Tag Archives: troubleshooting

Practice Makes Perfect: Testing Campaigns Before You Send Them

Post Syndicated from Zach Barbitta original https://aws.amazon.com/blogs/messaging-and-targeting/practice-makes-perfect-testing-campaigns-before-you-send-them/

In an article we posted to Medium in February, we talked about how to determine the best time to engage your customers by using Amazon Pinpoint’s built-in session heat map. The session heat map allows you to find the times that your customers are most likely to use your app. In this post, we continued on the topic of best practices—specifically, how to appropriately test a campaign before going live.

In this post, we’ll talk about the old adage “practice makes perfect,” and how it applies to the campaigns you send using Amazon Pinpoint. Let’s take a scenario many of our customers encounter daily: creating a campaign to engage users by sending a push notification.

As you can see from the preceding screenshot, the segment we plan to target has nearly 1.7M recipients, which is a lot! Of course, before we got to this step, we already put several best practices into practice. For example, we determined the best time to engage our audience, scheduled the message based on recipients’ local time zones, performed A/B/N testing, measured lift using a hold-out group, and personalized the content for maximum effectiveness. Now that we’re ready to send the notification, we should test the message before we send it to all of the recipients in our segment. The reason for testing the message is pretty straightforward: we want to make sure every detail of the message is accurate before we send it to all 1,687,575 customers.

Fortunately, Amazon Pinpoint makes it easy to test your messages—in fact, you don’t even have to leave the campaign wizard in order to do so. In step 3 of the campaign wizard, below the message editor, there’s a button labelled Test campaign.

When you choose the Test campaign button, you have three options: you can send the test message to a segment of 100 endpoints or less, or to a set of specific endpoint IDs (up to 10), or to a set of specific device tokens (up to 10), as shown in the following image.

In our case, we’ve already created a segment of internal recipients who will test our message. On the Test Campaign window, under Send a test message to, we choose A segment. Then, in the drop-down menu, we select our test segment, and then choose Send test message.

Because we’re sending the test message to a segment, Amazon Pinpoint automatically creates a new campaign dedicated to this test. This process executes a test campaign, complete with message analytics, which allows you to perform end-to-end testing as if you sent the message to your production audience. To see the analytics for your test campaign, go to the Campaigns tab, and then choose the campaign (the name of the campaign contains the word “test”, followed by four random characters, followed by the name of the campaign).

After you complete a successful test, you’re ready to launch your campaign. As a final check, the Review & Launch screen includes a reminder that indicates whether or not you’ve tested the campaign, as shown in the following image.

There are several other ways you can use this feature. For example, you could use it for troubleshooting a campaign, or for iterating on existing campaigns. To learn more about testing campaigns, see the Amazon Pinpoint User Guide.

Wanted: Vault Storage Engineer

Post Syndicated from Yev original https://www.backblaze.com/blog/wanted-vault-storage-engineer/

Want to work at a company that helps customers in 156 countries around the world protect the memories they hold dear? A company that stores over 500 petabytes of customers’ photos, music, documents and work files in a purpose-built cloud storage system?

Well here’s your chance. Backblaze is looking for a Vault Storage Engineer!

Company Description:

Founded in 2007, Backblaze started with a mission to make backup software elegant and provide complete peace of mind. Over the course of almost a decade, we have become a pioneer in robust, scalable low cost cloud backup. Recently, we launched B2 — robust and reliable object storage at just $0.005/gb/mo. Part of our differentiation is being able to offer the lowest price of any of the big players while still being profitable.

We’ve managed to nurture a team oriented culture with amazingly low turnover. We value our people and their families. Don’t forget to check out our “About Us” page to learn more about the people and some of our perks.

We have built a profitable, high growth business. While we love our investors, we have maintained control over the business. That means our corporate goals are simple – grow sustainably and profitably.

Some Backblaze Perks:

  • Competitive healthcare plans
  • Competitive compensation and 401k
  • All employees receive Option grants
  • Unlimited vacation days
  • Strong coffee
  • Fully stocked Micro kitchen
  • Catered breakfast and lunches
  • Awesome people who work on awesome projects
  • New Parent Childcare bonus
  • Normal work hours
  • Get to bring your pets into the office
  • San Mateo Office – located near Caltrain and Highways 101 & 280.

Want to know what you’ll be doing?

You will work on the core of the Backblaze: the vault cloud storage system (https://www.backblaze.com/blog/vault-cloud-storage-architecture/). The system accepts files uploaded from customers, stores them durably by distributing them across the data center, automatically handles drive failures, rebuilds data when drives are replaced, and maintains high availability for customers to download their files. There are significant enhancements in the works, and you’ll be a part of making them happen.

Must have a strong background in:

  • Computer Science
  • Multi-threaded programming
  • Distributed Systems
  • Java
  • Math (such as matrix algebra and statistics)
  • Building reliable, testable systems

Bonus points for:

  • Java
  • JavaScript
  • Python
  • Cassandra
  • SQL

Looking for an attitude of:

  • Passionate about building reliable clean interfaces and systems.
  • Likes to work closely with other engineers, support, and sales to help customers.
  • Customer Focused (!!) — always focus on the customer’s point of view and how to solve their problem!

Required for all Backblaze Employees:

  • Good attitude and willingness to do whatever it takes to get the job done
  • Strong desire to work for a small fast-paced company
  • Desire to learn and adapt to rapidly changing technologies and work environment
  • Rigorous adherence to best practices
  • Relentless attention to detail
  • Excellent interpersonal skills and good oral/written communication
  • Excellent troubleshooting and problem solving skills

This position is located in San Mateo, California but will also consider remote work as long as you’re no more than three time zones away and can come to San Mateo now and then.

Backblaze is an Equal Opportunity Employer.

Contact Us:
If this sounds like you, follow these steps:

  1. Send an email to jobscontact@backblaze.com with the position in the subject line.
  2. Include your resume.
  3. Tell us a bit about your programming experience.

The post Wanted: Vault Storage Engineer appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Needed: Software Engineering Director

Post Syndicated from Yev original https://www.backblaze.com/blog/needed-software-engineering-director/

Company Description:

Founded in 2007, Backblaze started with a mission to make backup software elegant and provide complete peace of mind. Over the course of almost a decade, we have become a pioneer in robust, scalable low cost cloud backup. Recently, we launched B2, robust and reliable object storage at just $0.005/gb/mo. We offer the lowest price of any of the big players and are still profitable.

Backblaze has a culture of openness. The hardware designs for our storage pods are open source. Key parts of the software, including the Reed-Solomon erasure coding are open-source. Backblaze is the only company that publishes hard drive reliability statistics.

We’ve managed to nurture a team-oriented culture with amazingly low turnover. We value our people and their families. The team is distributed across the U.S., but we work in Pacific Time, so work is limited to work time, leaving evenings and weekends open for personal and family time. Check out our “About Us” page to learn more about the people and some of our perks.

We have built a profitable, high growth business. While we love our investors, we have maintained control over the business. That means our corporate goals are simple – grow sustainably and profitably.

Our engineering team is 10 software engineers, and 2 quality assurance engineers. Most engineers are experienced, and a couple are more junior. The team will be growing as the company grows to meet the demand for our products; we plan to add at least 6 more engineers in 2018. The software includes the storage systems that run in the data center, the web APIs that clients access, the web site, and client programs that run on phones, tablets, and computers.

The Job:

As the Director of Engineering, you will be:

  • managing the software engineering team
  • ensuring consistent delivery of top-quality services to our customers
  • collaborating closely with the operations team
  • directing engineering execution to scale the business and build new services
  • transforming a self-directed, scrappy startup team into a mid-size engineering organization

A successful director will have the opportunity to grow into the role of VP of Engineering. Backblaze expects to continue our exponential growth of our storage services in the upcoming years, with matching growth in the engineering team..

This position is located in San Mateo, California.

Qualifications:

We are a looking for a director who:

  • has a good understanding of software engineering best practices
  • has experience scaling a large, distributed system
  • gets energized by creating an environment where engineers thrive
  • understands the trade-offs between building a solid foundation and shipping new features
  • has a track record of building effective teams

Required for all Backblaze Employees:

  • Good attitude and willingness to do whatever it takes to get the job done
  • Strong desire to work for a small fast-paced company
  • Desire to learn and adapt to rapidly changing technologies and work environment
  • Rigorous adherence to best practices
  • Relentless attention to detail
  • Excellent interpersonal skills and good oral/written communication
  • Excellent troubleshooting and problem solving skills

Some Backblaze Perks:

  • Competitive healthcare plans
  • Competitive compensation and 401k
  • All employees receive Option grants
  • Unlimited vacation days
  • Strong coffee
  • Fully stocked Micro kitchen
  • Catered breakfast and lunches
  • Awesome people who work on awesome projects
  • New Parent Childcare bonus
  • Normal work hours
  • Get to bring your pets into the office
  • San Mateo Office — located near Caltrain and Highways 101 & 280.

Contact Us:

If this sounds like you, follow these steps:

  1. Send an email to jobscontact@backblaze.com with the position in the subject line.
  2. Include your resume.
  3. Tell us a bit about your experience.

Backblaze is an Equal Opportunity Employer.

The post Needed: Software Engineering Director appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Needed: Senior Software Engineer

Post Syndicated from Yev original https://www.backblaze.com/blog/needed-senior-software-engineer/

Want to work at a company that helps customers in 156 countries around the world protect the memories they hold dear? A company that stores over 500 petabytes of customers’ photos, music, documents and work files in a purpose-built cloud storage system?

Well, here’s your chance. Backblaze is looking for a Sr. Software Engineer!

Company Description:

Founded in 2007, Backblaze started with a mission to make backup software elegant and provide complete peace of mind. Over the course of almost a decade, we have become a pioneer in robust, scalable low cost cloud backup. Recently, we launched B2 – robust and reliable object storage at just $0.005/gb/mo. Part of our differentiation is being able to offer the lowest price of any of the big players while still being profitable.

We’ve managed to nurture a team oriented culture with amazingly low turnover. We value our people and their families. Don’t forget to check out our “About Us” page to learn more about the people and some of our perks.

We have built a profitable, high growth business. While we love our investors, we have maintained control over the business. That means our corporate goals are simple – grow sustainably and profitably.

Some Backblaze Perks:

  • Competitive healthcare plans
  • Competitive compensation and 401k
  • All employees receive Option grants
  • Unlimited vacation days
  • Strong coffee
  • Fully stocked Micro kitchen
  • Catered breakfast and lunches
  • Awesome people who work on awesome projects
  • New Parent Childcare bonus
  • Normal work hours
  • Get to bring your pets into the office
  • San Mateo Office – located near Caltrain and Highways 101 & 280

Want to know what you’ll be doing?

You will work on the server side APIs that authenticate users when they log in, accept the backups, manage the data, and prepare restored data for customers. And you will help build new features as well as support tools to help chase down and diagnose customer issues.

Must be proficient in:

  • Java
  • Apache Tomcat
  • Large scale systems supporting thousands of servers and millions of customers
  • Cross platform (Linux/Macintosh/Windows) — don’t need to be an expert on all three, but cannot be afraid of any

Bonus points for:

  • Cassandra experience
  • JavaScript
  • ReactJS
  • Python
  • Struts
  • JSP’s

Looking for an attitude of:

  • Passionate about building friendly, easy to use Interfaces and APIs.
  • Likes to work closely with other engineers, support, and sales to help customers.
  • Believes the whole world needs backup, not just English speakers in the USA.
  • Customer Focused (!!) — always focus on the customer’s point of view and how to solve their problem!

Required for all Backblaze Employees:

  • Good attitude and willingness to do whatever it takes to get the job done
  • Strong desire to work for a small, fast-paced company
  • Desire to learn and adapt to rapidly changing technologies and work environment
  • Rigorous adherence to best practices
  • Relentless attention to detail
  • Excellent interpersonal skills and good oral/written communication
  • Excellent troubleshooting and problem solving skills

This position is located in San Mateo, California but will also consider remote work as long as you’re no more than three time zones away and can come to San Mateo now and then.

Backblaze is an Equal Opportunity Employer.

If this sounds like you —follow these steps:

  1. Send an email to [email protected] with the position in the subject line.
  2. Include your resume.
  3. Tell us a bit about your programming experience.

The post Needed: Senior Software Engineer appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Wanted: Senior Systems Administrator

Post Syndicated from Yev original https://www.backblaze.com/blog/wanted-senior-systems-administrator/

Wanted: Senior Systems Administrator

We’re looking for someone who enjoys solving difficult problems, running down elusive tech gremlins, and improving our environment one server at a time. If you enjoy being stretched, learning new skills, and want to look forward to seeing your co-workers every day, then we want you!

Backblaze is a small (in headcount) cloud storage (and backup!) company with a big mission, bringing feature-rich and accessible services to the masses, even if they don’t have unlimited VC funding (because we don’t either)! We believe in a fun and positive work environment where people can learn and grow, and where a sense of community is not just a buzzword from a company handbook (though you might probably find it in there).

What You’ll Be Doing

  • Mastering your craft, becoming a subject matter expert, and acting as an escalation point for areas of expertise (this means responding to pages in your areas of ownership as well)
  • Leading projects across a range of IT operations disciplines
  • Developing a thorough understanding of the environment and the skills necessary to troubleshoot all systems and services
  • Collaborating closely with other teams (Engineering, Infrastructure, etc.) to build out new systems and improve existing ones
  • Participating in on-call rotation when necessary
  • Petting the office dogs when appropriate

What You Should Have

  • 5+ years of work as a Systems Administrator (or equivalent college degree)
  • Expert knowledge of Linux systems administration (Debian preferred)
  • Ability to work under pressure in a fast-paced startup environment
  • A passion for build and improving all manner of systems and services
  • Excellent problem solving, investigative, and troubleshooting skills
  • Strong interpersonal communication skills
  • Local enough to commute to San Mateo office

Highly Desirable Skills

  • Experience working at a technology/software startup
  • Configuration management and automation software (Ansible preferred)
  • Familiarity with server and storage system hardware and configurations
  • Understanding of Java servlet containers (Tomcat preferred)
  • Skill in administration of different software suites and cloud-based integrations (G Suite, PagerDuty, etc.)
  • Comprehension of standard web services and packages (WordPress, Apache, etc.)

Some Backblaze Perks

  • Generous healthcare plans
  • Competitive compensation and 401k
  • All employees receive Option grants
  • Unlimited vacation days
  • Strong coffee
  • Fully stocked Micro kitchens
  • Weekly catered breakfast and lunches
  • Awesome people who work on awesome projects
  • Childcare bonus (human children only)
  • Get to bring your (well behaved) pets into the office
  • Backblaze is an Equal Opportunity Employer and we offer competitive salary and benefits, including our no policy vacation policy

If this sounds like you — follow these steps:

  1. Send an email to jobscontact@backblaze.com with the position in the subject line.
  2. Include your resume.
  3. Tell us a bit about your experience and why you’re excited to work with Backblaze.

The post Wanted: Senior Systems Administrator appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Transition from Scratch to Python with FutureLearn

Post Syndicated from Dan Fisher original https://www.raspberrypi.org/blog/futurelearn-scratch-to-python/

With the launch of our first new free online course of 2018 — Scratch to Python: Moving from Block- to Text-based Programming — two weeks away, I thought this would be a great opportunity to introduce you to the ins and outs of the course content so you know what to expect.

FutureLearn: Moving from Scratch to Python

Learn how to apply the thinking and programming skills you’ve learnt in Scratch to text-based programming languages like Python.

Take the plunge into text-based programming

The idea for this course arose from our conversations with educators who had set up a Code Club in their schools. Most people start a club by teaching Scratch, a block-based programming language, because it allows learners to drag and drop blocks of pre-written code into a window to create a program. The blocks automatically snap together, making it easy to build fun and educational projects that don’t require much troubleshooting. You can do almost anything a beginner could wish for with Scratch, even physical computing to control LEDs, buzzers, buttons, motors, and more!

Scratch to Python FutureLearn Raspberry Pi

However, on our face-to-face training programme Picademy, educators told us that they were finding it hard to engage children who had outgrown Scratch and needed a new challenge. It was easy for me to imagine: a young learner, who once felt confident about programming using Scratch, is now confused by the alien, seemingly awkward interface of Python. What used to take them minutes in Scratch now takes them hours to code, and they start to lose interest — not a good result, I’m sure you’ll agree. I wanted to help educators to navigate this period in their learners’ development, and so I’ve written a course that shows you how to take the programming and thinking skills you and your learners have developed in Scratch, and apply them to Python.

Scratch to Python FutureLearn Raspberry Pi

Who is the course for?

Educators from all backgrounds who are working with secondary school-aged learners. It will also be interesting to anyone who has spent time working with Scratch and wants to understand how programming concepts translate between different languages.

“It was great fun, and I thought that the ideas and resources would be great to use with Year 7 classes.”
Sue Grey, Classroom Teacher

What is covered?

After showing you the similarities and differences of Scratch and Python, and how the skills learned using one can be applied to the other, we will look at turning more complex Scratch scripts into Python programs. Through creating a Mad Libs game and developing a username generator, you will see how programs can be simplified in a text-based language. We will give you our top tips for debugging Python code, and you’ll have the chance to share your ideas for introducing more complex programs to your students.

Scratch to Python FutureLearn Raspberry Pi

After that, we will look at different data types in Python and write a script to calculate how old you are in dog years. Finally, you’ll dive deeper into the possibilities of Python by installing and using external Python libraries to perform some amazing tasks.

By the end of the course, you’ll be able to:

  • Transfer programming and thinking skills from Scratch to Python
  • Use fundamental Python programming skills
  • Identify errors in your Python code based on error messages, and debug your scripts
  • Produce tools to support students’ transition from block-based to text-based programming
  • Understand the power of text-based programming and what you can create with it

Where can I sign up?

The free four-week course starts on 12 March 2018, and you can sign up now on FutureLearn. While you’re there, be sure to check out our other free courses, such as Prepare to Run a Code Club, Teaching Physical Computing with a Raspberry Pi and Python, and our second new course Build a Makerspace for Young People — more information on it will follow in tomorrow’s blog post.

The post Transition from Scratch to Python with FutureLearn appeared first on Raspberry Pi.

Community Profile: Estefannie Explains It All

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/community-profile-estefannie/

This column is from The MagPi issue 59. You can download a PDF of the full issue for free, or subscribe to receive the print edition through your letterbox or the digital edition on your tablet. All proceeds from the print and digital editions help the Raspberry Pi Foundation achieve our charitable goals.

“Hey, world!” Estefannie exclaims, a wide grin across her face as the camera begins to roll for another YouTube tutorial video. With a growing number of followers and wonderful support from her fans, Estefannie is building a solid reputation as an online maker, creating unique, fun content accessible to all.

A woman sitting at a desk with a laptop and papers — Estefannie Explains it All Raspberry Pi

It’s as if she was born into performing and making for an audience, but this fun, enjoyable journey to social media stardom came not from a desire to be in front of the camera, but rather as a unique approach to her own learning. While studying, Estefannie decided the best way to confirm her knowledge of a subject was to create an educational video explaining it. If she could teach a topic successfully, she knew she’d retained the information. And so her YouTube channel, Estefannie Explains It All, came into being.

Note taking — Estefannie Explains it All

Her first videos featured pages of notes with voice-over explanations of data structure and algorithm analysis. Then she moved in front of the camera, and expanded her skills in the process.

But YouTube isn’t her only outlet. With nearly 50000 followers, Estefannie’s Instagram game is strong, adding to an increasing number of female coders taking to the platform. Across her Instagram grid, you’ll find insights into her daily routine, from programming on location for work to behind-the-scenes troubleshooting as she begins to create another tutorial video. It’s hard work, with content creation for both Instagram and YouTube forever on her mind as she continues to work and progress successfully as a software engineer.

A woman showing off a game on a tablet — Estefannie Explains it All Raspberry Pi

As a thank you to her Instagram fans for helping her reach 10000 followers, Estefannie created a free game for Android and iOS called Gravitris — imagine Tetris with balance issues!

