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Being a developer is awesome. Writing code, solving problems, and thinking of ingenious solutions for complicated algorithms is what we live for. But, the grass is not always so green on this side of the fence. Sooner or later, you need to get your hands dirty and deploy the app you worked so hard on. Deployments are not always easy. To be blunt, they can be challenging and time-consuming. That's what we'll solve in this tutorial. Goals Today you'll learn how to deploy a Node.js application to with the help of . AWS Lambda the Serverless Framework The walkthrough will also cover a real-life scenario of deploying a production version of your application, with environment variables, , and, of course, ; Oh, how I love seeing nice stack traces! proper monitoring easy debugging Prerequisites This tutorial will need you to have a few simple things set up already. Don't worry, it won't be long and will cover just the basics. AWS has 12 months of free tiers and plenty of lifetime free tiers, so don't worry about breaking the bank. : AWS account Here's a quick reference for making sure you have them installed. : Node.js and npm installed Check this out for an excellent reference and follow steps 1--3. : Serverless Framework installed This will give us the required overview to see what's happening to our app. : Dashbird account Ready? Let's go! . For copyable code snippets, see the original article What's AWS Lambda? AWS Lambda is a pay-as-you-go serverless compute service. Well, you just deploy your source code to AWS, and they handle the rest. Amazing! No need to fiddle with servers, ssh connections, Linux, or Vim. But, want to know what's even better? It scales automatically and has no downtime. I'll let that sink in... Which means what now? The technical definition for AWS Lambda would be a Function as a Service. You deploy some code; it gets invoked, processes some input, and returns a value. Simple! Hold up a sec. We need to mention a crucial fact. All lambda functions are stateless, meaning they cannot store persistent data. And, if I just said Function as a Service, how do we deploy a whole Node.js application to AWS Lambda? How Does It Work? When you think about it, it's not that complicated. A single lambda function is essentially just a tiny Node.js runtime environment. You can run whatever you want in there. That's what we'll do, package up a Node.js app and send it off to AWS Lambda. Sneaky. What Will We Deploy? The code we'll be deploying is just seven lines long to make this example as simple as possible. However, it could be as large as any Node.js application you have in production, and it would all work like a charm. Intriguing... How Do We Deploy It? In comes the Serverless Framework like a horse riding a white knight! Hold up; I may have that backward. Anyhow, this excellent framework enables us to both develop our apps locally, just like we're used to, but also deploy them with a straightforward command. Hmm... tell me more. Let's Get Coding! First thing's first. Open up your terminal; we need to install some packages. Yay, installing stuff, love it! 1. Install and Configure the Serverless Framework $ npm install -g serverless Prefix the command with if you're running this command on Linux. sudo Make sure to add your IAM User's public and secret key instead of the placeholders I specified above. The user needs programmatic access and admin permissions. . Here's the official guide in the AWS docs 2. Create the Boilerplate Code Let's create a new folder and give it a unique name. Jump over to your terminal and run the commands below. $ mkdir serverless-nodejs-app && cd serverless-nodejs-app Excellent! Now, what's left is to run the create command to generate some starter code for us. This is called a serverless service. $ sls create -t aws-nodejs -n serverless-nodejs-app Only one more step before opening a code editor. Installing Dependencies Like you can see in the code snippet above, we need to install a few modules first. Lucky for us, there are only two, and it's as simple as running one command. That's it! Let's open it up in a code editor and do some actual coding. Coding For Real Once you open up the code editor, you'll see three files. Ignoring the , let me explain what the is first, then I'll move on to the . The handler will hold all your app logic, all the code. In contrast, the is the configuration file for the resources you'll be creating on AWS. .gitignore file handler.js serverless.yml servereless.yml Go ahead and rename the to , to help us figure out what goes where. handler.js app.js Delete all the starter code and paste this code snippet into the file. app.js Seven lines of code? It looks familiar, right? Just like you're used to. That's it. Believe it or not, there's nothing more to it. Let's move on to the . serverless.yml Once again, delete all the boilerplate code and paste this in. Done! All that's left is to deploy it. Ready to Deploy! Switch back to the terminal window. By running one command, your app will be deployed. $ sls deploy The Serverless Framework will now wrap everything up into a nice bundle, create a CloudFormation file from the and shoot it off to AWS S3. Once the resources are created, and the code is deployed, you'll see an endpoint sent to you in the terminal. serverless.yml Open up the provided URL in a browser. You'll see that gets sent back to you. "Hello World!" Deploying to Production! This is great and all, but not ready for a production environment... yet. Worry not! You'll be surprised how simple it is to make it production-ready. Add a file for environment variables secrets.json For now, let's just add the in the . NODE_ENV secrets.json Add a reference for the in the secrets.json serverless.yml As simple as adding the secrets file, it's even easier to reference the file in the . serverless.yml Amazing, that's it! Delete the and folders from the service and run npm install again, but this time with the flag. node_modules .serverless --production $ npm install --- production Great! All that's left is to re-deploy the service, and you're set. $ sls deploy And this is what we end up with. I guess we're done? Not really. Having an app running in production just because you installed npm modules with doesn't cut it. To be able to sleep well at night, I need a bit more. Here's where proper system insight and monitoring tools come to play. --production Let me show you. How to Gain Insight Into Your System? The #1 problem with all serverless applications is their distributed nature. Plain and simple, it's impossibly hard to have an overview of all the things going on. Not to mention how hard it is to debug when something goes wrong. To calm my fears, I use . It's a simple monitoring tool that doesn't require me to change any code and has no overhead. So, no performance hits either. Nice! Dashbird Thankfully, they have sensible documentation, which makes the onboarding process a breeze. Go ahead and follow their . Don't forget to come back here, though. Quick Start guide Once you're done, all the requests will start piling in one by one, and you should see something like this. The function details will give you an overview of how the function behaved over time, so multiple invocations are aggregated here. If you click on a recent invocation at the bottom left, you will drill down to the invocation level of details that comes with tracing and logging data. Wrapping Up This was fun! Lambda is awesome! When combined with HTTP event triggers such as , development tools like the Serverless Framework, and observability tools such as , things just become so easy. API Gateway Dashbird This simple API example we coded above is just a proof of concept. But you can see the point. It gives you a starting point from where you can create awesome production apps! If you missed any of the steps above, . here's the repository with all the code First Published here Further reading: How to check NFT supply with AWS Lambda How to test serverless applications Top 6 AWS Lambda monitoring tools How to solve serverless cold starts AWS Lambda Node.js errors and exceptions

Go Serverless with AWS Lambda, Node.js and Dashbird

