Tag Archives: How-to

Announcing Local Build Support for AWS CodeBuild

Post Syndicated from Karthik Thirugnanasambandam original https://aws.amazon.com/blogs/devops/announcing-local-build-support-for-aws-codebuild/

Today, we’re excited to announce local build support in AWS CodeBuild.

AWS CodeBuild is a fully managed build service. There are no servers to provision and scale, or software to install, configure, and operate. You just specify the location of your source code, choose your build settings, and CodeBuild runs build scripts for compiling, testing, and packaging your code.

In this blog post, I’ll show you how to set up CodeBuild locally to build and test a sample Java application.

By building an application on a local machine you can:

  • Test the integrity and contents of a buildspec file locally.
  • Test and build an application locally before committing.
  • Identify and fix errors quickly from your local development environment.

Prerequisites

In this post, I am using AWS Cloud9 IDE as my development environment.

If you would like to use AWS Cloud9 as your IDE, follow the express setup steps in the AWS Cloud9 User Guide.

The AWS Cloud9 IDE comes with Docker and Git already installed. If you are going to use your laptop or desktop machine as your development environment, install Docker and Git before you start.

Steps to build CodeBuild image locally

Run git clone https://github.com/aws/aws-codebuild-docker-images.git to download this repository to your local machine.

$ git clone https://github.com/aws/aws-codebuild-docker-images.git

Lets build a local CodeBuild image for JDK 8 environment. The Dockerfile for JDK 8 is present in /aws-codebuild-docker-images/ubuntu/java/openjdk-8.

Edit the Dockerfile to remove the last line ENTRYPOINT [“dockerd-entrypoint.sh”] and save the file.

Run cd ubuntu/java/openjdk-8 to change the directory in your local workspace.

Run docker build -t aws/codebuild/java:openjdk-8 . to build the Docker image locally. This command will take few minutes to complete.

$ cd aws-codebuild-docker-images
$ cd ubuntu/java/openjdk-8
$ docker build -t aws/codebuild/java:openjdk-8 .

Steps to setup CodeBuild local agent

Run the following Docker pull command to download the local CodeBuild agent.

$ docker pull amazon/aws-codebuild-local:latest --disable-content-trust=false

Now you have the local agent image on your machine and can run a local build.

Run the following git command to download a sample Java project.

$ git clone https://github.com/karthiksambandam/sample-web-app.git

Steps to use the local agent to build a sample project

Let’s build the sample Java project using the local agent.

Execute the following Docker command to run the local agent and build the sample web app repository you cloned earlier.

$ docker run -it -v /var/run/docker.sock:/var/run/docker.sock -e "IMAGE_NAME=aws/codebuild/java:openjdk-8" -e "ARTIFACTS=/home/ec2-user/environment/artifacts" -e "SOURCE=/home/ec2-user/environment/sample-web-app" amazon/aws-codebuild-local

Note: We need to provide three environment variables namely  IMAGE_NAME, SOURCE and ARTIFACTS.

IMAGE_NAME: The name of your build environment image.

SOURCE: The absolute path to your source code directory.

ARTIFACTS: The absolute path to your artifact output folder.

When you run the sample project, you get a runtime error that says the YAML file does not exist. This is because a buildspec.yml file is not included in the sample web project. AWS CodeBuild requires a buildspec.yml to run a build. For more information about buildspec.yml, see Build Spec Example in the AWS CodeBuild User Guide.

Let’s add a buildspec.yml file with the following content to the sample-web-app folder and then rebuild the project.

version: 0.2

phases:
  build:
    commands:
      - echo Build started on `date`
      - mvn install

artifacts:
  files:
    - target/javawebdemo.war

$ docker run -it -v /var/run/docker.sock:/var/run/docker.sock -e "IMAGE_NAME=aws/codebuild/java:openjdk-8" -e "ARTIFACTS=/home/ec2-user/environment/artifacts" -e "SOURCE=/home/ec2-user/environment/sample-web-app" amazon/aws-codebuild-local

This time your build should be successful. Upon successful execution, look in the /artifacts folder for the final built artifacts.zip file to validate.

Conclusion:

In this blog post, I showed you how to quickly set up the CodeBuild local agent to build projects right from your local desktop machine or laptop. As you see, local builds can improve developer productivity by helping you identify and fix errors quickly.

I hope you found this post useful. Feel free to leave your feedback or suggestions in the comments.

CI/CD with Data: Enabling Data Portability in a Software Delivery Pipeline with AWS Developer Tools, Kubernetes, and Portworx

Post Syndicated from Kausalya Rani Krishna Samy original https://aws.amazon.com/blogs/devops/cicd-with-data-enabling-data-portability-in-a-software-delivery-pipeline-with-aws-developer-tools-kubernetes-and-portworx/

This post is written by Eric Han – Vice President of Product Management Portworx and Asif Khan – Solutions Architect

Data is the soul of an application. As containers make it easier to package and deploy applications faster, testing plays an even more important role in the reliable delivery of software. Given that all applications have data, development teams want a way to reliably control, move, and test using real application data or, at times, obfuscated data.

For many teams, moving application data through a CI/CD pipeline, while honoring compliance and maintaining separation of concerns, has been a manual task that doesn’t scale. At best, it is limited to a few applications, and is not portable across environments. The goal should be to make running and testing stateful containers (think databases and message buses where operations are tracked) as easy as with stateless (such as with web front ends where they are often not).

Why is state important in testing scenarios? One reason is that many bugs manifest only when code is tested against real data. For example, we might simply want to test a database schema upgrade but a small synthetic dataset does not exercise the critical, finer corner cases in complex business logic. If we want true end-to-end testing, we need to be able to easily manage our data or state.

In this blog post, we define a CI/CD pipeline reference architecture that can automate data movement between applications. We also provide the steps to follow to configure the CI/CD pipeline.

 

Stateful Pipelines: Need for Portable Volumes

As part of continuous integration, testing, and deployment, a team may need to reproduce a bug found in production against a staging setup. Here, the hosting environment is comprised of a cluster with Kubernetes as the scheduler and Portworx for persistent volumes. The testing workflow is then automated by AWS CodeCommit, AWS CodePipeline, and AWS CodeBuild.

Portworx offers Kubernetes storage that can be used to make persistent volumes portable between AWS environments and pipelines. The addition of Portworx to the AWS Developer Tools continuous deployment for Kubernetes reference architecture adds persistent storage and storage orchestration to a Kubernetes cluster. The example uses MongoDB as the demonstration of a stateful application. In practice, the workflow applies to any containerized application such as Cassandra, MySQL, Kafka, and Elasticsearch.

Using the reference architecture, a developer calls CodePipeline to trigger a snapshot of the running production MongoDB database. Portworx then creates a block-based, writable snapshot of the MongoDB volume. Meanwhile, the production MongoDB database continues serving end users and is uninterrupted.

Without the Portworx integrations, a manual process would require an application-level backup of the database instance that is outside of the CI/CD process. For larger databases, this could take hours and impact production. The use of block-based snapshots follows best practices for resilient and non-disruptive backups.

As part of the workflow, CodePipeline deploys a new MongoDB instance for staging onto the Kubernetes cluster and mounts the second Portworx volume that has the data from production. CodePipeline triggers the snapshot of a Portworx volume through an AWS Lambda function, as shown here

 

 

 

AWS Developer Tools with Kubernetes: Integrated Workflow with Portworx

In the following workflow, a developer is testing changes to a containerized application that calls on MongoDB. The tests are performed against a staging instance of MongoDB. The same workflow applies if changes were on the server side. The original production deployment is scheduled as a Kubernetes deployment object and uses Portworx as the storage for the persistent volume.

The continuous deployment pipeline runs as follows:

  • Developers integrate bug fix changes into a main development branch that gets merged into a CodeCommit master branch.
  • Amazon CloudWatch triggers the pipeline when code is merged into a master branch of an AWS CodeCommit repository.
  • AWS CodePipeline sends the new revision to AWS CodeBuild, which builds a Docker container image with the build ID.
  • AWS CodeBuild pushes the new Docker container image tagged with the build ID to an Amazon ECR registry.
  • Kubernetes downloads the new container (for the database client) from Amazon ECR and deploys the application (as a pod) and staging MongoDB instance (as a deployment object).
  • AWS CodePipeline, through a Lambda function, calls Portworx to snapshot the production MongoDB and deploy a staging instance of MongoDB• Portworx provides a snapshot of the production instance as the persistent storage of the staging MongoDB
    • The MongoDB instance mounts the snapshot.

At this point, the staging setup mimics a production environment. Teams can run integration and full end-to-end tests, using partner tooling, without impacting production workloads. The full pipeline is shown here.

 

Summary

This reference architecture showcases how development teams can easily move data between production and staging for the purposes of testing. Instead of taking application-specific manual steps, all operations in this CodePipeline architecture are automated and tracked as part of the CI/CD process.

This integrated experience is part of making stateful containers as easy as stateless. With AWS CodePipeline for CI/CD process, developers can easily deploy stateful containers onto a Kubernetes cluster with Portworx storage and automate data movement within their process.

The reference architecture and code are available on GitHub:

● Reference architecture: https://github.com/portworx/aws-kube-codesuite
● Lambda function source code for Portworx additions: https://github.com/portworx/aws-kube-codesuite/blob/master/src/kube-lambda.py

For more information about persistent storage for containers, visit the Portworx website. For more information about Code Pipeline, see the AWS CodePipeline User Guide.

Tinkernut’s hidden Coke bottle spy cam

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/tinkernuts-spy-cam/

Go undercover and keep an eye on your stuff with this brilliant secret Coke bottle spy cam from Tinkernut!

Secret Coke Bottle SPY CAM! – Weekend Hacker #1803

SPECIAL NOTE*** THE FULL TUTORIAL WILL BE AVAILABLE NEXT WEEK April Fools! What a terrible day. So many pranks. You can’t believe anything you read. People invading your space. The mental and physical anguish of enduring the day. It’s time to fight back! Let’s catch the perps in action by making a device that always watches.

Keeping tabs

A Raspberry Pi Zero W, a small camera, and a rechargeable Lithium Polymer (LiPo) battery constitute the bulk of this project’s tech. A pair of 3D-printed parts, and gelatine-solidified Coke Zero make up the fake fizzy body.

Tinkernut Coke bottle Raspberry Pi Spy Cam

“So let’s make this video as short as possible and just buy a cheap pre-made spy cam off of Amazon. Just kidding,” Tinkernut jokes in the tutorial video for the project, before going through the step-by-step process of using the Raspberry Pi to “DIY this the right way”.

After accessing the Zero W from his laptop via SSH, Tinkernut opted for using the rpi_camera_surveillance_system Python script written by GitHub user RuiSantosdotme to control the spy cam. Luckily, this meant no additional library setup, and basically no lag on the video feed.

What we want to do is create a script that activates the camera and serves it to a web page so that we can access it from any web browser. There are plenty of different ways to do this (Motion, Raspivid, etc), but I found a simple Python script that does everything I need it to do and doesn’t require any extra software or libraries to install. The best thing about it is that the lag time is practically unnoticeable.

With the code in place, every boot-up of the Raspberry Pi automatically launches both the script and a web page of the live video, allowing for constant monitoring of potential sneaks and thieves.

Tinkernut Coke bottle Raspberry Pi Spy Cam

The projects is powered by a 1500mAh LiPo battery and the Adafruit LiPo charger. It also includes a simple on/off switch, which Tinkernut wired to the charger and the Pi’s PP1 and PP6 connector pads.

Tinkernut Coke bottle Raspberry Pi Spy Cam

Tinkernut decided to use a Coke Zero bottle for the build, incorporating 3D-printed parts to house the Pi, and a mix of Coke and gelatine to create a realistic-looking filling for the bottle. However, the setup can be transferred to pretty much any hollow item in your home, say, a cookie jar or a cracker box. So get creative and get spying!

A complete spy cam how-to

If you’d like to make your own secret spy cam, you can find a tutorial for Tinkernut’s build at hackster.io, or follow along with his video below. Also make sure to subscribe his YouTube channel to be updated on all his newest builds — they’re rather splendid.

BUILD: Coke Bottle SPY CAM! – Tinkernut Workbench

Learn how to take a regular Coke Zero bottle, cram a Raspberry Pi and webcam inside of it, and have it still look like a regular Coke Zero bottle. Why would you want to do this? To spy on those irritating April Fooligans!!!

And if you’re interested in more spy-themed digital making projects, check out our complete 007 how-to guide for links to tutorials such as our Sense HAT puzzle box, Parent detector, and Laser tripwire.

The post Tinkernut’s hidden Coke bottle spy cam appeared first on Raspberry Pi.

How to migrate a Hue database from an existing Amazon EMR cluster

Post Syndicated from Anvesh Ragi original https://aws.amazon.com/blogs/big-data/how-to-migrate-a-hue-database-from-an-existing-amazon-emr-cluster/

Hadoop User Experience (Hue) is an open-source, web-based, graphical user interface for use with Amazon EMR and Apache Hadoop. The Hue database stores things like users, groups, authorization permissions, Apache Hive queries, Apache Oozie workflows, and so on.

There might come a time when you want to migrate your Hue database to a new EMR cluster. For example, you might want to upgrade from an older version of the Amazon EMR AMI (Amazon Machine Image), but your Hue application and its database have had a lot of customization.You can avoid re-creating these user entities and retain query/workflow histories in Hue by migrating the existing Hue database, or remote database in Amazon RDS, to a new cluster.