Estefannie was born and raised in Mexico, with ambitions to become a graphic designer and animator. However, a documentary on coding at Pixar, and the beauty of Merida’s hair in Brave, opened her mind to the opportunities of software engineering in animation. She altered her career path, moved to the United States, and switched to a Computer Science course.

A woman wearing safety goggles hugging a keyboard Estefannie Explains it All Raspberry Pi

With a constant desire to make and to learn, Estefannie combines her software engineering profession with her hobby to create fun, exciting content for YouTube.

While studying, Estefannie started a Computer Science Girls Club at the University of Houston, Texas, and she found herself eager to put more time and effort into the movement to increase the percentage of women in the industry. The club was a success, and still is to this day. While Estefannie has handed over the reins, she’s still very involved in the cause.

Through her YouTube videos, Estefannie continues the theme of inclusion, with every project offering a warm sense of approachability for all, regardless of age, gender, or skill. From exploring Scratch and Makey Makey with her young niece and nephew to creating her own Disney ‘Made with Magic’ backpack for a trip to Disney World, Florida, Estefannie’s videos are essentially a documentary of her own learning process, produced so viewers can learn with her — and learn from her mistakes — to create their own tech wonders.

Using the Raspberry Pi, she’s been able to broaden her skills and, in turn, her projects, creating a home-automated gingerbread house at Christmas, building a GPS-controlled GoPro for her trip to London, and making everyone’s life better with an Internet Button–controlled French press.

Estefannie Explains it All Raspberry Pi Home Automated Gingerbread House

Estefannie’s automated gingerbread house project was a labour of love, with electronics, wires, and candy strewn across both her living room and kitchen for weeks before completion. While she already was a skilled programmer, the world of physical digital making was still fairly new for Estefannie. Having ditched her hot glue gun in favour of a soldering iron in a previous video, she continued to experiment and try out new, interesting techniques that are now second nature to many members of the maker community. With the gingerbread house, Estefannie was able to research and apply techniques such as light controls, servos, and app making, although the latter was already firmly within her skill set. The result? A fun video of ups and downs that resulted in a wonderful, festive treat. She even gave her holiday home its own solar panel!

A DAY AT RASPBERRY PI TOWERS!! LINK IN BIO ⚡🎥 @raspberrypifoundation

1,910 Likes, 43 Comments – Estefannie Explains It All (@estefanniegg) on Instagram: “A DAY AT RASPBERRY PI TOWERS!! LINK IN BIO ⚡🎥 @raspberrypifoundation”

And that’s just the beginning of her adventures with Pi…but we won’t spoil her future plans by telling you what’s coming next. Sorry! However, since this article was written last year, Estefannie has released a few more Pi-based project videos, plus some awesome interviews and live-streams with other members of the maker community such as Simone Giertz. She even made us an awesome video for our Raspberry Pi YouTube channel! So be sure to check out her latest releases.

Best day yet!! I got to hangout, play Jenga with a huge arm robot, and have afternoon tea with @simonegiertz and robots!! 🤖👯 #shittyrobotnation

2,264 Likes, 56 Comments – Estefannie Explains It All (@estefanniegg) on Instagram: “Best day yet!! I got to hangout, play Jenga with a huge arm robot, and have afternoon tea with…”

While many wonderful maker videos show off a project without much explanation, or expect a certain level of skill from viewers hoping to recreate the project, Estefannie’s videos exist almost within their own category. We can’t wait to see where Estefannie Explains It All goes next!

The post Community Profile: Estefannie Explains It All appeared first on Raspberry Pi.

Troubleshooting event publishing issues in Amazon SES

Post Syndicated from Dustin Taylor original https://aws.amazon.com/blogs/ses/troubleshooting-event-publishing-issues-in-amazon-ses/

Over the past year, we’ve released several features that make it easier to track the metrics that are associated with your Amazon SES account. The first of these features, launched in November of last year, was event publishing.

Initially, event publishing let you capture basic metrics related to your email sending and publish them to other AWS services, such as Amazon CloudWatch and Amazon Kinesis Data Firehose. Some examples of these basic metrics include the number of emails that were sent and delivered, as well as the number that bounced or received complaints. A few months ago, we expanded this feature by adding engagement metrics—specifically, information about the number of emails that your customers opened or engaged with by clicking links.

As a former Cloud Support Engineer, I’ve seen Amazon SES customers do some amazing things with event publishing, but I’ve also seen some common issues. In this article, we look at some of these issues, and discuss the steps you can take to resolve them.

Before we begin

This post assumes that your Amazon SES account is already out of the sandbox, that you’ve verified an identity (such as an email address or domain), and that you have the necessary permissions to use Amazon SES and the service that you’ll publish event data to (such as Amazon SNS, CloudWatch, or Kinesis Data Firehose).

We also assume that you’re familiar with the process of creating configuration sets and specifying event destinations for those configuration sets. For more information, see Using Amazon SES Configuration Sets in the Amazon SES Developer Guide.

Amazon SNS event destinations

If you want to receive notifications when events occur—such as when recipients click a link in an email, or when they report an email as spam—you can use Amazon SNS as an event destination.

Occasionally, customers ask us why they’re not receiving notifications when they use an Amazon SNS topic as an event destination. One of the most common reasons for this issue is that they haven’t configured subscriptions for their Amazon SNS topic yet.

A single topic in Amazon SNS can have one or more subscriptions. When you subscribe to a topic, you tell that topic which endpoints (such as email addresses or mobile phone numbers) to contact when it receives a notification. If you haven’t set up any subscriptions, nothing will happen when an email event occurs.

For more information about setting up topics and subscriptions, see Getting Started in the Amazon SNS Developer Guide. For information about publishing Amazon SES events to Amazon SNS topics, see Set Up an Amazon SNS Event Destination for Amazon SES Event Publishing in the Amazon SES Developer Guide.

Kinesis Data Firehose event destinations

If you want to store your Amazon SES event data for the long term, choose Amazon Kinesis Data Firehose as a destination for Amazon SES events. With Kinesis Data Firehose, you can stream data to Amazon S3 or Amazon Redshift for storage and analysis.

The process of setting up Kinesis Data Firehose as an event destination is similar to the process for setting up Amazon SNS: you choose the types of events (such as deliveries, opens, clicks, or bounces) that you want to export, and the name of the Kinesis Data Firehose stream that you want to export to. However, there’s one important difference. When you set up a Kinesis Data Firehose event destination, you must also choose the IAM role that Amazon SES uses to send event data to Kinesis Data Firehose.

When you set up the Kinesis Data Firehose event destination, you can choose to have Amazon SES create the IAM role for you automatically. For many users, this is the best solution—it ensures that the IAM role has the appropriate permissions to move event data from Amazon SES to Kinesis Data Firehose.

Customers occasionally run into issues with the Kinesis Data Firehose event destination when they use an existing IAM role. If you use an existing IAM role, or create a new role for this purpose, make sure that the role includes the firehose:PutRecord and firehose:PutRecordBatch permissions. If the role doesn’t include these permissions, then the Amazon SES event data isn’t published to Kinesis Data Firehose. For more information, see Controlling Access with Amazon Kinesis Data Firehose in the Amazon Kinesis Data Firehose Developer Guide.

CloudWatch event destinations

By publishing your Amazon SES event data to Amazon CloudWatch, you can create dashboards that track your sending statistics in real time, as well as alarms that notify you when your event metrics reach certain thresholds.

The amount that you’re charged for using CloudWatch is based on several factors, including the number of metrics you use. In order to give you more control over the specific metrics you send to CloudWatch—and to help you avoid unexpected charges—you can limit the email sending events that are sent to CloudWatch.

When you choose CloudWatch as an event destination, you must choose a value source. The value source can be one of three options: a message tag, a link tag, or an email header. After you choose a value source, you then specify a name and a value. When you send an email using a configuration set that refers to a CloudWatch event destination, it only sends the metrics for that email to CloudWatch if the email contains the name and value that you specified as the value source. This requirement is commonly overlooked.

For example, assume that you chose Message Tag as the value source, and specified “CategoryId” as the dimension name and “31415” as the dimension value. When you want to send events for an email to CloudWatch, you must specify the name of the configuration set that uses the CloudWatch destination. You must also include a tag in your message. The name of the tag must be “CategoryId” and the value must be “31415”.

For more information about adding tags and email headers to your messages, see Send Email Using Amazon SES Event Publishing in the Amazon SES Developer Guide. For more information about adding tags to links, see Amazon SES Email Sending Metrics FAQs in the Amazon SES Developer Guide.

Troubleshooting event publishing for open and click data

Occasionally, customers ask why they’re not seeing open and click data for their emails. This issue most often occurs when the customer only sends text versions of their emails. Because of the way Amazon SES tracks open and click events, you can only see open and click data for emails that are sent as HTML. For more information about how Amazon SES modifies your emails when you enable open and click tracking, see Amazon SES Email Sending Metrics FAQs in the Amazon SES Developer Guide.

The process that you use to send HTML emails varies based on the email sending method you use. The Code Examples section of the Amazon SES Developer Guide contains examples of several methods of sending email by using the Amazon SES SMTP interface or an AWS SDK. All of the examples in this section include methods for sending HTML (as well as text-only) emails.

If you encounter any issues that weren’t covered in this post, please open a case in the Support Center and we’d be more than happy to assist.

Migrating .NET Classic Applications to Amazon ECS Using Windows Containers

Post Syndicated from Sundar Narasiman original https://aws.amazon.com/blogs/compute/migrating-net-classic-applications-to-amazon-ecs-using-windows-containers/

This post contributed by Sundar Narasiman, Arun Kannan, and Thomas Fuller.

AWS recently announced the general availability of Windows container management for Amazon Elastic Container Service (Amazon ECS). Docker containers and Amazon ECS make it easy to run and scale applications on a virtual machine by abstracting the complex cluster management and setup needed.

Classic .NET applications are developed with .NET Framework 4.7.1 or older and can run only on a Windows platform. These include Windows Communication Foundation (WCF), ASP.NET Web Forms, and an ASP.NET MVC web app or web API.

Why classic ASP.NET?

ASP.NET MVC 4.6 and older versions of ASP.NET occupy a significant footprint in the enterprise web application space. As enterprises move towards microservices for new or existing applications, containers are one of the stepping stones for migrating from monolithic to microservices architectures. Additionally, the support for Windows containers in Windows 10, Windows Server 2016, and Visual Studio Tooling support for Docker simplifies the containerization of ASP.NET MVC apps.

Getting started

In this post, you pick an ASP.NET 4.6.2 MVC application and get step-by-step instructions for migrating to ECS using Windows containers. The detailed steps, AWS CloudFormation template, Microsoft Visual Studio solution, ECS service definition, and ECS task definition are available in the aws-ecs-windows-aspnet GitHub repository.

To help you getting started running Windows containers, here is the reference architecture for Windows containers on GitHub: ecs-refarch-cloudformation-windows. This reference architecture is the layered CloudFormation stack, in that it calls the other stacks to create the environment. The CloudFormation YAML template in this reference architecture is referenced to create a single JSON CloudFormation stack, which is used in the steps for the migration.

Steps for Migration

The code and templates to implement this migration can be found on GitHub: https://github.com/aws-samples/aws-ecs-windows-aspnet.

  1. Your development environment needs to have the latest version and updates for Visual Studio 2017, Windows 10, and Docker for Windows Stable.
  2. Next, containerize the ASP.NET application and test it locally. The size of Windows container application images is generally larger compared to Linux containers. This is because the base image of the Windows container itself is large in size, typically greater than 9 GB.
  3. After the application is containerized, the container image needs to be pushed to Amazon Elastic Container Registry (Amazon ECR). Images stored in ECR are compressed to improve pull times and reduce storage costs. In this case, you can see that ECR compresses the image to around 1 GB, for an optimization factor of 90%.
  4. Create a CloudFormation stack using the template in the ‘CloudFormation template’ folder. This creates an ECS service, task definition (referring the containerized ASP.NET application), and other related components mentioned in the ECS reference architecture for Windows containers.
  5. After the stack is created, verify the successful creation of the ECS service, ECS instances, running tasks (with the threshold mentioned in the task definition), and the Application Load Balancer’s successful health check against running containers.
  6. Navigate to the Application Load Balancer URL and see the successful rendering of the containerized ASP.NET MVC app in the browser.

Key Notes

  • Generally, Windows container images occupy large amount of space (in the order of few GBs).
  • All the task definition parameters for Linux containers are not available for Windows containers. For more information, see Windows Task Definitions.
  • An Application Load Balancer can be configured to route requests to one or more ports on each container instance in a cluster. The dynamic port mapping allows you to have multiple tasks from a single service on the same container instance.
  • IAM roles for Windows tasks require extra configuration. For more information, see Windows IAM Roles for Tasks. For this post, configuration was handled by the CloudFormation template.
  • The ECS container agent log file can be accessed for troubleshooting Windows containers: C:\ProgramData\Amazon\ECS\log\ecs-agent.log

Summary

In this post, you migrated an ASP.NET MVC application to ECS using Windows containers.

The logical next step is to automate the activities for migration to ECS and build a fully automated continuous integration/continuous deployment (CI/CD) pipeline for Windows containers. This can be orchestrated by leveraging services such as AWS CodeCommit, AWS CodePipeline, AWS CodeBuild, Amazon ECR, and Amazon ECS. You can learn more about how this is done in the Set Up a Continuous Delivery Pipeline for Containers Using AWS CodePipeline and Amazon ECS post.

If you have questions or suggestions, please comment below.

Serverless @ re:Invent 2017

Post Syndicated from Chris Munns original https://aws.amazon.com/blogs/compute/serverless-reinvent-2017/

At re:Invent 2014, we announced AWS Lambda, what is now the center of the serverless platform at AWS, and helped ignite the trend of companies building serverless applications.

This year, at re:Invent 2017, the topic of serverless was everywhere. We were incredibly excited to see the energy from everyone attending 7 workshops, 15 chalk talks, 20 skills sessions and 27 breakout sessions. Many of these sessions were repeated due to high demand, so we are happy to summarize and provide links to the recordings and slides of these sessions.

Over the course of the week leading up to and then the week of re:Invent, we also had over 15 new features and capabilities across a number of serverless services, including AWS Lambda, Amazon API Gateway, AWS [email protected], AWS SAM, and the newly announced AWS Serverless Application Repository!

AWS Lambda

Amazon API Gateway

  • Amazon API Gateway Supports Endpoint Integrations with Private VPCs – You can now provide access to HTTP(S) resources within your VPC without exposing them directly to the public internet. This includes resources available over a VPN or Direct Connect connection!
  • Amazon API Gateway Supports Canary Release Deployments – You can now use canary release deployments to gradually roll out new APIs. This helps you more safely roll out API changes and limit the blast radius of new deployments.
  • Amazon API Gateway Supports Access Logging – The access logging feature lets you generate access logs in different formats such as CLF (Common Log Format), JSON, XML, and CSV. The access logs can be fed into your existing analytics or log processing tools so you can perform more in-depth analysis or take action in response to the log data.
  • Amazon API Gateway Customize Integration Timeouts – You can now set a custom timeout for your API calls as low as 50ms and as high as 29 seconds (the default is 30 seconds).
  • Amazon API Gateway Supports Generating SDK in Ruby – This is in addition to support for SDKs in Java, JavaScript, Android and iOS (Swift and Objective-C). The SDKs that Amazon API Gateway generates save you development time and come with a number of prebuilt capabilities, such as working with API keys, exponential back, and exception handling.

AWS Serverless Application Repository

Serverless Application Repository is a new service (currently in preview) that aids in the publication, discovery, and deployment of serverless applications. With it you’ll be able to find shared serverless applications that you can launch in your account, while also sharing ones that you’ve created for others to do the same.

AWS [email protected]

[email protected] now supports content-based dynamic origin selection, network calls from viewer events, and advanced response generation. This combination of capabilities greatly increases the use cases for [email protected], such as allowing you to send requests to different origins based on request information, showing selective content based on authentication, and dynamically watermarking images for each viewer.

AWS SAM

Twitch Launchpad live announcements

Other service announcements

Here are some of the other highlights that you might have missed. We think these could help you make great applications:

AWS re:Invent 2017 sessions

Coming up with the right mix of talks for an event like this can be quite a challenge. The Product, Marketing, and Developer Advocacy teams for Serverless at AWS spent weeks reading through dozens of talk ideas to boil it down to the final list.

From feedback at other AWS events and webinars, we knew that customers were looking for talks that focused on concrete examples of solving problems with serverless, how to perform common tasks such as deployment, CI/CD, monitoring, and troubleshooting, and to see customer and partner examples solving real world problems. To that extent we tried to settle on a good mix based on attendee experience and provide a track full of rich content.

Below are the recordings and slides of breakout sessions from re:Invent 2017. We’ve organized them for those getting started, those who are already beginning to build serverless applications, and the experts out there already running them at scale. Some of the videos and slides haven’t been posted yet, and so we will update this list as they become available.

Find the entire Serverless Track playlist on YouTube.

Talks for people new to Serverless

Advanced topics

Expert mode

Talks for specific use cases

Talks from AWS customers & partners

Looking to get hands-on with Serverless?

At re:Invent, we delivered instructor-led skills sessions to help attendees new to serverless applications get started quickly. The content from these sessions is already online and you can do the hands-on labs yourself!
Build a Serverless web application

Still looking for more?

We also recently completely overhauled the main Serverless landing page for AWS. This includes a new Resources page containing case studies, webinars, whitepapers, customer stories, reference architectures, and even more Getting Started tutorials. Check it out!

Now Available: A New AWS Quick Start Reference Deployment for CJIS

Post Syndicated from Emil Lerch original https://aws.amazon.com/blogs/security/now-available-a-new-aws-quick-start-reference-deployment-for-cjis/

CJIS logo

As part of the AWS Compliance Quick Start program, AWS has published a new Quick Start reference deployment for customers who need to align with Criminal Justice Information Services (CJIS) Security Policy 5.6 and process Criminal Justice Information (CJI) in accordance with this policy. The new Quick Start is AWS Enterprise Accelerator – Compliance: CJIS, and it makes it easier for you to address the list of supported controls you will find in the security controls matrix that accompanies the Quick Start.

As all AWS Quick Starts do, this Quick Start helps you automate the building of a recommended architecture that, when deployed as a package, provides a baseline AWS configuration. The Quick Start uses sets of nested AWS CloudFormation templates and user data scripts to create an example environment with a two-VPC, multi-tiered web service.

The new Quick Start also includes:

The recommended architecture built by the Quick Start supports a wide variety of AWS best practices (all of which are detailed in the Quick Start), including the use of multiple Availability Zones, isolation using public and private subnets, load balancing, and Auto Scaling.

The Quick Start package also includes a deployment guide with detailed instructions and a security controls matrix that describes how the deployment addresses CJIS Security Policy 5.6 controls. You should have your IT security assessors and risk decision makers review the security controls matrix so that they can understand the extent of the implementation of the controls within the architecture. The matrix also identifies the specific resources in the CloudFormation templates that affect each control, and contains cross-references to the CJIS Security Policy 5.6 security controls.

If you have questions about this new Quick Start, contact the AWS Compliance Quick Start team. For more information about the AWS CJIS program, see CJIS Compliance.

– Emil

Easier Certificate Validation Using DNS with AWS Certificate Manager

Post Syndicated from Todd Cignetti original https://aws.amazon.com/blogs/security/easier-certificate-validation-using-dns-with-aws-certificate-manager/

Secure Sockets Layer/Transport Layer Security (SSL/TLS) certificates are used to secure network communications and establish the identity of websites over the internet. Before issuing a certificate for your website, Amazon must validate that you control the domain name for your site. You can now use AWS Certificate Manager (ACM) Domain Name System (DNS) validation to establish that you control a domain name when requesting SSL/TLS certificates with ACM. Previously ACM supported only email validation, which required the domain owner to receive an email for each certificate request and validate the information in the request before approving it.

