Kirana Club is India&#x27;s largest community of Kirana Retailers.

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Kirana Club is India&#x27;s largest Kirana Network. Kirana Club blends the trust of the offline B2B trade with the effortless scale of the digital approach to build a strong bridge between the Kiranas and those working with the Kiranas.

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The terms &quot;We&quot; / &quot;Us&quot; / &quot;Our&quot;/”<strong>Company</strong>” individually and collectively refer to Ecologique Science Technik (India) Pvt. Ltd. (ESTPL) Promoter of <strong>KiranaClub</strong> Application and the terms &quot;You&quot; /&quot;Your&quot; / &quot;Yourself&quot; refer to the users. This Privacy Policy is an electronic record in the form of ...

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Kiran Group is a leading logistics service providing <strong>company</strong> in Kandla, Gujarat, India. We provide bulk liquid storage, warehousing, custom clearing, transportation, port handing, stevedoring and Drumming. Call Now: +91 2836266900

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Kirana Club is India's Largest Kirana Community Kirana Club is the LinkedIn for Kiranas. :) It is a platform where Kiranas can connect with each other, FMCG brands, and other stakeholders in the retail ecosystem. Kiranas can use Kirana Club to discover new products, learn about margins, and discuss business. We have onboarded over 15 lakh Kiranas in the last few months, and we are creating a bustling community of Kiranas who are working together to grow their businesses.

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One of the features of cybersport disciplines that distinguishes them from standard sports is that there is no strict segregation of genders. Women and men can compete at the same level. Cybersport has long ceased to be just fun for the children. Around the world, this industry is treated very seriously; it builds a reputation and earns a lot of money. Last year, Ukraine also joined the cohort of countries that have officially recognized e-sports. Moreover, the number of women and men among cybersport athletes has almost reached the balance — 41% and 59% respectively. Thus, there are still fewer women than men, and this difference is narrowing soon. Recently Riot Games, the company that represents League of Legends and , announced the holding of VCT Game Changers EMEA games for women and representatives of other genders. The tournament series will start this autumn. VALORANT The prize fund of the first two will amount to 20 thousand euros and the final to 50 thousand. Besiktaş Esports/XSET There are plenty of women’s teams in world cybersports. Besiktaş Esports/XSET is a CS:GO team representing the Turkish football club Besiktaş. In 2019 they won the qualifying tournament. They did not manage to be the first, but they showed that the women’s teams could be real competitors. Claudia “Clawdia” Che Multi-gaming American company Evil Geniuses announced in the middle of this year that two women joined their strictly male teams. The company joined VALORANT by introducing two female players — Claudia Clawdia Che and Capitan Christine Potter Che. It is known that the latter has shown unbeatable results from CS: GO and is also a knowledgeable analyst. Dignitas Female Dignitas Female is one of the highest-scoring female teams in CS: GO. They ranked first in the Intel Challenge Katowice women’s league, taking home 25,000 USD. Prior to that, they had already won victories at GIRLGAMER Esports in 2018 and the Copenhagen Games in 2019. Vaevictis eSports Russia created the Vaevictis eSports team from League of Legends, which included nine girls. In 2019, their debut was marked by discriminatory attacks from competitors. However, the women’s team was later accused of unprofessional behavior. As a matter of fact, Vaevictis eSports lost the competition, ending up in the last position. Julia “Juliano” Kiran As for the players, the real legend among the eSports players is Julia Juliano Kiran. The 27-year-old player began her career in 2010 and is now ranked as one of the best players in Counter-Strike. During this time, she has been invited by the world’s top teams to play — she has changed seven clubs, and her team became the most prestigious Intel Challenge Katowice for three consecutive years. Recently, the girl announced that she was changing disciplines and would now play for VALORANT. Bі axx Xia Chinese streamer Bі axx Xia is known not only for her achievements but also for the fact that she often plays on the roster of male teams in Dota 2. In 2017, the promising player was remembered by Newbee.Boss. Later she moved to the club Baze. There, the Chinese player once again showed unbeatable results and took the prize place during The International tournament. Kim “Geguri” Se-Yon The 22-year-old Korean Kim Geguri Se-Yon has made quite a splash in Overwatch, quickly making her way to the world’s top players. After being requested to play for the UW Artisan, she also qualified for the Nexus Cup. The results were so shocking that she was accused of cheating. However, after a lengthy investigation, the charge was dropped. In February 2021, Kim joined the Shanghai Dragons’ all-female team and became the first woman in the Overwatch League. Sasha “Scarlett” Hostyn Sasha Scarlett Hostyn, 27, from Canada, is known for her entry into the Guinness Book of World Records in 2016. She won the most prizes of any female cyber athlete — a total of 384.2 thousand dollars. In winter 2018, Sasha won the Intel Extreme Masters in Pyeongchang and became the first woman to win the StarCraft 2 World Championship. Antonina Predko And we finish the list of the most outstanding females in esports with the name of Ukraine’s Antonina Predko. The girl is considered one of the best esports hosts on the territory of Eastern Europe in esports, including CS: GO, Dota2, Point Blank, World of Tanks, PUBG Mobile, StarCraft, Valorant, etc.

