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On Combinations Amongst Masters or Workmen against Each Other

The 40 Under 40 Fueling Spain's Startup Economy

40 অনূর্ধ্ব 40 স্পেনের স্টার্টআপ অর্থনীতিতে জ্বালানি দিচ্ছে

Die 40 unter 40 treiben Spaniens Startup-Wirtschaft voran

Los 40 menores de 40 que impulsan la economía de las startups en España

Les 40 de moins de 40 ans alimentent l'économie des startups espagnoles

40 अंडर 40 स्पेन की स्टार्टअप अर्थव्यवस्था को बढ़ावा दे रहा है

スペインのスタートアップ経済を支える40歳未満の40人

스페인의 스타트업 경제를 촉진하는 40세 미만 40세

Os 40 abaixo dos 40 impulsionam a economia das startups na Espanha

40 Yaş Altı 40, İspanya'nın Startup Ekonomisini Güçlendiriyor

Nhóm 40 Under 40 thúc đẩy nền kinh tế khởi nghiệp của Tây Ban Nha

40 位 40 岁以下的人士推动了西班牙的创业经济

If you’ve been playing Animal Crossing: New Horizons recently, chances are you’re aware of the latest update that came out a couple of days ago. And unless you’ve been messing with your Switch’s system clock, chances are you still have yet to see everything from it. Here’s the Animal Crossing Direct discussing this new update, in case you missed it: For veteran players and newcomers alike, the latest update has introduced hundreds of new things you can work towards. Since it’s such a new update, however, there’s a lot of misinformation and confusion on when players can access this new content. In this article, I’ll be dispelling that misinformation and discussing the new content in-depth - including what needs to be done to access it. How to Access the New Content in the Animal Crossing Update Image via Unofficial Animal Crossing Wiki Animal Crossing: New Horizons isn’t a very story-heavy game, but players will need to progress through it to unlock the new update’s features. Outside of certain furniture and clothing and the wooden ladder set-up kit, there are two milestones players will need to reach to get most of the new content: Upgrading Resident Services (which unlocks the pro camera app and the Happy Home Paradise DLC) Getting a 3-star island and completing Project K (which allows Harv’s Island to expand, Kapp’n to show up, ordinances to be enacted, and The Roost to start construction, on top of cooking becoming available to the player) If you’re only just starting a new game of Animal Crossing: New Horizons, the updated content will only become fully available to you after around two weeks of playing. Of course, time travel is always an option, but there isn’t a need to rush - especially with just how much was really added. The Major Additions of the Animal Crossing: New Horizons Update These additions to the New Horizons update are a little complicated in their unlocking process, as well as how in-depth they are. You’ll want to read up on these if you’re having trouble figuring out how or what to unlock. The Roost - Secrets and How to Unlock Image via GameRant The Roost has a couple of extra steps in order to unlock. You’ll need to donate one item for each area of the museum, including artwork - meaning you’ll also have to donate a lot of items in order to unlock the ability to donate art in the first place. After doing all this and completing Project K, entering the museum on a new day will let you start the quest to get The Roost set up. After finding the owner of The Roost and visiting the museum again, you’ll need to wait until the day after tomorrow in order to take your first sip of coffee. Once that time comes, you’ll have access to the possibility of chatting with hundreds of fantastic Animal Crossing characters - including numerous characters that weren’t even in New Horizons until this update. There’s also a reward for visiting The Roost daily for a while, so make time in your schedule for a virtual coffee break. Harv’s Island Expansions - Shops and Rewards Image via Nintendo Life If you’ve met the requirements for The Roost, you’re most likely all set to help Harv expand his island. You’ll know for a fact once you receive a letter from him in the mail - if you haven’t gotten one yet, make sure you visited his island in the first place and talked about taking pictures with him. Once everything is said and done, you’ll be able to fund seven different shops on Harv’s Island for 100,000 bells apiece. You can only fund one shop a day, and each shop has a different purpose. Kicks, Redd, Leif, and Saharah all provide similar services that they do as traveling merchants. Reese and Cyrus will help you customize items, Tortimer lets you access your storage from Harv’s Island, and Katrina will predict your luck for the day. Harriet will also become available to customize your hair after you fund and open up at least one shop on Harv’s Island. It’s up to you which shops you open up, though many players recommend going with Leif’s shop first to get ahold of the new vegetables as part of the update. Happy Home Paradise - Completing the DLC Image via Nintendo's Official Website Once you gain access to the Happy Home Paradise DLC, unlocking everything in it is actually pretty straightforward. Other than some of the exclusive furniture, all of the features can be unlocked just by continuing to complete client requests. After 30 clients, you’ll gain the ability to renovate the homes of your fellow islanders - the last feature you need to unlock from the DLC. Animal Crossing: New Horizons Update - Additional Content and Extras Image via Nintendo Life This only scratches the surface of the new content added in this update. New and returning villagers were added for players to interact with; new music is available for players to listen to, and - in total - new items were brought into the game. There’s never been a better time to jump into New Horizons, and that goes for newcomers and veterans alike. over 9,000 More in Gaming: Top 10 Kirby Series Games Ranked by Sales | Hacker Noon Monster Hunter Stories 2 Review (Nintendo Switch): Smaller Scale, Greater Story | Hacker Noon The Ace Attorney Timeline: All Phoenix Wright Games in Chronological Order | Hacker Noon