With DNS validation, you write a CNAME record to your DNS configuration to establish control of your domain name. After you have configured the CNAME record, ACM can automatically renew DNS-validated certificates before they expire, as long as the DNS record has not changed. To make it even easier to validate your domain, ACM can update your DNS configuration for you if you manage your DNS records with Amazon Route 53. In this blog post, I demonstrate how to request a certificate for a website by using DNS validation. To perform the equivalent steps using the AWS CLI or AWS APIs and SDKs, see AWS Certificate Manager in the AWS CLI Reference and the ACM API Reference.

Requesting an SSL/TLS certificate by using DNS validation

In this section, I walk you through the four steps required to obtain an SSL/TLS certificate through ACM to identify your site over the internet. SSL/TLS provides encryption for sensitive data in transit and authentication by using certificates to establish the identity of your site and secure connections between browsers and applications and your site. DNS validation and SSL/TLS certificates provisioned through ACM are free.

Step 1: Request a certificate

To get started, sign in to the AWS Management Console and navigate to the ACM console. Choose Get started to request a certificate.

Screenshot of getting started in the ACM console

If you previously managed certificates in ACM, you will instead see a table with your certificates and a button to request a new certificate. Choose Request a certificate to request a new certificate.

Screenshot of choosing "Request a certificate"

Type the name of your domain in the Domain name box and choose Next. In this example, I type www.example.com. You must use a domain name that you control. Requesting certificates for domains that you don’t control violates the AWS Service Terms.

Screenshot of entering a domain name

Step 2: Select a validation method

With DNS validation, you write a CNAME record to your DNS configuration to establish control of your domain name. Choose DNS validation, and then choose Review.

Screenshot of selecting validation method

Step 3: Review your request

Review your request and choose Confirm and request to request the certificate.

Screenshot of reviewing request and confirming it

Step 4: Submit your request

After a brief delay while ACM populates your domain validation information, choose the down arrow (highlighted in the following screenshot) to display all the validation information for your domain.

Screenshot of validation information

ACM displays the CNAME record you must add to your DNS configuration to validate that you control the domain name in your certificate request. If you use a DNS provider other than Route 53 or if you use a different AWS account to manage DNS records in Route 53, copy the DNS CNAME information from the validation information, or export it to a file (choose Export DNS configuration to a file) and write it to your DNS configuration. For information about how to add or modify DNS records, check with your DNS provider. For more information about using DNS with Route 53 DNS, see the Route 53 documentation.

If you manage DNS records for your domain with Route 53 in the same AWS account, choose Create record in Route 53 to have ACM update your DNS configuration for you.

After updating your DNS configuration, choose Continue to return to the ACM table view.

ACM then displays a table that includes all your certificates. The certificate you requested is displayed so that you can see the status of your request. After you write the DNS record or have ACM write the record for you, it typically takes DNS 30 minutes to propagate the record, and it might take several hours for Amazon to validate it and issue the certificate. During this time, ACM shows the Validation status as Pending validation. After ACM validates the domain name, ACM updates the Validation status to Success. After the certificate is issued, the certificate status is updated to Issued. If ACM cannot validate your DNS record and issue the certificate after 72 hours, the request times out, and ACM displays a Timed out validation status. To recover, you must make a new request. Refer to the Troubleshooting Section of the ACM User Guide for instructions about troubleshooting validation or issuance failures.

Screenshot of a certificate issued and validation successful

You now have an ACM certificate that you can use to secure your application or website. For information about how to deploy certificates with other AWS services, see the documentation for Amazon CloudFront, Amazon API Gateway, Application Load Balancers, and Classic Load Balancers. Note that your certificate must be in the US East (N. Virginia) Region to use the certificate with CloudFront.

ACM automatically renews certificates that are deployed and in use with other AWS services as long as the CNAME record remains in your DNS configuration. To learn more about ACM DNS validation, see the ACM FAQs and the ACM documentation.

If you have comments about this post, submit them in the “Comments” section below. If you have questions about this blog post, start a new thread on the ACM forum or contact AWS Support.

– Todd

Ultimate 3D printer control with OctoPrint

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/octoprint/

Control and monitor your 3D printer remotely with a Raspberry Pi and OctoPrint.

Timelapse of OctoPrint Ornament

Printed on a bq Witbox STL file can be found here: http://www.thingiverse.com/thing:191635 OctoPrint is located here: http://www.octoprint.org

3D printing

Whether you have a 3D printer at home or use one at your school or local makerspace, it’s fair to assume you’ve had a failed print or two in your time. Filament knotting or running out, your print peeling away from the print bed — these are common issues for all 3D printing enthusiasts, and they can be costly if they’re discovered too late.

OctoPrint

OctoPrint is a free open-source software, created and maintained by Gina Häußge, that performs a multitude of useful 3D printing–related tasks, including remote control of your printer, live video, and data collection.

The OctoPrint logo

Control and monitoring

To control the print process, use OctoPrint on a Raspberry Pi connected to your 3D printer. First, ensure a safe uninterrupted run by using the software to restrict who can access the printer. Then, before starting your print, use the web app to work on your STL file. The app also allows you to reposition the print head at any time, as well as pause or stop printing if needed.

Live video streaming

Since OctoPrint can stream video of your print as it happens, you can watch out for any faults that may require you to abort and restart. Proud of your print? Record the entire process from start to finish and upload the time-lapse video to your favourite social media platform.

OctoPrint software graphic user interface screenshot

Data capture

Octoprint records real-time data, such as the temperature, giving you another way to monitor your print to ensure a smooth, uninterrupted process. Moreover, the records will help with troubleshooting if there is a problem.

OctoPrint software graphic user interface screenshot

Print the Millenium Falcon

OK, you can print anything you like. However, this design definitely caught our eye this week.

3D-Printed Fillenium Malcon (Timelapse)

This is a Timelapse of my biggest print project so far on my own designed/built printer. It’s 500x170x700(mm) and weights 3 Kilograms of Filament.

You can support the work of Gina and OctoPrint by visiting her Patreon account and following OctoPrint on Twitter, Facebook, or G+. And if you’ve set up a Raspberry Pi to run OctoPrint, or you’ve created some cool Pi-inspired 3D prints, make sure to share them with us on our own social media channels.

The post Ultimate 3D printer control with OctoPrint appeared first on Raspberry Pi.

Event-Driven Computing with Amazon SNS and AWS Compute, Storage, Database, and Networking Services

Post Syndicated from Christie Gifrin original https://aws.amazon.com/blogs/compute/event-driven-computing-with-amazon-sns-compute-storage-database-and-networking-services/

Contributed by Otavio Ferreira, Manager, Software Development, AWS Messaging

Like other developers around the world, you may be tackling increasingly complex business problems. A key success factor, in that case, is the ability to break down a large project scope into smaller, more manageable components. A service-oriented architecture guides you toward designing systems as a collection of loosely coupled, independently scaled, and highly reusable services. Microservices take this even further. To improve performance and scalability, they promote fine-grained interfaces and lightweight protocols.

However, the communication among isolated microservices can be challenging. Services are often deployed onto independent servers and don’t share any compute or storage resources. Also, you should avoid hard dependencies among microservices, to preserve maintainability and reusability.

If you apply the pub/sub design pattern, you can effortlessly decouple and independently scale out your microservices and serverless architectures. A pub/sub messaging service, such as Amazon SNS, promotes event-driven computing that statically decouples event publishers from subscribers, while dynamically allowing for the exchange of messages between them. An event-driven architecture also introduces the responsiveness needed to deal with complex problems, which are often unpredictable and asynchronous.

What is event-driven computing?

Given the context of microservices, event-driven computing is a model in which subscriber services automatically perform work in response to events triggered by publisher services. This paradigm can be applied to automate workflows while decoupling the services that collectively and independently work to fulfil these workflows. Amazon SNS is an event-driven computing hub, in the AWS Cloud, that has native integration with several AWS publisher and subscriber services.

Which AWS services publish events to SNS natively?

Several AWS services have been integrated as SNS publishers and, therefore, can natively trigger event-driven computing for a variety of use cases. In this post, I specifically cover AWS compute, storage, database, and networking services, as depicted below.

Compute services

  • Auto Scaling: Helps you ensure that you have the correct number of Amazon EC2 instances available to handle the load for your application. You can configure Auto Scaling lifecycle hooks to trigger events, as Auto Scaling resizes your EC2 cluster.As an example, you may want to warm up the local cache store on newly launched EC2 instances, and also download log files from other EC2 instances that are about to be terminated. To make this happen, set an SNS topic as your Auto Scaling group’s notification target, then subscribe two Lambda functions to this SNS topic. The first function is responsible for handling scale-out events (to warm up cache upon provisioning), whereas the second is in charge of handling scale-in events (to download logs upon termination).

  • AWS Elastic Beanstalk: An easy-to-use service for deploying and scaling web applications and web services developed in a number of programming languages. You can configure event notifications for your Elastic Beanstalk environment so that notable events can be automatically published to an SNS topic, then pushed to topic subscribers.As an example, you may use this event-driven architecture to coordinate your continuous integration pipeline (such as Jenkins CI). That way, whenever an environment is created, Elastic Beanstalk publishes this event to an SNS topic, which triggers a subscribing Lambda function, which then kicks off a CI job against your newly created Elastic Beanstalk environment.

  • Elastic Load Balancing: Automatically distributes incoming application traffic across Amazon EC2 instances, containers, or other resources identified by IP addresses.You can configure CloudWatch alarms on Elastic Load Balancing metrics, to automate the handling of events derived from Classic Load Balancers. As an example, you may leverage this event-driven design to automate latency profiling in an Amazon ECS cluster behind a Classic Load Balancer. In this example, whenever your ECS cluster breaches your load balancer latency threshold, an event is posted by CloudWatch to an SNS topic, which then triggers a subscribing Lambda function. This function runs a task on your ECS cluster to trigger a latency profiling tool, hosted on the cluster itself. This can enhance your latency troubleshooting exercise by making it timely.

Storage services

  • Amazon S3: Object storage built to store and retrieve any amount of data.You can enable S3 event notifications, and automatically get them posted to SNS topics, to automate a variety of workflows. For instance, imagine that you have an S3 bucket to store incoming resumes from candidates, and a fleet of EC2 instances to encode these resumes from their original format (such as Word or text) into a portable format (such as PDF).In this example, whenever new files are uploaded to your input bucket, S3 publishes these events to an SNS topic, which in turn pushes these messages into subscribing SQS queues. Then, encoding workers running on EC2 instances poll these messages from the SQS queues; retrieve the original files from the input S3 bucket; encode them into PDF; and finally store them in an output S3 bucket.

  • Amazon EFS: Provides simple and scalable file storage, for use with Amazon EC2 instances, in the AWS Cloud.You can configure CloudWatch alarms on EFS metrics, to automate the management of your EFS systems. For example, consider a highly parallelized genomics analysis application that runs against an EFS system. By default, this file system is instantiated on the “General Purpose” performance mode. Although this performance mode allows for lower latency, it might eventually impose a scaling bottleneck. Therefore, you may leverage an event-driven design to handle it automatically.Basically, as soon as the EFS metric “Percent I/O Limit” breaches 95%, CloudWatch could post this event to an SNS topic, which in turn would push this message into a subscribing Lambda function. This function automatically creates a new file system, this time on the “Max I/O” performance mode, then switches the genomics analysis application to this new file system. As a result, your application starts experiencing higher I/O throughput rates.

  • Amazon Glacier: A secure, durable, and low-cost cloud storage service for data archiving and long-term backup.You can set a notification configuration on an Amazon Glacier vault so that when a job completes, a message is published to an SNS topic. Retrieving an archive from Amazon Glacier is a two-step asynchronous operation, in which you first initiate a job, and then download the output after the job completes. Therefore, SNS helps you eliminate polling your Amazon Glacier vault to check whether your job has been completed, or not. As usual, you may subscribe SQS queues, Lambda functions, and HTTP endpoints to your SNS topic, to be notified when your Amazon Glacier job is done.

  • AWS Snowball: A petabyte-scale data transport solution that uses secure appliances to transfer large amounts of data.You can leverage Snowball notifications to automate workflows related to importing data into and exporting data from AWS. More specifically, whenever your Snowball job status changes, Snowball can publish this event to an SNS topic, which in turn can broadcast the event to all its subscribers.As an example, imagine a Geographic Information System (GIS) that distributes high-resolution satellite images to users via Web browser. In this example, the GIS vendor could capture up to 80 TB of satellite images; create a Snowball job to import these files from an on-premises system to an S3 bucket; and provide an SNS topic ARN to be notified upon job status changes in Snowball. After Snowball changes the job status from “Importing” to “Completed”, Snowball publishes this event to the specified SNS topic, which delivers this message to a subscribing Lambda function, which finally creates a CloudFront web distribution for the target S3 bucket, to serve the images to end users.

Database services

  • Amazon RDS: Makes it easy to set up, operate, and scale a relational database in the cloud.RDS leverages SNS to broadcast notifications when RDS events occur. As usual, these notifications can be delivered via any protocol supported by SNS, including SQS queues, Lambda functions, and HTTP endpoints.As an example, imagine that you own a social network website that has experienced organic growth, and needs to scale its compute and database resources on demand. In this case, you could provide an SNS topic to listen to RDS DB instance events. When the “Low Storage” event is published to the topic, SNS pushes this event to a subscribing Lambda function, which in turn leverages the RDS API to increase the storage capacity allocated to your DB instance. The provisioning itself takes place within the specified DB maintenance window.

  • Amazon ElastiCache: A web service that makes it easy to deploy, operate, and scale an in-memory data store or cache in the cloud.ElastiCache can publish messages using Amazon SNS when significant events happen on your cache cluster. This feature can be used to refresh the list of servers on client machines connected to individual cache node endpoints of a cache cluster. For instance, an ecommerce website fetches product details from a cache cluster, with the goal of offloading a relational database and speeding up page load times. Ideally, you want to make sure that each web server always has an updated list of cache servers to which to connect.To automate this node discovery process, you can get your ElastiCache cluster to publish events to an SNS topic. Thus, when ElastiCache event “AddCacheNodeComplete” is published, your topic then pushes this event to all subscribing HTTP endpoints that serve your ecommerce website, so that these HTTP servers can update their list of cache nodes.

  • Amazon Redshift: A fully managed data warehouse that makes it simple to analyze data using standard SQL and BI (Business Intelligence) tools.Amazon Redshift uses SNS to broadcast relevant events so that data warehouse workflows can be automated. As an example, imagine a news website that sends clickstream data to a Kinesis Firehose stream, which then loads the data into Amazon Redshift, so that popular news and reading preferences might be surfaced on a BI tool. At some point though, this Amazon Redshift cluster might need to be resized, and the cluster enters a ready-only mode. Hence, this Amazon Redshift event is published to an SNS topic, which delivers this event to a subscribing Lambda function, which finally deletes the corresponding Kinesis Firehose delivery stream, so that clickstream data uploads can be put on hold.At a later point, after Amazon Redshift publishes the event that the maintenance window has been closed, SNS notifies a subscribing Lambda function accordingly, so that this function can re-create the Kinesis Firehose delivery stream, and resume clickstream data uploads to Amazon Redshift.

  • AWS DMS: Helps you migrate databases to AWS quickly and securely. The source database remains fully operational during the migration, minimizing downtime to applications that rely on the database.DMS also uses SNS to provide notifications when DMS events occur, which can automate database migration workflows. As an example, you might create data replication tasks to migrate an on-premises MS SQL database, composed of multiple tables, to MySQL. Thus, if replication tasks fail due to incompatible data encoding in the source tables, these events can be published to an SNS topic, which can push these messages into a subscribing SQS queue. Then, encoders running on EC2 can poll these messages from the SQS queue, encode the source tables into a compatible character set, and restart the corresponding replication tasks in DMS. This is an event-driven approach to a self-healing database migration process.

Networking services

  • Amazon Route 53: A highly available and scalable cloud-based DNS (Domain Name System). Route 53 health checks monitor the health and performance of your web applications, web servers, and other resources.You can set CloudWatch alarms and get automated Amazon SNS notifications when the status of your Route 53 health check changes. As an example, imagine an online payment gateway that reports the health of its platform to merchants worldwide, via a status page. This page is hosted on EC2 and fetches platform health data from DynamoDB. In this case, you could configure a CloudWatch alarm for your Route 53 health check, so that when the alarm threshold is breached, and the payment gateway is no longer considered healthy, then CloudWatch publishes this event to an SNS topic, which pushes this message to a subscribing Lambda function, which finally updates the DynamoDB table that populates the status page. This event-driven approach avoids any kind of manual update to the status page visited by merchants.

  • AWS Direct Connect (AWS DX): Makes it easy to establish a dedicated network connection from your premises to AWS, which can reduce your network costs, increase bandwidth throughput, and provide a more consistent network experience than Internet-based connections.You can monitor physical DX connections using CloudWatch alarms, and send SNS messages when alarms change their status. As an example, when a DX connection state shifts to 0 (zero), indicating that the connection is down, this event can be published to an SNS topic, which can fan out this message to impacted servers through HTTP endpoints, so that they might reroute their traffic through a different connection instead. This is an event-driven approach to connectivity resilience.

More event-driven computing on AWS

In addition to SNS, event-driven computing is also addressed by Amazon CloudWatch Events, which delivers a near real-time stream of system events that describe changes in AWS resources. With CloudWatch Events, you can route each event type to one or more targets, including:

Many AWS services publish events to CloudWatch. As an example, you can get CloudWatch Events to capture events on your ETL (Extract, Transform, Load) jobs running on AWS Glue and push failed ones to an SQS queue, so that you can retry them later.

Conclusion

Amazon SNS is a pub/sub messaging service that can be used as an event-driven computing hub to AWS customers worldwide. By capturing events natively triggered by AWS services, such as EC2, S3 and RDS, you can automate and optimize all kinds of workflows, namely scaling, testing, encoding, profiling, broadcasting, discovery, failover, and much more. Business use cases presented in this post ranged from recruiting websites, to scientific research, geographic systems, social networks, retail websites, and news portals.

Start now by visiting Amazon SNS in the AWS Management Console, or by trying the AWS 10-Minute Tutorial, Send Fan-out Event Notifications with Amazon SNS and Amazon SQS.

 

Bringing Datacenter-Scale Hardware-Software Co-design to the Cloud with FireSim and Amazon EC2 F1 Instances

Post Syndicated from Mia Champion original https://aws.amazon.com/blogs/compute/bringing-datacenter-scale-hardware-software-co-design-to-the-cloud-with-firesim-and-amazon-ec2-f1-instances/

The recent addition of Xilinx FPGAs to AWS Cloud compute offerings is one way that AWS is enabling global growth in the areas of advanced analytics, deep learning and AI. The customized F1 servers use pooled accelerators, enabling interconnectivity of up to 8 FPGAs, each one including 64 GiB DDR4 ECC protected memory, with a dedicated PCIe x16 connection. That makes this a powerful engine with the capacity to process advanced analytical applications at scale, at a significantly faster rate. For example, AWS commercial partner Edico Genome is able to achieve an approximately 30X speedup in analyzing whole genome sequencing datasets using their DRAGEN platform powered with F1 instances.

While the availability of FPGA F1 compute on-demand provides clear accessibility and cost advantages, many mainstream users are still finding that the “threshold to entry” in developing or running FPGA-accelerated simulations is too high. Researchers at the UC Berkeley RISE Lab have developed “FireSim”, powered by Amazon FPGA F1 instances as an open-source resource, FireSim lowers that entry bar and makes it easier for everyone to leverage the power of an FPGA-accelerated compute environment. Whether you are part of a small start-up development team or working at a large datacenter scale, hardware-software co-design enables faster time-to-deployment, lower costs, and more predictable performance. We are excited to feature FireSim in this post from Sagar Karandikar and his colleagues at UC-Berkeley.

―Mia Champion, Sr. Data Scientist, AWS

Mapping an 8-node FireSim cluster simulation to Amazon EC2 F1

As traditional hardware scaling nears its end, the data centers of tomorrow are trending towards heterogeneity, employing custom hardware accelerators and increasingly high-performance interconnects. Prototyping new hardware at scale has traditionally been either extremely expensive, or very slow. In this post, I introduce FireSim, a new hardware simulation platform under development in the computer architecture research group at UC Berkeley that enables fast, scalable hardware simulation using Amazon EC2 F1 instances.