The Female Faces of the Esports Industry

Viral and the eBZaar team are on a mission to give Indian brick and mortar retail stores with no digital presence an easy way to sell goods online. Davis Baer : What’s your name, and what are you working on? My name is Viral Thakker, and I run eBZaar.com as the CEO & Co-founder. At eBZaar, we are on a mission to bring Indian brick & mortar retail stores online by giving them a zero cost digital store, with the idea that technology should be easily accessible to this segment of the society. We want to enable the local retailer to sell more and sell better by better-leveraging technology and the power of digital; without having to worry about creating websites, mobile apps, hosting, cataloging or digital marketing. On the demand side, we aim to make the customers’ favorite stores available 24X7, thereby enabling the customers to shop hassle free anytime and anywhere from their own trusted stores. Considering that customers are adopting digital at a rapid pace, this segment (50 million retail outlets in India, 14 million in food & grocery, 98% small & unorganized businesses) could soon become non-relevant to society if they failed to adopt digital technology. When acting as a consumer, the same business owner does use multiple digital platforms; however when acting as a retail store owner, the very same retailer is aloof to digital technology — and this is not always by choice. In our current journey, we act as their online channel partners. Over a period of time, we will become their omnichannel partners too. After all, why should technology be restricted to Amazon or Flipkart (acquired by Walmart) and why should omnichannel be available only to large conglomerates (e.g. Reliance Fresh, Future Group-owned Big Bazaar or similar models by business houses of Tata’s and Birla’s) alone. Overall, we see us working on a retail transformation agenda for the next few years. To start with we are tapping the Food & Grocery segment as A) In this segment, customers have the highest reliance on neighborhood stores, and B) Opportunity gap in the market is highest in this segment. For us, Food & Grocery includes Kirana, Fruits & Veggies, Cakes, Mithai, Meat & Sea Food & more — anything that customer typically prefers to buy from their local neighborhood. What’s your background? I have 13 years of corporate experience prior to starting up, spanning Banking & Financial Services. During this career, I have developed with a keen understanding of retail and wholesale banking, capital markets, trade finance, and cash management. My last Corporate role was as Executive Director at J.P.Morgan; a place which accounts for half of my corporate career and where I was responsible for selling structured and classical trade finance and cash management services to Fortune 500 companies. I gained in-house & external recognition and accolades for my work here. Prior to joining J.P.Morgan, I have worked with HBSC and ICICI Bank in roles across relationship management, audit and advisory services. Working across diverse MNC Banks, I also possess experience of working in a complex / matrix organizational structures, navigating through multiple stakeholders and working with people from diverse cultural and geographical background. I have traveled extensively both within and out of India, something that I relish; this aside, it also provides me the opportunity to network extensively with people from different backgrounds. We have worked with 30+ stores until now. With the release of our mobile application in September 2018, both customers and retail stores are showing increasing interest in joining hands with us. We have on-boarded (live plus mandates) 10 stores in the last 2 months alone, the adoption from stores being much higher with the release of our mobile application. We are being constrained only by our capacity to expand and are on a firm footing when it comes to taking our offering to more & more customers and merchants. What motivated you to get started with your company? As a CoFounder, I have 25 years of experience in running a grocery store; right from early childhood till few years overlapping with my corporate career. Even back then, we had aspirations of expanding our business but on the other extreme, we had no clue on how to overcome even basic insecurities like trusting the cash till to a manager OR how to track inventory (we were devoid of things like barcode and POS systems). Thus, even back then, the story was about access to the right technology and processes for business expansion at a small family run local store. As I was looking out to start out, post my corporate career, the story seemed pretty much the same. Considering the rapid pace of digital adoption by consumers, the local retail store faced an existential crisis if they failed to adopt a digital channel. If there was to be a bridge to overcome this gap, who is better placed then a bunch of individuals all of whom feel passionate about and understand retail. Aside from me, Pranav Mehta (COO & CoFounder) has implemented technology solutions at multi-billion US enterprises, having spent 7 out of 15 years in the US across different aspects of technology. Ravi Ranjan, CTO & CoFounder, has done his own startup in a similar space, where he managed technology and product. Besides, he has also built 4 products during his corporate career including leading a tech team at a Y Combinator Company (Bulk MRO, W2017 batch) and comes from a family of FMCG / Pharma products wholesalers and retailers. Everyone in the eBZaar team is deeply passionate about and experienced in retail; we strongly believe in the potential of technology to drive retail transformation for the benefit of all participants in the industry. We are driven by the fact that this section (business owners of small or medium-sized local stores) also needs to participate in, and have access to, the digital economy. A decade plus corporate career allowed me to save upon a little kitty as a starting capital. Aside, having a supportive family with a working spouse has ensured that we could keep the household going without dipping into contingency savings. However, of course, startup life has its demands and you do have to have a bit of moderation in your lifestyle. Till date, we have worked with 30+ stores and processed 1000 + orders with order ticket size being above industry average and an army of a small set of super faithful customers that keep us going. These customers also act as our reference points on idea validation. And with the launch of our mobile application, we can see the enthusiasm and adoption of our platform only improving further. What went into building the initial product? Building a product as a first-time entrepreneur and as a solo founder (which was the case until recently) is never easy. My first version of eBZaar was totally ridiculed by family & friends alike and we had to almost scrap it after 6 months of work. I learned that people do not like to merely search a product if it’s an eCommerce platform. They like visuals and imagery, and category listings just to browse and to figure out what they want to buy from your portal or app. It took 3 to 4 months of rework before we could relaunch the product and get our first external customer. A couple of important learning along the way: be very sure if you want to be working with another startup as a service provider; it is almost always better to work with experienced hands since you yourself are a startup and not sure about many a thing. Secondly, be sure to get the right team first; the mistake I committed was to read about technology during my notice period, I would have been better off reading about hiring and team building. You definitely cannot do it all alone. One part which worked right was that forward selling the product platform and features to some of our (would be) store partners. We had 5 store mandates before the product was officially live (1.X which was ridiculed)! Friends and family were extremely supportive along this journey. If one friend would read up my legal agreement to help me spot the gaps, my spouse would help create initial marketing collaterals and/or line up interviews while another friend would sit with me helping through initial interviews. One of my mentor, who later also wrote a cheque, particularly helped me find the right tech team to move us from a ridiculed first version of the product to one where we got our first external customer. All of this product build-out was self-funded, and that continued for another year before getting an external cheque. How have you attracted users and grown your company? We never had a formal big bang launch. Instead, we did demo products, researched markets, asked for referrals from family & friends, talked to everyone in the network, introspected on all the feedback & pointers from numerous discussions; all of which contributed in the evolution of our business model. While we had five store mandates before we started and all of them converted, the first superstar store that bought us a lot of customers was a result of a referral! Early into the journey, it helped us to emphasize the power of referral, which we have held dear ever since. In a way, I truly realized the power of listening to the knowledge that the market shares with you. Silly as it may sound, our first flyer lacked a CTA (Call to Action). I had barely located one solitude friend who had taken up a marketing career and he was the point us out. I learned the importance of nudging the users to an action we demand of them (say a “Register Now” versus “Order Now” — the nudge is important). And then I learned some more marketing mantras as he connected me to his manager, a very senior marketing pro. Thereafter, I just went and met some people by cold emailing people on LinkedIn and also meeting more marketing folks on every available conference, session or other such opportunities. Another interesting learning took place at another startup event where we just got a consolation prize. The coupon card hack (a gift card that is the size of a visiting card, which people keep in their wallet and do not throw away unlike a flyer). Doing in-store promotions, another suggestion we had received in one of the many meetings, turned out to be a real insight gatherer besides helping us sign up users. We learned that a) Consumers do not like to be disturbed while on their shopping journey, so do not think of converting them (sales) when they are in store b) however it is a great place to get feedback, reference of other stores they buy from, and to gain user signups Another hack that worked for us is, (when marketing in a closed user group) — was giving users something small but valuable (say even a free shipping in our case!). We executed this over WhatsApp marketing, getting us a lot of converts. However not being able to keep the freshness of the messaging meant that we soon faced Diminishing Marginal Utility. The other related issue with this one was that it was difficult to execute at scale. All along, continuously talking to a lot of different people, to learn and to experiment is one of the most important things that helped shape up a lot of things in terms of our business model. What’s your business model, and how have you grown your revenue? We simply charge a commission whenever our store partner gets an order on our platform. We believe that the core service — a zero fixed cost digital store, will always remain free. However as we add more value-added services and additional feature sets for the store to leverage, these may come at an incremental cost instead of being free. One thing we have been clear about has been to charge for the services we provided. We have been charging our partner stores from day one. We never found a good reason for not charging them. Having said that, we are still a fairly early stage and are only now raising money, with a significant amount to be spent on marketing and scaling up. One of the things that worked for us in our early days (and I think it still does) is to keep a fairly straightforward no-nonsense pricing structure, something that people do not find fault with easily. Secondly, we try & protect your base margins, with additional variable costs being billed as out of pocket wherever it is easy to identify back the cost to a particular transaction. This learning was a result of market feedback coupled with my corporate career where I had honed my skills about price negotiations and pricing models to an extent. What are your goals for the future? From a peak order volume of 10/15 orders per day, we want to scale to 200/250 orders per day. That is one very important goal we are tracking. One of the big roadblocks is going to be debottlenecking and automating some operationally intensive tasks — as capacity constraints on the supply side are more internal. This was an important realization as we started on our go to market execution post launch of our mobile application. We also realized that the mobile application improved the acceptability of the product in the market, but it is but hindered by our ability to push through more stores more quickly. Also having sufficient capital is an important aspect to reach where we want to reach. What are the biggest challenges you’ve faced and the obstacles you’ve overcome? If you had to start over, what would you do differently? One of the biggest challenges I have faced was team-building / hiring. Today, I can proudly say that we have one of the best core execution, leadership, and operational team. However to get there I have tried to absorb learning from multiple sources — reading blogs, attending some relevant sessions, even asking 3 to 4 of my hiring managers on why did they hire me and how do they usually go about hiring, also posing this same question to senior entrepreneur friends! If I have to start over again, I would definitely focus on getting the right team in, first upfront. Like I stated elsewhere, I used to read about technology in very early days of conceiving the startup. Rather I should have been reading about how to hire a CTO! Today as we are a team of over 10, I do worry about what is the right culture we want to set for the team. Also, I do see that the team is always watching you and you are the first role model to set examples for others to follow. The other thing, philosophically speaking, was: to continuously keep at it, at least on aspects of the business which were under our control, while one tries to solve for challenges present in the business. This approach ensured that at least some part of the business strengthened even if it took longer for some other part to sort itself out. Ultimately every situation is different. My suggestion would be to take the above with adequate discretion and thought and then adapt it to your own circumstances and situation. Have you found anything particularly helpful or advantageous? Not having co-founders until recently, meant that crowd-sourcing was pretty useful for a lot of idea generation, provoking thoughts and brainstorming. Having another solo founder at another venture as your cribbing buddy; as someone to share your highs and lows with, was another very important part. Aside, to have a “constant” group of 4/5 friends to speak to about your progress and some challenges can do you a world of good, especially when you are worried about the diversity of thoughts or whether you have been missing out on any important aspects of a situation. Secondly, as an entrepreneur, you should learn to be very resourceful: ask yourself if you have exhausted every available option, forum, platform, friend or group of friends, customers, vendors, past or existing service providers for help? Have you asked at enough places for answers? I discovered that people are usually very friendly, but they can or will help only when you ask for help. Following certain inspiring people around you, having senior entrepreneurs to learn from, nurturing relationships and helping people before asking for help, tracking your priorities and maintaining whatever lists you ought to maintain to work more efficiently; are other advantageous aspects of our startup journey. What’s your advice for entrepreneurs who are just starting out? My advice to folks who are starting out is this: do it if you have a really strong reason to do it, money alone is a very bad reason as it cannot keep you motivated long enough, when you are in the dumps; and you will be more than once, before you hit a goldmine. Once this part is sorted, keep it at it long enough but do not stop learning and listening to market feedback, and keep on removing one block at a time. Horizon is for real and not far enough. When people say that you ought to walk together to walk far, it is so very true. For me, sales & hiring are two topmost skills to posses — remember, you are selling even while hiring. I had to learn to sell the vision and the way we want to change the world before we started to get people interested in even listening to us. And lastly, follow some of the people who inspire you –and there are enough and more on LinkedIn and other similar platforms. Keep learning, keep growing and best wishes to all entrepreneurs. For me, Zero to One has many golden nuggets, I am more of a blog reader on LinkedIn and online and not a book reader. Pick up whatever works for you and do follow that. And lastly, still squeeze in time to chase your hobby once in a while just to bring diversity to your thoughts and to break the routine! Where can we go to learn more? For more questions on amazing things that we can do together or business inquiries, do reach out to me (Viral Thakker, CEO & CoFounder) on viral@yaminiindia.com Our Website > www.ebzaar.com Mobile App: https://play.google.com/store/apps/details?id=com.ebzaar.groceryonline&hl=en eBZaar Blog: https://ebzaar.com/blog Social Handles: Twitter, Instagram, and Facebook handle is: @ebzaar This interview is brought to you by , a tool to schedule and automatically repeat your posts on Facebook, Instagram, Twitter, Pinterest, LinkedIn, and Google My Business OneUp