In the 21st century, it’s quite easy to manipulate machines and computers. Our worries are no longer if something is doable, but if something can be perfected. Therefore, we mostly search for new ideas and ways to make our work impeccable. For example, if you’re using a particular software and you realize that the software is excellent, but it could be better in some ways that would allow you to work even faster, you’ll explore the alternatives. There are all kinds to choose from, and you will search for the one that is most suitable for your own needs. Every one of them has some perks of their own, and in others, you’ll notice some faults. In this article, we’ll make a brief introduction, and we’ll also talk about the differences between CloudWatch alerts vs. Dashbird alerts. Which one is better and why? What is AWS CloudWatch Used For? is built for system operators, site reliability engineers (SRE), IT managers, and developers. CloudWatch allows you to monitor your applications via data access and insights it provides. It can also recognize, understand and respond to all changes happening throughout the entire system. AWS CloudWatch CloudWatch is also collecting monitoring and operational data through metrics, events, and logs which further provides you with a unique view over the AWS resources, services, and apps that run on AWS, as well as in the localized servers. CloudWatch enables you to set alarms (or alerts), troubleshoot for issues, and discover the insights for application optimization which will ensure that the application runs smooth. AWS CloudWatch Alerts Explained A CloudWatch alert (a.k.a. alarm) can watch over a single CloudWatch metric or even a result of math expression found in CloudWatch metrics. Alerts will perform single or multiple actions based on the value of metric or expression which is relative to a threshold over a number of time periods. Adding alarms to AWS CloudWatch dashboard is enabled and that way you’ll be able to monitor them visually. There are three alarm states: – meaning that the expression or metric is found inside the already defined threshold; OK – implies that the expression or metric are located outside of the specified threshold; ALARM – this alert is shown when the alarm has already started but the metric is not available, or there’s not enough data for the metric to realize in which state the alarm is. INSUFFICIENT_DATA When creating an alarm, you are able to specify three settings that will allow CloudWatch to evaluate when to change the alarm state: – will enable you to evaluate the time length of metric or expression in order to create an individual data point for an alarm; Period – is the number of the recent data points you need to evaluate to be able to determine the state of the alarm; Evaluation Period – is the number of data points in the evaluation period that must be breached, so it’s causing the alarm to go to the ALARM state. These breaching data points must all be within the last number of data points which is equal to the Evaluation Period. Datapoints to Alarm There are a lot of features that apply to all AWS CloudWatch alarms, and we’ll go through some of them. For example, the number of evaluation periods for an alarm if multiplied by the length of every evaluation period can’t surpass the one-day limit. Another feature worth mentioning is that ASCII characters must be included in alarm naming. You are also able to create 5,000 alarms within every region per a single AWS account. Metrics CloudWatch gathers basic metrics, which further allows you to how the entire system performs. The collected metrics for Lambda functions are latency, invocations, errors, and concurrency. Since the chances of you checking the metrics precisely at the same time when something goes wrong are pretty slim, it would be wise to configure alarms upfront (more on this and best practices below). Do this in case an unexpected event meets a condition or threshold so the alarm could notify you in time. monitor How to Set Up Your Metric Alarms on AWS CloudWatch? You should first , but only if the predefined condition is fully met. The SNS trigger will then invoke a Lambda function to take action. This action will send notifications so you can further investigate the issue. configure a CloudWatch metric alarm to trigger an SNS topic Setting Up Metric Alerting on AWS (Best Practices) Can you recognize the optimal time to ? The answer depends on whether you’d like to receive alerts only in cases that require your immediate attention or not. Even if you set them up to alert you often, responding to each and every alert is not feasible. It means that it won’t be long before you miss a crucial alert, which is bound to happen either because of the noise or because you began ignoring alerts entirely. configure a metric alarm Try to understand all of it this way: Do you think it’s okay if 1% of all requests fail due to a single function? Is it of vital importance that all requests take no longer than 1 second? In these cases, you’d probably want to know if your Lambda is reaching a (account-wide). All these settings are completely individual for each application, and it usually takes some time and iterations before you can get it to an acceptable level. concurrency limit Another thing you should think about is configuring naturally preventive alerts. These alerts will trigger even when nothing has failed yet, but it might happen soon. A good example will be if a Lambda function is close to a timeout or even closer to fill its memory capacity – remember that CloudWatch attains metrics for invocation counts, latency, memory usage, and failures by default. High-Resolution Metric Alarms If you set the alarm on a high-resolution metric, you’re allowed to specify a high-resolution alarm in specific time groups (10 or 30 seconds). Moreover, you can set up a standard 60-second alarm multiple times. It’s also essential to know that high-resolution alarms are charged at a higher rate so be sure to do a cost analysis if that could be a potential issue. Standard AWS CloudWatch Alerts Features CloudWatch alarms have numerous features, and the ones listed below are common and apply to all alarms: Each AWS account can create up to 5,000 alarms for each Region. You should use the CloudWatch console if you wish to update an existing alarm or create a new one. Use the command put-metric-alarm in AWS CLI or utilize an API action called PutMetricAlarm that’s also available. Only ASCII characters are allowed in alarm name fields. You can either enlist some or even all of the alarms that are currently configured. You can also list any alarms within a particular state by utilizing the CW console by looking for an API action DescribeAlarms. Alternatively, look in for the command describe-alarms. AWS CLI You can also enable or disable alarms via the CloudWatch console. To do so requires you to look for API actions EnableAlarmActions and DisableAlarmActions. As an alternative, you can look for the following commands in AWS CLI: enable-alarm-actions and disable-alarm-actions. Set the alarm to any state to test it by utilizing either an API action: SetAlarmState, or by utilizing command in AWS CLI: set-alarm-state. Remember that when the next alarm comparison happens, this temporary state change will be over. It’s possible to create a custom metric alarm even before making this particular custom metric. You have to include all of the custom metric’s dimensions along with the metric name and metric namespace within the alarm definition to make it valid. You can do this via a command in the AWS CLI: put-metric-alarm, or via API action: PutMetricAlarm. Viewing an alarm’s history is possible via CloudWatch console as it saves alarm history for 14 days. You can either use a command in AWS CLI: describe-alarm-history, or an API action: DescribeAlarmHistory within the console. You should know that every state transition has its unique mark in the form of a timestamp. Make note that your history might show even more than one notification for a single state change. However, that’s quite rare. Also, you can confirm all unique state changes as the timestamp enables you to do so. The evaluation periods’ quantity for each alarm multiplied by the