Tag Archives: project

CoderDojo Coolest Projects 2017

Post Syndicated from Ben Nuttall original https://www.raspberrypi.org/blog/coderdojo-coolest-projects-2017/

When I heard we were merging with CoderDojo, I was delighted. CoderDojo is a wonderful organisation with a spectacular community, and it’s going to be great to join forces with the team and work towards our common goal: making a difference to the lives of young people by making technology accessible to them.

You may remember that last year Philip and I went along to Coolest Projects, CoderDojo’s annual event at which their global community showcase their best makes. It was awesome! This year a whole bunch of us from the Raspberry Pi Foundation attended Coolest Projects with our new Irish colleagues, and as expected, the projects on show were as cool as can be.

Coolest Projects 2017 attendee

Crowd at Coolest Projects 2017

This year’s coolest projects!

Young maker Benjamin demoed his brilliant RGB LED table tennis ball display for us, and showed off his brilliant project tutorial website codemakerbuddy.com, which he built with Python and Flask. [Click on any of the images to enlarge them.]

Coolest Projects 2017 LED ping-pong ball display
Coolest Projects 2017 Benjamin and Oly

Next up, Aimee showed us a recipes app she’d made with the MIT App Inventor. It was a really impressive and well thought-out project.

Coolest Projects 2017 Aimee's cook book
Coolest Projects 2017 Aimee's setup

This very successful OpenCV face detection program with hardware installed in a teddy bear was great as well:

Coolest Projects 2017 face detection bear
Coolest Projects 2017 face detection interface
Coolest Projects 2017 face detection database

Helen’s and Oly’s favourite project involved…live bees!

Coolest Projects 2017 live bees

BEEEEEEEEEEES!

Its creator, 12-year-old Amy, said she wanted to do something to help the Earth. Her project uses various sensors to record data on the bee population in the hive. An adjacent monitor displays the data in a web interface:

Coolest Projects 2017 Aimee's bees

Coolest robots

I enjoyed seeing lots of GPIO Zero projects out in the wild, including this robotic lawnmower made by Kevin and Zach:

Raspberry Pi Lawnmower

Kevin and Zach’s Raspberry Pi lawnmower project with Python and GPIO Zero, showed at CoderDojo Coolest Projects 2017

Philip’s favourite make was a Pi-powered robot you can control with your mind! According to the maker, Laura, it worked really well with Philip because he has no hair.

Philip Colligan on Twitter

This is extraordinary. Laura from @CoderDojo Romania has programmed a mind controlled robot using @Raspberry_Pi @coolestprojects

And here are some pictures of even more cool robots we saw:

Coolest Projects 2017 coolest robot no.1
Coolest Projects 2017 coolest robot no.2
Coolest Projects 2017 coolest robot no.3

Games, toys, activities

Oly and I were massively impressed with the work of Mogamad, Daniel, and Basheerah, who programmed a (borrowed) Amazon Echo to make a voice-controlled text-adventure game using Java and the Alexa API. They’ve inspired me to try something similar using the AIY projects kit and adventurelib!

Coolest Projects 2017 Mogamad, Daniel, Basheerah, Oly
Coolest Projects 2017 Alexa text-based game

Christopher Hill did a brilliant job with his Home Alone LEGO house. He used sensors to trigger lights and sounds to make it look like someone’s at home, like in the film. I should have taken a video – seeing it in action was great!

Coolest Projects 2017 Lego home alone house
Coolest Projects 2017 Lego home alone innards
Coolest Projects 2017 Lego home alone innards closeup

Meanwhile, the Northern Ireland Raspberry Jam group ran a DOTS board activity, which turned their area into a conductive paint hazard zone.

Coolest Projects 2017 NI Jam DOTS activity 1
Coolest Projects 2017 NI Jam DOTS activity 2
Coolest Projects 2017 NI Jam DOTS activity 3
Coolest Projects 2017 NI Jam DOTS activity 4
Coolest Projects 2017 NI Jam DOTS activity 5
Coolest Projects 2017 NI Jam DOTS activity 6

Creativity and ingenuity

We really enjoyed seeing so many young people collaborating, experimenting, and taking full advantage of the opportunity to make real projects. And we loved how huge the range of technologies in use was: people employed all manner of hardware and software to bring their ideas to life.

Philip Colligan on Twitter

Wow! Look at that room full of awesome young people. @coolestprojects #coolestprojects @CoderDojo

Congratulations to the Coolest Projects 2017 prize winners, and to all participants. Here are some of the teams that won in the different categories:

Coolest Projects 2017 winning team 1
Coolest Projects 2017 winning team 2
Coolest Projects 2017 winning team 3

Take a look at the gallery of all winners over on Flickr.

The wow factor

Raspberry Pi co-founder and Foundation trustee Pete Lomas came along to the event as well. Here’s what he had to say:

It’s hard to describe the scale of the event, and photos just don’t do it justice. The first thing that hit me was the sheer excitement of the CoderDojo ninjas [the children attending Dojos]. Everyone was setting up for their time with the project judges, and their pure delight at being able to show off their creations was evident in both halls. Time and time again I saw the ninjas apply their creativity to help save the planet or make someone’s life better, and it’s truly exciting that we are going to help that continue and expand.

Even after 8 hours, enthusiasm wasn’t flagging – the awards ceremony was just brilliant, with ninjas high-fiving the winners on the way to the stage. This speaks volumes about the ethos and vision of the CoderDojo founders, where everyone is a winner just by being part of a community of worldwide friends. It was a brilliant introduction, and if this weekend was anything to go by, our merger certainly is a marriage made in Heaven.

Join this awesome community!

If all this inspires you as much as it did us, consider looking for a CoderDojo near you – and sign up as a volunteer! There’s plenty of time for young people to build up skills and start working on a project for next year’s event. Check out coolestprojects.com for more information.

The post CoderDojo Coolest Projects 2017 appeared first on Raspberry Pi.

How to Create an AMI Builder with AWS CodeBuild and HashiCorp Packer – Part 2

Post Syndicated from Heitor Lessa original https://aws.amazon.com/blogs/devops/how-to-create-an-ami-builder-with-aws-codebuild-and-hashicorp-packer-part-2/

Written by AWS Solutions Architects Jason Barto and Heitor Lessa

 
In Part 1 of this post, we described how AWS CodeBuild, AWS CodeCommit, and HashiCorp Packer can be used to build an Amazon Machine Image (AMI) from the latest version of Amazon Linux. In this post, we show how to use AWS CodePipeline, AWS CloudFormation, and Amazon CloudWatch Events to continuously ship new AMIs. We use Ansible by Red Hat to harden the OS on the AMIs through a well-known set of security controls outlined by the Center for Internet Security in its CIS Amazon Linux Benchmark.

You’ll find the source code for this post in our GitHub repo.