FireSim benefits both hardware and software developers working on new rack-scale systems: software developers can use the simulated nodes with new hardware features as they would use a real machine, while hardware developers have full control over the hardware being simulated and can run real software stacks while hardware is still under development. In conjunction with this post, we’re releasing the first public demo of FireSim, which lets you deploy your own 8-node simulated cluster on an F1 Instance and run benchmarks against it. This demo simulates a pre-built “vanilla” cluster, but demonstrates FireSim’s high performance and usability.

Why FireSim + F1?

FPGA-accelerated hardware simulation is by no means a new concept. However, previous attempts to use FPGAs for simulation have been fraught with usability, scalability, and cost issues. FireSim takes advantage of EC2 F1 and open-source hardware to address the traditional problems with FPGA-accelerated simulation:
Problem #1: FPGA-based simulations have traditionally been expensive, difficult to deploy, and difficult to reproduce.
FireSim uses public-cloud infrastructure like F1, which means no upfront cost to purchase and deploy FPGAs. Developers and researchers can distribute pre-built AMIs and AFIs, as in this public demo (more details later in this post), to make experiments easy to reproduce. FireSim also automates most of the work involved in deploying an FPGA simulation, essentially enabling one-click conversion from new RTL to deploying on an FPGA cluster.

Problem #2: FPGA-based simulations have traditionally been difficult (and expensive) to scale.
Because FireSim uses F1, users can scale out experiments by spinning up additional EC2 instances, rather than spending hundreds of thousands of dollars on large FPGA clusters.

Problem #3: Finding open hardware to simulate has traditionally been difficult. Finding open hardware that can run real software stacks is even harder.
FireSim simulates RocketChip, an open, silicon-proven, RISC-V-based processor platform, and adds peripherals like a NIC and disk device to build up a realistic system. Processors that implement RISC-V automatically support real operating systems (such as Linux) and even support applications like Apache and Memcached. We provide a custom Buildroot-based FireSim Linux distribution that runs on our simulated nodes and includes many popular developer tools.

Problem #4: Writing hardware in traditional HDLs is time-consuming.
Both FireSim and RocketChip use the Chisel HDL, which brings modern programming paradigms to hardware description languages. Chisel greatly simplifies the process of building large, highly parameterized hardware components.

How to use FireSim for hardware/software co-design

FireSim drastically improves the process of co-designing hardware and software by acting as a push-button interface for collaboration between hardware developers and systems software developers. The following diagram describes the workflows that hardware and software developers use when working with FireSim.

Figure 2. The FireSim custom hardware development workflow.

The hardware developer’s view:

  1. Write custom RTL for your accelerator, peripheral, or processor modification in a productive language like Chisel.
  2. Run a software simulation of your hardware design in standard gate-level simulation tools for early-stage debugging.
  3. Run FireSim build scripts, which automatically build your simulation, run it through the Vivado toolchain/AWS shell scripts, and publish an AFI.
  4. Deploy your simulation on EC2 F1 using the generated simulation driver and AFI
  5. Run real software builds released by software developers to benchmark your hardware

The software developer’s view:

  1. Deploy the AMI/AFI generated by the hardware developer on an F1 instance to simulate a cluster of nodes (or scale out to many F1 nodes for larger simulated core-counts).
  2. Connect using SSH into the simulated nodes in the cluster and boot the Linux distribution included with FireSim. This distribution is easy to customize, and already supports many standard software packages.
  3. Directly prototype your software using the same exact interfaces that the software will see when deployed on the real future system you’re prototyping, with the same performance characteristics as observed from software, even at scale.

FireSim demo v1.0

Figure 3. Cluster topology simulated by FireSim demo v1.0.

This first public demo of FireSim focuses on the aforementioned “software-developer’s view” of the custom hardware development cycle. The demo simulates a cluster of 1 to 8 RocketChip-based nodes, interconnected by a functional network simulation. The simulated nodes work just like “real” machines:  they boot Linux, you can connect to them using SSH, and you can run real applications on top. The nodes can see each other (and the EC2 F1 instance on which they’re deployed) on the network and communicate with one another. While the demo currently simulates a pre-built “vanilla” cluster, the entire hardware configuration of these simulated nodes can be modified after FireSim is open-sourced.

In this post, I walk through bringing up a single-node FireSim simulation for experienced EC2 F1 users. For more detailed instructions for new users and instructions for running a larger 8-node simulation, see FireSim Demo v1.0 on Amazon EC2 F1. Both demos walk you through setting up an instance from a demo AMI/AFI and booting Linux on the simulated nodes. The full demo instructions also walk you through an example workload, running Memcached on the simulated nodes, with YCSB as a load generator to demonstrate network functionality.

Deploying the demo on F1

In this release, we provide pre-built binaries for driving simulation from the host and a pre-built AFI that contains the FPGA infrastructure necessary to simulate a RocketChip-based node.

Starting your F1 instances

First, launch an instance using the free FireSim Demo v1.0 product available on the AWS Marketplace on an f1.2xlarge instance. After your instance has booted, log in using the user name centos. On the first login, you should see the message “FireSim network config completed.” This sets up the necessary tap interfaces and bridge on the EC2 instance to enable communicating with the simulated nodes.

AMI contents

The AMI contains a variety of tools to help you run simulations and build software for RISC-V systems, including the riscv64 toolchain, a Buildroot-based Linux distribution that runs on the simulated nodes, and the simulation driver program. For more details, see the AMI Contents section on the FireSim website.

Single-node demo

First, you need to flash the FPGA with the FireSim AFI. To do so, run:

[[email protected]_ADDR ~]$ sudo fpga-load-local-image -S 0 -I agfi-00a74c2d615134b21

To start a simulation, run the following at the command line:

[[email protected]_ADDR ~]$ boot-firesim-singlenode

This automatically calls the simulation driver, telling it to load the Linux kernel image and root filesystem for the Linux distro. This produces output similar to the following:

Simulations Started. You can use the UART console of each simulated node by attaching to the following screens:

There is a screen on:

2492.fsim0      (Detached)

1 Socket in /var/run/screen/S-centos.

You could connect to the simulated UART console by connecting to this screen, but instead opt to use SSH to access the node instead.

First, ping the node to make sure it has come online. This is currently required because nodes may get stuck at Linux boot if the NIC does not receive any network traffic. For more information, see Troubleshooting/Errata. The node is always assigned the IP address 192.168.1.10:

[[email protected]_ADDR ~]$ ping 192.168.1.10

This should eventually produce the following output:

PING 192.168.1.10 (192.168.1.10) 56(84) bytes of data.

From 192.168.1.1 icmp_seq=1 Destination Host Unreachable

64 bytes from 192.168.1.10: icmp_seq=1 ttl=64 time=2017 ms

64 bytes from 192.168.1.10: icmp_seq=2 ttl=64 time=1018 ms

64 bytes from 192.168.1.10: icmp_seq=3 ttl=64 time=19.0 ms

At this point, you know that the simulated node is online. You can connect to it using SSH with the user name root and password firesim. It is also convenient to make sure that your TERM variable is set correctly. In this case, the simulation expects TERM=linux, so provide that:

[[email protected]_ADDR ~]$ TERM=linux ssh [email protected]

The authenticity of host ‘192.168.1.10 (192.168.1.10)’ can’t be established.

ECDSA key fingerprint is 63:e9:66:d0:5c:06:2c:1d:5c:95:33:c8:36:92:30:49.

Are you sure you want to continue connecting (yes/no)? yes

Warning: Permanently added ‘192.168.1.10’ (ECDSA) to the list of known hosts.

[email protected]’s password:

#

At this point, you’re connected to the simulated node. Run uname -a as an example. You should see the following output, indicating that you’re connected to a RISC-V system:

# uname -a

Linux buildroot 4.12.0-rc2 #1 Fri Aug 4 03:44:55 UTC 2017 riscv64 GNU/Linux

Now you can run programs on the simulated node, as you would with a real machine. For an example workload (running YCSB against Memcached on the simulated node) or to run a larger 8-node simulation, see the full FireSim Demo v1.0 on Amazon EC2 F1 demo instructions.

Finally, when you are finished, you can shut down the simulated node by running the following command from within the simulated node:

# poweroff

You can confirm that the simulation has ended by running screen -ls, which should now report that there are no detached screens.

Future plans

At Berkeley, we’re planning to keep improving the FireSim platform to enable our own research in future data center architectures, like FireBox. The FireSim platform will eventually support more sophisticated processors, custom accelerators (such as Hwacha), network models, and peripherals, in addition to scaling to larger numbers of FPGAs. In the future, we’ll open source the entire platform, including Midas, the tool used to transform RTL into FPGA simulators, allowing users to modify any part of the hardware/software stack. Follow @firesimproject on Twitter to stay tuned to future FireSim updates.

Acknowledgements

FireSim is the joint work of many students and faculty at Berkeley: Sagar Karandikar, Donggyu Kim, Howard Mao, David Biancolin, Jack Koenig, Jonathan Bachrach, and Krste Asanović. This work is partially funded by AWS through the RISE Lab, by the Intel Science and Technology Center for Agile HW Design, and by ASPIRE Lab sponsors and affiliates Intel, Google, HPE, Huawei, NVIDIA, and SK hynix.

Automating Security Group Updates with AWS Lambda

Post Syndicated from Ian Scofield original https://aws.amazon.com/blogs/compute/automating-security-group-updates-with-aws-lambda/

Customers often use public endpoints to perform cross-region replication or other application layer communication to remote regions. But a common problem is how do you protect these endpoints? It can be tempting to open up the security groups to the world due to the complexity of keeping security groups in sync across regions with a dynamically changing infrastructure.

Consider a situation where you are running large clusters of instances in different regions that all require internode connectivity. One approach would be to use a VPN tunnel between regions to provide a secure tunnel over which to send your traffic. A good example of this is the Transit VPC Solution, which is a published AWS solution to help customers quickly get up and running. However, this adds additional cost and complexity to your solution due to the newly required additional infrastructure.

Another approach, which I’ll explore in this post, is to restrict access to the nodes by whitelisting the public IP addresses of your hosts in the opposite region. Today, I’ll outline a solution that allows for cross-region security group updates, can handle remote region failures, and supports external actions such as manually terminating instances or adding instances to an existing Auto Scaling group.

Solution overview

The overview of this solution is diagrammed below. Although this post covers limiting access to your instances, you should still implement encryption to protect your data in transit.

If your entire infrastructure is running in a single region, you can reference a security group as the source, allowing your IP addresses to change without any updates required. However, if you’re going across the public internet between regions to perform things like application-level traffic or cross-region replication, this is no longer an option. Security groups are regional. When you go across regions it can be tempting to drop security to enable this communication.

Although using an Elastic IP address can provide you with a static IP address that you can define as a source for your security groups, this may not always be feasible, especially when automatic scaling is desired.

In this example scenario, you have a distributed database that requires full internode communication for replication. If you place a cluster in us-east-1 and us-west-2, you must provide a secure method of communication between the two. Because the database uses cloud best practices, you can add or remove nodes as the load varies.

To start the process of updating your security groups, you must know when an instance has come online to trigger your workflow. Auto Scaling groups have the concept of lifecycle hooks that enable you to perform custom actions as the group launches or terminates instances.

When Auto Scaling begins to launch or terminate an instance, it puts the instance into a wait state (Pending:Wait or Terminating:Wait). The instance remains in this state while you perform your various actions until either you tell Auto Scaling to Continue, Abandon, or the timeout period ends. A lifecycle hook can trigger a CloudWatch event, publish to an Amazon SNS topic, or send to an Amazon SQS queue. For this example, you use CloudWatch Events to trigger an AWS Lambda function that updates an Amazon DynamoDB table.

Component breakdown

Here’s a quick breakdown of the components involved in this solution:

• Lambda function
• CloudWatch event
• DynamoDB table

Lambda function

The Lambda function automatically updates your security groups, in the following way:

1. Determines whether a change was triggered by your Auto Scaling group lifecycle hook or manually invoked for a “true up” functionality, which I discuss later in this post.
2. Describes the instances in the Auto Scaling group and obtain public IP addresses for each instance.
3. Updates both local and remote DynamoDB tables.
4. Compares the list of public IP addresses for both local and remote clusters with what’s already in the local region security group. Update the security group.
5. Compares the list of public IP addresses for both local and remote clusters with what’s already in the remote region security group. Update the security group
6. Signals CONTINUE back to the lifecycle hook.

CloudWatch event

The CloudWatch event triggers when an instance passes through either the launching or terminating states. When the Lambda function gets invoked, it receives an event that looks like the following:

{
	"account": "123456789012",
	"region": "us-east-1",
	"detail": {
		"LifecycleHookName": "hook-launching",
		"AutoScalingGroupName": "",
		"LifecycleActionToken": "33965228-086a-4aeb-8c26-f82ed3bef495",
		"LifecycleTransition": "autoscaling:EC2_INSTANCE_LAUNCHING",
		"EC2InstanceId": "i-017425ec54f22f994"
	},
	"detail-type": "EC2 Instance-launch Lifecycle Action",
	"source": "aws.autoscaling",
	"version": "0",
	"time": "2017-05-03T02:20:59Z",
	"id": "cb930cf8-ce8b-4b6c-8011-af17966eb7e2",
	"resources": [
		"arn:aws:autoscaling:us-east-1:123456789012:autoScalingGroup:d3fe9d96-34d0-4c62-b9bb-293a41ba3765:autoScalingGroupName/"
	]
}

DynamoDB table

You use DynamoDB to store lists of remote IP addresses in a local table that is updated by the opposite region as a failsafe source of truth. Although you can describe your Auto Scaling group for the local region, you must maintain a list of IP addresses for the remote region.

To minimize the number of describe calls and prevent an issue in the remote region from blocking your local scaling actions, we keep a list of the remote IP addresses in a local DynamoDB table. Each Lambda function in each region is responsible for updating the public IP addresses of its Auto Scaling group for both the local and remote tables.

As with all the infrastructure in this solution, there is a DynamoDB table in both regions that mirror each other. For example, the following screenshot shows a sample DynamoDB table. The Lambda function in us-east-1 would update the DynamoDB entry for us-east-1 in both tables in both regions.

By updating a DynamoDB table in both regions, it allows the local region to gracefully handle issues with the remote region, which would otherwise prevent your ability to scale locally. If the remote region becomes inaccessible, you have a copy of the latest configuration from the table that you can use to continue to sync with your security groups. When the remote region comes back online, it pushes its updated public IP addresses to the DynamoDB table. The security group is updated to reflect the current status by the remote Lambda function.

 

Walkthrough

Note: All of the following steps are performed in both regions. The Launch Stack buttons will default to the us-east-1 region.

Here’s a quick overview of the steps involved in this process:

1. An instance is launched or terminated, which triggers an Auto Scaling group lifecycle hook, triggering the Lambda function via CloudWatch Events.
2. The Lambda function retrieves the list of public IP addresses for all instances in the local region Auto Scaling group.
3. The Lambda function updates the local and remote region DynamoDB tables with the public IP addresses just received for the local Auto Scaling group.
4. The Lambda function updates the local region security group with the public IP addresses, removing and adding to ensure that it mirrors what is present for the local and remote Auto Scaling groups.
5. The Lambda function updates the remote region security group with the public IP addresses, removing and adding to ensure that it mirrors what is present for the local and remote Auto Scaling groups.

Prerequisites

To deploy this solution, you need to have Auto Scaling groups, launch configurations, and a base security group in both regions. To expedite this process, this CloudFormation template can be launched in both regions.

Step 1: Launch the AWS SAM template in the first region

To make the deployment process easy, I’ve created an AWS Serverless Application Model (AWS SAM) template, which is a new specification that makes it easier to manage and deploy serverless applications on AWS. This template creates the following resources:

• A Lambda function, to perform the various security group actions
• A DynamoDB table, to track the state of the local and remote Auto Scaling groups
• Auto Scaling group lifecycle hooks for instance launching and terminating
• A CloudWatch event, to track the EC2 Instance-Launch Lifecycle-Action and EC2 Instance-terminate Lifecycle-Action events
• A pointer from the CloudWatch event to the Lambda function, and the necessary permissions

Download the template from here or click to launch.

Upon launching the template, you’ll be presented with a list of parameters which includes the remote/local names for your Auto Scaling Groups, AWS region, Security Group IDs, DynamoDB table names, as well as where the code for the Lambda function is located. Because this is the first region you’re launching the stack in, fill out all the parameters except for the RemoteTable parameter as it hasn’t been created yet (you fill this in later).

Step 2: Test the local region

After the stack has finished launching, you can test the local region. Open the EC2 console and find the Auto Scaling group that was created when launching the prerequisite stack. Change the desired number of instances from 0 to 1.

For both regions, check your security group to verify that the public IP address of the instance created is now in the security group.

Local region:

Remote region:

Now, change the desired number of instances for your group back to 0 and verify that the rules are properly removed.

Local region:

Remote region:

Step 3: Launch in the remote region

When you deploy a Lambda function using CloudFormation, the Lambda zip file needs to reside in the same region you are launching the template. Once you choose your remote region, create an Amazon S3 bucket and upload the Lambda zip file there. Next, go to the remote region and launch the same SAM template as before, but make sure you update the CodeBucket and CodeKey parameters. Also, because this is the second launch, you now have all the values and can fill out all the parameters, specifically the RemoteTable value.

 

Step 4: Update the local region Lambda environment variable

When you originally launched the template in the local region, you didn’t have the name of the DynamoDB table for the remote region, because you hadn’t created it yet. Now that you have launched the remote template, you can perform a CloudFormation stack update on the initial SAM template. This populates the remote DynamoDB table name into the initial Lambda function’s environment variables.

In the CloudFormation console in the initial region, select the stack. Under Actions, choose Update Stack, and select the SAM template used for both regions. Under Parameters, populate the remote DynamoDB table name, as shown below. Choose Next and let the stack update complete. This updates your Lambda function and completes the setup process.

 

Step 5: Final testing

You now have everything fully configured and in place to trigger security group changes based on instances being added or removed to your Auto Scaling groups in both regions. Test this by changing the desired capacity of your group in both regions.

True up functionality
If an instance is manually added or removed from the Auto Scaling group, the lifecycle hooks don’t get triggered. To account for this, the Lambda function supports a “true up” functionality in which the function can be manually invoked. If you paste in the following JSON text for your test event, it kicks off the entire workflow. For added peace of mind, you can also have this function fire via a CloudWatch event with a CRON expression for nearly continuous checking.

{
	"detail": {
		"AutoScalingGroupName": "<your ASG name>"
	},
	"trueup":true
}

Extra credit

Now that all the resources are created in both regions, go back and break down the policy to incorporate resource-level permissions for specific security groups, Auto Scaling groups, and the DynamoDB tables.

Although this post is centered around using public IP addresses for your instances, you could instead use a VPN between regions. In this case, you would still be able to use this solution to scope down the security groups to the cluster instances. However, the code would need to be modified to support private IP addresses.

 

Conclusion

At this point, you now have a mechanism in place that captures when a new instance is added to or removed from your cluster and updates the security groups in both regions. This ensures that you are locking down your infrastructure securely by allowing access only to other cluster members.

Keep in mind that this architecture (lifecycle hooks, CloudWatch event, Lambda function, and DynamoDB table) requires that the infrastructure to be deployed in both regions, to have synchronization going both ways.

Because this Lambda function is modifying security group rules, it’s important to have an audit log of what has been modified and who is modifying them. The out-of-the-box function provides logs in CloudWatch for what IP addresses are being added and removed for which ports. As these are all API calls being made, they are logged in CloudTrail and can be traced back to the IAM role that you created for your lifecycle hooks. This can provide historical data that can be used for troubleshooting or auditing purposes.

Security is paramount at AWS. We want to ensure that customers are protecting access to their resources. This solution helps you keep your security groups in both regions automatically in sync with your Auto Scaling group resources. Let us know if you have any questions or other solutions you’ve come up with!

