive. Grafana Alerting now allows you to define alerts based on any Grafana data (Loki logs, Mimir, Tempo traces). The engine allows you to define alert criteria, evaluation frequency, time duration for evaluation, and composite criteria and also set notification policies like where and to whom the alerts are routed to. You could mute alerts for a while, or stop receiving notifications for a specific alert altogether. That said, Prometheus AlertManager still has an edge within metrics as it allows for more complex alerts with complex queries and calculations, with better performance. Grafana Alerting uses a SQL database so performance may not be great. Visualization For , Grafana is the star. Its dashboards are customizable, intuitive, and designed for a great user experience. Prometheus, on the other hand, has a basic visualization interface. It's functional but lacks the polish and flexibility Grafana offers. \ data visualization If rich visuals and dashboards are your focus, Grafana is the clear choice. Prometheus provides the data; Grafana makes it look good. UI & UX Diving into UI and UX, Grafana offers a sleek, user-friendly interface, making dashboard creation and navigation a breeze. In contrast, Prometheus focuses more on its core functionalities, with a UI that's straightforward but not as refined. For those prioritizing a smooth user experience and intuitive layout, Grafana has the edge. However, if you're looking purely for functionality and don't mind a steeper learning curve, Prometheus gets the job done. Collaboration and team management With built-in features like user roles, permissions, and team-centric dashboards, Grafana enables easy collaboration. Prometheus, on the other hand, leans heavily on its robust metrics collection, lacking advanced team features. If seamless team coordination is your goal, Grafana takes the cake. Documentation Both provide thorough resources. Prometheus distinctly carves a niche with detailed help on the metric collection, including best practices and common pitfalls. Grafana, on the other hand, hosts an extensive library of resources, spanning tutorials on dashboards, panels, and its expanding list of plugins. While Prometheus's documentation reads like a deep, technical manual, Grafana offers a blend of user guides, tutorials, and community-contributed content. Both projects are very well-documented and have vibrant communities. Deployment Prometheus is straightforward to deploy banking on its standalone nature with configurations primarily via YAML files. This minimalism makes its initial setup somewhat swift. Grafana, conversely, offers a lot of integrations, making it versatile but forcing a steeper initial learning curve. Though Prometheus speaks the language of simplicity, Grafana whispers promises of adaptability. As for teams preferring a plug-and-play approach, Grafana might demand a bit more patience, but its flexibility is worth the elbow grease. Integrations Prometheus, with its dedicated exporters, zeroes in on extracting metrics from various services, ensuring a tailored fit. It excels within metrics. Grafana, however, plays a broader game. Its vast array of plugins supports numerous data sources, helping in seamless integration. This is just a function of whether you’re looking for metrics alone, or also for other observability. Pricing Both projects are 100% open-source. Prometheus has an Apache v2.0 license, while Grafana has an . AGPL license Prometheus does not have a cloud version. However, several other players offer hosted Prometheus- e.g., Amazon-managed service for Prometheus, Google Cloud-managed service for Prometheus, and many other independent players. Grafana on the other hand offers its which is paid for. It’s a robust, tightly integrated offering that brings the best of the proven Grafana stack and makes it available as a hosted solution, own cloud version Better Together? As we saw above, Grafana and Prometheus build on each other a lot and are happy partners in the open-source observability ecosystem. The decision is often not really Prometheus vs. Grafana, but how to use Prometheus and Grafana together in the best way possible. Grafana and Prometheus in Practice: Typical Combinations & Configurations In real-world observability scenarios, the flexibility of Prometheus and Grafana allows for a range of configurations, each tailored to suit different requirements. Here's a quick dive into how these tools are commonly set up together for metrics: Grafana-Prometheus configurations in Monitoring Within monitoring, companies do Grafana-only, Prometheus-only, or. combination of the two (see image below). : This setup isn't just popular—it's a powerhouse. Prometheus, with its focused metric scraping, provides raw, granular data. Grafana takes this data and transforms it into actionable insights via its advanced visualization. It's not just about collecting metrics but understanding them, and this combination excels here. Prometheus metrics server + Grafana visualization : Increasingly popular. Teams adopting this are looking for cohesion—Grafana not just as a dashboard tool, but as an all-encompassing observability platform. Mimir + Grafana visualization : This combo is less common. It's typically adopted by teams with specific needs or those that are in the nascent stages of their observability journey. However, as organizations scale and demand more intricate visualizations, they often switch to Grafana for a broader palette of visualization tools. Prometheus server + Prometheus visualization Grafana-Prometheus configurations in overall Observability stack Prometheus for metrics alone, Grafana for the rest This is where teams use Prometheus for just metrics back-end and Grafana for traces, and logs, with an integrated visualization layer. This allows for a single-pane-of-glass experience, where the developer sees all observability data on the same dashboard. It's also one of the most commonly preferred configurations. Most teams already have Prometheus setup as their monitoring tool and are used to it, so tend to prefer this model. The native compatibility between Prometheus and Grafana visualization makes this a popular choice. Grafana stack for everything This is the full Grafana observability option, widely known as the LGTM stack (Loki for logs, Grafana for visualization, Tempo for traces, and Mimir for metrics). This is being adopted by much more modern teams who’re either setting up their observability anew, or refreshing their stack, and are looking for less expensive options vs. the commercial players. This offers a tightly integrated experience much like a Datadog or NewRelic, while having the advantages of being open-source and flexible. What’s Next? AI Once you have your basic observability set up, what next? Recent developments in AI are set to dramatically change how we implement observability. Even with a strong observability stack, developers still need to navigate large volumes of data to zero in on incident-specific data that they’re looking for. When a production incident occurs, these AI observability workspaces pull from across Prometheus, Grafana, and the rest of your observability stack, and generate AI inferences on the most probable root causes. This helps drastically reduce MTTR and also offers a unified incident-specific dashboard for troubleshooting. You can sign up for early access . incident-specific data here Summary We looked at a comprehensive assessment of Prometheus vs. Grafana — their offerings, where they overlap and how they differ, how they perform across different dimensions, and how they’re often used together. They’re both robust offerings within their own categories and liberally borrow from each other. Both have contributed significantly to advancing the open-source observability ecosystem. Also published here.