At the end of this post, we will have the following architecture:

Requirements

 
To follow along, you will need Git and a text editor. Make sure Git is configured to work with AWS CodeCommit, as described in Part 1.

Technologies

 
In addition to the services and products used in Part 1 of this post, we also use these AWS services and third-party software:

AWS CloudFormation gives developers and systems administrators an easy way to create and manage a collection of related AWS resources, provisioning and updating them in an orderly and predictable fashion.

Amazon CloudWatch Events enables you to react selectively to events in the cloud and in your applications. Specifically, you can create CloudWatch Events rules that match event patterns, and take actions in response to those patterns.

AWS CodePipeline is a continuous integration and continuous delivery service for fast and reliable application and infrastructure updates. AWS CodePipeline builds, tests, and deploys your code every time there is a code change, based on release process models you define.

Amazon SNS is a fast, flexible, fully managed push notification service that lets you send individual messages or to fan out messages to large numbers of recipients. Amazon SNS makes it simple and cost-effective to send push notifications to mobile device users or email recipients. The service can even send messages to other distributed services.

Ansible is a simple IT automation system that handles configuration management, application deployment, cloud provisioning, ad-hoc task-execution, and multinode orchestration.

Getting Started

 
We use CloudFormation to bootstrap the following infrastructure:

Component Purpose
AWS CodeCommit repository Git repository where the AMI builder code is stored.
S3 bucket Build artifact repository used by AWS CodePipeline and AWS CodeBuild.
AWS CodeBuild project Executes the AWS CodeBuild instructions contained in the build specification file.
AWS CodePipeline pipeline Orchestrates the AMI build process, triggered by new changes in the AWS CodeCommit repository.
SNS topic Notifies subscribed email addresses when an AMI build is complete.
CloudWatch Events rule Defines how the AMI builder should send a custom event to notify an SNS topic.
Region AMI Builder Launch Template
N. Virginia (us-east-1)
Ireland (eu-west-1)

After launching the CloudFormation template linked here, we will have a pipeline in the AWS CodePipeline console. (Failed at this stage simply means we don’t have any data in our newly created AWS CodeCommit Git repository.)

Next, we will clone the newly created AWS CodeCommit repository.

If this is your first time connecting to a AWS CodeCommit repository, please see instructions in our documentation on Setup steps for HTTPS Connections to AWS CodeCommit Repositories.

To clone the AWS CodeCommit repository (console)

  1. From the AWS Management Console, open the AWS CloudFormation console.
  2. Choose the AMI-Builder-Blogpost stack, and then choose Output.
  3. Make a note of the Git repository URL.
  4. Use git to clone the repository.

For example: git clone https://git-codecommit.eu-west-1.amazonaws.com/v1/repos/AMI-Builder_repo

To clone the AWS CodeCommit repository (CLI)

# Retrieve CodeCommit repo URL
git_repo=$(aws cloudformation describe-stacks --query 'Stacks[0].Outputs[?OutputKey==`GitRepository`].OutputValue' --output text --stack-name "AMI-Builder-Blogpost")

# Clone repository locally
git clone ${git_repo}

Bootstrap the Repo with the AMI Builder Structure

 
Now that our infrastructure is ready, download all the files and templates required to build the AMI.

Your local Git repo should have the following structure:

.
├── ami_builder_event.json
├── ansible
├── buildspec.yml
├── cloudformation
├── packer_cis.json

Next, push these changes to AWS CodeCommit, and then let AWS CodePipeline orchestrate the creation of the AMI:

git add .
git commit -m "My first AMI"
git push origin master

AWS CodeBuild Implementation Details

 
While we wait for the AMI to be created, let’s see what’s changed in our AWS CodeBuild buildspec.yml file:

...
phases:
  ...
  build:
    commands:
      ...
      - ./packer build -color=false packer_cis.json | tee build.log
  post_build:
    commands:
      - egrep "${AWS_REGION}\:\sami\-" build.log | cut -d' ' -f2 > ami_id.txt
      # Packer doesn't return non-zero status; we must do that if Packer build failed
      - test -s ami_id.txt || exit 1
      - sed -i.bak "s/<<AMI-ID>>/$(cat ami_id.txt)/g" ami_builder_event.json
      - aws events put-events --entries file://ami_builder_event.json
      ...
artifacts:
  files:
    - ami_builder_event.json
    - build.log
  discard-paths: yes

In the build phase, we capture Packer output into a file named build.log. In the post_build phase, we take the following actions:

  1. Look up the AMI ID created by Packer and save its findings to a temporary file (ami_id.txt).
  2. Forcefully make AWS CodeBuild to fail if the AMI ID (ami_id.txt) is not found. This is required because Packer doesn’t fail if something goes wrong during the AMI creation process. We have to tell AWS CodeBuild to stop by informing it that an error occurred.
  3. If an AMI ID is found, we update the ami_builder_event.json file and then notify CloudWatch Events that the AMI creation process is complete.
  4. CloudWatch Events publishes a message to an SNS topic. Anyone subscribed to the topic will be notified in email that an AMI has been created.

Lastly, the new artifacts phase instructs AWS CodeBuild to upload files built during the build process (ami_builder_event.json and build.log) to the S3 bucket specified in the Outputs section of the CloudFormation template. These artifacts can then be used as an input artifact in any later stage in AWS CodePipeline.

For information about customizing the artifacts sequence of the buildspec.yml, see the Build Specification Reference for AWS CodeBuild.

CloudWatch Events Implementation Details

 
CloudWatch Events allow you to extend the AMI builder to not only send email after the AMI has been created, but to hook up any of the supported targets to react to the AMI builder event. This event publication means you can decouple from Packer actions you might take after AMI completion and plug in other actions, as you see fit.

For more information about targets in CloudWatch Events, see the CloudWatch Events API Reference.

In this case, CloudWatch Events should receive the following event, match it with a rule we created through CloudFormation, and publish a message to SNS so that you can receive an email.

Example CloudWatch custom event

[
        {
            "Source": "com.ami.builder",
            "DetailType": "AmiBuilder",
            "Detail": "{ \"AmiStatus\": \"Created\"}",
            "Resources": [ "ami-12cd5guf" ]
        }
]

Cloudwatch Events rule

{
  "detail-type": [
    "AmiBuilder"
  ],
  "source": [
    "com.ami.builder"
  ],
  "detail": {
    "AmiStatus": [
      "Created"
    ]
  }
}

Example SNS message sent in email

{
    "version": "0",
    "id": "f8bdede0-b9d7...",
    "detail-type": "AmiBuilder",
    "source": "com.ami.builder",
    "account": "<<aws_account_number>>",
    "time": "2017-04-28T17:56:40Z",
    "region": "eu-west-1",
    "resources": ["ami-112cd5guf "],
    "detail": {
        "AmiStatus": "Created"
    }
}

Packer Implementation Details

 
In addition to the build specification file, there are differences between the current version of the HashiCorp Packer template (packer_cis.json) and the one used in Part 1.