Using AWS Step Functions State Machines to Handle Workflow-Driven AWS CodePipeline Actions

Post Syndicated from Marcilio Mendonca original https://aws.amazon.com/blogs/devops/using-aws-step-functions-state-machines-to-handle-workflow-driven-aws-codepipeline-actions/

AWS CodePipeline is a continuous integration and continuous delivery service for fast and reliable application and infrastructure updates. It offers powerful integration with other AWS services, such as AWS CodeBuildAWS CodeDeployAWS CodeCommit, AWS CloudFormation and with third-party tools such as Jenkins and GitHub. These services make it possible for AWS customers to successfully automate various tasks, including infrastructure provisioning, blue/green deployments, serverless deployments, AMI baking, database provisioning, and release management.

Developers have been able to use CodePipeline to build sophisticated automation pipelines that often require a single CodePipeline action to perform multiple tasks, fork into different execution paths, and deal with asynchronous behavior. For example, to deploy a Lambda function, a CodePipeline action might first inspect the changes pushed to the code repository. If only the Lambda code has changed, the action can simply update the Lambda code package, create a new version, and point the Lambda alias to the new version. If the changes also affect infrastructure resources managed by AWS CloudFormation, the pipeline action might have to create a stack or update an existing one through the use of a change set. In addition, if an update is required, the pipeline action might enforce a safety policy to infrastructure resources that prevents the deletion and replacement of resources. You can do this by creating a change set and having the pipeline action inspect its changes before updating the stack. Change sets that do not conform to the policy are deleted.

This use case is a good illustration of workflow-driven pipeline actions. These are actions that run multiple tasks, deal with async behavior and loops, need to maintain and propagate state, and fork into different execution paths. Implementing workflow-driven actions directly in CodePipeline can lead to complex pipelines that are hard for developers to understand and maintain. Ideally, a pipeline action should perform a single task and delegate the complexity of dealing with workflow-driven behavior associated with that task to a state machine engine. This would make it possible for developers to build simpler, more intuitive pipelines and allow them to use state machine execution logs to visualize and troubleshoot their pipeline actions.

In this blog post, we discuss how AWS Step Functions state machines can be used to handle workflow-driven actions. We show how a CodePipeline action can trigger a Step Functions state machine and how the pipeline and the state machine are kept decoupled through a Lambda function. The advantages of using state machines include:

  • Simplified logic (complex tasks are broken into multiple smaller tasks).
  • Ease of handling asynchronous behavior (through state machine wait states).
  • Built-in support for choices and processing different execution paths (through state machine choices).
  • Built-in visualization and logging of the state machine execution.

The source code for the sample pipeline, pipeline actions, and state machine used in this post is available at https://github.com/awslabs/aws-codepipeline-stepfunctions.

Overview

This figure shows the components in the CodePipeline-Step Functions integration that will be described in this post. The pipeline contains two stages: a Source stage represented by a CodeCommit Git repository and a Prod stage with a single Deploy action that represents the workflow-driven action.

This action invokes a Lambda function (1) called the State Machine Trigger Lambda, which, in turn, triggers a Step Function state machine to process the request (2). The Lambda function sends a continuation token back to the pipeline (3) to continue its execution later and terminates. Seconds later, the pipeline invokes the Lambda function again (4), passing the continuation token received. The Lambda function checks the execution state of the state machine (5,6) and communicates the status to the pipeline. The process is repeated until the state machine execution is complete. Then the Lambda function notifies the pipeline that the corresponding pipeline action is complete (7). If the state machine has failed, the Lambda function will then fail the pipeline action and stop its execution (7). While running, the state machine triggers various Lambda functions to perform different tasks. The state machine and the pipeline are fully decoupled. Their interaction is handled by the Lambda function.

The Deploy State Machine

The sample state machine used in this post is a simplified version of the use case, with emphasis on infrastructure deployment. The state machine will follow distinct execution paths and thus have different outcomes, depending on:

  • The current state of the AWS CloudFormation stack.
  • The nature of the code changes made to the AWS CloudFormation template and pushed into the pipeline.

If the stack does not exist, it will be created. If the stack exists, a change set will be created and its resources inspected by the state machine. The inspection consists of parsing the change set results and detecting whether any resources will be deleted or replaced. If no resources are being deleted or replaced, the change set is allowed to be executed and the state machine completes successfully. Otherwise, the change set is deleted and the state machine completes execution with a failure as the terminal state.

Let’s dive into each of these execution paths.

Path 1: Create a Stack and Succeed Deployment

The Deploy state machine is shown here. It is triggered by the Lambda function using the following input parameters stored in an S3 bucket.

Create New Stack Execution Path

{
    "environmentName": "prod",
    "stackName": "sample-lambda-app",
    "templatePath": "infra/Lambda-template.yaml",
    "revisionS3Bucket": "codepipeline-us-east-1-418586629775",
    "revisionS3Key": "StepFunctionsDrivenD/CodeCommit/sjcmExZ"
}

Note that some values used here are for the use case example only. Account-specific parameters like revisionS3Bucket and revisionS3Key will be different when you deploy this use case in your account.

These input parameters are used by various states in the state machine and passed to the corresponding Lambda functions to perform different tasks. For example, stackName is used to create a stack, check the status of stack creation, and create a change set. The environmentName represents the environment (for example, dev, test, prod) to which the code is being deployed. It is used to prefix the name of stacks and change sets.

With the exception of built-in states such as wait and choice, each state in the state machine invokes a specific Lambda function.  The results received from the Lambda invocations are appended to the state machine’s original input. When the state machine finishes its execution, several parameters will have been added to its original input.

The first stage in the state machine is “Check Stack Existence”. It checks whether a stack with the input name specified in the stackName input parameter already exists. The output of the state adds a Boolean value called doesStackExist to the original state machine input as follows:

{
  "doesStackExist": true,
  "environmentName": "prod",
  "stackName": "sample-lambda-app",
  "templatePath": "infra/lambda-template.yaml",
  "revisionS3Bucket": "codepipeline-us-east-1-418586629775",
  "revisionS3Key": "StepFunctionsDrivenD/CodeCommit/sjcmExZ",
}

The following stage, “Does Stack Exist?”, is represented by Step Functions built-in choice state. It checks the value of doesStackExist to determine whether a new stack needs to be created (doesStackExist=true) or a change set needs to be created and inspected (doesStackExist=false).

If the stack does not exist, the states illustrated in green in the preceding figure are executed. This execution path creates the stack, waits until the stack is created, checks the status of the stack’s creation, and marks the deployment successful after the stack has been created. Except for “Stack Created?” and “Wait Stack Creation,” each of these stages invokes a Lambda function. “Stack Created?” and “Wait Stack Creation” are implemented by using the built-in choice state (to decide which path to follow) and the wait state (to wait a few seconds before proceeding), respectively. Each stage adds the results of their Lambda function executions to the initial input of the state machine, allowing future stages to process them.

Path 2: Safely Update a Stack and Mark Deployment as Successful

Safely Update a Stack and Mark Deployment as Successful Execution Path

If the stack indicated by the stackName parameter already exists, a different path is executed. (See the green states in the figure.) This path will create a change set and use wait and choice states to wait until the change set is created. Afterwards, a stage in the execution path will inspect  the resources affected before the change set is executed.

The inspection procedure represented by the “Inspect Change Set Changes” stage consists of parsing the resources affected by the change set and checking whether any of the existing resources are being deleted or replaced. The following is an excerpt of the algorithm, where changeSetChanges.Changes is the object representing the change set changes:

...
var RESOURCES_BEING_DELETED_OR_REPLACED = "RESOURCES-BEING-DELETED-OR-REPLACED";
var CAN_SAFELY_UPDATE_EXISTING_STACK = "CAN-SAFELY-UPDATE-EXISTING-STACK";
for (var i = 0; i < changeSetChanges.Changes.length; i++) {
    var change = changeSetChanges.Changes[i];
    if (change.Type == "Resource") {
        if (change.ResourceChange.Action == "Delete") {
            return RESOURCES_BEING_DELETED_OR_REPLACED;
        }
        if (change.ResourceChange.Action == "Modify") {
            if (change.ResourceChange.Replacement == "True") {
                return RESOURCES_BEING_DELETED_OR_REPLACED;
            }
        }
    }
}
return CAN_SAFELY_UPDATE_EXISTING_STACK;

The algorithm returns different values to indicate whether the change set can be safely executed (CAN_SAFELY_UPDATE_EXISTING_STACK or RESOURCES_BEING_DELETED_OR_REPLACED). This value is used later by the state machine to decide whether to execute the change set and update the stack or interrupt the deployment.

The output of the “Inspect Change Set” stage is shown here.

{
  "environmentName": "prod",
  "stackName": "sample-lambda-app",
  "templatePath": "infra/lambda-template.yaml",
  "revisionS3Bucket": "codepipeline-us-east-1-418586629775",
  "revisionS3Key": "StepFunctionsDrivenD/CodeCommit/sjcmExZ",
  "doesStackExist": true,
  "changeSetName": "prod-sample-lambda-app-change-set-545",
  "changeSetCreationStatus": "complete",
  "changeSetAction": "CAN-SAFELY-UPDATE-EXISTING-STACK"
}

At this point, these parameters have been added to the state machine’s original input:

  • changeSetName, which is added by the “Create Change Set” state.
  • changeSetCreationStatus, which is added by the “Get Change Set Creation Status” state.
  • changeSetAction, which is added by the “Inspect Change Set Changes” state.

The “Safe to Update Infra?” step is a choice state (its JSON spec follows) that simply checks the value of the changeSetAction parameter. If the value is equal to “CAN-SAFELY-UPDATE-EXISTING-STACK“, meaning that no resources will be deleted or replaced, the step will execute the change set by proceeding to the “Execute Change Set” state. The deployment is successful (the state machine completes its execution successfully).

"Safe to Update Infra?": {
      "Type": "Choice",
      "Choices": [
        {
          "Variable": "$.taskParams.changeSetAction",
          "StringEquals": "CAN-SAFELY-UPDATE-EXISTING-STACK",
          "Next": "Execute Change Set"
        }
      ],
      "Default": "Deployment Failed"
 }

Path 3: Reject Stack Update and Fail Deployment

Reject Stack Update and Fail Deployment Execution Path

If the changeSetAction parameter is different from “CAN-SAFELY-UPDATE-EXISTING-STACK“, the state machine will interrupt the deployment by deleting the change set and proceeding to the “Deployment Fail” step, which is a built-in Fail state. (Its JSON spec follows.) This state causes the state machine to stop in a failed state and serves to indicate to the Lambda function that the pipeline deployment should be interrupted in a fail state as well.

 "Deployment Failed": {
      "Type": "Fail",
      "Cause": "Deployment Failed",
      "Error": "Deployment Failed"
    }

In all three scenarios, there’s a state machine’s visual representation available in the AWS Step Functions console that makes it very easy for developers to identify what tasks have been executed or why a deployment has failed. Developers can also inspect the inputs and outputs of each state and look at the state machine Lambda function’s logs for details. Meanwhile, the corresponding CodePipeline action remains very simple and intuitive for developers who only need to know whether the deployment was successful or failed.

The State Machine Trigger Lambda Function

The Trigger Lambda function is invoked directly by the Deploy action in CodePipeline. The CodePipeline action must pass a JSON structure to the trigger function through the UserParameters attribute, as follows:

{
  "s3Bucket": "codepipeline-StepFunctions-sample",
  "stateMachineFile": "state_machine_input.json"
}

The s3Bucket parameter specifies the S3 bucket location for the state machine input parameters file. The stateMachineFile parameter specifies the file holding the input parameters. By being able to specify different input parameters to the state machine, we make the Trigger Lambda function and the state machine reusable across environments. For example, the same state machine could be called from a test and prod pipeline action by specifying a different S3 bucket or state machine input file for each environment.

The Trigger Lambda function performs two main tasks: triggering the state machine and checking the execution state of the state machine. Its core logic is shown here:

exports.index = function (event, context, callback) {
    try {
        console.log("Event: " + JSON.stringify(event));
        console.log("Context: " + JSON.stringify(context));
        console.log("Environment Variables: " + JSON.stringify(process.env));
        if (Util.isContinuingPipelineTask(event)) {
            monitorStateMachineExecution(event, context, callback);
        }
        else {
            triggerStateMachine(event, context, callback);
        }
    }
    catch (err) {
        failure(Util.jobId(event), callback, context.invokeid, err.message);
    }
}

Util.isContinuingPipelineTask(event) is a utility function that checks if the Trigger Lambda function is being called for the first time (that is, no continuation token is passed by CodePipeline) or as a continuation of a previous call. In its first execution, the Lambda function will trigger the state machine and send a continuation token to CodePipeline that contains the state machine execution ARN. The state machine ARN is exposed to the Lambda function through a Lambda environment variable called stateMachineArn. Here is the code that triggers the state machine:

function triggerStateMachine(event, context, callback) {
    var stateMachineArn = process.env.stateMachineArn;
    var s3Bucket = Util.actionUserParameter(event, "s3Bucket");
    var stateMachineFile = Util.actionUserParameter(event, "stateMachineFile");
    getStateMachineInputData(s3Bucket, stateMachineFile)
        .then(function (data) {
            var initialParameters = data.Body.toString();
            var stateMachineInputJSON = createStateMachineInitialInput(initialParameters, event);
            console.log("State machine input JSON: " + JSON.stringify(stateMachineInputJSON));
            return stateMachineInputJSON;
        })
        .then(function (stateMachineInputJSON) {
            return triggerStateMachineExecution(stateMachineArn, stateMachineInputJSON);
        })
        .then(function (triggerStateMachineOutput) {
            var continuationToken = { "stateMachineExecutionArn": triggerStateMachineOutput.executionArn };
            var message = "State machine has been triggered: " + JSON.stringify(triggerStateMachineOutput) + ", continuationToken: " + JSON.stringify(continuationToken);
            return continueExecution(Util.jobId(event), continuationToken, callback, message);
        })
        .catch(function (err) {
            console.log("Error triggering state machine: " + stateMachineArn + ", Error: " + err.message);
            failure(Util.jobId(event), callback, context.invokeid, err.message);
        })
}

The Trigger Lambda function fetches the state machine input parameters from an S3 file, triggers the execution of the state machine using the input parameters and the stateMachineArn environment variable, and signals to CodePipeline that the execution should continue later by passing a continuation token that contains the state machine execution ARN. In case any of these operations fail and an exception is thrown, the Trigger Lambda function will fail the pipeline immediately by signaling a pipeline failure through the putJobFailureResult CodePipeline API.

If the Lambda function is continuing a previous execution, it will extract the state machine execution ARN from the continuation token and check the status of the state machine, as shown here.

function monitorStateMachineExecution(event, context, callback) {
    var stateMachineArn = process.env.stateMachineArn;
    var continuationToken = JSON.parse(Util.continuationToken(event));
    var stateMachineExecutionArn = continuationToken.stateMachineExecutionArn;
    getStateMachineExecutionStatus(stateMachineExecutionArn)
        .then(function (response) {
            if (response.status === "RUNNING") {
                var message = "Execution: " + stateMachineExecutionArn + " of state machine: " + stateMachineArn + " is still " + response.status;
                return continueExecution(Util.jobId(event), continuationToken, callback, message);
            }
            if (response.status === "SUCCEEDED") {
                var message = "Execution: " + stateMachineExecutionArn + " of state machine: " + stateMachineArn + " has: " + response.status;
                return success(Util.jobId(event), callback, message);
            }
            // FAILED, TIMED_OUT, ABORTED
            var message = "Execution: " + stateMachineExecutionArn + " of state machine: " + stateMachineArn + " has: " + response.status;
            return failure(Util.jobId(event), callback, context.invokeid, message);
        })
        .catch(function (err) {
            var message = "Error monitoring execution: " + stateMachineExecutionArn + " of state machine: " + stateMachineArn + ", Error: " + err.message;
            failure(Util.jobId(event), callback, context.invokeid, message);
        });
}

If the state machine is in the RUNNING state, the Lambda function will send the continuation token back to the CodePipeline action. This will cause CodePipeline to call the Lambda function again a few seconds later. If the state machine has SUCCEEDED, then the Lambda function will notify the CodePipeline action that the action has succeeded. In any other case (FAILURE, TIMED-OUT, or ABORT), the Lambda function will fail the pipeline action.

This behavior is especially useful for developers who are building and debugging a new state machine because a bug in the state machine can potentially leave the pipeline action hanging for long periods of time until it times out. The Trigger Lambda function prevents this.

Also, by having the Trigger Lambda function as a means to decouple the pipeline and state machine, we make the state machine more reusable. It can be triggered from anywhere, not just from a CodePipeline action.

The Pipeline in CodePipeline

Our sample pipeline contains two simple stages: the Source stage represented by a CodeCommit Git repository and the Prod stage, which contains the Deploy action that invokes the Trigger Lambda function. When the state machine decides that the change set created must be rejected (because it replaces or deletes some the existing production resources), it fails the pipeline without performing any updates to the existing infrastructure. (See the failed Deploy action in red.) Otherwise, the pipeline action succeeds, indicating that the existing provisioned infrastructure was either created (first run) or updated without impacting any resources. (See the green Deploy stage in the pipeline on the left.)

The Pipeline in CodePipeline

The JSON spec for the pipeline’s Prod stage is shown here. We use the UserParameters attribute to pass the S3 bucket and state machine input file to the Lambda function. These parameters are action-specific, which means that we can reuse the state machine in another pipeline action.

{
  "name": "Prod",
  "actions": [
      {
          "inputArtifacts": [
              {
                  "name": "CodeCommitOutput"
              }
          ],
          "name": "Deploy",
          "actionTypeId": {
              "category": "Invoke",
              "owner": "AWS",
              "version": "1",
              "provider": "Lambda"
          },
          "outputArtifacts": [],
          "configuration": {
              "FunctionName": "StateMachineTriggerLambda",
              "UserParameters": "{\"s3Bucket\": \"codepipeline-StepFunctions-sample\", \"stateMachineFile\": \"state_machine_input.json\"}"
          },
          "runOrder": 1
      }
  ]
}

Conclusion

In this blog post, we discussed how state machines in AWS Step Functions can be used to handle workflow-driven actions. We showed how a Lambda function can be used to fully decouple the pipeline and the state machine and manage their interaction. The use of a state machine greatly simplified the associated CodePipeline action, allowing us to build a much simpler and cleaner pipeline while drilling down into the state machine’s execution for troubleshooting or debugging.

Here are two exercises you can complete by using the source code.

Exercise #1: Do not fail the state machine and pipeline action after inspecting a change set that deletes or replaces resources. Instead, create a stack with a different name (think of blue/green deployments). You can do this by creating a state machine transition between the “Safe to Update Infra?” and “Create Stack” stages and passing a new stack name as input to the “Create Stack” stage.

Exercise #2: Add wait logic to the state machine to wait until the change set completes its execution before allowing the state machine to proceed to the “Deployment Succeeded” stage. Use the stack creation case as an example. You’ll have to create a Lambda function (similar to the Lambda function that checks the creation status of a stack) to get the creation status of the change set.

Have fun and share your thoughts!

About the Author

Marcilio Mendonca is a Sr. Consultant in the Canadian Professional Services Team at Amazon Web Services. He has helped AWS customers design, build, and deploy best-in-class, cloud-native AWS applications using VMs, containers, and serverless architectures. Before he joined AWS, Marcilio was a Software Development Engineer at Amazon. Marcilio also holds a Ph.D. in Computer Science. In his spare time, he enjoys playing drums, riding his motorcycle in the Toronto GTA area, and spending quality time with his family.

Implementing Default Directory Indexes in Amazon S3-backed Amazon CloudFront Origins Using [email protected]

Post Syndicated from Ronnie Eichler original https://aws.amazon.com/blogs/compute/implementing-default-directory-indexes-in-amazon-s3-backed-amazon-cloudfront-origins-using-lambdaedge/

With the recent launch of [email protected], it’s now possible for you to provide even more robust functionality to your static websites. Amazon CloudFront is a content distribution network service. In this post, I show how you can use [email protected] along with the CloudFront origin access identity (OAI) for Amazon S3 and still provide simple URLs (such as www.example.com/about/ instead of www.example.com/about/index.html).

