Trending February 2024 # Toshiba Officially Kills Hd Dvd, May You Rest In Peace # Suggested March 2024 # Top 9 Popular

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It’s true folks, we all knew the slow death was coming and as of today, Tuesday, February 19, 2008, HD DVD is pronounced dead. According to Gizmodo Japan, Toshiba’s press release is out in wild. So there you have it, the Format War is finally over; long live BluRay. Jump over for the full press release.

TOKYO–Toshiba Corporation today announced that it has undertaken a thorough review of its overall strategy for HD DVD and has decided it will no longer develop, manufacture and market HD DVD players and recorders. This decision has been made following recent major changes in the market. Toshiba will continue, however, to provide full product support and after-sales service for all owners of Toshiba HD DVD products.

HD DVD was developed to offer consumers access at an affordable price to high-quality, high definition content and prepare them for the digital convergence of tomorrow where the fusion of consumer electronics and IT will continue to progress.

“We carefully assessed the long-term impact of continuing the so-called ‘next-generation format war’ and concluded that a swift decision will best help the market develop,” said Atsutoshi Nishida, President and CEO of Toshiba Corporation. “While we are disappointed for the company and more importantly, for the consumer, the real mass market opportunity for high definition content remains untapped and Toshiba is both able and determined to use our talent, technology and intellectual property to make digital convergence a reality.”

Toshiba will continue to lead innovation, in a wide range of technologies that will drive mass market access to high definition content. These include high capacity NAND flash memory, small form factor hard disk drives, next generation CPUs, visual processing, and wireless and encryption technologies. The company expects to make forthcoming announcements around strategic progress in these convergence technologies.

Toshiba will begin to reduce shipments of HD DVD players and recorders to retail channels, aiming for cessation of these businesses by the end of March 2008. Toshiba also plans to end volume production of HD DVD disk drives for such applications as PCs and games in the same timeframe, yet will continue to make efforts to meet customer requirements. The company will continue to assess the position of notebook PCs with integrated HD DVD drives within the overall PC business relative to future market demand.

This decision will not impact on Toshiba’s commitment to standard DVD, and the company will continue to market conventional DVD players and recorders. Toshiba intends to continue to contribute to the development of the DVD industry, as a member of the DVD Forum, an international organization with some 200 member companies, committed to the discussion and defining of optimum optical disc formats for the consumer and the related industries.

Toshiba also intends to maintain collaborative relations with the companies who joined with Toshiba in working to build up the HD DVD market, including Universal Studios, Paramount Pictures, and DreamWorks Animation and major Japanese and European content providers on the entertainment side, as well as leaders in the IT industry, including Microsoft, Intel, and HP. Toshiba will study possible collaboration with these companies for future business opportunities, utilizing the many assets generated through the development of HD DVD.

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How To Type Peace Symbol (Text) In Word

In today’s article, you’ll learn how to use some keyboard shortcuts and other methods to type or insert the Peace Symbol (text) in MS Word for Windows.

Just before we begin, I’ll like to tell you that you can also use the button below to copy and paste the Peace symbol text into your work for free.

However, if you just want to type this symbol on your keyboard, the actionable steps below will show you everything you need to know.

To type the Peace Symbol on Word for Windows, simply press down the Alt key and type 9774 using the numeric keypad, then let go of the Alt key. Alternatively, place your insertion pointer where you need this symbol, then type 262Eand press Alt+X to get the Peace symbol.

These shortcuts work only on MS Word.

The below table contains all the information you need to type this Symbol on the keyboard on Word for Windows PC.

Symbol NamePeace SignSymbol Text☮Alt Code9774Shortcut for WordAlt+9774

The quick guide above provides some useful shortcuts and alt codes on how to type the Peace symbol in Word on Windows.

For more details, below are some other methods you can also use to insert this symbol into your work such as Word or Excel document.

Microsoft Office provides several methods for typing Peace Symbol or inserting symbols that do not have dedicated keys on the keyboard.