Animal Crossing: New Horizons Update - Everything You Need to Know

20 Mouthwatering WordPress Restaurant Themes to Make You and Your Business Salivate 

Learn how I created an app to help with the COVID-19 outbreak in less than a day using Stryke and Glitch. Photo by Capturing the human heart. on Unsplash Sometimes you need to act fast! We are currently facing some challenging times with the COVID-19 outbreak. In Barcelona we are confined to our homes, and are encouraged to fill in a to show to the authorities in case we are stopped on the street. self-justification form Considering the amount of data this type of requirement will generate, I set out to create an app that would help record this info and visualise it using a heatmap. Heatmaps are a great tool to visualise where people are concentrating while maintaining the information anonymous. Knowing which are the places where a lot of people are gathering is very important in the current COVID-19 outbreak, as one of the strategies to fight it, is to avoid such situations. Authorities could use this tool to monitor and potentially act on areas of high concentration. Even more importantly, it could be used preventively by citizens. A publicly accessible heatmap could guide people in choosing their routes or places to go to (example: preferring one shop to another) in order to avoid hotspots. Heatmap visualisation of anonymous data stored in Stryke representing journeys and points where crowds gathered. Disclaimer : In this project I make use of a platform called Stryke . I work for Stryke so my opinion can be biased. There are other platforms with which you could develop a similar solution. However, I feel that Stryke is a really good fit for this solution based on the features it provides and how it allows to get something functional very quickly. TL;DR; We will create an app that displays the journeys people took over a specific period of time on a heatmap. The information is entered anonymously by users through a form (or an API call) where they just need to provide the start and destination of their journeys. I wanted to have something working quickly so I resorted to 2 platforms that aim to speed up the development of cloud apps: and . Stryke Glitch In this article I will explain how I put together this project. All code is available here: https://github.com/strykeio/covid-heatmap You can see a live demo here: https://covidheatmap.glitch.me/ Feel free to use my version or create one of your own. I am happy if this can help anybody so do not hesitate to share it or use it in your community. The Idea Photo by Mehrad Vosoughi on Unsplash I needed to go to the supermarket. So I went to fill in the online form provided by the authorities when breaking the COVID-19 confinement. There and then I realised that it would be pretty straightforward to create a useful visualisation of how people are moving and where they are gathering if we could send this data to an app every time a person filled in this form. With such a tool I could decide easily which supermarket to go to: it would allow me to check which of my neighbouring shops is the least busy, before even leaving the house. A heatmap would provide the perfect visualisation for this data. With that in mind we can formulate the following requirements. Data submission should be done through a very simple form (2 fields) or API request (so that it could be called from existing pages) Data submitted should be anonymous The format in which the data is stored should be efficient for display but also understandable by a person The data should be easily accessible and available to export The heatmap should display data for a given timeframe The heatmap should be in a dedicated page The Architecture I want this solution to be as simple as possible. Here is what we need based on our requirements: A back end to store data, accept new submissions and retrieve data with a criteria via an API. A front end to visualise data in a heatmap and to allow people to submit new data. A Mapping API capable to retrieve locations, calculate routes and draw visualisations on a map. The “time to final solution” is key in this project. We will use two platforms that allow reaching a working solution very quickly: and . For all tasks related with mapping we will use the . Stryke Glitch Google Maps APIs We will create an app in that will serve as the backend where data is persisted and can be created and retrieved via an API. Creating an app in Stryke also automatically creates a user interface where we can check and manage the data if necessary. Stryke Stryke is a great platform to implement a project like this as it is very quick to setup the following features: Data persistence Define the business logic through short JS code snippets Create a callable via HTTP requests custom API to keep things secure Authentication A to do back office UI for the app Standard Stryke user interface for our app. Great for back office work. On the front end side of things we will develop two simple pages: A page that holds the heatmap A second page that contains the data entry form We will use to create these simple HTML pages and host them. Glitch is a great tool to put things together quickly and easily. Glitch Data entry (finding places easily and getting their coordinates), route calculation, as well as the heatmap visualisation require working with geo locations and maps. provides a host of very useful APIs to achieve these tasks. To achieve all of the above we will use the following APIs: The Google Maps Platform Maps JavaScript API Places API Directions API The Back End — Stryke is a platform for developers to create cloud applications easily. It speeds up development by providing many infrastructural features and an easy environment. Stryke In this section we will go over how to: create an app in Stryke, define the entity that will hold our data, create the API that will be called to create a new route and to retrieve routes. Important Note We will go through all the steps to create a Stryke app from scratch. You can choose to do the same or simply from the export file you can find . import the app here Create an App First of all, let’s login to Stryke to create a new app for our project. If you do not have a Stryke user, you can create one by to the platform. signing up From the Stryke dashboard let’s create our “COVID-19 Heatmap app” (choose a unique name for your new app). Entity We want this app to store routes in a Database. In order to achieve that, we need to create an Entity in Stryke (which will be the table in the DB). Once your app is created, from your app’s dashboard click on “New” under the entity section. For each route generated by a user, we will store: The starting point (latitude and longitude) The destination point (latitude and longitude) The full path of the route between start and destination A descriptive name for this route In order to make things efficient we will store the route’s path as an . This is a format created by google that allows storing data for many points in an encoded string. The contains an encoded polyline for the route called overview_polyline. It is very straightforward to draw encoded polylines, we will see how to do that when discussing the heatmap page. encoded polyline response from the directions API _jv{F_zhLp@ @g@ `AHLHM~CsEfCqDl@y@^h@R\DAL?^Hb@RhGtElBtAvAdARWNQCe@RRR[^e@fBgCHMHLvC|DzBdDnFjIvN|Sf@r@~HhLpEvGzG~JfJvMfA|Al@}@lAmBj@eA` `@SzBiD Sample encoded route Since most of the fields in a route record are not very easy to read by a person, we will add a descriptive name. This name will be put together when creating the route and will contain the transport type and the full address of the start and end points. walking : Carrer de Mallorca, , Barcelona, Spain to: Rambla de Catalunya, , Barcelona, Spain from 481 08013 2 08007 Sample route name You can see the JSON that defines this . entity here The API Now that we have an entity to store routes, we want to be able to submit and retrieve them from clients. We could create a retrieve route records using the standard , which is automatically constructed for the app when we create it, via POST and GET requests. Stryke’s REST API [GET] https: //api.stryke.io/v0/{appName}/data?entityName=route Example use of the standard Stryke API However, in our case we want to execute some additional logic when instructed to create or retrieve routes. When a route is created, we want to calculate the path of the route from the start and end points. Additionally, when retrieving routes we want to filter out data with a certain. We will create two in Stryke for this. API actions allow us to write a JS script in Stryke that can be called via an HTTP request. This script can perform our business logic as well as create records or retrieve and return them. API actions [POST] https: { : , : , : , : , : } //api.stryke.io/v0/{appName}/action/submitRoute "start_lat" 41.315296 "start_lng" 2.0133208 "end_lat" 41.4134488 "end_lng" 2.0182425 "routeType" "driving" Submit route request with sample payload “Submit Route” Action We want users to have to enter as little data as possible when submitting their journey, namely just the starting point and end point (and optionally the transport type: walking, driving, etc). From this information we will extract a route path using the . This method of coming up with the route may not be 100% accurate when looking at a single case, since users may travel in different ways. However, when looking at a large number of routes, it should provide a description of how people are moving with an acceptable degree of error. Google direction’s API The action will first retrieve the start and end points, and method of transport from the request’s payload: payload = stryke.data.requestData; startPoint = ; endPoint = ; routeType = payload.routeType ? payload.routeType : ; const const ` , ` ${payload.start_lat} ${payload.start_lng} const ` , ` ${payload.end_lat} ${payload.end_lng} const 'walking' Then it will call the Google API to calculate a route: url = ; response = axios.get(url); const `https://maps.googleapis.com/maps/api/directions/json?origin= &destination= &key= &mode= ` ${startPoint} ${endPoint} ${apiKey} ${routeType} const await Finally, it will store the route details in a new route record in Stryke selectedRoute = response.data.routes[ ]; routeLegs = selectedRoute.legs; route = { : selectedRoute.overview_polyline.points, : routeLegs[ ].start_location.lat, : routeLegs[ ].start_location.lng, : routeLegs[routeLegs.length ].end_location.lat, : routeLegs[routeLegs.length ].end_location.lng, : } stryke.create( , route); const 0 const const path start_lat 0 start_lng 0 end_lat -1 end_lng -1 routeName ` from: to: ` ${routeType} ${routeLegs[ ].start_address} 0 ${routeLegs[routeLegs.length ].end_address} -1 await 'route' See the full source code . here “Get Routes” Action This action will retrieve routes that match a certain criteria. It will retrieve routes within a specific time period. We also want to be able to easily enhance this criteria in the future. To achieve this, we will create another custom action available through the API which will query data and only return the relevant records. DATA_WINDOW_DAYS = ; startDate = moment().add(-DATA_WINDOW_DAYS, ); routes = stryke.find( ); const 7 const 'days' const await `{
  Route ( filter: { created : { gt : " " }}       
  ) {
      start_lat, start_lng, end_lat, end_lng, path
  }
}` ${startDate.toISOString()} Queries in Stryke are written using notation. For every entity created, Stryke will provide callable to retrieve all records, retrieve a record by ID or retrieve data filtered by criteria. In this case we will query using a filter that will only retrieve records created in the last 7 days. GraphQL GraphQL queries See the full source code . here The Front End — Glitch I used to develop and host the heat map page and data entry form. I chose this platform because it allows me to focus on the HTML, JS and CSS and not have to worry about hosting, deploying, it even does source control for you behind the scenes! Glitch We will develop both pages using vanilla HTML+JS+CSS without using any infrastructural framework. The pages are simple enough to be developed without the aid of a framework. This has two additional advantages: code is easy to integrate in existing pages, not using a specific framework makes it easy to illustrate the point without getting into proprietary stuff. The Heatmap My objective with the page was to draw the routes people took on a map. We will do this by using a heatmap to highlight segments and places of higher traffic. I first started implementing the heatmap layer using . However, I soon discovered that, for the heatmap API to draw full lines instead of isolated dots along a route I needed to send to the front end a very large number of points. This made the page quite slow. Google’s JS Heatmap API Instead of doing that, we will follow an approach that still uses the heatmap API to highlight key points on the map, in conjunction with drawing simple polylines to represent the segments of the routes. The key points along the route are the start and end points. These are conceptually places where people would stay for some time. It makes sense to still draw these using the heatmap API, so that places of gathering are highlighted with both intensity and colour. As mentioned before, the routes are stored as encoded polylines. In order to draw them we need to convert the encoded polyline into an array of points. Google provides a useful utility that makes this task a one-liner. polyObj = google.maps.geometry.encoding.decodePath(route.path); path = google.maps.Polyline({ : polyObj, : , : , : , : }); path.setMap(map); // decode the polyline const // define the look of the polyline var new path geodesic true strokeColor '#8ADE42' strokeOpacity 0.3 strokeWeight 7 // draw the polyline on the map By drawing the lines with a low opacity value, we can achieve a heatmap-like effect, where segments that are drawn fewer times are more transparent, while overlapping segments show bolder. Route Input — Google Places Submitting a route should be something really fast for users. We will use the to help fill in addresses quickly, as well as an option to auto-fill the current location. Both these options rely on the browser’s location service, which need to be allowed by the user. Google’s address autocomplete component The search used to do the address autocomplete is biased using the current location, as described in the . example from Google While the current location option retrieves the latitude and longitude of the current position provided by the browser. navigator.geolocation.getCurrentPosition( {
    routeDetails.start_lat = position.coords.latitude;
    routeDetails.start_lng = position.coords.longitude; .getElementById( ).value = ;
}); ( ) function position document "start" 'Current location' Live Demo You can see a live demo of what we have discussed so far, here: https://covidheatmap.glitch.me/ Conclusion In this article we saw how it is possible to develop a fully functional application very quickly if we leverage the right tools. The solution we developed can definitely be enhanced. For example, we could allow the used to control the time window for which we retrieve data, retrieve only routes within the currently visible window of the map, or use different colours when drawing overlapping routes to highlight intensity. However, what we have here is a good functional foundation. The heatmap can show where the places of high concentration are and as a result help guide decisions. Data is stored anonymously and both the data input (submitting routes) and visualisation can work as standalone tools or be easily integrated into other pages. If you think that your community could use a tool like this, feel free to implement it and use it. I would be very happy to hear about it!

