Tag Archives: apt

Roguelike Simulator

Post Syndicated from Eevee original https://eev.ee/release/2017/12/09/roguelike-simulator/

Screenshot of a monochromatic pixel-art game designed to look mostly like ASCII text

On a recent game night, glip and I stumbled upon bitsy — a tiny game maker for “games where you can walk around and talk to people and be somewhere.” It’s enough of a genre to have become a top tag on itch, so we flicked through a couple games.

What we found were tiny windows into numerous little worlds, ill-defined yet crisply rendered in chunky two-colored pixels. Indeed, all you can do is walk around and talk to people and be somewhere, but the somewheres are strangely captivating. My favorite was the last days of our castle, with a day on the town in a close second (though it cheated and extended the engine a bit), but there are several hundred of these tiny windows available. Just single, short, minimal, interactive glimpses of an idea.

I’ve been wanting to do more of that, so I gave it a shot today. The result is Roguelike Simulator, a game that condenses the NetHack experience into about ninety seconds.


Constraints breed creativity, and bitsy is practically made of constraints — the only place you can even make any decisions at all is within dialogue trees. There are only three ways to alter the world: the player can step on an ending tile to end the game, step on an exit tile to instantly teleport to a tile on another map (or not), or pick up an item. That’s it. You can’t even implement keys; the best you can do is make an annoying maze of identical rooms, then have an NPC tell you the solution.

In retrospect, a roguelike — a genre practically defined by its randomness — may have been a poor choice.

I had a lot of fun faking it, though, and it worked well enough to fool at least one person for a few minutes! Some choice hacks follow. Probably play the game a couple times before reading them?

  • Each floor reveals itself, of course, by teleporting you between maps with different chunks of the floor visible. I originally intended for this to be much more elaborate, but it turns out to be a huge pain to juggle multiple copies of the same floor layout.

  • Endings can’t be changed or randomized; even the text is static. I still managed to implement multiple variants on the “ascend” ending! See if you can guess how. (It’s not that hard.)

  • There are no Boolean operators, but there are arithmetic operators, so in one place I check whether you have both of two items by multiplying together how many of each you have.

  • Monsters you “defeat” are actually just items you pick up. They’re both drawn in the same color, and you can’t see your inventory, so you can’t tell the difference.

Probably the best part was writing the text, which is all completely ridiculous. I really enjoy writing a lot of quips — which I guess is why I like Twitter — and I’m happy to see they’ve made people laugh!


I think this has been a success! It’s definitely made me more confident about making smaller things — and about taking the first idea I have and just running with it. I’m going to keep an eye out for other micro game engines to play with, too.

The Raspberry Pi Christmas shopping list 2017

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/christmas-shopping-list-2017/

Looking for the perfect Christmas gift for a beloved maker in your life? Maybe you’d like to give a relative or friend a taste of the world of coding and Raspberry Pi? Whatever you’re looking for, the Raspberry Pi Christmas shopping list will point you in the right direction.

An ice-skating Raspberry Pi - The Raspberry Pi Christmas Shopping List 2017

For those getting started

Thinking about introducing someone special to the wonders of Raspberry Pi during the holidays? Although you can set up your Pi with peripherals from around your home, such as a mobile phone charger, your PC’s keyboard, and the old mouse dwelling in an office drawer, a starter kit is a nice all-in-one package for the budding coder.



Check out the starter kits from Raspberry Pi Approved Resellers such as Pimoroni, The Pi Hut, ModMyPi, Adafruit, CanaKit…the list is pretty long. Our products page will direct you to your closest reseller, or you can head to element14 to pick up the official Raspberry Pi Starter Kit.



You can also buy the Raspberry Pi Press’s brand-new Raspberry Pi Beginners Book, which includes a Raspberry Pi Zero W, a case, a ready-made SD card, and adapter cables.

Once you’ve presented a lucky person with their first Raspberry Pi, it’s time for them to spread their maker wings and learn some new skills.

MagPi Essentials books - The Raspberry Pi Christmas Shopping List 2017

To help them along, you could pick your favourite from among the Official Projects Book volume 3, The MagPi Essentials guides, and the brand-new third edition of Carrie Anne Philbin’s Adventures in Raspberry Pi. (She is super excited about this new edition!)

And you can always add a link to our free resources on the gift tag.

For the maker in your life

If you’re looking for something for a confident digital maker, you can’t go wrong with adding to their arsenal of electric and electronic bits and bobs that are no doubt cluttering drawers and boxes throughout their house.



Components such as servomotors, displays, and sensors are staples of the maker world. And when it comes to jumper wires, buttons, and LEDs, one can never have enough.



You could also consider getting your person a soldering iron, some helpings hands, or small tools such as a Dremel or screwdriver set.

And to make their life a little less messy, pop it all inside a Really Useful Box…because they’re really useful.



For kit makers

While some people like to dive into making head-first and to build whatever comes to mind, others enjoy working with kits.



The Naturebytes kit allows you to record the animal visitors of your garden with the help of a camera and a motion sensor. Footage of your local badgers, birds, deer, and more will be saved to an SD card, or tweeted or emailed to you if it’s in range of WiFi.

Cortec Tiny 4WD - The Raspberry Pi Christmas Shopping List 2017

Coretec’s Tiny 4WD is a kit for assembling a Pi Zero–powered remote-controlled robot at home. Not only is the robot adorable, building it also a great introduction to motors and wireless control.



Bare Conductive’s Touch Board Pro Kit offers everything you need to create interactive electronics projects using conductive paint.

Pi Hut Arcade Kit - The Raspberry Pi Christmas Shopping List 2017

Finally, why not help your favourite maker create their own gaming arcade using the Arcade Building Kit from The Pi Hut?

For the reader

For those who like to curl up with a good read, or spend too much of their day on public transport, a book or magazine subscription is the perfect treat.

For makers, hackers, and those interested in new technologies, our brand-new HackSpace magazine and the ever popular community magazine The MagPi are ideal. Both are available via a physical or digital subscription, and new subscribers to The MagPi also receive a free Raspberry Pi Zero W plus case.