Variables

  "variables": {
    "vpc": "{{env `BUILD_VPC_ID`}}",
    "subnet": "{{env `BUILD_SUBNET_ID`}}",
         “ami_name”: “Prod-CIS-Latest-AMZN-{{isotime \”02-Jan-06 03_04_05\”}}”
  },
  • ami_name: Prefixes a name used by Packer to tag resources during the Builders sequence.
  • vpc and subnet: Environment variables defined by the CloudFormation stack parameters.

We no longer assume a default VPC is present and instead use the VPC and subnet specified in the CloudFormation parameters. CloudFormation configures the AWS CodeBuild project to use these values as environment variables. They are made available throughout the build process.

That allows for more flexibility should you need to change which VPC and subnet will be used by Packer to launch temporary resources.

Builders

  "builders": [{
    ...
    "ami_name": “{{user `ami_name`| clean_ami_name}}”,
    "tags": {
      "Name": “{{user `ami_name`}}”,
    },
    "run_tags": {
      "Name": “{{user `ami_name`}}",
    },
    "run_volume_tags": {
      "Name": “{{user `ami_name`}}",
    },
    "snapshot_tags": {
      "Name": “{{user `ami_name`}}",
    },
    ...
    "vpc_id": "{{user `vpc` }}",
    "subnet_id": "{{user `subnet` }}"
  }],

We now have new properties (*_tag) and a new function (clean_ami_name) and launch temporary resources in a VPC and subnet specified in the environment variables. AMI names can only contain a certain set of ASCII characters. If the input in project deviates from the expected characters (for example, includes whitespace or slashes), Packer’s clean_ami_name function will fix it.

For more information, see functions on the HashiCorp Packer website.

Provisioners

  "provisioners": [
    {
        "type": "shell",
        "inline": [
            "sudo pip install ansible"
        ]
    }, 
    {
        "type": "ansible-local",
        "playbook_file": "ansible/playbook.yaml",
        "role_paths": [
            "ansible/roles/common"
        ],
        "playbook_dir": "ansible",
        "galaxy_file": "ansible/requirements.yaml"
    },
    {
      "type": "shell",
      "inline": [
        "rm .ssh/authorized_keys ; sudo rm /root/.ssh/authorized_keys"
      ]
    }

We used shell provisioner to apply OS patches in Part 1. Now, we use shell to install Ansible on the target machine and ansible-local to import, install, and execute Ansible roles to make our target machine conform to our standards.

Packer uses shell to remove temporary keys before it creates an AMI from the target and temporary EC2 instance.

Ansible Implementation Details

 
Ansible provides OS patching through a custom Common role that can be easily customized for other tasks.

CIS Benchmark and Cloudwatch Logs are implemented through two Ansible third-party roles that are defined in ansible/requirements.yaml as seen in the Packer template.

The Ansible provisioner uses Ansible Galaxy to download these roles onto the target machine and execute them as instructed by ansible/playbook.yaml.

For information about how these components are organized, see the Playbook Roles and Include Statements in the Ansible documentation.

The following Ansible playbook (ansible</playbook.yaml) controls the execution order and custom properties:

---
- hosts: localhost
  connection: local
  gather_facts: true    # gather OS info that is made available for tasks/roles
  become: yes           # majority of CIS tasks require root
  vars:
    # CIS Controls whitepaper:  http://bit.ly/2mGAmUc
    # AWS CIS Whitepaper:       http://bit.ly/2m2Ovrh
    cis_level_1_exclusions:
    # 3.4.2 and 3.4.3 effectively blocks access to all ports to the machine
    ## This can break automation; ignoring it as there are stronger mechanisms than that
      - 3.4.2 
      - 3.4.3
    # CloudWatch Logs will be used instead of Rsyslog/Syslog-ng
    ## Same would be true if any other software doesn't support Rsyslog/Syslog-ng mechanisms
      - 4.2.1.4
      - 4.2.2.4
      - 4.2.2.5
    # Autofs is not installed in newer versions, let's ignore
      - 1.1.19
    # Cloudwatch Logs role configuration
    logs:
      - file: /var/log/messages
        group_name: "system_logs"
  roles:
    - common
    - anthcourtney.cis-amazon-linux
    - dharrisio.aws-cloudwatch-logs-agent

Both third-party Ansible roles can be easily configured through variables (vars). We use Ansible playbook variables to exclude CIS controls that don’t apply to our case and to instruct the CloudWatch Logs agent to stream the /var/log/messages log file to CloudWatch Logs.

If you need to add more OS or application logs, you can easily duplicate the playbook and make changes. The CloudWatch Logs agent will ship configured log messages to CloudWatch Logs.

For more information about parameters you can use to further customize third-party roles, download Ansible roles for the Cloudwatch Logs Agent and CIS Amazon Linux from the Galaxy website.

Committing Changes

 
Now that Ansible and CloudWatch Events are configured as a part of the build process, commiting any changes to the AWS CodeComit Git Repository will triger a new AMI build process that can be followed through the AWS CodePipeline console.

When the build is complete, an email will be sent to the email address you provided as a part of the CloudFormation stack deployment. The email serves as notification that an AMI has been built and is ready for use.

Summary

 
We used AWS CodeCommit, AWS CodePipeline, AWS CodeBuild, Packer, and Ansible to build a pipeline that continuously builds new, hardened CIS AMIs. We used Amazon SNS so that email addresses subscribed to a SNS topic are notified upon completion of the AMI build.

By treating our AMI creation process as code, we can iterate and track changes over time. In this way, it’s no different from a software development workflow. With that in mind, software patches, OS configuration, and logs that need to be shipped to a central location are only a git commit away.

Next Steps

 
Here are some ideas to extend this AMI builder:

  • Hook up a Lambda function in Cloudwatch Events to update EC2 Auto Scaling configuration upon completion of the AMI build.
  • Use AWS CodePipeline parallel steps to build multiple Packer images.
  • Add a commit ID as a tag for the AMI you created.
  • Create a scheduled Lambda function through Cloudwatch Events to clean up old AMIs based on timestamp (name or additional tag).
  • Implement Windows support for the AMI builder.
  • Create a cross-account or cross-region AMI build.

Cloudwatch Events allow the AMI builder to decouple AMI configuration and creation so that you can easily add your own logic using targets (AWS Lambda, Amazon SQS, Amazon SNS) to add events or recycle EC2 instances with the new AMI.

If you have questions or other feedback, feel free to leave it in the comments or contribute to the AMI Builder repo on GitHub.