Founder Interviews: Viral Thakker of eBZaar

is an open source system for automatically deploying, scaling, and managing containerized applications. Containerization is a lightweight alternative to full machine virtualization that involves encapsulating an application in a container with its own operating environment. Containerization has matured over the past few years and has led to greater adoption of Kubernetes across the tech industry. Kubernetes Kubernetes greatly increases a user’s ability to run testing and simulations. At a high level, it allows users to expand their technical resources outside of their traditional limits; for example, users can simulate a 101-node blockchain cluster without needing to own 101 physical machines. This is ideal for quickly standing up staging environments for a blockchain cluster. It is also particularly useful for simulating blockchain networks with proof of stake (PoS) or delegated proof of stake (DPoS) consensus mechanisms, which involve a finite amount (e.g., 101) of geographically distributed block producing / verifying nodes. Starting with our , IoTeX has used Kubernetes to deploy and optimize our Testnet infrastructure. Overall, Kubernetes has greatly improved our operations — in this blog, we share our experience and some tips to run your own containerized applications and environments on Kubernetes. Testnet Beta “Epik” release Setting up the IoTeX cluster on Kubernetes We begin by defining two type of Kubernetes : services used to boot up the whole IoTeX cluster. It is the entry point of any new iotex nodes and helps new nodes join the P2P network. All nodes identify each other through their internal Pod’s IP address. bootnode service: exposes the JSON-RPC API from all iotex nodes. The JSON-RPC interface is used by our and our action injection tool. iotex service: open source IoTeX Explorer All node configurations are deployed through the Kubernetes , including the genesis block. This allows us to redeploy a new cluster without waiting for a new docker image to be built. ConfigMap Applications running in Kubernetes Pods are stateless by default, which means saved data from any applications will not be available after a redeployment. To counteract this, we use the following setup process. In our scenario each IoTeX node will utilize the same copy of data (i.e., distributed ledger), so it is not necessary to persist every nodes’ data. Therefore, we mount the Kubernetes to only one iotex node — in our case, we mounted it to our boot node. We also back up our persisted data to object storage spaces like S3 and Digital Ocean. Since all nodes except the boot node will not have saved data after a redeployment, they will need to first download the backup before startup. We use init container to achieve this. This process enables our cluster to restart from a crash our failure without worrying about losing previous data. Persistent Volume Cluster Monitoring It is not trivial to track the status of 21 nodes (let alone 101 nodes down the road), and it becomes more complex with a larger amount of nodes. To have better observability within our IoTeX cluster, we set up monitoring capabilities in Kubernetes, which consists of a logging stack, metrics stack, and alert manager. For the logging stack, we use + + + . Fluentd sends logs from each node to the Elastic Search client, and the Elastic Search master server indexes the logs which are query-able through Kibana. Finally, we use the Elastic Search Curator to clean up the outdated logs. Fluentd Elastic Search Kibana Elastic Search Curator Refer from Log aggregation with ElasticSearch, Fluentd and Kibana stack on ARM64 Kubernetes cluster For the metrics stack, we use + + . provides an easy way to configure Prometheus running in a Kubernetes cluster. We can hook up the IoTeX node’s Prometheus client with the Prometheus server by simply exposing a metrics service. Prometheus Prometheus Operator Grafana CoreOS Prometheus Operator Refer from CoreOS: Prometheus Operator We are also working on setting up alerts with Prometheus Alert Manager to send out alerts to our on-call engineer when we have abnormal metrics. Using Helm to Manage Multiple Configurations Using yaml configuration to deploy different types of applications on Kubernetes is fairly straightforward for a single environment. However, when you have multiple environments (e.g., testing, staging, production) and different node twists / configurations (e.g., 21 vs. 101 delegates), users encounter higher overhead and redundancy to manage various Kubernetes yaml configurations for the same application. To solve this issue, we use Helm. is a package manager for Kubernetes applications. It allows us to create our IoTeX cluster as a chart package with version and default values. With Helm, we don’t need to manage duplicate Kubernetes yaml files; rather, we only need to manage a smaller subset of configurations for different environments and twists which overwrite the default value. It also simplifies the command we need to run to start a fresh IoTeX cluster into one single command: Helm Helm also allows us to publish a chart easily. In the future, we will publish our IoTeX Helm chart to the community, so you can setup an IoTeX cluster within minutes. More to come! With the power of Kubernetes and other operational tools, IoTeX’s Development team not only saves time on cluster operation, but also digests issues quicker and iterates even faster than before. We look forward to sharing more technical perspectives in the future — feel free to reach out with any questions to support@iotex.io. About IoTeX IoTeX is the world’s first privacy-centric blockchain platform that is fast, flexible, and Internet of Things (IoT) friendly. IoTeX’s global team is comprised of Ph.Ds in Cryptography, Distributed Systems, and Machine Learning, top tier engineers, and experienced ecosystem builders. Designed and optimized for IoT, IoTeX uses state-of-the-art privacy, consensus, and subchain innovations to capture the full potential of IoT. By enabling trusted data, interoperability, and M2M automation, IoTeX connects the physical and digital worlds and brings trusted machine economies to the masses. Stay connected with us! Website: Twitter: Telegram Announcement Channel: Telegram Group: Medium: Reddit: Join us: https://iotex.io/ https://twitter.com/iotex_io https://t.me/iotexchannel https://t.me/IoTeXGroup https://medium.com/@iotex https://www.reddit.com/r/IoTeX/ https://iotex.io/careers