length of each and every evaluation period can’t exceed 24 hours. Dashbird Alarms and Notifications Explained Dashbird’s instant alerting system will notify you if any issue shows up within any part of your application. Issues such as crashes, , runtime errors, timeouts, configuration errors, and early exits. Its system offers messages and realistic logs that humans can easily read and understand, which saves you and your company meaningful debugging time. Dashbird monitors your application and is able to detect all kinds of errors in various runtime environments. cold starts The Events page does everything an observability tool should – showcase all errors occurring within your system. With Dashbird, you can customize which events to track and how you want to get notified. Everything mentioned above works for programming languages supported by AWS Lambda, including Node.js, Python, Java, and C#. All the required data to successfully go through troubleshooting events and resolve any app issues are entirely at your disposal. A human-friendly interface will present you with any previous occurrences, stack traces, etc. Logs and trends for every error or problem are also available. You can use a “more info” button for every single error you face, and that way, you’ll see the error page with all the needed info for debugging the current issue. Configuring an Alert Policy (Alarms) with Dashbird To configure an alert policy, simply visit Events from the left navigation menu and click on the Settings tab on the Events panel. From here, click on Alarms and click + ADD: Dashbird gives you complete control over Alarms as it allows you to choose which error reports you should receive. You’re required to set and configure the Alarms rules. That way, you’re adding a new policy to the list, and from there, you can start configuring how you want to be notified of Alarms. Defining Alarms for any ‘error overall functions’ is possible, but you can also set the system to alert you when a specific microservice or function experiences invocation timeouts. Setting Up Notifications Proactive alerts are another one of Dashbird’s features. You could set up an email notification system or seamlessly integrate it with Slack. We also support SNS and webhooks. All policies require at least one alert condition along with one notification channel. An alert state consists of different functions and error conditions, while a notification channel can either be an email address or even Slack. The Best Ways to Handle Alerts Consider alerting as an ongoing process. Therefore, we at Dashbird recommend you to test different policies. These are some basic principles and suggestions on how to handle alerts when they emerge: You should add new Alarms whenever you need them, but you should also mute or delete unnecessary ones so you would avoid alarm fatigue. Even if you think production Lambdas will never fail, you should set alerts for them too, as unexpected circumstances tend to happen from time to time. If you’ve fixed alerts within the code, it’s always mandatory to resolve them as well. By doing so, if the same problem happens again, you’ll receive a new notification. Conclusion Both CloudWatch and Dashbird have their pros and cons, and we’ll wrap up here after mentioning a few. While Cloudwatch is mostly an excellent choice for users who are already inside the AWS ecosystem, it’s not all that great for the ones who aren’t, and they should find a simpler solution. The alerting options for CloudWatch are not as boundless since they’re available with third-party services. Moreover, CloudWatch doesn’t offer pre-configured alerts. It would be best if you create custom alerts by yourself, which means you must be very familiar with how everything works in order to create them properly. On the other hand, Dashbird’s alert notification system is automated and instant, which undoubtedly provides you comfort and ease if something happens within your application. During the past three years, we’ve heavily invested in the new Dashbird app interface and building the next stages of our platform. We’ve expanded our offer, and it now includes services like API Gateway, Step-Functions, DynamoDB, Kinesis, ECS, and SQS, and many more to come soon.

Setting Up AWS CloudWatch Alerts (vs Dashbird Alerts) To Monitor Your Applications

The serverless space is relatively new and rapidly changing and improving (just look at the monster list of launches and updates from re:Invent). To keep up and maintain top efficiency, so should serverless developers. This is our round-up of all the enhanced serverless observability feature releases Dashbird launched in 2020 that will help serverless users troubleshoot and scale better in 2021. So here we go! Inventory Having previously exclusively served AWS Lambda, earlier this year, Dashbird added more AWS services to our roster for serverless monitoring, observability, and optimization opportunities. The Inventory list includes: AWS SQS AWS DynamoDB AWS API Gateway AWS ECS AWS Kinesis AWS Step Functions The addition of these instrumental services and resources to the Dashbird monitoring platform gives you even greater autonomy and understanding of your Serverless infrastructure. With the ability to drill down into the details, set up alerts, and watch for trends and patterns, Dashbird offers . All without any code or architectural changes. You just have to deploy CloudFormation template to your AWS, and can start receiving pre-configured automated alerts and working with your data immediately. one of the fullest Serverless observability platforms available today Well-Architected and Best-Practice Insights Dashbird is constantly running users’ infrastructure data through its WAF rule engines detecting anomalies and weaknesses within the architecture, and notifying the users of critical errors and/or when improvements should be made. Dashbird features a continuous rule engine for detecting and enforcing Serverless Well-Architected practices. This allows developers to build and operate complex Well-Architected serverless applications without compromising reliability over time and also be able to layer on complexity later on. Metric Alarms In addition to the Inventory, we also added the Metric Alarms feature. Its simplicity – choosing a metric important to you, setting its status to ‘critical’ or ‘warning’, and pointing to the relevant resources and the person this alarm should reach – , meaning alert fatigue and complex alarms can be a thing of the past. You can be confident that important information reaches the correct person in time! lets you cut through the noise Used in conjunction with Dashbird Insights Engine, Metric Alarm allows users to and to customize their infrastructure based on solid insight. gain greater assurance over SLAs/SLIs For example, here we have configured a critical alarm to be triggered when the queue has reached 10 or more items. This alarm helps detect situations like when the queue consumers cannot keep up with new messages in the queue – that is when messages arrive faster than they can be processed. So whenever that happens, Alex from our dev team gets notified in real-time via Slack (his preferred channel). Detailed Usage Report The Detailed Usage Report provides another element to the metrics and insights Dashbird already offers. Shown by; Usage by Region and by Type, Data Ingested by Region and by Lambda, and Lambda Invocation, it’s one step easier to . spot trends and patterns for better optimization For example, understanding the pattern of a well-used Lambda from this starting point, users can then look into its related billing and resource usage patterns to identify if savings can be made in either AWS or Dashbird. Notifications Channel & New Communication Channels Dashbird has now enabled alerts and alarms to be sent through AWS SNS and Webhooks, making automation and autoscaling even easier and more seamless. Out-of-the-box notifications can be set up, as can the customized ones. Understanding that each company’s workflow is different, the Notification Channel now makes it easier to point alerts to a specific team member or group for faster internal communication, acknowledgment, and remediation. Dashbird app got a shiny new look The new and improved Dashbird app’s main screen is designed to give you a quick overview of the most important events and metrics across your serverless environment. Jump in and check it out! Previously published at https://dashbird.io/blog/dashbird-feature-releases-2020/