In this section, I will make available for you 3 different methods you can use to type or insert this and any other symbol on your PC, like in MS Word for Windows.

Without any further ado, let’s get started.

See Also: How to type Gender Symbol in Word

The Peace Symbol alt code is 9774.

Even though this Symbol has no dedicated key on the keyboard, you can still type it on the keyboard with the Alt code method. To do this, press and hold the Alt key whilst pressing the Peace Alt code (i.e. 9774) using the numeric keypad.

This method works on Windows only. And your keyboard must also have a numeric keypad.

Below is a break-down of the steps you can take to type the Peace Sign on your Windows PC:

Place your insertion pointer where you need the Peace Symbol text.

Press and hold one of the Alt keys on your keyboard.

Whilst holding on to the Alt key, press the Peace Symbol’s alt code (9774). You must use the numeric keypad to type the alt code. If you are using a laptop without the numeric keypad, this method may not work for you. On some laptops, there’s a hidden numeric keypad which you can enable by pressing Fn+NmLk on the keyboard.

Release the Alt key after typing the Alt code to insert the Symbol into your document.

This is how you may type this symbol in Word using the Alt Code method.

Another easy way to get the Peace Symbol on any PC is to use my favorite method: copy and paste.

All you have to do is to copy the symbol from somewhere like a web page, or the character map for windows users, and head over to where you need the symbol (say in Word or Excel), then hit Ctrl+V to paste.

Below is the symbol for you to copy and paste into your Word document. Just select it and press Ctrl+C to copy, switch over to Microsoft Word, place your insertion pointer at the desired location, and press Ctrl+V to paste.

Alternatively, just use the copy button at the beginning of this post.

For windows users, obey the following instructions to copy and paste the Peace Symbol using the character map dialog box.

The Character Map dialog will appear.

This is how you may use the Character Map dialog to copy and paste any symbol on Windows PC.

Obey the following steps to insert this symbol (☮) in Word or Excel using the insert symbol dialog box.

The Symbol dialog box will appear.

Close the dialog.

The symbol will then be inserted exactly where you placed the insertion pointer.

These are the steps you may use to insert this Symbol in Word.

As you can see, there are several different methods you can use to type the Peace Sign in Microsoft Word.

Using the alt code shortcut for Word makes the fastest option for this task. Shortcuts are always fast.

Thank you very much for reading this blog.

The Next Novel You Read May Be In Facebook Messenger

We aren’t exactly a nation of readers. On a typical day, just 15 percent of men and 22 percent of women read for pleasure. In the last year, one in four Americans haven’t read a single book in any format—paperback, audiobook, or otherwise.

But social media and smartphone app companies think they may have the solution to our reading aversion. From Silicon Valley heavyweights to bookworm-run startups, efforts to bring fiction to our smartphones are proliferating. While reading a book in Facebook Messenger or “chat fiction” on Snapchat might seem strange, silly, or tedious, each new initiative is pushing up against the boundaries of the book cover.

Last year, James Patterson, one of the most commercially-successful authors of all time (Forbes pegged his 2024 income at $95 million), and his team approached Facebook about adapting one of his forthcoming novel to its messaging app. So the author, who believes Americans need “a shared literature,” offered Messenger its choice of two soon-to-debut books. The company selected a narrative about a New Orleans-based detective, who runs a well-known food truck with his ex-wife. After a few months of harried development, The Chef rolled out Tuesday morning on Messenger. You can find it now by searching “The Chef by James Patterson” in the app.

Without a standalone book portal in the Messenger app (a designer says they’re working on that now), every piece of the Patterson novel had to be created within the app’s pre-existing design parameters. Books usually require page-turning, but the Messenger novel unfurls itself to readers each time they press a knife emoji. The text comes through in a typical message bubble, or several at once. Each passage fills a single page on your smartphone—and not a centimeter more—so you don’t have to scroll.