Deploy, operate, and monitor IoTeX cluster with Kubernetes

“Is that it?” “No. That’s a wall.” “It could be disguised.” “You’re not very good at looking for things, are you?” “I’m good at looking for walls. Look, I found another one.” ― Derek Landy, Kingdom of the Wicked hen a system was monolithic we had access to the full execution stack trace. However, in Microservices architecture, any single operation in any service can trigger a chain of downstream microservice calls, as all are isolated among themselves which leads to a challenging task to debug an actual flow. W And let’s be honest, we all hate those “ ” or “ ” system errors. something went wrong unknown Well, this situation can be smoothly handled if we externalize and centralize the storage of our logs. Which I believe will increase our chances of tracking down and fixing issues. Complete project can be found here : https: //github.com/vrushofficial/Utter-Microservices-Territory Checklist E-L-K Stack ElasticSearch Kibana Beats LogStash Logs as Streams of events Logging with LogBack & SLF4J Enhancing log in with tracing and JSONformat Example Playing with Docker images Spinning docker containers Investigation of logs on Kibana server E-L-K Stack ELK stack is basically a combination of 4 open source softs for processing log files and storing them at a centralized place. It helps to identify the issues spanning through multiple servers by correlating their logs within a specific time frame. www.elastic.co ELK Stack: Elasticsearch, Logstash, Kibana | Elastic What is the ELK Stack? The ELK Stack is an acronym for a combination of three widely used open source projects… Let’s have a quick look at each component of Elastic Stack. Elasticsearch Elasticsearch is a distributed analytics and search engine built over , a Java-based search and indexing library. Elasticsearch provides distributed cluster features and sharding for Lucene indexes, advanced search and aggregation functionality, as well as high availability, security, a snapshot/restore module, and other data management features. The platform also provides such features as thread-pooling, node/cluster monitoring API, queues, data monitoring API, cluster management, etc. Apache Lucene Kibana Kibana is a data visualization and management tool for Elasticsearch that provides real-time histograms, line graphs, pie charts, and maps. Kibana also includes advanced applications such as Canvas, which allows users to create custom dynamic infographics based on their data, and Elastic Maps for visualizing geospatial data. Logstash Logstash, one of the core products of the Elastic Stack, is used to aggregate and process data and send it to Elasticsearch. Logstash is an open source, server-side data processing pipeline that enables you to ingest data from multiple sources simultaneously and enrich and transform it before it is indexed into Elasticsearch. Place All In Pipeline The following illustration shows how the components of Elastic Stack interact with each other In few Words: FileBeats will collect logs from .log file and sends to buffering or one can directly send to Logstash. Logstash enhance data within particular formate and sends to Elasticsearch. Elasticsearch saves data and create inverted index. Kibana works for visualization and analytics. It simplifies the huge volumes of data and reflects the real-time changes in the Elasticsearch queries. Logs as Streams of events Logs are the continuous events of aggregated, time-ordered events collected from the output streams of all running processes and backing services. Logs in their raw form are typically a text format with one event per line (though backtraces from exceptions may span multiple lines). Logs have no fixed beginning or end, but flow continuously as long as the app is operating. By following this methodology logs can be continuously routed to a file or can be watched in real-time on terminal. Logging with LogBack & SLF4J Playing with spring boot applications Logback dependency will be pulled by default and will be used as a default logging mechanism. Logback preferably used with just like following, SLF4J @Slf4j
public {
   log.trace( );
   log.debug( );
   log.info( );
   log.warn( );
   log.error( ); log.debug( , list.size());
} class Vrush "Logging at TRACE level" "Logging at DEBUG level" "Logging at INFO level" "Logging at WARN level" "Logging at ERROR level" //or any Parametrized logs "Found {} results" Logback can be in the file, located under the folder. In this configuration file, we can take advantage of Spring profiles and the templating features provided by Spring Boot. configured logback-spring.xml resources Enhancing log in with tracing and Json format A simple user action might trigger a chain of downstream microservice calls. It would be tedious to trace the logs related to a particular user action across microservices. In addition to that, we might want to track down why a certain microservice call is taking so much time. One solution to this is at the beginning of the call chain we can create a and add it to all log statements. Along with it, send as a header to all the downstream services as well so that those downstream services also use in logs. This way we can identify all the log statements related to a particular action across services. CORRELATION_ID CORRELATION_ID CORRELATION_ID We can implement this solution using feature of Logging frameworks. Typically we will have a WebRequest Interceptor where you can check whether there is a header. If there is no in the header then create a new one and set it in MDC. The logging frameworks include the information set in MDC with all log statements. MDC CORRELATION_ID CORRELATION_ID But, instead of doing all this work, we can use which will do all this and much more for us. Spring Cloud Sleuth <dependency> <artifactId>spring-cloud-starter-sleuth< dependency> org.springframework.cloud < > groupId </ > groupId /artifactId>
</ By adding Spring Cloud Sleuth dependency in pom.xml file, interactions with the downstream services will be instrumented automatically and the trace and span ids will be added to the SLF4J’s (MDC), which will be included in the logs. Mapped Diagnostic Context Sleuth includes the pattern in logs from the MDC. [appname,traceId,spanId,exportable] In logs you can find log statements something like: booking-service – – : : WARN [booking-service, c472a08e5cda0] returning BAD REQUEST response vrushofficial@gmail.com request due Room is not available 2021 01 01 15 31 29.189 903 for 7513 In logs you can find log statements something like: consumer-service – – : : WARN[consumer service, c472a08e5cda0,af68249ac3a6902] [io -exec ] 2021 01 01 15 31 29.189 903 64712 -8181 -12 Observe that TraceID is the same in both booking-service and consumer-service for the same REST API call. This way we can easily correlate the logs across services. 903c472a08e5cda0 Export logs in JSON By default, logback stores logs in plain text format. And we are going to store logs in Elasticsearch which operates on JSON index. To accomplish it, we can use the or LoggingEventCompositeJsonEncoder. Logstash Logback Encoder In general, the microservices will run in Docker containers so that we can leave the responsibility of writing the log files to Docker. For a simple and quick configuration, we could use LogstashEncoder, which comes with a : pre-defined set of providers <configuration> <appender name="jsonConsoleAppender" class="ch.qos.logback.core.ConsoleAppender">
        <encoder class="net.logstash.logback.encoder.LogstashEncoder"/>
    </appender>
    <root level="INFO">
        <appender-ref ref="jsonConsoleAppender"/>
    </root>
    