I Built A Low-Code App To Monitor Crowds During the COVID-19 Outbreak

Apache DolphinScheduler is a distributed, decentralized big data workflow scheduling system that supports big data task scheduling. To set DolphinScheduler to start automatically upon system boot, you typically need to configure it as a system service. The following are general steps, which may vary depending on your operating system: Linux System Create a system service file: Edit a file named dolphinscheduler.service, usually placed in the /etc/systemd/system/directory. Fill in the service file according to your DolphinScheduler installation path and configuration. Example: [Unit]
Description=DolphinScheduler Service
After=network.target
[Service]
type=exec
ExecStart=/opt/module/dolphinscheduler/bin/start-all.sh
ExecStop=/opt/module/dolphinscheduler/bin/stop-all.sh
RemainAfterExit=yes
[Install]
WantedBy=multi-user.target 2. Start the service, and set it to start on boot: Use the systemctl command to start the service, and enable it to start on boot: systemctl daemon-reload
systemctl start dolphinscheduler.service
systemctl enable dolphinscheduler.service 3. Verify the service status: Check the service status using the following command: systemctl status dolphinscheduler.service Windows System Create a batch file: Create a batch file (e.g., start-dolphinscheduler.bat) to start DolphinScheduler.


Add the command to start DolphinScheduler in the batch file. 2. Add the batch file to the Startup folder: Place the batch file in the Startup folder to run automatically when the system boots. Press Win + R to open the Run dialog. Type shell:startup to open the Startup folder. Drag and drop the batch file into the Startup folder. 3. Restart Verification: Restart the computer to check if DolphinScheduler starts with the system. Please note that the above steps may need to be adjusted based on your specific environment and the DolphinScheduler version. Ensure you have sufficient permissions and a good understanding of system services to avoid potential service conflicts or system instability. If the DolphinScheduler service briefly shows as green (indicating the service is running) after restarting and then changes to a non-green state (typically indicating the service is no longer running), but the related processes are still running, this may be because the service manager (systemd) thinks the service has stopped because it does not detect a continuously running main process. This situation usually occurs in the following cases: Service Script Issues: If the service script (start-all.sh) completes without keeping any process running in the background, systemd will think the service has stopped.


Incorrect Service Type Configuration: In the systemd service file, the Type configuration may need to be corrected. If the service is set to simple, it should be forking or oneshot, systemd might think the service has stopped as soon as the script finishes executing.