Cover of CoderDojo Nano Make your own game

Marc Scott Beginner's Guide to Coding Book

You can also check out other publications from the Raspberry Pi family, including CoderDojo’s new CoderDojo Nano: Make Your Own Game, Eben Upton and Gareth Halfacree’s Raspberry Pi User Guide, and Marc Scott’s A Beginner’s Guide to Coding. And have I mentioned Carrie Anne’s Adventures in Raspberry Pi yet?

Stocking fillers for everyone

Looking for something small to keep your loved ones occupied on Christmas morning? Or do you have to buy a Secret Santa gift for the office tech? Here are some wonderful stocking fillers to fill your boots with this season.

Pi Hut 3D Christmas Tree - The Raspberry Pi Christmas Shopping List 2017

The Pi Hut 3D Xmas Tree: available as both a pre-soldered and a DIY version, this gadget will work with any 40-pin Raspberry Pi and allows you to create your own mini light show.



Google AIY Voice kit: build your own home assistant using a Raspberry Pi, the MagPi Essentials guide, and this brand-new kit. “Google, play Mariah Carey again…”



Pimoroni’s Raspberry Pi Zero W Project Kits offer everything you need, including the Pi, to make your own time-lapse cameras, music players, and more.



The official Raspberry Pi Sense HAT, Camera Module, and cases for the Pi 3 and Pi Zero will complete the collection of any Raspberry Pi owner, while also opening up exciting project opportunities.

STEAM gifts that everyone will love

Awesome Astronauts | Building LEGO’s Women of NASA!

LEGO Idea’s bought out this amazing ‘Women of NASA’ set, and I thought it would be fun to build, play and learn from these inspiring women! First up, let’s discover a little more about Sally Ride and Mae Jemison, two AWESOME ASTRONAUTS!

Treat the kids, and big kids, in your life to the newest LEGO Ideas set, the Women of NASA — starring Nancy Grace Roman, Margaret Hamilton, Sally Ride, and Mae Jemison!



Explore the world of wearables with Pimoroni’s sewable, hackable, wearable, adorable Bearables kits.



Add lights and motors to paper creations with the Activating Origami Kit, available from The Pi Hut.




We all loved Hidden Figures, and the STEAM enthusiast you know will do too. The film’s available on DVD, and you can also buy the original book, along with other fascinating non-fiction such as Rebecca Skloot’s The Immortal Life of Henrietta Lacks, Rachel Ignotofsky’s Women in Science, and Sydney Padua’s (mostly true) The Thrilling Adventures of Lovelace and Babbage.

Have we missed anything?

With so many amazing kits, HATs, and books available from members of the Raspberry Pi community, it’s hard to only pick a few. Have you found something splendid for the maker in your life? Maybe you’ve created your own kit that uses the Raspberry Pi? Share your favourites with us in the comments below or via our social media accounts.

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Movie & TV Companies Tackle Pirate IPTV in Australia Federal Court

Post Syndicated from Andy original https://torrentfreak.com/movie-tv-companies-tackle-pirate-iptv-in-australia-federal-court-171207/

As movie and TV show piracy has migrated from the desktop towards mobile and living room-based devices, copyright holders have found the need to adapt to a new enemy.

Dealing with streaming services is now high on the agenda, with third-party Kodi addons and various Android apps posing the biggest challenge. Alongside is the much less prevalent but rapidly growing pay IPTV market, in which thousands of premium channels are delivered to homes for a relatively small fee.

In Australia, copyright holders are treating these services in much the same way as torrent sites. They feel that if they can force ISPs to block them, the problem can be mitigated. Most recently, movie and TV show giants Village Roadshow, Disney, Universal, Warner Bros, Twentieth Century Fox, and Paramount filed an application targeting HDSubs+, a pirate IPTV operation servicing thousands of Australians.

Filed in October, the application for the injunction targets Australia’s largest ISPs including Telstra, Optus, TPG, and Vocus, plus their subsidiaries. The movie and TV show companies want them to quickly block HDSubs+, to prevent it from reaching its audience.

HDSubs+ IPTV package
However, blocking isn’t particularly straightforward. Due to the way IPTV services are setup a number of domains need to be blocked, including their sales platforms, EPG (electronic program guide), software (such as an Android app), updates, and sundry other services. In HDSubs+ case around ten domains need to be restricted but in court today, Village Roadshow revealed that probably won’t deal with the problem.

HDSubs+ appears to be undergoing some kind of transformation, possibly to mitigate efforts to block it in Australia. ComputerWorld reports that it is now directing subscribers to update to a new version that works in a more evasive manner.

If they agree, HDSubs+ customers are being migrated over to a service called PressPlayPlus. It works in the same way as the old system but no longer uses the domain names cited in Village Roadshow’s injunction application. This means that DNS blocks, the usual weapon of choice for local ISPs, will prove futile.

Village Roadshow says that with this in mind it may be forced to seek enhanced IP address blocking, unless it is granted a speedy hearing for its application. This, in turn, may result in the normally cooperative ISPs returning to court to argue their case.

“If that’s what you want to do, then you’ll have to amend the orders and let the parties know,” Judge John Nicholas said.

“It’s only the former [DNS blocking] that carriage service providers have agreed to in the past.”

As things stand, Village Roadshow will return to court on December 15 for a case management hearing but in the meantime, the Federal Court must deal with another IPTV-related blocking request.

In common with its Australian and US-based counterparts, Hong Kong-based broadcaster Television Broadcasts Limited (TVB) has launched a similar case asking local ISPs to block another IPTV service.

“Television Broadcasts Limited can confirm that we have commenced legal action in Australia to protect our copyright,” a TVB spokesperson told Computerworld.

TVB wants ISPs including Telstra, Optus, Vocus, and TPG plus their subsidiaries to block access to seven Android-based services named as A1, BlueTV, EVPAD, FunTV, MoonBox, Unblock, and hTV5.