By default, Hue user information and query histories are stored in a local MySQL database on the EMR cluster’s master node. However, you can create one or more Hue-enabled clusters using a configuration stored in Amazon S3 and a remote MySQL database in Amazon RDS. This allows you to preserve user information and query history that Hue creates without keeping your Amazon EMR cluster running.

This post describes the step-by-step process for migrating the Hue database from an existing EMR cluster.

Note: Amazon EMR supports different Hue versions across different AMI releases. Keep in mind the compatibility of Hue versions between the old and new clusters in this migration activity. Currently, Hue 3.x.x versions are not compatible with Hue 4.x.x versions, and therefore a migration between these two Hue versions might create issues. In addition, Hue 3.10.0 is not backward compatible with its previous 3.x.x versions.

Before you begin

First, let’s create a new testUser in Hue on an existing EMR cluster, as shown following:

You will use these credentials later to log in to Hue on the new EMR cluster and validate whether you have successfully migrated the Hue database.

Let’s get started!

Migration how-to

Follow these steps to migrate your database to a new EMR cluster and then validate the migration process.

1.) Make a backup of the existing Hue database.

Use SSH to connect to the master node of the old cluster, as shown following (if you are using Linux/Unix/macOS), and dump the Hue database to a JSON file.

$ ssh -i ~/key.pem [email protected]
$ /usr/lib/hue/build/env/bin/hue dumpdata > ./hue-mysql.json

Edit the hue-mysql.json output file by removing all JSON objects that have useradmin.userprofile in the model field, and save the file. For example, remove the objects as shown following:

{
  "pk": 1,
  "model": "useradmin.userprofile",
  "fields": {
    "last_activity": "2018-01-10T11:41:04",
    "creation_method": "HUE",
    "first_login": false,
    "user": 1,
    "home_directory": "/user/hue_admin"
  }
},

2.) Store the hue-mysql.json file on persistent storage like Amazon S3.

You can copy the file from the old EMR cluster to Amazon S3 using the AWS CLI or Secure Copy (SCP) client. For example, the following uses the AWS CLI:

$ aws s3 cp ./hue-mysql.json s3://YourBucketName/folder/

3.) Recover/reload the backed-up Hue database into the new EMR cluster.

a.) Use SSH to connect to the master node of the new EMR cluster, and stop the Hue service that is already running.

$ ssh -i ~/key.pem [email protected]
$ sudo stop hue
hue stop/waiting

b.) Connect to the Hue database—either the local MySQL database or the remote database in Amazon RDS for your cluster as shown following, using the mysql client.

$ mysql -h HOST –u USER –pPASSWORD

For a local MySQL database, you can find the hostname, user name, and password for connecting to the database in the /etc/hue/conf/hue.ini file on the master node.

[[database]]
    engine = mysql
    name = huedb
    case_insensitive_collation = utf8_unicode_ci
    test_charset = utf8
    test_collation = utf8_bin
    host = ip-172-31-37-133.us-west-2.compute.internal
    user = hue
    test_name = test_huedb
    password = QdWbL3Ai6GcBqk26
    port = 3306

Based on the preceding example configuration, the sample command is as follows. (Replace the host, user, and password details based on your EMR cluster settings.)

$ mysql -h ip-172-31-37-133.us-west-2.compute.internal -u hue -pQdWbL3Ai6GcBqk26

c.) Drop the existing Hue database with the name huedb from the MySQL server.

mysql> DROP DATABASE IF EXISTS huedb;

d.) Create a new empty database with the same name huedb.

mysql> CREATE DATABASE huedb DEFAULT CHARACTER SET utf8 DEFAULT COLLATE=utf8_bin;

e.) Now, synchronize Hue with its database huedb.

$ sudo /usr/lib/hue/build/env/bin/hue syncdb --noinput
$ sudo /usr/lib/hue/build/env/bin/hue migrate

(This populates the new huedb with all Hue tables that are required.)

f.) Log in to MySQL again, and drop the foreign key to clean tables.

mysql> SHOW CREATE TABLE huedb.auth_permission;

In the following example, replace <id value> with the actual value from the preceding output.

mysql> ALTER TABLE huedb.auth_permission DROP FOREIGN KEY
content_type_id_refs_id_<id value>;

g.) Delete the contents of the django_content_type

mysql> DELETE FROM huedb.django_content_type;

h.) Download the backed-up Hue database dump from Amazon S3 to the new EMR cluster, and load it into Hue.

$ aws s3 cp s3://YourBucketName/folder/hue-mysql.json ./
$ sudo /usr/lib/hue/build/env/bin/hue loaddata ./hue-mysql.json

i.) In MySQL, add the foreign key content_type_id back to the auth_permission

mysql> use huedb;
mysql> ALTER TABLE huedb.auth_permission ADD FOREIGN KEY (`content_type_id`) REFERENCES `django_content_type` (`id`);

j.) Start the Hue service again.

$ sudo start hue
hue start/running, process XXXX

That’s it! Now, verify whether you can successfully access the Hue UI, and sign in using your existing testUser credentials.

After a successful sign in to Hue on the new EMR cluster, you should see a similar Hue homepage as shown following with testUser as the user signed in:

Conclusion

You have now learned how to migrate an existing Hue database to a new Amazon EMR cluster and validate the migration process. If you have any similar Amazon EMR administration topics that you want to see covered in a future post, please let us know in the comments below.


Additional Reading

If you found this post useful, be sure to check out Anomaly Detection Using PySpark, Hive, and Hue on Amazon EMR and Dynamically Create Friendly URLs for Your Amazon EMR Web Interfaces.


About the Author


Anvesh Ragi is a Big Data Support Engineer with Amazon Web Services. He works closely with AWS customers to provide them architectural and engineering assistance for their data processing workflows. In his free time, he enjoys traveling and going for hikes.

Your Hard Drive Crashed — Get Working Again Fast with Backblaze

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/how-to-recover-your-files-with-backblaze/

holding a hard drive and diagnostic tools
The worst thing for a computer user has happened. The hard drive on your computer crashed, or your computer is lost or completely unusable.

Fortunately, you’re a Backblaze customer with a current backup in the cloud. That’s great. The challenge is that you’ve got a presentation to make in just 48 hours and the document and materials you need for the presentation were on the hard drive that crashed.

Relax. Backblaze has your data (and your back). The question is, how do you get what you need to make that presentation deadline?

Here are some strategies you could use.

One — The first approach is to get back the presentation file and materials you need to meet your presentation deadline as quickly as possible. You can use another computer (maybe even your smartphone) to make that presentation.

Two — The second approach is to get your computer (or a new computer, if necessary) working again and restore all the files from your Backblaze backup.

Let’s start with Option One, which gets you back to work with just the files you need now as quickly as possible.

Option One — You’ve Got a Deadline and Just Need Your Files

Getting Back to Work Immediately

You want to get your computer working again as soon as possible, but perhaps your top priority is getting access to the files you need for your presentation. The computer can wait.

Find a Computer to Use

First of all. You’re going to need a computer to use. If you have another computer handy, you’re all set. If you don’t, you’re going to need one. Here are some ideas on where to find one:

  • Family and Friends
  • Work
  • Neighbors
  • Local library
  • Local school
  • Community or religious organization
  • Local computer shop
  • Online store

Laptop computer

If you have a smartphone that you can use to give your presentation or to print materials, that’s great. With the Backblaze app for iOS and Android, you can download files directly from your Backblaze account to your smartphone. You also have the option with your smartphone to email or share files from your Backblaze backup so you can use them elsewhere.

Laptop with smartphone

Download The File(s) You Need

Once you have the computer, you need to connect to your Backblaze backup through a web browser or the Backblaze smartphone app.

Backblaze Web Admin

Sign into your Backblaze account. You can download the files directly or use the share link to share files with yourself or someone else.

If you need step-by-step instructions on retrieving your files, see Restore the Files to the Drive section below. You also can find help at https://help.backblaze.com/hc/en-us/articles/217665888-How-to-Create-a-Restore-from-Your-Backblaze-Backup.

Smartphone App

If you have an iOS or Android smartphone, you can use the Backblaze app and retrieve the files you need. You then could view the file on your phone, use a smartphone app with the file, or email it to yourself or someone else.

Backblaze Smartphone app (iOS)

Backblaze Smartphone app (iOS)

Using one of the approaches above, you got your files back in time for your presentation. Way to go!

Now, the next step is to get the computer with the bad drive running again and restore all your files, or, if that computer is no longer usable, restore your Backblaze backup to a new computer.

Option Two — You Need a Working Computer Again

Getting the Computer with the Failed Drive Running Again (or a New Computer)

If the computer with the failed drive can’t be saved, then you’re going to need a new computer. A new computer likely will come with the operating system installed and ready to boot. If you’ve got a running computer and are ready to restore your files from Backblaze, you can skip forward to Restore the Files to the Drive.

If you need to replace the hard drive in your computer before you restore your files, you can continue reading.

Buy a New Hard Drive to Replace the Failed Drive

The hard drive is gone, so you’re going to need a new drive. If you have a computer or electronics store nearby, you could get one there. Another choice is to order a drive online and pay for one or two-day delivery. You have a few choices:

  1. Buy a hard drive of the same type and size you had
  2. Upgrade to a drive with more capacity
  3. Upgrade to an SSD. SSDs cost more but they are faster, more reliable, and less susceptible to jolts, magnetic fields, and other hazards that can affect a drive. Otherwise, they work the same as a hard disk drive (HDD) and most likely will work with the same connector.


Hard Disk Drive (HDD)Solid State Drive (SSD)

Hard Disk Drive (HDD)

Solid State Drive (SSD)


Be sure that the drive dimensions are compatible with where you’re going to install the drive in your computer, and the drive connector is compatible with your computer system (SATA, PCIe, etc.) Here’s some help.

Install the Drive

If you’re handy with computers, you can install the drive yourself. It’s not hard, and there are numerous videos on YouTube and elsewhere on how to do this. Just be sure to note how everything was connected so you can get everything connected and put back together correctly. Also, be sure that you discharge any static electricity from your body by touching something metallic before you handle anything inside the computer. If all this sounds like too much to handle, find a friend or a local computer store to help you.

Note:  If the drive that failed is a boot drive for your operating system (either Macintosh or Windows), you need to make sure that the drive is bootable and has the operating system files on it. You may need to reinstall from an operating system source disk or install files.

Restore the Files to the Drive

To start, you will need to sign in to the Backblaze website with your registered email address and password. Visit https://secure.backblaze.com/user_signin.htm to login.

Sign In to Your Backblaze Account

Selecting the Backup

Once logged in, you will be brought to the account Overview page. On this page, all of the computers registered for backup under your account are shown with some basic information about each. Select the backup from which you wish to restore data by using the appropriate “Restore” button.

Screenshot of Admin for Selecting the Type of Restore

Selecting the Type of Restore

Backblaze offers three different ways in which you can receive your restore data: downloadable ZIP file, USB flash drive, or USB hard drive. The downloadable ZIP restore option will create a ZIP file of the files you request that is made available for download for 7 days. ZIP restores do not have any additional cost and are a great option for individual files or small sets of data.

Depending on the speed of your internet connection to the Backblaze data center, downloadable restores may not always be the best option for restoring very large amounts of data. ZIP restores are limited to 500 GB per request and a maximum of 5 active requests can be submitted under a single account at any given time.

USB flash and hard drive restores are built with the data you request and then shipped to an address of your choosing via FedEx Overnight or FedEx Priority International. USB flash restores cost $99 and can contain up to 128 GB (110,000 MB of data) and USB hard drive restores cost $189 and can contain up to 4TB max (3,500,000 MB of data). Both include the cost of shipping.

You can return the ZIP drive within 30 days for a full refund with our Restore Return Refund Program, effectively making the process of restoring free, even with a shipped USB drive.

Screenshot of Admin for Selecting the Backup

Selecting Files for Restore

Using the left hand file viewer, navigate to the location of the files you wish to restore. You can use the disclosure triangles to see subfolders. Clicking on a folder name will display the folder’s files in the right hand file viewer. If you are attempting to restore files that have been deleted or are otherwise missing or files from a failed or disconnected secondary or external hard drive, you may need to change the time frame parameters.

Put checkmarks next to disks, files or folders you’d like to recover. Once you have selected the files and folders you wish to restore, select the “Continue with Restore” button above or below the file viewer. Backblaze will then build the restore via the option you select (ZIP or USB drive). You’ll receive an automated email notifying you when the ZIP restore has been built and is ready for download or when the USB restore drive ships.

If you are using the downloadable ZIP option, and the restore is over 2 GB, we highly recommend using the Backblaze Downloader for better speed and reliability. We have a guide on using the Backblaze Downloader for Mac OS X or for Windows.

For additional assistance, visit our help files at https://help.backblaze.com/hc/en-us/articles/217665888-How-to-Create-a-Restore-from-Your-Backblaze-Backup

Screenshot of Admin for Selecting Files for Restore

Extracting the ZIP

Recent versions of both macOS and Windows have built-in capability to extract files from a ZIP archive. If the built-in capabilities aren’t working for you, you can find additional utilities for Macintosh and Windows.