A Comprehensive Comparison of Prometheus and Grafana in 2023

Scientific American Supplement, No. 365, December 30, 1882 by Various, is part of the HackerNoon Books Series. You can jump to any chapter in this book . SPECTRUM GRATINGS. here SPECTRUM GRATINGS. At a recent meeting of the London Physical Society, Prof. Rowland, of Baltimore, exhibited a number of his new concave gratings for giving a diffraction spectrum. He explained the theory of their action. Gratings can be ruled on any surface, if the lines are at a proper distance apart and of the proper form. The best surface, however, is a cylindrical or spherical one. The gratings are solid slabs of polished speculum metal ruled with lines equidistant by a special machine of Prof. Rowland's invention. An account of this machine will be published shortly. The number of lines per inch varied in the specimens shown from 5,000 to 42,000, but higher numbers can be engraved by the cutting diamond. The author has designed an ingenious mechanical arrangement for keeping the photographic plates in focus. In this way photographs of great distinctness can be obtained. Prof. Rowland exhibited some 10 inches long, which showed the E line doubled, and the large B group very clearly. Lines are divided by this method which have never been divided before, and the work of photographing takes a mere fraction of the time formerly required. A photographic plate sensitive throughout its length is got by means of a mixture of eosene, iodized collodion, and bromized collodion. Prof. Rowland and Captain Abney, R.E., are at present engaged in preparing a new map of the whole spectrum with a focus of 18 feet. In reply to Mr. Hilger, F.R.A.S., the author stated that if the metal is the true speculum metal used by Lord Rosse, it would stand the effects of climate, he thought; but if too much copper were put in, it might not. In reply to Mr. Warren de la Rue, Prof. Rowland said that 42,000 was the largest number of lines he had yet required to engrave on the metal. Prof. Guthrie read a letter from Captain Abney, pointing out that Prof. Rowland's plates gave clearer spectra than any others; they were free from "ghosts," caused by periodicity in the ruling, and the speculum metal had no particular absorption. Prof. Dewar, F.R.S., observed that Prof. Liveing and he had been engaged for three years past in preparing a map of the ultra-violet spectrum, which would soon be published. He considered the concave gratings to make a new departure in the subject, and that they would have greatly facilitated the preparation of his map. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Various (2006). Scientific American Supplement, No. 365, December 30, 1882. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/18763/pg18763-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