Background

Amazon S3 is a great platform for hosting a static website. You don’t need to worry about managing servers or underlying infrastructure—you just publish your static to content to an S3 bucket. S3 provides a DNS name such as <bucket-name>.s3-website-<AWS-region>.amazonaws.com. Use this name for your website by creating a CNAME record in your domain’s DNS environment (or Amazon Route 53) as follows:

www.example.com -> <bucket-name>.s3-website-<AWS-region>.amazonaws.com

You can also put CloudFront in front of S3 to further scale the performance of your site and cache the content closer to your users. CloudFront can enable HTTPS-hosted sites, by either using a custom Secure Sockets Layer (SSL) certificate or a managed certificate from AWS Certificate Manager. In addition, CloudFront also offers integration with AWS WAF, a web application firewall. As you can see, it’s possible to achieve some robust functionality by using S3, CloudFront, and other managed services and not have to worry about maintaining underlying infrastructure.

One of the key concerns that you might have when implementing any type of WAF or CDN is that you want to force your users to go through the CDN. If you implement CloudFront in front of S3, you can achieve this by using an OAI. However, in order to do this, you cannot use the HTTP endpoint that is exposed by S3’s static website hosting feature. Instead, CloudFront must use the S3 REST endpoint to fetch content from your origin so that the request can be authenticated using the OAI. This presents some challenges in that the REST endpoint does not support redirection to a default index page.

CloudFront does allow you to specify a default root object (index.html), but it only works on the root of the website (such as http://www.example.com > http://www.example.com/index.html). It does not work on any subdirectory (such as http://www.example.com/about/). If you were to attempt to request this URL through CloudFront, CloudFront would do a S3 GetObject API call against a key that does not exist.

Of course, it is a bad user experience to expect users to always type index.html at the end of every URL (or even know that it should be there). Until now, there has not been an easy way to provide these simpler URLs (equivalent to the DirectoryIndex Directive in an Apache Web Server configuration) to users through CloudFront. Not if you still want to be able to restrict access to the S3 origin using an OAI. However, with the release of [email protected], you can use a JavaScript function running on the CloudFront edge nodes to look for these patterns and request the appropriate object key from the S3 origin.

Solution

In this example, you use the compute power at the CloudFront edge to inspect the request as it’s coming in from the client. Then re-write the request so that CloudFront requests a default index object (index.html in this case) for any request URI that ends in ‘/’.

When a request is made against a web server, the client specifies the object to obtain in the request. You can use this URI and apply a regular expression to it so that these URIs get resolved to a default index object before CloudFront requests the object from the origin. Use the following code:

'use strict';
exports.handler = (event, context, callback) => {
    
    // Extract the request from the CloudFront event that is sent to [email protected] 
    var request = event.Records[0].cf.request;

    // Extract the URI from the request
    var olduri = request.uri;

    // Match any '/' that occurs at the end of a URI. Replace it with a default index
    var newuri = olduri.replace(/\/$/, '\/index.html');
    
    // Log the URI as received by CloudFront and the new URI to be used to fetch from origin
    console.log("Old URI: " + olduri);
    console.log("New URI: " + newuri);
    
    // Replace the received URI with the URI that includes the index page
    request.uri = newuri;
    
    // Return to CloudFront
    return callback(null, request);

};

To get started, create an S3 bucket to be the origin for CloudFront:

Create bucket

On the other screens, you can just accept the defaults for the purposes of this walkthrough. If this were a production implementation, I would recommend enabling bucket logging and specifying an existing S3 bucket as the destination for access logs. These logs can be useful if you need to troubleshoot issues with your S3 access.

Now, put some content into your S3 bucket. For this walkthrough, create two simple webpages to demonstrate the functionality:  A page that resides at the website root, and another that is in a subdirectory.

<s3bucketname>/index.html

<!doctype html>
<html>
    <head>
        <meta charset="utf-8">
        <title>Root home page</title>
    </head>
    <body>
        <p>Hello, this page resides in the root directory.</p>
    </body>
</html>

<s3bucketname>/subdirectory/index.html

<!doctype html>
<html>
    <head>
        <meta charset="utf-8">
        <title>Subdirectory home page</title>
    </head>
    <body>
        <p>Hello, this page resides in the /subdirectory/ directory.</p>
    </body>
</html>

When uploading the files into S3, you can accept the defaults. You add a bucket policy as part of the CloudFront distribution creation that allows CloudFront to access the S3 origin. You should now have an S3 bucket that looks like the following:

Root of bucket

Subdirectory in bucket

Next, create a CloudFront distribution that your users will use to access the content. Open the CloudFront console, and choose Create Distribution. For Select a delivery method for your content, under Web, choose Get Started.

On the next screen, you set up the distribution. Below are the options to configure:

  • Origin Domain Name:  Select the S3 bucket that you created earlier.
  • Restrict Bucket Access: Choose Yes.
  • Origin Access Identity: Create a new identity.
  • Grant Read Permissions on Bucket: Choose Yes, Update Bucket Policy.
  • Object Caching: Choose Customize (I am changing the behavior to avoid having CloudFront cache objects, as this could affect your ability to troubleshoot while implementing the Lambda code).
    • Minimum TTL: 0
    • Maximum TTL: 0
    • Default TTL: 0

You can accept all of the other defaults. Again, this is a proof-of-concept exercise. After you are comfortable that the CloudFront distribution is working properly with the origin and Lambda code, you can re-visit the preceding values and make changes before implementing it in production.

CloudFront distributions can take several minutes to deploy (because the changes have to propagate out to all of the edge locations). After that’s done, test the functionality of the S3-backed static website. Looking at the distribution, you can see that CloudFront assigns a domain name:

CloudFront Distribution Settings

Try to access the website using a combination of various URLs:

http://<domainname>/:  Works

› curl -v http://d3gt20ea1hllb.cloudfront.net/
*   Trying 54.192.192.214...
* TCP_NODELAY set
* Connected to d3gt20ea1hllb.cloudfront.net (54.192.192.214) port 80 (#0)
> GET / HTTP/1.1
> Host: d3gt20ea1hllb.cloudfront.net
> User-Agent: curl/7.51.0
> Accept: */*
>
< HTTP/1.1 200 OK
< ETag: "cb7e2634fe66c1fd395cf868087dd3b9"
< Accept-Ranges: bytes
< Server: AmazonS3
< X-Cache: Miss from cloudfront
< X-Amz-Cf-Id: -D2FSRwzfcwyKZKFZr6DqYFkIf4t7HdGw2MkUF5sE6YFDxRJgi0R1g==
< Content-Length: 209
< Content-Type: text/html
< Last-Modified: Wed, 19 Jul 2017 19:21:16 GMT
< Via: 1.1 6419ba8f3bd94b651d416054d9416f1e.cloudfront.net (CloudFront), 1.1 iad6-proxy-3.amazon.com:80 (Cisco-WSA/9.1.2-010)
< Connection: keep-alive
<
<!doctype html>
<html>
    <head>
        <meta charset="utf-8">
        <title>Root home page</title>
    </head>
    <body>
        <p>Hello, this page resides in the root directory.</p>
    </body>
</html>
* Curl_http_done: called premature == 0
* Connection #0 to host d3gt20ea1hllb.cloudfront.net left intact

This is because CloudFront is configured to request a default root object (index.html) from the origin.

http://<domainname>/subdirectory/:  Doesn’t work

› curl -v http://d3gt20ea1hllb.cloudfront.net/subdirectory/
*   Trying 54.192.192.214...
* TCP_NODELAY set
* Connected to d3gt20ea1hllb.cloudfront.net (54.192.192.214) port 80 (#0)
> GET /subdirectory/ HTTP/1.1
> Host: d3gt20ea1hllb.cloudfront.net
> User-Agent: curl/7.51.0
> Accept: */*
>
< HTTP/1.1 200 OK
< ETag: "d41d8cd98f00b204e9800998ecf8427e"
< x-amz-server-side-encryption: AES256
< Accept-Ranges: bytes
< Server: AmazonS3
< X-Cache: Miss from cloudfront
< X-Amz-Cf-Id: Iqf0Gy8hJLiW-9tOAdSFPkL7vCWBrgm3-1ly5tBeY_izU82ftipodA==
< Content-Length: 0
< Content-Type: application/x-directory
< Last-Modified: Wed, 19 Jul 2017 19:21:24 GMT
< Via: 1.1 6419ba8f3bd94b651d416054d9416f1e.cloudfront.net (CloudFront), 1.1 iad6-proxy-3.amazon.com:80 (Cisco-WSA/9.1.2-010)
< Connection: keep-alive
<
* Curl_http_done: called premature == 0
* Connection #0 to host d3gt20ea1hllb.cloudfront.net left intact

If you use a tool such like cURL to test this, you notice that CloudFront and S3 are returning a blank response. The reason for this is that the subdirectory does exist, but it does not resolve to an S3 object. Keep in mind that S3 is an object store, so there are no real directories. User interfaces such as the S3 console present a hierarchical view of a bucket with folders based on the presence of forward slashes, but behind the scenes the bucket is just a collection of keys that represent stored objects.

http://<domainname>/subdirectory/index.html:  Works

› curl -v http://d3gt20ea1hllb.cloudfront.net/subdirectory/index.html
*   Trying 54.192.192.130...
* TCP_NODELAY set
* Connected to d3gt20ea1hllb.cloudfront.net (54.192.192.130) port 80 (#0)
> GET /subdirectory/index.html HTTP/1.1
> Host: d3gt20ea1hllb.cloudfront.net
> User-Agent: curl/7.51.0
> Accept: */*
>
< HTTP/1.1 200 OK
< Date: Thu, 20 Jul 2017 20:35:15 GMT
< ETag: "ddf87c487acf7cef9d50418f0f8f8dae"
< Accept-Ranges: bytes
< Server: AmazonS3
< X-Cache: RefreshHit from cloudfront
< X-Amz-Cf-Id: bkh6opXdpw8pUomqG3Qr3UcjnZL8axxOH82Lh0OOcx48uJKc_Dc3Cg==
< Content-Length: 227
< Content-Type: text/html
< Last-Modified: Wed, 19 Jul 2017 19:21:45 GMT
< Via: 1.1 3f2788d309d30f41de96da6f931d4ede.cloudfront.net (CloudFront), 1.1 iad6-proxy-3.amazon.com:80 (Cisco-WSA/9.1.2-010)
< Connection: keep-alive
<
<!doctype html>
<html>
    <head>
        <meta charset="utf-8">
        <title>Subdirectory home page</title>
    </head>
    <body>
        <p>Hello, this page resides in the /subdirectory/ directory.</p>
    </body>
</html>
* Curl_http_done: called premature == 0
* Connection #0 to host d3gt20ea1hllb.cloudfront.net left intact

This request works as expected because you are referencing the object directly. Now, you implement the [email protected] function to return the default index.html page for any subdirectory. Looking at the example JavaScript code, here’s where the magic happens:

var newuri = olduri.replace(/\/$/, '\/index.html');

You are going to use a JavaScript regular expression to match any ‘/’ that occurs at the end of the URI and replace it with ‘/index.html’. This is the equivalent to what S3 does on its own with static website hosting. However, as I mentioned earlier, you can’t rely on this if you want to use a policy on the bucket to restrict it so that users must access the bucket through CloudFront. That way, all requests to the S3 bucket must be authenticated using the S3 REST API. Because of this, you implement a [email protected] function that takes any client request ending in ‘/’ and append a default ‘index.html’ to the request before requesting the object from the origin.

In the Lambda console, choose Create function. On the next screen, skip the blueprint selection and choose Author from scratch, as you’ll use the sample code provided.

Next, configure the trigger. Choosing the empty box shows a list of available triggers. Choose CloudFront and select your CloudFront distribution ID (created earlier). For this example, leave Cache Behavior as * and CloudFront Event as Origin Request. Select the Enable trigger and replicate box and choose Next.

Lambda Trigger

Next, give the function a name and a description. Then, copy and paste the following code:

'use strict';
exports.handler = (event, context, callback) => {
    
    // Extract the request from the CloudFront event that is sent to [email protected] 
    var request = event.Records[0].cf.request;

    // Extract the URI from the request
    var olduri = request.uri;

    // Match any '/' that occurs at the end of a URI. Replace it with a default index
    var newuri = olduri.replace(/\/$/, '\/index.html');
    
    // Log the URI as received by CloudFront and the new URI to be used to fetch from origin
    console.log("Old URI: " + olduri);
    console.log("New URI: " + newuri);
    
    // Replace the received URI with the URI that includes the index page
    request.uri = newuri;
    
    // Return to CloudFront
    return callback(null, request);

};

Next, define a role that grants permissions to the Lambda function. For this example, choose Create new role from template, Basic Edge Lambda permissions. This creates a new IAM role for the Lambda function and grants the following permissions:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "logs:CreateLogGroup",
                "logs:CreateLogStream",
                "logs:PutLogEvents"
            ],
            "Resource": [
                "arn:aws:logs:*:*:*"
            ]
        }
    ]
}

In a nutshell, these are the permissions that the function needs to create the necessary CloudWatch log group and log stream, and to put the log events so that the function is able to write logs when it executes.

After the function has been created, you can go back to the browser (or cURL) and re-run the test for the subdirectory request that failed previously:

› curl -v http://d3gt20ea1hllb.cloudfront.net/subdirectory/
*   Trying 54.192.192.202...
* TCP_NODELAY set
* Connected to d3gt20ea1hllb.cloudfront.net (54.192.192.202) port 80 (#0)
> GET /subdirectory/ HTTP/1.1
> Host: d3gt20ea1hllb.cloudfront.net
> User-Agent: curl/7.51.0
> Accept: */*
>
< HTTP/1.1 200 OK
< Date: Thu, 20 Jul 2017 21:18:44 GMT
< ETag: "ddf87c487acf7cef9d50418f0f8f8dae"
< Accept-Ranges: bytes
< Server: AmazonS3
< X-Cache: Miss from cloudfront
< X-Amz-Cf-Id: rwFN7yHE70bT9xckBpceTsAPcmaadqWB9omPBv2P6WkIfQqdjTk_4w==
< Content-Length: 227
< Content-Type: text/html
< Last-Modified: Wed, 19 Jul 2017 19:21:45 GMT
< Via: 1.1 3572de112011f1b625bb77410b0c5cca.cloudfront.net (CloudFront), 1.1 iad6-proxy-3.amazon.com:80 (Cisco-WSA/9.1.2-010)
< Connection: keep-alive
<
<!doctype html>
<html>
    <head>
        <meta charset="utf-8">
        <title>Subdirectory home page</title>
    </head>
    <body>
        <p>Hello, this page resides in the /subdirectory/ directory.</p>
    </body>
</html>
* Curl_http_done: called premature == 0
* Connection #0 to host d3gt20ea1hllb.cloudfront.net left intact

You have now configured a way for CloudFront to return a default index page for subdirectories in S3!

Summary

In this post, you used [email protected] to be able to use CloudFront with an S3 origin access identity and serve a default root object on subdirectory URLs. To find out some more about this use-case, see [email protected] integration with CloudFront in our documentation.

If you have questions or suggestions, feel free to comment below. For troubleshooting or implementation help, check out the Lambda forum.

Predict Billboard Top 10 Hits Using RStudio, H2O and Amazon Athena

Post Syndicated from Gopal Wunnava original https://aws.amazon.com/blogs/big-data/predict-billboard-top-10-hits-using-rstudio-h2o-and-amazon-athena/

Success in the popular music industry is typically measured in terms of the number of Top 10 hits artists have to their credit. The music industry is a highly competitive multi-billion dollar business, and record labels incur various costs in exchange for a percentage of the profits from sales and concert tickets.

Predicting the success of an artist’s release in the popular music industry can be difficult. One release may be extremely popular, resulting in widespread play on TV, radio and social media, while another single may turn out quite unpopular, and therefore unprofitable. Record labels need to be selective in their decision making, and predictive analytics can help them with decision making around the type of songs and artists they need to promote.

In this walkthrough, you leverage H2O.ai, Amazon Athena, and RStudio to make predictions on whether a song might make it to the Top 10 Billboard charts. You explore the GLM, GBM, and deep learning modeling techniques using H2O’s rapid, distributed and easy-to-use open source parallel processing engine. RStudio is a popular IDE, licensed either commercially or under AGPLv3, for working with R. This is ideal if you don’t want to connect to a server via SSH and use code editors such as vi to do analytics. RStudio is available in a desktop version, or a server version that allows you to access R via a web browser. RStudio’s Notebooks feature is used to demonstrate the execution of code and output. In addition, this post showcases how you can leverage Athena for query and interactive analysis during the modeling phase. A working knowledge of statistics and machine learning would be helpful to interpret the analysis being performed in this post.

Walkthrough

Your goal is to predict whether a song will make it to the Top 10 Billboard charts. For this purpose, you will be using multiple modeling techniques―namely GLM, GBM and deep learning―and choose the model that is the best fit.

This solution involves the following steps:

  • Install and configure RStudio with Athena
  • Log in to RStudio
  • Install R packages
  • Connect to Athena
  • Create a dataset
  • Create models

Install and configure RStudio with Athena

Use the following AWS CloudFormation stack to install, configure, and connect RStudio on an Amazon EC2 instance with Athena.

Launching this stack creates all required resources and prerequisites:

  • Amazon EC2 instance with Amazon Linux (minimum size of t2.large is recommended)
  • Provisioning of the EC2 instance in an existing VPC and public subnet
  • Installation of Java 8
  • Assignment of an IAM role to the EC2 instance with the required permissions for accessing Athena and Amazon S3
  • Security group allowing access to the RStudio and SSH ports from the internet (I recommend restricting access to these ports)
  • S3 staging bucket required for Athena (referenced within RStudio as ATHENABUCKET)
  • RStudio username and password
  • Setup logs in Amazon CloudWatch Logs (if needed for additional troubleshooting)
  • Amazon EC2 Systems Manager agent, which makes it easy to manage and patch

All AWS resources are created in the US-East-1 Region. To avoid cross-region data transfer fees, launch the CloudFormation stack in the same region. To check the availability of Athena in other regions, see Region Table.

Log in to RStudio

The instance security group has been automatically configured to allow incoming connections on the RStudio port 8787 from any source internet address. You can edit the security group to restrict source IP access. If you have trouble connecting, ensure that port 8787 isn’t blocked by subnet network ACLS or by your outgoing proxy/firewall.

  1. In the CloudFormation stack, choose Outputs, Value, and then open the RStudio URL. You might need to wait for a few minutes until the instance has been launched.
  2. Log in to RStudio with the and password you provided during setup.

Install R packages

Next, install the required R packages from the RStudio console. You can download the R notebook file containing just the code.

#install pacman – a handy package manager for managing installs
if("pacman" %in% rownames(installed.packages()) == FALSE)
{install.packages("pacman")}  
library(pacman)
p_load(h2o,rJava,RJDBC,awsjavasdk)
h2o.init(nthreads = -1)
##  Connection successful!
## 
## R is connected to the H2O cluster: 
##     H2O cluster uptime:         2 hours 42 minutes 
##     H2O cluster version:        3.10.4.6 
##     H2O cluster version age:    4 months and 4 days !!! 
##     H2O cluster name:           H2O_started_from_R_rstudio_hjx881 
##     H2O cluster total nodes:    1 
##     H2O cluster total memory:   3.30 GB 
##     H2O cluster total cores:    4 
##     H2O cluster allowed cores:  4 
##     H2O cluster healthy:        TRUE 
##     H2O Connection ip:          localhost 
##     H2O Connection port:        54321 
##     H2O Connection proxy:       NA 
##     H2O Internal Security:      FALSE 
##     R Version:                  R version 3.3.3 (2017-03-06)
## Warning in h2o.clusterInfo(): 
## Your H2O cluster version is too old (4 months and 4 days)!
## Please download and install the latest version from http://h2o.ai/download/
#install aws sdk if not present (pre-requisite for using Athena with an IAM role)
if (!aws_sdk_present()) {
  install_aws_sdk()
}

load_sdk()
## NULL

Connect to Athena

Next, establish a connection to Athena from RStudio, using an IAM role associated with your EC2 instance. Use ATHENABUCKET to specify the S3 staging directory.