Scale Your Serverless App with Dashbird's New Features

With AWS Lambda, we get scalability and resilience out-of-the-box. What’s more, AWS also provides built-in monitoring, logging and tracing support through CloudWatch and X-Ray. These built-in tools provide a good starting point but many developers eventually outgrow them as their serverless application becomes more complex. In this post, let’s take a serverless application and see how can help you debug the application. Dashbird Challenges with serverless observability When it comes to observability, serverless has introduced some interesting challenges. For so long, we have relied on the use of agents and daemons to collect metrics and logs. They run silently in the background, away from our critical paths where we are concerned with minimizing user-facing latencies. They collect, buffer and publish these observability data in batches to improve efficiency. As a practice, they are so deeply ingrained into how we monitor our applications, until now. When it comes to serverless, specifically with managed platforms such as AWS Lambda, there’s simply nowhere for us to install these agents! To collect metrics and logs as part of your function’s invocation would understandably add overhead to its invocation time. Since AWS is collecting logs from your function asynchronously already, and publishing them to CloudWatch Logs. A common workaround is to subscribe to these logs and perform post-processing on them. Indeed, that is how Dashbird collects data about your function’s execution. It subscribes the CloudWatch Logs log groups to a Kinesis stream and then processes the events from there. You can read about the advantages of this approach in . this article As our serverless applications become more complex, it’s important for us to be able to trace executions across multiple functions. As the demo app demonstrates, even a simple user transaction can span across multiple event sources as well as Lambda functions. The demo app Imagine you’re building a Twitter clone. One of the core features of the system is to distribute a user’s post to his followers’ feeds. To implement this feature, imagine we have two separate API endpoints: POST posts/create : to create a new post for the current user GET followers/{userId} : to fetch a user’s followers Each endpoint is handled by a separate Lambda function — and respectively. create-post get-followers When a user publishes a new post, the function would save the post in the DynamoDB table and also publish a event into a Kinesis stream. This event then triggers a function. This function would query the endpoint and then add the post to the followers’ feeds. The function would query the DynamoDB table to return the IDs of the user’s followers. create-post posts post-created distribute-post GET followers/{userId} get-followers followers For brevity sake I have omitted the logic for actually distributing the posts. So the overall architecture for our demo app looks like the following. To make things more interesting, each of the Lambda functions are hardwired to error or timeout based on a configurable probability. The source code for the demo app is , so feel free to try it out yourself. available on Github Introducing Dashbird Even with a simple serverless application like the one outlined above, we have quite a few functions to look after. Let’s see how we can use Dashbird to help us monitor this application and debug issues. As soon as I log in, I have a high level dashboard for my account. In addition to the data I get in the AWS Lambda console (see below), the Dashbird dashboard has two useful data points: Average memory utilization for the functions Cost for the Lambda invocations Next, in Dashbird’s Lambda console, I can see a high level summary of my functions and their activities over the last 24 hours. What I find very useful here is the fact that it highlights functions that have been idle for 10 days as inactive. As your serverless architecture expands and you end up with hundreds of functions, maintained by different teams, it’s very difficult to track which functions are no longer needed. Having redundant functions lying around is a security risk as they remain an attack surface that can be exploited. While this view alone cannot tell you definitively which functions are no longer used. Many functions are not run on a regular basis. Maybe they are part of a cron job that only runs once a month. Or maybe they are only used during disaster recovery scenarios. Nonetheless, being aware of which functions are inactive encourages teams to ask the question “Is this function still needed?”. From here, maybe better practices can emerge. For example, use tags to mark functions that are expected to be used sparingly so they are not flagged by these checks. If I navigate to one of the functions, then I have a function-centric view of invocation time, error, cost and memory utilization. In addition, I can also see a list of the recent invocations. What’s really useful here is that cold starts and retries are clearly labelled. When debugging live issues this lets me quickly narrow down the invocations that I need to pay attention to. Straight away, I can see that 3 of the invocations timed out after 6 seconds. What’s more, the original Kinesis event was retried 3 times and finally succeeded on the third retry. If I click on the “+” button next to an invocation then I can drill down to the invocation itself. Here I can see the logs and X-Ray trace for this invocation in one screen. This is great as it saves me from having to constantly jump between different AWS console. Debugging with Dashbird As mentioned before, the demo app is hardwired to error and timeout. And sure enough, when these failure cases happen, Dashbird’s built-in alerting kicks in and I promptly received emails notifying me that something went wrong. While this built-in alerting is great, I couldn’t see any settings to adjust the alert sensitivity. As I followed the links in the emails, to the function, then the failed invocation. Dashbird neatly groups the related invocations — the initial timeout, and the subsequent retries — together. I can quickly see that the failed Kinesis event was successfully processed on the 3rd retry. Dashbird also tracks the open issues in the Errors console. Now that I know the problem has resolved itself I can go ahead and resolve the error. Tailing function logs Another nice feature of Dashbird is that it’s able to tail the logs for multiple functions at the same time. For the demo app, I want to see the logs for both the and functions as I curl the endpoint. create-post distribute-post POST posts/create That way, I can see that the event was successfully published into the Kinesis stream, and was subsequently received by the function. distribute-post Conclusion Overall I was impressed with what Dashbird has to offer, and it’s clear that a lot of thought has gone into the product. It has many nice touches that makes debugging much easier. For example, grouping retries together, and integrating X-Ray traces and logs in one screen. These might seem like trivial niceties, but they can make a big difference under the high-pressure scenarios of dealing with a live issue. From what I have been able to see, I think Dashbird is a really great tool. The main thing missing for me is the ability to trace executions end-to-end. Personally, I’d really like to see the entire execution traced from the API call to create a post, all the way to the function performing a Query against the DynamoDB table. get-followers followers If you’re solely relying on the built-in AWS tools (CloudWatch, CloudWatch Logs, and X-Ray) then you should give Dashbird a try. Why not sign up for a free trial, and deploy the demo app to your environment so you can see how Dashbird can help you debug your serverless application? Originally published at dashbird.io on January 25, 2019.