Reactivating your Backblaze Account

Now that you’ve got a working computer again, you’re going to need to reinstall Backblaze Backup (if it’s not on the system already) and connect with your existing account. Start by downloading and reinstalling Backblaze.

If you’ve restored the files from your Backblaze Backup to your new computer or drive, you don’t want to have to reupload the same files again to your Backblaze backup. To let Backblaze know that this computer is on the same account and has the same files, you need to use “Inherit Backup State.” See https://help.backblaze.com/hc/en-us/articles/217666358-Inherit-Backup-State

Screenshot of Admin for Inherit Backup State

That’s It

You should be all set, either with the files you needed for your presentation, or with a restored computer that is again ready to do productive work.

We hope your presentation wowed ’em.

If you have any additional questions on restoring from a Backblaze backup, please ask away in the comments. Also, be sure to check out our help resources at https://www.backblaze.com/help.html.

The post Your Hard Drive Crashed — Get Working Again Fast with Backblaze appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

HackSpace magazine 4: the wearables issue

Post Syndicated from Andrew Gregory original https://www.raspberrypi.org/blog/hackspace-4-wearables/

Big things are afoot in the world of HackSpace magazine! This month we’re running our first special issue, with wearables projects throughout the magazine. Moreover, we’re giving away our first subscription gift free to all 12-month print subscribers. Lastly, and most importantly, we’ve made the cover EXTRA SHINY!

HackSpace magazine issue 4 cover

Prepare your eyeballs — it’s HackSpace magazine issue 4!

Wearables

In this issue, we’re taking an in-depth look at wearable tech. Not Fitbits or Apple Watches — we’re talking stuff you can make yourself, from projects that take a couple of hours to put together, to the huge, inspiring builds that are bringing technology to the runway. If you like wearing clothes and you like using your brain to make things better, then you’ll love this feature.

We’re continuing our obsession with Nixie tubes, with the brilliant Time-To-Go-Clock – Trump edition. This ingenious bit of kit uses obsolete Russian electronics to count down the time until the end of the 45th president’s term in office. However, you can also program it to tell the time left to any predictable event, such as the deadline for your tax return or essay submission, or the date England gets knocked out of the World Cup.

HackSpace magazine page 08
HackSpace magazine page 70
HackSpace magazine issue 4 page 98

We’re also talking to Dr Lucy Rogers — NASA alumna, Robot Wars judge, and fellow of the Institution of Mechanical Engineers — about the difference between making as a hobby and as a job, and about why we need the Guild of Makers. Plus, issue 4 has a teeny boat, the most beautiful Raspberry Pi cases you’ve ever seen, and it explores the results of what happens when you put a bunch of hardware hackers together in a French chateau — sacré bleu!

Tutorials

As always, we’ve got more how-tos than you can shake a soldering iron at. Fittingly for the current climate here in the UK, there’s a hot water monitor, which shows you how long you have before your morning shower turns cold, and an Internet of Tea project to summon a cuppa from your kettle via the web. Perhaps not so fittingly, there’s also an ESP8266 project for monitoring a solar power station online. Readers in the southern hemisphere, we’ll leave that one for you — we haven’t seen the sun here for months!

And there’s more!

We’re super happy to say that all our 12-month print subscribers have been sent an Adafruit Circuit Playground Express with this new issue:

Adafruit Circuit Playground Express HackSpace

This gadget was developed primarily with wearables in mind and comes with all sorts of in-built functionality, so subscribers can get cracking with their latest wearable project today! If you’re not a 12-month print subscriber, you’ll miss out, so subscribe here to get your magazine and your device,  and let us know what you’ll make.

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‘Pirate’ Kodi Addon Devs & Distributors Told to Cease-and-Desist

Post Syndicated from Andy original https://torrentfreak.com/pirate-kodi-addon-devs-distributors-told-to-cease-and-desist-180214/

Last November, following a year of upheaval for third-party addon creators and distributors, yet more turmoil hit the community in the form of threats from the world’s most powerful anti-piracy coalition – the Alliance for Creativity and Entertainment (ACE).

Comprised of 30 companies including the studios of the MPAA, Amazon, Netflix, CBS, HBO, BBC, Sky, Bell Canada, CBS, Hulu, Lionsgate, Foxtel, Village Roadshow, and many more, ACE warned several developers to shut down – or else.

The letter: shut down – or else

Now it appears that ACE is on the warpath again, this time targeting a broader range of individuals involved in the Kodi addon scene, from developers and distributors to those involved in the production of how-to videos on YouTube.

The first report of action came from TVAddons, who noted that the lead developer at the Noobs and Nerds repository had been targeted with a cease-and-desist notice, adding that people from the site had been “visited at their homes.”

As seen in the image below, the Noobs and Nerds website is currently down. The site’s Twitter account has also been disabled.

Noobs and Nerds – gone

While TVAddons couldn’t precisely confirm the source of the threat, information gathered from individuals involved in the addon scene all point to the involvement of ACE.

In particular, a man known online as Teverz, who develops his own builds, runs a repo, and creates Kodi-themed YouTube videos, confirmed that ACE had been in touch.

An apparently unconcerned Teverz….

“I am not a dev so they really don’t scare me lmao,” he added.

Teverz claims to be from Canada and it appears that others in the country are also facing cease and desist notices. An individual known as Doggmatic, who also identifies as Canadian and has Kodi builds under his belt, says he too was targeted.

Another target in Canada

Doggmatic, who appears to be part of the Illuminati repo, says he had someone call the people who sent the cease-and-desist but like Teverz, he doesn’t seem overly concerned, at least for now.

“I have a legal representative calling them. The letters they sent aren’t legal documents. No lawyer signed them and no law firm mentioned,” Doggmatic said.

But the threats don’t stop there. Blamo, the developer of the Neptune Rising addon accessible from the Blamo repo, also claims to have been threatened.

SpinzTV, who offers unofficial Kodi builds and an associated repository, is also under the spotlight. Unlike his Canadian counterparts, he has already thrown in the towel, according to a short announcement on Twitter.

For SpinzTV it’s all over…

TorrentFreak contacted the Alliance for Creativity and Entertainment, asking them if they could confirm the actions and provide any additional details. At the time of publication they had no information for us but we’ll update if and when that comes in.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN discounts, offers and coupons

The Fisher Piano: make music in the air

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/air-piano/

Piano keys are so limiting! Why not swap them out for LEDs and the wealth of instruments in Pygame to build air keys, as demonstrated by Instructables maker 2fishy?

Raspberry Pi LED Light Schroeder Piano – Twinkle Little Star

Raspberry Pi LED Light Schroeder Piano – Twinkle Little Star

Keys? Where we’re going you don’t need keys!

This project, created by either Yolanda or Ken Fisher (or both!), uses an array of LEDs and photoresistors to form a MIDI sequencer. Twelve LEDs replace piano keys, and another three change octaves and access the menu.

Each LED is paired with a photoresistor, which detects the emitted light to form a closed circuit. Interrupting the light beam — in this case with a finger — breaks the circuit, telling the Python program to perform an action.

2fishy LED light piano raspberry pi

We’re all hoping this is just the scaled-down prototype of a full-sized LED grand piano

Using Pygame, the 2fishy team can access 75 different instruments and 128 notes per instrument, making their wooden piano more than just a one-hit wonder.

Piano building

The duo made the piano’s body out of plywood, hardboard, and dowels, and equipped it with a Raspberry Pi 2, a speaker, and the aforementioned LEDs and photoresistors.

2fishy LED light piano raspberry pi

A Raspberry Pi 2 and speaker sit within the wooden body, with LEDs and photoresistors in place of the keys.

A complete how-to for the build, including some rather fancy and informative schematics, is available at Instructables, where 2fishy received a bronze medal for their project. Congratulations!

Learn more

If you’d like to learn more about using Pygame, check out The MagPi’s Make Games with Python Essentials Guide, available both in print and as a free PDF download.

And for more music-based projects using a variety of tech, be sure to browse our free resources.

Lastly, if you’d like to see more piano-themed Raspberry Pi projects, take a look at our Big Minecraft Piano, these brilliant piano stairs, this laser-guided piano teacher, and our video below about the splendid Street Fighter duelling pianos we witnessed at Maker Faire.

Pianette: Piano Street Fighter at Maker Faire NYC 2016

Two pianos wired up as Playstation 2 controllers allow users to battle…musically! We caught up with makers Eric Redon and Cyril Chapellier of foobarflies a…

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Thank you for my new Raspberry Pi, Santa! What next?

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/thank-you-for-my-new-raspberry-pi-santa-what-next/

Note: the Pi Towers team have peeled away from their desks to spend time with their families over the festive season, and this blog will be quiet for a while as a result. We’ll be back in the New Year with a bushel of amazing projects, awesome resources, and much merriment and fun times. Happy holidays to all!

Now back to the matter at hand. Your brand new Christmas Raspberry Pi.

Your new Raspberry Pi

Did you wake up this morning to find a new Raspberry Pi under the tree? Congratulations, and welcome to the Raspberry Pi community! You’re one of us now, and we’re happy to have you on board.

But what if you’ve never seen a Raspberry Pi before? What are you supposed to do with it? What’s all the fuss about, and why does your new computer look so naked?

Setting up your Raspberry Pi

Are you comfy? Good. Then let us begin.

Download our free operating system

First of all, you need to make sure you have an operating system on your micro SD card: we suggest Raspbian, the Raspberry Pi Foundation’s official supported operating system. If your Pi is part of a starter kit, you might find that it comes with a micro SD card that already has Raspbian preinstalled. If not, you can download Raspbian for free from our website.

An easy way to get Raspbian onto your SD card is to use a free tool called Etcher. Watch The MagPi’s Lucy Hattersley show you what you need to do. You can also use NOOBS to install Raspbian on your SD card, and our Getting Started guide explains how to do that.

Plug it in and turn it on

Your new Raspberry Pi 3 comes with four USB ports and an HDMI port. These allow you to plug in a keyboard, a mouse, and a television or monitor. If you have a Raspberry Pi Zero, you may need adapters to connect your devices to its micro USB and micro HDMI ports. Both the Raspberry Pi 3 and the Raspberry Pi Zero W have onboard wireless LAN, so you can connect to your home network, and you can also plug an Ethernet cable into the Pi 3.

Make sure to plug the power cable in last. There’s no ‘on’ switch, so your Pi will turn on as soon as you connect the power. Raspberry Pi uses a micro USB power supply, so you can use a phone charger if you didn’t receive one as part of a kit.

Learn with our free projects

If you’ve never used a Raspberry Pi before, or you’re new to the world of coding, the best place to start is our projects site. It’s packed with free projects that will guide you through the basics of coding and digital making. You can create projects right on your screen using Scratch and Python, connect a speaker to make music with Sonic Pi, and upgrade your skills to physical making using items from around your house.

Here’s James to show you how to build a whoopee cushion using a Raspberry Pi, paper plates, tin foil and a sponge:

Whoopee cushion PRANK with a Raspberry Pi: HOW-TO

Explore the world of Raspberry Pi physical computing with our free FutureLearn courses: http://rpf.io/futurelearn Free make your own Whoopi Cushion resource: http://rpf.io/whoopi For more information on Raspberry Pi and the charitable work of the Raspberry Pi Foundation, including Code Club and CoderDojo, visit http://rpf.io Our resources are free to use in schools, clubs, at home and at events.

Diving deeper

You’ve plundered our projects, you’ve successfully rigged every chair in the house to make rude noises, and now you want to dive deeper into digital making. Good! While you’re digesting your Christmas dinner, take a moment to skim through the Raspberry Pi blog for inspiration. You’ll find projects from across our worldwide community, with everything from home automation projects and retrofit upgrades, to robots, gaming systems, and cameras.

You’ll also find bucketloads of ideas in The MagPi magazine, the official monthly Raspberry Pi publication, available in both print and digital format. You can download every issue for free. If you subscribe, you’ll get a Raspberry Pi Zero W to add to your new collection. HackSpace magazine is another fantastic place to turn for Raspberry Pi projects, along with other maker projects and tutorials.

And, of course, simply typing “Raspberry Pi projects” into your preferred search engine will find thousands of ideas. Sites like Hackster, Hackaday, Instructables, Pimoroni, and Adafruit all have plenty of fab Raspberry Pi tutorials that they’ve devised themselves and that community members like you have created.

And finally

If you make something marvellous with your new Raspberry Pi – and we know you will – don’t forget to share it with us! Our Twitter, Facebook, Instagram and Google+ accounts are brimming with chatter, projects, and events. And our forums are a great place to visit if you have questions about your Raspberry Pi or if you need some help.

It’s good to get together with like-minded folks, so check out the growing Raspberry Jam movement. Raspberry Jams are community-run events where makers and enthusiasts can meet other makers, show off their projects, and join in with workshops and discussions. Find your nearest Jam here.

Have a great festive holiday and welcome to the community. We’ll see you in 2018!

The post Thank you for my new Raspberry Pi, Santa! What next? appeared first on Raspberry Pi.

Power data ingestion into Splunk using Amazon Kinesis Data Firehose

Post Syndicated from Tarik Makota original https://aws.amazon.com/blogs/big-data/power-data-ingestion-into-splunk-using-amazon-kinesis-data-firehose/

In late September, during the annual Splunk .conf, Splunk and Amazon Web Services (AWS) jointly announced that Amazon Kinesis Data Firehose now supports Splunk Enterprise and Splunk Cloud as a delivery destination. This native integration between Splunk Enterprise, Splunk Cloud, and Amazon Kinesis Data Firehose is designed to make AWS data ingestion setup seamless, while offering a secure and fault-tolerant delivery mechanism. We want to enable customers to monitor and analyze machine data from any source and use it to deliver operational intelligence and optimize IT, security, and business performance.