URL <- 'https://s3.amazonaws.com/athena-downloads/drivers/AthenaJDBC41-1.0.1.jar'
fil <- basename(URL)
#download the file into current working directory
if (!file.exists(fil)) download.file(URL, fil)
#verify that the file has been downloaded successfully
list.files()
## [1] "AthenaJDBC41-1.0.1.jar"
drv <- JDBC(driverClass="com.amazonaws.athena.jdbc.AthenaDriver", fil, identifier.quote="'")

con <- jdbcConnection <- dbConnect(drv, 'jdbc:awsathena://athena.us-east-1.amazonaws.com:443/',
                                   s3_staging_dir=Sys.getenv("ATHENABUCKET"),
                                   aws_credentials_provider_class="com.amazonaws.auth.DefaultAWSCredentialsProviderChain")

Verify the connection. The results returned depend on your specific Athena setup.

con
## <JDBCConnection>
dbListTables(con)
##  [1] "gdelt"               "wikistats"           "elb_logs_raw_native"
##  [4] "twitter"             "twitter2"            "usermovieratings"   
##  [7] "eventcodes"          "events"              "billboard"          
## [10] "billboardtop10"      "elb_logs"            "gdelthist"          
## [13] "gdeltmaster"         "twitter"             "twitter3"

Create a dataset

For this analysis, you use a sample dataset combining information from Billboard and Wikipedia with Echo Nest data in the Million Songs Dataset. Upload this dataset into your own S3 bucket. The table below provides a description of the fields used in this dataset.

Field Description
yearYear that song was released
songtitleTitle of the song
artistnameName of the song artist
songidUnique identifier for the song
artistidUnique identifier for the song artist
timesignatureVariable estimating the time signature of the song
timesignature_confidenceConfidence in the estimate for the timesignature
loudnessContinuous variable indicating the average amplitude of the audio in decibels
tempoVariable indicating the estimated beats per minute of the song
tempo_confidenceConfidence in the estimate for tempo
keyVariable with twelve levels indicating the estimated key of the song (C, C#, B)
key_confidenceConfidence in the estimate for key
energyVariable that represents the overall acoustic energy of the song, using a mix of features such as loudness
pitchContinuous variable that indicates the pitch of the song
timbre_0_min thru timbre_11_minVariables that indicate the minimum values over all segments for each of the twelve values in the timbre vector
timbre_0_max thru timbre_11_maxVariables that indicate the maximum values over all segments for each of the twelve values in the timbre vector
top10Indicator for whether or not the song made it to the Top 10 of the Billboard charts (1 if it was in the top 10, and 0 if not)

Create an Athena table based on the dataset

In the Athena console, select the default database, sampled, or create a new database.

Run the following create table statement.

create external table if not exists billboard
(
year int,
songtitle string,
artistname string,
songID string,
artistID string,
timesignature int,
timesignature_confidence double,
loudness double,
tempo double,
tempo_confidence double,
key int,
key_confidence double,
energy double,
pitch double,
timbre_0_min double,
timbre_0_max double,
timbre_1_min double,
timbre_1_max double,
timbre_2_min double,
timbre_2_max double,
timbre_3_min double,
timbre_3_max double,
timbre_4_min double,
timbre_4_max double,
timbre_5_min double,
timbre_5_max double,
timbre_6_min double,
timbre_6_max double,
timbre_7_min double,
timbre_7_max double,
timbre_8_min double,
timbre_8_max double,
timbre_9_min double,
timbre_9_max double,
timbre_10_min double,
timbre_10_max double,
timbre_11_min double,
timbre_11_max double,
Top10 int
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
STORED AS TEXTFILE
LOCATION 's3://aws-bigdata-blog/artifacts/predict-billboard/data'
;

Inspect the table definition for the ‘billboard’ table that you have created. If you chose a database other than sampledb, replace that value with your choice.

dbGetQuery(con, "show create table sampledb.billboard")
##                                      createtab_stmt
## 1       CREATE EXTERNAL TABLE `sampledb.billboard`(
## 2                                       `year` int,
## 3                               `songtitle` string,
## 4                              `artistname` string,
## 5                                  `songid` string,
## 6                                `artistid` string,
## 7                              `timesignature` int,
## 8                `timesignature_confidence` double,
## 9                                `loudness` double,
## 10                                  `tempo` double,
## 11                       `tempo_confidence` double,
## 12                                       `key` int,
## 13                         `key_confidence` double,
## 14                                 `energy` double,
## 15                                  `pitch` double,
## 16                           `timbre_0_min` double,
## 17                           `timbre_0_max` double,
## 18                           `timbre_1_min` double,
## 19                           `timbre_1_max` double,
## 20                           `timbre_2_min` double,
## 21                           `timbre_2_max` double,
## 22                           `timbre_3_min` double,
## 23                           `timbre_3_max` double,
## 24                           `timbre_4_min` double,
## 25                           `timbre_4_max` double,
## 26                           `timbre_5_min` double,
## 27                           `timbre_5_max` double,
## 28                           `timbre_6_min` double,
## 29                           `timbre_6_max` double,
## 30                           `timbre_7_min` double,
## 31                           `timbre_7_max` double,
## 32                           `timbre_8_min` double,
## 33                           `timbre_8_max` double,
## 34                           `timbre_9_min` double,
## 35                           `timbre_9_max` double,
## 36                          `timbre_10_min` double,
## 37                          `timbre_10_max` double,
## 38                          `timbre_11_min` double,
## 39                          `timbre_11_max` double,
## 40                                     `top10` int)
## 41                             ROW FORMAT DELIMITED 
## 42                         FIELDS TERMINATED BY ',' 
## 43                            STORED AS INPUTFORMAT 
## 44       'org.apache.hadoop.mapred.TextInputFormat' 
## 45                                     OUTPUTFORMAT 
## 46  'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
## 47                                        LOCATION
## 48    's3://aws-bigdata-blog/artifacts/predict-billboard/data'
## 49                                  TBLPROPERTIES (
## 50            'transient_lastDdlTime'='1505484133')

Run a sample query

Next, run a sample query to obtain a list of all songs from Janet Jackson that made it to the Billboard Top 10 charts.

dbGetQuery(con, " SELECT songtitle,artistname,top10   FROM sampledb.billboard WHERE lower(artistname) =     'janet jackson' AND top10 = 1")
##                       songtitle    artistname top10
## 1                       Runaway Janet Jackson     1
## 2               Because Of Love Janet Jackson     1
## 3                         Again Janet Jackson     1
## 4                            If Janet Jackson     1
## 5  Love Will Never Do (Without You) Janet Jackson 1
## 6                     Black Cat Janet Jackson     1
## 7               Come Back To Me Janet Jackson     1
## 8                       Alright Janet Jackson     1
## 9                      Escapade Janet Jackson     1
## 10                Rhythm Nation Janet Jackson     1

Determine how many songs in this dataset are specifically from the year 2010.

dbGetQuery(con, " SELECT count(*)   FROM sampledb.billboard WHERE year = 2010")
##   _col0
## 1   373

The sample dataset provides certain song properties of interest that can be analyzed to gauge the impact to the song’s overall popularity. Look at one such property, timesignature, and determine the value that is the most frequent among songs in the database. Timesignature is a measure of the number of beats and the type of note involved.

Running the query directly may result in an error, as shown in the commented lines below. This error is a result of trying to retrieve a large result set over a JDBC connection, which can cause out-of-memory issues at the client level. To address this, reduce the fetch size and run again.

#t<-dbGetQuery(con, " SELECT timesignature FROM sampledb.billboard")
#Note:  Running the preceding query results in the following error: 
#Error in .jcall(rp, "I", "fetch", stride, block): java.sql.SQLException: The requested #fetchSize is more than the allowed value in Athena. Please reduce the fetchSize and try #again. Refer to the Athena documentation for valid fetchSize values.
# Use the dbSendQuery function, reduce the fetch size, and run again
r <- dbSendQuery(con, " SELECT timesignature     FROM sampledb.billboard")
dftimesignature<- fetch(r, n=-1, block=100)
dbClearResult(r)
## [1] TRUE
table(dftimesignature)
## dftimesignature
##    0    1    3    4    5    7 
##   10  143  503 6787  112   19
nrow(dftimesignature)
## [1] 7574

From the results, observe that 6787 songs have a timesignature of 4.

Next, determine the song with the highest tempo.

dbGetQuery(con, " SELECT songtitle,artistname,tempo   FROM sampledb.billboard WHERE tempo = (SELECT max(tempo) FROM sampledb.billboard) ")
##                   songtitle      artistname   tempo
## 1 Wanna Be Startin' Somethin' Michael Jackson 244.307

Create the training dataset

Your model needs to be trained such that it can learn and make accurate predictions. Split the data into training and test datasets, and create the training dataset first.  This dataset contains all observations from the year 2009 and earlier. You may face the same JDBC connection issue pointed out earlier, so this query uses a fetch size.

#BillboardTrain <- dbGetQuery(con, "SELECT * FROM sampledb.billboard WHERE year <= 2009")
#Running the preceding query results in the following error:-
#Error in .verify.JDBC.result(r, "Unable to retrieve JDBC result set for ", : Unable to retrieve #JDBC result set for SELECT * FROM sampledb.billboard WHERE year <= 2009 (Internal error)
#Follow the same approach as before to address this issue.

r <- dbSendQuery(con, "SELECT * FROM sampledb.billboard WHERE year <= 2009")
BillboardTrain <- fetch(r, n=-1, block=100)
dbClearResult(r)
## [1] TRUE
BillboardTrain[1:2,c(1:3,6:10)]
##   year           songtitle artistname timesignature
## 1 2009 The Awkward Goodbye    Athlete             3
## 2 2009        Rubik's Cube    Athlete             3
##   timesignature_confidence loudness   tempo tempo_confidence
## 1                    0.732   -6.320  89.614   0.652
## 2                    0.906   -9.541 117.742   0.542
nrow(BillboardTrain)
## [1] 7201

Create the test dataset

BillboardTest <- dbGetQuery(con, "SELECT * FROM sampledb.billboard where year = 2010")
BillboardTest[1:2,c(1:3,11:15)]
##   year              songtitle        artistname key
## 1 2010 This Is the House That Doubt Built A Day to Remember  11
## 2 2010        Sticks & Bricks A Day to Remember  10
##   key_confidence    energy pitch timbre_0_min
## 1          0.453 0.9666556 0.024        0.002
## 2          0.469 0.9847095 0.025        0.000
nrow(BillboardTest)
## [1] 373

Convert the training and test datasets into H2O dataframes

train.h2o <- as.h2o(BillboardTrain)
## 
  |                                                                       
  |                                                                 |   0%
  |                                                                       
  |=================================================================| 100%
test.h2o <- as.h2o(BillboardTest)
## 
  |                                                                       
  |                                                                 |   0%
  |                                                                       
  |=================================================================| 100%

Inspect the column names in your H2O dataframes.

colnames(train.h2o)
##  [1] "year"                     "songtitle"               
##  [3] "artistname"               "songid"                  
##  [5] "artistid"                 "timesignature"           
##  [7] "timesignature_confidence" "loudness"                
##  [9] "tempo"                    "tempo_confidence"        
## [11] "key"                      "key_confidence"          
## [13] "energy"                   "pitch"                   
## [15] "timbre_0_min"             "timbre_0_max"            
## [17] "timbre_1_min"             "timbre_1_max"            
## [19] "timbre_2_min"             "timbre_2_max"            
## [21] "timbre_3_min"             "timbre_3_max"            
## [23] "timbre_4_min"             "timbre_4_max"            
## [25] "timbre_5_min"             "timbre_5_max"            
## [27] "timbre_6_min"             "timbre_6_max"            
## [29] "timbre_7_min"             "timbre_7_max"            
## [31] "timbre_8_min"             "timbre_8_max"            
## [33] "timbre_9_min"             "timbre_9_max"            
## [35] "timbre_10_min"            "timbre_10_max"           
## [37] "timbre_11_min"            "timbre_11_max"           
## [39] "top10"

Create models

You need to designate the independent and dependent variables prior to applying your modeling algorithms. Because you’re trying to predict the ‘top10’ field, this would be your dependent variable and everything else would be independent.

Create your first model using GLM. Because GLM works best with numeric data, you create your model by dropping non-numeric variables. You only use the variables in the dataset that describe the numerical attributes of the song in the logistic regression model. You won’t use these variables:  “year”, “songtitle”, “artistname”, “songid”, or “artistid”.

y.dep <- 39
x.indep <- c(6:38)
x.indep
##  [1]  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
## [24] 29 30 31 32 33 34 35 36 37 38

Create Model 1: All numeric variables

Create Model 1 with the training dataset, using GLM as the modeling algorithm and H2O’s built-in h2o.glm function.

modelh1 <- h2o.glm( y = y.dep, x = x.indep, training_frame = train.h2o, family = "binomial")
## 
  |                                                                       
  |                                                                 |   0%
  |                                                                       
  |=====                                                            |   8%
  |                                                                       
  |=================================================================| 100%

Measure the performance of Model 1, using H2O’s built-in performance function.

h2o.performance(model=modelh1,newdata=test.h2o)
## H2OBinomialMetrics: glm
## 
## MSE:  0.09924684
## RMSE:  0.3150347
## LogLoss:  0.3220267
## Mean Per-Class Error:  0.2380168
## AUC:  0.8431394
## Gini:  0.6862787
## R^2:  0.254663
## Null Deviance:  326.0801
## Residual Deviance:  240.2319
## AIC:  308.2319
## 
## Confusion Matrix (vertical: actual; across: predicted) for F1-optimal threshold:
##          0   1    Error     Rate
## 0      255  59 0.187898  =59/314
## 1       17  42 0.288136   =17/59
## Totals 272 101 0.203753  =76/373
## 
## Maximum Metrics: Maximum metrics at their respective thresholds
##                         metric threshold    value idx
## 1                       max f1  0.192772 0.525000 100
## 2                       max f2  0.124912 0.650510 155
## 3                 max f0point5  0.416258 0.612903  23
## 4                 max accuracy  0.416258 0.879357  23
## 5                max precision  0.813396 1.000000   0
## 6                   max recall  0.037579 1.000000 282
## 7              max specificity  0.813396 1.000000   0
## 8             max absolute_mcc  0.416258 0.455251  23
## 9   max min_per_class_accuracy  0.161402 0.738854 125
## 10 max mean_per_class_accuracy  0.124912 0.765006 155
## 
## Gains/Lift Table: Extract with `h2o.gainsLift(<model>, <data>)` or ` 
h2o.auc(h2o.performance(modelh1,test.h2o)) 
## [1] 0.8431394

The AUC metric provides insight into how well the classifier is able to separate the two classes. In this case, the value of 0.8431394 indicates that the classification is good. (A value of 0.5 indicates a worthless test, while a value of 1.0 indicates a perfect test.)

Next, inspect the coefficients of the variables in the dataset.

dfmodelh1 <- as.data.frame(h2o.varimp(modelh1))
dfmodelh1
##                       names coefficients sign
## 1              timbre_0_max  1.290938663  NEG
## 2                  loudness  1.262941934  POS
## 3                     pitch  0.616995941  NEG
## 4              timbre_1_min  0.422323735  POS
## 5              timbre_6_min  0.349016024  NEG
## 6                    energy  0.348092062  NEG
## 7             timbre_11_min  0.307331997  NEG
## 8              timbre_3_max  0.302225619  NEG
## 9             timbre_11_max  0.243632060  POS
## 10             timbre_4_min  0.224233951  POS
## 11             timbre_4_max  0.204134342  POS
## 12             timbre_5_min  0.199149324  NEG
## 13             timbre_0_min  0.195147119  POS
## 14 timesignature_confidence  0.179973904  POS
## 15         tempo_confidence  0.144242598  POS
## 16            timbre_10_max  0.137644568  POS
## 17             timbre_7_min  0.126995955  NEG
## 18            timbre_10_min  0.123851179  POS
## 19             timbre_7_max  0.100031481  NEG
## 20             timbre_2_min  0.096127636  NEG
## 21           key_confidence  0.083115820  POS
## 22             timbre_6_max  0.073712419  POS
## 23            timesignature  0.067241917  POS
## 24             timbre_8_min  0.061301881  POS
## 25             timbre_8_max  0.060041698  POS
## 26                      key  0.056158445  POS
## 27             timbre_3_min  0.050825116  POS
## 28             timbre_9_max  0.033733561  POS
## 29             timbre_2_max  0.030939072  POS
## 30             timbre_9_min  0.020708113  POS
## 31             timbre_1_max  0.014228818  NEG
## 32                    tempo  0.008199861  POS
## 33             timbre_5_max  0.004837870  POS
## 34                                    NA <NA>

Typically, songs with heavier instrumentation tend to be louder (have higher values in the variable “loudness”) and more energetic (have higher values in the variable “energy”). This knowledge is helpful for interpreting the modeling results.

You can make the following observations from the results:

  • The coefficient estimates for the confidence values associated with the time signature, key, and tempo variables are positive. This suggests that higher confidence leads to a higher predicted probability of a Top 10 hit.
  • The coefficient estimate for loudness is positive, meaning that mainstream listeners prefer louder songs with heavier instrumentation.
  • The coefficient estimate for energy is negative, meaning that mainstream listeners prefer songs that are less energetic, which are those songs with light instrumentation.

These coefficients lead to contradictory conclusions for Model 1. This could be due to multicollinearity issues. Inspect the correlation between the variables “loudness” and “energy” in the training set.

cor(train.h2o$loudness,train.h2o$energy)
## [1] 0.7399067

This number indicates that these two variables are highly correlated, and Model 1 does indeed suffer from multicollinearity. Typically, you associate a value of -1.0 to -0.5 or 1.0 to 0.5 to indicate strong correlation, and a value of 0.1 to 0.1 to indicate weak correlation. To avoid this correlation issue, omit one of these two variables and re-create the models.

You build two variations of the original model:

  • Model 2, in which you keep “energy” and omit “loudness”
  • Model 3, in which you keep “loudness” and omit “energy”

You compare these two models and choose the model with a better fit for this use case.

