Tag Archives: Choice

Pioneers: only you can save us

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/pioneers-third-challenge/

Pioneers, we just received this message through our network — have you seen it?

Can you see me? Only YOU can save us!

Uploaded by Raspberry Pi on 2017-09-14.

Only you can save us

We have no choice – we must help her! If things are as bad as she says they are, our only hope of survival is to work together.

We know you have the skills and imagination required to make something. We’ve seen that in previous Pioneers challenges. That’s why we’re coming directly to you with this: we know you won’t let her down.

What you need to do

We’ve watched back through the recording and pulled out as much information as we can:

  • To save us, you have ten weeks to create something using tech. This means you need to be done on 1 December, or it will be too late!
  • The build you will create needs to help her in the treacherous situation she’s in. What you decide to make is completely up to you.
  • Her call is for those of you aged between 11 and 16 who are based in the UK or Republic of Ireland. You need to work in groups of up to five, and you need to find someone aged 18 or over to act as a mentor and support your project.
  • Any tech will do. We work for the Raspberry Pi Foundation, but this doesn’t mean you need to use a Raspberry Pi. Use anything at all — from microcontrollers to repurposed devices such as laptops and cameras.

To keep in contact with you, it looks like she’s created a form for you to fill in and share your team name and details with her. In return she will trade some items with you — things that will help inspire you in your mission. We’ve managed to find the link to the form: you can fill it in here.

Only you can save us - Raspberry Pi Pioneers

In order to help her (and any others who might still be out there!) to recreate your project, you need to make sure you record your working process. Take photos and footage to document how you build your make, and put together a video to send to her when you’re done making.

If you manage to access social media, you could also share your progress as you go along! Make sure to use #MakeYourIdeas, so that other survivors can see your work.

We’ve assembled some more information on the Pioneers website to create a port of call for you. Check it out, and let us know if you have any questions. We will do whatever we can to help you protect the world.

Good luck, everybody! It’s up to you now.

Only you can save us.

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Strategies for Backing Up Windows Computers

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/strategies-for-backing-up-windows-computers/

Windows 7, Windows 8, Windows 10 logos

There’s a little company called Apple making big announcements this week, but about 45% of you are on Windows machines, so we thought it would be a good idea to devote a blog post today to Windows users and the options they have for backing up Windows computers.

We’ll be talking about the various options for backing up Windows desktop OS’s 7, 8, and 10, and Windows servers. We’ve written previously about this topic in How to Back Up Windows, and Computer Backup Options, but we’ll be covering some new topics and ways to combine strategies in this post. So, if you’re a Windows user looking for shelter from all the Apple hoopla, welcome to our Apple Announcement Day Windows Backup Day post.

Windows laptop

First, Let’s Talk About What We Mean by Backup

This might seem to our readers like an unneeded appetizer on the way to the main course of our post, but we at Backblaze know that people often mean very different things when they use backup and related terms. Let’s start by defining what we mean when we say backup, cloud storage, sync, and archive.

Backup
A backup is an active copy of the system or files that you are using. It is distinguished from an archive, which is the storing of data that is no longer in active use. Backups fall into two main categories: file and image. File backup software will back up whichever files you designate by either letting you include files you wish backed up or by excluding files you don’t want backed up, or both. An image backup, sometimes called a disaster recovery backup or a system clone, is useful if you need to recreate your system on a new drive or computer.
The first backup generally will be a full backup of all files. After that, the backup will be incremental, meaning that only files that have been changed since the full backup will be added. Often, the software will keep changed versions of the files for some period of time, so you can maintain a number of previous revisions of your files in case you wish to return to something in an earlier version of your file.
The destination for your backup could be another drive on your computer, an attached drive, a network-attached drive (NAS), or the cloud.
Cloud Storage
Cloud storage vendors supply data storage just as a utility company supplies power, gas, or water. Cloud storage can be used for data backups, but it can also be used for data archives, application data, records, or libraries of photos, videos, and other media.
You contract with the service for storing any type of data, and the storage location is available to you via the internet. Cloud storage providers generally charge by some combination of data ingress, egress, and the amount of data stored.
Sync
File sync is useful for files that you wish to have access to from different places or computers, or for files that you wish to share with others. While sync has its uses, it has limitations for keeping files safe and how much it could cost you to store large amounts of data. As opposed to backup, which keeps revision of files, sync is designed to keep two or more locations exactly the same. Sync costs are based on how much data you sync and can get expensive for large amounts of data.
Archive
A data archive is for data that is no longer in active use but needs to be saved, and may or may not ever be retrieved again. In old-style storage parlance, it is called cold storage. An archive could be stored with a cloud storage provider, or put on a hard drive or flash drive that you disconnect and put in the closet, or mail to your brother in Idaho.

What’s the Best Strategy for Backing Up?

Now that we’ve got our terminology clear, let’s talk backup strategies for Windows.

At Backblaze, we advocate the 3-2-1 strategy for safeguarding your data, which means that you should maintain three copies of any valuable data — two copies stored locally and one stored remotely. I follow this strategy at home by working on the active data on my Windows 10 desktop computer (copy one), which is backed up to a Drobo RAID device attached via USB (copy two), and backing up the desktop to Backblaze’s Personal Backup in the cloud (copy three). I also keep an image of my primary disk on a separate drive and frequently update it using Windows 10’s image tool.

I use Dropbox for sharing specific files I am working on that I might wish to have access to when I am traveling or on another computer. Once my subscription with Dropbox expires, I’ll use the latest release of Backblaze that has individual file preview with sharing built-in.

Before you decide which backup strategy will work best for your situation, you’ll need to ask yourself a number of questions. These questions include where you wish to store your backups, whether you wish to supply your own storage media, whether the backups will be manual or automatic, and whether limited or unlimited data storage will work best for you.

Strategy 1 — Back Up to a Local or Attached Drive

The first copy of the data you are working on is often on your desktop or laptop. You can create a second copy of your data on another drive or directory on your computer, or copy the data to a drive directly attached to your computer, such as via USB.

external hard drive and RAID NAS devices

Windows has built-in tools for both file and image level backup. Depending on which version of Windows you use, these tools are called Backup and Restore, File History, or Image. These tools enable you to set a schedule for automatic backups, which ensures that it is done regularly. You also have the choice to use Windows Explorer (aka File Explorer) to manually copy files to another location. Some external disk drives and USB Flash Drives come with their own backup software, and other backup utilities are available for free or for purchase.

Windows Explorer File History screenshot

This is a supply-your-own media solution, meaning that you need to have a hard disk or other medium available of sufficient size to hold all your backup data. When a disk becomes full, you’ll need to add a disk or swap out the full disk to continue your backups.

We’ve written previously on this strategy at Should I use an external drive for backup?

Strategy 2 — Back Up to a Local Area Network (LAN)

Computers, servers, and network-attached-storage (NAS) on your local network all can be used for backing up data. Microsoft’s built-in backup tools can be used for this job, as can any utility that supports network protocols such as NFS or SMB/CIFS, which are common protocols that allow shared access to files on a network for Windows and other operatings systems. There are many third-party applications available as well that provide extensive options for managing and scheduling backups and restoring data when needed.

NAS cloud

Multiple computers can be backed up to a single network-shared computer, server, or NAS, which also could then be backed up to the cloud, which rounds out a nice backup strategy, because it covers both local and remote copies of your data. System images of multiple computers on the LAN can be included in these backups if desired.

Again, you are managing the backup media on the local network, so you’ll need to be sure you have sufficient room on the destination drives to store all your backup data.

Strategy 3 — Back Up to Detached Drive at Another Location

You may have have read our recent blog post, Getting Data Archives Out of Your Closet, in which we discuss the practice of filling hard drives and storing them in a closet. Of course, to satisfy the off-site backup guideline, these drives would need to be stored in a closet that’s in a different geographical location than your main computer. If you’re willing to do all the work of copying the data to drives and transporting them to another location, this is a viable option.

stack of hard drives

The only limitation to the amount of backup data is the number of hard drives you are willing to purchase — and maybe the size of your closet.

Strategy 4 — Back Up to the Cloud

Backing up to the cloud has become a popular option for a number of reasons. Internet speeds have made moving large amounts of data possible, and not having to worry about supplying the storage media simplifies choices for users. Additionally, cloud vendors implement features such as data protection, deduplication, and encryption as part of their services that make cloud storage reliable, secure, and efficient. Unlimited cloud storage for data from a single computer is a popular option.

A backup vendor likely will provide a software client that runs on your computer and backs up your data to the cloud in the background while you’re doing other things, such as Backblaze Personal Backup, which has clients for Windows computers, Macintosh computers, and mobile apps for both iOS and Android. For restores, Backblaze users can download one or all of their files for free from anywhere in the world. Optionally, a 128 GB flash drive or 4 TB drive can be overnighted to the customer, with a refund available if the drive is returned.

Storage Pod in the cloud

Backblaze B2 Cloud Storage is an option for those who need capabilities beyond Backblaze’s Personal Backup. B2 provides cloud storage that is priced based on the amount of data the customer uses, and is suitable for long-term data storage. B2 supports integrations with NAS devices, as well as Windows, Macintosh, and Linux computers and servers.

Services such as BackBlaze B2 are often called Cloud Object Storage or IaaS (Infrastructure as a Service), because they provide a complete solution for storing all types of data in partnership with vendors who integrate various solutions for working with B2. B2 has its own API (Application Programming Interface) and CLI (Command-line Interface) to work with B2, but B2 becomes even more powerful when paired with any one of a number of other solutions for data storage and management provided by third parties who offer both hardware and software solutions.

Backing Up Windows Servers

Windows Servers are popular workstations for some users, and provide needed network services for others. They also can be used to store backups from other computers on the network. They, in turn, can be backed up to attached drives or the cloud. While our Personal Backup client doesn’t support Windows servers, our B2 Cloud Storage has a number of integrations with vendors who supply software or hardware for storing data both locally and on B2. We’ve written a number of blog posts and articles that address these solutions, including How to Back Up your Windows Server with B2 and CloudBerry.

Sometimes the Best Strategy is to Mix and Match

The great thing about computers, software, and networks is that there is an endless number of ways to combine them. Our users and hardware and software partners are ingenious in configuring solutions that save data locally, copy it to an attached or network drive, and then store it to the cloud.

image of cloud backup

Among our B2 partners, Synology, CloudBerry Archiware, QNAP, Morro Data, and GoodSync have integrations that allow their NAS devices to store and retrieve data to and from B2 Cloud Storage. For a drag-and-drop experience on the desktop, take a look at CyberDuck, MountainDuck, and Dropshare, which provide users with an easy and interactive way to store and use data in B2.

If you’d like to explore more options for combining software, hardware, and cloud solutions, we invite you to browse the integrations for our many B2 partners.

Have Questions?

Windows versions, tools, and backup terminology all can be confusing, and we know how hard it can be to make sense of all of it. If there’s something we haven’t addressed here, or if you have a question or contribution, please let us know in the comments.

And happy Windows Backup Day! (Just don’t tell Apple.)

The post Strategies for Backing Up Windows Computers appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Delivering Graphics Apps with Amazon AppStream 2.0

Post Syndicated from Deepak Suryanarayanan original https://aws.amazon.com/blogs/compute/delivering-graphics-apps-with-amazon-appstream-2-0/

Sahil Bahri, Sr. Product Manager, Amazon AppStream 2.0

Do you need to provide a workstation class experience for users who run graphics apps? With Amazon AppStream 2.0, you can stream graphics apps from AWS to a web browser running on any supported device. AppStream 2.0 offers a choice of GPU instance types. The range includes the newly launched Graphics Design instance, which allows you to offer a fast, fluid user experience at a fraction of the cost of using a graphics workstation, without upfront investments or long-term commitments.

In this post, I discuss the Graphics Design instance type in detail, and how you can use it to deliver a graphics application such as Siemens NX―a popular CAD/CAM application that we have been testing on AppStream 2.0 with engineers from Siemens PLM.

Graphics Instance Types on AppStream 2.0

First, a quick recap on the GPU instance types available with AppStream 2.0. In July, 2017, we launched graphics support for AppStream 2.0 with two new instance types that Jeff Barr discussed on the AWS Blog:

  • Graphics Desktop
  • Graphics Pro

Many customers in industries such as engineering, media, entertainment, and oil and gas are using these instances to deliver high-performance graphics applications to their users. These instance types are based on dedicated NVIDIA GPUs and can run the most demanding graphics applications, including those that rely on CUDA graphics API libraries.

Last week, we added a new lower-cost instance type: Graphics Design. This instance type is a great fit for engineers, 3D modelers, and designers who use graphics applications that rely on the hardware acceleration of DirectX, OpenGL, or OpenCL APIs, such as Siemens NX, Autodesk AutoCAD, or Adobe Photoshop. The Graphics Design instance is based on AMD’s FirePro S7150x2 Server GPUs and equipped with AMD Multiuser GPU technology. The instance type uses virtualized GPUs to achieve lower costs, and is available in four instance sizes to scale and match the requirements of your applications.

Instance vCPUs Instance RAM (GiB) GPU Memory (GiB)
stream.graphics-design.large 2 7.5 GiB 1
stream.graphics-design.xlarge 4 15.3 GiB 2
stream.graphics-design.2xlarge 8 30.5 GiB 4
stream.graphics-design.4xlarge 16 61 GiB 8

The following table compares all three graphics instance types on AppStream 2.0, along with example applications you could use with each.

  Graphics Design Graphics Desktop Graphics Pro
Number of instance sizes 4 1 3
GPU memory range
1–8 GiB 4 GiB 8–32 GiB
vCPU range 2–16 8 16–32
Memory range 7.5–61 GiB 15 GiB 122–488 GiB
Graphics libraries supported AMD FirePro S7150x2 NVIDIA GRID K520 NVIDIA Tesla M60
Price range (N. Virginia AWS Region) $0.25 – $2.00/hour $0.5/hour $2.05 – $8.20/hour
Example applications Adobe Premiere Pro, AutoDesk Revit, Siemens NX AVEVA E3D, SOLIDWORKS AutoDesk Maya, Landmark DecisionSpace, Schlumberger Petrel

Example graphics instance set up with Siemens NX

In the section, I walk through setting up Siemens NX with Graphics Design instances on AppStream 2.0. After set up is complete, users can able to access NX from within their browser and also access their design files from a file share. You can also use these steps to set up and test your own graphics applications on AppStream 2.0. Here’s the workflow:

  1. Create a file share to load and save design files.
  2. Create an AppStream 2.0 image with Siemens NX installed.
  3. Create an AppStream 2.0 fleet and stack.
  4. Invite users to access Siemens NX through a browser.
  5. Validate the setup.

To learn more about AppStream 2.0 concepts and set up, see the previous post Scaling Your Desktop Application Streams with Amazon AppStream 2.0. For a deeper review of all the setup and maintenance steps, see Amazon AppStream 2.0 Developer Guide.

Step 1: Create a file share to load and save design files

To launch and configure the file server

  1. Open the EC2 console and choose Launch Instance.
  2. Scroll to the Microsoft Windows Server 2016 Base Image and choose Select.
  3. Choose an instance type and size for your file server (I chose the general purpose m4.large instance). Choose Next: Configure Instance Details.
  4. Select a VPC and subnet. You launch AppStream 2.0 resources in the same VPC. Choose Next: Add Storage.
  5. If necessary, adjust the size of your EBS volume. Choose Review and Launch, Launch.
  6. On the Instances page, give your file server a name, such as My File Server.
  7. Ensure that the security group associated with the file server instance allows for incoming traffic from the security group that you select for your AppStream 2.0 fleets or image builders. You can use the default security group and select the same group while creating the image builder and fleet in later steps.