</configuration> < = = = /> springProperty scope "context" name "application_name" source "spring.application.name" The above configuration will produce the following log output (just bear in mind that the actual output is a single line, but it’s been formatted below for better visualization): { : , : , : , : , : , : , : , : , : , : , : , : , : , : } "@timestamp" "2021-01-01T05:01:38.967+01:00" "@version" "1" "message" "Finding details of room with id 7052" "logger_name" "com.vrush.microservices.booking.service" "thread_name" "http-nio-8001-exec-3" "level" "WARN" "level_value" 20000 "application_name" "booking-service" "traceId" "c52d9ff782fa8f6e" "spanId" "c52d9ff782fa8f6e" "spanExportable" "false" "X-Span-Export" "false" "X-B3-SpanId" "c52d9ff782fa8f6e" "X-B3-TraceId" "c52d9ff782fa8f6e" The composite encoder has no providers configured by default, so we must add the we want to customize the output. Which you can find on my page. providers github It’s time to get your hands dirty :) Running on Docker As we will have multiple containers, we will use to manage them. Application’s all services are configured in a file. Then, by running a shell script, we create and start all the services from our configuration. All you need to do is hit a Docker Compose YAML build.sh . And take a Coffee or two!! In the given example have a look at how the services are defined and configured in the . What’s important to highlight is the fact that have been added to some services. Labels are simply metadata that only have meaning for who’s using them. Let’s have a quick looks at the labels that have been defined for the services: docker-compose-backend-services.yml labels Filebeat will collect the logs produced by the Docker container by adding and will autodiscover the Docker containers that have this property - collect_logs_with_filebeat=true decode_log_event_to_json_object=true. Filebeat collects and stores the log event as a string in the message property of a JSON document. If the events are logged as JSON, as described in above point that Filebeat should decode the JSON string stored in the message property to an actual JSON object. Send the log events to Logstash which runs on the port 5044 When applications run on containers, they become moving targets to the monitoring system. So we’ll use the feature from Filebeat, which allows it to track the containers and adapt settings as changes happen. By defining configuration templates, the autodiscover subsystem can monitor services as they start running. autodiscover Booking, Financial, Searching, Consumer services will produce logs to the standard output ( ). By default, Docker captures the standard output (and standard error) of all your containers, and writes them to files in JSON format, using the json-file driver. The logs files are stored in the directory and each log file contains information about only one container. In file, Filebeat is configured as following: stdout /var/lib/docker/containers filebeat.docker.yml filebeat.autodiscover:
  providers:
    - type: docker
      labels.dedot: templates:
        - condition:
            contains:
              container.labels.collect_logs_with_filebeat: config:
            - type: container
              format: docker
              paths:
                - processors:
                - decode_json_fields:
                    when.equals:
                      docker.container.labels.decode_log_event_to_json_object: fields: [ ]
                    target: overwrite_keys: output.logstash:
  hosts: true "true" "/var/lib/docker/containers/${data.docker.container.id}/*.log" "true" "message" "" true "logstash:5044" The above configuration uses a single processor. If we need, we could add more processors, which will be chained and executed in the order they are defined in the configuration file. Each processor receives an event, applies a defined action to the event, and the processed event is the input of the next processor until the end of the chain. Once the log event is collected and processed by Filebeat, it is sent to Logstash, which provides a rich set of plugins for further processing the events. The Logstash pipeline has two required elements, input and output, and one optional element, filter. The consume data from a source, the modify the data as we specify, and the write the data to a destination. input plugins filter plugins output plugins In the file, Logstash is configured to: logstash.conf Receive events coming from Beats in the port . Process the events by adding the tag sends the processed events to Elasticsearch which runs on the port 5044 logstash_filter_applied 9200 input {
  beats {
    port => }
}
filter {
  mutate {
    add_tag => [ ]
  }
}
output {
  elasticsearch {
    hosts => }
} 5044 "logstash_filter_applied" "elasticsearch:9200" Elasticsearch will store and index the log events and, finally, we will be able to visualize the logs in Kibana, which exposes a UI in the port . 5601 All over but the shouting : Example Before starting, ensure you at least , and set up. Then clone the repository from GitHub: Java 11 Maven 3.x Docker git clone https: //github.com/vrushofficial/Utter-Microservices-Territory In given above example there are multiple service — booking, financial, searching, consumer. Each are producing external logs file in specified locations in docker container. There are multiple files created for creating docker images and all these YMLs are configured with docker compose from file. .yml build.sh Visualizing logs in KibanaOpen Kibana in your favourite browser: . Kibana comes with sample data in case we want to play with it. To explore the data generate by our applications, click the link. On the left hand side, click the icon. http://localhost:5601 Explore on my own Discover Then pick a field for filtering the data by time. Choose and click the button. @timestamp Create index pattern The index pattern will be created. Click again in the icon and the log events of booking service start up will be shown: Discover To filter log events from the booking service, for example, enter application_name : "booking-service" in the search box. Click the button and now you’ll see log events from the booking service only. Update In the left-hand side, there’s a list of fields available. Hover over the list of fields and an button will be shown for each field. Add a few fields such as and message. Now let’s see how to trace a request. Pick a trace id from the logs and, in the filter box, input where is the trace id you want to use as filter criteria. Then click the button and you will able to see logs that match that trace id. Add application_name, trace.trace_id, trace.span_id trace.trace_id: "<value>" <value> Update Further Notes Configurations! and hopefully been able to understand and get your basics clear on all the terms and technologies that shall be used to implement the logs tracing application. If you found this story helpful, please share to help others find it! Come back here and tell me about the before-and-after. I bet you’ll have something to say! Hasta La Vista Baby!

Aggregate Logs with Elasticsearch, Kibana, Logstash & Docker

is a distributed, RESTful search and analytics engine that can handle an expanding range of use cases. It offers a full-text search engine with a multitenant capability, an HTTP web interface, and schema-free JSON documents, all with simple installation. Elasticsearch Elasticsearch is a search engine based on the Lucene library. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents. In this article, I will be walking you through how to set up elasticsearch on your PC. How to setup, install, and configure Elasticsearch for Mac In order to download Elasticsearch, go to the , select the respective operating system and click on the download button as shown below: Elasticsearch Download Page After downloading, extract the compressed file using the command below: $ tar -xzvf elasticsearch-8.4.1-darwin-aarch64.tar.gz Configuring Elasticsearch After extracting elasticsearch, depending on your use case, you may want to configure elasticsearch to suit you. You can take a look at the config file using the command below: $ vi elasticsearch-8.4.1/config/elasticsearch.yml You can have multiple nodes in your cluster, in order to do this, you need to ensure that the cluster name on your nodes matches. Cluster: # -------------------------------- Cluster ----------------------------------
#
# Use a descriptive name for your cluster:
#
cluster.name: air-elastic
# This is an identifier for the individual nodes in your cluster Node: # -------------------------------- Node ----------------------------------
#
# Use a descriptive name for your cluster:
#
node.name: airscholar-node
# For other configurations, check to fine-tune the configs to suit your use case. elasticsearch.yml Starting up Elasticsearch In order to start up elasticsearch engine use the command below: $ bin/elasticsearch When starting up for the first time, you will be presented with a password that needs to be changed: elasticsearch Changing your password for the first time You should change your password after starting up the elasticsearch for the first time, of course, you can do that using this command: bin/elasticsearch-reset-password -u elastic NOTE: ENSURE YOU KEEP YOUR ELASTIC PASSWORD SAFE, IT WILL BE REQUIRED IN ORDER TO CONNECT TO ELASTICSEARCH Testing Elasticsearch In your browser, goto or the specified hostname and port in your . You will be asked for (elastic) and (the password you changed to when starting elasticsearch for the first time) https://localhost:9200 elasticonfig.yml username password You should see something like this: {
  "name" : "airscholar-node",
  "cluster_name" : "air-elastic",
  "cluster_uuid" : "7g3LmMYFRmyxTyR6ZI-b7A",
  "version" : {
    "number" : "8.4.1",
    "build_flavor" : "default",
    "build_type" : "tar",
    "build_hash" : "2bd229c8e56650b42e40992322a76e7914258f0c",
    "build_date" : "2022-08-26T12:11:43.232597118Z",
    "build_snapshot" : false,
    "lucene_version" : "9.3.0",
    "minimum_wire_compatibility_version" : "7.17.0",
    "minimum_index_compatibility_version" : "7.0.0"
  },
  "tagline" : "You Know, for Search"
} If you have any challenges setting up elasticsearch, feel free to drop a comment, and I will try to respond. You can also check the for more detailed information about elasticsearch. official elasticsearch documentation Kibana Kibana is a source-available data visualization dashboard software for Elasticsearch, whose free and open source successor in OpenSearch is OpenSearch Dashboards. How does Kibana help us in this case you may ask? is a great visualization tool that helps us with our elasticsearch queries and manipulation. It is part of B.E.L.K (Beats, Elasticsearch, Logstash, and Kibana) stack. In case you don't know much about it, follow along and you will be just fine! (A separate article on Kibana can be written as an addendum to this series if you want, let me know in the comment section!) Kibana Setting up and Configuring Kibana In order to download Kibana, go to , and click on the download button. Kibana Download Page After downloading, extract the zip file using this command: $ tar -xzvf kibana-8.4.1-darwin-aarch64.tar.gz
$ cd kibana-8.4.1 There are two ways to configure and connect Kibana to elasticsearch, one way is to manually input the required keys into (in ) or run Kibana and configure it on the UI. I'm choosing the latter 😉! kibana.yml config/kibana.yml Run kibana using this command: bin/kibana When starting Kibana for the first time, you will be presented with this screen: There are two ways to tackle this; Use the elastic enrollment token (usually generated on your terminal when starting elastic for the first time) see below; Copy and paste the enrollment token above to Kibana and click . You will be fine. Configure Elastic Manually configure Kibana with elastic using the elastic URL. Click on check address and click . Configure elastic Enter password generated. Or click forgot password (if you can not recall the password), and paste the command below to your terminal to reset the password. kibana_system bin/elasticsearch-reset-password --username kibana_system You will be asked to enter a verification code and check your terminal (where you are running kibana to retrieve it) That's it! You are all set up. Enter your elastic username and password to log in to kibana and you are in! Querying elastic using Kibana Let's run a sample query to test our connection, shall we? Click on the hamburger (three lines on the top left corner of your screen) Scroll down to the bottom of the page Click on Dev tools You should see something like this: On the left pane is where queries are written, and the right pane is where the outputs are presented. Ensure you click on any part of the query and select the play button. The output of the query will be presented on the right pane. Loading data into elasticsearch To enable us to write our code effectively, we need data loaded into our . We will be using a sample dataset from Kaggle ( ). elasticsearch Download it here Follow the FOUR steps below to load it up into elasticsearch: Open up Kibana ( ) http://localhost:5601 Under Click on Upload a file: Get started by adding integrations Click on import and enter the name of the index you want to put the data in. Click on import (final page) If you made it to this point, you have successfully imported data into elasticsearch. Querying for sample Goto DevTools (Hamburger on the top left corner of the screen > Scroll down to Management > DevTools) Run the query below (select it and click on the play button) GET tmdb_movies/_search If you see this, we are good to go! Now, Let's dive into coding, shall we 😊? NestJS NestJS is a progressive Node. js framework that helps build server-side applications. Nest extends Node. js frameworks like Express or Fastify adding modular organization and a wide range of other libraries to take care of repetitive tasks. It's open-source, uses TypeScript, and is a very versatile Node. Creating a NestJs Application Run the command below to install nestcli and create a new NestJs Application (in the article, the name of the app will be nest-elastic). $ npm i -g @nestjs/cli
$ nest new nest-elastic You will be asked to select a package manager, you can select npm, yarn, or pnpm. I will be selecting yarn (you can choose any other one you want 😉). Your project will be set up and we should be ready to code! Adding elasticsearch to your app Run the command below to add elasticsearch to your and other dependencies: nest-elastic yarn add @elastic/elasticsearch @nestjs/elasticsearch @nestjs/config In your root folder add your file with the following contents: .env ELASTICSEARCH_NODE=https://localhost:9200
ELASTICSEARCH_USERNAME=elastic
ELASTICSEARCH_PASSWORD=elasticPasswordGoesHere
ELASTICSEARCH_MAX_RETRIES=10
ELASTICSEARCH_REQ_TIMEOUT=50000
ELASTICSEARCH_INDEX=tmdb_movies Let's create a separate module that handles only search using elasticsearch. A simple shortcut is to use the command below (you are welcome to do it manually if you want to): nest g resource search Update the to have the content below: search.module.ts import { Module } from '@nestjs/common';
import { SearchService } from './search.service';
import { SearchController } from './search.controller';
import { ConfigModule, ConfigService } from '@nestjs/config';
import { ElasticsearchModule } from '@nestjs/elasticsearch';