Process Exit Code Issues: If the service script correctly starts all necessary processes but exits quickly (e.g., because the commands in the script finish soon), systemd might determine the service status based on the script's exit code. To resolve this issue, you can try the following steps: Check the Service Script: Ensure the start-all.shscript keeps running after starting all necessary processes. If the script does not keep any process running in the background after execution, you might need to add some logic to keep it running. Adjust the Service File: If the script correctly starts all necessary processes and those processes run in the background, you may need to adjust the Type and other configuration options in the systemd service file. Use Type=forking: If your service script is a forking script, ensure you use Type=forking in the systemdservice file. This way, systemd will wait for the child process created by the script. Use Type=notify (if supported): If DolphinScheduler supports communication with the service manager, you can use Type=notify. This way, the service can send status notifications to systemd via the sd_notify() function. Use RemainAfterExit=yes: If the processes started by the service script run independently of the script itself, you can use RemainAfterExit=yes in the systemd service file. This way, even if the script exits, systemd will consider the service to be still running.
Reload and Restart the Service: After modifying the service file, reload systemd and restart the service: systemctl daemon-reload 
systemctl restart dolphinscheduler.service Check Service Status: Check the service status again to see if it is active (running). If the above steps do not resolve the issue, you may need to examine the DolphinScheduler log files or the output of the service script in more detail to determine why systemd believes the service has stopped. Finally, the configuration works well. Apache DolphinScheduler is a distributed, decentralized big data workflow scheduling system that supports big data task scheduling. To set DolphinScheduler to start automatically upon system boot, you typically need to configure it as a system service. The following are general steps, which may vary depending on your operating system: Linux System Create a system service file: Create a system service file: Edit a file named dolphinscheduler.service, usually placed in the /etc/systemd/system/directory. Edit a file named dolphinscheduler.service , usually placed in the /etc/systemd/system/ directory. dolphinscheduler.service /etc/systemd/system/ Fill in the service file according to your DolphinScheduler installation path and configuration. Example: Fill in the service file according to your DolphinScheduler installation path and configuration. Example: [Unit]
Description=DolphinScheduler Service
After=network.target
[Service]
type=exec
ExecStart=/opt/module/dolphinscheduler/bin/start-all.sh
ExecStop=/opt/module/dolphinscheduler/bin/stop-all.sh
RemainAfterExit=yes
[Install]
WantedBy=multi-user.target [Unit]
Description=DolphinScheduler Service
After=network.target
[Service]
type=exec
ExecStart=/opt/module/dolphinscheduler/bin/start-all.sh
ExecStop=/opt/module/dolphinscheduler/bin/stop-all.sh
RemainAfterExit=yes
[Install]
WantedBy=multi-user.target 2. Start the service, and set it to start on boot: Use the systemctl command to start the service, and enable it to start on boot: Use the systemctl command to start the service, and enable it to start on boot: systemctl systemctl daemon-reload
systemctl start dolphinscheduler.service
systemctl enable dolphinscheduler.service systemctl daemon-reload
systemctl start dolphinscheduler.service
systemctl enable dolphinscheduler.service 3. Verify the service status: Check the service status using the following command: Check the service status using the following command: systemctl status dolphinscheduler.service systemctl status dolphinscheduler.service Windows System Create a batch file: Create a batch file: Create a batch file (e.g., start-dolphinscheduler.bat) to start DolphinScheduler. Add the command to start DolphinScheduler in the batch file. Create a batch file (e.g., start-dolphinscheduler.bat) to start DolphinScheduler. Create a batch file (e.g., start-dolphinscheduler.bat ) to start DolphinScheduler. start-dolphinscheduler.bat Add the command to start DolphinScheduler in the batch file. Add the command to start DolphinScheduler in the batch file. 2. Add the batch file to the Startup folder: Place the batch file in the Startup folder to run automatically when the system boots. Place the batch file in the Startup folder to run automatically when the system boots. Press Win + R to open the Run dialog. Press Win + R to open the Run dialog. Type shell:startup to open the Startup folder. Type shell:startup to open the Startup folder. shell:startup Drag and drop the batch file into the Startup folder. Drag and drop the batch file into the Startup folder. 3. Restart Verification: Restart the computer to check if DolphinScheduler starts with the system. Restart the computer to check if DolphinScheduler starts with the system. Please note that the above steps may need to be adjusted based on your specific environment and the DolphinScheduler version. Ensure you have sufficient permissions and a good understanding of system services to avoid potential service conflicts or system instability. If the DolphinScheduler service briefly shows as green (indicating the service is running) after restarting and then changes to a non-green state (typically indicating the service is no longer running), but the related processes are still running, this may be because the service manager ( systemd ) thinks the service has stopped because it does not detect a continuously running main process. systemd This situation usually occurs in the following cases: Service Script Issues: If the service script (start-all.sh) completes without keeping any process running in the background, systemd will think the service has stopped. Incorrect Service Type Configuration: In the systemd service file, the Type configuration may need to be corrected. If the service is set to simple, it should be forking or oneshot, systemd might think the service has stopped as soon as the script finishes executing. Process Exit Code Issues: If the service script correctly starts all necessary processes but exits quickly (e.g., because the commands in the script finish soon), systemd might determine the service status based on the script's exit code. Service Script Issues: If the service script (start-all.sh) completes without keeping any process running in the background, systemd will think the service has stopped. Service Script Issues: If the service script ( start-all.sh ) completes without keeping any process running in the background, systemd will think the service has stopped. start-all.sh Incorrect Service Type Configuration: In the systemd service file, the Type configuration may need to be corrected. If the service is set to simple, it should be forking or oneshot, systemd might think the service has stopped as soon as the script finishes executing. Incorrect Service Type Configuration: In the systemd service file, the Type configuration may need to be corrected. If the service is set to simple, it should be forking or oneshot , systemd might think the service has stopped as soon as the script finishes executing. systemd Type forking oneshot systemd Process Exit Code Issues: If the service script correctly starts all necessary processes but exits quickly (e.g., because the commands in the script finish soon), systemd might determine the service status based on the script's exit code. Process Exit Code Issues: If the service script correctly starts all necessary processes but exits quickly (e.g., because the commands in the script finish soon), systemd might determine the service status based on the script's exit code. systemd To resolve this issue, you can try the following steps: Check the Service Script: Ensure the start-all.shscript keeps running after starting all necessary processes. If the script does not keep any process running in the background after execution, you might need to add some logic to keep it running. Check the Service Script: Ensure the start-all.sh script keeps running after starting all necessary processes. If the script does not keep any process running in the background after execution, you might need to add some logic to keep it running. start-all.sh Adjust the Service File: If the script correctly starts all necessary processes and those processes run in the background, you may need to adjust the Type and other configuration options in the systemd service file. Adjust the Service File: If the script correctly starts all necessary processes and those processes run in the background, you may need to adjust the Type and other configuration options in the systemd service file. Use Type=forking: If your service script is a forking script, ensure you use Type=forking in the systemdservice file. This way, systemd will wait for the child process created by the script. Use Type=forking : If your service script is a forking script, ensure you use Type=forking in the systemd service file. This way, systemd will wait for the child process created by the script. Type=forking Type=forking systemd systemd Use Type=notify (if supported): If DolphinScheduler supports communication with the service manager, you can use Type=notify. This way, the service can send status notifications to systemd via the sd_notify() function. Use Type=notify (if supported): If DolphinScheduler supports communication with the service manager, you can use Type=notify . This way, the service can send status notifications to systemd via the sd_notify() function. Type=notify Type=notify systemd sd_notify() Use RemainAfterExit=yes: If the processes started by the service script run independently of the script itself, you can use RemainAfterExit=yes in the systemd service file. This way, even if the script exits, systemd will consider the service to be still running. Reload and Restart the Service: After modifying the service file, reload systemd and restart the service: Use RemainAfterExit=yes : If the processes started by the service script run independently of the script itself, you can use RemainAfterExit=yes in the systemd service file. This way, even if the script exits, systemd will consider the service to be still running. RemainAfterExit=yes RemainAfterExit=yes systemd systemd Reload and Restart the Service: After modifying the service file, reload systemd and restart the service: systemd systemctl daemon-reload 
systemctl restart dolphinscheduler.service systemctl daemon-reload 
systemctl restart dolphinscheduler.service Check Service Status: Check the service status again to see if it is active (running). Check Service Status: Check the service status again to see if it is active (running). If the above steps do not resolve the issue, you may need to examine the DolphinScheduler log files or the output of the service script in more detail to determine why systemd believes the service has stopped. If the above steps do not resolve the issue, you may need to examine the DolphinScheduler log files or the output of the service script in more detail to determine why systemd believes the service has stopped. systemd Finally, the configuration works well.

How to Enable Auto-Start for Apache DolphinScheduler

Evaluating Feasibility and Accuracy of Parrot Training Models

Every manager has to undertake a tricky task of teaching employees to make the right decisions, the ones that will be profitable for the business. As my team was growing, I also came to realize I had to delegate responsibility. I don’t like to be micromanaged — so I don’t micromanage my team. My job is to lead people, not to be a bottleneck. I was lucky that Skyeng is all about principle-based decision-making; this is what makes an annual growth of 200% possible. I only had to take those corporate principles, or rules, and apply them to my field of commercial products. I’ll talk about my rules in this article. Rule #1. Estimate the monetary value Any business operates to make money. Estimate the monetary value of a decision to see whether or not it is profitable. Money is a universal unit of measurement that helps to compare decisions, prioritize projects and be sure that you made the best decision. I am convinced that you can estimate the monetary value of anything — it’s not as difficult as it sounds. You have to always ask yourself, “Will I make a profit? Is it worth the resources spent?” By using this rule we don’t make unjustified decisions. The more difficult a decision, the better it has to be justified. It’s better to take extra time to think that to take a reckless action with a thought “Why not?” Rule #2. Value the goal over the process How our team achieves the goal is not important to us. What we care about is the result even if it is achieved through unconventional ways. It gives our employees the freedom to think and be creative. Quite often the biggest difficulties lead to the biggest breakthroughs. Our team can and even have to re-create the established processes and make them better. If someone knows how to achieve the goal faster and more efficiently, they have to advocate for the change, to demolish the old processes and build new ones.However, it’s important to keep a balance. Let’s say we want to reduce the cost of customer acquisition. We can fire the whole sales department, it will make the process cheaper. But it will also cut the conversion rate. The lower conversion rate will do more harm than the reduced acquisition cost will do good. We will technically achieve our goal, but the company won’t benefit from it. That’s why it’s important to use Rule #2 together with Rule #1 and estimate the monetary value of a change. Rule #3. Any decision is better than none In 2017, we ran a complete makeover of our website’s front page. It took two months and up to six stakeholders. Along with that, there were many interested people who wanted to give tips. Obviously, we couldn’t consider and use all the advice as the whole process would take forever. We did not have time for that. That’s when we came up with this rule together with the rule to implement new processes if it helps to achieve the goal (see Rule #2). We made a draft and presented it for discussion. While people were giving their ideas, we were already making the next version, not considering the current comments. We implemented the revision with a lag of one version or didn’t implement them at all. It wasn’t our goal to consider all the comments, our goal was to make a good front page. For really big decision we took a break and said, “Ok, guys, you have 24 hours to comment if you want to see your revisions in the next version.” Thanks to that, only people who were really interested in the project got to make a decision. We cut all the unnecessary communication regardless of the participants’ positions and made a new front page with a considerably better conversion rate before the deadline. Science tells us that moving is living. I beg you to live at work. There shouldn’t be moments when you sit at your desk and don’t know what to do. This leads to procrastination. A stall can occur due to external or internal reasons. If an employee hesitates what decision to make, it’s better to make any, get feedback and try again. The iterative approach makes all the doubts disappear. If it takes anyone — me, your manager, your counterpart from another team — more than 24 hours to make a decision, you can make it yourself without approval. The person involved had enough time; if they didn’t use it, it was not that important to them. What if you make a wrong decision? You definitely will. That’s why we came up with the next rule. Rule #4. Make more mistakes and learn from them It may sound odd, but to make mistakes is crucial. I always encourage my team to make mistakes as that’s how they can get experience. That’s how I learned. Sometimes I see that someone from my team is going to make a mistake and I don’t warn them letting them make this mistake. The next time they won’t wait for me to guide them and will trust their own knowledge and intuition. This way employees become more independent and make better decisions. But sometimes it turns out that the employee was right and I was wrong! That’s what I enjoy the most, realizing that I hired someone with more competence than I have. However, making mistakes can lead to fast growth and better results only in case when each mistake is thoroughly analyzed, conclusions are made and the rules and principles are amended. I keep close tabs on that. Rule #5. Take responsibility for your work With great freedom comes great responsibility. Employees can decide whether they want to manage, make decisions and take risks or to do their job and let me take all the risks. Both positions are ok, it’s the matter of preference. Each employee is important. But if someone chooses to risk and then makes the same mistakes over and over again (which means that he or she does not learn), he or she will be fired. If you don’t reach your targets, you’re fired. If you don’t follow the principles, you’re fired. If you stall, you take unimportant projects. If you earn a profit, you grow fast and work on the most interesting and creative projects. Career lifts can take you either way; you can go up or down very fast depending on how much profit you earn for the company. This rule applies to everyone at the company, from specialists to co-founders. Rule #6. Make decisions openly At all times. If you make a decision in a private chat, it’s a bad decision. Share information honestly and openly, especially if your decision involves risk. The greater the risk, the more you need to share. It helps you to get feedback even before you make a mistake (yes, it’s better not to make some mistakes despite Rule #5). Your colleagues can see overlooked flaws in your plan. When you work alone or in a small team, it’s easy to lose touch with reality. But when you share information, you can get a heads-up from a more experienced or just more attentive colleague much faster than you could spot a mistake yourself. Rule # 7. Change rules if it means better results Every new hire has to learn and follow our rules. But they are not set in stone. Rules exist to help make better decisions, not to limit your freedom. If you follow the rules and get a negative result, it’s great! I hope you share it with the team. It gives you an opportunity to make the world around you slightly better, even if it’s just a tiny rule. Not only people need to constantly grow and change; rules for decision-making need to grow and change too. The rule of no control When you travel on the speed of light, it’s hard to keep everything under control. Sometimes our test servers fail. According to Rule #3, we don’t wait for them to be fixed and release projects with no testing. We can implement such a risky process, thanks to Rule #2. Of course, it can result in system fails, from which we learn according to Rule #4, and we are not afraid of these fails as we’ve estimated their monetary value according to Rule #1. We accepted the responsibility for such mistakes according to Rule #5 and informed everyone openly according to Rule #6. I’m sure that the day will come when our rules will no longer be effective and we’ll have to change them. It’s good we can do it thanks to Rule #7.Rules don’t give you more control but help achieve better results for less effort. And the free time you’ll have can be invested in personal growth. Also appears . here