SPECTRUM GRATINGS

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

We are excited to announce that starting from integrates effortlessly with , the popular open-source data visualization and monitoring platform. By leveraging the default MySQL connector, you can now visualize Manticore Search data in a more dynamic and informative way. This integration brings new possibilities for search performance optimization, trend analysis, and real-time insights. Manticore Search 6.2.0 Grafana In the following sections, we will guide you through the process of connecting Manticore Search to and help you create your first chart. Grafana Step-by-step tutorial Step 1: Install and Configure Manticore Search Before connecting to , make sure you have installed and configured on your server. Follow the for installation and setup instructions. Manticore Search Grafana Manticore Search official Manticore Search documentation Step 2: Set up Grafana To set up Grafana, follow the official . Grafana installation guide Step 3: Prepare Manticore Search data to visualize In the next step, you need to prepare and create tables in Manticore Search, then load them with data that will help us generate charts and visualize them using Grafana. Connect to Manticore using a MySQL client and execute the following queries: Here is the structure we will use: CREATE TABLE btc_usd_trading (
id bigint,
time timestamp,
open float,
high float,
low float,
close float
); Now, let’s add some data to populate the table. Just open your terminal and run following comands: curl -sSL https://gist.githubusercontent.com/donhardman/df109ba6c5e690f73198b95f3768e73f/raw/0fab3aee69d7007fad012f4e97f38901a64831fb/btc_usd_trading.sql | mysql -h0 -P9306 Step 4: Connect Manticore Search to Grafana Log in to your dashboard and click on “Configuration” (the gear icon) in the left sidebar. Grafana Select “Data Sources” and click on “Add data source.” Choose “MySQL” from the list of available data sources. In the settings page, provide the following details: Name: A name for the data source (e.g., “Manticore Search”). Host: The hostname or IP address of your Manticore Search server. Database: Your Manticore Search database name. User: The username with access to the specified Manticore Search database. Password: The password for the specified user. Click on “Save & Test” to verify the connection. Step 5: Create a Dashboard and Add a Chart In the Grafana dashboard, click on the “+” icon in the left sidebar, and select “New dashboard.” Click on the “Add visualization” button to start configuring your chart. Choose the datasource with Manticore Search that connected by using MySQL connector. Choose the type of chart you want to create (e.g., bar chart, line chart, pie chart) from the list of available visualization options. We will choose “Time Series”. Step 6: Configure the Chart Use Grafana’s query builder or simply write an SQL query to fetch the data you want to visualize from your Manticore Search database. Customize the chart’s appearance, labels, and other settings as needed. Click on “Apply” to save your chart. Step 7: Check your chart’s result Here is a sample of the results we obtained by using the provided data. It’s easy to change the style of a chart, as in this example, we used Bitcoin prices. Simply choose Candlestick and adjust the query builder by selecting all fields. This will provide an entirely different view of your data in . Grafana You can also use different types of visualizations available in , depending on the data in your database. Simply connect your Manticore Search, select the table, and create a chart with the desired parameters using the default MySQL connector. Grafana Manticore Search Build your own dashboard by combining different charts of your data! Conclusion With the seamless integration of and using the default MySQL connector, you can now unlock the full potential of your search data. Visualize trends, performance metrics, and real-time insights with ease, all within the intuitive interface. Get started today and elevate your data analysis capabilities to new heights. Manticore Search Grafana Grafana Also published . here