@Module({
  imports: [
    ConfigModule,
    ElasticsearchModule.registerAsync({
      imports: [ConfigModule],
      useFactory: async (configService: ConfigService) => ({
        node: configService.get('ELASTICSEARCH_NODE'),
        auth: {
          username: configService.get('ELASTICSEARCH_USERNAME'),
          password: configService.get('ELASTICSEARCH_PASSWORD'),
        },
        maxRetries: configService.get('ELASTICSEARCH_MAX_RETRIES'),
        requestTimeout: configService.get('ELASTICSEARCH_REQ_TIMEOUT'),
      }),
      inject: [ConfigService],
    }),
  ],
  controllers: [SearchController],
  providers: [SearchService],
  exports: [SearchService],
})
export class SearchModule {} Update with the content below: search.service.ts import { ConfigService } from '@nestjs/config';
import { Injectable } from '@nestjs/common';
import { ElasticsearchService } from '@nestjs/elasticsearch';

type dataResponse = {
  UnitPrice: number;
  Description: string;
  Quantity: number;
  Country: string;
  InvoiceNo: string;
  InvoiceDate: Date;
  CustomerID: number;
  StockCode: string;
};

@Injectable()
export class SearchService {
  constructor(
    private readonly esService: ElasticsearchService,
    private readonly configService: ConfigService,
  ) {}

  async search(search: {key: string}) {
    let results = new Set();
    const response = await this.esService.search({
      index: this.configService.get('ELASTICSEARCH_INDEX'),
      body: {
        size: 50,
        query: {
          match_phrase: search
        },
      },
    });
    const hits = response.hits.hits;
    hits.map((item) => {
      results.add(item._source as dataResponse);
    });

    return { results: Array.from(results), total: response.hits.total };
  }
} Now, let's add movies modules: nest g resource movies Update with the content below: movies.controller.ts import { SearchService } from './../search/search.service';
import { Body, Controller, Post } from '@nestjs/common';

@Controller('movies')
export class MoviesController {
  constructor(private readonly searchService: SearchService) {}

  @Post('search')
  async search(@Body() body) {
    return await this.searchService.search(body.data);
  }
} Then movies.module.ts import { SearchModule } from './../search/search.module';
import { Module } from '@nestjs/common';
import { MoviesService } from './movies.service';
import { MoviesController } from './movies.controller';

@Module({
  imports: [SearchModule],
  controllers: [MoviesController],
  providers: [MoviesService],
})
export class MoviesModule {} Finally, update app.module.ts import { MoviesModule } from './movies/movies.module';
import { ConfigModule } from '@nestjs/config';
import { Module } from '@nestjs/common';
import { AppController } from './app.controller';
import { AppService } from './app.service';
import { SearchModule } from './search/search.module';

@Module({
  imports: [MoviesModule, ConfigModule.forRoot(), SearchModule],
  controllers: [AppController],
  providers: [AppService],
})
export class AppModule {} Your should look like this: package.json {
  "name": "nest-elastic",
  "version": "0.0.1",
  "description": "",
  "author": "Yusuf Ganiyu",
  "private": true,
  "license": "UNLICENSED",
  "scripts": {
    "prebuild": "rimraf dist",
    "build": "nest build",
    "format": "prettier --write \"src/**/*.ts\" \"test/**/*.ts\"",
    "start": "nest start",
    "start:dev": "nest start --watch",
    "start:debug": "nest start --debug --watch",
    "start:prod": "node dist/main",
    "lint": "eslint \"{src,apps,libs,test}/**/*.ts\" --fix",
    "test": "jest",
    "test:watch": "jest --watch",
    "test:cov": "jest --coverage",
    "test:debug": "node --inspect-brk -r tsconfig-paths/register -r ts-node/register node_modules/.bin/jest --runInBand",
    "test:e2e": "jest --config ./test/jest-e2e.json"
  },
  "dependencies": {
    "@elastic/elasticsearch": "^8.4.0",
    "@nestjs/common": "^9.0.0",
    "@nestjs/config": "^2.2.0",
    "@nestjs/core": "^9.0.0",
    "@nestjs/elasticsearch": "^9.0.0",
    "@nestjs/mapped-types": "*",
    "@nestjs/platform-express": "^9.0.0",
    "reflect-metadata": "^0.1.13",
    "rimraf": "^3.0.2",
    "rxjs": "^7.2.0"
  },
  "devDependencies": {
    "@nestjs/cli": "^9.0.0",
    "@nestjs/schematics": "^9.0.0",
    "@nestjs/testing": "^9.0.0",
    "@types/express": "^4.17.13",
    "@types/jest": "28.1.4",
    "@types/node": "^16.0.0",
    "@types/supertest": "^2.0.11",
    "@typescript-eslint/eslint-plugin": "^5.0.0",
    "@typescript-eslint/parser": "^5.0.0",
    "eslint": "^8.0.1",
    "eslint-config-prettier": "^8.3.0",
    "eslint-plugin-prettier": "^4.0.0",
    "jest": "28.1.2",
    "prettier": "^2.3.2",
    "source-map-support": "^0.5.20",
    "supertest": "^6.1.3",
    "ts-jest": "28.0.5",
    "ts-loader": "^9.2.3",
    "ts-node": "^10.0.0",
    "tsconfig-paths": "4.0.0",
    "typescript": "^4.3.5"
  },
  "jest": {
    "moduleFileExtensions": [
      "js",
      "json",
      "ts"
    ],
    "rootDir": "src",
    "testRegex": ".*\\.spec\\.ts$",
    "transform": {
      "^.+\\.(t|j)s$": "ts-jest"
    },
    "collectCoverageFrom": [
      "**/*.(t|j)s"
    ],
    "coverageDirectory": "../coverage",
    "testEnvironment": "node"
  }
} Running the app You can fire up the app in the dev environment using yarn start:dev Testing You can access the full . source code here Setting up a react project You can set up a simple react app using this command (or check out this ): detailed react documentation $ npx create-react-app nest-elastic-frontend Once your app is set up, open it in your favorite IDE, mine is VSCode. We will need to install as a dependency. If you prefer package manager, you will have to run but if you preferred yarn, . axios npm npm install axios yarn add axios We need to update three files public/index.html You need to add bootstrap CDN. (PS: I have removed the comments to reduce the length of the file). <!DOCTYPE html>
<html lang="en">
  <head>
    <meta charset="utf-8" />
    <link rel="icon" href="%PUBLIC_URL%/favicon.ico" />
    <meta name="viewport" content="width=device-width, initial-scale=1" />
    <meta name="theme-color" content="#000000" />
    <meta name="description" content="Web site created using create-react-app" />
    <link rel="apple-touch-icon" href="%PUBLIC_URL%/logo192.png" />
    <link rel="manifest" href="%PUBLIC_URL%/manifest.json" />
    <link
      href="//maxcdn.bootstrapcdn.com/bootstrap/4.1.1/css/bootstrap.min.css"
      rel="stylesheet"
      id="bootstrap-css"
    />
    <title>React App</title>
  </head>
  <body>
    <noscript>You need to enable JavaScript to run this app.</noscript>
    <div id="root"></div>
    <script src="//maxcdn.bootstrapcdn.com/bootstrap/4.1.1/js/bootstrap.min.js"></script>
    <script src="//cdnjs.cloudflare.com/ajax/libs/jquery/3.2.1/jquery.min.js"></script>
  </body>
</html> src/App.js import './App.css';
import axios from 'axios';
import { useState } from 'react';
const App = () => {
  const [searchResponse, setSearchResponse] = useState([]);
  const [totalValue, setTotalValue] = useState();