Log in to the file server using a remote access client such as Microsoft Remote Desktop. For more information about connecting to an EC2 Windows instance, see Connect to Your Windows Instance.

To enable file sharing

  1. Create a new folder (such as C:\My Graphics Files) and upload the shared files to make available to your users.
  2. From the Windows control panel, enable network discovery.
  3. Choose Server Manager, File and Storage Services, Volumes.
  4. Scroll to Shares and choose Start the Add Roles and Features Wizard. Go through the wizard to install the File Server and Share role.
  5. From the left navigation menu, choose Shares.
  6. Choose Start the New Share Wizard to set up your folder as a file share.
  7. Open the context (right-click) menu on the share and choose Properties, Permissions, Customize Permissions.
  8. Choose Permissions, Add. Add Read and Execute permissions for everyone on the network.

Step 2:  Create an AppStream 2.0 image with Siemens NX installed

To connect to the image builder and install applications

  1. Open the AppStream 2.0 management console and choose Images, Image Builder, Launch Image Builder.
  2. Create a graphics design image builder in the same VPC as your file server.
  3. From the Image builder tab, select your image builder and choose Connect. This opens a new browser tab and display a desktop to log in to.
  4. Log in to your image builder as ImageBuilderAdmin.
  5. Launch the Image Assistant.
  6. Download and install Siemens NX and other applications on the image builder. I added Blender and Firefox, but you could replace these with your own applications.
  7. To verify the user experience, you can test the application performance on the instance.

Before you finish creating the image, you must mount the file share by enabling a few Microsoft Windows services.

To mount the file share

  1. Open services.msc and check the following services:
  • DNS Client
  • Function Discovery Resource Publication
  • SSDP Discovery
  • UPnP Device H
  1. If any of the preceding services have Startup Type set to Manual, open the context (right-click) menu on the service and choose Start. Otherwise, open the context (right-click) menu on the service and choose Properties. For Startup Type, choose Manual, Apply. To start the service, choose Start.
  2. From the Windows control panel, enable network discovery.
  3. Create a batch script that mounts a file share from the storage server set up earlier. The file share is mounted automatically when a user connects to the AppStream 2.0 environment.

Logon Script Location: C:\Users\Public\logon.bat

Script Contents:

:loop

net use H: \\path\to\network\share 

PING localhost -n 30 >NUL

IF NOT EXIST H:\ GOTO loop

  1. Open gpedit.msc and choose User Configuration, Windows Settings, Scripts. Set logon.bat as the user logon script.
  2. Next, create a batch script that makes the mounted drive visible to the user.

Logon Script Location: C:\Users\Public\startup.bat

Script Contents:
REG DELETE “HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\CurrentVersion\Policies\Explorer” /v “NoDrives” /f

  1. Open Task Scheduler and choose Create Task.
  2. Choose General, provide a task name, and then choose Change User or Group.
  3. For Enter the object name to select, enter SYSTEM and choose Check Names, OK.
  4. Choose Triggers, New. For Begin the task, choose At startup. Under Advanced Settings, change Delay task for to 5 minutes. Choose OK.
  5. Choose Actions, New. Under Settings, for Program/script, enter C:\Users\Public\startup.bat. Choose OK.
  6. Choose Conditions. Under Power, clear the Start the task only if the computer is on AC power Choose OK.
  7. To view your scheduled task, choose Task Scheduler Library. Close Task Scheduler when you are done.

Step 3:  Create an AppStream 2.0 fleet and stack

To create a fleet and stack

  1. In the AppStream 2.0 management console, choose Fleets, Create Fleet.
  2. Give the fleet a name, such as Graphics-Demo-Fleet, that uses the newly created image and the same VPC as your file server.
  3. Choose Stacks, Create Stack. Give the stack a name, such as Graphics-Demo-Stack.
  4. After the stack is created, select it and choose Actions, Associate Fleet. Associate the stack with the fleet you created in step 1.

Step 4:  Invite users to access Siemens NX through a browser

To invite users

  1. Choose User Pools, Create User to create users.
  2. Enter a name and email address for each user.
  3. Select the users just created, and choose Actions, Assign Stack to provide access to the stack created in step 2. You can also provide access using SAML 2.0 and connect to your Active Directory if necessary. For more information, see the Enabling Identity Federation with AD FS 3.0 and Amazon AppStream 2.0 post.

Your user receives an email invitation to set up an account and use a web portal to access the applications that you have included in your stack.

Step 5:  Validate the setup

Time for a test drive with Siemens NX on AppStream 2.0!

  1. Open the link for the AppStream 2.0 web portal shared through the email invitation. The web portal opens in your default browser. You must sign in with the temporary password and set a new password. After that, you get taken to your app catalog.
  2. Launch Siemens NX and interact with it using the demo files available in the shared storage folder – My Graphics Files. 

After I launched NX, I captured the screenshot below. The Siemens PLM team also recorded a video with NX running on AppStream 2.0.

Summary

In this post, I discussed the GPU instances available for delivering rich graphics applications to users in a web browser. While I demonstrated a simple setup, you can scale this out to launch a production environment with users signing in using Active Directory credentials,  accessing persistent storage with Amazon S3, and using other commonly requested features reviewed in the Amazon AppStream 2.0 Launch Recap – Domain Join, Simple Network Setup, and Lots More post.

To learn more about AppStream 2.0 and capabilities added this year, see Amazon AppStream 2.0 Resources.

Parallel Processing in Python with AWS Lambda

Post Syndicated from Oz Akan original https://aws.amazon.com/blogs/compute/parallel-processing-in-python-with-aws-lambda/

If you develop an AWS Lambda function with Node.js, you can call multiple web services without waiting for a response due to its asynchronous nature.  All requests are initiated almost in parallel, so you can get results much faster than a series of sequential calls to each web service. Considering the maximum execution duration for Lambda, it is beneficial for I/O bound tasks to run in parallel.

If you develop a Lambda function with Python, parallelism doesn’t come by default. Lambda supports Python 2.7 and Python 3.6, both of which have multiprocessing and threading modules. The multiprocessing module supports multiple cores so it is a better choice, especially for CPU intensive workloads. With the threading module, all threads are going to run on a single core though performance difference is negligible for network-bound tasks.

In this post, I demonstrate how the Python multiprocessing module can be used within a Lambda function to run multiple I/O bound tasks in parallel.

Example use case

In this example, you call Amazon EC2 and Amazon EBS API operations to find the total EBS volume size for all your EC2 instances in a region.

This is a two-step process:

  • The Lambda function calls EC2 to list all EC2 instances
  • The function calls EBS for each instance to find attached EBS volumes

Sequential Execution

If you make these calls sequentially, during the second step, your code has to loop over all the instances and wait for each response before moving to the next request.

The class named VolumesSequential has the following methods:

  • __init__ creates an EC2 resource.
  • total_size returns all EC2 instances and passes these to the instance_volumes method.
  • instance_volumes finds the total size of EBS volumes for the instance.
  • total_size adds all sizes from all instances to find total size for the EBS volumes.

Source Code for Sequential Execution

import time
import boto3

class VolumesSequential(object):
    """Finds total volume size for all EC2 instances"""
    def __init__(self):
        self.ec2 = boto3.resource('ec2')

    def instance_volumes(self, instance):
        """
        Finds total size of the EBS volumes attached
        to an EC2 instance
        """
        instance_total = 0
        for volume in instance.volumes.all():
            instance_total += volume.size
        return instance_total

    def total_size(self):
        """
        Lists all EC2 instances in the default region
        and sums result of instance_volumes
        """
        print "Running sequentially"
        instances = self.ec2.instances.all()
        instances_total = 0
        for instance in instances:
            instances_total += self.instance_volumes(instance)
        return instances_total

def lambda_handler(event, context):
    volumes = VolumesSequential()
    _start = time.time()
    total = volumes.total_size()
    print "Total volume size: %s GB" % total
    print "Sequential execution time: %s seconds" % (time.time() - _start)

Parallel Execution

The multiprocessing module that comes with Python 2.7 lets you run multiple processes in parallel. Due to the Lambda execution environment not having /dev/shm (shared memory for processes) support, you can’t use multiprocessing.Queue or multiprocessing.Pool.

If you try to use multiprocessing.Queue, you get an error similar to the following:

[Errno 38] Function not implemented: OSError
…
    sl = self._semlock = _multiprocessing.SemLock(kind, value, maxvalue)
OSError: [Errno 38] Function not implemented

On the other hand, you can use multiprocessing.Pipe instead of multiprocessing.Queue to accomplish what you need without getting any errors during the execution of the Lambda function.

The class named VolumeParallel has the following methods:

  • __init__ creates an EC2 resource
  • instance_volumes finds the total size of EBS volumes attached to an instance
  • total_size finds all instances and runs instance_volumes for each to find the total size of all EBS volumes attached to all EC2 instances.

Source Code for Parallel Execution

import time
from multiprocessing import Process, Pipe
import boto3

class VolumesParallel(object):
    """Finds total volume size for all EC2 instances"""
    def __init__(self):
        self.ec2 = boto3.resource('ec2')

    def instance_volumes(self, instance, conn):
        """
        Finds total size of the EBS volumes attached
        to an EC2 instance
        """
        instance_total = 0
        for volume in instance.volumes.all():
            instance_total += volume.size
        conn.send([instance_total])
        conn.close()

    def total_size(self):
        """
        Lists all EC2 instances in the default region
        and sums result of instance_volumes
        """
        print "Running in parallel"

        # get all EC2 instances
        instances = self.ec2.instances.all()
        
        # create a list to keep all processes
        processes = []

        # create a list to keep connections
        parent_connections = []
        
        # create a process per instance
        for instance in instances:            
            # create a pipe for communication
            parent_conn, child_conn = Pipe()
            parent_connections.append(parent_conn)

            # create the process, pass instance and connection
            process = Process(target=self.instance_volumes, args=(instance, child_conn,))
            processes.append(process)

        # start all processes
        for process in processes:
            process.start()

        # make sure that all processes have finished
        for process in processes:
            process.join()

        instances_total = 0
        for parent_connection in parent_connections:
            instances_total += parent_connection.recv()[0]

        return instances_total


def lambda_handler(event, context):
    volumes = VolumesParallel()
    _start = time.time()
    total = volumes.total_size()
    print "Total volume size: %s GB" % total
    print "Sequential execution time: %s seconds" % (time.time() - _start)

Performance

There are a few differences between two Lambda functions when it comes to the execution environment. The parallel function requires more memory than the sequential one. You may run the parallel Lambda function with a relatively large memory setting to see how much memory it uses. The amount of memory required by the Lambda function depends on what the function does and how many processes it runs in parallel. To restrict maximum memory usage, you may want to limit the number of parallel executions.

In this case, when you give 1024 MB for both Lambda functions, the parallel function runs about two times faster than the sequential function. I have a handful of EC2 instances and EBS volumes in my account so the test ran way under the maximum execution limit for Lambda. Remember that parallel execution doesn’t guarantee that the runtime for the Lambda function will be under the maximum allowed duration but does speed up the overall execution time.

Sequential Run Time Output

START RequestId: 4c370b12-f9d3-11e6-b46b-b5d41afd648e Version: $LATEST
Running sequentially
Total volume size: 589 GB
Sequential execution time: 3.80066084862 seconds
END RequestId: 4c370b12-f9d3-11e6-b46b-b5d41afd648e
REPORT RequestId: 4c370b12-f9d3-11e6-b46b-b5d41afd648e Duration: 4091.59 ms Billed Duration: 4100 ms  Memory Size: 1024 MB Max Memory Used: 46 MB

Parallel Run Time Output

START RequestId: 4f1328ed-f9d3-11e6-8cd1-c7381c5c078d Version: $LATEST
Running in parallel
Total volume size: 589 GB
Sequential execution time: 1.89170885086 seconds
END RequestId: 4f1328ed-f9d3-11e6-8cd1-c7381c5c078d
REPORT RequestId: 4f1328ed-f9d3-11e6-8cd1-c7381c5c078d Duration: 2069.33 ms Billed Duration: 2100 ms  Memory Size: 1024 MB Max Memory Used: 181 MB 

Summary

In this post, I demonstrated how to run multiple I/O bound tasks in parallel by developing a Lambda function with the Python multiprocessing module. With the help of this module, you freed the CPU from waiting for I/O and fired up several tasks to fit more I/O bound operations into a given time frame. This might be the trick to reduce the overall runtime of a Lambda function especially when you have to run so many and don’t want to split the work into smaller chunks.

A Hardware Privacy Monitor for iPhones

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2017/09/a_hardware_priv.html

Andrew “bunnie” Huang and Edward Snowden have designed a hardware device that attaches to an iPhone and monitors it for malicious surveillance activities, even in instances where the phone’s operating system has been compromised. They call it an Introspection Engine, and their use model is a journalist who is concerned about government surveillance:

Our introspection engine is designed with the following goals in mind:

  1. Completely open source and user-inspectable (“You don’t have to trust us”)
  2. Introspection operations are performed by an execution domain completely separated from the phone”s CPU (“don’t rely on those with impaired judgment to fairly judge their state”)

  3. Proper operation of introspection system can be field-verified (guard against “evil maid” attacks and hardware failures)

  4. Difficult to trigger a false positive (users ignore or disable security alerts when there are too many positives)

  5. Difficult to induce a false negative, even with signed firmware updates (“don’t trust the system vendor” — state-level adversaries with full cooperation of system vendors should not be able to craft signed firmware updates that spoof or bypass the introspection engine)

  6. As much as possible, the introspection system should be passive and difficult to detect by the phone’s operating system (prevent black-listing/targeting of users based on introspection engine signatures)

  7. Simple, intuitive user interface requiring no specialized knowledge to interpret or operate (avoid user error leading to false negatives; “journalists shouldn’t have to be cryptographers to be safe”)

  8. Final solution should be usable on a daily basis, with minimal impact on workflow (avoid forcing field reporters into the choice between their personal security and being an effective journalist)

This looks like fantastic work, and they have a working prototype.

Of course, this does nothing to stop all the legitimate surveillance that happens over a cell phone: location tracking, records of who you talk to, and so on.

BoingBoing post.

No, Google Drive is Definitely Not The New Pirate Bay

Post Syndicated from Andy original https://torrentfreak.com/no-google-drive-is-definitely-not-the-new-pirate-bay-170910/

Running close to two decades old, the world of true mainstream file-sharing is less of a mystery to the general public than it’s ever been.

Most people now understand the concept of shifting files from one place to another, and a significant majority will be aware of the opportunities to do so with infringing content.

Unsurprisingly, this is a major thorn in the side of rightsholders all over the world, who have been scrambling since the turn of the century in a considerable effort to stem the tide. The results of their work have varied, with some sectors hit harder than others.

One area that has taken a bit of a battering recently involves the dominant peer-to-peer platforms reliant on underlying BitTorrent transfers. Several large-scale sites have shut down recently, not least KickassTorrents, Torrentz, and ExtraTorrent, raising questions of what bad news may arrive next for inhabitants of Torrent Land.

Of course, like any other Internet-related activity, sharing has continued to evolve over the years, with streaming and cloud-hosting now a major hit with consumers. In the main, sites which skirt the borders of legality have been the major hosting and streaming players over the years, but more recently it’s become clear that even the most legitimate companies can become unwittingly involved in the piracy scene.

As reported here on TF back in 2014 and again several times this year (1,2,3), cloud-hosting services operated by Google, including Google Drive, are being used to store and distribute pirate content.

That news was echoed again this week, with a report on Gadgets360 reiterating that Google Drive is still being used for movie piracy. What followed were a string of follow up reports, some of which declared Google’s service to be ‘The New Pirate Bay.’

No. Just no.