How to Teach People to Make the Right Decisions and Delegate Important Business Tasks With no Stress

How We Enhanced Remotion Performance through GPU-Accelerated Server-Side Rendering Running with hardware acceleration in headless mode can be more challenging than it appears. We embarked on this journey with , which is an excellent framework that enables developers to "Make Videos Programmatically.” On our way, explore various Nvidia driver versions, including those from Nvidia's website and Ubuntu's official repositories. Google Chrome Remotion We collaborated with the vibrant Open-source community to find the answer for using . After encountering some setbacks on our way, we were able to make it work for Remotion eventually. We consolidated our findings in the Remotion Docs at and simplified the instructions for Remotion developers to make the most out of it. Although the whole research was done for , the same process will work for any headless application that requires GPU power, especially in headless mode. Remotion GPU with Remotion for server-side rendering Using the GPU in the Cloud Remotion Hardware Acceleration in Browser Rendering Hardware Acceleration is to your browser what " " is for a gasoline car. It uses the power of specialized hardware like GPUs to make your web pages load faster, and videos play smoothly. Hardware acceleration improves the part where the web page is displayed and shown to you, known as rendering. This results in a smoother and snappier browsing experience. Nitrogen Fuel DLAMI from Marketplace? One may wonder why we didn't opt for the AWS Deep Learning AMI or DLAMI. But we needed fine-grained control over the GPU drivers and utilities and the outdated DLAMIs despite all the bloatware was not the answer. Such tasks are often challenging to achieve with pre-packaged AMIs, as discussed in " .” Tailoring the environment to our exact requirements also helped us troubleshoot issues effectively. Why Deep Learning AMI is Holding You Back We experimented with multiple packages to identify the key ones needed for Google Chrome to recognize the GPU. Despite these efforts, GPU recognition remained elusive, and in some instances, GPU drivers were crashing even at initialization. The long and arduous journey has brought us to the end of the tunnel, yet the darkness persists, and the solution remains as elusive as ever. As we decided to call it a day, We stumbled upon on 's Github issues, which did not give us any hope but did give us a new perspective and drive to achieve our goal. this thread Puppeteer The Fresh Start It was time to return to the drawing board and start afresh. We started with a clean base Ubuntu AMI on AWS EC2 Instance. After that, we downloaded and installed the Nvidia Tesla T4G drivers from Nvidia's official website. Once it was confirmed that the GPU was working properly using , we installed Google Chrome and tested it for hardware acceleration. To our relief, Google Chrome finally gave a nod to work with Hardware Acceleration. nvidia-smi Launching an EC2 Instance Launching an EC2 instance can be achieved in various ways, with the AWS Web Console and AWS CLI being two notable options. Regardless of the method, the important parameters are instance type and AMI selection. At the time of writing, the AMI ID for our chosen instance is . For launching the EC2 instance via , the following command can be used after replacing the placeholders with actual values. g4dn.xlarge ami-053b0d53c279acc90 aws-cli aws ec2 run-instances \
  --instance-initiated-shutdown-behavior "terminate" \
  --image-id "ami-053b0d53c279acc90" \
  --instance-type "g4dn.xlarge" \
  --key-name "<KEY_NAME>" \
  --subnet-id "<SUBNET_ID>" \
  --security-group-ids "<SECURITY_GROUP_ID>" \
  --iam-instance-profile Arn="<ARN>" \
  --block-device-mappings '[
  {
    "DeviceName": "/dev/sda1",
    "Ebs": {
      "VolumeSize": 8,
      "VolumeType": "gp3"
    }
  }
]' Ubuntu and Kernel Upgrade Ubuntu 22.04 AMI on AWS comes with Linux Kernel (specifically ). At this point, we have two options either we stick with Kernel or Upgrade to ( ), we decided to upgrade the Kernel. This decision is crucial as the Nvidia driver compiled for one version will not work for the other. ami-053b0d53c279acc90 v5 5.19.0-1025-aws v5 v6 v6.2.0-1013-aws After launching the instance, SSH into the instance and initiate the Ubuntu packages upgrade process. To update all the installed packages, including Linux Kernel, execute the following one-liner. sudo bash -c "apt update && export DEBIAN_FRONTEND=noninteractive && export NEEDRESTART_MODE=a && apt upgrade -y && reboot" We can also split , and . But the reason behind doing it like this is to install these updates in non-interactive mode. apt update apt upgrade -y reboot After the execution, the system will reboot with Linux Kernel Version 6. We can confirm the kernel update with . Rebooting is essential when upgrading the Ubuntu system because it loads the new Kernel on the next boot, and Nvidia drivers will subsequently be built for the updated version. uname -r Nvidia GPU Driver Installation The process of installing GPU drivers is straightforward when we have the correct driver for the GPU and the corresponding compatible version. Before starting the installation, we need to install a couple of packages: and . The former is a bundle with a variety of compilation tools required to compile the Nvidia driver. The latter, while not mandatory, is required by Google Chrome. Therefore, it's a good idea to install it beforehand to enable support for Vulkan ICD loader for Nvidia, should we require it in the future. build-essential libvulkan1 sudo apt install -y build-essential libvulkan1 After build tools are installed, we can proceed to download and install the GPU drivers using the following commands: DRIVER_URL="https://us.download.nvidia.com/tesla/535.104.12/NVIDIA-Linux-x86_64-535.104.12.run"
DRIVER_NAME="NVIDIA-Linux-driver.run"
wget -O "$DRIVER_NAME" "$DRIVER_URL"
sudo sh "$DRIVER_NAME" --disable-nouveau --silent
rm "$DRIVER_NAME" Now we can run to confirm the GPU driver installation. The output will look similar to this: nvidia-smi Configuring the Startup Service We learned this the hard way: running once is needed for the proper initialization of and . Google Chrome and Chromium fail to initialize without this preliminary setup. To automate this process, we will create a service that runs at boot time using the following commands: nvidia-smi EGL ANGLE EGL nvidia-smi echo '[Unit]
Description=Run nvidia-smi at system startup