With Kinesis Data Firehose, customers can use a fully managed, reliable, and scalable data streaming solution to Splunk. In this post, we tell you a bit more about the Kinesis Data Firehose and Splunk integration. We also show you how to ingest large amounts of data into Splunk using Kinesis Data Firehose.

Push vs. Pull data ingestion

Presently, customers use a combination of two ingestion patterns, primarily based on data source and volume, in addition to existing company infrastructure and expertise:

  1. Pull-based approach: Using dedicated pollers running the popular Splunk Add-on for AWS to pull data from various AWS services such as Amazon CloudWatch or Amazon S3.
  2. Push-based approach: Streaming data directly from AWS to Splunk HTTP Event Collector (HEC) by using AWS Lambda. Examples of applicable data sources include CloudWatch Logs and Amazon Kinesis Data Streams.

The pull-based approach offers data delivery guarantees such as retries and checkpointing out of the box. However, it requires more ops to manage and orchestrate the dedicated pollers, which are commonly running on Amazon EC2 instances. With this setup, you pay for the infrastructure even when it’s idle.

On the other hand, the push-based approach offers a low-latency scalable data pipeline made up of serverless resources like AWS Lambda sending directly to Splunk indexers (by using Splunk HEC). This approach translates into lower operational complexity and cost. However, if you need guaranteed data delivery then you have to design your solution to handle issues such as a Splunk connection failure or Lambda execution failure. To do so, you might use, for example, AWS Lambda Dead Letter Queues.

How about getting the best of both worlds?

Let’s go over the new integration’s end-to-end solution and examine how Kinesis Data Firehose and Splunk together expand the push-based approach into a native AWS solution for applicable data sources.

By using a managed service like Kinesis Data Firehose for data ingestion into Splunk, we provide out-of-the-box reliability and scalability. One of the pain points of the old approach was the overhead of managing the data collection nodes (Splunk heavy forwarders). With the new Kinesis Data Firehose to Splunk integration, there are no forwarders to manage or set up. Data producers (1) are configured through the AWS Management Console to drop data into Kinesis Data Firehose.

You can also create your own data producers. For example, you can drop data into a Firehose delivery stream by using Amazon Kinesis Agent, or by using the Firehose API (PutRecord(), PutRecordBatch()), or by writing to a Kinesis Data Stream configured to be the data source of a Firehose delivery stream. For more details, refer to Sending Data to an Amazon Kinesis Data Firehose Delivery Stream.

You might need to transform the data before it goes into Splunk for analysis. For example, you might want to enrich it or filter or anonymize sensitive data. You can do so using AWS Lambda. In this scenario, Kinesis Data Firehose buffers data from the incoming source data, sends it to the specified Lambda function (2), and then rebuffers the transformed data to the Splunk Cluster. Kinesis Data Firehose provides the Lambda blueprints that you can use to create a Lambda function for data transformation.

Systems fail all the time. Let’s see how this integration handles outside failures to guarantee data durability. In cases when Kinesis Data Firehose can’t deliver data to the Splunk Cluster, data is automatically backed up to an S3 bucket. You can configure this feature while creating the Firehose delivery stream (3). You can choose to back up all data or only the data that’s failed during delivery to Splunk.

In addition to using S3 for data backup, this Firehose integration with Splunk supports Splunk Indexer Acknowledgments to guarantee event delivery. This feature is configured on Splunk’s HTTP Event Collector (HEC) (4). It ensures that HEC returns an acknowledgment to Kinesis Data Firehose only after data has been indexed and is available in the Splunk cluster (5).

Now let’s look at a hands-on exercise that shows how to forward VPC flow logs to Splunk.

How-to guide

To process VPC flow logs, we implement the following architecture.

Amazon Virtual Private Cloud (Amazon VPC) delivers flow log files into an Amazon CloudWatch Logs group. Using a CloudWatch Logs subscription filter, we set up real-time delivery of CloudWatch Logs to an Kinesis Data Firehose stream.

Data coming from CloudWatch Logs is compressed with gzip compression. To work with this compression, we need to configure a Lambda-based data transformation in Kinesis Data Firehose to decompress the data and deposit it back into the stream. Firehose then delivers the raw logs to the Splunk Http Event Collector (HEC).

If delivery to the Splunk HEC fails, Firehose deposits the logs into an Amazon S3 bucket. You can then ingest the events from S3 using an alternate mechanism such as a Lambda function.

When data reaches Splunk (Enterprise or Cloud), Splunk parsing configurations (packaged in the Splunk Add-on for Kinesis Data Firehose) extract and parse all fields. They make data ready for querying and visualization using Splunk Enterprise and Splunk Cloud.

Walkthrough

Install the Splunk Add-on for Amazon Kinesis Data Firehose

The Splunk Add-on for Amazon Kinesis Data Firehose enables Splunk (be it Splunk Enterprise, Splunk App for AWS, or Splunk Enterprise Security) to use data ingested from Amazon Kinesis Data Firehose. Install the Add-on on all the indexers with an HTTP Event Collector (HEC). The Add-on is available for download from Splunkbase.

HTTP Event Collector (HEC)

Before you can use Kinesis Data Firehose to deliver data to Splunk, set up the Splunk HEC to receive the data. From Splunk web, go to the Setting menu, choose Data Inputs, and choose HTTP Event Collector. Choose Global Settings, ensure All tokens is enabled, and then choose Save. Then choose New Token to create a new HEC endpoint and token. When you create a new token, make sure that Enable indexer acknowledgment is checked.

When prompted to select a source type, select aws:cloudwatch:vpcflow.

Create an S3 backsplash bucket

To provide for situations in which Kinesis Data Firehose can’t deliver data to the Splunk Cluster, we use an S3 bucket to back up the data. You can configure this feature to back up all data or only the data that’s failed during delivery to Splunk.

Note: Bucket names are unique. Thus, you can’t use tmak-backsplash-bucket.

aws s3 create-bucket --bucket tmak-backsplash-bucket --create-bucket-configuration LocationConstraint=ap-northeast-1

Create an IAM role for the Lambda transform function

Firehose triggers an AWS Lambda function that transforms the data in the delivery stream. Let’s first create a role for the Lambda function called LambdaBasicRole.

Note: You can also set this role up when creating your Lambda function.

$ aws iam create-role --role-name LambdaBasicRole --assume-role-policy-document file://TrustPolicyForLambda.json

Here is TrustPolicyForLambda.json.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "Service": "lambda.amazonaws.com"
      },
      "Action": "sts:AssumeRole"
    }
  ]
}

 

After the role is created, attach the managed Lambda basic execution policy to it.

$ aws iam attach-role-policy 
  --policy-arn arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole 
  --role-name LambdaBasicRole

 

Create a Firehose Stream

On the AWS console, open the Amazon Kinesis service, go to the Firehose console, and choose Create Delivery Stream.

In the next section, you can specify whether you want to use an inline Lambda function for transformation. Because incoming CloudWatch Logs are gzip compressed, choose Enabled for Record transformation, and then choose Create new.

From the list of the available blueprint functions, choose Kinesis Data Firehose CloudWatch Logs Processor. This function unzips data and place it back into the Firehose stream in compliance with the record transformation output model.

Enter a name for the Lambda function, choose Choose an existing role, and then choose the role you created earlier. Then choose Create Function.

Go back to the Firehose Stream wizard, choose the Lambda function you just created, and then choose Next.

Select Splunk as the destination, and enter your Splunk Http Event Collector information.

Note: Amazon Kinesis Data Firehose requires the Splunk HTTP Event Collector (HEC) endpoint to be terminated with a valid CA-signed certificate matching the DNS hostname used to connect to your HEC endpoint. You receive delivery errors if you are using a self-signed certificate.

In this example, we only back up logs that fail during delivery.

To monitor your Firehose delivery stream, enable error logging. Doing this means that you can monitor record delivery errors.

Create an IAM role for the Firehose stream by choosing Create new, or Choose. Doing this brings you to a new screen. Choose Create a new IAM role, give the role a name, and then choose Allow.

If you look at the policy document, you can see that the role gives Kinesis Data Firehose permission to publish error logs to CloudWatch, execute your Lambda function, and put records into your S3 backup bucket.

You now get a chance to review and adjust the Firehose stream settings. When you are satisfied, choose Create Stream. You get a confirmation once the stream is created and active.

Create a VPC Flow Log

To send events from Amazon VPC, you need to set up a VPC flow log. If you already have a VPC flow log you want to use, you can skip to the “Publish CloudWatch to Kinesis Data Firehose” section.

On the AWS console, open the Amazon VPC service. Then choose VPC, Your VPC, and choose the VPC you want to send flow logs from. Choose Flow Logs, and then choose Create Flow Log. If you don’t have an IAM role that allows your VPC to publish logs to CloudWatch, choose Set Up Permissions and Create new role. Use the defaults when presented with the screen to create the new IAM role.

Once active, your VPC flow log should look like the following.

Publish CloudWatch to Kinesis Data Firehose

When you generate traffic to or from your VPC, the log group is created in Amazon CloudWatch. The new log group has no subscription filter, so set up a subscription filter. Setting this up establishes a real-time data feed from the log group to your Firehose delivery stream.

At present, you have to use the AWS Command Line Interface (AWS CLI) to create a CloudWatch Logs subscription to a Kinesis Data Firehose stream. However, you can use the AWS console to create subscriptions to Lambda and Amazon Elasticsearch Service.

To allow CloudWatch to publish to your Firehose stream, you need to give it permissions.

$ aws iam create-role --role-name CWLtoKinesisFirehoseRole --assume-role-policy-document file://TrustPolicyForCWLToFireHose.json


Here is the content for TrustPolicyForCWLToFireHose.json.

{
  "Statement": {
    "Effect": "Allow",
    "Principal": { "Service": "logs.us-east-1.amazonaws.com" },
    "Action": "sts:AssumeRole"
  }
}

 

Attach the policy to the newly created role.

$ aws iam put-role-policy 
    --role-name CWLtoKinesisFirehoseRole 
    --policy-name Permissions-Policy-For-CWL 
    --policy-document file://PermissionPolicyForCWLToFireHose.json

Here is the content for PermissionPolicyForCWLToFireHose.json.

{
    "Statement":[
      {
        "Effect":"Allow",
        "Action":["firehose:*"],
        "Resource":["arn:aws:firehose:us-east-1:YOUR-AWS-ACCT-NUM:deliverystream/ FirehoseSplunkDeliveryStream"]
      },
      {
        "Effect":"Allow",
        "Action":["iam:PassRole"],
        "Resource":["arn:aws:iam::YOUR-AWS-ACCT-NUM:role/CWLtoKinesisFirehoseRole"]
      }
    ]
}

Finally, create a subscription filter.

$ aws logs put-subscription-filter 
   --log-group-name " /vpc/flowlog/FirehoseSplunkDemo" 
   --filter-name "Destination" 
   --filter-pattern "" 
   --destination-arn "arn:aws:firehose:us-east-1:YOUR-AWS-ACCT-NUM:deliverystream/FirehoseSplunkDeliveryStream" 
   --role-arn "arn:aws:iam::YOUR-AWS-ACCT-NUM:role/CWLtoKinesisFirehoseRole"

When you run the AWS CLI command preceding, you don’t get any acknowledgment. To validate that your CloudWatch Log Group is subscribed to your Firehose stream, check the CloudWatch console.

As soon as the subscription filter is created, the real-time log data from the log group goes into your Firehose delivery stream. Your stream then delivers it to your Splunk Enterprise or Splunk Cloud environment for querying and visualization. The screenshot following is from Splunk Enterprise.

In addition, you can monitor and view metrics associated with your delivery stream using the AWS console.

Conclusion

Although our walkthrough uses VPC Flow Logs, the pattern can be used in many other scenarios. These include ingesting data from AWS IoT, other CloudWatch logs and events, Kinesis Streams or other data sources using the Kinesis Agent or Kinesis Producer Library. We also used Lambda blueprint Kinesis Data Firehose CloudWatch Logs Processor to transform streaming records from Kinesis Data Firehose. However, you might need to use a different Lambda blueprint or disable record transformation entirely depending on your use case. For an additional use case using Kinesis Data Firehose, check out This is My Architecture Video, which discusses how to securely centralize cross-account data analytics using Kinesis and Splunk.

 


Additional Reading

If you found this post useful, be sure to check out Integrating Splunk with Amazon Kinesis Streams and Using Amazon EMR and Hunk for Rapid Response Log Analysis and Review.


About the Authors

Tarik Makota is a solutions architect with the Amazon Web Services Partner Network. He provides technical guidance, design advice and thought leadership to AWS’ most strategic software partners. His career includes work in an extremely broad software development and architecture roles across ERP, financial printing, benefit delivery and administration and financial services. He holds an M.S. in Software Development and Management from Rochester Institute of Technology.