Raffeiner: My Ubuntu for mobile devices post mortem analysis

Post Syndicated from corbet original https://lwn.net/Articles/726034/rss

Simon Raffeiner describes
in detail
the reasons he sees for the failure of the Ubuntu phone
project.
I understand there weren’t enough developers to fix everything at
once, but instead of deciding to either make a good phone OR a good tablet
with Convergence, we had devices which couldn’t really do anything
right. The whole project also always always had this ‘these are developer
devices, it’s not important to do it fast, we will win in the long run’ air
around it – until the management quite obviously realised that this was all
way too expensive and too much time had already been lost.

Building Loosely Coupled, Scalable, C# Applications with Amazon SQS and Amazon SNS

Post Syndicated from Tara Van Unen original https://aws.amazon.com/blogs/compute/building-loosely-coupled-scalable-c-applications-with-amazon-sqs-and-amazon-sns/

 
Stephen Liedig, Solutions Architect

 

One of the many challenges professional software architects and developers face is how to make cloud-native applications scalable, fault-tolerant, and highly available.

Fundamental to your project success is understanding the importance of making systems highly cohesive and loosely coupled. That means considering the multi-dimensional facets of system coupling to support the distributed nature of the applications that you are building for the cloud.

By that, I mean addressing not only the application-level coupling (managing incoming and outgoing dependencies), but also considering the impacts of of platform, spatial, and temporal coupling of your systems. Platform coupling relates to the interoperability, or lack thereof, of heterogeneous systems components. Spatial coupling deals with managing components at a network topology level or protocol level. Temporal, or runtime coupling, refers to the ability of a component within your system to do any kind of meaningful work while it is performing a synchronous, blocking operation.

The AWS messaging services, Amazon SQS and Amazon SNS, help you deal with these forms of coupling by providing mechanisms for:

  • Reliable, durable, and fault-tolerant delivery of messages between application components
  • Logical decomposition of systems and increased autonomy of components
  • Creating unidirectional, non-blocking operations, temporarily decoupling system components at runtime
  • Decreasing the dependencies that components have on each other through standard communication and network channels

Following on the recent topic, Building Scalable Applications and Microservices: Adding Messaging to Your Toolbox, in this post, I look at some of the ways you can introduce SQS and SNS into your architectures to decouple your components, and show how you can implement them using C#.

Walkthrough

To illustrate some of these concepts, consider a web application that processes customer orders. As good architects and developers, you have followed best practices and made your application scalable and highly available. Your solution included implementing load balancing, dynamic scaling across multiple Availability Zones, and persisting orders in a Multi-AZ Amazon RDS database instance, as in the following diagram.


In this example, the application is responsible for handling and persisting the order data, as well as dealing with increases in traffic for popular items.

One potential point of vulnerability in the order processing workflow is in saving the order in the database. The business expects that every order has been persisted into the database. However, any potential deadlock, race condition, or network issue could cause the persistence of the order to fail. Then, the order is lost with no recourse to restore the order.

With good logging capability, you may be able to identify when an error occurred and which customer’s order failed. This wouldn’t allow you to “restore” the transaction, and by that stage, your customer is no longer your customer.

As illustrated in the following diagram, introducing an SQS queue helps improve your ordering application. Using the queue isolates the processing logic into its own component and runs it in a separate process from the web application. This, in turn, allows the system to be more resilient to spikes in traffic, while allowing work to be performed only as fast as necessary in order to manage costs.


In addition, you now have a mechanism for persisting orders as messages (with the queue acting as a temporary database), and have moved the scope of your transaction with your database further down the stack. In the event of an application exception or transaction failure, this ensures that the order processing can be retired or redirected to the Amazon SQS Dead Letter Queue (DLQ), for re-processing at a later stage. (See the recent post, Using Amazon SQS Dead-Letter Queues to Control Message Failure, for more information on dead-letter queues.)

Scaling the order processing nodes

This change allows you now to scale the web application frontend independently from the processing nodes. The frontend application can continue to scale based on metrics such as CPU usage, or the number of requests hitting the load balancer. Processing nodes can scale based on the number of orders in the queue. Here is an example of scale-in and scale-out alarms that you would associate with the scaling policy.

Scale-out Alarm

aws cloudwatch put-metric-alarm --alarm-name AddCapacityToCustomerOrderQueue --metric-name ApproximateNumberOfMessagesVisible --namespace "AWS/SQS" 
--statistic Average --period 300 --threshold 3 --comparison-operator GreaterThanOrEqualToThreshold --dimensions Name=QueueName,Value=customer-orders
--evaluation-periods 2 --alarm-actions <arn of the scale-out autoscaling policy>

Scale-in Alarm

aws cloudwatch put-metric-alarm --alarm-name RemoveCapacityFromCustomerOrderQueue --metric-name ApproximateNumberOfMessagesVisible --namespace "AWS/SQS" 
 --statistic Average --period 300 --threshold 1 --comparison-operator LessThanOrEqualToThreshold --dimensions Name=QueueName,Value=customer-orders
 --evaluation-periods 2 --alarm-actions <arn of the scale-in autoscaling policy>

In the above example, use the ApproximateNumberOfMessagesVisible metric to discover the queue length and drive the scaling policy of the Auto Scaling group. Another useful metric is ApproximateAgeOfOldestMessage, when applications have time-sensitive messages and developers need to ensure that messages are processed within a specific time period.

Scaling the order processing implementation

On top of scaling at an infrastructure level using Auto Scaling, make sure to take advantage of the processing power of your Amazon EC2 instances by using as many of the available threads as possible. There are several ways to implement this. In this post, we build a Windows service that uses the BackgroundWorker class to process the messages from the queue.

Here’s a closer look at the implementation. In the first section of the consuming application, use a loop to continually poll the queue for new messages, and construct a ReceiveMessageRequest variable.

public static void PollQueue()
{
    while (_running)
    {
        Task<ReceiveMessageResponse> receiveMessageResponse;

        // Pull messages off the queue
        using (var sqs = new AmazonSQSClient())
        {
            const int maxMessages = 10;  // 1-10

            //Receiving a message
            var receiveMessageRequest = new ReceiveMessageRequest
            {
                // Get URL from Configuration
                QueueUrl = _queueUrl, 
                // The maximum number of messages to return. 
                // Fewer messages might be returned. 
                MaxNumberOfMessages = maxMessages, 
                // A list of attributes that need to be returned with message.
                AttributeNames = new List<string> { "All" },
                // Enable long polling. 
                // Time to wait for message to arrive on queue.
                WaitTimeSeconds = 5 
            };

            receiveMessageResponse = sqs.ReceiveMessageAsync(receiveMessageRequest);
        }

The WaitTimeSeconds property of the ReceiveMessageRequest specifies the duration (in seconds) that the call waits for a message to arrive in the queue before returning a response to the calling application. There are a few benefits to using long polling:

  • It reduces the number of empty responses by allowing SQS to wait until a message is available in the queue before sending a response.
  • It eliminates false empty responses by querying all (rather than a limited number) of the servers.
  • It returns messages as soon any message becomes available.

For more information, see Amazon SQS Long Polling.