Court documents list 21 URLs maintaining the services. They will all need to be blocked by DNS or other means, if the former proves futile. Online reports suggest that there are similarities among the IPTV products listed above. A demo for the FunTV IPTV service is shown below.

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

GPIO expander: access a Pi’s GPIO pins on your PC/Mac

Post Syndicated from Gordon Hollingworth original https://www.raspberrypi.org/blog/gpio-expander/

Use the GPIO pins of a Raspberry Pi Zero while running Debian Stretch on a PC or Mac with our new GPIO expander software! With this tool, you can easily access a Pi Zero’s GPIO pins from your x86 laptop without using SSH, and you can also take advantage of your x86 computer’s processing power in your physical computing projects.

A Raspberry Pi zero connected to a laptop - GPIO expander

What is this magic?

Running our x86 Stretch distribution on a PC or Mac, whether installed on the hard drive or as a live image, is a great way of taking advantage of a well controlled and simple Linux distribution without the need for a Raspberry Pi.

The downside of not using a Pi, however, is that there aren’t any GPIO pins with which your Scratch or Python programs could communicate. This is a shame, because it means you are limited in your physical computing projects.

I was thinking about this while playing around with the Pi Zero’s USB booting capabilities, having seen people employ the Linux gadget USB mode to use the Pi Zero as an Ethernet device. It struck me that, using the udev subsystem, we could create a simple GUI application that automatically pops up when you plug a Pi Zero into your computer’s USB port. Then the Pi Zero could be programmed to turn into an Ethernet-connected computer running pigpio to provide you with remote GPIO pins.

So we went ahead and built this GPIO expander application, and your PC or Mac can now have GPIO pins which are accessible through Scratch or the GPIO Zero Python library. Note that you can only use this tool to access the Pi Zero.

You can also install the application on the Raspberry Pi. Theoretically, you could connect a number of Pi Zeros to a single Pi and (without a USB hub) use a maximum of 140 pins! But I’ve not tested this — one for you, I think…

Making the GPIO expander work

If you’re using a PC or Mac and you haven’t set up x86 Debian Stretch yet, you’ll need to do that first. An easy way to do it is to download a copy of the Stretch release from this page and image it onto a USB stick. Boot from the USB stick (on most computers, you just need to press F10 during booting and select the stick when asked), and then run Stretch directly from the USB key. You can also install it to the hard drive, but be aware that installing it will overwrite anything that was on your hard drive before.

Whether on a Mac, PC, or Pi, boot through to the Stretch desktop, open a terminal window, and install the GPIO expander application:

sudo apt install usbbootgui

Next, plug in your Raspberry Pi Zero (don’t insert an SD card), and after a few seconds the GUI will appear.

A screenshot of the GPIO expander GUI

The Raspberry Pi USB programming GUI

Select GPIO expansion board and click OK. The Pi Zero will now be programmed as a locally connected Ethernet port (if you run ifconfig, you’ll see the new interface usb0 coming up).

What’s really cool about this is that your plugged-in Pi Zero is now running pigpio, which allows you to control its GPIOs through the network interface.

With Scratch 2

To utilise the pins with Scratch 2, just click on the start bar and select Programming > Scratch 2.

In Scratch, click on More Blocks, select Add an Extension, and then click Pi GPIO.

Two new blocks will be added: the first is used to set the output pin, the second is used to get the pin value (it is true if the pin is read high).

This a simple application using a Pibrella I had hanging around:

A screenshot of a Scratch 2 program - GPIO expander

With Python

This is a Python example using the GPIO Zero library to flash an LED:

[email protected]:~ $ export GPIOZERO_PIN_FACTORY=pigpio
[email protected]:~ $ export PIGPIO_ADDR=fe80::1%usb0
[email protected]:~ $ python3
>>> from gpiozero import LED
>>> led = LED(17)
>>> led.blink()
A Raspberry Pi zero connected to a laptop - GPIO expander

The pinout command line tool is your friend

Note that in the code above the IP address of the Pi Zero is an IPv6 address and is shortened to fe80::1%usb0, where usb0 is the network interface created by the first Pi Zero.

With pigs directly

Another option you have is to use the pigpio library and the pigs application and redirect the output to the Pi Zero network port running IPv6. To do this, you’ll first need to set some environment variable for the redirection:

[email protected]:~ $ export PIGPIO_ADDR=fe80::1%usb0
[email protected]:~ $ pigs bc2 0x8000
[email protected]:~ $ pigs bs2 0x8000

With the commands above, you should be able to flash the LED on the Pi Zero.

The secret sauce

I know there’ll be some people out there who would be interested in how we put this together. And I’m sure many people are interested in the ‘buildroot’ we created to run on the Pi Zero — after all, there are lots of things you can create if you’ve got a Pi Zero on the end of a piece of IPv6 string! For a closer look, find the build scripts for the GPIO expander here and the source code for the USB boot GUI here.

And be sure to share your projects built with the GPIO expander by tagging us on social media or posting links in the comments!

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Stretch for PCs and Macs, and a Raspbian update

Post Syndicated from Simon Long original https://www.raspberrypi.org/blog/stretch-pcs-macs-raspbian-update/

Today, we are launching the first Debian Stretch release of the Raspberry Pi Desktop for PCs and Macs, and we’re also releasing the latest version of Raspbian Stretch for your Pi.

Raspberry Pi Desktop Stretch splash screen

For PCs and Macs

When we released our custom desktop environment on Debian for PCs and Macs last year, we were slightly taken aback by how popular it turned out to be. We really only created it as a result of one of those “Wouldn’t it be cool if…” conversations we sometimes have in the office, so we were delighted by the Pi community’s reaction.

Seeing how keen people were on the x86 version, we decided that we were going to try to keep releasing it alongside Raspbian, with the ultimate aim being to make simultaneous releases of both. This proved to be tricky, particularly with the move from the Jessie version of Debian to the Stretch version this year. However, we have now finished the job of porting all the custom code in Raspbian Stretch to Debian, and so the first Debian Stretch release of the Raspberry Pi Desktop for your PC or Mac is available from today.