Debugging Serverless Applications with Dashbird

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Let's learn about via these 74 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Observability /Learn Repo 1. So, How are Observability and Monitoring Different, Actually? The similarities and differences between monitoring and observability and how to pair the two strategies 2. Observability Tips and Tricks For Using Grafana and Prometheus Rapid prototyping tips when using Grafana and Prometheus observability and monitoring stack. 3. Observability and Monitoring in a nutshell A guide to understanding the concepts and differences between monitoring and observability. 4. Debugging AWS Lambda Runtime Errors Debugging AWS Lambda can get tricky in Amazon CloudWatch. KloudMate lets you get to Lambda errors quickly and effectively using Lambda error logs. 5. An Intro to Dynamic CDK Dashboards How to create a construct based on other constructs in the same stack without forcing the developer to call a method for each? That's exactly what aspects do! 6. N/A please delete this 7. Developer Trends from Thoughtworks’ Technology Radar 27 Thoughtworks’ Technology Radar is a regular time to take a look at what trends are changing our industry. 8. How Observability and Monitoring Produces Better Software An article focused on deep diving into observability and its significance in software. Its history, goals, the importance of observability, and the issues that 9. How to Build Your Own Observability Platform on Kubernettes End-to-end full observability using Odigos and other back-end observability tools. 10. Maintaining Quality When Transitioning from Monolith to Microservices Piece by piece, legacy monolith applications are being broken down and replaced by microservices. 11. How to Monitor Apache Flink with OpenTelemetry Apache Flink monitoring support is now available in the open source OpenTelemetry collector. 12. Observability vs Monitoring: What's the Difference? Monitoring has been a basic system to track the health of servers for years. Now it is not enough. 13. Observability and Monitoring Have a Symbiotic Relationship, but They Are Different Observability vs. monitoring, what is the difference? Monitoring is the what to observability’s why. Here we dig into the differences. 14. Microservice.add(observability) != Microservice.add(monitoring) You are reading this content, which means that you are not novice to the microservices field. So let me just  scratch the surface of it before moving to Observable Microservices.  Once upon a time Monolith Application was now transformed into Microservices based application. 15. How Do You Make Cloud Migration Easier? Engage in intelligent change. 16. Application Monitoring: Closing Observability Gaps with Custom Metrics Which application metrics should you collect for your microservices? 17. The Challenges of Modern Observability It’s tough, impossible, to get a holistic view of your entire application when it’s running on 200 pods deployed to 12 nodes distributed across the world. 18. Why OpenTelemetry Should Matter to Network and Systems Admins OpenTelemetry is young, even by internet standards. Born out of the merger of OpenTracing and OpenCensus projects at the Cloud Native Computing Foundation CNCF 19. What is the ideal memory size to lower costs of running a task on Lambda? Should you increase Lambda memory? It might sound crazy, but increasing your AWS Lambda memory could actually lower your bills. Find out how. 20. What "Shifting Left" in Software Really Means for Blameless DevOps A truly "blameless culture" in software must to evolve from incident reporting, to telemetry aimed at Proactive Observability in DevOps 21. CI/CD/CF — The "Missing-Link" in the DevOps Toolchain? Everyone is familiar with CI and CD processes, but whatever happened to Continuous Feedback? Leveraging observability in dev creates a new type of dev process 22. Why Your Monitoring Dashboard May Be Feeding You Phantom Metrics We trust our metrics to show us the status of our system and where it misbehaves. But do our metrics show us what really happened? 23. Cloud Native Applications: 4 Building Blocks You Need in Your Platform To create amazing cloud native applications your platform needs these components: Kubernetes, Continuous Delivery Pipelines, Edge Stack, and Observability. This article will explain the importance of each component. 24. Comparing Different Serverless Monitoring Platforms Technology touches almost every corner of the world economy. Even when it’s an indirect relation, in many cases tech is an essential, vital part of our societies. It just can’t fail without causing too much distress and losses. Not only financially, but especially to the human aspect. 25. Get Started With Sidekick Open Source Live Debugger in 5 Mins Sidekick is a live application debugger that lets you troubleshoot your applications while they keep on running. Here is how you can start using it in 5 minutes 26. Logging in Observability - Part 1 rsyslog vs journald vs filebeat 27. Managing Collectors on K8 – Why We Chose the OpenTelemetry Collector for Odigos Without proper implementation and continuous configuration of your collectors, observability tools will be limited at best at best and many times ineffectual. 28. On Dynamic Observability and Team Culture with Liran Haimovitch, Rookout CTO Rookout Co-Founder and CTO, Liran Haimovitch, shares the origin story of their debugging tool, what excites him about the startup life, PLG, and more. 29. 9 Data Trends You’ll See in 2023 2022 saw the data space grow by leaps and bounds. Here are the top 9 things our team of data experts expects to see in 2023. 30. Observability and AI Partner Well to Tackle All of That Data Gone are the days when operations and development were a back-office affair. 31. Logging in Observability - Part 2 In this article, I want to describe how to work with logs, analyze them in the command line, and we will consider new modern tools to visualize logs. 32. Tracing: An Essential Component of Observability In this article, I am going to shed light on one important aspect of observability - tracing. 33. Microservice Observability Patterns [Part 1] Logging is one of the most important parts of software systems. Whether you have just started working on a new piece of software, or your system is running in a large scale production environment, you’ll always find yourself seeking help from log files. Logs are the first thing people look for when something goes wrong, or something doesn’t work as expected. 34. What is Observability? Is it Cultural? In order to leverage observability we need a significant shift in our corporate culture that encapsulates the entire company & goes beyond the tools. 35. Murre - the lightweight K8s metrics monitoring tool 36. Introductory Guide to Debugging Lambda Configuration Errors The “Lambda configuration error” is as generic as it gets but at the end of the day, it's a pathing issue. Let's go over how to resolve this. 37. Contextual Intelligence and Observability: Without the Former, You Really Don’t Have the Latter Observability is a hot term in the industry, but don’t let it fool you: having visibility into your organization's apps and services only gives you a piece. 38. The Everything Guide to Data Collection in DevSecOps Collecting wide swaths of observability and security data is key to a high-quality digital experience. Find out what you need to know to get started. 39. Better Performance and Security by Monitoring Logs, Metrics, and More Monitoring is a crucial part of observability. Learn how monitoring can specifically improve security, performance, and reliability. 40. Goodbye, Cost-depth Trade Off: Cloud-Native Observability, Redefined It turns out that, by rethinking the fundamentals of your approach to monitoring, you can have it all 41. Prometheus — Easy In, Difficult Out Setting up a Prometheus server can be easy, but getting actionable information out is a very long and never-ending journey. 