 

 

 

Roy Arsan is a solutions architect in the Splunk Partner Integrations team. He has a background in product development, cloud architecture, and building consumer and enterprise cloud applications. More recently, he has architected Splunk solutions on major cloud providers, including an AWS Quick Start for Splunk that enables AWS users to easily deploy distributed Splunk Enterprise straight from their AWS console. He’s also the co-author of the AWS Lambda blueprints for Splunk. He holds an M.S. in Computer Science Engineering from the University of Michigan.

 

 

 

How to Enable Caching for AWS CodeBuild

Post Syndicated from Karthik Thirugnanasambandam original https://aws.amazon.com/blogs/devops/how-to-enable-caching-for-aws-codebuild/

AWS CodeBuild is a fully managed build service. There are no servers to provision and scale, or software to install, configure, and operate. You just specify the location of your source code, choose your build settings, and CodeBuild runs build scripts for compiling, testing, and packaging your code.

A typical application build process includes phases like preparing the environment, updating the configuration, downloading dependencies, running unit tests, and finally, packaging the built artifact.

Downloading dependencies is a critical phase in the build process. These dependent files can range in size from a few KBs to multiple MBs. Because most of the dependent files do not change frequently between builds, you can noticeably reduce your build time by caching dependencies.

In this post, I will show you how to enable caching for AWS CodeBuild.

Requirements

  • Create an Amazon S3 bucket for storing cache archives (You can use existing s3 bucket as well).
  • Create a GitHub account (if you don’t have one).

Create a sample build project:

1. Open the AWS CodeBuild console at https://console.aws.amazon.com/codebuild/.

2. If a welcome page is displayed, choose Get started.

If a welcome page is not displayed, on the navigation pane, choose Build projects, and then choose Create project.

3. On the Configure your project page, for Project name, type a name for this build project. Build project names must be unique across each AWS account.

4. In Source: What to build, for Source provider, choose GitHub.

5. In Environment: How to build, for Environment image, select Use an image managed by AWS CodeBuild.

  • For Operating system, choose Ubuntu.
  • For Runtime, choose Java.
  • For Version,  choose aws/codebuild/java:openjdk-8.
  • For Build specification, select Insert build commands.

Note: The build specification file (buildspec.yml) can be configured in two ways. You can package it along with your source root directory, or you can override it by using a project environment configuration. In this example, I will use the override option and will use the console editor to specify the build specification.

6. Under Build commands, click Switch to editor to enter the build specification.

Copy the following text.

version: 0.2

phases:
  build:
    commands:
      - mvn install
      
cache:
  paths:
    - '/root/.m2/**/*'

Note: The cache section in the build specification instructs AWS CodeBuild about the paths to be cached. Like the artifacts section, the cache paths are relative to $CODEBUILD_SRC_DIR and specify the directories to be cached. In this example, Maven stores the downloaded dependencies to the /root/.m2/ folder, but other tools use different folders. For example, pip uses the /root/.cache/pip folder, and Gradle uses the /root/.gradle/caches folder. You might need to configure the cache paths based on your language platform.

7. In Artifacts: Where to put the artifacts from this build project:

  • For Type, choose No artifacts.

8. In Cache:

  • For Type, choose Amazon S3.
  • For Bucket, choose your S3 bucket.
  • For Path prefix, type cache/archives/

9. In Service role, the Create a service role in your account option will display a default role name.  You can accept the default name or type your own.

If you already have an AWS CodeBuild service role, choose Choose an existing service role from your account.

10. Choose Continue.

11. On the Review page, to run a build, choose Save and build.

Review build and cache behavior:

Let us review our first build for the project.

In the first run, where no cache exists, overall build time would look something like below (notice the time for DOWNLOAD_SOURCE, BUILD and POST_BUILD):

If you check the build logs, you will see log entries for dependency downloads. The dependencies are downloaded directly from configured external repositories. At the end of the log, you will see an entry for the cache uploaded to your S3 bucket.

Let’s review the S3 bucket for the cached archive. You’ll see the cache from our first successful build is uploaded to the configured S3 path.

Let’s try another build with the same CodeBuild project. This time the build should pick up the dependencies from the cache.

In the second run, there was a cache hit (cache was generated from the first run):

You’ll notice a few things:

  1. DOWNLOAD_SOURCE took slightly longer. Because, in addition to the source code, this time the build also downloaded the cache from user’s s3 bucket.
  2. BUILD time was faster. As the dependencies didn’t need to get downloaded, but were reused from cache.
  3. POST_BUILD took slightly longer, but was relatively the same.

Overall, build duration was improved with cache.

Best practices for cache

  • By default, the cache archive is encrypted on the server side with the customer’s artifact KMS key.
  • You can expire the cache by manually removing the cache archive from S3. Alternatively, you can expire the cache by using an S3 lifecycle policy.
  • You can override cache behavior by updating the project. You can use the AWS CodeBuild the AWS CodeBuild console, AWS CLI, or AWS SDKs to update the project. You can also invalidate cache setting by using the new InvalidateProjectCache API. This API forces a new InvalidationKey to be generated, ensuring that future builds receive an empty cache. This API does not remove the existing cache, because this could cause inconsistencies with builds currently in flight.
  • The cache can be enabled for any folders in the build environment, but we recommend you only cache dependencies/files that will not change frequently between builds. Also, to avoid unexpected application behavior, don’t cache configuration and sensitive information.

Conclusion

In this blog post, I showed you how to enable and configure cache setting for AWS CodeBuild. As you see, this can save considerable build time. It also improves resiliency by avoiding external network connections to an artifact repository.

I hope you found this post useful. Feel free to leave your feedback or suggestions in the comments.

Using AWS CodeCommit Pull Requests to request code reviews and discuss code

Post Syndicated from Chris Barclay original https://aws.amazon.com/blogs/devops/using-aws-codecommit-pull-requests-to-request-code-reviews-and-discuss-code/