After you have returned messages from the queue, you can start to process them by looping through each message in the response and invoking a new BackgroundWorker thread.

// Process messages
if (receiveMessageResponse.Result.Messages != null)
{
    foreach (var message in receiveMessageResponse.Result.Messages)
    {
        Console.WriteLine("Received SQS message, starting worker thread");

        // Create background worker to process message
        BackgroundWorker worker = new BackgroundWorker();
        worker.DoWork += (obj, e) => ProcessMessage(message);
        worker.RunWorkerAsync();
    }
}
else
{
    Console.WriteLine("No messages on queue");
}

The event handler, ProcessMessage, is where you implement business logic for processing orders. It is important to have a good understanding of how long a typical transaction takes so you can set a message VisibilityTimeout that is long enough to complete your operation. If order processing takes longer than the specified timeout period, the message becomes visible on the queue. Other nodes may pick it and process the same order twice, leading to unintended consequences.

Handling Duplicate Messages

In order to manage duplicate messages, seek to make your processing application idempotent. In mathematics, idempotent describes a function that produces the same result if it is applied to itself:

f(x) = f(f(x))

No matter how many times you process the same message, the end result is the same (definition from Enterprise Integration Patterns: Designing, Building, and Deploying Messaging Solutions, Hohpe and Wolf, 2004).

There are several strategies you could apply to achieve this:

  • Create messages that have inherent idempotent characteristics. That is, they are non-transactional in nature and are unique at a specified point in time. Rather than saying “place new order for Customer A,” which adds a duplicate order to the customer, use “place order <orderid> on <timestamp> for Customer A,” which creates a single order no matter how often it is persisted.
  • Deliver your messages via an Amazon SQS FIFO queue, which provides the benefits of message sequencing, but also mechanisms for content-based deduplication. You can deduplicate using the MessageDeduplicationId property on the SendMessage request or by enabling content-based deduplication on the queue, which generates a hash for MessageDeduplicationId, based on the content of the message, not the attributes.
var sendMessageRequest = new SendMessageRequest
{
    QueueUrl = _queueUrl,
    MessageBody = JsonConvert.SerializeObject(order),
    MessageGroupId = Guid.NewGuid().ToString("N"),
    MessageDeduplicationId = Guid.NewGuid().ToString("N")
};
  • If using SQS FIFO queues is not an option, keep a message log of all messages attributes processed for a specified period of time, as an alternative to message deduplication on the receiving end. Verifying the existence of the message in the log before processing the message adds additional computational overhead to your processing. This can be minimized through low latency persistence solutions such as Amazon DynamoDB. Bear in mind that this solution is dependent on the successful, distributed transaction of the message and the message log.

Handling exceptions

Because of the distributed nature of SQS queues, it does not automatically delete the message. Therefore, you must explicitly delete the message from the queue after processing it, using the message ReceiptHandle property (see the following code example).

However, if at any stage you have an exception, avoid handling it as you normally would. The intention is to make sure that the message ends back on the queue, so that you can gracefully deal with intermittent failures. Instead, log the exception to capture diagnostic information, and swallow it.

By not explicitly deleting the message from the queue, you can take advantage of the VisibilityTimeout behavior described earlier. Gracefully handle the message processing failure and make the unprocessed message available to other nodes to process.

In the event that subsequent retries fail, SQS automatically moves the message to the configured DLQ after the configured number of receives has been reached. You can further investigate why the order process failed. Most importantly, the order has not been lost, and your customer is still your customer.

private static void ProcessMessage(Message message)
{
    using (var sqs = new AmazonSQSClient())
    {
        try
        {
            Console.WriteLine("Processing message id: {0}", message.MessageId);

            // Implement messaging processing here
            // Ensure no downstream resource contention (parallel processing)
            // <your order processing logic in here…>
            Console.WriteLine("{0} Thread {1}: {2}", DateTime.Now.ToString("s"), Thread.CurrentThread.ManagedThreadId, message.MessageId);
            
            // Delete the message off the queue. 
            // Receipt handle is the identifier you must provide 
            // when deleting the message.
            var deleteRequest = new DeleteMessageRequest(_queueName, message.ReceiptHandle);
            sqs.DeleteMessageAsync(deleteRequest);
            Console.WriteLine("Processed message id: {0}", message.MessageId);

        }
        catch (Exception ex)
        {
            // Do nothing.
            // Swallow exception, message will return to the queue when 
            // visibility timeout has been exceeded.
            Console.WriteLine("Could not process message due to error. Exception: {0}", ex.Message);
        }
    }
}

Using SQS to adapt to changing business requirements

One of the benefits of introducing a message queue is that you can accommodate new business requirements without dramatically affecting your application.

If, for example, the business decided that all orders placed over $5000 are to be handled as a priority, you could introduce a new “priority order” queue. The way the orders are processed does not change. The only significant change to the processing application is to ensure that messages from the “priority order” queue are processed before the “standard order” queue.

The following diagram shows how this logic could be isolated in an “order dispatcher,” whose only purpose is to route order messages to the appropriate queue based on whether the order exceeds $5000. Nothing on the web application or the processing nodes changes other than the target queue to which the order is sent. The rates at which orders are processed can be achieved by modifying the poll rates and scalability settings that I have already discussed.

Extending the design pattern with Amazon SNS

Amazon SNS supports reliable publish-subscribe (pub-sub) scenarios and push notifications to known endpoints across a wide variety of protocols. It eliminates the need to periodically check or poll for new information and updates. SNS supports:

  • Reliable storage of messages for immediate or delayed processing
  • Publish / subscribe – direct, broadcast, targeted “push” messaging
  • Multiple subscriber protocols
  • Amazon SQS, HTTP, HTTPS, email, SMS, mobile push, AWS Lambda

With these capabilities, you can provide parallel asynchronous processing of orders in the system and extend it to support any number of different business use cases without affecting the production environment. This is commonly referred to as a “fanout” scenario.

Rather than your web application pushing orders to a queue for processing, send a notification via SNS. The SNS messages are sent to a topic and then replicated and pushed to multiple SQS queues and Lambda functions for processing.

As the diagram above shows, you have the development team consuming “live” data as they work on the next version of the processing application, or potentially using the messages to troubleshoot issues in production.

Marketing is consuming all order information, via a Lambda function that has subscribed to the SNS topic, inserting the records into an Amazon Redshift warehouse for analysis.

All of this, of course, is happening without affecting your order processing application.

Summary

While I haven’t dived deep into the specifics of each service, I have discussed how these services can be applied at an architectural level to build loosely coupled systems that facilitate multiple business use cases. I’ve also shown you how to use infrastructure and application-level scaling techniques, so you can get the most out of your EC2 instances.

One of the many benefits of using these managed services is how quickly and easily you can implement powerful messaging capabilities in your systems, and lower the capital and operational costs of managing your own messaging middleware.