Maps, Instagram posts, and more are embedded in The Chef by James Patterson on Messenger. Courtesy of Messenger

A more established reading app might offer some insight into what’s to come for Facebook. Launched in 2024, Hooked forgoes adaptation in favor of commissioning its own made-for-social stories. These original works are part of a digital genre the company calls “chat fiction”—stories written in the form of text messages, which appear sequentially on-screen.

Husband-and-wife startup duo Parag Chordia and Prerna Gupta went through all kinds of iterations before the launch. The couple originally had high hopes for image-driven media, inspired by comic books, and excerpts of bestselling novels. But completion rates among their target audience of 13- to 24-year-olds was low: Gupta says just 35 percent of readers finished the excerpts. Chat fiction, however, thrived. The 1,000-word story arcs, which involve two or more characters updating each other on plot development in text messages, boasted completion rates in the 80th and 90th percentile.

This week, Hooked released its longest piece of chat fiction yet, on a dedicated Snapchat channel. The 30,000-word-long story, Dark Matter, appeared in “episodes” (more conventionally known as “chapters”) of 5,000 to 8,000 words. Like all Hooked stories, the tale leans heavily on cliffhangers to keep readers “hooked” from message to message. There’s a liberal use of ellipses, and a tension-building mystery. “When you’re on mobile, you are in a constant battle for attention,” Gupta says. “Users need to feel that there’s some payoff in one episode,” or they won’t come back for more.

Chat fiction in action. Courtesy of Hooked

A “Snapchat-based book” sounds like a postmodern word salad, but Hooked’s serialization strategy has actually worked for centuries. In the Victorian era, most authors published their stories in bits and pieces in newspapers, with one novella doled out over weeks or months. The Pickwick Papers by Charles Dickens, * The Count of Monte Cristo* by Alexandre Dumas, and the character Sherlock Holmes all first appeared in periodical form.

But something else is at work, too. Publishing has never not been in a state of disruption. From monks in scriptoria painting words into books no one could read to the dawn of the printing press to the era of ebooks, how we read is always changing. The only thing that’s remained consistent is that the words themselves matter most.

Selling someone who loves the crack of a fresh hardcover spine, on a Facebook Messenger-based novel is a challenge. So is convincing a Snapchat-loving teenager to read a musty library book. But goading someone who loves literary fiction into reading a James Patterson novel—whether it’s a paperback, ebook, or Messenger bubble—is even harder. The medium and the message feel increasingly indistinguishable, but the message still matters more.

Ultimately, Messenger’s The Chef and Hooked’s Dark Matter aren’t for everyone, and they don’t claim to be. From Patterson to Gupta, the stated goal has always been to get more Americans reading in whatever form they prefer. It’s OK to support a proliferation of reading platforms, and still stick to your paperbacks.

This May Be The Friendly Robot Face You See Before You Die

Hello, human. Are you dying? Let’s try and fix that. Stan Horaczek

When it comes to robots in our home, there are a few well-worn tropes to which we’ve grown accustomed. There’s the friendly Rosie the Robot butler that brings us our futuristic food and slippers. Then there’s the fatalistic, sci-fi view in which any humanoid robot is just a step towards a Terminator- or Matrix-style dystopia in which humans are reduced to a nuisance or a power supply, respectively. At this year’s Consumer Electronics Show, however, there was an assortment of robots designed to monitor a person’s health and intervene if something goes wrong. And those helpful little bots may be the last thing you see before the squishy, inefficient, meat-based machine we call a body gives up the ghost.

The Bot Care interface let’s caretakers monitor a patient’s vitals from afar. Stan Horaczek

The most high-profile health helper bot at the show was Samsung’s Bot Care. Revealed during Samsung’s massive press conference, the hip-high robot is part of Samsung’s new robotics platform; others include an automated pal designed to help people in retail shopping environments, and another designed to filter pollution from the air in your home.

From the outside, Bot Care looks a lot like the robots we’ve come to expect at CES. It has a decidedly Pixar vibe with friendly eyes plastered across a digital display that doubles as its face. It’s adorable, which makes sense for a device that’s meant to act as a companion for a human.