While it’s always tempting for publications to squeeze a reference to The Pirate Bay into a piracy article due to the site’s popularity, it’s particularly out of place in this comparison. In no way, shape, or form can a centralized store of data like Google Drive ever replace the underlying technology of sites like The Pirate Bay.

While the casual pirate might love the idea of streaming a movie with a couple of clicks to a browser of his or her choice, the weakness of the cloud system cannot be understated. To begin with, anything hosted by Google is vulnerable to immediate takedown on demand, usually within a matter of hours.

“Google Drive has a variety of piracy counter-measures in place,” a spokesperson told Mashable this week, “and we are continuously working to improve our protections to prevent piracy across all of our products.”

When will we ever hear anything like that from The Pirate Bay? Answer: When hell freezes over. But it’s not just compliance with takedown requests that make Google Drive-hosted files vulnerable.

At the point Google Drive responds to a takedown request, it takes down the actual file. On the other hand, even if Pirate Bay responded to notices (which it doesn’t), it would be unable to do anything about the sharing going on underneath. Removing a torrent file or magnet link from TPB does nothing to negatively affect the decentralized swarm of people sharing files among themselves. Those files stay intact and sharing continues, no matter what happens to the links above.

Importantly, people sharing using BitTorrent do so without any need for central servers – the whole process is decentralized as long as a user can lay his or her hands on a torrent file or magnet link. Those using Google Drive, however, rely on a totally centralized system, where not only is Google king, but it can and will stop the entire party after receiving a few lines of text from a rightsholder.

There is a very good reason why sites like The Pirate Bay have been around for close to 15 years while platforms such as Megaupload, Hotfile, Rapidshare, and similar platforms have all met their makers. File-hosting platforms are expensive-to-run warehouses full of files, each of which brings direct liability for their hosts, once they’re made aware that those files are infringing. These days the choice is clear – take the files down or get brought down, it’s as simple as that.

The Pirate Bay, on the other hand, is nothing more than a treasure map (albeit a valuable one) that points the way to content spread all around the globe in the most decentralized way possible. There are no files to delete, no content to disappear. Comparing a vulnerable Google Drive to this kind of robust system couldn’t be further from the mark.

That being said, this is the way things are going. The cloud, it seems, is here to stay in all its forms. Everyone has access to it and uploading content is easier – much easier – than uploading it to a BitTorrent network. A Google Drive upload is simplicity itself for anyone with a mouse and a file; the same cannot be said about The Pirate Bay.

For this reason alone, platforms like Google Drive and the many dozens of others offering a similar service will continue to become havens for pirated content, until the next big round of legislative change. At the moment, each piece of content has to be removed individually but in the future, it’s possible that pre-emptive filters will kill uploads of pirated content before they see the light of day.

When this comes to pass, millions of people will understand why Google Drive, with its bots checking every file upload for alleged infringement, is not The Pirate Bay. At this point, if people have left it too long, it might be too late to reinvigorate BitTorrent networks to their former glory.

People will try to rebuild them, of course, but realizing why they shouldn’t have been left behind at all is probably the best protection.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

New Network Load Balancer – Effortless Scaling to Millions of Requests per Second

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-network-load-balancer-effortless-scaling-to-millions-of-requests-per-second/

Elastic Load Balancing (ELB)) has been an important part of AWS since 2009, when it was launched as part of a three-pack that also included Auto Scaling and Amazon CloudWatch. Since that time we have added many features, and also introduced the Application Load Balancer. Designed to support application-level, content-based routing to applications that run in containers, Application Load Balancers pair well with microservices, streaming, and real-time workloads.

Over the years, our customers have used ELB to support web sites and applications that run at almost any scale — from simple sites running on a T2 instance or two, all the way up to complex applications that run on large fleets of higher-end instances and handle massive amounts of traffic. Behind the scenes, ELB monitors traffic and automatically scales to meet demand. This process, which includes a generous buffer of headroom, has become quicker and more responsive over the years and works well even for our customers who use ELB to support live broadcasts, “flash” sales, and holidays. However, in some situations such as instantaneous fail-over between regions, or extremely spiky workloads, we have worked with our customers to pre-provision ELBs in anticipation of a traffic surge.

New Network Load Balancer
Today we are introducing the new Network Load Balancer (NLB). It is designed to handle tens of millions of requests per second while maintaining high throughput at ultra low latency, with no effort on your part. The Network Load Balancer is API-compatible with the Application Load Balancer, including full programmatic control of Target Groups and Targets. Here are some of the most important features:

Static IP Addresses – Each Network Load Balancer provides a single IP address for each VPC subnet in its purview. If you have targets in a subnet in us-west-2a and other targets in a subnet in us-west-2c, NLB will create and manage two IP addresses (one per subnet); connections to that IP address will spread traffic across the instances in the subnet. You can also specify an existing Elastic IP for each subnet for even greater control. With full control over your IP addresses, Network Load Balancer can be used in situations where IP addresses need to be hard-coded into DNS records, customer firewall rules, and so forth.

Zonality – The IP-per-subnet feature reduces latency with improved performance, improves availability through isolation and fault tolerance and makes the use of Network Load Balancers transparent to your client applications. Network Load Balancers also attempt to route a series of requests from a particular source to targets in a single subnet while still allowing automatic failover.

Source Address Preservation – With Network Load Balancer, the original source IP address and source ports for the incoming connections remain unmodified, so application software need not support X-Forwarded-For, proxy protocol, or other workarounds. This also means that normal firewall rules, including VPC Security Groups, can be used on targets.

Long-running Connections – NLB handles connections with built-in fault tolerance, and can handle connections that are open for months or years, making them a great fit for IoT, gaming, and messaging applications.

Failover – Powered by Route 53 health checks, NLB supports failover between IP addresses within and across regions.

Creating a Network Load Balancer
I can create a Network Load Balancer opening up the EC2 Console, selecting Load Balancers, and clicking on Create Load Balancer:

I choose Network Load Balancer and click on Create, then enter the details. I can choose an Elastic IP address for each subnet in the target VPC and I can tag the Network Load Balancer:

Then I click on Configure Routing and create a new target group. I enter a name, and then choose the protocol and port. I can also set up health checks that go to the traffic port or to the alternate of my choice:

Then I click on Register Targets and the EC2 instances that will receive traffic, and click on Add to registered:

I make sure that everything looks good and then click on Create:

The state of my new Load Balancer is provisioning, switching to active within a minute or so:

For testing purposes, I simply grab the DNS name of the Load Balancer from the console (in practice I would use Amazon Route 53 and a more friendly name):

Then I sent it a ton of traffic (I intended to let it run for just a second or two but got distracted and it created a huge number of processes, so this was a happy accident):

$ while true;
> do
>   wget http://nlb-1-6386cc6bf24701af.elb.us-west-2.amazonaws.com/phpinfo2.php &
> done

A more disciplined test would use a tool like Bees with Machine Guns, of course!

I took a quick break to let some traffic flow and then checked the CloudWatch metrics for my Load Balancer, finding that it was able to handle the sudden onslaught of traffic with ease:

I also looked at my EC2 instances to see how they were faring under the load (really well, it turns out):

It turns out that my colleagues did run a more disciplined test than I did. They set up a Network Load Balancer and backed it with an Auto Scaled fleet of EC2 instances. They set up a second fleet composed of hundreds of EC2 instances, each running Bees with Machine Guns and configured to generate traffic with highly variable request and response sizes. Beginning at 1.5 million requests per second, they quickly turned the dial all the way up, reaching over 3 million requests per second and 30 Gbps of aggregate bandwidth before maxing out their test resources.

Choosing a Load Balancer
As always, you should consider the needs of your application when you choose a load balancer. Here are some guidelines:

Network Load Balancer (NLB) – Ideal for load balancing of TCP traffic, NLB is capable of handling millions of requests per second while maintaining ultra-low latencies. NLB is optimized to handle sudden and volatile traffic patterns while using a single static IP address per Availability Zone.

Application Load Balancer (ALB) – Ideal for advanced load balancing of HTTP and HTTPS traffic, ALB provides advanced request routing that supports modern application architectures, including microservices and container-based applications.

Classic Load Balancer (CLB) – Ideal for applications that were built within the EC2-Classic network.

For a side-by-side feature comparison, see the Elastic Load Balancer Details table.

If you are currently using a Classic Load Balancer and would like to migrate to a Network Load Balancer, take a look at our new Load Balancer Copy Utility. This Python tool will help you to create a Network Load Balancer with the same configuration as an existing Classic Load Balancer. It can also register your existing EC2 instances with the new load balancer.

Pricing & Availability
Like the Application Load Balancer, pricing is based on Load Balancer Capacity Units, or LCUs. Billing is $0.006 per LCU, based on the highest value seen across the following dimensions:

  • Bandwidth – 1 GB per LCU.
  • New Connections – 800 per LCU.
  • Active Connections – 100,000 per LCU.

Most applications are bandwidth-bound and should see a cost reduction (for load balancing) of about 25% when compared to Application or Classic Load Balancers.

Network Load Balancers are available today in all AWS commercial regions except China (Beijing), supported by AWS CloudFormation, Auto Scaling, and Amazon ECS.

Jeff;

 

Director of Kim Dotcom Documentary Speaks Out on Piracy

Post Syndicated from Ernesto original https://torrentfreak.com/director-of-kim-dotcom-documentary-speaks-out-on-piracy-170902/

When you make a documentary about Kim Dotcom, someone who’s caught up in one of the largest criminal copyright infringement cases in history, the piracy issue is unavoidable.

And indeed, the topic is discussed in depth in “Kim Dotcom: Caught in the Web,” which enjoyed its digital release early last week.

As happens with most digital releases, a pirated copy soon followed. While no filmmaker would actively encourage people not to pay for their work, director Annie Goldson wasn’t surprised at all when she saw the first unauthorized copies appear online.

The documentary highlights that piracy is in part triggered by lacking availability, so it was a little ironic that the film itself wasn’t released worldwide on all services. However, Goldson had no direct influence on the distribution process.

“It was inevitable really. We have tried to adopt a distribution model that we hope will encourage viewers to buy legal copies making it available as widely as possible,” Goldson informs TorrentFreak.

“We had sold the rights, so didn’t have complete control over reach or pricing which I think are two critical variables that do impact on the degree of piracy. Although I think our sales agent did make good strides towards a worldwide release.”

Now that millions of pirates have access to her work for free, it will be interesting to see how this impacts sales. For now, however, there’s still plenty of legitimate interest, with the film now appearing in the iTunes top ten of independent films.

In any case, Goldson doesn’t subscribe to the ‘one instance of piracy is a lost sale’ theory and notes that views about piracy are sharply polarized.

“Some claim financial devastation while others argue that infringement leads to ‘buzz,’ that this can generate further sales – so we shall see. At one level, watching this unfold is quite an interesting research exercise into distribution, which ironically is one of the big themes of the film of course,” Goldson notes.

Piracy overall doesn’t help the industry forward though, she says, as it hurts the development of better distribution models.

“I’m opposed to copyright infringement and piracy as it muddies the waters when it comes to devising a better model for distribution, one that would nurture and support artists and creatives, those that do the hard yards.”

Kim Dotcom: Caught in the Web trailer

The director has no issues with copyright enforcement either. Not just to safeguard financial incentives, but also because the author does have moral and ethical rights about how their works are distributed. That said, instead of pouring money into enforcement, it might be better spent on finding a better business model.

“I’m with Wikipedia founder Jimmy Wales who says [in the documentary] that the problem is primarily with the existing business model. If you make films genuinely available at prices people can afford, at the same time throughout the world, piracy would drop to low levels.

“I think most people would prefer to access their choice of entertainment legally rather than delving into dark corners of the Internet. I might be wrong of course,” Goldson adds.

In any case, ‘simply’ enforcing piracy into oblivion seems to be an unworkable prospect – not without massive censorship, or the shutdown of the entire Internet.

“I feel the risk is that anti-piracy efforts will step up and erode important freedoms. Or we have to close down the Internet altogether. After all, the unwieldy beast is a giant copying machine – making copies is what it does well,” Goldson says.

The problems is that the industry is keeping piracy intact through its own business model. When people can’t get what they want, when, and where they want it, they often turn to pirate sites.

“One problem is that the industry has been slow to change and hence we now have generations of viewers who have had to regularly infringe to be part of a global conversation.

“I do feel if the industry is promoting and advertising works internationally, using globalized communication and social media, then denying viewers from easily accessing works, either through geo-blocking or price points, obviously, digitally-savvy viewers will find them regardless,” Goldson adds.

And yes, this ironically also applies to her own documentary.

The solution is to continue to improve the legal options. This is easier said than done, as Goldson and her team tried hard, so it won’t happen overnight. However, universal access for a decent price would seem to be the future.

Unless the movie industry prefers to shut down the Internet entirely, of course.

For those who haven’t seen “Kim Dotcom: Caught in the Web yet,” the film is available globally on Vimeo OnDemand, and in a lot of territories on iTunes, the PlayStation Store, Amazon, Google Play, and the Microsoft/Xbox Store. In the US there is also Vudu, Fandango Now & Verizon.

If that doesn’t work, then…

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

How Much Does ‘Free’ Premier League Piracy Cost These Days?

Post Syndicated from Andy original https://torrentfreak.com/how-much-does-free-premier-league-piracy-cost-these-days-170902/

Right now, the English Premier League is engaged in perhaps the most aggressively innovative anti-piracy operation the Internet has ever seen. After obtaining a new High Court order, it now has the ability to block ‘pirate’ streams of matches, in real-time, with no immediate legal oversight.

If the Premier League believes a server is streaming one of its matches, it can ask ISPs in the UK to block it, immediately. That’s unprecedented anywhere on the planet.

As previously reported, this campaign caused a lot of problems for people trying to access free and premium streams at the start of the season. Many IPTV services were blocked in the UK within minutes of matches starting, with free streams also dropping like flies. According to information obtained by TF, more than 600 illicit streams were blocked during that weekend.

While some IPTV providers and free streams continued without problems, it seems likely that it’s only a matter of time before the EPL begins to pick off more and more suppliers. To be clear, the EPL isn’t taking services or streams down, it’s only blocking them, which means that people using circumvention technologies like VPNs can get around the problem.

However, this raises the big issue again – that of continuously increasing costs. While piracy is often painted as free, it is not, and as setups get fancier, costs increase too.

Below, we take a very general view of a handful of the many ‘pirate’ configurations currently available, to work out how much ‘free’ piracy costs these days. The list is not comprehensive by any means (and excludes more obscure methods such as streaming torrents, which are always free and rarely blocked), but it gives an idea of costs and how the balance of power might eventually tip.

Basic beginner setup

On a base level, people who pirate online need at least some equipment. That could be an Android smartphone and easily installed free software such as Mobdro or Kodi. An Internet connection is a necessity and if the EPL blocks those all important streams, a VPN provider is required to circumvent the bans.

Assuming people already have a phone and the Internet, a VPN can be bought for less than £5 per month. This basic setup is certainly cheap but overall it’s an entry level experience that provides quality equal to the effort and money expended.

Equipment: Phone, tablet, PC
Comms: Fast Internet connection, decent VPN provider
Overal performance: Low quality, unpredictable, often unreliable
Cost: £5pm approx for VPN, plus Internet costs

Big screen, basic

For those who like their matches on the big screen, stepping up the chain costs more money. People need a TV with an HDMI input and a fast Internet connection as a minimum, alongside some kind of set-top device to run the necessary software.

Android devices are the most popular and are roughly split into two groups – the small standalone box type and the plug-in ‘stick’ variant such as Amazon’s Firestick.

A cheap Android set-top box

These cost upwards of £30 to £40 but the software to install on them is free. Like the phone, Mobdro is an option, but most people look to a Kodi setup with third-party addons. That said, all streams received on these setups are now vulnerable to EPL blocking so in the long-term, users will need to run a paid VPN.