Using Amazon SQS and Amazon SNS together can provide you with a powerful mechanism for decoupling application components. This should be part of design considerations as you architect for the cloud.

For more information, see the Amazon SQS Developer Guide and Amazon SNS Developer Guide. You’ll find tutorials on all the concepts covered in this post, and more. To can get started using the AWS console or SDK of your choice visit:

Happy messaging!

Schaller: Fedora Workstation 26 and beyond

Post Syndicated from corbet original https://lwn.net/Articles/725992/rss

Christian Schaller has posted an
extensive look forward
at the changes coming to the Fedora desktop.
Another major project we been working on for a long time in Fleet
Commander. Fleet Commander is a tool to allow you to manage Fedora and RHEL
desktops centrally. This is a tool targeted at for instance Universities or
companies with tens, hundreds or thousands of workstation installation. It
gives you a graphical browser based UI (accessible through Cockpit) to
create configuration profiles and deploy across your organization.

Shelfchecker Smart Shelf: build a home library system

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/smart-shelf-home-library/

Are you tired of friends borrowing your books and never returning them? Maybe you’re sure you own 1984 but can’t seem to locate it? Do you find a strange satisfaction in using the supermarket self-checkout simply because of the barcode beep? With the ShelfChecker smart shelf from maker Annelynn described on Instructables, you can be your own librarian and never misplace your books again! Beep!

Shelfchecker smart shelf annelynn Raspberry Pi

Harry Potter and the Aesthetically Pleasing Smart Shelf

The ShelfChecker smart shelf

Annelynn built her smart shelf utilising a barcode scanner, LDR light sensors, a Raspberry Pi, plus a few other peripherals and some Python scripts. She has created a fully integrated library checkout system with accompanying NeoPixel location notification for your favourite books.

This build allows you to issue your book-borrowing friends their own IDs and catalogue their usage of your treasured library. On top of that, you’ll be able to use LED NeoPixels to highlight your favourite books, registering their removal and return via light sensor tracking.

Using light sensors for book cataloguing

Once Annelynn had built the shelf, she drilled holes to fit the eight LDRs that would guard her favourite books, and separated them with corner brackets to prevent confusion.

Shelfchecker smart shelf annelynn Raspberry Pi

Corner brackets keep the books in place without confusion between their respective light sensors

Due to the limitations of the MCP3008 Adafruit microchip, the smart shelf can only keep track of eight of your favourite books. But this limitation won’t stop you from cataloguing your entire home library; it simply means you get to pick your ultimate favourites that will occupy the prime real estate on your wall.

Obviously, the light sensors sense light. So when you remove or insert a book, light floods or is blocked from that book’s sensor. The sensor sends this information to the Raspberry Pi. In response, an Arduino controls the NeoPixel strip along the ‘favourites’ shelf to indicate the book’s status.

Shelfchecker smart shelf annelynn Raspberry Pi

The book you are looking for is temporarily unavailable

Code your own library

While keeping a close eye on your favourite books, the system also allows creation of a complete library catalogue system with the help of a MySQL database. Users of the library can log into the system with a barcode scanner, and take out or return books recorded in the database guided by an LCD screen attached to the Pi.

Shelfchecker smart shelf annelynn Raspberry Pi

Beep!

I won’t go into an extensive how-to on creating MySQL databases here on the blog, because my glamourous assistant Janina has pulled up these MySQL tutorials to help you get started. Annelynn’s Github scripts are also packed with useful comments to keep you on track.

Raspberry Pi and books

We love books and libraries. And considering the growing number of Code Clubs and makespaces into libraries across the world, and the host of book-based Pi builds we’ve come across, the love seems to be mutual.

We’ve seen the Raspberry Pi introduced into the Wordery bookseller warehouse, a Pi-powered page-by-page book scanner by Jonathon Duerig, and these brilliant text-to-speech and page turner projects that use our Pis!

Did I say we love books? In fact we love them so much that members of our team have even written a few.*

If you’ve set up any sort of digital making event in a library, have in some way incorporated Raspberry Pi into your own personal book collection, or even managed to recreate the events of your favourite story using digital making, make sure to let us know in the comments below.

* Shameless plug**

Fancy adding some Pi to your home library? Check out these publications from the Raspberry Pi staff:

A Beginner’s Guide to Coding by Marc Scott

Adventures in Raspberry Pi by Carrie Anne Philbin

Getting Started with Raspberry Pi by Matt Richardson

Raspberry Pi User Guide by Eben Upton

The MagPi Magazine, Essentials Guides and Project Books

Make Your Own Game and Build Your Own Website by CoderDojo

** Shameless Pug

 

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All Systems Go! 2017 CfP Open

Post Syndicated from Lennart Poettering original http://0pointer.net/blog/all-systems-go-2017-cfp-open.html

The All Systems Go! 2017 Call for Participation is Now Open!

We’d like to invite presentation proposals for All Systems Go! 2017!

All Systems Go! is an Open Source community conference focused on the projects and technologies at the foundation of modern Linux systems — specifically low-level user-space technologies. Its goal is to provide a friendly and collaborative gathering place for individuals and communities working to push these technologies forward.

All Systems Go! 2017 takes place in Berlin, Germany on October 21st+22nd.

All Systems Go! is a 2-day event with 2-3 talks happening in parallel. Full presentation slots are 30-45 minutes in length and lightning talk slots are 5-10 minutes.

We are now accepting submissions for presentation proposals. In particular, we are looking for sessions including, but not limited to, the following topics:

  • Low-level container executors and infrastructure
  • IoT and embedded OS infrastructure
  • OS, container, IoT image delivery and updating
  • Building Linux devices and applications
  • Low-level desktop technologies
  • Networking
  • System and service management
  • Tracing and performance measuring
  • IPC and RPC systems
  • Security and Sandboxing

While our focus is definitely more on the user-space side of things, talks about kernel projects are welcome too, as long as they have a clear and direct relevance for user-space.

Please submit your proposals by September 3rd. Notification of acceptance will be sent out 1-2 weeks later.

To submit your proposal now please visit our CFP submission web site.

For further information about All Systems Go! visit our conference web site.

systemd.conf will not take place this year in lieu of All Systems Go!. All Systems Go! welcomes all projects that contribute to Linux user space, which, of course, includes systemd. Thus, anything you think was appropriate for submission to systemd.conf is also fitting for All Systems Go!

Weekly roundup: Successful juggling

Post Syndicated from Eevee original https://eev.ee/dev/2017/06/19/weekly-roundup-successful-juggling/

Despite flipping my sleep, as I seem to end up doing every month now, I’ve had a pretty solid week. We finally got our hands on a Switch, so I just played Zelda to stay up a ridiculously long time and restore my schedule pretty quickly.

  • potluck: I started building the potluck game in LÖVE, and it’s certainly come along much faster — I have map transitions, dialogue, and a couple moving platforms working. I still don’t quite know what this game is, but I’m starting to get some ideas.