Bot Care robots don’t have arms so they can’t carry you to safety, but they can monitor your health and call for help if needed. Stan Horaczek

The demo also included the robot’s ability to take heart rate and blood pressure measurements. This is where platforms like this start to tie into the burgeoning number of medical-grade health devices also on display at this year’s CES trade show.

The Omron HeartGuard helps provide regular blood pressure readings for more consistent monitoring of vitals. Stan Horaczek

The $500 Omron HeartGuide blood pressure watch, for instance, uses an inflating device to allow instant blood pressure monitoring, the results of which can transfer to Apple’s HealthKit (with Google and other platform support coming down the line). The latest Apple Watch also famously added an ECG functionality that can monitor for irregular heartbeats and detect when the wearer falls down and may be hurt. The information that these devices collect is somewhat scattered at the moment, but the summation of that data could eventually inform AI meant to help companion robots be friendlier, more helpful, and even better at telling when we might die.

Samsung’s Bot Care platform, for instance, has a compatible fall detector that a patient can wear on their clothes. If it detects a fall, the Bot Care robot can move to the person’s location and try to figure out whether or not to contact an emergency contact or even a doctor or paramedics.

Because Bot Care came out of Samsung’s AI division, it’s meant to interact with patients via regular conversations, which could have health benefits in and of itself. Researchers at University of Southern California have been using a humanoid robot to interact with patients at Los Angeles County + USC Medical Center and the Alzheimer’s care section of Silverado Senior Living center. The research claims the robotic interactions can help motivate patients to participate in healthier behaviors like exercise that they otherwise wouldn’t.

These caretaker robots aren’t a totally new concept across the board. Some have been around for more than a decade. A robot called Dinsow has been in the works since roughly 2009 and has reportedly made its way into some elder care facilities in Japan. The $2,500 bot handles familiar tasks like handing out medication reminders and keeping track of patents’ positions so it can notice if someone hasn’t moved over an extended period of time.

This health aid is stuck inside of a stationary screen, for now. Stan Horaczek

Another virtual health aid called Addison comes from Texas-based health monitoring company SameDay Security. The virtual nurse exists on a screen in the home rather than on a roving robot, but it handles many of the same functions and adds a decidedly human avatar with which patients and interact. The system is impressively conversational and friendly. It’s easy to see how a more human appearance and personality could pair with a robotic body.

While it will still be quite some time before this becomes a standard method of elder care, you can see the technologies in the AI field converging into something that could work in this capacity. After all, machine learning is getting better at learning about your current physical and emotional state by simply reading your facial expressions. One of the key things missing from that equation is context about the rest of the person’s habits and traits, something a live-in robot could collect along the way.

As more wearable devices collect health information, we’re bound to see more devices like this that can leverage it. Right now, a bot is likely to call for help, but it’s not hard to imagine a future in which it could perform some potentially life-saving tasks. Or, all this will make the robot takeover that much simpler.

Or, maybe all future healthcare workers will be replaced by this robotic nurse with a bear’s face.

Graphql Vs. Rest In 2023: Top 4 Advantages & Disadvantages

Since the release of GraphQL in 2024, there have been comparisons between GraphQL and REST due to their similar end results and GraphQL’s innovative approach. In some instances, GraphQL is even seen as a direct alternative or has been used in conjunction with REST.

Despite GraphQL’s innovative approach and acclaimed potential, in 2023, only 19% of companies used GraphQL, while 82% used REST.

While REST is far more widely used than GraphQL at the moment, industry leaders and big companies such as Facebook, Airbnb, and Github are adopting GraphQL.

GraphQL vs. REST

Figure 1: Representation of the GraphQL process 

GraphQL is an open-source query and manipulation language for APIs developed by Facebook in 2012. Contrary to REST architecture, GraphQL is not an API specification; it’s a runtime for fulfilling queries with existing data. Backbend GraphQL provides a type system that describes a schema for data; in return, this gives front-end API consumers the ability to request the exact data they need.