42. Choosing a Computing Method: a Serverless SWOT Analysis If you ever find yourself deciding for or against serverless the following tries to make the decision easier for you. 43. Setting Up AWS CloudWatch Alerts (vs Dashbird Alerts) To Monitor Your Applications Learn about the best practices for AWS Cloudwatch Alerts and Dashbird Alarms, to not miss out on critical info about your serverless app. 44. Exploring Differences Between Monitoring And Observability Monitoring vs Observability: in this article, we're explaining what is observability exactly and how does it differ from monitoring. 45. Kuma and Prometheus for Observability in Kubernetes Microservices Clusters A year ago, Harry Bagdi wrote an amazingly helpful blog post (link at bottom of article) on observability for microservices. And by comparing titles, it becomes obvious that my blog post draws inspiration from his work. 46. To Log Or Not To Log, Is The (Eternal) Question An alternative logging strategy to make loggers your friends, not enemies 47. 2023 Observability Predictions: Open Source is the Key to Explosive Growth The observability industry is projected to grow about 8% in 2023, a continuation of a strong growth trend since the 90’s. 48. How Goji Investments Enhances Developer Experience via Observability This post was written by Dean Record, Engineer at Goji Investments. 49. What is API Observability? APIs are the blood vessels of digital business. 50. A Guide to Deploying Jaeger on Kubernetes in Production Logs, metrics, and traces are the three pillars of the Observability world. The distributed tracing world, in particular, has seen a lot of innovation in recent months, with OpenTelemetry standardization and with Jaeger open source project graduating from the CNCF incubation. 51. How To Use a FastAPI Abstraction To Query AWS Data Lake Our guide in creating FastAPI abstraction that allows us to query the AWS Glue metadata catalog - a Data Discovery API for AWS Data Lake. 52. Sizing Your Cloud Resources: 10 Common Mistakes to Avoid Learn the common pitfalls of moving to the cloud and how you can avoid them to truly benefit from the cloud’s elasticity. 53. CNCF Tools Overview: Are You Cloud-Native? Cloud computing is becoming more and more of a household name, with even the most conservative fields of business figuring out how to make the best use of it. Cloud computing usually starts with running a private cloud solution on premises before venturing onto the public cloud. Of course, the cloud is not a single uniform being. It may come from different providers, Amazon Web Services, Google Cloud Platform, and Microsoft Azure being the biggest players here. Or it may come with different visibility and hosting, that is, public (resides with the provider), private (self-hosted), or hybrid (which uses a bit of both). And the cloud can use different tools and APIs for management as well. 54. Microservice Observability Patterns [Part 2] In my previous article, I talked about the importance of logs and the differences between structured and unstructured logging. Logs are easy to integrate into your application and provide the ability to represent any type of data in the form of strings. 55. Improving Observability Through Service Level Metrics 56. Observability on Heroku: How to Monitor Apps on a Managed Infrastructure Cloud platforms like Heroku make it easier than ever to host applications: just upload your code, and they’ll deploy it for you. But a common misconception is that, because you don’t own the infrastructure, you can’t really monitor your applications or see under the hood. 57. Setting Up Notification Systems to Observe and Analyze Your Application By building observability and analytics into your notification system, you can identify and quickly resolve issues by monitoring how your product is performing. 58. Better Failure Detection in Serverless Apps Having reliable failure detection in place is a must for any application in production. Here's how to best achieve it for AWS Lambda. 59. 4 Key Observability Metrics for Distributed Applications A common architectural design pattern these days is to break up an application monolith into smaller microservices. Each microservice is then responsible for a specific aspect or feature of your app. For example, one microservice might be responsible for serving external API requests, while another might handle data fetching for your frontend. 60. Lua-Land CPU Flame Graphs in OpenResty XRay This post will introduce the idea of Lua-land CPU flame graphs and use OpenResty XRay to produce real flame graphs for several small and standalone Lua examples 61. 5 Key Takeaways from New Relic’s 2022 State of Logs Report The 2022 State of Logs Report provides a detailed view of how this practice is shaping engineering and the technologies of the future. 62. Introduction to Observability in ITOM and AIOps Observability is a best practice implemented by AIOps, enabling automation and expanding visibility into the entire organizational ecosystem. 63. Internal AWS Monitoring is Hard to Grasp Learm how a serverless monitoring solution can catch problems for you without the painful learning curve connected to serverless failure detection. 64. Jaeger Persistent Storage with Elasticsearch, Cassandra and Kafka Running systems in production involve requirements for high availability, resilience and recovery from failure. When running cloud-native applications this becomes even more critical, as the base assumption in such environments is that compute nodes will suffer outages, Kubernetes nodes will go down and microservices instances are likely to fail, yet the service is expected to remain up and running. 65. How to Monitor Serverless Applications With AWS CloudWatch Alarms Running any application in production assumes reliable monitoring to be in place and serverless applications are no exception. 66. How to Optimize Large Scale Serverless Applications for Operational Excellence Managing applications at scale often comes up as one of the biggest concerns for businesses; How can it work smoothly? How do we monitor so many resources? How do we maintain best practices with constantly evolving infrastructure? In this article, we run through the best approach for operational excellence looking at serverless monitoring strategy, serverless alerting strategy, and security and compliance best practices. 67. Fixing The ClickHouse Node Failure On Distributed Systems - A How-To Guide Part One: ClickHouse Failures, by Marcel Birkner 68. Introduction To Distributed Tracing Pattern A distributed architecture brings in several challenges when it comes to operability and monitoring. Here, one may be dealing with tens if not hundreds of microservices, each of which may or may not have been built by the same team. 69. 10 Threats to an Open API Ecosystem Despite tight economic situations worldwide, the API economy continues to grow. 70. Data Access for Microservices If you want to access data in a distributed environment such as in a microservice architecture, then data services are the way to go. The idea is to create a data abstraction layer (DAL) that the rest of the system’s applications and services can share. Thus, a data service gives you a generalized interface to the data you’re exposing and provides access to it in a standard manner. This would be in a well-understood protocol and a known data format. For example, a popular approach is to use JSON via HTTP/S. 71. Rethinking Programming: Automated Observability Introduction 72. Watching the Requests Go By: Reconstructing an API Spec with APIClarity In this post, we’re going to look at what API reconstruction is and how APIClarity solves the API observability problem. 73. Navigate Your Way to Production Bliss with Caretta The easiest way to map our cluster: Caretta - a standalone OSS tool, leveraging eBPF technology to be lightweight and frictionless 74. The Importance of Monitoring Big Data Analytics Pipelines In this article, we first explain the requirements for monitoring your big data analytics pipeline and then we go into the key aspects that you need to consider to build a system that provides holistic observability. Thank you for checking out the 74 most read stories about Observability on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