The problem here is that some devices (including the 1st gen Firestick) aren’t ideal for running a VPN on top of a stream, so people will need to dump their old device and buy something more capable. That could cost another £30 to £40 and more, depending on requirements.

Importantly, none of this investment guarantees a decent stream – that’s down to what’s available on the day – but invariably the quality is low and/or intermittent, at best.

Equipment: TV, decent Android set-top box or equivalent
Comms: Fast Internet connection, decent VPN provider
Overall performance: Low to acceptable quality, unpredictable, often unreliable
Cost: £30 to £50 for set-top box, £5pm approx for VPN, plus Internet

Premium IPTV – PC or Android based

At this point, premium IPTV services come into play. People have a choice of spending varying amounts of money, depending on the quality of experience they require.

First of all, a monthly IPTV subscription with an established provider that isn’t going to disappear overnight is required, which can be a challenge to find in itself. We’re not here to review or recommend services but needless to say, like official TV packages they come in different flavors to suit varying wallet sizes. Some stick around, many don’t.

A decent one with a Sky-like EPG costs between £7 and £15 per month, depending on the quality and depth of streams, and how far in front users are prepared to commit.

Fairly typical IPTV with EPG (VOD shown)

Paying for a year in advance tends to yield better prices but with providers regularly disappearing and faltering in their service levels, people are often reluctant to do so. That said, some providers experience few problems so it’s a bit like gambling – research can improve the odds but there’s never a guarantee.

However, even when a provider, price, and payment period is decided upon, the process of paying for an IPTV service can be less than straightforward.

While some providers are happy to accept PayPal, many will only deal in credit cards, bitcoin, or other obscure payment methods. That sets up more barriers to entry that might deter the less determined customer. And, if time is indeed money, fussing around with new payment processors can be pricey, at least to begin with.

Once subscribed though, watching these streams is pretty straightforward. On a base level, people can use a phone, tablet, or set-top device to receive them, using software such as Perfect Player IPTV, for example. Currently available in free (ad supported) and premium (£2) variants, this software can be setup in a few clicks and will provide a decent user experience, complete with EPG.

Perfect Player IPTV

Those wanting to go down the PC route have more options but by far the most popular is receiving IPTV via a Kodi setup. For the complete novice, it’s not always easy to setup but some IPTV providers supply their own free addons, which streamline the process massively. These can also be used on Android-based Kodi setups, of course.

Nevertheless, if the EPL blocks the provider, a VPN is still going to be needed to access the IPTV service.

An Android tablet running Kodi

So, even if we ignore the cost of the PC and Internet connection, users could still find themselves paying between £10 and £20 per month for an IPTV service and a decent VPN. While more channels than simply football will be available from most providers, this is getting dangerously close to the £18 Sky are asking for its latest football package.

Equipment: TV, PC, or decent Android set-top box or equivalent
Comms: Fast Internet connection, IPTV subscription, decent VPN provider
Overal performance: High quality, mostly reliable, user-friendly (once setup)
Cost: PC or £30/£50 for set-top box, IPTV subscription £7 to £15pm, £5pm approx for VPN, plus Internet, plus time and patience for obscure payment methods.
Note: There are zero refunds when IPTV providers disappoint or disappear

Premium IPTV – Deluxe setup

Moving up to the top of the range, things get even more costly. Those looking to give themselves the full home entertainment-like experience will often move away from the PC and into the living room in front of the TV, armed with a dedicated set-top box. Weapon of choice: the Mag254.

Like Amazon’s FireStick, PC or Android tablet, the Mag254 is an entirely legal, content agnostic device. However, enter the credentials provided by many illicit IPTV suppliers and users are presented with a slick Sky-like experience, far removed from anything available elsewhere. The device is operated by remote control and integrates seamlessly with any HDMI-capable TV.

Mag254 IPTV box

Something like this costs around £70 in the UK, plus the cost of a WiFi adaptor on top, if needed. The cost of the IPTV provider needs to be figured in too, plus a VPN subscription if the provider gets blocked by EPL, which is likely. However, in this respect the Mag254 has a problem – it can’t run a VPN natively. This means that if streams get blocked and people need to use a VPN, they’ll need to find an external solution.

Needless to say, this costs more money. People can either do all the necessary research and buy a VPN-capable router/modem that’s also compatible with their provider (this can stretch to a couple of hundred pounds) or they’ll need to invest in a small ‘travel’ router with VPN client features built in.

‘Travel’ router (with tablet running Mobdro for scale)

These devices are available on Amazon for around £25 and sit in between the Mag254 (or indeed any other wireless device) and the user’s own regular router. Once the details of the VPN subscription are entered into the router, all traffic passing through is encrypted and will tunnel through web blocking measures. They usually solve the problem (ymmv) but of course, this is another cost.

Equipment: Mag254 or similar, with WiFi
Comms: Fast Internet connection, IPTV subscription, decent VPN provider
Overall performance: High quality, mostly reliable, very user-friendly
Cost: Mag254 around £75 with WiFi, IPTV subscription £7 to £15pm, £5pm for VPN (plus £25 for mini router), plus Internet, plus patience for obscure payment methods.
Note: There are zero refunds when IPTV providers disappoint or disappear

Conclusion

On the whole, people who want a reliable and high-quality Premier League streaming experience cannot get one for free, no matter where they source the content. There are many costs involved, some of which cannot be avoided.

If people aren’t screwing around with annoying and unreliable Kodi streams, they’ll be paying for an IPTV provider, VPN and other equipment. Or, if they want an easy life, they’ll be paying Sky, BT or Virgin Media. That might sound harsh to many pirates but it’s the only truly reliable solution.

However, for those looking for something that’s merely adequate, costs drop significantly. Indeed, if people don’t mind the hassle of wondering whether a sub-VHS quality stream will appear before the big match and stay on throughout, it can all be done on a shoestring.

But perhaps the most important thing to note in respect of costs is the recent changes to the pricing of Premier League content in the UK. As mentioned earlier, Sky now delivers a sports package for £18pm, which sounds like the best deal offered to football fans in recent years. It will be tempting for sure and has all the hallmarks of a price point carefully calculated by Sky.

The big question is whether it will be low enough to tip significant numbers of people away from piracy. The reality is that if another couple of thousand streams get hit hard again this weekend – and the next – and the next – many pirating fans will be watching the season drift away for yet another month, unviewed. That’s got to be frustrating.

The bottom line is that high-quality streaming piracy is becoming a little bit pricey just for football so if it becomes unreliable too – and that’s the Premier League’s goal – the balance of power could tip. At this point, the EPL will need to treat its new customers with respect, in order to keep them feeling both entertained and unexploited.

Fail on those counts – especially the latter – and the cycle will start again.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

How to Configure an LDAPS Endpoint for Simple AD

Post Syndicated from Cameron Worrell original https://aws.amazon.com/blogs/security/how-to-configure-an-ldaps-endpoint-for-simple-ad/

Simple AD, which is powered by Samba  4, supports basic Active Directory (AD) authentication features such as users, groups, and the ability to join domains. Simple AD also includes an integrated Lightweight Directory Access Protocol (LDAP) server. LDAP is a standard application protocol for the access and management of directory information. You can use the BIND operation from Simple AD to authenticate LDAP client sessions. This makes LDAP a common choice for centralized authentication and authorization for services such as Secure Shell (SSH), client-based virtual private networks (VPNs), and many other applications. Authentication, the process of confirming the identity of a principal, typically involves the transmission of highly sensitive information such as user names and passwords. To protect this information in transit over untrusted networks, companies often require encryption as part of their information security strategy.

In this blog post, we show you how to configure an LDAPS (LDAP over SSL/TLS) encrypted endpoint for Simple AD so that you can extend Simple AD over untrusted networks. Our solution uses Elastic Load Balancing (ELB) to send decrypted LDAP traffic to HAProxy running on Amazon EC2, which then sends the traffic to Simple AD. ELB offers integrated certificate management, SSL/TLS termination, and the ability to use a scalable EC2 backend to process decrypted traffic. ELB also tightly integrates with Amazon Route 53, enabling you to use a custom domain for the LDAPS endpoint. The solution needs the intermediate HAProxy layer because ELB can direct traffic only to EC2 instances. To simplify testing and deployment, we have provided an AWS CloudFormation template to provision the ELB and HAProxy layers.

This post assumes that you have an understanding of concepts such as Amazon Virtual Private Cloud (VPC) and its components, including subnets, routing, Internet and network address translation (NAT) gateways, DNS, and security groups. You should also be familiar with launching EC2 instances and logging in to them with SSH. If needed, you should familiarize yourself with these concepts and review the solution overview and prerequisites in the next section before proceeding with the deployment.

Note: This solution is intended for use by clients requiring an LDAPS endpoint only. If your requirements extend beyond this, you should consider accessing the Simple AD servers directly or by using AWS Directory Service for Microsoft AD.

Solution overview

The following diagram and description illustrates and explains the Simple AD LDAPS environment. The CloudFormation template creates the items designated by the bracket (internal ELB load balancer and two HAProxy nodes configured in an Auto Scaling group).

Diagram of the the Simple AD LDAPS environment

Here is how the solution works, as shown in the preceding numbered diagram:

  1. The LDAP client sends an LDAPS request to ELB on TCP port 636.
  2. ELB terminates the SSL/TLS session and decrypts the traffic using a certificate. ELB sends the decrypted LDAP traffic to the EC2 instances running HAProxy on TCP port 389.
  3. The HAProxy servers forward the LDAP request to the Simple AD servers listening on TCP port 389 in a fixed Auto Scaling group configuration.
  4. The Simple AD servers send an LDAP response through the HAProxy layer to ELB. ELB encrypts the response and sends it to the client.

Note: Amazon VPC prevents a third party from intercepting traffic within the VPC. Because of this, the VPC protects the decrypted traffic between ELB and HAProxy and between HAProxy and Simple AD. The ELB encryption provides an additional layer of security for client connections and protects traffic coming from hosts outside the VPC.

Prerequisites

  1. Our approach requires an Amazon VPC with two public and two private subnets. The previous diagram illustrates the environment’s VPC requirements. If you do not yet have these components in place, follow these guidelines for setting up a sample environment:
    1. Identify a region that supports Simple AD, ELB, and NAT gateways. The NAT gateways are used with an Internet gateway to allow the HAProxy instances to access the internet to perform their required configuration. You also need to identify the two Availability Zones in that region for use by Simple AD. You will supply these Availability Zones as parameters to the CloudFormation template later in this process.
    2. Create or choose an Amazon VPC in the region you chose. In order to use Route 53 to resolve the LDAPS endpoint, make sure you enable DNS support within your VPC. Create an Internet gateway and attach it to the VPC, which will be used by the NAT gateways to access the internet.
    3. Create a route table with a default route to the Internet gateway. Create two NAT gateways, one per Availability Zone in your public subnets to provide additional resiliency across the Availability Zones. Together, the routing table, the NAT gateways, and the Internet gateway enable the HAProxy instances to access the internet.
    4. Create two private routing tables, one per Availability Zone. Create two private subnets, one per Availability Zone. The dual routing tables and subnets allow for a higher level of redundancy. Add each subnet to the routing table in the same Availability Zone. Add a default route in each routing table to the NAT gateway in the same Availability Zone. The Simple AD servers use subnets that you create.
    5. The LDAP service requires a DNS domain that resolves within your VPC and from your LDAP clients. If you do not have an existing DNS domain, follow the steps to create a private hosted zone and associate it with your VPC. To avoid encryption protocol errors, you must ensure that the DNS domain name is consistent across your Route 53 zone and in the SSL/TLS certificate (see Step 2 in the “Solution deployment” section).
  2. Make sure you have completed the Simple AD Prerequisites.
  3. We will use a self-signed certificate for ELB to perform SSL/TLS decryption. You can use a certificate issued by your preferred certificate authority or a certificate issued by AWS Certificate Manager (ACM).
    Note: To prevent unauthorized connections directly to your Simple AD servers, you can modify the Simple AD security group on port 389 to block traffic from locations outside of the Simple AD VPC. You can find the security group in the EC2 console by creating a search filter for your Simple AD directory ID. It is also important to allow the Simple AD servers to communicate with each other as shown on Simple AD Prerequisites.

Solution deployment

This solution includes five main parts:

  1. Create a Simple AD directory.
  2. Create a certificate.
  3. Create the ELB and HAProxy layers by using the supplied CloudFormation template.
  4. Create a Route 53 record.
  5. Test LDAPS access using an Amazon Linux client.

1. Create a Simple AD directory

With the prerequisites completed, you will create a Simple AD directory in your private VPC subnets:

  1. In the Directory Service console navigation pane, choose Directories and then choose Set up directory.
  2. Choose Simple AD.
    Screenshot of choosing "Simple AD"
  3. Provide the following information:
    • Directory DNS – The fully qualified domain name (FQDN) of the directory, such as corp.example.com. You will use the FQDN as part of the testing procedure.
    • NetBIOS name – The short name for the directory, such as CORP.
    • Administrator password – The password for the directory administrator. The directory creation process creates an administrator account with the user name Administrator and this password. Do not lose this password because it is nonrecoverable. You also need this password for testing LDAPS access in a later step.
    • Description – An optional description for the directory.
    • Directory Size – The size of the directory.
      Screenshot of the directory details to provide
  4. Provide the following information in the VPC Details section, and then choose Next Step:
    • VPC – Specify the VPC in which to install the directory.
    • Subnets – Choose two private subnets for the directory servers. The two subnets must be in different Availability Zones. Make a note of the VPC and subnet IDs for use as CloudFormation input parameters. In the following example, the Availability Zones are us-east-1a and us-east-1c.
      Screenshot of the VPC details to provide
  5. Review the directory information and make any necessary changes. When the information is correct, choose Create Simple AD.

It takes several minutes to create the directory. From the AWS Directory Service console , refresh the screen periodically and wait until the directory Status value changes to Active before continuing. Choose your Simple AD directory and note the two IP addresses in the DNS address section. You will enter them when you run the CloudFormation template later.

Note: Full administration of your Simple AD implementation is out of scope for this blog post. See the documentation to add users, groups, or instances to your directory. Also see the previous blog post, How to Manage Identities in Simple AD Directories.

2. Create a certificate

In the previous step, you created the Simple AD directory. Next, you will generate a self-signed SSL/TLS certificate using OpenSSL. You will use the certificate with ELB to secure the LDAPS endpoint. OpenSSL is a standard, open source library that supports a wide range of cryptographic functions, including the creation and signing of x509 certificates. You then import the certificate into ACM that is integrated with ELB.

  1. You must have a system with OpenSSL installed to complete this step. If you do not have OpenSSL, you can install it on Amazon Linux by running the command, sudo yum install openssl. If you do not have access to an Amazon Linux instance you can create one with SSH access enabled to proceed with this step. Run the command, openssl version, at the command line to see if you already have OpenSSL installed.
    [[email protected] ~]$ openssl version
    OpenSSL 1.0.1k-fips 8 Jan 2015

  2. Create a private key using the command, openssl genrsa command.
    [[email protected] tmp]$ openssl genrsa 2048 > privatekey.pem
    Generating RSA private key, 2048 bit long modulus
    ......................................................................................................................................................................+++
    ..........................+++
    e is 65537 (0x10001)

  3. Generate a certificate signing request (CSR) using the openssl req command. Provide the requested information for each field. The Common Name is the FQDN for your LDAPS endpoint (for example, ldap.corp.example.com). The Common Name must use the domain name you will later register in Route 53. You will encounter certificate errors if the names do not match.
    [[email protected] tmp]$ openssl req -new -key privatekey.pem -out server.csr
    You are about to be asked to enter information that will be incorporated into your certificate request.