The new Stretch releases

As with the Jessie release, you can either run this as a live image from a DVD, USB stick, or SD card or install it as the native operating system on the hard drive of an old laptop or desktop computer. Please note that installing this software will erase anything else on the hard drive — do not install this over a machine running Windows or macOS that you still need to use for its original purpose! It is, however, safe to boot a live image on such a machine, since your hard drive will not be touched by this.

We’re also pleased to announce that we are releasing the latest version of Raspbian Stretch for your Pi today. The Pi and PC versions are largely identical: as before, there are a few applications (such as Mathematica) which are exclusive to the Pi, but the user interface, desktop, and most applications will be exactly the same.

For Raspbian, this new release is mostly bug fixes and tweaks over the previous Stretch release, but there are one or two changes you might notice.

File manager

The file manager included as part of the LXDE desktop (on which our desktop is based) is a program called PCManFM, and it’s very feature-rich; there’s not much you can’t do in it. However, having used it for a few years, we felt that it was perhaps more complex than it needed to be — the sheer number of menu options and choices made some common operations more awkward than they needed to be. So to try to make file management easier, we have implemented a cut-down mode for the file manager.

Raspberry Pi Desktop Stretch - file manager

Most of the changes are to do with the menus. We’ve removed a lot of options that most people are unlikely to change, and moved some other options into the Preferences screen rather than the menus. The two most common settings people tend to change — how icons are displayed and sorted — are now options on the toolbar and in a top-level menu rather than hidden away in submenus.

The sidebar now only shows a single hierarchical view of the file system, and we’ve tidied the toolbar and updated the icons to make them match our house style. We’ve removed the option for a tabbed interface, and we’ve stomped a few bugs as well.

One final change was to make it possible to rename a file just by clicking on its icon to highlight it, and then clicking on its name. This is the way renaming works on both Windows and macOS, and it’s always seemed slightly awkward that Unix desktop environments tend not to support it.

As with most of the other changes we’ve made to the desktop over the last few years, the intention is to make it simpler to use, and to ease the transition from non-Unix environments. But if you really don’t like what we’ve done and long for the old file manager, just untick the box for Display simplified user interface and menus in the Layout page of Preferences, and everything will be back the way it was!

Raspberry Pi Desktop Stretch - preferences GUI

Battery indicator for laptops

One important feature missing from the previous release was an indication of the amount of battery life. Eben runs our desktop on his Mac, and he was becoming slightly irritated by having to keep rebooting into macOS just to check whether his battery was about to die — so fixing this was a priority!

We’ve added a battery status icon to the taskbar; this shows current percentage charge, along with whether the battery is charging, discharging, or connected to the mains. When you hover over the icon with the mouse pointer, a tooltip with more details appears, including the time remaining if the battery can provide this information.

Raspberry Pi Desktop Stretch - battery indicator

While this battery monitor is mainly intended for the PC version, it also supports the first-generation pi-top — to see it, you’ll only need to make sure that I2C is enabled in Configuration. A future release will support the new second-generation pi-top.

New PC applications

We have included a couple of new applications in the PC version. One is called PiServer — this allows you to set up an operating system, such as Raspbian, on the PC which can then be shared by a number of Pi clients networked to it. It is intended to make it easy for classrooms to have multiple Pis all running exactly the same software, and for the teacher to have control over how the software is installed and used. PiServer is quite a clever piece of software, and it’ll be covered in more detail in another blog post in December.

We’ve also added an application which allows you to easily use the GPIO pins of a Pi Zero connected via USB to a PC in applications using Scratch or Python. This makes it possible to run the same physical computing projects on the PC as you do on a Pi! Again, we’ll tell you more in a separate blog post this month.

Both of these applications are included as standard on the PC image, but not on the Raspbian image. You can run them on a Pi if you want — both can be installed from apt.

How to get the new versions

New images for both Raspbian and Debian versions are available from the Downloads page.

It is possible to update existing installations of both Raspbian and Debian versions. For Raspbian, this is easy: just open a terminal window and enter

sudo apt-get update
sudo apt-get dist-upgrade

Updating Raspbian on your Raspberry Pi

How to update to the latest version of Raspbian on your Raspberry Pi. Download Raspbian here: More information on the latest version of Raspbian: Buy a Raspberry Pi:

It is slightly more complex for the PC version, as the previous release was based around Debian Jessie. You will need to edit the files /etc/apt/sources.list and /etc/apt/sources.list.d/raspi.list, using sudo to do so. In both files, change every occurrence of the word “jessie” to “stretch”. When that’s done, do the following:

sudo apt-get update 
sudo dpkg --force-depends -r libwebkitgtk-3.0-common
sudo apt-get -f install
sudo apt-get dist-upgrade
sudo apt-get install python3-thonny
sudo apt-get install sonic-pi=2.10.0~repack-rpt1+2
sudo apt-get install piserver
sudo apt-get install usbbootgui

At several points during the upgrade process, you will be asked if you want to keep the current version of a configuration file or to install the package maintainer’s version. In every case, keep the existing version, which is the default option. The update may take an hour or so, depending on your network connection.

As with all software updates, there is the possibility that something may go wrong during the process, which could lead to your operating system becoming corrupted. Therefore, we always recommend making a backup first.

Enjoy the new versions, and do let us know any feedback you have in the comments or on the forums!

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Seven Years of Hadopi: Nine Million Piracy Warnings, 189 Convictions

Post Syndicated from Andy original https://torrentfreak.com/seven-years-of-hadopi-nine-million-piracy-warnings-189-convictions-171201/

More than seven years ago, it was predicted that the next big thing in anti-piracy enforcement would be the graduated response scheme.

Commonly known as “three strikes” or variants thereof, these schemes were promoted as educational in nature, with alleged pirates receiving escalating warnings designed to discourage further infringing behavior.

In the fall of 2010, France became one of the pioneers of the warning system and now almost more than seven years later, a new report from the country’s ‘Hadopi’ anti-piracy agency has revealed the extent of its operations.