  const handleChange = async e => {
    const { data } = await axios.post('http://localhost:8000/movies/search', {
      data: {
        title: e.target.value,
      },
    });

    setSearchResponse(data.results);
    setTotalValue(data.total.value);
  };
  return (
    <div className='App'>
      <div className='container search-table'>
        <div className='search-box'>
          <div className='row'>
            <div className='col-md-3'>
              <h5>Search All Fields</h5>
            </div>
            <div className='col-md-9'>
              <input
                type='text'
                id='myInput'
                onChange={handleChange}
                className='form-control'
                placeholder='Search IMDB movies'></input>
            </div>
          </div>
        </div>
        <div className='search-list'>
          <h3>
            {totalValue ?? 0} {totalValue > 1 ? 'Records' : 'Record'} Found
          </h3>
          <table className='table' id='myTable'>
            <thead>
              <tr>
                <th>Title</th>
                <th>Overview</th>
                <th>Revenue:Budget ($)</th>
              </tr>
            </thead>
            <tbody>
              {searchResponse.map((res, idx) => (
                <tr key={idx}>
                  <td className='title'>{res.title}</td>
                  <td>
                    <p>{res.overview}</p>
                    <sub>"{res.tagline}"</sub>
                  </td>
                  <td>
                    <p>
                      <sub>
                        {res.revenue.toLocaleString()}:{res.budget.toLocaleString()}
                      </sub>
                    </p>
                  </td>
                </tr>
              ))}
            </tbody>
          </table>
        </div>
      </div>
    </div>
  );
};

export default App; Lastly, src/index.css body {
  margin: 0;
  font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Oxygen', 'Ubuntu',
    'Cantarell', 'Fira Sans', 'Droid Sans', 'Helvetica Neue', sans-serif;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}

code {
  font-family: source-code-pro, Menlo, Monaco, Consolas, 'Courier New', monospace;
}

.search-table {
  padding: 10%;
  margin-top: -6%;
}
.search-box {
  background: #c1c1c1;
  border: 1px solid #ababab;
  padding: 3%;
}
.search-box input:focus {
  box-shadow: none;
  border: 2px solid #eeeeee;
}
.search-list {
  background: #fff;
  border: 1px solid #ababab;
  border-top: none;
}
.search-list h3 {
  background: #eee;
  padding: 3%;
  margin-bottom: 0%;
}

.title {
  word-wrap: normal;
  width: 200px;
} Running your app Start your react app with or depending on your preferred package manager. yarn start npm start Testing your app Thanks for staying tuned! Here is the link to . the source code Also published . here

Create a Full Autocomplete Search Application with Elasticsearch, Kibana, NestJS and React

Check out this exhaustive list of Kubernetes tools and utilities to take control of every part of your Kubernetes cluster. Kubernetes has become the new norm for container orchestration. Enterprises going cloud-native consider Kubernetes as one of the important elements contributing to the success of implementing a cloud-first strategy. Kubernetes is a system that helps with the deployment, scaling and management of containerized applications. As per the 2021 Cloud-Native Survey , Kubernetes, the dominant container orchestration program has reached its highest level ever, with 96% of organizations using or evaluating the technology. But with its benefits, K8s management brings in profound challenges for  software teams, planning to adopt Kubernetes architecture. Here are 3 tools to manage a Kubernetes cluster. So, let’s take a look at some of the popular Kubernetes management tools, here in this blog. Kubernetes Operations (kOps) kOps (shortened form of Kubernetes Operations) is a set of tools that helps users create, upgrade and manage production-grade Kubernetes clusters. This open-source project automates clusters in AWS & GCE and also supports cloud-native features and automatic updates. kOps is considered to be one of the best tools for deploying small production-grade clusters. It is built on a state-sync model that provides dry-runs and automatic idempotency. Some of the incredible features of this Kubernetes tool include, - It helps in automating the provisioning of highly available Kubernetes clusters and supports zero-config managed cluster add-ons. - Its additional features include command line autocompletion, YAML manifest-based API configuration, and dry-run modes for creating manifests. - It allows adding of containers, as hooks, and files to nodes through a cluster manifest file. - It provides support for add-ons, upgrading from kube-up, and the ability to generate Terraform configurations. Source Kubespray Next on the list of Kubernetes management tools is Kubespray. Kubespray is an automation tool that can help automate the provisioning of Kubernetes with Ansible, an open-source IT automation engine. It enables quicker, more flexible and hassle-free deployment of Kubernetes clusters allowing customization of the implementation process. Kubespray can be defined as a combination of Kubernetes and Ansible. It operates on bare metal and most cloud services. Moreover, it supports popular Linux distributions, such as Ubuntu, CentOS, Debian, Fedora, OpenSUSE, and Flatcar. Kubespray is one of the most popular ways to automate a Kubernetes cluster. Its extraordinary features include, - Within Kubespray, tasks such as creating clusters, composing inventory files, and cluster deployment can all be automated. - Kubespray can make the deployment process very easy as users may need to run just one Ansible playbook to deploy the K8s cluster. - Based on the cluster requirements, additional components such as Ansible playbooks or provisioning tools can be plugged in. Kubespray is one of the most effective options for organizations that run a hybrid environment with more than one public cloud on their premises. Source [Good Read: What The C-Suite Needs To Know About Using Kubernetes! ] BuildPiper BuildPiper is an end-to-end Kubernetes & Microservices Application Delivery Platform that eases the task of the DevOps team as it frees them from the task of keeping a check on the implementation details of Kubernetes components and making sure that the cluster is healthy, secure, and up to date. Here are some of the other out-of-the-box functionalities of this Kubernetes tool, Easy creation of Ingress, Namespaces, Config Maps, Secrets. This can easily be done through a Guided UI or by uploading the Manifest file via Gitops. Namespace/ Ingress Management Easy and production-grade setup of supporting tools for security such as Hashicorp Vault, Consul, Sonar, Istio, Nginx from a single, centralized console. Security Tools Hassle-free and enterprise-grade setup of Monitoring tools such as Prometheus, Grafana, Alert Manager and Log Management tools such as ElasticSearch, Fluentd, and Kibana. Monitoring & Log Management Tools Easy and seamless configuration, maintenance and management of K8s cluster with deep insights and 360-degree observability. 360-degree Cluster Observability Allowing DevOps teams to use spot instances for running containerized workloads. Cost Optimization with Spot Instances Source Stay tuned for more information & recent updates on Kubernetes tools, in our upcoming blogs! Have a HAPPY Reading till then! Also Published Here