  4. Use the openssl x509 command to sign the certificate. The following example uses the private key from the previous step (privatekey.pem) and the signing request (server.csr) to create a public certificate named server.crt that is valid for 365 days. This certificate must be updated within 365 days to avoid disruption of LDAPS functionality.
    [[email protected] tmp]$ openssl x509 -req -sha256 -days 365 -in server.csr -signkey privatekey.pem -out server.crt
    Signature ok
    subject=/C=XX/L=Default City/O=Default Company Ltd/CN=ldap.corp.example.com
    Getting Private key

  5. You should see three files: privatekey.pem, server.crt, and server.csr.
    [[email protected] tmp]$ ls
    privatekey.pem server.crt server.csr

    Restrict access to the private key.

    [[email protected] tmp]$ chmod 600 privatekey.pem

    Keep the private key and public certificate for later use. You can discard the signing request because you are using a self-signed certificate and not using a Certificate Authority. Always store the private key in a secure location and avoid adding it to your source code.

  6. In the ACM console, choose Import a certificate.
  7. Using your favorite Linux text editor, paste the contents of your server.crt file in the Certificate body box.
  8. Using your favorite Linux text editor, paste the contents of your privatekey.pem file in the Certificate private key box. For a self-signed certificate, you can leave the Certificate chain box blank.
  9. Choose Review and import. Confirm the information and choose Import.

3. Create the ELB and HAProxy layers by using the supplied CloudFormation template

Now that you have created your Simple AD directory and SSL/TLS certificate, you are ready to use the CloudFormation template to create the ELB and HAProxy layers.

  1. Load the supplied CloudFormation template to deploy an internal ELB and two HAProxy EC2 instances into a fixed Auto Scaling group. After you load the template, provide the following input parameters. Note: You can find the parameters relating to your Simple AD from the directory details page by choosing your Simple AD in the Directory Service console.
Input parameter Input parameter description
HAProxyInstanceSize The EC2 instance size for HAProxy servers. The default size is t2.micro and can scale up for large Simple AD environments.
MyKeyPair The SSH key pair for EC2 instances. If you do not have an existing key pair, you must create one.
VPCId The target VPC for this solution. Must be in the VPC where you deployed Simple AD and is available in your Simple AD directory details page.
SubnetId1 The Simple AD primary subnet. This information is available in your Simple AD directory details page.
SubnetId2 The Simple AD secondary subnet. This information is available in your Simple AD directory details page.
MyTrustedNetwork Trusted network Classless Inter-Domain Routing (CIDR) to allow connections to the LDAPS endpoint. For example, use the VPC CIDR to allow clients in the VPC to connect.
SimpleADPriIP The primary Simple AD Server IP. This information is available in your Simple AD directory details page.
SimpleADSecIP The secondary Simple AD Server IP. This information is available in your Simple AD directory details page.
LDAPSCertificateARN The Amazon Resource Name (ARN) for the SSL certificate. This information is available in the ACM console.
  1. Enter the input parameters and choose Next.
  2. On the Options page, accept the defaults and choose Next.
  3. On the Review page, confirm the details and choose Create. The stack will be created in approximately 5 minutes.

4. Create a Route 53 record

The next step is to create a Route 53 record in your private hosted zone so that clients can resolve your LDAPS endpoint.

  1. If you do not have an existing DNS domain for use with LDAP, create a private hosted zone and associate it with your VPC. The hosted zone name should be consistent with your Simple AD (for example, corp.example.com).
  2. When the CloudFormation stack is in CREATE_COMPLETE status, locate the value of the LDAPSURL on the Outputs tab of the stack. Copy this value for use in the next step.
  3. On the Route 53 console, choose Hosted Zones and then choose the zone you used for the Common Name box for your self-signed certificate. Choose Create Record Set and enter the following information:
    1. Name – The label of the record (such as ldap).
    2. Type – Leave as A – IPv4 address.
    3. Alias – Choose Yes.
    4. Alias Target – Paste the value of the LDAPSURL on the Outputs tab of the stack.
  4. Leave the defaults for Routing Policy and Evaluate Target Health, and choose Create.
    Screenshot of finishing the creation of the Route 53 record

5. Test LDAPS access using an Amazon Linux client

At this point, you have configured your LDAPS endpoint and now you can test it from an Amazon Linux client.

  1. Create an Amazon Linux instance with SSH access enabled to test the solution. Launch the instance into one of the public subnets in your VPC. Make sure the IP assigned to the instance is in the trusted IP range you specified in the CloudFormation parameter MyTrustedNetwork in Step 3.b.
  2. SSH into the instance and complete the following steps to verify access.
    1. Install the openldap-clients package and any required dependencies:
      sudo yum install -y openldap-clients.
    2. Add the server.crt file to the /etc/openldap/certs/ directory so that the LDAPS client will trust your SSL/TLS certificate. You can copy the file using Secure Copy (SCP) or create it using a text editor.
    3. Edit the /etc/openldap/ldap.conf file and define the environment variables BASE, URI, and TLS_CACERT.
      • The value for BASE should match the configuration of the Simple AD directory name.
      • The value for URI should match your DNS alias.
      • The value for TLS_CACERT is the path to your public certificate.

Here is an example of the contents of the file.

BASE dc=corp,dc=example,dc=com
URI ldaps://ldap.corp.example.com
TLS_CACERT /etc/openldap/certs/server.crt

To test the solution, query the directory through the LDAPS endpoint, as shown in the following command. Replace corp.example.com with your domain name and use the Administrator password that you configured with the Simple AD directory

$ ldapsearch -D "[email protected]corp.example.com" -W sAMAccountName=Administrator

You should see a response similar to the following response, which provides the directory information in LDAP Data Interchange Format (LDIF) for the administrator distinguished name (DN) from your Simple AD LDAP server.

# extended LDIF
#
# LDAPv3
# base <dc=corp,dc=example,dc=com> (default) with scope subtree
# filter: sAMAccountName=Administrator
# requesting: ALL
#

# Administrator, Users, corp.example.com
dn: CN=Administrator,CN=Users,DC=corp,DC=example,DC=com
objectClass: top
objectClass: person
objectClass: organizationalPerson
objectClass: user
description: Built-in account for administering the computer/domain
instanceType: 4
whenCreated: 20170721123204.0Z
uSNCreated: 3223
name: Administrator
objectGUID:: l3h0HIiKO0a/ShL4yVK/vw==
userAccountControl: 512
…

You can now use the LDAPS endpoint for directory operations and authentication within your environment. If you would like to learn more about how to interact with your LDAPS endpoint within a Linux environment, here are a few resources to get started:

Troubleshooting

If you receive an error such as the following error when issuing the ldapsearch command, there are a few things you can do to help identify issues.

ldap_sasl_bind(SIMPLE): Can't contact LDAP server (-1)
  • You might be able to obtain additional error details by adding the -d1 debug flag to the ldapsearch command in the previous section.
    $ ldapsearch -D "[email protected]" -W sAMAccountName=Administrator –d1

  • Verify that the parameters in ldap.conf match your configured LDAPS URI endpoint and that all parameters can be resolved by DNS. You can use the following dig command, substituting your configured endpoint DNS name.
    $ dig ldap.corp.example.com

  • Confirm that the client instance from which you are connecting is in the CIDR range of the CloudFormation parameter, MyTrustedNetwork.
  • Confirm that the path to your public SSL/TLS certificate configured in ldap.conf as TLS_CAERT is correct. You configured this in Step 5.b.3. You can check your SSL/TLS connection with the command, substituting your configured endpoint DNS name for the string after –connect.
    $ echo -n | openssl s_client -connect ldap.corp.example.com:636

  • Verify that your HAProxy instances have the status InService in the EC2 console: Choose Load Balancers under Load Balancing in the navigation pane, highlight your LDAPS load balancer, and then choose the Instances

Conclusion

You can use ELB and HAProxy to provide an LDAPS endpoint for Simple AD and transport sensitive authentication information over untrusted networks. You can explore using LDAPS to authenticate SSH users or integrate with other software solutions that support LDAP authentication. This solution’s CloudFormation template is available on GitHub.

If you have comments about this post, submit them in the “Comments” section below. If you have questions about or issues implementing this solution, start a new thread on the Directory Service forum.

– Cameron and Jeff

The Pronunciation Training Machine

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/pronunciation-training-machine/

Using a Raspberry Pi, an Arduino, an Adafruit NeoPixel Ring and a servomotor, Japanese makers HomeMadeGarbage produced this Pronunciation Training Machine to help their parents distinguish ‘L’s and ‘R’s when speaking English.

L R 発音矯正ギブス お母ちゃん編 Pronunciation training machine #right #light #raspberrypi #arduino #neopixel

23 Likes, 1 Comments – Home Made Garbage (@homemadegarbage) on Instagram: “L R 発音矯正ギブス お母ちゃん編 Pronunciation training machine #right #light #raspberrypi #arduino #neopixel”

How does an Pronunciation Training Machine work?

As you can see in the video above, the machine utilises the Google Cloud Speech API to recognise their parents’ pronunciation of the words ‘right’ and ‘light’. Correctly pronounce the former, and the servo-mounted arrow points to the right. Pronounce the later and the NeoPixel Ring illuminates because, well, you just said “light”.

An image showing how the project works - English Pronunciation TrainingYou can find the full code for the project on its hackster page here.

Variations on the idea

It’s a super-cute project with great potential, and the concept could easily be amended for other training purposes. How about using motion sensors to help someone learn their left from their right?

A photo of hands with left and right written on them - English Pronunciation Training

Wait…your left or my left?
image c/o tattly

Or use random.choice to switch on LEDs over certain images, and speech recognition to reward a correct answer? Light up a picture of a cat, for example, and when the player says “cat”, they receive a ‘purr’ or a treat?

A photo of a kitten - English Pronunciation Training

Obligatory kitten picture
image c/o somewhere on the internet!

Raspberry Pi-based educational aids do not have to be elaborate builds. They can use components as simple as a servo and an LED, and still have the potential to make great improvements in people’s day-to-day lives.

Your own projects

If you’ve created an educational tool using a Raspberry Pi, we’d love to see it. The Raspberry Pi itself is an educational tool, so you’re helping it to fulfil its destiny! Make sure you share your projects with us on social media, or pop a link in the comments below. We’d also love to see people using the Pronunciation Training Machine (or similar projects), so make sure you share those too!

A massive shout out to Artie at hackster.io for this heads-up, and for all the other Raspberry Pi projects he sends my way. What a star!

The post The Pronunciation Training Machine appeared first on Raspberry Pi.

From Data Lake to Data Warehouse: Enhancing Customer 360 with Amazon Redshift Spectrum

Post Syndicated from Dylan Tong original https://aws.amazon.com/blogs/big-data/from-data-lake-to-data-warehouse-enhancing-customer-360-with-amazon-redshift-spectrum/

Achieving a 360o-view of your customer has become increasingly challenging as companies embrace omni-channel strategies, engaging customers across websites, mobile, call centers, social media, physical sites, and beyond. The promise of a web where online and physical worlds blend makes understanding your customers more challenging, but also more important. Businesses that are successful in this medium have a significant competitive advantage.

The big data challenge requires the management of data at high velocity and volume. Many customers have identified Amazon S3 as a great data lake solution that removes the complexities of managing a highly durable, fault tolerant data lake infrastructure at scale and economically.

AWS data services substantially lessen the heavy lifting of adopting technologies, allowing you to spend more time on what matters most—gaining a better understanding of customers to elevate your business. In this post, I show how a recent Amazon Redshift innovation, Redshift Spectrum, can enhance a customer 360 initiative.

Customer 360 solution

A successful customer 360 view benefits from using a variety of technologies to deliver different forms of insights. These could range from real-time analysis of streaming data from wearable devices and mobile interactions to historical analysis that requires interactive, on demand queries on billions of transactions. In some cases, insights can only be inferred through AI via deep learning. Finally, the value of your customer data and insights can’t be fully realized until it is operationalized at scale—readily accessible by fleets of applications. Companies are leveraging AWS for the breadth of services that cover these domains, to drive their data strategy.

A number of AWS customers stream data from various sources into a S3 data lake through Amazon Kinesis. They use Kinesis and technologies in the Hadoop ecosystem like Spark running on Amazon EMR to enrich this data. High-value data is loaded into an Amazon Redshift data warehouse, which allows users to analyze and interact with data through a choice of client tools. Redshift Spectrum expands on this analytics platform by enabling Amazon Redshift to blend and analyze data beyond the data warehouse and across a data lake.

The following diagram illustrates the workflow for such a solution.

This solution delivers value by:

  • Reducing complexity and time to value to deeper insights. For instance, an existing data model in Amazon Redshift may provide insights across dimensions such as customer, geography, time, and product on metrics from sales and financial systems. Down the road, you may gain access to streaming data sources like customer-care call logs and website activity that you want to blend in with the sales data on the same dimensions to understand how web and call center experiences maybe correlated with sales performance. Redshift Spectrum can join these dimensions in Amazon Redshift with data in S3 to allow you to quickly gain new insights, and avoid the slow and more expensive alternative of fully integrating these sources with your data warehouse.
  • Providing an additional avenue for optimizing costs and performance. In cases like call logs and clickstream data where volumes could be many TBs to PBs, storing the data exclusively in S3 yields significant cost savings. Interactive analysis on massive datasets may now be economically viable in cases where data was previously analyzed periodically through static reports generated by inexpensive batch processes. In some cases, you can improve the user experience while simultaneously lowering costs. Spectrum is powered by a large-scale infrastructure external to your Amazon Redshift cluster, and excels at scanning and aggregating large volumes of data. For instance, your analysts maybe performing data discovery on customer interactions across millions of consumers over years of data across various channels. On this large dataset, certain queries could be slow if you didn’t have a large Amazon Redshift cluster. Alternatively, you could use Redshift Spectrum to achieve a better user experience with a smaller cluster.

Proof of concept walkthrough

To make evaluation easier for you, I’ve conducted a Redshift Spectrum proof-of-concept (PoC) for the customer 360 use case. For those who want to replicate the PoC, the instructions, AWS CloudFormation templates, and public data sets are available in the GitHub repository.

The remainder of this post is a journey through the project, observing best practices in action, and learning how you can achieve business value. The walkthrough involves:

  • An analysis of performance data from the PoC environment involving queries that demonstrate blending and analysis of data across Amazon Redshift and S3. Observe that great results are achievable at scale.
  • Guidance by example on query tuning, design, and data preparation to illustrate the optimization process. This includes tuning a query that combines clickstream data in S3 with customer and time dimensions in Amazon Redshift, and aggregates ~1.9 B out of 3.7 B+ records in under 10 seconds with a small cluster!
  • Guidance and measurements to help assess deciding between two options: accessing and analyzing data exclusively in Amazon Redshift, or using Redshift Spectrum to access data left in S3.

Stream ingestion and enrichment

The focus of this post isn’t stream ingestion and enrichment on Kinesis and EMR, but be mindful of performance best practices on S3 to ensure good streaming and query performance:

  • Use random object keys: The data files provided for this project are prefixed with SHA-256 hashes to prevent hot partitions. This is important to ensure that optimal request rates to support PUT requests from the incoming stream in addition to certain queries from large Amazon Redshift clusters that could send a large number of parallel GET requests.
  • Micro-batch your data stream: S3 isn’t optimized for small random write workloads. Your datasets should be micro-batched into large files. For instance, the “parquet-1” dataset provided batches >7 million records per file. The optimal file size for Redshift Spectrum is usually in the 100 MB to 1 GB range.

If you have an edge case that may pose scalability challenges, AWS would love to hear about it. For further guidance, talk to your solutions architect.