Between July 2016 and June 2017, Hadopi sent a total of 889 cases to court, a 30% uplift on the 684 cases handed over during the same period 2015/2016. This boost is notable, not least since the use of peer-to-peer protocols (such as BitTorrent, which Hadopi closely monitors) is declining in favor of streaming methods.

When all the seven years of the scheme are added together ending August 31, 2017, the numbers are even more significant.

“Since the launch of the graduated response scheme, more than 2,000 cases have been sent to prosecutors for possible prosecution,” Hadopi’s report reads.

“The number of cases sent to the prosecutor’s office has increased every year, with a significant increase in the last two years. Three-quarters of all the cases sent to prosecutors have been sent since July 2015.”

In all, the Hadopi agency has sent more than nine million first warning notices to alleged pirates since 2012, with more than 800,000 follow-up warnings on top, 200,000 of them during 2016-2017. But perhaps of most interest is the number of French citizens who, despite all the warnings, carried on with their pirating behavior and ended up prosecuted as a result.

Since the program’s inception, 583 court decisions have been handed down against pirates. While 394 of them resulted in a small fine, a caution, or other community-based punishment, 189 citizens walked away with a criminal conviction.

These can include fines of up to 1,500 euros or in more extreme cases, up to three years in prison and/or a 300,000 euro fine.

While this approach looks set to continue into 2018, Hadopi’s report highlights the need to adapt to a changing piracy landscape, one which requires a multi-faceted approach. In addition to tracking pirates, Hadopi also has a mission to promote legal offerings while educating the public. However, it is fully aware that these strategies alone won’t be enough.

To that end, the agency is calling for broader action, such as faster blocking of sites, expanding to the blocking of mirror sites, tackling unauthorized streaming platforms and, of course, dealing with the “fully-loaded” set-top box phenomenon that’s been sweeping the world for the past two years.

The full report can be downloaded here (pdf, French)

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

NSA "Red Disk" Data Leak

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2017/11/nsa_red_disk_da.html

ZDNet is reporting about another data leak, this one from US Army’s Intelligence and Security Command (INSCOM), which is also within to the NSA.

The disk image, when unpacked and loaded, is a snapshot of a hard drive dating back to May 2013 from a Linux-based server that forms part of a cloud-based intelligence sharing system, known as Red Disk. The project, developed by INSCOM’s Futures Directorate, was slated to complement the Army’s so-called distributed common ground system (DCGS), a legacy platform for processing and sharing intelligence, surveillance, and reconnaissance information.

[…]

Red Disk was envisioned as a highly customizable cloud system that could meet the demands of large, complex military operations. The hope was that Red Disk could provide a consistent picture from the Pentagon to deployed soldiers in the Afghan battlefield, including satellite images and video feeds from drones trained on terrorists and enemy fighters, according to a Foreign Policy report.

[…]

Red Disk was a modular, customizable, and scalable system for sharing intelligence across the battlefield, like electronic intercepts, drone footage and satellite imagery, and classified reports, for troops to access with laptops and tablets on the battlefield. Marking files found in several directories imply the disk is “top secret,” and restricted from being shared to foreign intelligence partners.

A couple of points. One, this isn’t particularly sensitive. It’s an intelligence distribution system under development. It’s not raw intelligence. Two, this doesn’t seem to be classified data. Even the article hedges, using the unofficial term of “highly sensitive.” Three, it doesn’t seem that Chris Vickery, the researcher that discovered the data, has published it.

Chris Vickery, director of cyber risk research at security firm UpGuard, found the data and informed the government of the breach in October. The storage server was subsequently secured, though its owner remains unknown.

This doesn’t feel like a big deal to me.

Slashdot thread.

MagPi 64: get started with electronics

Post Syndicated from Rob Zwetsloot original https://www.raspberrypi.org/blog/magpi-64/

Hey folks, Rob here again! You get a double dose of me this month, as today marks the release of The MagPi 64. In this issue we give you a complete electronics starter guide to help you learn how to make circuits that connect to your Raspberry Pi!

The front cover of MagPi 64

MAGPI SIXTY-FOOUUUR!

Wires, wires everywhere!

In the electronics feature, we’ll teach you how to identify different components in circuit diagrams, we’ll explain what they do, and we’ll give you some basic wiring instructions so you can take your first steps. The feature also includes step-by-step tutorials on how to make a digital radio and a range-finder, meaning you can test out your new electronics skills immediately!

Christmas tutorials

Electronics are cool, but what else is in this issue? Well, we have exciting news about the next Google AIY Projects Vision kit, which forgoes audio for images, allowing you to build a smart camera with your Raspberry Pi.

We’ve also included guides on how to create your own text-based adventure game and a kaleidoscope camera. And, just in time for the festive season, there’s a tutorial for making a 3D-printed Pi-powered Christmas tree star. All this in The MagPi 64, along with project showcases, reviews, and much more!

Kaleido Cam

Using a normal web cam or the Raspberry Pi camera produce real time live kaleidoscope effects with the Raspberry Pi. This video shows the normal mode, along with an auto pre-rotate, and a horizontal and vertical flip.

Get The MagPi 64

Issue 64 is available today from WHSmith, Tesco, Sainsbury’s, and Asda. If you live in the US, head over to your local Barnes & Noble or Micro Center in the next few days. You can also get the new issue online from our store, or digitally via our Android and iOS apps. And don’t forget, there’s always the free PDF as well.

Subscribe for free goodies

Want to support the Raspberry Pi Foundation and the magazine, and get some cool free stuff? If you take out a twelve-month print subscription to The MagPi, you’ll get a Pi Zero W, Pi Zero case, and adapter cables absolutely free! This offer does not currently have an end date.

We hope you enjoy this issue!

Nintendo Sixty-FOOOOOOOOOOUR

Brandon gets an n64 for christmas 1998 and gets way too excited inquiries about usage / questions / comments? [email protected] © n64kids.com

The post MagPi 64: get started with electronics appeared first on Raspberry Pi.