[Service]
ExecStart=/usr/bin/nvidia-smi
Type=oneshot
RemainAfterExit=yes

[Install]
WantedBy=multi-user.target' | sudo tee /etc/systemd/system/nvidia-smi.service
sudo systemctl enable nvidia-smi.service
sudo systemctl start nvidia-smi.service Testing the Installation To test hardware acceleration with Google Chrome or Chromium, we can follow these steps. It's worth noting that Google Chrome has more dependencies, making it an easier choice if we plan to use the Node.js library, Puppeteer. On the other hand, Chromium is a lightweight option with a smaller footprint of dependencies, but we can still use Puppeteer with Chromium by installing a few extra dependencies. Installing Chromium Installation Installing Chromium is straightforward, as it is readily available with Ubuntu's official software repositories. We can install it using with the following command. apt sudo apt install -y chromium-browser Testing Chromium is fully prepared to handle Hardware Acceleration tasks. We can verify its readiness by launching Chromium with the specified parameters. chromium-browser --headless --use-gl=angle \
   --use-angle=gl-egl --use-cmd-decoder=passthrough \
   --print-to-pdf=output.pdf 'chrome://gpu' Installing Puppeteer Dependencies To use Puppeteer with Chromium instead of Google Chrome, there are a few extra dependencies that should be installed. sudo apt install ca-certificates fonts-liberation \
    libasound2 libatk-bridge2.0-0 libatk1.0-0 libc6 \
    libcairo2 libcups2 libdbus-1-3 libexpat1 libfontconfig1 \
    libgbm1 libgcc1 libglib2.0-0 libgtk-3-0 libnspr4 libnss3 \
    libpango-1.0-0 libpangocairo-1.0-0 libstdc++6 libx11-6 \
    libx11-xcb1 libxcb1 libxcomposite1 libxcursor1 libxdamage1 \
    libxext6 libxfixes3 libxi6 libxrandr2 libxrender1 libxss1 \
    libxtst6 lsb-release wget xdg-utils Installing Google Chrome Installation Google Chrome can be installed from multiple options, but we will use the Google Chrome official repository for Debian. We need to add it to Ubuntu's repo list along with the public signing key. curl -fsSL https://dl.google.com/linux/linux_signing_key.pub | sudo gpg --dearmor -o /usr/share/keyrings/googlechrom-keyring.gpg
echo "deb [arch=amd64 signed-by=/usr/share/keyrings/googlechrom-keyring.gpg] http://dl.google.com/linux/chrome/deb/ stable main" | sudo tee /etc/apt/sources.list.d/google-chrome.list
sudo apt update
sudo apt install -y google-chrome-stable Testing Google Chrome is ready for our Hardware Acceleration tasks. We can test it by launching Google Chrome with the following parameters. google-chrome-stable --headless --use-gl=angle \
    --use-angle=gl-egl --use-cmd-decoder=passthrough \
    --print-to-pdf=output.pdf 'chrome://gpu' The above commands will generate , and transfer it to your local machine to check the status of Hardware Acceleration in Google Chrome or Chromium. If the process goes smoothly, the resultant will look like this. output.pdf PDF Working with Puppeteer To verify GPU acceleration using the Node.js library Puppeteer, follow these steps: Installation Install Node.js, as this clean Ubuntu AMI does not include any unnecessary packages. Use the following commands: curl -fsSL https://deb.nodesource.com/gpgkey/nodesource-repo.gpg.key | sudo gpg --dearmor -o /etc/apt/keyrings/nodesource.gpg
NODE_MAJOR=18
echo "deb [signed-by=/etc/apt/keyrings/nodesource.gpg] https://deb.nodesource.com/node_$NODE_MAJOR.x nodistro main" | sudo tee /etc/apt/sources.list.d/nodesource.list
sudo apt update
sudo apt install nodejs Testing After installing Node.js, we can install Puppeteer by running in our code directory. Here's a sample file for checking hardware acceleration: npm i puppeteer index.js const puppeteer = require('puppeteer');
(async () => {
  const browser = await puppeteer.launch({
    headless: true, 
    args: ['--use-gl=angle', '--use-angle=gl-egl'], 
  });
  const page = await browser.newPage();
  await page.goto('chrome://gpu');
  await page.waitForTimeout(2000);
  await page.screenshot({ path: 'output.png' });
  await browser.close();
})(); Run the script using . This will generate an file containing hardware acceleration information. node index.js output.png Note from the Creator has been immensely helpful in helping the “ ” community adopt GPU-accelerated rendering.He's researched and written about how to obtain, configure and run EC2 containers and headless Chrome correctly in order to take advantage of graphics acceleration.This area has been especially hard to crack, and without we would not have been able to unlock huge speed boosts. Mirza Remotion Mirza We are super grateful for that! -- Jonny Burger Creator / Remotion.dev Conclusion With hardware acceleration now readily available in headless mode, we can harness the power of GPUs for faster and more complex rendering tasks. Our next steps involve creating a custom AMI from this instance and streamlining the process using AWS Image Builder Pipelines for efficiency. Additionally, we plan to extend hardware capabilities to Docker containers, further extending our options. Resources Remotion - Make videos programmatically: https://remotion.dev Remotion - Using the GPU in the cloud: https://www.remotion.dev/docs/miscellaneous/cloud-gpu Cannot enable GPU acceleration: github.com/puppeteer/puppeteer/issues/3637 : Recommend a workflow for using the GPU on a AWS instance https://github.com/remotion-dev/remotion/issues/2889 Getting Started with Headless Chrome: developer.chrome.com/blog/headless-chrome Why Your AWS Deep Learning AMI is Holding You Back and How to Fix: mirzabilal.com/why-your-aws-deep-learning-a.. Deep Learning with “AWS Graviton2 + NVIDIA Tensor T4G” for as low as free* with CUDA 12.2: mirzabilal.com/deep-learning-with-aws-gravi.. How to install FFmpeg with Harware Accelaration on AWS: mirzabilal.com/how-to-install-ffmpeg-with-h.. CPU vs GPU for Video Transcoding - Challenging the Cost-Speed Myth: https://mirzabilal.com/cpu-vs-gpu-for-video-transcoding-challenging-the-cost-speed-myth Also published . here

Unlocking GPU-Accelerated Server-Side Rendering in Remotion With Chrome, Chromium & Puppeteer