Environment

The project consists of the following environment:

  • Amazon Redshift cluster: 4 X dc1.large
  • Data:
    • Time and customer dimension tables are stored on all Amazon Redshift nodes (ALL distribution style):
      • The data originates from the DWDATE and CUSTOMER tables in the Star Schema Benchmark
      • The customer table contains attributes for 3 million customers.
      • The time data is at the day-level granularity, and spans 7 years, from the start of 1992 to the end of 1998.
    • The clickstream data is stored in an S3 bucket, and serves as a fact table.
      • Various copies of this dataset in CSV and Parquet format have been provided, for reasons to be discussed later.
      • The data is a modified version of the uservisits dataset from AMPLab’s Big Data Benchmark, which was generated by Intel’s Hadoop benchmark tools.
      • Changes were minimal, so that existing test harnesses for this test can be adapted:
        • Increased the 751,754,869-row dataset 5X to 3,758,774,345 rows.
        • Added surrogate keys to support joins with customer and time dimensions. These keys were distributed evenly across the entire dataset to represents user visits from six customers over seven years.
        • Values for the visitDate column were replaced to align with the 7-year timeframe, and the added time surrogate key.

Queries across the data lake and data warehouse 

Imagine a scenario where a business analyst plans to analyze clickstream metrics like ad revenue over time and by customer, market segment and more. The example below is a query that achieves this effect: 

The query part highlighted in red retrieves clickstream data in S3, and joins the data with the time and customer dimension tables in Amazon Redshift through the part highlighted in blue. The query returns the total ad revenue for three customers over the last three months, along with info on their respective market segment.

Unfortunately, this query takes around three minutes to run, and doesn’t enable the interactive experience that you want. However, there’s a number of performance optimizations that you can implement to achieve the desired performance.

Performance analysis

Two key utilities provide visibility into Redshift Spectrum:

  • EXPLAIN
    Provides the query execution plan, which includes info around what processing is pushed down to Redshift Spectrum. Steps in the plan that include the prefix S3 are executed on Redshift Spectrum. For instance, the plan for the previous query has the step “S3 Seq Scan clickstream.uservisits_csv10”, indicating that Redshift Spectrum performs a scan on S3 as part of the query execution.
  • SVL_S3QUERY_SUMMARY
    Statistics for Redshift Spectrum queries are stored in this table. While the execution plan presents cost estimates, this table stores actual statistics for past query runs.

You can get the statistics of your last query by inspecting the SVL_S3QUERY_SUMMARY table with the condition (query = pg_last_query_id()). Inspecting the previous query reveals that the entire dataset of nearly 3.8 billion rows was scanned to retrieve less than 66.3 million rows. Improving scan selectivity in your query could yield substantial performance improvements.

Partitioning

Partitioning is a key means to improving scan efficiency. In your environment, the data and tables have already been organized, and configured to support partitions. For more information, see the PoC project setup instructions. The clickstream table was defined as:

CREATE EXTERNAL TABLE clickstream.uservisits_csv10
…
PARTITIONED BY(customer int4, visitYearMonth int4)

The entire 3.8 billion-row dataset is organized as a collection of large files where each file contains data exclusive to a particular customer and month in a year. This allows you to partition your data into logical subsets by customer and year/month. With partitions, the query engine can target a subset of files:

  • Only for specific customers
  • Only data for specific months
  • A combination of specific customers and year/months

You can use partitions in your queries. Instead of joining your customer data on the surrogate customer key (that is, c.c_custkey = uv.custKey), the partition key “customer” should be used instead:

SELECT c.c_name, c.c_mktsegment, t.prettyMonthYear, SUM(uv.adRevenue)
…
ON c.c_custkey = uv.customer
…
ORDER BY c.c_name, c.c_mktsegment, uv.yearMonthKey  ASC

This query should run approximately twice as fast as the previous query. If you look at the statistics for this query in SVL_S3QUERY_SUMMARY, you see that only half the dataset was scanned. This is expected because your query is on three out of six customers on an evenly distributed dataset. However, the scan is still inefficient, and you can benefit from using your year/month partition key as well:

SELECT c.c_name, c.c_mktsegment, t.prettyMonthYear, SUM(uv.adRevenue)
…
ON c.c_custkey = uv.customer
…
ON uv.visitYearMonth = t.d_yearmonthnum
…
ORDER BY c.c_name, c.c_mktsegment, uv.visitYearMonth ASC

All joins between the tables are now using partitions. Upon reviewing the statistics for this query, you should observe that Redshift Spectrum scans and returns the exact number of rows, 66,270,117. If you run this query a few times, you should see execution time in the range of 8 seconds, which is a 22.5X improvement on your original query!

Predicate pushdown and storage optimizations 

Previously, I mentioned that Redshift Spectrum performs processing through large-scale infrastructure external to your Amazon Redshift cluster. It is optimized for performing large scans and aggregations on S3. In fact, Redshift Spectrum may even out-perform a medium size Amazon Redshift cluster on these types of workloads with the proper optimizations. There are two important variables to consider for optimizing large scans and aggregations:

  • File size and count. As a general rule, use files 100 MB-1 GB in size, as Redshift Spectrum and S3 are optimized for reading this object size. However, the number of files operating on a query is directly correlated with the parallelism achievable by a query. There is an inverse relationship between file size and count: the bigger the files, the fewer files there are for the same dataset. Consequently, there is a trade-off between optimizing for object read performance, and the amount of parallelism achievable on a particular query. Large files are best for large scans as the query likely operates on sufficiently large number of files. For queries that are more selective and for which fewer files are operating, you may find that smaller files allow for more parallelism.
  • Data format. Redshift Spectrum supports various data formats. Columnar formats like Parquet can sometimes lead to substantial performance benefits by providing compression and more efficient I/O for certain workloads. Generally, format types like Parquet should be used for query workloads involving large scans, and high attribute selectivity. Again, there are trade-offs as formats like Parquet require more compute power to process than plaintext. For queries on smaller subsets of data, the I/O efficiency benefit of Parquet is diminished. At some point, Parquet may perform the same or slower than plaintext. Latency, compression rates, and the trade-off between user experience and cost should drive your decision.

To help illustrate how Redshift Spectrum performs on these large aggregation workloads, run a basic query that aggregates the entire ~3.7 billion record dataset on Redshift Spectrum, and compared that with running the query exclusively on Amazon Redshift:

SELECT uv.custKey, COUNT(uv.custKey)
FROM <your clickstream table> as uv
GROUP BY uv.custKey
ORDER BY uv.custKey ASC

For the Amazon Redshift test case, the clickstream data is loaded, and distributed evenly across all nodes (even distribution style) with optimal column compression encodings prescribed by the Amazon Redshift’s ANALYZE command.

The Redshift Spectrum test case uses a Parquet data format with each file containing all the data for a particular customer in a month. This results in files mostly in the range of 220-280 MB, and in effect, is the largest file size for this partitioning scheme. If you run tests with the other datasets provided, you see that this data format and size is optimal and out-performs others by ~60X. 

Performance differences will vary depending on the scenario. The important takeaway is to understand the testing strategy and the workload characteristics where Redshift Spectrum is likely to yield performance benefits. 

The following chart compares the query execution time for the two scenarios. The results indicate that you would have to pay for 12 X DC1.Large nodes to get performance comparable to using a small Amazon Redshift cluster that leverages Redshift Spectrum. 

Chart showing simple aggregation on ~3.7 billion records

So you’ve validated that Spectrum excels at performing large aggregations. Could you benefit by pushing more work down to Redshift Spectrum in your original query? It turns out that you can, by making the following modification:

The clickstream data is stored at a day-level granularity for each customer while your query rolls up the data to the month level per customer. In the earlier query that uses the day/month partition key, you optimized the query so that it only scans and retrieves the data required, but the day level data is still sent back to your Amazon Redshift cluster for joining and aggregation. The query shown here pushes aggregation work down to Redshift Spectrum as indicated by the query plan:

In this query, Redshift Spectrum aggregates the clickstream data to the month level before it is returned to the Amazon Redshift cluster and joined with the dimension tables. This query should complete in about 4 seconds, which is roughly twice as fast as only using the partition key. The speed increase is evident upon reviewing the SVL_S3QUERY_SUMMARY table:

  • Bytes scanned is 21.6X less because of the Parquet data format.
  • Only 90 records are returned back to the Amazon Redshift cluster as a result of the push-down, instead of ~66.2 million, leading to substantially less join overhead, and about 530 MB less data sent back to your cluster.
  • No adverse change in average parallelism.

Assessing the value of Amazon Redshift vs. Redshift Spectrum

At this point, you might be asking yourself, why would I ever not use Redshift Spectrum? Well, you still get additional value for your money by loading data into Amazon Redshift, and querying in Amazon Redshift vs. querying S3.

In fact, it turns out that the last version of our query runs even faster when executed exclusively in native Amazon Redshift, as shown in the following chart:

Chart comparing Amazon Redshift vs. Redshift Spectrum with pushdown aggregation over 3 months of data

As a general rule, queries that aren’t dominated by I/O and which involve multiple joins are better optimized in native Amazon Redshift. For instance, the performance difference between running the partition key query entirely in Amazon Redshift versus with Redshift Spectrum is twice as large as that that of the pushdown aggregation query, partly because the former case benefits more from better join performance.

Furthermore, the variability in latency in native Amazon Redshift is lower. For use cases where you have tight performance SLAs on queries, you may want to consider using Amazon Redshift exclusively to support those queries.

On the other hand, when you perform large scans, you could benefit from the best of both worlds: higher performance at lower cost. For instance, imagine that you wanted to enable your business analysts to interactively discover insights across a vast amount of historical data. In the example below, the pushdown aggregation query is modified to analyze seven years of data instead of three months:

SELECT c.c_name, c.c_mktsegment, t.prettyMonthYear, uv.totalRevenue
…
WHERE customer <= 3 and visitYearMonth >= 199201
… 
FROM dwdate WHERE d_yearmonthnum >= 199201) as t
…
ORDER BY c.c_name, c.c_mktsegment, uv.visitYearMonth ASC

This query requires scanning and aggregating nearly 1.9 billion records. As shown in the chart below, Redshift Spectrum substantially speeds up this query. A large Amazon Redshift cluster would have to be provisioned to support this use case. With the aid of Redshift Spectrum, you could use an existing small cluster, keep a single copy of your data in S3, and benefit from economical, durable storage while only paying for what you use via the pay per query pricing model.

Chart comparing Amazon Redshift vs. Redshift Spectrum with pushdown aggregation over 7 years of data

Summary

Redshift Spectrum lowers the time to value for deeper insights on customer data queries spanning the data lake and data warehouse. It can enable interactive analysis on datasets in cases that weren’t economically practical or technically feasible before.

There are cases where you can get the best of both worlds from Redshift Spectrum: higher performance at lower cost. However, there are still latency-sensitive use cases where you may want native Amazon Redshift performance. For more best practice tips, see the 10 Best Practices for Amazon Redshift post.

Please visit the Amazon Redshift Spectrum PoC Environment Github page. If you have questions or suggestions, please comment below.

 


Additional Reading

Learn more about how Amazon Redshift Spectrum extends data warehousing out to exabytes – no loading required.


About the Author

Dylan Tong is an Enterprise Solutions Architect at AWS. He works with customers to help drive their success on the AWS platform through thought leadership and guidance on designing well architected solutions. He has spent most of his career building on his expertise in data management and analytics by working for leaders and innovators in the space.

 

 

Amazon AppStream 2.0 Launch Recap – Domain Join, Simple Network Setup, and Lots More

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/amazon-appstream-2-0-launch-recap-domain-join-simple-network-setup-and-lots-more/

We (the AWS Blog Team) work to maintain a delicate balance between coverage and volume! On the one hand, we want to make sure that you are aware of as many features as possible. On the other, we don’t want to bury you in blog posts. As a happy medium between these two extremes we sometimes let interesting new features pile up for a couple of weeks and then pull them together in the form of a recap post such as this one.

Today I would like to tell you about the latest and greatest additions to Amazon AppStream 2.0, our application streaming service (read Amazon AppStream 2.0 – Stream Desktop Apps from AWS to learn more). We launched GPU-powered streaming instances just a month ago and have been adding features rapidly; here are some recent launches that did not get covered in individual posts at launch time:

  • Microsoft Active Directory Domains – Connect AppStream 2.0 streaming instances to your Microsoft Active Directory domain.
  • User Management & Web Portal – Create and manage users from within the AppStream 2.0 management console.
  • Persistent Storage for User Files – Use persistent, S3-backed storage for user home folders.
  • Simple Network Setup – Enable Internet access for image builder and instance fleets more easily.
  • Custom VPC Security Groups – Use VPC security groups to control network traffic.
  • Audio-In – Use microphones with your streaming applications.

These features were prioritized based on early feedback from AWS customers who are using or are considering the use of AppStream 2.0 in their enterprises. Let’s take a quick look at each one.

Domain Join
This much-requested feature allows you to connect your AppStream 2.0 streaming instances to your Microsoft Active Directory (AD) domain. After you do this you can apply existing policies to your streaming instances, and provide your users with single sign-on access to intranet resources such as web sites, printers, and file shares. Your users are authenticated using the SAML 2.0 provider of your choice, and can access applications that require a connection to your AD domain.

To get started, visit the AppStream 2.0 Console, create and store a Directory Configuration:

Newly created image builders and newly launched fleets can then use the stored Directory Configuration to join the AD domain in an Organizational Unit (OU) that you provide:

To learn more, read Using Active Directory Domains with AppStream 2.0 and follow the Setting Up the Active Directory tutorial. You can also learn more in the What’s New.

User Management & Web Portal
This feature makes it easier for you to give new users access to the applications that you are streaming with AppStream 2.0 if you are not using the Domain Join feature that I described earlier.

You can create and manage users, give them access to applications through a web portal, and send them welcome emails, all with a couple of clicks:

AppStream 2.0 sends each new user a welcome email that directs them to a web portal where they will be prompted to create a permanent password. Once they are logged in they are able to access the applications that have been assigned to them.

To learn more, read Using the AppStream 2.0 User Pool and the What’s New.

Persistent Storage
This feature allows users of streaming applications to store files for use in later AppStream 2.0 sessions. Each user is given a home folder which is stored in Amazon Simple Storage Service (S3) between sessions. The folder is made available to the streaming instance at the start of the session and changed files are periodically synced back to S3. To enable this feature, simply check Enable Home Folders when you create your next fleet:

All folders (and the files within) are stored in an S3 bucket that is automatically created within your account when the feature is enabled. There is no limit on total file storage but we recommend that individual files be limited to 5 gigabytes.

Regular S3 pricing applies; to learn more about this feature read about Persistent Storage with AppStream 2.0 Home Folders and check out the What’s New.

Simple Network Setup
Setting up Internet access for your image builder and your streaming instances was once a multi-step process. You had to create a Network Address Translation (NAT) gateway in a public subnet of one of your VPCs and configure traffic routing rules.

Now, you can do this by marking the image builder or the fleet for Internet access, selecting a VPC that has at least one public subnet, and choosing the public subnet(s), all from the AppStream 2.0 Console:

To learn more, read Network Settings for Fleet and Image Builder Instances and Enabling Internet Access Using a Public Subnet and check out the What’s New.

Custom VPC Security Groups
You can create VPC security groups and associate them with your image builders and your fleets. This gives you fine-grained control over inbound and outbound traffic to databases, license servers, file shares, and application servers. Read the What’s New to learn more.

Audio-In
You can use analog and USB microphones, mixing consoles, and other audio input devices with your streaming applications. Simply click on Enable Microphone in the AppStream 2.0 toolbar to get started. Read the What’s New to learn more.

Available Now
All of these features are available now and you can start using them today in all AWS Regions where Amazon AppStream 2.0 is available.

Jeff;

PS – If you are new to AppStream 2.0, try out some pre-installed applications. No setup needed and you’ll get to experience the power of streaming applications first-hand.