Warrant Protections against Police Searches of Our Data

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2017/11/warrant_protect.html

The cell phones we carry with us constantly are the most perfect surveillance device ever invented, and our laws haven’t caught up to that reality. That might change soon.

This week, the Supreme Court will hear a case with profound implications on your security and privacy in the coming years. The Fourth Amendment’s prohibition of unlawful search and seizure is a vital right that protects us all from police overreach, and the way the courts interpret it is increasingly nonsensical in our computerized and networked world. The Supreme Court can either update current law to reflect the world, or it can further solidify an unnecessary and dangerous police power.

The case centers on cell phone location data and whether the police need a warrant to get it, or if they can use a simple subpoena, which is easier to obtain. Current Fourth Amendment doctrine holds that you lose all privacy protections over any data you willingly share with a third party. Your cellular provider, under this interpretation, is a third party with whom you’ve willingly shared your movements, 24 hours a day, going back months — even though you don’t really have any choice about whether to share with them. So police can request records of where you’ve been from cell carriers without any judicial oversight. The case before the court, Carpenter v. United States, could change that.

Traditionally, information that was most precious to us was physically close to us. It was on our bodies, in our homes and offices, in our cars. Because of that, the courts gave that information extra protections. Information that we stored far away from us, or gave to other people, afforded fewer protections. Police searches have been governed by the “third-party doctrine,” which explicitly says that information we share with others is not considered private.

The Internet has turned that thinking upside-down. Our cell phones know who we talk to and, if we’re talking via text or e-mail, what we say. They track our location constantly, so they know where we live and work. Because they’re the first and last thing we check every day, they know when we go to sleep and when we wake up. Because everyone has one, they know whom we sleep with. And because of how those phones work, all that information is naturally shared with third parties.

More generally, all our data is literally stored on computers belonging to other people. It’s our e-mail, text messages, photos, Google docs, and more ­ all in the cloud. We store it there not because it’s unimportant, but precisely because it is important. And as the Internet of Things computerizes the rest our lives, even more data will be collected by other people: data from our health trackers and medical devices, data from our home sensors and appliances, data from Internet-connected “listeners” like Alexa, Siri, and your voice-activated television.

All this data will be collected and saved by third parties, sometimes for years. The result is a detailed dossier of your activities more complete than any private investigator –­ or police officer –­ could possibly collect by following you around.

The issue here is not whether the police should be allowed to use that data to help solve crimes. Of course they should. The issue is whether that information should be protected by the warrant process that requires the police to have probable cause to investigate you and get approval by a court.

Warrants are a security mechanism. They prevent the police from abusing their authority to investigate someone they have no reason to suspect of a crime. They prevent the police from going on “fishing expeditions.” They protect our rights and liberties, even as we willingly give up our privacy to the legitimate needs of law enforcement.

The third-party doctrine never made a lot of sense. Just because I share an intimate secret with my spouse, friend, or doctor doesn’t mean that I no longer consider it private. It makes even less sense in today’s hyper-connected world. It’s long past time the Supreme Court recognized that a months’-long history of my movements is private, and my e-mails and other personal data deserve the same protections, whether they’re on my laptop or on Google’s servers.

This essay previously appeared in the Washington Post.

Details on the case. Two opinion pieces.

I signed on to two amicus briefs on the case.

EDITED TO ADD (12/1): Good commentary on the Supreme Court oral arguments.

Amazon EC2 Update – Streamlined Access to Spot Capacity, Smooth Price Changes, Instance Hibernation

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/amazon-ec2-update-streamlined-access-to-spot-capacity-smooth-price-changes-instance-hibernation/

EC2 Spot Instances give you access to spare compute capacity in the AWS Cloud. Our customers use fleets of Spot Instances to power their CI/CD environments & traffic generators, host web servers & microservices, render movies, and to run many types of analytics jobs, all at prices that offer significant savings in comparison to On-Demand Instances.

New Streamlined Access
Today we are introducing a new, streamlined access model for Spot Instances. You simply indicate your desire to use Spot capacity when you launch an instance via the RunInstances function, the run-instances command, or the AWS Management Console to submit a request that will be fulfilled as long as the capacity is available. With no extra effort on your part you’ll save up to 90% off of the On-Demand price for the instance type, allowing you to boost your overall application throughput by up to 10x for the same budget. The instances that you launch in this way will continue to run until you terminate them or if EC2 needs to reclaim them for On-Demand usage. At that point the instance will be given the usual 2-minute warning and then reclaimed, making this a great fit for applications that are fault-tolerant.

Unlike the old model which required an understanding of Spot markets, bidding, and calls to a standalone asynchronous API, the new model is synchronous and as easy to use as On-Demand. Your code or your script receives an Instance ID immediately and need not check back to see if the request has been processed and accepted.

We’ve made this as clean and as simple as possible, with the expectation that it will be easy to modify many current scripts and applications to request and make use of Spot capacity. If you want to exercise additional control over your Spot instance budget, you have the option to specify a maximum price when you make a request for capacity. If you use Spot capacity to power your Amazon EMR, Amazon ECS, or AWS Batch clusters, or if you launch Spot instances by way of a AWS CloudFormation template or Auto Scaling Group, you will benefit from this new model without having to make any changes.

Applications that are built around RequestSpotInstances or RequestSpotFleet will continue to work just fine with no changes. However, you now have the option to make requests that do not include the SpotPrice parameter.

Smooth Price Changes
As part of today’s launch we are also changing the way that Spot prices change, moving to a model where prices adjust more gradually, based on longer-term trends in supply and demand. As I mentioned earlier, you will continue to save an average of 70-90% off the On-Demand price, and you will continue to pay the Spot price that’s in effect for the time period your instances are running. Applications built around our Spot Fleet feature will continue to automatically diversify placement of their Spot Instances across the most cost-effective pools based on the configuration you specified when you created the fleet.

Spot in Action
To launch a Spot Instance from the command line; simply specify the Spot market:

$ aws ec2 run-instances –-market Spot --image-id ami-1a2b3c4d --count 1 --instance-type c3.large 

Instance Hibernation
If you run workloads that keep a lot of state in memory, you will love this new feature!