Welcome to other chapters of Let’s Understand Chrome V8 When we learn V8, the SharedFunction and JSFunction are two very common and important terminologies that may confuse beginners. So what are they? What is the difference between them? In general, the main role of the ShardFunction is the place that lays the bytecodes, there is other stuff like the trampoline which I’ll talk about in the future. “Shared”, you can roughly think of the SharedFunction as a DLL library that can be called in anywhere. The compiler compiles a JavaScript function to a SharedFunction, and the SharedFunction can be used anywhere, like a DLL. This is the reason its name is Shared-function. Roughly, a JSFunction equals a SharedFunction + a context. When V8 is going to call a SharedFunction, V8 needs to bind a context to the ShareFunction, the bind is a term of V8 which means it binds a SharedFuncton and a context together, namely a JSFunction. A JSFunction is an executable instance that can be executed by V8 interpreter ignition. 1. SharedFunction The following code is SharedFucntion class. 1.  // SharedFunctionInfo describes the JSFunction information that can be
2.  // shared by multiple instances of the function.
3.  class SharedFunctionInfo : public HeapObject {
4.  public:
5.  V8_EXPORT_PRIVATE Code GetCode() const;
6.  V8_EXPORT_PRIVATE static void SetScript(
7.      Handle<SharedFunctionInfo> shared, Handle<Object> script_object,
8.       int function_literal_id, bool reset_preparsed_scope_data = true);
9.   V8_EXPORT_PRIVATE int EndPosition() const;
10.    V8_EXPORT_PRIVATE int StartPosition() const;
11.    V8_EXPORT_PRIVATE void SetPosition(int start_position, int end_position);
12.    inline bool IsApiFunction() const;
13.    inline bool is_class_constructor() const;
14.    inline FunctionTemplateInfo get_api_func_data();
15.    inline void set_api_func_data(FunctionTemplateInfo data);
16.    inline bool HasBytecodeArray() const;
17.    inline BytecodeArray GetBytecodeArray() const;
18.    inline void set_bytecode_array(BytecodeArray bytecode);
19.    inline Code InterpreterTrampoline() const;
20.    inline bool HasInterpreterData() const;
21.    inline InterpreterData interpreter_data() const;
22.    inline void set_interpreter_data(InterpreterData interpreter_data);
23.    // builtin_id corresponds to the auto-generated Builtins::Name id.
24.    inline bool HasBuiltinId() const;
25.    inline int builtin_id() const;
26.    inline void set_builtin_id(int builtin_id);
27.    inline bool HasUncompiledData() const;
28.    inline UncompiledData uncompiled_data() const;
29.    inline void set_uncompiled_data(UncompiledData data);
30.    inline bool HasUncompiledDataWithPreparseData() const;
31.    inline UncompiledDataWithPreparseData uncompiled_data_with_preparse_data()
32.        const;
33.    inline void set_uncompiled_data_with_preparse_data(
34.        UncompiledDataWithPreparseData data);
35.    inline bool HasUncompiledDataWithoutPreparseData() const;
36.    inline LanguageMode language_mode() const;
37.    inline void set_language_mode(LanguageMode language_mode);
38.    DECL_PRIMITIVE_ACCESSORS(syntax_kind, FunctionSyntaxKind)
39.    inline bool is_wrapped() const;
40.    DECL_BOOLEAN_ACCESSORS(has_duplicate_parameters)
41.    DECL_BOOLEAN_ACCESSORS(native)
42.    DECL_BOOLEAN_ACCESSORS(is_asm_wasm_broken)
43.    DECL_BOOLEAN_ACCESSORS(name_should_print_as_anonymous)
44.    DECL_BOOLEAN_ACCESSORS(is_oneshot_iife)
45.    DECL_BOOLEAN_ACCESSORS(are_properties_final)
46.    DECL_BOOLEAN_ACCESSORS(is_safe_to_skip_arguments_adaptor)
47.    DECL_BOOLEAN_ACCESSORS(has_reported_binary_coverage)
48.    DECL_BOOLEAN_ACCESSORS(private_name_lookup_skips_outer_class)
49.    inline FunctionKind kind() const;
50.    DECL_INT_ACCESSORS(function_map_index)
51.    inline void clear_padding();
52.    inline void UpdateFunctionMapIndex();
53.    inline bool optimization_disabled() const;
54.    inline BailoutReason disable_optimization_reason() const;
55.    void DisableOptimization(BailoutReason reason);
56.    DECL_BOOLEAN_ACCESSORS(requires_instance_members_initializer)
57.    bool HasSourceCode() const;
58.    static Handle<Object> GetSourceCode(Handle<SharedFunctionInfo> shared);
59.    static Handle<Object> GetSourceCodeHarmony(Handle<SharedFunctionInfo> shared);
60.    inline bool IsSubjectToDebugging();
61.    inline bool IsUserJavaScript();
62.    inline bool CanDiscardCompiled() const;
63.    void UpdateExpectedNofPropertiesFromEstimate(FunctionLiteral* literal);
64.    void UpdateAndFinalizeExpectedNofPropertiesFromEstimate(
65.        FunctionLiteral* literal);
66.    DECL_CAST(SharedFunctionInfo)
67.    // Constants.
68.    static const uint16_t kDontAdaptArgumentsSentinel = static_cast<uint16_t>(-1);
69.    static const int kMaximumFunctionTokenOffset = kMaxUInt16 - 1;
70.    static const uint16_t kFunctionTokenOutOfRange = static_cast<uint16_t>(-1);
71.    STATIC_ASSERT(kMaximumFunctionTokenOffset + 1 == kFunctionTokenOutOfRange);
72.    DEFINE_FIELD_OFFSET_CONSTANTS(HeapObject::kHeaderSize,
73.                                  TORQUE_GENERATED_SHARED_FUNCTION_INFO_FIELDS)
74.//............omit..............
75.    }; Lines 1–2 comment that a SharedFunction can be shared by multiple instances. Here, I give you some important members of a SharedFunction. Line 5, the GetCode() returns code, I would shed some more light on that the code is a Builtin code but not your JavaScript source code, the Builtin code is used to construct the function prologue for interpreter ignition. Before calling a function, V8 needs to construct a call stack and get the first instruction address, for which all of this is responsible by the Builtin code; Line 6, SetScript() sets JavaScript source code into the SharedFunction; Lines 9–11, they set offset that describes the corresponding position between bytecode and JavaScript source code; Lines 12–15, they describe if the SharedFunction is an API function; Lines 16–18, they get and set bytecode array for a SharedFunction; Line 19, it is the InterpreterTrampoline address that will be talked about in the future; Lines 27–35, they are the optimization reason for TurboFan. The following list is the optimization reason. #define BAILOUT_MESSAGES_LIST(V)                                            \
  V(kNoReason, "no reason")                                                 \
                                                                            \
  V(kBailedOutDueToDependencyChange, "Bailed out due to dependency change") \
  V(kCodeGenerationFailed, "Code generation failed")                        \
  V(kCyclicObjectStateDetectedInEscapeAnalysis,                             \
    "Cyclic object state detected by escape analysis")                      \
  V(kFunctionBeingDebugged, "Function is being debugged")                   \
  V(kGraphBuildingFailed, "Optimized graph construction failed")            \
  V(kFunctionTooBig, "Function is too big to be optimized")                 \
  V(kLiveEdit, "LiveEdit")                                                  \
  V(kNativeFunctionLiteral, "Native function literal")                      \
  V(kNotEnoughVirtualRegistersRegalloc,                                     \
    "Not enough virtual registers (regalloc)")                              \
  V(kOptimizationDisabled, "Optimization disabled")                         \
  V(kNeverOptimize, "Optimization is always disabled") Lines 36–37, set the mode as strict or sloppy; Lines 40–48, the macro DECL_BOOLEAN_ACCESSORS’ definition is below: #define DECL_PRIMITIVE_ACCESSORS(name, type) \
  inline type name() const;                  \
  inline void set_##name(type value);