Figure 2: Representation of REST API 

1. Specificity 

GraphQL can provide customers with the exact data they need. One of the most common problems with traditional REST APIs is that they tend to cause overfecthing, obtaining more information than needed. A REST query will extract all the data from a specific resource, while GraphQL will only get what is dictated in a query (see Figure 3). 

Figure 3: Rest API query vs. GraphQL query

Source: Medium.

2. Performance

GraphQL can process customized queries which contribute to enhanced performance. Processing customized queries reduce the number of API calls. 

Contrary to REST, GraphQL has a single endpoint, it is much more predictable, and there is a lower chance of unnecessary API calls. Research shows that mitigating GraphQL from REST increases performance by 66%

3. Flexibility

GraphQL allows its user to integrate multiple systems, and it can fetch data from existing systems. This allows GraphQL to be utilized without needing to uninstall existing infrastructures, and it can work with existing API management tools.

4. Less effort to implement 1. Single endpoint bottleneck

While a single endpoint is one of the strengths of GraphqL, it can become a bottleneck in certain circumstances. HTTP’s built-in cache function in REST APIs produces faster results than GraphQL in almost every scenario. This is because REST APIs’ multiple endpoints allow them to use HTTP caching to avoid reloading resources. GraphQL’s single endpoint pushes the user to rely on an additional library. 

2. Security 

REST’s vast popularity and authentication methods make it a better option for security reasons than GraphQL. While REST has built-in HTTP authentication methods, GraphQL does not provide a specific process to ensure security. The user must figure out their own security methods, whether authentication or authorization. However, users can overcome this issue with a well-planned security plan for using GraphQL.

3. Complexity  4. Cost

One of the significant drawbacks to using GraphQL is that it is more difficult to specify the API rate limit than REST. This creates the risk of the cost of queries being unexpectedly large, leading to computation, resource, and infrastructure overload.

To overcome such risks, the user must calculate a query’s cost before executing it. However, calculating GraphQL’s queries is challenging due to its nested structure. Thus, it is best to use a machine-learning approach to estimate.

If you want to explore specific software, feel free to check our data-driven list of testing tools and data-driven test automation tools vendor list. If you have other questions, we can help:

He received his bachelor’s degree in Political Science and Public Administration from Bilkent University and he received his master’s degree in International Politics from KU Leuven .

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Airflow For Orchestrating Rest Api Applications

This article was published as a part of the Data Science Blogathon.

“Apache Airflow is the most widely-adopted, open-source workflow management platform for data engineering pipelines. It started at Airbnb in October 2014 as a solution to manage the company’s increasingly complex workflows. Most organizations today with complex data pipelines to be managed leverage Apache Airflow to schedule, sequence, monitor the workflows.”

Airflow provides an easy-to-use, intuitive workflow system where you can declaratively define the sequencing of tasks (also known as

DAG

or Directed Acyclic Graph). The Airflow workflow scheduler works out the magic and takes care of scheduling, triggering, and retrying the tasks in the correct order.  

“Start Task4 only after Task1, Task2, and Task3 have been completed….”

“Retry Task2 upto 3 times with an interval of 1 minute if it fails…”

“Which task took the longest in the workflow? …”

“What time did taskN take today vs one week back? …”

“Email the team when a critical task fails…”

The Use Case for Airflow

So, where does a workflow management system fit? And how do you know you need to use it? Let’s say you are working for the IT division of a health care organization, and you need to run some analytics on patient records that you receive from a vendor hospital. You have developed that awesome Apache Spark-based application, which is working like a charm. You need that application run daily against the data that comes in from the hospital. A further requirement is that the output of that analysis needs to be pushed as input to a time-critical downstream application which determines the composition and quantity of factory production units for a test medicine for that day.

Initially, a simple cron job or a Jenkins-based job might suffice until things get bigger. Let’s say two more upstream hospitals get added to the fray. One pushes data to an S3 bucket; another gives a REST API-based interface from which you need to fetch data, and yet another in-house system dumps data to a database. You need to now run your analytics application against the data from all these upstream systems before running the downstream app. This is where the beauty of Airflow comes into play.