The 3 Best Tools for Kubernetes Cluster Management

Intro: Grafana vs Kibana vs Knowi In today’s digital world when a person uses the term “Big Data”, the first thing which comes to mind is the sea of data that humans, social networks, and IoT devices are generating. But we often miss the big data deluge that is getting generated by servers, cloud infrastructure applications, and web-based business tools across the globe. Daily, these systems are generating tons of data in their logs. These log datasets are crucial to properly monitor these systems. But a person cannot monitor and analyze such huge amounts of log data in raw format manually. This is where data visualization tools can be a big help. Log data visualization dashboards can be useful for monitoring server and application logs to make an IT team more efficient. Grafana, Kibana. and Knowi are some of the best visualization tools available in the market--especially for . In this article, we shall give you a comparison of Grafana vs Kibana vs Knowi so that you can make the correct choice for your log management needs. Let us first understand each of them in more detail. Elasticsearch analytics Grafana Grafana is an open-source tool that can be used to visualize any data. It can be used to create multiple dashboards that support a variety of beautiful graphs and visualizations for different users and stakeholders. It has support for integration with a few different data sources, including Graphite, InfluxDB, and Elasticsearch. Grafana Dashboard (Source: Wikipedia.com) Kibana Kibana is the visualization tool of the very popular ELK stack. ELK stack is a collection of 3 open-sourced tools: Elasticsearch, Logstash, and Kibana. The ELK stack is used for end-to-end log management and monitoring solutions for Elasticsearch data. Kibana, which is the ‘K’ of ELK stack, is used for visualizing and analyzing the log data in Elasticsearch. Kibana does not support integration with any other data source and works only with Elasticsearch. Kibana Dashboard (Source: Elastic.co) Knowi Knowi is a modern business intelligence platform that has a host of features for both data engineers and data scientists. It has native integrations to plenty of SQL and NoSQL based data sources. Knowi provides rich visualizations for advanced analytics, on-demand reporting, and user dashboards. Keeping with the current disruption of artificial intelligence, Knowi also offers integration of machine learning and natural language processing. The ML feature helps users to build machine learning-based triggers in the workflow. While the feature allows non-technical users the ability to query the data just like they would ask questions in a google search. natural language business intelligence Knowi Dashboard from knowi.com Grafana vs Kibana vs Knowi Let us now do an in-depth comparison between these three tools on various parameters and understand which of the three will suit your requirements. Visualization Both Grafana and Kibana are essentially visualization tools and they offer a plethora of features to create graphs and dashboards. But Grafana is more popular for producing beautiful and visually appealing graphs and dashboards. Moreover, Grafana is known to be more customizable and flexible when compared to Kibana. But at the same time, Kibana is easier to set up. On the other hand, as a BI platform, Knowi also offers rich visualization. As a matter of fact, Knowi provides over 30+ visualizations while Kibana only supports 17. In spite of these differences, Grafana, Kibana, and Knowi are neck-to-neck in their visualization features. If you are not looking for something specific, then visualization should be least of your worries in deciding between Grafana, Kibana, and Kibana. A more important point to think about might be data source integration. Integration with Data Sources Grafana officially supports integration with 14 data sources like  MySQL, Graphite, InfluxDB, PostgreSQL, and Prometheus to name a few. Knowi, on the other hand, supports a wider range of 36 different data sources which include, Oracle, MySQL, MongoDB, PostgreSQL, Elasticsearch, etc. And those are just the listed native integrations. With its ability to connect to any REST API, the total number of possible data sources is limitless. The ability to tap data from multiple data sources makes both Grafana and Knowi truly versatile options for any sort of log management architecture. Kibana, however, only supports integration with Elasticsearch. It might look like a big shortcoming of Kibana, but on the positive side, if your project uses only Elasticsearch (and you do not think it will expand in the future to include more data sources) then you should consider Kibana as it is coupled tightly in the ELK stack. Grafana and Knowi, however, do still fully support Elasticsearch integration and can be considered over Kibana if they bring more on the table for your particular use-case. Log Search and Analysis While doing some analysis or debugging you may need to search through the logs. Both Knowi and  Kibana support querying on text data. Hence log analysis can be performed with these two tools. However, this support is not currently present in Grafana and is seen as a major drawback of choosing that platform. One use-case where this querying feature may be a good reason for you to choose either Knowi or Kibana over Grafana, is if you intend to build a monitoring solution for a technical support team that analyzes issues from logs. In such a use-case text-data querying would be essential. Proactive Alerts A crucial aspect of monitoring sensitive systems is to detect certain issues proactively so that action can be taken in advance before it leads to severe consequences. For example, we can set a rule to send alerts when a server memory utilization crosses 90%.  This will help the operations team to bring down the memory utilization and hence avoid any unwanted outage on the system. Both Grafana and Knowi have built-in features for raising alerts against a set of rules that can be defined by us. Kibana, however, does not offer support for Alerts out of the box. It instead requires 3rd party integration to enable Alerts. User Authorization In a big enterprise where there are many users and stakeholders, you would not like all users to access the data. This holds true even for monitoring dashboards. You would like certain users to access only certain dashboards. Or maybe your requirement is to create a dashboard specific to certain users. The good thing is that both Grafana and Knowi provide user management features out of the box. You can create users and roles, and authorize specific dashboard access to them. Kibana does not offer any user management features. This means whoever has the link to the Kibana dashboard can see the data. This might not always be a good way of working. But the saving grace is that we can still implement user and role restrictions in Kibana with the help of 3rd party integrations. Online Community Both Grafana and Kibana are open source and are under active development on GitHub. This means there is good online community support available for both if needed. If the popularity of both projects has to be measured, then it is worth noting that on Github, Kibana has more commits than Grafana. Knowi, however, is not an open-source tool currently. Still, they do have their own forum on their website where you can seek help from other users. But what they lack in community may be made up for in customer support.  As a relatively young startup looking to make a name for themselves, they have a dynamic, responsive, and strongly motivated team of solution engineers looking to prove that their product is the best on the market. Machine Learning Currently, machine learning is making inroads in all possible spaces globally where there is data. If you have loads of big data you would not like to miss the opportunity to apply machine learning to get hidden insights and patterns from data. Imagine, if you can predict any potential risk from logs data well in advance and take precautionary measures. This can take the log monitoring process to a whole new level. Unfortunately, neither Kibana nor Grafana support machine learning. But Knowi has built-in integration with many machine learning algorithms like Classification, Regression, and Time-Series Anomaly Detection type Machine Learning use cases, with clustering and deep learning coming soon. This enables Knowi users to do predictive analysis using machine learning and set up a triggering alert based on the prediction in the monitoring workflow. Purpose Till now, we have done a comparison of Grafana vs Kibana vs Knowi with respect to the features they offer. But we need to understand one thing that the underlying purpose of using the tools is slightly different. Kibana is useful to convert log data from the ELK stack into visualizations and it also supports querying logs. Grafana is more of a general-purpose visualization tool that can work with a handful of data sets that may or may not be “logs”. But it does not support text querying. Knowi is an end-to-end data analytics platform that can sit on top of and across multiple databases, structured or unstructured. Knowi literally supports all the features and integrations that either Kibana or Grafana offers plus much more. And it does have a big edge due to it’s out of the box machine learning solution. That said, both Kibana and Grafana can be deployed for free or at a very low cost, while Knowi is an enterprise solution that does have licensing fees. But with those added expenses come a lot of support, which can be nice when getting a complex data analytics solution off the ground. Conclusions People who work in the operation support team will tell you how important it is to monitor the health of applications, servers, and infrastructures. They continuously need to monitor these systems since an outage can result in a huge loss to the business. Grafana or Kibana or Knowi can be a good choice for your monitoring solution. As we saw, they have their own pros and cons, and differences in features. What will work best for you depends on your use case and you should evaluate your project needs before making the choice between the three platforms. About the Author Sean Knight has a M.S. in Data Science and a B.S. in Astrophysics. He has worked at particle accelerators, NASA JPL, and is now in the startup world as a growth hacker. Disclaimer: Sean works as Head of Growth at . Knowi Previously published at https://www.knowi.com/blog/grafana-vs-kibana/

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