    I also launched GAMES MADE QUICK??? 1½, a game jam for making a game while watching GDQ, instead of just plain watching GDQ. I intend to spend the week working on the potluck game, though I’m not sure whether I’ll finish it then.

  • fox flux: I started planning out a more interesting overworld and doodled a couple relevant tiles. Terrain is still hard. Also some more player frames.

  • art: I finally finished a glorious new banner, which now hangs proudly above my Twitter and Patreon. I did a bedtime slate doodle. I made and animated a low-poly Yoshi. I sketched Styx based on a photo.

    I keep wishing I have time to dedicate to painting experiments, but I guess this is pretty good output.

  • veekun: Wow! I touched veekun on three separate occasions. I have basic item data actually physically dumping now, I fixed some stuff with Pokémon, and I got evolutions working. Progress! Getting there! So close!

  • blog: Per request, I wrote about digital painting software, though it was hampered slightly by the fact that most of it doesn’t run on my operating system.

I seem to be maintaining tangible momentum on multiple big projects, which is fantastic. And there’s still 40% of the month left! I’m feeling pretty good about where I’m standing; if I can get potluck and veekun done soon, that’ll be a medium and a VERY LARGE weight off my shoulders.

AIMS Desktop 2017.1 released

Post Syndicated from corbet original https://lwn.net/Articles/725712/rss

The AIMS desktop is a
Debian-derived distribution aimed at mathematical and scientific use. This
project’s first public release, based on Debian 9, is now available.
It is a GNOME-based distribution with a bunch of add-on software.
It is maintained by AIMS (The African Institute for Mathematical
Sciences), a pan-African network of centres of excellence enabling Africa’s
talented students to become innovators driving the continent’s scientific,
educational and economic self-sufficiency.

Debian 9 “Stretch” released

Post Syndicated from corbet original https://lwn.net/Articles/725696/rss

The Debian 9 “Stretch” release is now available. “Debian 9 is
dedicated to the project’s founder Ian Murdock, who passed away on 28
December 2015.
” There are a lot of changes in this release,
including a switch to MariaDB, the return of Firefox and Thunderbird under
those names, 90% reproducible-build coverage, a rootless X server, and
more.

Mira, tiny robot of joyful delight

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/mira-robot-alonso-martinez/

The staff of Pi Towers are currently melting into puddles while making ‘Aaaawwwwwww’ noises as Mira, the adorable little Pi-controlled robot made by Pixar 3D artist Alonso Martinez, steals their hearts.

Mira the robot playing peek-a-boo

If you want to get updates on Mira’s progress, sign up for the mailing list! http://eepurl.com/bteigD Mira is a desk companion that makes your life better one smile at a time. This project explores human robot interactivity and emotional intelligence. Currently Mira uses face tracking to interact with the users and loves playing the game “peek-a-boo”.

Introducing Mira

Honestly, I can’t type words – I am but a puddle! If I could type at all, I would only produce a stream of affectionate fragments. Imagine walking into a room full of kittens. What you would sound like is what I’d type.

No! I can do this. I’m a professional. I write for a living! I can…

SHE BLINKS OHMYAAAARGH!!!

Mira Alonso Martinez Raspberry Pi

Weebl & Bob meets South Park’s Ike Broflovski in an adorable 3D-printed bundle of ‘Aaawwwww’

Introducing Mira (I promise I can do this)

Right. I’ve had a nap and a drink. I’ve composed myself. I am up for this challenge. As long as I don’t look directly at her, I’ll be fine!

Here I go.

As one of the many über-talented 3D artists at Pixar, Alonso Martinez knows a thing or two about bringing adorable-looking characters to life on screen. However, his work left him wondering:

In movies you see really amazing things happening but you actually can’t interact with them – what would it be like if you could interact with characters?

So with the help of his friends Aaron Nathan and Vijay Sundaram, Alonso set out to bring the concept of animation to the physical world by building a “character” that reacts to her environment. His experiments with robotics started with Gertie, a ball-like robot reminiscent of his time spent animating bouncing balls when he was learning his trade. From there, he moved on to Mira.

Mira Alonso Martinez

Many, many of the views of this Tested YouTube video have come from me. So many.

Mira swivels to follow a person’s face, plays games such as peekaboo, shows surprise when you finger-shoot her, and giggles when you give her a kiss.

Mira’s inner workings

To get Mira to turn her head in three dimensions, Alonso took inspiration from the Microsoft Sidewinder Pro joystick he had as a kid. He purchased one on eBay, took it apart to understand how it works, and replicated its mechanism for Mira’s Raspberry Pi-powered innards.

Mira Alonso Martinez

Alonso used the smallest components he could find so that they would fit inside Mira’s tiny body.

Mira’s axis of 3D-printed parts moves via tiny Power HD DSM44 servos, while a camera and OpenCV handle face-tracking, and a single NeoPixel provides a range of colours to indicate her emotions. As for the blinking eyes? Two OLED screens boasting acrylic domes fit within the few millimeters between all the other moving parts.

More on Mira, including her history and how she works, can be found in this wonderful video released by Tested this week.

Pixar Artist’s 3D-Printed Animated Robots!

We’re gushing with grins and delight at the sight of these adorable animated robots created by artist Alonso Martinez. Sean chats with Alonso to learn how he designed and engineered his family of robots, using processes like 3D printing, mold-making, and silicone casting. They’re amazing!

You can also sign up for Alonso’s newsletter here to stay up-to-date about this little robot. Hopefully one of these newsletters will explain how to buy or build your own Mira, as I for one am desperate to see her adorable little face on my desk every day for the rest of my life.

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[$] The Brave web browser

Post Syndicated from jake original https://lwn.net/Articles/725261/rss

The Brave web browser is a project from
a new company called Brave Software. It was founded by Brendan Eich, who is the
inventor of JavaScript and former developer and CTO at Mozilla; he
hopes to dramatically re-invent the advertising model of the web while
strengthening user anonymity and security. Brave’s value proposition is
that instead of being served advertisements from web sites that use the
revenue to pay their bills, users can opt to directly pay the content
providers of their choosing with cryptocurrency. Also, there is a
recognition of the
utility of targeted advertising, so users have an option of saving a local,
protected profile that can be used anonymously to obtain targeted
advertisements instead of having their online behavior tracked and sold by
a third party.

[$] Alioth moving toward pagure

Post Syndicated from jake original https://lwn.net/Articles/724986/rss

Since 2003, the Debian project has been running a server
called Alioth to host source code
version control systems. The server will hit the end of life of the Debian
LTS release (Wheezy) next year; that deadline raised some questions
regarding the plans for the server over the coming years. Naturally, that
led to a discussion regarding possible replacements.