Airflow as a mainstream DevOps tool has been widely adopted since it was launched eight years ago to orchestrate BigData and ETL pipelines. As your systems and processes become bigger, managing the scalability and monitoring using custom scripts or cron-based solutions becomes difficult—this is where it fits in.

Airflow UI

times the task started/ended.

The Tree View UI shows you the historical runs broken down by tasks – this is most useful when you want to compare performance between historical runs.

REST API with Python Operators

There are  several operators and provider packages that Apache Airflow supports. Depending on your use case, you get to pick and choose what is most suitable. When I started learning Airflow, what I found most helpful and flexible were the Python-based operators. My applications were running in less than 24 hours with the combination of PythonOperator and PythonSensor 

With these two, you should be able to fit in the general use case described above. All you need is basic Python knowledge!

Structure of an A G

1. First come the imports:

2. Then comes the definition of the DAG constructor/initialization.

Here’s where you give the name of the workflow process that you want to see in the UI, the default retries for tasks, etc

dag = DAG( 'patient_data_analysis', default_args={'retries': 1}, start_date=datetime(2024, 1, 1), catchup=False, ) dag.doc_md = __doc__ ## Operators start = DummyOperator(task_id='start', dag=dag) op1 = PythonOperator( task_id='watch_for_data_dump_on_s3_bucket_pushed_byx_upstream_application_1', python_callable= _placeholder_function1, dag=dag) op2 = PythonOperator( task_id='fetch_data_from_upstream_REST_application2_and_dump_to_s3', python_callable= _placeholder_function2, dag=dag) op3 = PythonOperator( task_id='fetch_data_from_upstream_cloudant_application3_and_dump_to_s3', python_callable= _placeholder_function3, dag=dag) op4 = PythonOperator( task_id='run_analysis_on_all_patient_data_on_s3_dumps', python_callable= _placeholder_function4, dag=dag) determine_production_dosage = BranchPythonOperator( task_id='determine_production_dosage', python_callable=_determine_production_dosage, dag=dag ) production_path_1 = PythonOperator( task_id='production_path_1', python_callable= _placeholder_function5, dag=dag) production_path_2 = PythonOperator( task_id='production_path_2', python_callable= _placeholder_function6, dag=dag) end = DummyOperator(task_id='end',trigger_rule='one_success', dag=dag)

Here is where we have the breakdown of tasks in the flow. We have used three kinds of Operators.

PythonOperator –  which calls the Python callable or function which contains the actual task processing logic

BranchPythonOperator  – which is useful when you want the workflow to take different paths based on some conditional logic.

DummyOperator – which is a convenience operator to try out some POC flow quickly or in this case- gives a structure to the flow  – start and end.

Note that all the operators are connected using the same “dag” object reference.

4. Sequence your tasks

## Flow

The dependencies between your tasks can be declared using this intuitive flow notation.

The start operator will kick off three tasks in parallel – op1, op2, op3

Only when op1, op2, and op3 are done the op4 task will get started

The determine_production_dosage can result in either of the paths production_path_1 or production_path_2

And finally, execution of either path results in the end.



In this case, I have just given placeholder functions. We’ll get into what it should hold in the next section.  Special mention to the _determine_production_dosage(). This is the function called by the branch operator. As the code illustrates, this function’s return value is the operator’s name in the workflow.

PythonOperator and PythonSensor Combo

The following working code covers the following concepts.