An Invitation for CrashPlan Customers: Try Backblaze

Post Syndicated from Gleb Budman original https://www.backblaze.com/blog/crashplan-alternative-backup-solution/

Welcome CrashPlan Users
With news coming out this morning of CrashPlan exiting the consumer market, we know some of you may be considering which backup provider to call home. We welcome you to try us.

For over a decade, Backblaze has provided unlimited cloud backup for Windows and Macintosh computers at $5 per month (or $50 per year).

Backblaze is excellent if you’re looking for the cheapest online backup option that still offers serious file protection.” — Dann Berg, Tom’s Guide.

That’s it. Ready to make sure your data is safe? Try Backblaze for free — it’ll take you less than a minute and you don’t need a credit card to start protecting your data.

Our customers don’t have to choose between competing feature sets or hard to understand fine print. There are no extra charges and no limits on the size of your files — no matter how many videos you want to back up. And when we say unlimited, we mean unlimited; there are no restrictions on files, gigabytes, or restores. Customers also love the choices they have for getting their data back — web, mobile apps, and our free Restore by Mail option. We’re also the fastest to back up your data. While other services throttle your upload speeds, we want to get you protected as quickly as possible.

Backblaze vs. Carbonite

We know that CrashPlan is encouraging customers to look at Carbonite as an alternative. We would like to offer you another option: Backblaze. We cost less, we offer more, we store over 350 Petabytes of data, we have restored over 20 billion files, and customers in over 120 countries around the world trust us with their data.

Backblaze Carbonite Basic Carbonite Prime
Price per Computer $50/year $59.99/year $149.99/year
Back Up All User Data By Default – No Picking And Choosing Yes No No
Automatically Back Up Files Of Any Size, Including Videos Yes No Yes1
Back Up Multiple USB External Hard Drives Yes No No
Restore by Mail for Free Yes No No
Locate Computer Yes No No
Manage Families & Teams Yes No No
Protect Accounts Via Two Factor VerificationSMS & Authenticator Apps Yes No No
Protect Data Via Private Encryption Key Yes No No2
(1) All videos and files over 4GB require manual selection.  (2) Available on Windows Only

To get just some of the features offered by Backblaze for $50/year, you would need to purchase Carbonite Prime at $149.99/year.

Reminder: Sync is Not Backup

“Backblaze is my favorite online backup service, mostly because everything about it is so simple, especially its pricing and software.“ Tim Fisher — Lifewire: 22 Online Backup Services Reviewed

Of course, there are plenty of options in the marketplace. We encourage you to choose one to make sure you stay backed up. One thing we tell our own friends and family: sync is not backup.

If you’re considering using a sync service — Dropbox, Google Drive, OneDrive, iCloud, etc. — you should know that these services are not designed to back up all your data. Typically, they only sync data from a specific directory or folder. If the service detects a file was deleted from your sync folder, it also will delete it from their server, and you’re out of luck. In addition, most don’t support external drives and have tiered pricing that gets quite expensive.

Backblaze is the Simple, Reliable, and Affordable Choice for Unlimited Backup of All Your Data
People have trusted Backblaze to protect their digital photos, music, movies, and documents for the past 10 years. We look forward to doing the same for your valuable data.

Your CrashPlan service may not be getting shut off today. But there’s no reason to wait until your data is at risk. Try Backblaze for FREE today — all you need to do is pick an email/password and click download.

The post An Invitation for CrashPlan Customers: Try Backblaze appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Unfixable Automobile Computer Security Vulnerability

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2017/08/unfixable_autom.html

There is an unpatchable vulnerability that affects most modern cars. It’s buried in the Controller Area Network (CAN):

Researchers say this flaw is not a vulnerability in the classic meaning of the word. This is because the flaw is more of a CAN standard design choice that makes it unpatchable.

Patching the issue means changing how the CAN standard works at its lowest levels. Researchers say car manufacturers can only mitigate the vulnerability via specific network countermeasures, but cannot eliminate it entirely.

Details on how the attack works are here:

The CAN messages, including errors, are called “frames.” Our attack focuses on how CAN handles errors. Errors arise when a device reads values that do not correspond to the original expected value on a frame. When a device detects such an event, it writes an error message onto the CAN bus in order to “recall” the errant frame and notify the other devices to entirely ignore the recalled frame. This mishap is very common and is usually due to natural causes, a transient malfunction, or simply by too many systems and modules trying to send frames through the CAN at the same time.

If a device sends out too many errors, then­ — as CAN standards dictate — ­it goes into a so-called Bus Off state, where it is cut off from the CAN and prevented from reading and/or writing any data onto the CAN. This feature is helpful in isolating clearly malfunctioning devices and stops them from triggering the other modules/systems on the CAN.

This is the exact feature that our attack abuses. Our attack triggers this particular feature by inducing enough errors such that a targeted device or system on the CAN is made to go into the Bus Off state, and thus rendered inert/inoperable. This, in turn, can drastically affect the car’s performance to the point that it becomes dangerous and even fatal, especially when essential systems like the airbag system or the antilock braking system are deactivated. All it takes is a specially-crafted attack device, introduced to the car’s CAN through local access, and the reuse of frames already circulating in the CAN rather than injecting new ones (as previous attacks in this manner have done).

Slashdot thread.

Analyzing AWS Cost and Usage Reports with Looker and Amazon Athena

Post Syndicated from Dillon Morrison original https://aws.amazon.com/blogs/big-data/analyzing-aws-cost-and-usage-reports-with-looker-and-amazon-athena/

This is a guest post by Dillon Morrison at Looker. Looker is, in their own words, “a new kind of analytics platform–letting everyone in your business make better decisions by getting reliable answers from a tool they can use.” 

As the breadth of AWS products and services continues to grow, customers are able to more easily move their technology stack and core infrastructure to AWS. One of the attractive benefits of AWS is the cost savings. Rather than paying upfront capital expenses for large on-premises systems, customers can instead pay variables expenses for on-demand services. To further reduce expenses AWS users can reserve resources for specific periods of time, and automatically scale resources as needed.

The AWS Cost Explorer is great for aggregated reporting. However, conducting analysis on the raw data using the flexibility and power of SQL allows for much richer detail and insight, and can be the better choice for the long term. Thankfully, with the introduction of Amazon Athena, monitoring and managing these costs is now easier than ever.

In the post, I walk through setting up the data pipeline for cost and usage reports, Amazon S3, and Athena, and discuss some of the most common levers for cost savings. I surface tables through Looker, which comes with a host of pre-built data models and dashboards to make analysis of your cost and usage data simple and intuitive.

Analysis with Athena

With Athena, there’s no need to create hundreds of Excel reports, move data around, or deploy clusters to house and process data. Athena uses Apache Hive’s DDL to create tables, and the Presto querying engine to process queries. Analysis can be performed directly on raw data in S3. Conveniently, AWS exports raw cost and usage data directly into a user-specified S3 bucket, making it simple to start querying with Athena quickly. This makes continuous monitoring of costs virtually seamless, since there is no infrastructure to manage. Instead, users can leverage the power of the Athena SQL engine to easily perform ad-hoc analysis and data discovery without needing to set up a data warehouse.

After the data pipeline is established, cost and usage data (the recommended billing data, per AWS documentation) provides a plethora of comprehensive information around usage of AWS services and the associated costs. Whether you need the report segmented by product type, user identity, or region, this report can be cut-and-sliced any number of ways to properly allocate costs for any of your business needs. You can then drill into any specific line item to see even further detail, such as the selected operating system, tenancy, purchase option (on-demand, spot, or reserved), and so on.

Walkthrough

By default, the Cost and Usage report exports CSV files, which you can compress using gzip (recommended for performance). There are some additional configuration options for tuning performance further, which are discussed below.

Prerequisites

If you want to follow along, you need the following resources:

Enable the cost and usage reports

First, enable the Cost and Usage report. For Time unit, select Hourly. For Include, select Resource IDs. All options are prompted in the report-creation window.

The Cost and Usage report dumps CSV files into the specified S3 bucket. Please note that it can take up to 24 hours for the first file to be delivered after enabling the report.

Configure the S3 bucket and files for Athena querying

In addition to the CSV file, AWS also creates a JSON manifest file for each cost and usage report. Athena requires that all of the files in the S3 bucket are in the same format, so we need to get rid of all these manifest files. If you’re looking to get started with Athena quickly, you can simply go into your S3 bucket and delete the manifest file manually, skip the automation described below, and move on to the next section.

To automate the process of removing the manifest file each time a new report is dumped into S3, which I recommend as you scale, there are a few additional steps. The folks at Concurrency labs wrote a great overview and set of scripts for this, which you can find in their GitHub repo.

These scripts take the data from an input bucket, remove anything unnecessary, and dump it into a new output bucket. We can utilize AWS Lambda to trigger this process whenever new data is dropped into S3, or on a nightly basis, or whatever makes most sense for your use-case, depending on how often you’re querying the data. Please note that enabling the “hourly” report means that data is reported at the hour-level of granularity, not that a new file is generated every hour.

Following these scripts, you’ll notice that we’re adding a date partition field, which isn’t necessary but improves query performance. In addition, converting data from CSV to a columnar format like ORC or Parquet also improves performance. We can automate this process using Lambda whenever new data is dropped in our S3 bucket. Amazon Web Services discusses columnar conversion at length, and provides walkthrough examples, in their documentation.

As a long-term solution, best practice is to use compression, partitioning, and conversion. However, for purposes of this walkthrough, we’re not going to worry about them so we can get up-and-running quicker.

Set up the Athena query engine

In your AWS console, navigate to the Athena service, and click “Get Started”. Follow the tutorial and set up a new database (we’ve called ours “AWS Optimizer” in this example). Don’t worry about configuring your initial table, per the tutorial instructions. We’ll be creating a new table for cost and usage analysis. Once you walked through the tutorial steps, you’ll be able to access the Athena interface, and can begin running Hive DDL statements to create new tables.

One thing that’s important to note, is that the Cost and Usage CSVs also contain the column headers in their first row, meaning that the column headers would be included in the dataset and any queries. For testing and quick set-up, you can remove this line manually from your first few CSV files. Long-term, you’ll want to use a script to programmatically remove this row each time a new file is dropped in S3 (every few hours typically). We’ve drafted up a sample script for ease of reference, which we run on Lambda. We utilize Lambda’s native ability to invoke the script whenever a new object is dropped in S3.

For cost and usage, we recommend using the DDL statement below. Since our data is in CSV format, we don’t need to use a SerDe, we can simply specify the “separatorChar, quoteChar, and escapeChar”, and the structure of the files (“TEXTFILE”). Note that AWS does have an OpenCSV SerDe as well, if you prefer to use that.

 

CREATE EXTERNAL TABLE IF NOT EXISTS cost_and_usage	 (
identity_LineItemId String,
identity_TimeInterval String,
bill_InvoiceId String,
bill_BillingEntity String,
bill_BillType String,
bill_PayerAccountId String,
bill_BillingPeriodStartDate String,
bill_BillingPeriodEndDate String,
lineItem_UsageAccountId String,
lineItem_LineItemType String,
lineItem_UsageStartDate String,
lineItem_UsageEndDate String,
lineItem_ProductCode String,
lineItem_UsageType String,
lineItem_Operation String,
lineItem_AvailabilityZone String,
lineItem_ResourceId String,
lineItem_UsageAmount String,
lineItem_NormalizationFactor String,
lineItem_NormalizedUsageAmount String,
lineItem_CurrencyCode String,
lineItem_UnblendedRate String,
lineItem_UnblendedCost String,
lineItem_BlendedRate String,
lineItem_BlendedCost String,
lineItem_LineItemDescription String,
lineItem_TaxType String,
product_ProductName String,
product_accountAssistance String,
product_architecturalReview String,
product_architectureSupport String,
product_availability String,
product_bestPractices String,
product_cacheEngine String,
product_caseSeverityresponseTimes String,
product_clockSpeed String,
product_currentGeneration String,
product_customerServiceAndCommunities String,
product_databaseEdition String,
product_databaseEngine String,
product_dedicatedEbsThroughput String,
product_deploymentOption String,
product_description String,
product_durability String,
product_ebsOptimized String,
product_ecu String,
product_endpointType String,
product_engineCode String,
product_enhancedNetworkingSupported String,
product_executionFrequency String,
product_executionLocation String,
product_feeCode String,
product_feeDescription String,
product_freeQueryTypes String,
product_freeTrial String,
product_frequencyMode String,
product_fromLocation String,
product_fromLocationType String,
product_group String,
product_groupDescription String,
product_includedServices String,
product_instanceFamily String,
product_instanceType String,
product_io String,
product_launchSupport String,
product_licenseModel String,
product_location String,
product_locationType String,
product_maxIopsBurstPerformance String,
product_maxIopsvolume String,
product_maxThroughputvolume String,
product_maxVolumeSize String,
product_maximumStorageVolume String,
product_memory String,
product_messageDeliveryFrequency String,
product_messageDeliveryOrder String,
product_minVolumeSize String,
product_minimumStorageVolume String,
product_networkPerformance String,
product_operatingSystem String,
product_operation String,
product_operationsSupport String,
product_physicalProcessor String,
product_preInstalledSw String,
product_proactiveGuidance String,
product_processorArchitecture String,
product_processorFeatures String,
product_productFamily String,
product_programmaticCaseManagement String,
product_provisioned String,
product_queueType String,
product_requestDescription String,
product_requestType String,
product_routingTarget String,
product_routingType String,
product_servicecode String,
product_sku String,
product_softwareType String,
product_storage String,
product_storageClass String,
product_storageMedia String,
product_technicalSupport String,
product_tenancy String,
product_thirdpartySoftwareSupport String,
product_toLocation String,
product_toLocationType String,
product_training String,
product_transferType String,
product_usageFamily String,
product_usagetype String,
product_vcpu String,
product_version String,
product_volumeType String,
product_whoCanOpenCases String,
pricing_LeaseContractLength String,
pricing_OfferingClass String,
pricing_PurchaseOption String,
pricing_publicOnDemandCost String,
pricing_publicOnDemandRate String,
pricing_term String,
pricing_unit String,
reservation_AvailabilityZone String,
reservation_NormalizedUnitsPerReservation String,
reservation_NumberOfReservations String,
reservation_ReservationARN String,
reservation_TotalReservedNormalizedUnits String,
reservation_TotalReservedUnits String,
reservation_UnitsPerReservation String,
resourceTags_userName String,
resourceTags_usercostcategory String  


)
    ROW FORMAT DELIMITED
      FIELDS TERMINATED BY ','
      ESCAPED BY '\\'
      LINES TERMINATED BY '\n'

STORED AS TEXTFILE
    LOCATION 's3://<<your bucket name>>';

Once you’ve successfully executed the command, you should see a new table named “cost_and_usage” with the below properties. Now we’re ready to start executing queries and running analysis!

Start with Looker and connect to Athena

Setting up Looker is a quick process, and you can try it out for free here (or download from Amazon Marketplace). It takes just a few seconds to connect Looker to your Athena database, and Looker comes with a host of pre-built data models and dashboards to make analysis of your cost and usage data simple and intuitive. After you’re connected, you can use the Looker UI to run whatever analysis you’d like. Looker translates this UI to optimized SQL, so any user can execute and visualize queries for true self-service analytics.

Major cost saving levers

Now that the data pipeline is configured, you can dive into the most popular use cases for cost savings. In this post, I focus on:

  • Purchasing Reserved Instances vs. On-Demand Instances
  • Data transfer costs
  • Allocating costs over users or other Attributes (denoted with resource tags)

On-Demand, Spot, and Reserved Instances

Purchasing Reserved Instances vs On-Demand Instances is arguably going to be the biggest cost lever for heavy AWS users (Reserved Instances run up to 75% cheaper!). AWS offers three options for purchasing instances:

  • On-Demand—Pay as you use.
  • Spot (variable cost)—Bid on spare Amazon EC2 computing capacity.
  • Reserved Instances—Pay for an instance for a specific, allotted period of time.