Making Waves: print out sound waves with the Raspberry Pi

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/printed-sound-wave/

For fun, Eunice Lee, Matthew Zhang, and Bomani McClendon have worked together to create Waves, an audiovisual project that records people’s spoken responses to personal questions and prints them in the form of a sound wave as a gift for being truthful.

Waves

Waves is a Raspberry Pi project centered around transforming the transience of the spoken word into something concrete and physical. In our setup, a user presses a button corresponding to an intimate question (ex: what’s your motto?) and answers it into a microphone while pressing down on the button.

What are you grateful for?

“I’m grateful for finishing this project,” admits maker Eunice Lee as she presses a button and speaks into the microphone that is part of the Waves project build. After a brief moment, her confession appears on receipt paper as a waveform, and she grins toward the camera, happy with the final piece.

Eunice testing Waves

Waves is a Raspberry Pi project centered around transforming the transience of the spoken word into something concrete and physical. In our setup, a user presses a button corresponding to an intimate question (ex: what’s your motto?) and answers it into a microphone while pressing down on the button.

Sound wave machine

Alongside a Raspberry Pi 3, the Waves device is comprised of four tactile buttons, a standard USB microphone, and a thermal receipt printer. This type of printer has become easily available for the maker movement from suppliers such as Adafruit and Pimoroni.

Eunice Lee, Matthew Zhang, Bomani McClendon - Sound Wave Raspberry Pi

Definitely more fun than a polygraph test

The trio designed four colour-coded cards that represent four questions, each of which has a matching button on the breadboard. Press the button that belongs to the question to be answered, and Python code directs the Pi to record audio via the microphone. Releasing the button stops the audio recording. “Once the recording has been saved, the script viz.py is launched,” explains Lee. “This script takes the audio file and, using Python matplotlib magic, turns it into a nice little waveform image.”

From there, the Raspberry Pi instructs the thermal printer to produce a printout of the sound wave image along with the question.

Making for fun

Eunice, Bomani, and Matt, students of design and computer science at Northwestern University in Illinois, built Waves as a side project. They wanted to make something at the intersection of art and technology and were motivated by the pure joy of creating.

Eunice Lee, Matthew Zhang, Bomani McClendon - Sound Wave Raspberry Pi

Making makes people happy

They have noted improvements that can be made to increase the scope of their sound wave project. We hope to see many more interesting builds from these three, and in the meantime we invite you all to look up their code on Eunice’s GitHub to create your own Waves at home.

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Elixir Cross Referencer: new way to browse kernel sources

Post Syndicated from ris original https://lwn.net/Articles/725302/rss

Free electrons has released the initial
version of the Elixir
Cross-Referencer
, a Linux source code cross-referencing online tool.
Elixir uses a new engine written in Python that replaces LXR, the
engine used in free electron’s previous online tool. “Another reason that motivated a complete rewrite was that we wanted to provide an up-to-date reference (including the latest revisions) while keeping it immutable, so that external links to the source code wouldn’t get broken in the future. As a direct consequence, we would need to index many different revisions for each project, with potentially a lot of redundant information between them. That’s when we realized we could leverage the data model of Git to deal with this redundancy in an efficient manner, by indexing Git blobs, which are shared between revisions. In order to make sure queries under this strategy would be fast enough, we wrote a proof-of-concept in Python, and thus Elixir was born.

Firefox 54 released

Post Syndicated from ris original https://lwn.net/Articles/725275/rss

Firefox 54.0 has been released. The release
notes
are somewhat sparse, however this
blog post
contains more information about some changes under-the-hood.
To make Firefox run even complex sites faster, we’ve been changing it to run using multiple operating system processes. Translation? The old Firefox used a single process to run all the tabs in a browser. Modern browsers split the load into several independent processes. We named our project to split Firefox into multiple processes ‘Electrolysis (E10S)’ after the chemical process that divides water into its core elements. E10S is the largest change to Firefox code in our history. And today we’re launching our next big phase of the E10S initiative.

Estefannie’s GPS-Controlled GoPro Photo Taker

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

Are you tired of having to take selfies physically? Do you only use your GoPro for the occasional beach vacation? Are you maybe even wondering what to do with the load of velcro you bought on a whim? Then we have good news for you: Estefannie‘s back to help you out with her Personal Automated GPS-Controlled Portable Photo Taker…PAGCPPT for short…or pagsssspt, if you like.

RASPBERRY PI + GPS CONTROLLED PHOTO TAKER

Hey World! Do you like vacation pictures but don’t like taking them? Make your own Personal Automated GPS Controlled Portable Photo Taker! The code, components, and instructions are in my Hackster.io account: https://www.hackster.io/estefanniegg/automated-gps-controlled-photo-taker-3fc84c For this build, I decided to put together a backpack to take pictures of me when I am close to places that like.

The Personal Automated GPS-Controlled Portable Photo Taker

Try saying that five times in a row.

Go on. I’ll wait.

Using a Raspberry Pi 3, a GPS module, a power pack, and a GoPro plus GoPro Stick, Estefannie created the PAGCPPT as a means of automatically taking selfies at pre-specified tourist attractions across London.

Estefannie Explains it All Raspberry Pi GPS GoPro Camera

There’s pie in my backpack too…but it’s a bit messy

With velcro and hot glue, she secured the tech in place on (and inside) a backpack. Then it was simply a case of programming her set up to take pictures while she walked around the city.

Estefannie Explains it All Raspberry Pi GPS GoPro Camera

Making the GoPro…go

Estefannie made use of a GoPro API library to connect her GoPro to the Raspberry Pi via WiFi. With the help of this library, she wrote a Python script that made the GoPro take a photograph whenever her GPS module placed her within a ten-metre radius of a pre-selected landmark such as Tower Bridge, Abbey Road, or Platform 9 3/4.

Estefannie Explains it All Raspberry Pi GPS GoPro Camera

“Accio selfie.”

The full script, as well as details regarding the components she used for the project, can be found on her hackster.io page here.

Estefannie Explains it All

You’ll have noticed that we’ve covered Estefannie once or twice before on the Raspberry Pi blog. We love project videos that convey a sense of ‘Oh hey, I can totally build one of those!’, and hers always tick that box. They are imaginative, interesting, quirky, and to be totally honest with you, I’ve been waiting for this particular video since she hinted at it on her visit to Pi Towers in May. I got the inside scoop, yo!

What’s better than taking pictures? Not taking pictures. But STILL having pictures. I made a personal automated GPS controlled Portable Photo Taker ⚡ NEW VIDEO ALERT⚡ Link in bio.

1,351 Likes, 70 Comments – Estefannie Explains It All (@estefanniegg) on Instagram: “What’s better than taking pictures? Not taking pictures. But STILL having pictures. I made a…”

Make sure to follow her on YouTube and Instagram for more maker content and random shenanigans. And if you have your own maker social media channel, YouTube account, blog, etc, this is your chance to share it for the world to see in the comments below!

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