How to use the  PythonOperator and callable to make REST API calls to generate a Bearer Token

And use that Bearer Token in subsequent API calls that call some business logic (in this case, it is calling a Spark application on a cloud provider API)

Concept of passing data between tasks using xcom

How to use PythonSensor operator to poll/wait for asynchronous task completion

How to dynamically construct the REST API endpoint based on the value returned from a previous task ( NOTE: This is one use case where I found the power and simplicity of PythonOperator come into play. I had initially tried the SimpleHttpOperator – but found the PythonOperator to be more flexible! )

Source code for serverless_spark_pipeline.py

## Import statements and DAG definition

import json import requests from datetime import datetime from airflow import DAG from airflow.operators.python import PythonOperator from airflow.sensors.python import PythonSensor dag = DAG( 'serverless_spark_pipeline', default_args={'retries': 1}, start_date=datetime(2024, 1, 1), catchup=False, ) dag.doc_md = __doc__

## Python callable for getting a Bearer Token

api_key='CHANGEME' def _get_iam_token(ti): headers={"Authorization": "Basic Yng6Yng=", "Content-Type": "application/x-www-form-urlencoded"} data="grant_type=urn:ibm:params:oauth:grant-type:apikey&apikey="+api_key res = requests.post(url=iam_end_point,headers=headers,data=data) ## Push the token using key, value ti.xcom_push(key='access_token', value= access_token)

## Python Operator for getting the Bearer Token; It calls the Python callable _get_iam_token

generate_iam_token = PythonOperator( task_id = 'get_iam_token', python_callable= _get_iam_token, dag=dag)

## Python callable for calling a REST API

instance_id='CHANGEME' def _submit_spark_application(ti): # Pull the bearer token and use it to submit to REST API access_token=ti.xcom_pull(key='access_token') headers = {"Authorization": "Bearer " + access_token, "Content-type": "application/json"} finalurl = url+instance_id+'/spark_applications' data=json.dumps({"application_details": {"application": "/opt/ibm/spark/examples/src/main/python/wordcount.py", "arguments": ["/opt/ibm/spark/examples/src/main/resources/people.txt"]}}) res = requests.post(finalurl,headers=headers,data=data) # Push the application id - to be used on a downstream task ti.xcom_push(key='application_id', value= application_id)

## Python Operator for submitting the Spark Application; It calls the Python callable _submit_spark_application

submit_spark_application = PythonOperator( task_id = 'submit_spark_application', python_callable= _submit_spark_application, dag=dag) def _track_application(ti): # Pull the application id from an upstream task and use it.. application_id=ti.xcom_pull(key='application_id') access_token=ti.xcom_pull(key='access_token') headers = {'Authorization': 'Bearer ' + access_token} # Construct the REST API endpoint dynamically based on the data # from a previous API call finalurl = ae_url+instance_id+'/spark_applications/'+application_id+'/state' res = requests.get(finalurl,headers=headers) # Keep polling the REST API to check state of application submission until a # terminal state is reached if state == 'finished' or state == 'failed': # Push the value of state as xcom key, value pair. # It can be later used for example in a BranchPythonOperator t1.xcom_push(key='state',value=state) return True else: return False

## Python Sensor for tracking a REST APU. It calls the Python callable _track_application

  track_application = PythonSensor( task_id = 'track_application', python_callable= _track_application, dag=dag)

## Operator flow

This example is based on a REST API call to a cloud provider API that submits a spark application, gets the application ID, and keeps polling for the application’s state based on that application ID. And finally, when the application either finishes or fails, it ends the workflow execution.

The Python callable functions make use of the standard requests module. In the example above, POST and GET. You can use the same approach for other REST API calls, PATCH, PUT, DELETE, etc.

End Notes

Here’s a snapshot of the main DAG UI page. If you are starting Airflow, here are some newbie tips.

You need to toggle and enable the DAG to make it active and execute automatically through the tasks.

Also, be aware that whenever you make a change in the DAG file, it takes about 1 minute to refresh your code and reflect it in the DAG Code tab in the UI.  (The DAG files, which are nothing but python files, are located in the airflow/dags folder of your installation)

This article showed you how to get quickly started with

A simple working DAG that you can get it up and running by defining the sequencing of tasks

Introduction to Python-based operators and sensors that can be easily adapted to call any backend REST API services/applications

How to orchestrate various asynchronous REST API services by polling and passing the relevant data between tasks for further processing

Depending on the use case, your tasks’ data source and data sink. You will need to evaluate what Airflow operators are suitable. Many tuning knobs for airflow can be further configured as you get deeper

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