#define DECL_BOOLEAN_ACCESSORS(name) DECL_PRIMITIVE_ACCESSORS(name, bool) Line 72, it defines the SharedFunction’s memory layout, the following is the source code and Figure 1 shows the memory layout: #define DEFINE_FIELD_OFFSET_CONSTANTS(StartOffset, LIST_MACRO) \
  enum {                                                       \
    LIST_MACRO##_StartOffset = StartOffset - 1,                \
    LIST_MACRO(DEFINE_ONE_FIELD_OFFSET)                        \
  };
//==================omit===========================
#define TORQUE_GENERATED_SHARED_FUNCTION_INFO_FIELDS(V) \
V(kStartOfWeakFieldsOffset, 0) \
V(kFunctionDataOffset, kTaggedSize) \
V(kEndOfWeakFieldsOffset, 0) \
V(kStartOfStrongFieldsOffset, 0) \
V(kNameOrScopeInfoOffset, kTaggedSize) \
V(kOuterScopeInfoOrFeedbackMetadataOffset, kTaggedSize) \
V(kScriptOrDebugInfoOffset, kTaggedSize) \
V(kEndOfStrongFieldsOffset, 0) \
V(kLengthOffset, kUInt16Size) \
V(kFormalParameterCountOffset, kUInt16Size) \
V(kExpectedNofPropertiesOffset, kUInt16Size) \
V(kFunctionTokenOffsetOffset, kUInt16Size) \
V(kFlagsOffset, kInt32Size) \
V(kFunctionLiteralIdOffset, kInt32Size) \
V(kUniqueIdOffset, kInt32Size) \
V(kSize, 0) \
//==============omit====================
#define FLAGS_BIT_FIELDS(V, _)//see line 70 above 2. JSFunction The function NewFunctionFromSharedFunctionInfo reads a SharedFunction and generates the corresponding JSFunction. The following is the source code: 1.  Handle<JSFunction> Factory::NewFunctionFromSharedFunctionInfo(
2.      Handle<SharedFunctionInfo> info, Handle<Context> context,
3.      AllocationType allocation) {
4.    Handle<Map> initial_map(
5.        Map::cast(context->native_context().get(info->function_map_index())),
6.        isolate());
7.    return NewFunctionFromSharedFunctionInfo(initial_map, info, context,
8.                                             allocation);
9.  }
10.  //==============分隔线============
11.  Handle<JSFunction> Factory::NewFunction(Handle<Map> map,
12.                                          Handle<SharedFunctionInfo> info,
13.                                          Handle<Context> context,
14.                                          AllocationType allocation) {
15.    Handle<JSFunction> function(JSFunction::cast(New(map, allocation)),
16.                                isolate());
17.    function->initialize_properties(isolate());
18.    function->initialize_elements();
19.    function->set_shared(*info);
20.    function->set_code(info->GetCode());
21.    function->set_context(*context);
22.    function->set_raw_feedback_cell(*many_closures_cell());
23. //........omit...............
24.  } In the above code, line 7 calls the NewFunction(). Here, I give you some details about the NewFunction. Lines 17–18, they describe some properties and elements that a SharedFunction doesn’t have. Line 21, it is the bound context that I mentioned above. Lines 19–20, they set the corresponding SharedFunction and Builtins code into the JSFunction. Okay, a SharedFunction can be shared by multiple instances and a JSFunction is an executable instance. Okay, that wraps it up for this share. I’ll see you guys next time, take care! Please reach out to me if you have any issues. : qq9123013 : WeChat Email v8blink@outlook.com Also published . here

Understanding Chrome V8 - Chapter 24: How does V8 Describe Your JavaScript Function

Meet the Winners of Startups in North and Central America

A universal truth we’ve experienced as both an IT software vendor and as an application delivery team is that application owners are constantly trying to deliver better software faster. We all try different things to help make that happen, and when appropriate, share our findings with others. As we set out to Dockerize our self-hosted application components, we found out some great lessons for dockerizing applications. Let’s face it – organizations of all sizes are shifting away from classic software delivery models. As they strive to deliver better software faster, packaging binaries and shipping pieces of a distributed application to customers is no longer a sufficient way to build and release software. At Instana, we face many of the same challenge As our customers do in the never ending effort to innovate, automate, and deliver the best user experience possible. Thus, we completely revamped how our on-prem customers install the Instana Backend. Instana’s self-hosted platform now utilizes a fully process. Docker based installation This article looks at the reasons for dockerizing our application (including why Docker was our technology of choice), why we decided now was the right time, and some key lessons we learned about dockerization along the way. Why Dockerize Our Application Now? At Instana, we offer our Application Performance Monitoring (APM) platform as both SaaS and self-hosted on-premise offerings. Until now, we shipped our SaaS to self-hosted platforms by packaging the binaries we created for our SaaS platform into RPM/DEB packages to support different Linux distributions. We had all of the components wired together with a that had grown to cover all the edge use cases we discovered over time. Chef cookbook This approach served us fairly well for the past 4 years. As our customer base grew and demands shifted, we were faced with challenges supporting various linux distributions, outgrowing the simple single host installation, and needing to run in various environments like AWS, GCP, and Azure, as well as private data centers. This had a drastic impact on our release cycle predictability. We also wanted to make it easier for our customers to update from one version to the next. The increasing diversity of supported Linux distributions, the demands for deployment flexibility, and growing release complexity slowing down our release cycles led to a situation where a change was required. Once we completed customer conversations and interviews, we had a better understanding of ways we could improve their operational experience. Armed with this knowledge, we started looking for a solution that would enable us to ship an enterprise ready self-hosted version of our product that is scalable, continuously upgradable, with as little effort as possible. Why Docker is Our Technology of Choice Docker was an obvious choice based on its wide adoption in the enterprise. We found that even those organizations that still do not have containerized production workloads use processes to allow vendors to bring Docker in. Additionally, when we looked at the containerization ecosystem we found that the standard that Docker helped establish, is the most widely adopted and respected. This ensures that any new or upcoming runtimes will be compliant, future proofing our technology of choice. OCI (Open Containers Initiative) Another reason we chose this route is that we already use containerized packages in our SaaS platform. Those containers are scheduled by Nomad as well as . By bringing this same technology strategy to our self-hosted platform, we are able to use the EXACT same artifacts we leverage for our SaaS platform. Kubernetes (K8s) The Instana components are already containerized and run in our SaaS platform, but we still needed to create containers for our databases, Clickhouse, Cassandra, etc., and set up the release pipeline for them. Most of the complexity is not in creating a container with the database running, but in the management of the configuration and how to pass it down in a maintainable way to the corresponding component. We dealt with this complexity by providing the configurations through a mount point in the container. This has the added benefit of allowing us to use that pattern in all of our containers regardless of whether it’s a database or processing component. Additionally, it’s easy to maintain as all changes are only touching the configuration files that are accessible on the host system. It also simplifies the docker run directive and enables us to collect debug bundles for troubleshooting purposes without the need to hook into the container. The new Docker-based installer has been rolled out to our customers. We have benefited from this change by increasing our deployment flexibility, making our platform more scalable, and drastically reducing the complexity of our releases which has made our release cycles more predictable. We also have a much faster Operating System (OS) support qualification cycle, helping us and our self-hosted customers shrink the time to production value to match up with our SaaS users. Three Lessons About Dockerizing Applications All in all, the development, testing and deployment process were as quick to put together and as simple to roll out as expected, but we did come across some interesting issues along the way. Here are some subtle, but important, items to consider and examine when you dockerize your own applications. ulimits on CentOS Many of our customers use CentOs . As we rolled out the first version of our new dockerized setup to CentOS, we found that Clickhouse was not able to properly write to disk. We suspected a database configuration issue, but once we gathered all log files and compared the system settings to other operating systems that ran smoothly, we identified the problem in a very low default setting of ulimits. Knowing this, the next migrations and initial setups went off without a hitch. First we evaluated the option to manipulate the ulimit on the host, but realized quickly that every customer setup could be very unique where and how the setting is set. Then we found out that the for the docker container overrides the default setting resolving the issues for our databases and is the last parameter in the override hierarchy so we can rely on it to be set properly now –ulimit setting . Eliminating Docker Network Overhead The only other concern that remained was what impact adding a Docker layer would have on the overhead. We did not want to move away from a single host deployment model, so we tapped directly into the host network, eliminating the bottleneck that could have caused overhead issues on the Docker network device. This eliminated our overhead worries. Binding Systemd Services Soon we recognized that customers are executing maintenance windows for system restarts or upgrades with the confidence that the Instana components will behave correctly and take no additional effort while doing so. Taking a mandatory startup order into account it was not sufficient to rely on docker startup policy. We added multiple systemd bindings to docker.service within our Systemd setting. [Unit]
After=docker.service
BindsTo=docker.service
[Service]
Type=oneshot
ExecStart= bin/instana start -s
ExecStop= bin/instana stop
Restart=no
RemainAfterExit=yes
[Install]
WantedBy=docker.service /usr/ /usr/ This helps us ensure our components are always up and healthy. Should You Dockerize Your Application? It’s been 4 months now, and we couldn’t be happier with the results — nor could our customers. Through 4 releases, we’ve been able to keep the self-hosted version of our back-end in lock step with our SaaS version, which has extra benefits: Customers, regardless of back-end, all have access to new features immediately. Dev and Ops get to work on a single development and deployment cycle. Customer Success doesn’t have to concern themselves with unique versions in the field. So if you were to ask our Ops team, Customer Success, Developers, our Solution Architects, they would tell you “Don’t wait! Dockerize Applications now! So what’s next for our journey of Dockerization? One of the things we want to do is to provide an easy path to scalable out your system to process more transactions, by leveraging the microservice architecture of Instana. Spoiler alert: We will extend our self-hosted offering to be deployable on K8s, stay tuned 😀 P.S. Curious about observability? We've built an interactive sandbox for you to play with!

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