Thank you to Michael Edge, Senior Cloud Architect, for a great blog on CodeCommit pull requests.

~~~~~~~

AWS CodeCommit is a fully managed service for securely hosting private Git repositories. CodeCommit now supports pull requests, which allows repository users to review, comment upon, and interactively iterate on code changes. Used as a collaboration tool between team members, pull requests help you to review potential changes to a CodeCommit repository before merging those changes into the repository. Each pull request goes through a simple lifecycle, as follows:

  • The new features to be merged are added as one or more commits to a feature branch. The commits are not merged into the destination branch.
  • The pull request is created, usually from the difference between two branches.
  • Team members review and comment on the pull request. The pull request might be updated with additional commits that contain changes made in response to comments, or include changes made to the destination branch.
  • Once team members are happy with the pull request, it is merged into the destination branch. The commits are applied to the destination branch in the same order they were added to the pull request.

Commenting is an integral part of the pull request process, and is used to collaborate between the developers and the reviewer. Reviewers add comments and questions to a pull request during the review process, and developers respond to these with explanations. Pull request comments can be added to the overall pull request, a file within the pull request, or a line within a file.

To make the comments more useful, sign in to the AWS Management Console as an AWS Identity and Access Management (IAM) user. The username will then be associated with the comment, indicating the owner of the comment. Pull request comments are a great quality improvement tool as they allow the entire development team visibility into what reviewers are looking for in the code. They also serve as a record of the discussion between team members at a point in time, and shouldn’t be deleted.

AWS CodeCommit is also introducing the ability to add comments to a commit, another useful collaboration feature that allows team members to discuss code changed as part of a commit. This helps you discuss changes made in a repository, including why the changes were made, whether further changes are necessary, or whether changes should be merged. As is the case with pull request comments, you can comment on an overall commit, on a file within a commit, or on a specific line or change within a file, and other repository users can respond to your comments. Comments are not restricted to commits, they can also be used to comment on the differences between two branches, or between two tags. Commit comments are separate from pull request comments, i.e. you will not see commit comments when reviewing a pull request – you will only see pull request comments.

A pull request example

Let’s get started by running through an example. We’ll take a typical pull request scenario and look at how we’d use CodeCommit and the AWS Management Console for each of the steps.

To try out this scenario, you’ll need:

  • An AWS CodeCommit repository with some sample code in the master branch. We’ve provided sample code below.
  • Two AWS Identity and Access Management (IAM) users, both with the AWSCodeCommitPowerUser managed policy applied to them.
  • Git installed on your local computer, and access configured for AWS CodeCommit.
  • A clone of the AWS CodeCommit repository on your local computer.

In the course of this example, you’ll sign in to the AWS CodeCommit console as one IAM user to create the pull request, and as the other IAM user to review the pull request. To learn more about how to set up your IAM users and how to connect to AWS CodeCommit with Git, see the following topics:

  • Information on creating an IAM user with AWS Management Console access.
  • Instructions on how to access CodeCommit using Git.
  • If you’d like to use the same ‘hello world’ application as used in this article, here is the source code:
package com.amazon.helloworld;

public class Main {
	public static void main(String[] args) {

		System.out.println("Hello, world");
	}
}

The scenario below uses the us-east-2 region.

Creating the branches

Before we jump in and create a pull request, we’ll need at least two branches. In this example, we’ll follow a branching strategy similar to the one described in GitFlow. We’ll create a new branch for our feature from the main development branch (the default branch). We’ll develop the feature in the feature branch. Once we’ve written and tested the code for the new feature in that branch, we’ll create a pull request that contains the differences between the feature branch and the main development branch. Our team lead (the second IAM user) will review the changes in the pull request. Once the changes have been reviewed, the feature branch will be merged into the development branch.

Figure 1: Pull request link

Sign in to the AWS CodeCommit console with the IAM user you want to use as the developer. You can use an existing repository or you can go ahead and create a new one. We won’t be merging any changes to the master branch of your repository, so it’s safe to use an existing repository for this example. You’ll find the Pull requests link has been added just above the Commits link (see Figure 1), and below Commits you’ll find the Branches link. Click Branches and create a new branch called ‘develop’, branched from the ‘master’ branch. Then create a new branch called ‘feature1’, branched from the ‘develop’ branch. You’ll end up with three branches, as you can see in Figure 2. (Your repository might contain other branches in addition to the three shown in the figure).

Figure 2: Create a feature branch

If you haven’t cloned your repo yet, go to the Code link in the CodeCommit console and click the Connect button. Follow the instructions to clone your repo (detailed instructions are here). Open a terminal or command line and paste the git clone command supplied in the Connect instructions for your repository. The example below shows cloning a repository named codecommit-demo:

git clone https://git-codecommit.us-east-2.amazonaws.com/v1/repos/codecommit-demo

If you’ve previously cloned the repo you’ll need to update your local repo with the branches you created. Open a terminal or command line and make sure you’re in the root directory of your repo, then run the following command:

git remote update origin

You’ll see your new branches pulled down to your local repository.

$ git remote update origin
Fetching origin
From https://git-codecommit.us-east-2.amazonaws.com/v1/repos/codecommit-demo
 * [new branch]      develop    -> origin/develop
 * [new branch]      feature1   -> origin/feature1

You can also see your new branches by typing:

git branch --all

$ git branch --all
* master
  remotes/origin/develop
  remotes/origin/feature1
  remotes/origin/master

Now we’ll make a change to the ‘feature1’ branch. Open a terminal or command line and check out the feature1 branch by running the following command:

git checkout feature1

$ git checkout feature1
Branch feature1 set up to track remote branch feature1 from origin.
Switched to a new branch 'feature1'

Make code changes

Edit a file in the repo using your favorite editor and save the changes. Commit your changes to the local repository, and push your changes to CodeCommit. For example:

git commit -am 'added new feature'
git push origin feature1

$ git commit -am 'added new feature'
[feature1 8f6cb28] added new feature
1 file changed, 1 insertion(+), 1 deletion(-)

$ git push origin feature1
Counting objects: 9, done.
Delta compression using up to 4 threads.
Compressing objects: 100% (4/4), done.
Writing objects: 100% (9/9), 617 bytes | 617.00 KiB/s, done.
Total 9 (delta 2), reused 0 (delta 0)
To https://git-codecommit.us-east-2.amazonaws.com/v1/repos/codecommit-demo
   2774a53..8f6cb28  feature1 -> feature1

Creating the pull request

Now we have a ‘feature1’ branch that differs from the ‘develop’ branch. At this point we want to merge our changes into the ‘develop’ branch. We’ll create a pull request to notify our team members to review our changes and check whether they are ready for a merge.

In the AWS CodeCommit console, click Pull requests. Click Create pull request. On the next page select ‘develop’ as the destination branch and ‘feature1’ as the source branch. Click Compare. CodeCommit will check for merge conflicts and highlight whether the branches can be automatically merged using the fast-forward option, or whether a manual merge is necessary. A pull request can be created in both situations.

Figure 3: Create a pull request

After comparing the two branches, the CodeCommit console displays the information you’ll need in order to create the pull request. In the ‘Details’ section, the ‘Title’ for the pull request is mandatory, and you may optionally provide comments to your reviewers to explain the code change you have made and what you’d like them to review. In the ‘Notifications’ section, there is an option to set up notifications to notify subscribers of changes to your pull request. Notifications will be sent on creation of the pull request as well as for any pull request updates or comments. And finally, you can review the changes that make up this pull request. This includes both the individual commits (a pull request can contain one or more commits, available in the Commits tab) as well as the changes made to each file, i.e. the diff between the two branches referenced by the pull request, available in the Changes tab. After you have reviewed this information and added a title for your pull request, click the Create button. You will see a confirmation screen, as shown in Figure 4, indicating that your pull request has been successfully created, and can be merged without conflicts into the ‘develop’ branch.

Figure 4: Pull request confirmation page

Reviewing the pull request

Now let’s view the pull request from the perspective of the team lead. If you set up notifications for this CodeCommit repository, creating the pull request would have sent an email notification to the team lead, and he/she can use the links in the email to navigate directly to the pull request. In this example, sign in to the AWS CodeCommit console as the IAM user you’re using as the team lead, and click Pull requests. You will see the same information you did during creation of the pull request, plus a record of activity related to the pull request, as you can see in Figure 5.

Figure 5: Team lead reviewing the pull request

Commenting on the pull request

You now perform a thorough review of the changes and make a number of comments using the new pull request comment feature. To gain an overall perspective on the pull request, you might first go to the Commits tab and review how many commits are included in this pull request. Next, you might visit the Changes tab to review the changes, which displays the differences between the feature branch code and the develop branch code. At this point, you can add comments to the pull request as you work through each of the changes. Let’s go ahead and review the pull request. During the review, you can add review comments at three levels:

  • The overall pull request
  • A file within the pull request
  • An individual line within a file

The overall pull request
In the Changes tab near the bottom of the page you’ll see a ‘Comments on changes’ box. We’ll add comments here related to the overall pull request. Add your comments as shown in Figure 6 and click the Save button.

Figure 6: Pull request comment

A specific file in the pull request
Hovering your mouse over a filename in the Changes tab will cause a blue ‘comments’ icon to appear to the left of the filename. Clicking the icon will allow you to enter comments specific to this file, as in the example in Figure 7. Go ahead and add comments for one of the files changed by the developer. Click the Save button to save your comment.

Figure 7: File comment

A specific line in a file in the pull request
A blue ‘comments’ icon will appear as you hover over individual lines within each file in the pull request, allowing you to create comments against lines that have been added, removed or are unchanged. In Figure 8, you add comments against a line that has been added to the source code, encouraging the developer to review the naming standards. Go ahead and add line comments for one of the files changed by the developer. Click the Save button to save your comment.

Figure 8: Line comment

A pull request that has been commented at all three levels will look similar to Figure 9. The pull request comment is shown expanded in the ‘Comments on changes’ section, while the comments at file and line level are shown collapsed. A ‘comment’ icon indicates that comments exist at file and line level. Clicking the icon will expand and show the comment. Since you are expecting the developer to make further changes based on your comments, you won’t merge the pull request at this stage, but will leave it open awaiting feedback. Each comment you made results in a notification being sent to the developer, who can respond to the comments. This is great for remote working, where developers and team lead may be in different time zones.

Figure 9: Fully commented pull request

Adding a little complexity

A typical development team is going to be creating pull requests on a regular basis. It’s highly likely that the team lead will merge other pull requests into the ‘develop’ branch while pull requests on feature branches are in the review stage. This may result in a change to the ‘Mergable’ status of a pull request. Let’s add this scenario into the mix and check out how a developer will handle this.

To test this scenario, we could create a new pull request and ask the team lead to merge this to the ‘develop’ branch. But for the sake of simplicity we’ll take a shortcut. Clone your CodeCommit repo to a new folder, switch to the ‘develop’ branch, and make a change to one of the same files that were changed in your pull request. Make sure you change a line of code that was also changed in the pull request. Commit and push this back to CodeCommit. Since you’ve just changed a line of code in the ‘develop’ branch that has also been changed in the ‘feature1’ branch, the ‘feature1’ branch cannot be cleanly merged into the ‘develop’ branch. Your developer will need to resolve this merge conflict.

A developer reviewing the pull request would see the pull request now looks similar to Figure 10, with a ‘Resolve conflicts’ status rather than the ‘Mergable’ status it had previously (see Figure 5).

Figure 10: Pull request with merge conflicts

Reviewing the review comments

Once the team lead has completed his review, the developer will review the comments and make the suggested changes. As a developer, you’ll see the list of review comments made by the team lead in the pull request Activity tab, as shown in Figure 11. The Activity tab shows the history of the pull request, including commits and comments. You can reply to the review comments directly from the Activity tab, by clicking the Reply button, or you can do this from the Changes tab. The Changes tab shows the comments for the latest commit, as comments on previous commits may be associated with lines that have changed or been removed in the current commit. Comments for previous commits are available to view and reply to in the Activity tab.

In the Activity tab, use the shortcut link (which looks like this </>) to move quickly to the source code associated with the comment. In this example, you will make further changes to the source code to address the pull request review comments, so let’s go ahead and do this now. But first, you will need to resolve the ‘Resolve conflicts’ status.

Figure 11: Pull request activity

Resolving the ‘Resolve conflicts’ status

The ‘Resolve conflicts’ status indicates there is a merge conflict between the ‘develop’ branch and the ‘feature1’ branch. This will require manual intervention to restore the pull request back to the ‘Mergable’ state. We will resolve this conflict next.

Open a terminal or command line and check out the develop branch by running the following command:

git checkout develop

$ git checkout develop
Switched to branch 'develop'
Your branch is up-to-date with 'origin/develop'.

To incorporate the changes the team lead made to the ‘develop’ branch, merge the remote ‘develop’ branch with your local copy:

git pull

$ git pull
remote: Counting objects: 9, done.
Unpacking objects: 100% (9/9), done.
From https://git-codecommit.us-east-2.amazonaws.com/v1/repos/codecommit-demo
   af13c82..7b36f52  develop    -> origin/develop
Updating af13c82..7b36f52
Fast-forward
 src/main/java/com/amazon/helloworld/Main.java | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

Then checkout the ‘feature1’ branch:

git checkout feature1

$ git checkout feature1
Switched to branch 'feature1'
Your branch is up-to-date with 'origin/feature1'.

Now merge the changes from the ‘develop’ branch into your ‘feature1’ branch:

git merge develop

$ git merge develop
Auto-merging src/main/java/com/amazon/helloworld/Main.java
CONFLICT (content): Merge conflict in src/main/java/com/amazon/helloworld/Main.java
Automatic merge failed; fix conflicts and then commit the result.

Yes, this fails. The file Main.java has been changed in both branches, resulting in a merge conflict that can’t be resolved automatically. However, Main.java will now contain markers that indicate where the conflicting code is, and you can use these to resolve the issues manually. Edit Main.java using your favorite IDE, and you’ll see it looks something like this:

package com.amazon.helloworld;

import java.util.*;

/**
 * This class prints a hello world message
 */

public class Main {
   public static void main(String[] args) {

<<<<<<< HEAD
        Date todaysdate = Calendar.getInstance().getTime();

        System.out.println("Hello, earthling. Today's date is: " + todaysdate);
=======
      System.out.println("Hello, earth");
>>>>>>> develop
   }
}

The code between HEAD and ‘===’ is the code the developer added in the ‘feature1’ branch (HEAD represents ‘feature1’ because this is the current checked out branch). The code between ‘===’ and ‘>>> develop’ is the code added to the ‘develop’ branch by the team lead. We’ll resolve the conflict by manually merging both changes, resulting in an updated Main.java:

package com.amazon.helloworld;

import java.util.*;

/**
 * This class prints a hello world message
 */

public class Main {
   public static void main(String[] args) {

        Date todaysdate = Calendar.getInstance().getTime();

        System.out.println("Hello, earth. Today's date is: " + todaysdate);
   }
}

After saving the change you can add and commit it to your local repo:

git add src/
git commit -m 'fixed merge conflict by merging changes'

Fixing issues raised by the reviewer

Now you are ready to address the comments made by the team lead. If you are no longer pointing to the ‘feature1’ branch, check out the ‘feature1’ branch by running the following command:

git checkout feature1

$ git checkout feature1
Branch feature1 set up to track remote branch feature1 from origin.
Switched to a new branch 'feature1'

Edit the source code in your favorite IDE and make the changes to address the comments. In this example, the developer has updated the source code as follows:

package com.amazon.helloworld;

import java.util.*;

/**
 *  This class prints a hello world message
 *
 * @author Michael Edge
 * @see HelloEarth
 * @version 1.0
 */

public class Main {
   public static void main(String[] args) {

        Date todaysDate = Calendar.getInstance().getTime();

        System.out.println("Hello, earth. Today's date is: " + todaysDate);
   }
}

After saving the changes, commit and push to the CodeCommit ‘feature1’ branch as you did previously:

git commit -am 'updated based on review comments'
git push origin feature1

Responding to the reviewer

Now that you’ve fixed the code issues you will want to respond to the review comments. In the AWS CodeCommit console, check that your latest commit appears in the pull request Commits tab. You now have a pull request consisting of more than one commit. The pull request in Figure 12 has four commits, which originated from the following activities:

  • 8th Nov: the original commit used to initiate this pull request
  • 10th Nov, 3 hours ago: the commit by the team lead to the ‘develop’ branch, merged into our ‘feature1’ branch
  • 10th Nov, 24 minutes ago: the commit by the developer that resolved the merge conflict
  • 10th Nov, 4 minutes ago: the final commit by the developer addressing the review comments

Figure 12: Pull request with multiple commits

Let’s reply to the review comments provided by the team lead. In the Activity tab, reply to the pull request comment and save it, as shown in Figure 13.

Figure 13: Replying to a pull request comment

At this stage, your code has been committed and you’ve updated your pull request comments, so you are ready for a final review by the team lead.

Final review

The team lead reviews the code changes and comments made by the developer. As team lead, you own the ‘develop’ branch and it’s your decision on whether to merge the changes in the pull request into the ‘develop’ branch. You can close the pull request with or without merging using the Merge and Close buttons at the bottom of the pull request page (see Figure 13). Clicking Close will allow you to add comments on why you are closing the pull request without merging. Merging will perform a fast-forward merge, incorporating the commits referenced by the pull request. Let’s go ahead and click the Merge button to merge the pull request into the ‘develop’ branch.

Figure 14: Merging the pull request

After merging a pull request, development of that feature is complete and the feature branch is no longer needed. It’s common practice to delete the feature branch after merging. CodeCommit provides a check box during merge to automatically delete the associated feature branch, as seen in Figure 14. Clicking the Merge button will merge the pull request into the ‘develop’ branch, as shown in Figure 15. This will update the status of the pull request to ‘Merged’, and will close the pull request.

Conclusion

This blog has demonstrated how pull requests can be used to request a code review, and enable reviewers to get a comprehensive summary of what is changing, provide feedback to the author, and merge the code into production. For more information on pull requests, see the documentation.

A Raspberry Pi Halloween projects spectacular

Post Syndicated from Janina Ander original https://www.raspberrypi.org/blog/halloween-projects-2017/

Come with us on a journey to discover the 2017 Raspberry Pi Halloween projects that caught our eye, raised our hair, or sent us screaming into the night.

A clip of someone being pulled towards a trap door by hands reaching up from it - Raspberry Pi Halloween projects

Happy Halloween

Whether you’re easily scared or practically unshakeable, you can celebrate Halloween with Pi projects of any level of creepiness.

Even makers of a delicate constitution will enjoy making this Code Club Ghostbusters game, or building an interactive board game using Halloween lights with this MagPi tutorial by Mike Cook. And how about a wearable, cheerily LED-enhanced pumpkin created with the help of this CoderDojo resource? Cute, no?

Felt pumpkin with blinking LED smiley face - Raspberry Pi Halloween projects

Speaking of wearables, Derek Woodroffe’s be-tentacled hat may writhe disconcertingly, but at least it won’t reach out for you. Although, you could make it do that, if you were a terrible person.

Slightly queasy Halloween

Your decorations don’t have to be terrifying: this carved Pumpkin Pi and the Poplawskis’ Halloween decorations are controlled remotely via the web, but they’re more likely to give you happy goosebumps than cold sweats.

A clip of blinking Halloween decorations covering a house - Raspberry Pi Halloween projects

The Snake Eyes Bonnet pumpkin and the monster-face projection controlled by Pis that we showed you in our Halloween Twitter round-up look fairly friendly. Even the 3D-printed jack-o’-lantern by wermy, creator of mintyPi, is kind of adorable, if you ignore the teeth. And who knows, that AlexaPi-powered talking skull that’s staring at you could be an affable fellow who just fancies a chat, right? Right?

Horror-struck Halloween

OK, fine. You’re after something properly frightening. How about the haunted magic mirror by Kapitein Haak, or this one, with added Philips Hue effects, by Ben Eagan. As if your face first thing in the morning wasn’t shocking enough.

Haunted magic mirror demonstration - Raspberry Pi Halloween projects

If you find those rigid-faced, bow-lipped, plastic dolls more sinister than sweet – and you’re right to do so: they’re horrible – you won’t like this evil toy. Possessed by an unquiet shade, it’s straight out of my nightmares.

Earlier this month we covered Adafruit’s haunted portrait how-to. This build by Dominick Marino takes that concept to new, terrifying, heights.

Haunted portrait project demo - Raspberry Pi Halloween projects

Why not add some motion-triggered ghost projections to your Halloween setup? They’ll go nicely with the face-tracking, self-winding, hair-raising jack-in-the-box you can make thanks to Sean Hodgins’ YouTube tutorial.

And then, last of all, there’s this.

The Saw franchise's Billy the puppet on a tricycle - Raspberry Pi Halloween projects

NO.

This recreation of Billy the Puppet from the Saw franchise is Pi-powered, it’s mobile, and it talks. You can remotely control it, and I am not even remotely OK with it. That being said, if you’re keen to have one of your own, be my guest. Just follow the guide on Instructables. It’s your funeral.

Make your Halloween

It’s been a great year for scary Raspberry Pi makes, and we hope you have a blast using your Pi to get into the Halloween spirit.

And speaking of spirits, Matt Reed of RedPepper has created a Pi-based ghost detector! It uses Google’s Speech Neural Network AI to listen for voices in the ether, and it’s live-streaming tonight. Perfect for watching while you’re waiting for the trick-or-treaters to show up.

The post A Raspberry Pi Halloween projects spectacular appeared first on Raspberry Pi.

Prank your friends with the WhooPi Cushion

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/whoopi-cushion/

Learn about using switches and programming GPIO pins while you prank your friends with the Raspberry Pi-powered whoopee WhooPi Cushion!

Whoopee cushion PRANK with a Raspberry Pi: HOW-TO

Explore the world of Raspberry Pi physical computing with our free FutureLearn courses: http://rpf.io/futurelearn Free make your own Whoopi Cushion resource: http://rpf.io/whoopi For more information on Raspberry Pi and the charitable work of the Raspberry Pi Foundation, including Code Club and CoderDojo, visit http://rpf.io Our resources are free to use in schools, clubs, at home and at events.

The WhooPi Cushion

You might remember Carrie Anne and me showing off the WhooPi Cushion live on Facebook last year. The project was created as a simple proof of concept during a Pi Towers maker day. However, our viewers responded so enthusastically that we set about putting together a how-to resource for it.

A cartoon of a man sitting on a whoopee cushion - Raspberry Pi WhooPi Cushion Resource

When we made the resource available, it turned out to be so popular that we decided to include the project in one of our first FutureLearn courses and produced a WhooPi Cushion video tutorial to go with it.

A screen shot from our Raspberry Pi WhooPi Cushion Resource video

Our FutureLearn course attendees love the video, so last week we uploaded it to YouTube! Now everyone can follow along with James Robinson to make their own WhooPi Cushion out of easy-to-gather household items such as tinfoil, paper plates, and spongy material.

Build upon the WhooPi Cushion

Once you’ve completed your prank cushion, you’ll have learnt new skills that you can incorporate into other projects.

For example, you’ll know how to program an action in response to a button press — so how about playing a sound when the button is released instead? Just like that, you’ll have created a simple pressure-based alarm system. Or you could upgrade the functionality of the cushion by including a camera that takes a photo of your unwitting victim’s reaction!

A cartoon showing the stages of the Raspberry Pi Digital Curriculum from Creator to Builder, Developer and Maker

Building upon your skills to increase your knowledge of programming constructs and manufacturing techniques is key to becoming a digital maker. When you use the free Raspberry Pi resources, you’re also working through our digital curriculum, which guides you on this learning journey.

FutureLearn courses for free

Our FutureLearn courses are completely free and cover a variety of topics and skills, including object-oriented programming and teaching physical computing.

A GIF of a man on an island learning with FutureLearn

Regardless of your location, you can learn with us online to improve your knowledge of teaching digital making as well as your own hands-on digital skill set.

The post Prank your friends with the WhooPi Cushion appeared first on Raspberry Pi.

Improved Search for Backblaze’s Blog

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/using-relevannssi-wordpress-search/

Improved Search for Backblaze's Blog
Search has become the most powerful method to find content on the Web, both for finding websites themselves and for discovering information within websites. Our blog readers find content in both ways — using Google, Bing, Yahoo, Ask, DuckDuckGo, and other search engines to follow search results directly to our blog, and using the site search function once on our blog to find content in the blog posts themselves.

There’s a Lot of Great Content on the Backblaze Blog

Backblaze’s CEO Gleb Budman wrote the first post for this blog in March of 2008. Since that post there have been 612 more. There’s a lot of great content on this blog, as evidenced by the more than two million page views we’ve had since the beginning of this year. We typically publish two blog posts per week on a variety of topics, but we focus primarily on cloud storage technology and data backup, company news, and how-to articles on how to use cloud storage and various hardware and software solutions.

Earlier this year we initiated a series of posts on entrepreneurship by our CEO and co-founder, Gleb Budman, which has proven tremendously popular. We also occasionally publish something a little lighter, such as our current Halloween video contest — there’s still time to enter!

Blog search box

The Site Search Box — Your gateway to Backblaze blog content

We Could do a Better Job of Helping You Find It

I joined Backblaze as Content Director in July of this year. During the application process, I spent quite a bit of time reading through the blog to understand the company, the market, and its customers. That’s a lot of reading. I used the site search many times to uncover topics and posts, and discovered that site search had a number of weaknesses that made it less-than-easy to find what I was looking for.

These site search weaknesses included:

Searches were case sensitive
Visitor could easily miss content capitalized differently than the search terms
Results showed no date or author information
Visitor couldn’t tell how recent the post was or who wrote it
Search terms were not highlighted in context
Visitor had to scrutinize the results to find the terms in the post
No indication of the number of results or number of pages of results
Visitor didn’t know how fruitful the search was
No record of search terms used by visitors
We couldn’t tell what our visitors were searching for!

I wanted to make it easier for blog visitors to find all the great content on the Backblaze blog and help me understand what our visitors are searching for. To do that, we needed to upgrade our site search.

I started with a list of goals I wanted for site search.

  1. Make it easier to find content on the blog
  2. Provide a summary of what was found
  3. Search the comments as well as the posts
  4. Highlight the search terms in the results to help find them in context
  5. Provide a record of searches to help me understand what interests our readers

I had the goals, now how could I find a solution to achieve them?

Our blog is built on WordPress, which has a built-in site search function that could be described as simply adequate. The most obvious of its limitations is that search results are listed chronologically, not based on “most popular,” most occurring,” or any other metric that might make the result more relevant to your interests.

The Search for Improved (Site) Search

An obvious choice to improve site search would be to adopt Google Site Search, as many websites and blogs have done. Unfortunately, I quickly discovered that Google is sunsetting Site Search by April of 2018. That left the choice among a number of third-party services or WordPress-specific solutions. My immediate inclination was to see what is available specifically for WordPress.

There are a handful of search plugins for WordPress. One stood out to me for the number of installations (100,000+) and overwhelmingly high reviews: Relevanssi. Still, I had a number of questions. The first question was whether the plugin retained any search data from our site — I wanted to make sure that the privacy of our visitors is maintained, and even harvesting anonymous search data would not be acceptable to Backblaze. I wrote to the developer and was pleased by the responsiveness from Relevanssi’s creator, Mikko Saari. He explained to me that Relevanssi doesn’t have access to any of the search data from the sites using his plugin. Receiving a quick response from a developer is always a good sign. Other signs of a good WordPress plugin are recent updates and an active support forum.

Our solution: Relevanssi for Site Search

The WordPress plugin Relevanssi met all of our criteria, so we installed the plugin and switched to using it for site search in September.

In addition to solving the problems listed above, our search results are now displayed based on relevance instead of date, which is the default behavior of WordPress search. That capability is very useful on our blog where a lot of the content from years ago is still valuable — often called evergreen content. The new site search also enables visitors to search using the boolean expressions AND and OR. For example, a visitor can search for “seagate AND drive,” and see results that only include both words. Alternatively, a visitor can search for “seagate OR drive” and see results that include either word.

screenshot of relevannssi wordpress search results

Search results showing total number of results, hits and their location, and highlighted search terms in context

Visitors can put search terms in quotation marks to search for an entire phrase. For example, a visitor can search for “2016 drive stats” and see results that include only that exact phrase. In addition, the site search results come with a summary, showing where the results were found (title, post, or comments). Search terms are highlighted in yellow in the content, showing exactly where the search result was found.

Here’s an example of a popular post that shows up in searches. Hard Drive Stats for Q1 2017 was published on May 9, 2017. Since September 4, it has shown up over 150 times in site searches and in the last 90 days in has been viewed over 53,000 times on our blog.

Hard Drive Stats for Q1 2017

The Results Tell the Story

Since initiating the new search on our blog on September 4, there have been almost 23,000 site searches conducted, so we know you are using it. We’ve implemented pagination for the blog feed and search results so you know how many pages of results there are and made it easier to navigate to them.

Now that we have this site search data, you likely are wondering which are the most popular search terms on our blog. Here are some of the top searches:

What Do You Search For?

Please tell us how you use site search and whether there are any other capabilities you’d like to see that would make it easier to find content on our blog.

The post Improved Search for Backblaze’s Blog appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

AWS Developer Tools Expands Integration to Include GitHub

Post Syndicated from Balaji Iyer original https://aws.amazon.com/blogs/devops/aws-developer-tools-expands-integration-to-include-github/

AWS Developer Tools is a set of services that include AWS CodeCommit, AWS CodePipeline, AWS CodeBuild, and AWS CodeDeploy. Together, these services help you securely store and maintain version control of your application’s source code and automatically build, test, and deploy your application to AWS or your on-premises environment. These services are designed to enable developers and IT professionals to rapidly and safely deliver software.

As part of our continued commitment to extend the AWS Developer Tools ecosystem to third-party tools and services, we’re pleased to announce AWS CodeStar and AWS CodeBuild now integrate with GitHub. This will make it easier for GitHub users to set up a continuous integration and continuous delivery toolchain as part of their release process using AWS Developer Tools.

In this post, I will walk through the following:

Prerequisites:

You’ll need an AWS account, a GitHub account, an Amazon EC2 key pair, and administrator-level permissions for AWS Identity and Access Management (IAM), AWS CodeStar, AWS CodeBuild, AWS CodePipeline, Amazon EC2, Amazon S3.

 

Integrating GitHub with AWS CodeStar

AWS CodeStar enables you to quickly develop, build, and deploy applications on AWS. Its unified user interface helps you easily manage your software development activities in one place. With AWS CodeStar, you can set up your entire continuous delivery toolchain in minutes, so you can start releasing code faster.

When AWS CodeStar launched in April of this year, it used AWS CodeCommit as the hosted source repository. You can now choose between AWS CodeCommit or GitHub as the source control service for your CodeStar projects. In addition, your CodeStar project dashboard lets you centrally track GitHub activities, including commits, issues, and pull requests. This makes it easy to manage project activity across the components of your CI/CD toolchain. Adding the GitHub dashboard view will simplify development of your AWS applications.

In this section, I will show you how to use GitHub as the source provider for your CodeStar projects. I’ll also show you how to work with recent commits, issues, and pull requests in the CodeStar dashboard.