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Let's learn about via these 73 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Deployment /Learn Repo 1. When (and why) to go 'Serverless' Serverless doesn’t mean operating an application without a server, it means that you can operate the app without having to manage a server. 2. How to Deploy a React App to GitHub Pages This article outlines how to smoothly deploy a React App through Github while avoiding common problems that such a process is likely to cause for you. 3. Why Micro Frontends Are the Future of Frontend Development Microservices have helped the backend world to divide backends into smaller, more manageable, and more efficient pieces. 4. Approaching AI Model Deployment the More Efficient Way Organizations using Artificial Intelligence (AI) and Machine Learning (ML) solutions face a challenging problem. 5. Golang: Using systemd for Zero Downtime Restarts and Deploys By configuring a systemd socket, you can tell systemd to listen on the configured ports and start your service with a copy of the listening sockets. 6. Feel the Release Technology performing at human speed is no longer an option. 7. The Easiest Way To Deploy With Gradle How to easily deploy on Gradle for your Linux server without needing an entire development pipeline that the process normally requires you to have on hand. 8. What is Lead Time and Why Does it Have So Many Definitions? In this post, I look at why there are so many definitions of lead time and how you can put them to use. 9. Spin Up a BOSH VM Faster Using BoshLite BOSH-lite is a tool for running Cloud Foundry on Kubernetes and I’m guessing most BOSH directors are associated with a Cloud Foundry deployment... 10. Efficient Deployment and Release Management “Do not deploy on Friday” is not a joke after all even if you have continuous deployment. With deciding to deploy anyway comes the subtle concern... 11. The Collaboration Tools that Support on-premise Deployment Read this blog to discover the relevance of the On-Premise deployment model, as well as collaboration tools like Troop Messenger, AnyDesk, etc. that support it. 12. Go Serverless with AWS Lambda, Node.js and Dashbird Learn how to deploy a Node.js application to AWS Lambda using the Serverless Framework and Dashbird for insights. 13. How To Deploy Angular App On Heroku Hi All, Today we will talk about that how we can change our Angular app environment at run time when deploying the code on Heroku Server. 14. Debugging with Render: Two Effective Methods When you use Render to deploy your Node.js application with Postgres database, you can debug easily with integrated tools like Datadog and Log Streams. 15. Kubernetes Deprecates Docker to Support the Open Container Initiative Kubernetes Deprecating Docker?! Let Me Explain Containers in K8s. 16. Deploying Deep Learning Models with Model Server Learn how to deploy deep learning models with Model Server. 17. Using MLflow and Modzy for Automated Model Deployment As the world of artificial intelligence (AI) and machine learning (ML) continues to grow, the demand for leveraging AI capabilities is slowly becoming overshado 18. Top Debugging Methods for Quicker Deployment in Python In this article, I will cover most debugging methods, basic and modern ones, introduce some hands-on tools and show some basic configurations and instructions. 19. Hosting an Angular application on GitHub Pages using GitHub Actions Application example built with Angular 15 and hosted on GitHub Pages using GitHub Actions. 20. Websites Censored In Venezuela And How to Access Them Venezuela's government has been clamping down on the internet in an attempt to control what Venezuelans know and understand about the world, including websites. 21. Tech Teams in Brazil: 42% Deploy Daily, Another 38% Deploy Weekly [Study] Between January 29th and March 5th, we conducted a survey to get a technology landscape of the Brazilian startups and companies. We released the full report with the results of the study. 22. What Is Release Management And How Can Your Team Adopt It Exploring the essence of the release management process, its anatomy, and ways of its improvement, and how it all ties in with agile software development. 23. How ML Challenges Software Engineering Inherent uncertainty, data-driven behavior, rapid experimentation: characteristics of Machine Learning that disrupt traditional software engineering practices. 24. Deploy Computer Vision Models with Triton Inference Server There are a lot of Machine Learning courses, and we are pretty good at modeling and improving our accuracy or other metrics. 25. Publishing A Three.js Project On GitHub Pages Looking for a way to showcase your Three.js projects for free? Use GitHub Pages. 26. An In-Depth Guide to Blue-Green Deployments with Materialize Minimizing downtime during any deployment is a key part of any successful deployment strategy. There are many ways to achieve this. 27. 5 Effective Deployment Strategies for Machine Learning Models Understanding model deployment challenges and effective strategies that counteract them. 28. How to Deploy React JS Applications for Free on Firebase In this article , I’ll take you through all the steps involved in deploying a your React app on firebase. 29. Using Codeship to Deploy a Dotnet app on Oracle Kubernetes So, I was looking at an alternative to Azure DevOps and Jenkins to build a CI CD pipeline for a new project.  A friend had asked me for a recommendation. His wanted to host microservices in Oracle Kubernetes Service. 30. How To Create Deployment Objects in Kubernetes and Deploy Using kubectl Learn what Deployments are in Kubernetes and how to use them. Deployments are a high-level abstraction that controls how we deploy and maintain a set of Pods. 31. Linux-based Containers under the Hood Containers under Linux - building a container from scratch. 32. Deployment Choice: Code Promotion vs Artifact Promotion When it comes to deployment, a choice must be made. Find out what's the difference in code promotion and artifact promotion 33. How to Deploy a Ruby on Rails App on Heroku The most satisfying thing beyond building something is to make it available to the world. Heroku is great for beginners because it’s a free and “simple” push-to-deploy system. 34. Path-Based Routing in Render with Kong API Gateway It's good practice to put an API gateway in front of your microservices-backed application. Path-based routing makes this possible when deploying to Render. 35. Lessons Learned Using Docker from Development to Production Like many tools in the software developer's toolbox, docker is relatively easy to jump into and takes some time to master. Using it for a variety of projects over the years I've learned a few lessons along the way. 36. Connecting Dots: Go, Docker and k8s [Part 2] Nowadays, the successful application often consists of containers and some sort of container management system to ease scaling, reduce downtime, and more. 37. How to Handle Configuration for Multiple Environments in Console App Service? How I automated the process of service deployment for a console application after issues caused due to multiple configuration environments. 38. Post Mortem: Kubernetes Node OOM Production issues are never fun. They always seem to happen when you’re not at work, and the cause always seems to be silly. We recently had issues in our production Kubernetes cluster with nodes running out of memory, but the node recovered very quickly without any noticeable interruptions. In this story we will go over the specific issue that happened in our cluster, what the impact was, and how we will avoid this issue in the future. 39. 3 Key Questions You Need To Answer Before You Push That Code Before you commit that code and push it to the server deployment, ask yourself these three questions. 40. Scaling Web Applications Is Like Walking a Tight Rope in a Storm Scaling web applications is a skill. It’s like walking a tight rope, in the midst of a storm. There are multiple factors acting on you, 41. How to Migrate Your Apps From Heroku to Qoddi Migrating from Heroku to Qoddi App Platform is very easy and requires a small amount of time. This guide will guide you in the process on moving your app. 42. How to Debug Microservices Swiftly and Efficiently with KubeOrbit KubeOrbit, a cloud-native platform, has made deploying environments and debugging easier than before. 43. A Simple Guide to Github Page Deployment Long story short: Jekyll is a template engine changing markdown documents on static HTML webpages, that you can then host anywyere, because you don't need databases or server that has PHP or Python. 44. Why Tracking Vulnerabilities After Deployment Is Important Explore why you should track vulnerabilities after deploying your software,