You can arrange for instances to save their in-memory state when they are reclaimed, allowing the instances and the applications on them to pick up where they left off when capacity is once again available, just like closing and then opening your laptop. This feature works on C3, C4, and certain sizes of R3, R4, and M4 instances running Amazon Linux, Ubuntu, or Windows Server, and is supported by the EC2 Hibernation Agent.

The in-memory state is written to the root EBS volume of the instance using space that is set-aside when the instance launches. The private IP address and any Elastic IP Addresses are also preserved across a stop/start cycle.

Jeff;

Potential impact of the Intel ME vulnerability

Post Syndicated from Matthew Garrett original https://mjg59.dreamwidth.org/49611.html

(Note: this is my personal opinion based on public knowledge around this issue. I have no knowledge of any non-public details of these vulnerabilities, and this should not be interpreted as the position or opinion of my employer)

Intel’s Management Engine (ME) is a small coprocessor built into the majority of Intel CPUs[0]. Older versions were based on the ARC architecture[1] running an embedded realtime operating system, but from version 11 onwards they’ve been small x86 cores running Minix. The precise capabilities of the ME have not been publicly disclosed, but it is at minimum capable of interacting with the network[2], display[3], USB, input devices and system flash. In other words, software running on the ME is capable of doing a lot, without requiring any OS permission in the process.

Back in May, Intel announced a vulnerability in the Advanced Management Technology (AMT) that runs on the ME. AMT offers functionality like providing a remote console to the system (so IT support can connect to your system and interact with it as if they were physically present), remote disk support (so IT support can reinstall your machine over the network) and various other bits of system management. The vulnerability meant that it was possible to log into systems with enabled AMT with an empty authentication token, making it possible to log in without knowing the configured password.

This vulnerability was less serious than it could have been for a couple of reasons – the first is that “consumer”[4] systems don’t ship with AMT, and the second is that AMT is almost always disabled (Shodan found only a few thousand systems on the public internet with AMT enabled, out of many millions of laptops). I wrote more about it here at the time.

How does this compare to the newly announced vulnerabilities? Good question. Two of the announced vulnerabilities are in AMT. The previous AMT vulnerability allowed you to bypass authentication, but restricted you to doing what AMT was designed to let you do. While AMT gives an authenticated user a great deal of power, it’s also designed with some degree of privacy protection in mind – for instance, when the remote console is enabled, an animated warning border is drawn on the user’s screen to alert them.

This vulnerability is different in that it allows an authenticated attacker to execute arbitrary code within the AMT process. This means that the attacker shouldn’t have any capabilities that AMT doesn’t, but it’s unclear where various aspects of the privacy protection are implemented – for instance, if the warning border is implemented in AMT rather than in hardware, an attacker could duplicate that functionality without drawing the warning. If the USB storage emulation for remote booting is implemented as a generic USB passthrough, the attacker could pretend to be an arbitrary USB device and potentially exploit the operating system through bugs in USB device drivers. Unfortunately we don’t currently know.

Note that this exploit still requires two things – first, AMT has to be enabled, and second, the attacker has to be able to log into AMT. If the attacker has physical access to your system and you don’t have a BIOS password set, they will be able to enable it – however, if AMT isn’t enabled and the attacker isn’t physically present, you’re probably safe. But if AMT is enabled and you haven’t patched the previous vulnerability, the attacker will be able to access AMT over the network without a password and then proceed with the exploit. This is bad, so you should probably (1) ensure that you’ve updated your BIOS and (2) ensure that AMT is disabled unless you have a really good reason to use it.

The AMT vulnerability applies to a wide range of versions, everything from version 6 (which shipped around 2008) and later. The other vulnerability that Intel describe is restricted to version 11 of the ME, which only applies to much more recent systems. This vulnerability allows an attacker to execute arbitrary code on the ME, which means they can do literally anything the ME is able to do. This probably also means that they are able to interfere with any other code running on the ME. While AMT has been the most frequently discussed part of this, various other Intel technologies are tied to ME functionality.

Intel’s Platform Trust Technology (PTT) is a software implementation of a Trusted Platform Module (TPM) that runs on the ME. TPMs are intended to protect access to secrets and encryption keys and record the state of the system as it boots, making it possible to determine whether a system has had part of its boot process modified and denying access to the secrets as a result. The most common usage of TPMs is to protect disk encryption keys – Microsoft Bitlocker defaults to storing its encryption key in the TPM, automatically unlocking the drive if the boot process is unmodified. In addition, TPMs support something called Remote Attestation (I wrote about that here), which allows the TPM to provide a signed copy of information about what the system booted to a remote site. This can be used for various purposes, such as not allowing a compute node to join a cloud unless it’s booted the correct version of the OS and is running the latest firmware version. Remote Attestation depends on the TPM having a unique cryptographic identity that is tied to the TPM and inaccessible to the OS.

PTT allows manufacturers to simply license some additional code from Intel and run it on the ME rather than having to pay for an additional chip on the system motherboard. This seems great, but if an attacker is able to run code on the ME then they potentially have the ability to tamper with PTT, which means they can obtain access to disk encryption secrets and circumvent Bitlocker. It also means that they can tamper with Remote Attestation, “attesting” that the system booted a set of software that it didn’t or copying the keys to another system and allowing that to impersonate the first. This is, uh, bad.

Intel also recently announced Intel Online Connect, a mechanism for providing the functionality of security keys directly in the operating system. Components of this are run on the ME in order to avoid scenarios where a compromised OS could be used to steal the identity secrets – if the ME is compromised, this may make it possible for an attacker to obtain those secrets and duplicate the keys.

It’s also not entirely clear how much of Intel’s Secure Guard Extensions (SGX) functionality depends on the ME. The ME does appear to be required for SGX Remote Attestation (which allows an application using SGX to prove to a remote site that it’s the SGX app rather than something pretending to be it), and again if those secrets can be extracted from a compromised ME it may be possible to compromise some of the security assumptions around SGX. Again, it’s not clear how serious this is because it’s not publicly documented.