Create Model 2: Keep energy and omit loudness

colnames(train.h2o)
##  [1] "year"                     "songtitle"               
##  [3] "artistname"               "songid"                  
##  [5] "artistid"                 "timesignature"           
##  [7] "timesignature_confidence" "loudness"                
##  [9] "tempo"                    "tempo_confidence"        
## [11] "key"                      "key_confidence"          
## [13] "energy"                   "pitch"                   
## [15] "timbre_0_min"             "timbre_0_max"            
## [17] "timbre_1_min"             "timbre_1_max"            
## [19] "timbre_2_min"             "timbre_2_max"            
## [21] "timbre_3_min"             "timbre_3_max"            
## [23] "timbre_4_min"             "timbre_4_max"            
## [25] "timbre_5_min"             "timbre_5_max"            
## [27] "timbre_6_min"             "timbre_6_max"            
## [29] "timbre_7_min"             "timbre_7_max"            
## [31] "timbre_8_min"             "timbre_8_max"            
## [33] "timbre_9_min"             "timbre_9_max"            
## [35] "timbre_10_min"            "timbre_10_max"           
## [37] "timbre_11_min"            "timbre_11_max"           
## [39] "top10"
y.dep <- 39
x.indep <- c(6:7,9:38)
x.indep
##  [1]  6  7  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
## [24] 30 31 32 33 34 35 36 37 38
modelh2 <- h2o.glm( y = y.dep, x = x.indep, training_frame = train.h2o, family = "binomial")
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Measure the performance of Model 2.

h2o.performance(model=modelh2,newdata=test.h2o)
## H2OBinomialMetrics: glm
## 
## MSE:  0.09922606
## RMSE:  0.3150017
## LogLoss:  0.3228213
## Mean Per-Class Error:  0.2490554
## AUC:  0.8431933
## Gini:  0.6863867
## R^2:  0.2548191
## Null Deviance:  326.0801
## Residual Deviance:  240.8247
## AIC:  306.8247
## 
## Confusion Matrix (vertical: actual; across: predicted) for F1-optimal threshold:
##          0  1    Error     Rate
## 0      280 34 0.108280  =34/314
## 1       23 36 0.389831   =23/59
## Totals 303 70 0.152815  =57/373
## 
## Maximum Metrics: Maximum metrics at their respective thresholds
##                         metric threshold    value idx
## 1                       max f1  0.254391 0.558140  69
## 2                       max f2  0.113031 0.647208 157
## 3                 max f0point5  0.413999 0.596026  22
## 4                 max accuracy  0.446250 0.876676  18
## 5                max precision  0.811739 1.000000   0
## 6                   max recall  0.037682 1.000000 283
## 7              max specificity  0.811739 1.000000   0
## 8             max absolute_mcc  0.254391 0.469060  69
## 9   max min_per_class_accuracy  0.141051 0.716561 131
## 10 max mean_per_class_accuracy  0.113031 0.761821 157
## 
## Gains/Lift Table: Extract with `h2o.gainsLift(<model>, <data>)` or `h2o.gainsLift(<model>, valid=<T/F>, xval=<T/F>)`
dfmodelh2 <- as.data.frame(h2o.varimp(modelh2))
dfmodelh2
##                       names coefficients sign
## 1                     pitch  0.700331511  NEG
## 2              timbre_1_min  0.510270513  POS
## 3              timbre_0_max  0.402059546  NEG
## 4              timbre_6_min  0.333316236  NEG
## 5             timbre_11_min  0.331647383  NEG
## 6              timbre_3_max  0.252425901  NEG
## 7             timbre_11_max  0.227500308  POS
## 8              timbre_4_max  0.210663865  POS
## 9              timbre_0_min  0.208516163  POS
## 10             timbre_5_min  0.202748055  NEG
## 11             timbre_4_min  0.197246582  POS
## 12            timbre_10_max  0.172729619  POS
## 13         tempo_confidence  0.167523934  POS
## 14 timesignature_confidence  0.167398830  POS
## 15             timbre_7_min  0.142450727  NEG
## 16             timbre_8_max  0.093377516  POS
## 17            timbre_10_min  0.090333426  POS
## 18            timesignature  0.085851625  POS
## 19             timbre_7_max  0.083948442  NEG
## 20           key_confidence  0.079657073  POS
## 21             timbre_6_max  0.076426046  POS
## 22             timbre_2_min  0.071957831  NEG
## 23             timbre_9_max  0.071393189  POS
## 24             timbre_8_min  0.070225578  POS
## 25                      key  0.061394702  POS
## 26             timbre_3_min  0.048384697  POS
## 27             timbre_1_max  0.044721121  NEG
## 28                   energy  0.039698433  POS
## 29             timbre_5_max  0.039469064  POS
## 30             timbre_2_max  0.018461133  POS
## 31                    tempo  0.013279926  POS
## 32             timbre_9_min  0.005282143  NEG
## 33                                    NA <NA>

h2o.auc(h2o.performance(modelh2,test.h2o)) 
## [1] 0.8431933

You can make the following observations:

  • The AUC metric is 0.8431933.
  • Inspecting the coefficient of the variable energy, Model 2 suggests that songs with high energy levels tend to be more popular. This is as per expectation.
  • As H2O orders variables by significance, the variable energy is not significant in this model.

You can conclude that Model 2 is not ideal for this use , as energy is not significant.

CreateModel 3: Keep loudness but omit energy

colnames(train.h2o)
##  [1] "year"                     "songtitle"               
##  [3] "artistname"               "songid"                  
##  [5] "artistid"                 "timesignature"           
##  [7] "timesignature_confidence" "loudness"                
##  [9] "tempo"                    "tempo_confidence"        
## [11] "key"                      "key_confidence"          
## [13] "energy"                   "pitch"                   
## [15] "timbre_0_min"             "timbre_0_max"            
## [17] "timbre_1_min"             "timbre_1_max"            
## [19] "timbre_2_min"             "timbre_2_max"            
## [21] "timbre_3_min"             "timbre_3_max"            
## [23] "timbre_4_min"             "timbre_4_max"            
## [25] "timbre_5_min"             "timbre_5_max"            
## [27] "timbre_6_min"             "timbre_6_max"            
## [29] "timbre_7_min"             "timbre_7_max"            
## [31] "timbre_8_min"             "timbre_8_max"            
## [33] "timbre_9_min"             "timbre_9_max"            
## [35] "timbre_10_min"            "timbre_10_max"           
## [37] "timbre_11_min"            "timbre_11_max"           
## [39] "top10"
y.dep <- 39
x.indep <- c(6:12,14:38)
x.indep
##  [1]  6  7  8  9 10 11 12 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
## [24] 30 31 32 33 34 35 36 37 38
modelh3 <- h2o.glm( y = y.dep, x = x.indep, training_frame = train.h2o, family = "binomial")
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perfh3<-h2o.performance(model=modelh3,newdata=test.h2o)
perfh3
## H2OBinomialMetrics: glm
## 
## MSE:  0.0978859
## RMSE:  0.3128672
## LogLoss:  0.3178367
## Mean Per-Class Error:  0.264925
## AUC:  0.8492389
## Gini:  0.6984778
## R^2:  0.2648836
## Null Deviance:  326.0801
## Residual Deviance:  237.1062
## AIC:  303.1062
## 
## Confusion Matrix (vertical: actual; across: predicted) for F1-optimal threshold:
##          0  1    Error     Rate
## 0      286 28 0.089172  =28/314
## 1       26 33 0.440678   =26/59
## Totals 312 61 0.144772  =54/373
## 
## Maximum Metrics: Maximum metrics at their respective thresholds
##                         metric threshold    value idx
## 1                       max f1  0.273799 0.550000  60
## 2                       max f2  0.125503 0.663265 155
## 3                 max f0point5  0.435479 0.628931  24
## 4                 max accuracy  0.435479 0.882038  24
## 5                max precision  0.821606 1.000000   0
## 6                   max recall  0.038328 1.000000 280
## 7              max specificity  0.821606 1.000000   0
## 8             max absolute_mcc  0.435479 0.471426  24
## 9   max min_per_class_accuracy  0.173693 0.745763 120
## 10 max mean_per_class_accuracy  0.125503 0.775073 155
## 
## Gains/Lift Table: Extract with `h2o.gainsLift(<model>, <data>)` or `h2o.gainsLift(<model>, valid=<T/F>, xval=<T/F>)`
dfmodelh3 <- as.data.frame(h2o.varimp(modelh3))
dfmodelh3
##                       names coefficients sign
## 1              timbre_0_max 1.216621e+00  NEG
## 2                  loudness 9.780973e-01  POS
## 3                     pitch 7.249788e-01  NEG
## 4              timbre_1_min 3.891197e-01  POS
## 5              timbre_6_min 3.689193e-01  NEG
## 6             timbre_11_min 3.086673e-01  NEG
## 7              timbre_3_max 3.025593e-01  NEG
## 8             timbre_11_max 2.459081e-01  POS
## 9              timbre_4_min 2.379749e-01  POS
## 10             timbre_4_max 2.157627e-01  POS
## 11             timbre_0_min 1.859531e-01  POS
## 12             timbre_5_min 1.846128e-01  NEG
## 13 timesignature_confidence 1.729658e-01  POS
## 14             timbre_7_min 1.431871e-01  NEG
## 15            timbre_10_max 1.366703e-01  POS
## 16            timbre_10_min 1.215954e-01  POS
## 17         tempo_confidence 1.183698e-01  POS
## 18             timbre_2_min 1.019149e-01  NEG
## 19           key_confidence 9.109701e-02  POS
## 20             timbre_7_max 8.987908e-02  NEG
## 21             timbre_6_max 6.935132e-02  POS
## 22             timbre_8_max 6.878241e-02  POS
## 23            timesignature 6.120105e-02  POS
## 24                      key 5.814805e-02  POS
## 25             timbre_8_min 5.759228e-02  POS
## 26             timbre_1_max 2.930285e-02  NEG
## 27             timbre_9_max 2.843755e-02  POS
## 28             timbre_3_min 2.380245e-02  POS
## 29             timbre_2_max 1.917035e-02  POS
## 30             timbre_5_max 1.715813e-02  POS
## 31                    tempo 1.364418e-02  NEG
## 32             timbre_9_min 8.463143e-05  NEG
## 33                                    NA <NA>
h2o.sensitivity(perfh3,0.5)
## Warning in h2o.find_row_by_threshold(object, t): Could not find exact
## threshold: 0.5 for this set of metrics; using closest threshold found:
## 0.501855569251422. Run `h2o.predict` and apply your desired threshold on a
## probability column.
## [[1]]
## [1] 0.2033898
h2o.auc(perfh3)
## [1] 0.8492389

You can make the following observations:

  • The AUC metric is 0.8492389.
  • From the confusion matrix, the model correctly predicts that 33 songs will be top 10 hits (true positives). However, it has 26 false positives (songs that the model predicted would be Top 10 hits, but ended up not being Top 10 hits).
  • Loudness has a positive coefficient estimate, meaning that this model predicts that songs with heavier instrumentation tend to be more popular. This is the same conclusion from Model 2.
  • Loudness is significant in this model.

Overall, Model 3 predicts a higher number of top 10 hits with an accuracy rate that is acceptable. To choose the best fit for production runs, record labels should consider the following factors:

  • Desired model accuracy at a given threshold
  • Number of correct predictions for top10 hits
  • Tolerable number of false positives or false negatives

Next, make predictions using Model 3 on the test dataset.

predict.regh <- h2o.predict(modelh3, test.h2o)
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print(predict.regh)
##   predict        p0          p1
## 1       0 0.9654739 0.034526052
## 2       0 0.9654748 0.034525236
## 3       0 0.9635547 0.036445318
## 4       0 0.9343579 0.065642149
## 5       0 0.9978334 0.002166601
## 6       0 0.9779949 0.022005078
## 
## [373 rows x 3 columns]
predict.regh$predict
##   predict
## 1       0
## 2       0
## 3       0
## 4       0
## 5       0
## 6       0
## 
## [373 rows x 1 column]
dpr<-as.data.frame(predict.regh)
#Rename the predicted column 
colnames(dpr)[colnames(dpr) == 'predict'] <- 'predict_top10'
table(dpr$predict_top10)
## 
##   0   1 
## 312  61

The first set of output results specifies the probabilities associated with each predicted observation.  For example, observation 1 is 96.54739% likely to not be a Top 10 hit, and 3.4526052% likely to be a Top 10 hit (predict=1 indicates Top 10 hit and predict=0 indicates not a Top 10 hit).  The second set of results list the actual predictions made.  From the third set of results, this model predicts that 61 songs will be top 10 hits.

Compute the baseline accuracy, by assuming that the baseline predicts the most frequent outcome, which is that most songs are not Top 10 hits.

table(BillboardTest$top10)
## 
##   0   1 
## 314  59

Now observe that the baseline model would get 314 observations correct, and 59 wrong, for an accuracy of 314/(314+59) = 0.8418231.

It seems that Model 3, with an accuracy of 0.8552, provides you with a small improvement over the baseline model. But is this model useful for record labels?

View the two models from an investment perspective:

  • A production company is interested in investing in songs that are more likely to make it to the Top 10. The company’s objective is to minimize the risk of financial losses attributed to investing in songs that end up unpopular.
  • How many songs does Model 3 correctly predict as a Top 10 hit in 2010? Looking at the confusion matrix, you see that it predicts 33 top 10 hits correctly at an optimal threshold, which is more than half the number
  • It will be more useful to the record label if you can provide the production company with a list of songs that are highly likely to end up in the Top 10.
  • The baseline model is not useful, as it simply does not label any song as a hit.

Considering the three models built so far, you can conclude that Model 3 proves to be the best investment choice for the record label.

GBM model

H2O provides you with the ability to explore other learning models, such as GBM and deep learning. Explore building a model using the GBM technique, using the built-in h2o.gbm function.

Before you do this, you need to convert the target variable to a factor for multinomial classification techniques.

train.h2o$top10=as.factor(train.h2o$top10)
gbm.modelh <- h2o.gbm(y=y.dep, x=x.indep, training_frame = train.h2o, ntrees = 500, max_depth = 4, learn_rate = 0.01, seed = 1122,distribution="multinomial")
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perf.gbmh<-h2o.performance(gbm.modelh,test.h2o)
perf.gbmh
## H2OBinomialMetrics: gbm
## 
## MSE:  0.09860778
## RMSE:  0.3140188
## LogLoss:  0.3206876
## Mean Per-Class Error:  0.2120263
## AUC:  0.8630573
## Gini:  0.7261146
## 
## Confusion Matrix (vertical: actual; across: predicted) for F1-optimal threshold:
##          0  1    Error     Rate
## 0      266 48 0.152866  =48/314
## 1       16 43 0.271186   =16/59
## Totals 282 91 0.171582  =64/373
## 
## Maximum Metrics: Maximum metrics at their respective thresholds
##                       metric threshold    value idx
## 1                     max f1  0.189757 0.573333  90
## 2                     max f2  0.130895 0.693717 145
## 3               max f0point5  0.327346 0.598802  26
## 4               max accuracy  0.442757 0.876676  14
## 5              max precision  0.802184 1.000000   0
## 6                 max recall  0.049990 1.000000 284
## 7            max specificity  0.802184 1.000000   0
## 8           max absolute_mcc  0.169135 0.496486 104
## 9 max min_per_class_accuracy  0.169135 0.796610 104
## 10 max mean_per_class_accuracy  0.169135 0.805948 104
## 
## Gains/Lift Table: Extract with `h2o.gainsLift(<model>, <data>)` or `
h2o.sensitivity(perf.gbmh,0.5)
## Warning in h2o.find_row_by_threshold(object, t): Could not find exact
## threshold: 0.5 for this set of metrics; using closest threshold found:
## 0.501205344484314. Run `h2o.predict` and apply your desired threshold on a
## probability column.
## [[1]]
## [1] 0.1355932
h2o.auc(perf.gbmh)
## [1] 0.8630573

This model correctly predicts 43 top 10 hits, which is 10 more than the number predicted by Model 3. Moreover, the AUC metric is higher than the one obtained from Model 3.

As seen above, H2O’s API provides the ability to obtain key statistical measures required to analyze the models easily, using several built-in functions. The record label can experiment with different parameters to arrive at the model that predicts the maximum number of Top 10 hits at the desired level of accuracy and threshold.

H2O also allows you to experiment with deep learning models. Deep learning models have the ability to learn features implicitly, but can be more expensive computationally.

Now, create a deep learning model with the h2o.deeplearning function, using the same training and test datasets created before. The time taken to run this model depends on the type of EC2 instance chosen for this purpose.  For models that require more computation, consider using accelerated computing instances such as the P2 instance type.

system.time(
  dlearning.modelh <- h2o.deeplearning(y = y.dep,
                                      x = x.indep,
                                      training_frame = train.h2o,
                                      epoch = 250,
                                      hidden = c(250,250),
                                      activation = "Rectifier",
                                      seed = 1122,
                                      distribution="multinomial"
  )
)
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##    user  system elapsed 
##   1.216   0.020 166.508
perf.dl<-h2o.performance(model=dlearning.modelh,newdata=test.h2o)
perf.dl
## H2OBinomialMetrics: deeplearning
## 
## MSE:  0.1678359
## RMSE:  0.4096778
## LogLoss:  1.86509
## Mean Per-Class Error:  0.3433013
## AUC:  0.7568822
## Gini:  0.5137644
## 
## Confusion Matrix (vertical: actual; across: predicted) for F1-optimal threshold:
##          0  1    Error     Rate
## 0      290 24 0.076433  =24/314
## 1       36 23 0.610169   =36/59
## Totals 326 47 0.160858  =60/373
## 
## Maximum Metrics: Maximum metrics at their respective thresholds
##                       metric threshold    value idx
## 1                     max f1  0.826267 0.433962  46
## 2                     max f2  0.000000 0.588235 239
## 3               max f0point5  0.999929 0.511811  16
## 4               max accuracy  0.999999 0.865952  10
## 5              max precision  1.000000 1.000000   0
## 6                 max recall  0.000000 1.000000 326
## 7            max specificity  1.000000 1.000000   0
## 8           max absolute_mcc  0.999929 0.363219  16
## 9 max min_per_class_accuracy  0.000004 0.662420 145
## 10 max mean_per_class_accuracy  0.000000 0.685334 224
## 
## Gains/Lift Table: Extract with `h2o.gainsLift(<model>, <data>)` or `h2o.gainsLift(<model>, valid=<T/F>, xval=<T/F>)`
h2o.sensitivity(perf.dl,0.5)
## Warning in h2o.find_row_by_threshold(object, t): Could not find exact
## threshold: 0.5 for this set of metrics; using closest threshold found:
## 0.496293348880151. Run `h2o.predict` and apply your desired threshold on a
## probability column.
## [[1]]
## [1] 0.3898305
h2o.auc(perf.dl)
## [1] 0.7568822

The AUC metric for this model is 0.7568822, which is less than what you got from the earlier models. I recommend further experimentation using different hyper parameters, such as the learning rate, epoch or the number of hidden layers.

H2O’s built-in functions provide many key statistical measures that can help measure model performance. Here are some of these key terms.

MetricDescription
SensitivityMeasures the proportion of positives that have been correctly identified. It is also called the true positive rate, or recall.
SpecificityMeasures the proportion of negatives that have been correctly identified. It is also called the true negative rate.
ThresholdCutoff point that maximizes specificity and sensitivity. While the model may not provide the highest prediction at this point, it would not be biased towards positives or negatives.
PrecisionThe fraction of the documents retrieved that are relevant to the information needed, for example, how many of the positively classified are relevant
AUC

Provides insight into how well the classifier is able to separate the two classes. The implicit goal is to deal with situations where the sample distribution is highly skewed, with a tendency to overfit to a single class.

0.90 – 1 = excellent (A)

0.8 – 0.9 = good (B)

0.7 – 0.8 = fair (C)

.6 – 0.7 = poor (D)

0.5 – 0.5 = fail (F)

Here’s a summary of the metrics generated from H2O’s built-in functions for the three models that produced useful results.

Metric Model 3GBM ModelDeep Learning Model

Accuracy

(max)

0.882038

(t=0.435479)

0.876676

(t=0.442757)

0.865952

(t=0.999999)

Precision

(max)

1.0

(t=0.821606)

1.0

(t=0802184)

1.0

(t=1.0)

Recall

(max)

1.01.0

1.0

(t=0)

Specificity

(max)

1.01.0

1.0

(t=1)

Sensitivity

 

0.20338980.1355932

0.3898305

(t=0.5)

AUC0.84923890.86305730.756882

Note: ‘t’ denotes threshold.

Your options at this point could be narrowed down to Model 3 and the GBM model, based on the AUC and accuracy metrics observed earlier.  If the slightly lower accuracy of the GBM model is deemed acceptable, the record label can choose to go to production with the GBM model, as it can predict a higher number of Top 10 hits.  The AUC metric for the GBM model is also higher than that of Model 3.

Record labels can experiment with different learning techniques and parameters before arriving at a model that proves to be the best fit for their business. Because deep learning models can be computationally expensive, record labels can choose more powerful EC2 instances on AWS to run their experiments faster.

Conclusion

In this post, I showed how the popular music industry can use analytics to predict the type of songs that make the Top 10 Billboard charts. By running H2O’s scalable machine learning platform on AWS, data scientists can easily experiment with multiple modeling techniques and interactively query the data using Amazon Athena, without having to manage the underlying infrastructure. This helps record labels make critical decisions on the type of artists and songs to promote in a timely fashion, thereby increasing sales and revenue.

If you have questions or suggestions, please comment below.


Additional Reading

Learn how to build and explore a simple geospita simple GEOINT application using SparkR.


About the Authors

gopalGopal Wunnava is a Partner Solution Architect with the AWS GSI Team. He works with partners and customers on big data engagements, and is passionate about building analytical solutions that drive business capabilities and decision making. In his spare time, he loves all things sports and movies related and is fond of old classics like Asterix, Obelix comics and Hitchcock movies.

 

 

Bob Strahan, a Senior Consultant with AWS Professional Services, contributed to this post.