ways to track vulnerabilities, and how to keep your users and business safe. 45. How to Setup Different Environments for Dockerized Angular App with Nginx Here I’m going to suggest to you a very simple deployment practice that we can follow in regards to deploy an Angular-based front-end app. 46. Who Let the Birds Out? Modern day ‘Canary release’ or ‘canary deployment’ is a widely-used deployment pattern for the software development cycle. 47. Deployment Automation via SSH With Python Fabric: How It Works One of the most basic ways in which a project gets deployed is by SSHing into a remote host followed by executing a few basic commmands. In this article I'll be taking a look on how to deploy a basic project to a remote server through Gitlab CI using Python Fabric. 48. How to Deploy AWS Lambda with Docker Containers You can now build your serverless workloads in a Docker container with AWS Lambda. We gave it a try and here's our review (with ETL examples). 49. Step-By-Step Tutorial To Deploy A Node.js App To Kubernetes The Cloud Foundry project cf-for-k8s, along with a Cloud Native Buildpacks implementation called Paketo Buildpacks, can provide a path for JavaScript applicatio 50. Understanding Pods in Kubernetes Pods are Kubernetes Objects that are the basic unit for running our containers inside our Kubernetes cluster. 51. Why DevOps Is Important During the COVID-19 Pandemic Disruption is the name of the game these days, from current remote work settings due to COVID-19 to even more normal, every-day disruptions (like the process of moving and renovating a home, which I’m currently experiencing first-hand). As a habit-driven developer myself, I have come to the realization that the stable work environment I’ve come to rely on is no more. 52. Your Serverless Guide is Ready Learn everything you need to know about Serverless, including case studies, essential concepts, guidelines, and best practices. 53. Deploying Apache Kafka With Kubernetes Deploying Kafka on Kubernetes is a low-effort approach to setting up an event-driven architecture to support your API ecosystem in the cloud. 54. Automating Smart Contract Development with Docker Setting up a dockerized container to launch project setups that'll help deploy and run smart contracts. 55. 4 Essential Steps To Convert a Kubernetes Fullstack App to Heroku In the last several years, Google’s Kubernetes project has generated huge buzz. The project has grown and evolved into a titan of the cloud infrastructure world. 56. Fleet Serverless Node.js Function Introduction In February this year, we announced Fleet (Formerly Hole), a FaaS platform built on Node.js to be faster than other platforms and to create a more faithful integration with the ecosystem. In this post, I will clarify how all of this works and what we are bringing differently to the Serverless ecosystem, and what it means for startups and companies. 57. Everything about Blue Green Deployment Strategy! How the Blue Green Deployment Strategy can be used to deal with problems on websites that normal development strategies cannot normally be used to resolve. 58. The Developer's Guide to Preparing Django Applications for Production Configuration of production and development settings in Django.
Can we read from different databases? What about getting notifications on view exceptions? 59. A Guide to Deploying Ruby Bot Live on Heroku The steps below could also work for other Ruby bots (and not Telegram bot only) as well. 60. Increasing Developer Productivity with skaffold Primary focus of Developer is to write code. Build, Test and Deploy of the application are better left to be managed by tools. skaffold can help in automating some of mundane tasks that comes with using kubernetes. 61. Kubernetes Tutorial: Using The System For Personal Projects Learn how to deploy a simple rest API with ExpressJS and expose it using a service and ingress. Great for Kubernetes personal projects. 62. What Is the Difference Between Continuous Delivery and Continuous Deployment? Continuous Delivery and Continuous Deployment are two complementary practices that help teams get their software into the hands of users faster. 63. Hosting an Angular application on GitHub Pages using Travis CI Application example built with Angular 15 and hosted on GitHub Pages using Travis CI. 64. A Quick Guide to Using Deployment Slots with Azure Functions And how do they differ from non-serverless plans? 65. 4 Ways to Introduce New Technology to Work Every day, there are new tools that come into the market to make us more productive. More so since the global pandemic took hold. 66. 5 Ways that Microservices can be Deployed Effectively The perfect place to host a microservice application is largely determined by its size and scaling requirements. So, let’s go over the 5 main ways we can deploy 67. Going From Open API Specification To Development Portal API Economy is taking off exponentially and its every business need to expose their digital services and assets in a secure way. I'll walkthrough in this article how one can take a API-first , design first approach to take into the account, the needs of API consumers and various steps that lead to creating a Developer Portal using all the tools that we've currently. Please note that these tools have alternatives in the market, but these are what got me to the goal post in the fastest way and case could differ for others. 68. Gradual Code Releases: An In-House Kubernetes Canary Controller Our short-term strategy involved developing an in-house Canary deployment process powered by a custom Kubernetes controller. 69. How Infrastructure as Code Is Forcing a Revolution in Cost Management You might have read our beta launch blog post, which describes how Infrastructure as Code (IaC) is driving the third datacenter revolution (the first two being virtualization and the move to public clouds). In this post, I want to go deeper into looking at how IaC is driving a new need and approach to cost visibility and management. 70. How To Create a CD pipeline with Kubernetes, Ansible, and Jenkins What does CI/CD try to solve? 71. From TF to TFLite: Deploying ML Models on Mobile [Part 1] tl;dr - Link to code: TensorFlow GAN model. So the other day I was talking to my rubber ducky about how G-Board predicts my next word, even when those words are entirely made up by me, in that how it actually learns on-device. How amazingly Netflix, Amazon, Google Maps make use of machine learning in their apps. How does machine learning on apps even work? Does the model learn even after being deployed? Can I deploy a GAN model on mobile? 72. How Do You Manage Helm Charts? Whether you love it or hate it, Helm is a ubiquitous tool for managing Kubernetes applications. You can use it in many different ways which is great, but can also be overwhelming. 73. Deploying a Django Project to PythonAnywhere - A How-To Guide When I was first starting with Django, one of the most challenging obstacles I faced was deploying my application. In this tutorial, I will show you guys how to deploy your Django applications to PythonAnywhere and hopefully help you avoid the pitfalls I made. Thank you for checking out the 73 most read stories about Deployment on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

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