When purchasing a Reserved Instance, you can also choose to pay all-upfront, partial-upfront, or monthly. The more you pay upfront, the greater the discount.

If your company has been using AWS for some time now, you should have a good sense of your overall instance usage on a per-month or per-day basis. Rather than paying for these instances On-Demand, you should try to forecast the number of instances you’ll need, and reserve them with upfront payments.

The total amount of usage with Reserved Instances versus overall usage with all instances is called your coverage ratio. It’s important not to confuse your coverage ratio with your Reserved Instance utilization. Utilization represents the amount of reserved hours that were actually used. Don’t worry about exceeding capacity, you can still set up Auto Scaling preferences so that more instances get added whenever your coverage or utilization crosses a certain threshold (we often see a target of 80% for both coverage and utilization among savvy customers).

Calculating the reserved costs and coverage can be a bit tricky with the level of granularity provided by the cost and usage report. The following query shows your total cost over the last 6 months, broken out by Reserved Instance vs other instance usage. You can substitute the cost field for usage if you’d prefer. Please note that you should only have data for the time period after the cost and usage report has been enabled (though you can opt for up to 3 months of historical data by contacting your AWS Account Executive). If you’re just getting started, this query will only show a few days.

 

SELECT 
	DATE_FORMAT(from_iso8601_timestamp(cost_and_usage.lineitem_usagestartdate),'%Y-%m') AS "cost_and_usage.usage_start_month",
	COALESCE(SUM(cost_and_usage.lineitem_unblendedcost ), 0) AS "cost_and_usage.total_unblended_cost",
	COALESCE(SUM(CASE WHEN (CASE
         WHEN cost_and_usage.lineitem_lineitemtype = 'DiscountedUsage' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'RIFee' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'Fee' THEN 'RI Line Item'
         ELSE 'Non RI Line Item'
        END = 'RI Line Item') THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0) AS "cost_and_usage.total_reserved_unblended_cost",
	1.0 * (COALESCE(SUM(CASE WHEN (CASE
         WHEN cost_and_usage.lineitem_lineitemtype = 'DiscountedUsage' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'RIFee' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'Fee' THEN 'RI Line Item'
         ELSE 'Non RI Line Item'
        END = 'RI Line Item') THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0)) / NULLIF((COALESCE(SUM(cost_and_usage.lineitem_unblendedcost ), 0)),0)  AS "cost_and_usage.percent_spend_on_ris",
	COALESCE(SUM(CASE WHEN (CASE
         WHEN cost_and_usage.lineitem_lineitemtype = 'DiscountedUsage' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'RIFee' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'Fee' THEN 'RI Line Item'
         ELSE 'Non RI Line Item'
        END = 'Non RI Line Item') THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0) AS "cost_and_usage.total_non_reserved_unblended_cost",
	1.0 * (COALESCE(SUM(CASE WHEN (CASE
         WHEN cost_and_usage.lineitem_lineitemtype = 'DiscountedUsage' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'RIFee' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'Fee' THEN 'RI Line Item'
         ELSE 'Non RI Line Item'
        END = 'Non RI Line Item') THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0)) / NULLIF((COALESCE(SUM(cost_and_usage.lineitem_unblendedcost ), 0)),0)  AS "cost_and_usage.percent_spend_on_non_ris"
FROM aws_optimizer.cost_and_usage  AS cost_and_usage

WHERE 
	(((from_iso8601_timestamp(cost_and_usage.lineitem_usagestartdate)) >= ((DATE_ADD('month', -5, DATE_TRUNC('MONTH', CAST(NOW() AS DATE))))) AND (from_iso8601_timestamp(cost_and_usage.lineitem_usagestartdate)) < ((DATE_ADD('month', 6, DATE_ADD('month', -5, DATE_TRUNC('MONTH', CAST(NOW() AS DATE))))))))
GROUP BY 1
ORDER BY 2 DESC
LIMIT 500

The resulting table should look something like the image below (I’m surfacing tables through Looker, though the same table would result from querying via command line or any other interface).

With a BI tool, you can create dashboards for easy reference and monitoring. New data is dumped into S3 every few hours, so your dashboards can update several times per day.

It’s an iterative process to understand the appropriate number of Reserved Instances needed to meet your business needs. After you’ve properly integrated Reserved Instances into your purchasing patterns, the savings can be significant. If your coverage is consistently below 70%, you should seriously consider adjusting your purchase types and opting for more Reserved instances.

Data transfer costs

One of the great things about AWS data storage is that it’s incredibly cheap. Most charges often come from moving and processing that data. There are several different prices for transferring data, broken out largely by transfers between regions and availability zones. Transfers between regions are the most costly, followed by transfers between Availability Zones. Transfers within the same region and same availability zone are free unless using elastic or public IP addresses, in which case there is a cost. You can find more detailed information in the AWS Pricing Docs. With this in mind, there are several simple strategies for helping reduce costs.

First, since costs increase when transferring data between regions, it’s wise to ensure that as many services as possible reside within the same region. The more you can localize services to one specific region, the lower your costs will be.

Second, you should maximize the data you’re routing directly within AWS services and IP addresses. Transfers out to the open internet are the most costly and least performant mechanisms of data transfers, so it’s best to keep transfers within AWS services.

Lastly, data transfers between private IP addresses are cheaper than between elastic or public IP addresses, so utilizing private IP addresses as much as possible is the most cost-effective strategy.

The following query provides a table depicting the total costs for each AWS product, broken out transfer cost type. Substitute the “lineitem_productcode” field in the query to segment the costs by any other attribute. If you notice any unusually high spikes in cost, you’ll need to dig deeper to understand what’s driving that spike: location, volume, and so on. Drill down into specific costs by including “product_usagetype” and “product_transfertype” in your query to identify the types of transfer costs that are driving up your bill.

SELECT 
	cost_and_usage.lineitem_productcode  AS "cost_and_usage.product_code",
	COALESCE(SUM(cost_and_usage.lineitem_unblendedcost), 0) AS "cost_and_usage.total_unblended_cost",
	COALESCE(SUM(CASE WHEN REGEXP_LIKE(cost_and_usage.product_usagetype, 'DataTransfer')    THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0) AS "cost_and_usage.total_data_transfer_cost",
	COALESCE(SUM(CASE WHEN REGEXP_LIKE(cost_and_usage.product_usagetype, 'DataTransfer-In')    THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0) AS "cost_and_usage.total_inbound_data_transfer_cost",
	COALESCE(SUM(CASE WHEN REGEXP_LIKE(cost_and_usage.product_usagetype, 'DataTransfer-Out')    THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0) AS "cost_and_usage.total_outbound_data_transfer_cost"
FROM aws_optimizer.cost_and_usage  AS cost_and_usage

WHERE 
	(((from_iso8601_timestamp(cost_and_usage.lineitem_usagestartdate)) >= ((DATE_ADD('month', -5, DATE_TRUNC('MONTH', CAST(NOW() AS DATE))))) AND (from_iso8601_timestamp(cost_and_usage.lineitem_usagestartdate)) < ((DATE_ADD('month', 6, DATE_ADD('month', -5, DATE_TRUNC('MONTH', CAST(NOW() AS DATE))))))))
GROUP BY 1
ORDER BY 2 DESC
LIMIT 500

When moving between regions or over the open web, many data transfer costs also include the origin and destination location of the data movement. Using a BI tool with mapping capabilities, you can get a nice visual of data flows. The point at the center of the map is used to represent external data flows over the open internet.

Analysis by tags

AWS provides the option to apply custom tags to individual resources, so you can allocate costs over whatever customized segment makes the most sense for your business. For a SaaS company that hosts software for customers on AWS, maybe you’d want to tag the size of each customer. The following query uses custom tags to display the reserved, data transfer, and total cost for each AWS service, broken out by tag categories, over the last 6 months. You’ll want to substitute the cost_and_usage.resourcetags_customersegment and cost_and_usage.customer_segment with the name of your customer field.

 

SELECT * FROM (
SELECT *, DENSE_RANK() OVER (ORDER BY z___min_rank) as z___pivot_row_rank, RANK() OVER (PARTITION BY z__pivot_col_rank ORDER BY z___min_rank) as z__pivot_col_ordering FROM (
SELECT *, MIN(z___rank) OVER (PARTITION BY "cost_and_usage.product_code") as z___min_rank FROM (
SELECT *, RANK() OVER (ORDER BY CASE WHEN z__pivot_col_rank=1 THEN (CASE WHEN "cost_and_usage.total_unblended_cost" IS NOT NULL THEN 0 ELSE 1 END) ELSE 2 END, CASE WHEN z__pivot_col_rank=1 THEN "cost_and_usage.total_unblended_cost" ELSE NULL END DESC, "cost_and_usage.total_unblended_cost" DESC, z__pivot_col_rank, "cost_and_usage.product_code") AS z___rank FROM (
SELECT *, DENSE_RANK() OVER (ORDER BY CASE WHEN "cost_and_usage.customer_segment" IS NULL THEN 1 ELSE 0 END, "cost_and_usage.customer_segment") AS z__pivot_col_rank FROM (
SELECT 
	cost_and_usage.lineitem_productcode  AS "cost_and_usage.product_code",
	cost_and_usage.resourcetags_customersegment  AS "cost_and_usage.customer_segment",
	COALESCE(SUM(cost_and_usage.lineitem_unblendedcost ), 0) AS "cost_and_usage.total_unblended_cost",
	1.0 * (COALESCE(SUM(CASE WHEN REGEXP_LIKE(cost_and_usage.product_usagetype, 'DataTransfer')    THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0)) / NULLIF((COALESCE(SUM(cost_and_usage.lineitem_unblendedcost ), 0)),0)  AS "cost_and_usage.percent_spend_data_transfers_unblended",
	1.0 * (COALESCE(SUM(CASE WHEN (CASE
         WHEN cost_and_usage.lineitem_lineitemtype = 'DiscountedUsage' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'RIFee' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'Fee' THEN 'RI Line Item'
         ELSE 'Non RI Line Item'
        END = 'Non RI Line Item') THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0)) / NULLIF((COALESCE(SUM(cost_and_usage.lineitem_unblendedcost ), 0)),0)  AS "cost_and_usage.unblended_percent_spend_on_ris"
FROM aws_optimizer.cost_and_usage_raw  AS cost_and_usage

WHERE 
	(((from_iso8601_timestamp(cost_and_usage.lineitem_usagestartdate)) >= ((DATE_ADD('month', -5, DATE_TRUNC('MONTH', CAST(NOW() AS DATE))))) AND (from_iso8601_timestamp(cost_and_usage.lineitem_usagestartdate)) < ((DATE_ADD('month', 6, DATE_ADD('month', -5, DATE_TRUNC('MONTH', CAST(NOW() AS DATE))))))))
GROUP BY 1,2) ww
) bb WHERE z__pivot_col_rank <= 16384
) aa
) xx
) zz
 WHERE z___pivot_row_rank <= 500 OR z__pivot_col_ordering = 1 ORDER BY z___pivot_row_rank

The resulting table in this example looks like the results below. In this example, you can tell that we’re making poor use of Reserved Instances because they represent such a small portion of our overall costs.

Again, using a BI tool to visualize these costs and trends over time makes the analysis much easier to consume and take action on.

Summary

Saving costs on your AWS spend is always an iterative, ongoing process. Hopefully with these queries alone, you can start to understand your spending patterns and identify opportunities for savings. However, this is just a peek into the many opportunities available through analysis of the Cost and Usage report. Each company is different, with unique needs and usage patterns. To achieve maximum cost savings, we encourage you to set up an analytics environment that enables your team to explore all potential cuts and slices of your usage data, whenever it’s necessary. Exploring different trends and spikes across regions, services, user types, etc. helps you gain comprehensive understanding of your major cost levers and consistently implement new cost reduction strategies.

Note that all of the queries and analysis provided in this post were generated using the Looker data platform. If you’re already a Looker customer, you can get all of this analysis, additional pre-configured dashboards, and much more using Looker Blocks for AWS.


About the Author

Dillon Morrison leads the Platform Ecosystem at Looker. He enjoys exploring new technologies and architecting the most efficient data solutions for the business needs of his company and their customers. In his spare time, you’ll find Dillon rock climbing in the Bay Area or nose deep in the docs of the latest AWS product release at his favorite cafe (“Arlequin in SF is unbeatable!”).

 

 

 

OK Google, be aesthetically pleasing

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/aesthetically-pleasing-ok-google/

Maker Andrew Jones took a Raspberry Pi and the Google Assistant SDK and created a gorgeous-looking, and highly functional, alternative to store-bought smart speakers.

Raspberry Pi Google AI Assistant

In this video I get an “Ok Google” voice activated AI assistant running on a raspberry pi. I also hand make a nice wooden box for it to live in.

OK Google, what are you?

Google Assistant is software of the same ilk as Amazon’s Alexa, Apple’s Siri and Microsoft’s Cortana. It’s a virtual assistant that allows you to request information, play audio, and control smart home devices via voice commands.

Infinite Looping Siri, Alexa and Google Home

One can barely see the iPhone’s screen. That’s because I have a privacy protection screen. Sorry, did not check the camera angle. Learn how to create your own loop, why we put Cortana out of the loop, and how to train Siri to an artificial voice: https://www.danrl.com/2016/12/01/looping-ais-siri-alexa-google-home.html

You probably have a digital assistant on your mobile phone, and if you go to the home of someone even mildly tech-savvy, you may see a device awaiting commands via a wake word such the device’s name or, for the Google Assistant, the phrase “OK, Google”.

Homebrew versions

Understanding the maker need to ‘put tech into stuff’ and upgrade everyday objects into everyday objects 2.0, the creators of these virtual assistants have allowed access for developers to run their software on devices such as the Raspberry Pi. This means that your common-or-garden homemade robot can now be controlled via voice, and your shed-built home automation system can have easy-to-use internet connectivity via a reliable, multi-device platform.

Andrew’s Google Assistant build

Andrew gives a peerless explanation of how the Google Assistant works:

There’s Google’s Cloud. You log into Google’s Cloud and you do a bunch of cloud configuration cloud stuff. And then on the Raspberry Pi you install some Python software and you do a bunch of configuration. And then the cloud and the Pi talk the clouds kitten rainbow protocol and then you get a Google AI assistant.

It all makes perfect sense. Though for more extra detail, you could always head directly to Google.

Andrew Jones Raspberry Pi OK Google Assistant

I couldn’t have explained it better myself

Andrew decided to take his Google Assistant-enabled Raspberry Pi and create a new body for it. One that was more aesthetically pleasing than the standard Pi-inna-box. After wiring his build and cannibalising some speakers and a microphone, he created a sleek, wooden body that would sit quite comfortably in any Bang & Olufsen shop window.

Find the entire build tutorial on Instructables.

Make your own

It’s more straightforward than Andrew’s explanation suggests, we promise! And with an array of useful resources online, you should be able to incorporate your choice of virtual assistants into your build.

There’s The Raspberry Pi Guy’s tutorial on setting up Amazon Alexa on the Raspberry Pi. If you’re looking to use Siri on your Pi, YouTube has a plethora of tutorials waiting for you. And lastly, check out Microsoft’s site for using Cortana on the Pi!

If you’re looking for more information on Google Assistant, check out issue 57 of The MagPi Magazine, free to download as a PDF. The print edition of this issue came with a free AIY Projects Voice Kit, and you can sign up for The MagPi newsletter to be the first to know about the kit’s availability for purchase.

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