Manticore Search Now Integrates With Grafana

Hi! I am an experienced engineer and I decided to participate in the contest by Hackernoon and Aptible. DevOps I want to share the story of migrating logging and tracing services from Elastic Stack to Grafana Stack and what came out of it. Before the migration, my team used quite classical schemes: Logstash → Elasticsearch → Kibana for logs Jaeger → Elasticsearch → Kibana (Jaeger UI) for tracers This is a common variant among projects. It suited us for the first year and a half of the project's life. But as time passed, microservices proliferated like mushrooms after rain, and the volume of client requests grew. Expansion of resources of logging and tracing systems became more and more frequent. More and more storage and computing power was required. On top of that, the X-Pack license was pushing the price even higher. When problems with licenses and access to Elastic's products began to appear, it became clear that we couldn't go on living like this. While searching for the best solution, we tried different variants of components, wrote Kubernetes operators, and collected two buckets of trouble. In the end, the schemes took the following form: Vector → Loki → Grafana Jaeger → Tempo → Grafana This is how we managed to unite the 3 most important aspects of monitoring: metrics, logs, and tracers in one Grafana workspace and get several benefits from it. The main ones are: Reduced hardware space for the same amount of data Reduced amount of computational resources for system operation No need to purchase a license Free access to the product Simple enough customization of the autoscaling mechanism I hope this article will be helpful for those who are just choosing a logging/tracing system and those with similar difficulties. Migration preconditions: logging It was 2022, and as mentioned above, we were using a fairly standard centralized logging scheme. About 1 TB of logs were accumulated per day. The cluster consisted of about 10 Elasticsearch data nodes, X-Pack license was purchased (mainly for domain authorization and alerting). Applications were mostly deployed in the Kubernetes cluster. Fluent Bit was used to send them, followed by Kafka as a buffer and a Logstash pool under each namespace. In the process of system operation, we encountered various problems. Some of them were solved quite easily. For some, only a workaround was suitable, and some could not be solved at all. Combining the second and third groups of problems prompted us to search for another solution. Let me first list the 4 most significant of these problems. Loss of logs during collection Strangely enough, the first component that started having problems was . From time to time, it stopped sending individual pod logs. Analyzing debug logs, tuning buffers, and updating the version did not produce the desired effect. was taken as a replacement. As it turned out later, it also had similar problems. But it was fixed in version 0.21.0. Fluent Bit Kubernetes Vector Workarounds with DLQ The next annoyance was the forced use of workarounds when enabling DLQ on Logstash. Logstash does not know how to rotate logs that get into this queue. As an , it was suggested to simply restart the instance after reaching the threshold volume. This did not affect the system negatively since Kafka was used as input, and the service was terminated in graceful mode. But it was a pain to see the constantly growing number of restarts of pods. Restarts sometimes masked other problems. almost official workaround Painful writing alerts Not a very convenient description of alerting rules. You can click through the Kibana web interface, but using the description through code is more convenient, like in Prometheus. The syntax is rather non-obvious and vomitous. Resource consumption But the main problems were related to the increasing cost of resources consumed and, as a result, the cost of the system. The most greedy components turned out to be Logstash and Elasticsearch because JVM is notoriously partial to the amount of memory. Tracing Jaeger was used for centralized tracers collection. It sent data via Kafka to a separate Elastics cluster. Tracers were not getting smaller. We had to scale the system to accommodate hundreds of gigabytes of traces daily. The scheme looked like this: A common inconvenience was also the use of different web interfaces for different aspects of monitoring: Grafana - for metrics Kibana - for logs Jaeger UI - for tracers Of course, Grafana allows both Elasticsearch and Jaeger to be connected as data sources, but the need to manipulate different data query syntaxes remains. Our search for new logging and tracing solutions began with these assumptions. We did not conduct any comparative analysis of different systems. Now on the market, there are mostly serious products that require licensing. So we decided to take the open-source project Grafana Loki, which had already been successfully implemented in some companies. Migration to Loki So, why was chosen: Loki It is an opensource product that allows us to implement all the features we need Loki components consume significantly fewer resources under the same loads All components can be run in a Kubernetes cluster, and you can use or to scale them automatically HPA Keda Data takes up several times less space because it is stored in compressed form Query construction is very similar to PromQL The description of alerts is similar to Prometheus alerts The system is perfectly integrated with Grafana for message visualization and graphing was chosen as the deployment method. Object Storage was chosen as the chunk storage. The more lightweight Vector replaced Logstash. The system is quite functional out of the box at low volumes (several hundred messages per second). Logs can be saved almost in real-time mode, search for fresh data works almost as fast as in Kibana. The resulting scheme looks as follows. Distributed helm-chart The best settings in Loki As the load increases, the recording chain starts to suffer. Both ingester and distributor start dropping logs, returning timeouts, etc. And when requesting data missing in the ingester's cache, the response timeout starts to approach a minute or even drop by timeout. Just in case, here is a diagram of how the installation of Loki via Distributed chart looks like. In case of recording problems, it is worth paying attention to the parameters: limits_config.ingestrion_burst _size_mb
limits_config.rate_mb They are responsible for bandwidth. When traffic approaches the thresholds, messages will be discarded. To increase search speed, you should use Memcached or Redis caching. You can also play with the parameters: limits.config.split_queries_by_interal
frontend_worker.parallelisim Loki can use caching of 4 types of data: Chunks Indexes Responses to previous queries Data caching for deduplication needs It is worth using at least the first three. After tuning component settings, scaling, and enabling the cache, logging delays disappeared, and the search began to work in an acceptable time (within 10 seconds). As for alerting rules, they are written similarly to Prometheus rules. - alert: low_log_rate_common