Sign in to the AWS Management Console and from the Services menu, choose CodeStar. In the CodeStar console, choose Create a new project. You should see the Choose a project template page.

CodeStar Project

Choose an option by programming language, application category, or AWS service. I am going to choose the Ruby on Rails web application that will be running on Amazon EC2.

On the Project details page, you’ll now see the GitHub option. Type a name for your project, and then choose Connect to GitHub.

Project details

You’ll see a message requesting authorization to connect to your GitHub repository. When prompted, choose Authorize, and then type your GitHub account password.

Authorize

This connects your GitHub identity to AWS CodeStar through OAuth. You can always review your settings by navigating to your GitHub application settings.

Installed GitHub Apps

You’ll see AWS CodeStar is now connected to GitHub:

Create project

You can choose a public or private repository. GitHub offers free accounts for users and organizations working on public and open source projects and paid accounts that offer unlimited private repositories and optional user management and security features.

In this example, I am going to choose the public repository option. Edit the repository description, if you like, and then choose Next.

Review your CodeStar project details, and then choose Create Project. On Choose an Amazon EC2 Key Pair, choose Create Project.

Key Pair

On the Review project details page, you’ll see Edit Amazon EC2 configuration. Choose this link to configure instance type, VPC, and subnet options. AWS CodeStar requires a service role to create and manage AWS resources and IAM permissions. This role will be created for you when you select the AWS CodeStar would like permission to administer AWS resources on your behalf check box.

Choose Create Project. It might take a few minutes to create your project and resources.

Review project details

When you create a CodeStar project, you’re added to the project team as an owner. If this is the first time you’ve used AWS CodeStar, you’ll be asked to provide the following information, which will be shown to others:

  • Your display name.
  • Your email address.

This information is used in your AWS CodeStar user profile. User profiles are not project-specific, but they are limited to a single AWS region. If you are a team member in projects in more than one region, you’ll have to create a user profile in each region.

User settings

User settings

Choose Next. AWS CodeStar will create a GitHub repository with your configuration settings (for example, https://github.com/biyer/ruby-on-rails-service).

When you integrate your integrated development environment (IDE) with AWS CodeStar, you can continue to write and develop code in your preferred environment. The changes you make will be included in the AWS CodeStar project each time you commit and push your code.

IDE

After setting up your IDE, choose Next to go to the CodeStar dashboard. Take a few minutes to familiarize yourself with the dashboard. You can easily track progress across your entire software development process, from your backlog of work items to recent code deployments.

Dashboard

After the application deployment is complete, choose the endpoint that will display the application.

Pipeline

This is what you’ll see when you open the application endpoint:

The Commit history section of the dashboard lists the commits made to the Git repository. If you choose the commit ID or the Open in GitHub option, you can use a hotlink to your GitHub repository.

Commit history

Your AWS CodeStar project dashboard is where you and your team view the status of your project resources, including the latest commits to your project, the state of your continuous delivery pipeline, and the performance of your instances. This information is displayed on tiles that are dedicated to a particular resource. To see more information about any of these resources, choose the details link on the tile. The console for that AWS service will open on the details page for that resource.

Issues

You can also filter issues based on their status and the assigned user.

Filter

AWS CodeBuild Now Supports Building GitHub Pull Requests

CodeBuild is a fully managed build service that compiles source code, runs tests, and produces software packages that are ready to deploy. With CodeBuild, you don’t need to provision, manage, and scale your own build servers. CodeBuild scales continuously and processes multiple builds concurrently, so your builds are not left waiting in a queue. You can use prepackaged build environments to get started quickly or you can create custom build environments that use your own build tools.

We recently announced support for GitHub pull requests in AWS CodeBuild. This functionality makes it easier to collaborate across your team while editing and building your application code with CodeBuild. You can use the AWS CodeBuild or AWS CodePipeline consoles to run AWS CodeBuild. You can also automate the running of AWS CodeBuild by using the AWS Command Line Interface (AWS CLI), the AWS SDKs, or the AWS CodeBuild Plugin for Jenkins.

AWS CodeBuild

In this section, I will show you how to trigger a build in AWS CodeBuild with a pull request from GitHub through webhooks.

Open the AWS CodeBuild console at https://console.aws.amazon.com/codebuild/. Choose Create project. If you already have a CodeBuild project, you can choose Edit project, and then follow along. CodeBuild can connect to AWS CodeCommit, S3, BitBucket, and GitHub to pull source code for builds. For Source provider, choose GitHub, and then choose Connect to GitHub.

Configure

After you’ve successfully linked GitHub and your CodeBuild project, you can choose a repository in your GitHub account. CodeBuild also supports connections to any public repository. You can review your settings by navigating to your GitHub application settings.

GitHub Apps

On Source: What to Build, for Webhook, select the Rebuild every time a code change is pushed to this repository check box.

Note: You can select this option only if, under Repository, you chose Use a repository in my account.

Source

In Environment: How to build, for Environment image, select Use an image managed by AWS CodeBuild. For Operating system, choose Ubuntu. For Runtime, choose Base. For Version, choose the latest available version. For Build specification, you can provide a collection of build commands and related settings, in YAML format (buildspec.yml) or you can override the build spec by inserting build commands directly in the console. AWS CodeBuild uses these commands to run a build. In this example, the output is the string “hello.”

Environment

On Artifacts: Where to put the artifacts from this build project, for Type, choose No artifacts. (This is also the type to choose if you are just running tests or pushing a Docker image to Amazon ECR.) You also need an AWS CodeBuild service role so that AWS CodeBuild can interact with dependent AWS services on your behalf. Unless you already have a role, choose Create a role, and for Role name, type a name for your role.

Artifacts

In this example, leave the advanced settings at their defaults.

If you expand Show advanced settings, you’ll see options for customizing your build, including:

  • A build timeout.
  • A KMS key to encrypt all the artifacts that the builds for this project will use.
  • Options for building a Docker image.
  • Elevated permissions during your build action (for example, accessing Docker inside your build container to build a Dockerfile).
  • Resource options for the build compute type.
  • Environment variables (built-in or custom). For more information, see Create a Build Project in the AWS CodeBuild User Guide.

Advanced settings

You can use the AWS CodeBuild console to create a parameter in Amazon EC2 Systems Manager. Choose Create a parameter, and then follow the instructions in the dialog box. (In that dialog box, for KMS key, you can optionally specify the ARN of an AWS KMS key in your account. Amazon EC2 Systems Manager uses this key to encrypt the parameter’s value during storage and decrypt during retrieval.)

Create parameter

Choose Continue. On the Review page, either choose Save and build or choose Save to run the build later.

Choose Start build. When the build is complete, the Build logs section should display detailed information about the build.

Logs

To demonstrate a pull request, I will fork the repository as a different GitHub user, make commits to the forked repo, check in the changes to a newly created branch, and then open a pull request.

Pull request

As soon as the pull request is submitted, you’ll see CodeBuild start executing the build.

Build

GitHub sends an HTTP POST payload to the webhook’s configured URL (highlighted here), which CodeBuild uses to download the latest source code and execute the build phases.

Build project

If you expand the Show all checks option for the GitHub pull request, you’ll see that CodeBuild has completed the build, all checks have passed, and a deep link is provided in Details, which opens the build history in the CodeBuild console.

Pull request

Summary:

In this post, I showed you how to use GitHub as the source provider for your CodeStar projects and how to work with recent commits, issues, and pull requests in the CodeStar dashboard. I also showed you how you can use GitHub pull requests to automatically trigger a build in AWS CodeBuild — specifically, how this functionality makes it easier to collaborate across your team while editing and building your application code with CodeBuild.


About the author:

Balaji Iyer is an Enterprise Consultant for the Professional Services Team at Amazon Web Services. In this role, he has helped several customers successfully navigate their journey to AWS. His specialties include architecting and implementing highly scalable distributed systems, serverless architectures, large scale migrations, operational security, and leading strategic AWS initiatives. Before he joined Amazon, Balaji spent more than a decade building operating systems, big data analytics solutions, mobile services, and web applications. In his spare time, he enjoys experiencing the great outdoors and spending time with his family.

 

Using AWS CodePipeline, AWS CodeBuild, and AWS Lambda for Serverless Automated UI Testing

Post Syndicated from Prakash Palanisamy original https://aws.amazon.com/blogs/devops/using-aws-codepipeline-aws-codebuild-and-aws-lambda-for-serverless-automated-ui-testing/

Testing the user interface of a web application is an important part of the development lifecycle. In this post, I’ll explain how to automate UI testing using serverless technologies, including AWS CodePipeline, AWS CodeBuild, and AWS Lambda.

I built a website for UI testing that is hosted in S3. I used Selenium to perform cross-browser UI testing on Chrome, Firefox, and PhantomJS, a headless WebKit browser with Ghost Driver, an implementation of the WebDriver Wire Protocol. I used Python to create test cases for ChromeDriver, FirefoxDriver, or PhatomJSDriver based the browser against which the test is being executed.

Resources referred to in this post, including the AWS CloudFormation template, test and status websites hosted in S3, AWS CodeBuild build specification files, AWS Lambda function, and the Python script that performs the test are available in the serverless-automated-ui-testing GitHub repository.

S3 Hosted Test Website:

AWS CodeBuild supports custom containers so we can use the Selenium/standalone-Firefox and Selenium/standalone-Chrome containers, which include prebuild Firefox and Chrome browsers, respectively. Xvfb performs the graphical operation in virtual memory without any display hardware. It will be installed in the CodeBuild containers during the install phase.

Build Spec for Chrome and Firefox

The build specification for Chrome and Firefox testing includes multiple phases:

  • The environment variables section contains a set of default variables that are overridden while creating the build project or triggering the build.
  • As part of install phase, required packages like Xvfb and Selenium are installed using yum.
  • During the pre_build phase, the test bed is prepared for test execution.
  • During the build phase, the appropriate DISPLAY is set and the tests are executed.
version: 0.2

env:
  variables:
    BROWSER: "chrome"
    WebURL: "https://sampletestweb.s3-eu-west-1.amazonaws.com/website/index.html"
    ArtifactBucket: "codebuild-demo-artifact-repository"
    MODULES: "mod1"
    ModuleTable: "test-modules"
    StatusTable: "blog-test-status"

phases:
  install:
    commands:
      - apt-get update
      - apt-get -y upgrade
      - apt-get install xvfb python python-pip build-essential -y
      - pip install --upgrade pip
      - pip install selenium
      - pip install awscli
      - pip install requests
      - pip install boto3
      - cp xvfb.init /etc/init.d/xvfb
      - chmod +x /etc/init.d/xvfb
      - update-rc.d xvfb defaults
      - service xvfb start
      - export PATH="$PATH:`pwd`/webdrivers"
  pre_build:
    commands:
      - python prepare_test.py
  build:
    commands:
      - export DISPLAY=:5
      - cd tests
      - echo "Executing simple test..."
      - python testsuite.py

Because Ghost Driver runs headless, it can be executed on AWS Lambda. In keeping with a fire-and-forget model, I used CodeBuild to create the PhantomJS Lambda function and trigger the test invocations on Lambda in parallel. This is powerful because many tests can be executed in parallel on Lambda.

Build Spec for PhantomJS

The build specification for PhantomJS testing also includes multiple phases. It is a little different from the preceding example because we are using AWS Lambda for the test execution.

  • The environment variables section contains a set of default variables that are overridden while creating the build project or triggering the build.
  • As part of install phase, the required packages like Selenium and the AWS CLI are installed using yum.
  • During the pre_build phase, the test bed is prepared for test execution.
  • During the build phase, a zip file that will be used to create the PhantomJS Lambda function is created and tests are executed on the Lambda function.
version: 0.2

env:
  variables:
    BROWSER: "phantomjs"
    WebURL: "https://sampletestweb.s3-eu-west-1.amazonaws.com/website/index.html"
    ArtifactBucket: "codebuild-demo-artifact-repository"
    MODULES: "mod1"
    ModuleTable: "test-modules"
    StatusTable: "blog-test-status"
    LambdaRole: "arn:aws:iam::account-id:role/role-name"

phases:
  install:
    commands:
      - apt-get update
      - apt-get -y upgrade
      - apt-get install python python-pip build-essential -y
      - apt-get install zip unzip -y
      - pip install --upgrade pip
      - pip install selenium
      - pip install awscli
      - pip install requests
      - pip install boto3
  pre_build:
    commands:
      - python prepare_test.py
  build:
    commands:
      - cd lambda_function
      - echo "Packaging Lambda Function..."
      - zip -r /tmp/lambda_function.zip ./*
      - func_name=`echo $CODEBUILD_BUILD_ID | awk -F ':' '{print $1}'`-phantomjs
      - echo "Creating Lambda Function..."
      - chmod 777 phantomjs
      - |
         func_list=`aws lambda list-functions | grep FunctionName | awk -F':' '{print $2}' | tr -d ', "'`
         if echo "$func_list" | grep -qw $func_name
         then
             echo "Lambda function already exists."
         else
             aws lambda create-function --function-name $func_name --runtime "python2.7" --role $LambdaRole --handler "testsuite.lambda_handler" --zip-file fileb:///tmp/lambda_function.zip --timeout 150 --memory-size 1024 --environment Variables="{WebURL=$WebURL, StatusTable=$StatusTable}" --tags Name=$func_name
         fi
      - export PhantomJSFunction=$func_name
      - cd ../tests/
      - python testsuite.py

The list of test cases and the test modules that belong to each case are stored in an Amazon DynamoDB table. Based on the list of modules passed as an argument to the CodeBuild project, CodeBuild gets the test cases from that table and executes them. The test execution status and results are stored in another Amazon DynamoDB table. It will read the test status from the status table in DynamoDB and display it.

AWS CodeBuild and AWS Lambda perform the test execution as individual tasks. AWS CodePipeline plays an important role here by enabling continuous delivery and parallel execution of tests for optimized testing.

Here’s how to do it:

In AWS CodePipeline, create a pipeline with four stages:

  • Source (AWS CodeCommit)
  • UI testing (AWS Lambda and AWS CodeBuild)
  • Approval (manual approval)
  • Production (AWS Lambda)

Pipeline stages, the actions in each stage, and transitions between stages are shown in the following diagram.

This design implemented in AWS CodePipeline looks like this:

CodePipeline automatically detects a change in the source repository and triggers the execution of the pipeline.

In the UITest stage, there are two parallel actions:

  • DeployTestWebsite invokes a Lambda function to deploy the test website in S3 as an S3 website.
  • DeployStatusPage invokes another Lambda function to deploy in parallel the status website in S3 as an S3 website.

Next, there are three parallel actions that trigger the CodeBuild project:

  • TestOnChrome launches a container to perform the Selenium tests on Chrome.
  • TestOnFirefox launches another container to perform the Selenium tests on Firefox.
  • TestOnPhantomJS creates a Lambda function and invokes individual Lambda functions per test case to execute the test cases in parallel.

You can monitor the status of the test execution on the status website, as shown here:

When the UI testing is completed successfully, the pipeline continues to an Approval stage in which a notification is sent to the configured SNS topic. The designated team member reviews the test status and approves or rejects the deployment. Upon approval, the pipeline continues to the Production stage, where it invokes a Lambda function and deploys the website to a production S3 bucket.

I used a CloudFormation template to set up my continuous delivery pipeline. The automated-ui-testing.yaml template, available from GitHub, sets up a full-featured pipeline.

When I use the template to create my pipeline, I specify the following:

  • AWS CodeCommit repository.
  • SNS topic to send approval notification.
  • S3 bucket name where the artifacts will be stored.

The stack name should follow the rules for S3 bucket naming because it will be part of the S3 bucket name.

When the stack is created successfully, the URLs for the test website and status website appear in the Outputs section, as shown here:

Conclusion

In this post, I showed how you can use AWS CodePipeline, AWS CodeBuild, AWS Lambda, and a manual approval process to create a continuous delivery pipeline for serverless automated UI testing. Websites running on Amazon EC2 instances or AWS Elastic Beanstalk can also be tested using similar approach.


About the author

Prakash Palanisamy is a Solutions Architect for Amazon Web Services. When he is not working on Serverless, DevOps or Alexa, he will be solving problems in Project Euler. He also enjoys watching educational documentaries.