  expr: sum(count_over_time({namespace="common"}[15m])) < 50

  for: 5m

  labels:

      severity: warning

      annotations:

        summary: Count is less than 50 from {{ $labels.namespace }}. Current VALUE = {{ $value }} is used to access messages. It is similar in syntax to PromQL. In terms of visualization, everything looks like in Kibana. LogQL query language A very convenient feature is setting up links from log messages to the corresponding tracers. In this case, the tracer is opened when you click on the link in the right half of the workspace. Migration to Tempo is a younger product (introduced in 2020) with a very similar architecture to Loki. Distributed helm-chart was chosen as the deployment method. Tempo Distributors can connect to Kafka directly, but in this form, it was impossible to achieve read speeds commensurate with write speeds. By using the Grafana agent, it was possible to solve this problem. The best settings in Tempo The following points should be paid attention to during the configuration process: If Jaeger is used, spans from Grafana agent to Distributor will be sent via GRPC. In a Kubernetes cluster, even distribution to Distributors will require configuring a balancer such as Envoy (optionally via Istio). In case of write speed issues, it is worth increasing the parameters: overrides.ingestion_burst_size_bytes overrides.ingestion_rate_limit_bytes overrides.max_bytes_per_trace You can also increase the timeout for waiting for a response from ingester - ingester_client.remote_timeout. They don't always manage to respond in 5 seconds. In addition to the tracer visualization, you can also get a connected graph showing all the components involved in processing the request. Conclusion From the initial installation of Loki in the dev environment to its implementation in production took about 2 months. Tempo took the same amount of time. As a result, we were able to: Reduce the cost of the systems by about 7 times Combine visualization of metrics, logs, and tracers in a single system Organize alerting in a similar way to Prometheus Set up autoscaling of the system depending on the amount of incoming data It is worth mentioning what we have not managed to achieve yet: A log output speed similar to Elastic if the data is not in the cache. This is partially solved by adding the most frequently used fields to the index. Obtaining aggregated values for a long period (more than a day). We mean, for example, counting the number of log messages with the count_over_time function. Performance in saving spans as in the scheme with Elasticsearch. So far, we managed to solve it by horizontal scaling and increasing the number of partitions in Kafka. In general, I can say that the experience of switching to Grafana Stack was successful, but the process of improving the logging and tracing schemes did not end.

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