Various other things also run on the ME, including stuff like video DRM (ensuring that high resolution video streams can’t be intercepted by the OS). It may be possible to obtain encryption keys from a compromised ME that allow things like Netflix streams to be decoded and dumped. From a user privacy or security perspective, these things seem less serious.

The big problem at the moment is that we have no idea what the actual process of compromise is. Intel state that it requires local access, but don’t describe what kind. Local access in this case could simply require the ability to send commands to the ME (possible on any system that has the ME drivers installed), could require direct hardware access to the exposed ME (which would require either kernel access or the ability to install a custom driver) or even the ability to modify system flash (possible only if the attacker has physical access and enough time and skill to take the system apart and modify the flash contents with an SPI programmer). The other thing we don’t know is whether it’s possible for an attacker to modify the system such that the ME is persistently compromised or whether it needs to be re-compromised every time the ME reboots. Note that even the latter is more serious than you might think – the ME may only be rebooted if the system loses power completely, so even a “temporary” compromise could affect a system for a long period of time.

It’s also almost impossible to determine if a system is compromised. If the ME is compromised then it’s probably possible for it to roll back any firmware updates but still report that it’s been updated, giving admins a false sense of security. The only way to determine for sure would be to dump the system flash and compare it to a known good image. This is impractical to do at scale.

So, overall, given what we know right now it’s hard to say how serious this is in terms of real world impact. It’s unlikely that this is the kind of vulnerability that would be used to attack individual end users – anyone able to compromise a system like this could just backdoor your browser instead with much less effort, and that already gives them your banking details. The people who have the most to worry about here are potential targets of skilled attackers, which means activists, dissidents and companies with interesting personal or business data. It’s hard to make strong recommendations about what to do here without more insight into what the vulnerability actually is, and we may not know that until this presentation next month.

Summary: Worst case here is terrible, but unlikely to be relevant to the vast majority of users.

[0] Earlier versions of the ME were built into the motherboard chipset, but as portions of that were incorporated onto the CPU package the ME followed
[1] A descendent of the SuperFX chip used in Super Nintendo cartridges such as Starfox, because why not
[2] Without any OS involvement for wired ethernet and for wireless networks in the system firmware, but requires OS support for wireless access once the OS drivers have loaded
[3] Assuming you’re using integrated Intel graphics
[4] “Consumer” is a bit of a misnomer here – “enterprise” laptops like Thinkpads ship with AMT, but are often bought by consumers.

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Using Amazon Redshift Spectrum, Amazon Athena, and AWS Glue with Node.js in Production

Post Syndicated from Rafi Ton original https://aws.amazon.com/blogs/big-data/using-amazon-redshift-spectrum-amazon-athena-and-aws-glue-with-node-js-in-production/

This is a guest post by Rafi Ton, founder and CEO of NUVIAD. NUVIAD is, in their own words, “a mobile marketing platform providing professional marketers, agencies and local businesses state of the art tools to promote their products and services through hyper targeting, big data analytics and advanced machine learning tools.”

At NUVIAD, we’ve been using Amazon Redshift as our main data warehouse solution for more than 3 years.

We store massive amounts of ad transaction data that our users and partners analyze to determine ad campaign strategies. When running real-time bidding (RTB) campaigns in large scale, data freshness is critical so that our users can respond rapidly to changes in campaign performance. We chose Amazon Redshift because of its simplicity, scalability, performance, and ability to load new data in near real time.

Over the past three years, our customer base grew significantly and so did our data. We saw our Amazon Redshift cluster grow from three nodes to 65 nodes. To balance cost and analytics performance, we looked for a way to store large amounts of less-frequently analyzed data at a lower cost. Yet, we still wanted to have the data immediately available for user queries and to meet their expectations for fast performance. We turned to Amazon Redshift Spectrum.

In this post, I explain the reasons why we extended Amazon Redshift with Redshift Spectrum as our modern data warehouse. I cover how our data growth and the need to balance cost and performance led us to adopt Redshift Spectrum. I also share key performance metrics in our environment, and discuss the additional AWS services that provide a scalable and fast environment, with data available for immediate querying by our growing user base.

Amazon Redshift as our foundation

The ability to provide fresh, up-to-the-minute data to our customers and partners was always a main goal with our platform. We saw other solutions provide data that was a few hours old, but this was not good enough for us. We insisted on providing the freshest data possible. For us, that meant loading Amazon Redshift in frequent micro batches and allowing our customers to query Amazon Redshift directly to get results in near real time.

The benefits were immediately evident. Our customers could see how their campaigns performed faster than with other solutions, and react sooner to the ever-changing media supply pricing and availability. They were very happy.

However, this approach required Amazon Redshift to store a lot of data for long periods, and our data grew substantially. In our peak, we maintained a cluster running 65 DC1.large nodes. The impact on our Amazon Redshift cluster was evident, and we saw our CPU utilization grow to 90%.

Why we extended Amazon Redshift to Redshift Spectrum

Redshift Spectrum gives us the ability to run SQL queries using the powerful Amazon Redshift query engine against data stored in Amazon S3, without needing to load the data. With Redshift Spectrum, we store data where we want, at the cost that we want. We have the data available for analytics when our users need it with the performance they expect.

Seamless scalability, high performance, and unlimited concurrency

Scaling Redshift Spectrum is a simple process. First, it allows us to leverage Amazon S3 as the storage engine and get practically unlimited data capacity.

Second, if we need more compute power, we can leverage Redshift Spectrum’s distributed compute engine over thousands of nodes to provide superior performance – perfect for complex queries running against massive amounts of data.

Third, all Redshift Spectrum clusters access the same data catalog so that we don’t have to worry about data migration at all, making scaling effortless and seamless.

Lastly, since Redshift Spectrum distributes queries across potentially thousands of nodes, they are not affected by other queries, providing much more stable performance and unlimited concurrency.

Keeping it SQL

Redshift Spectrum uses the same query engine as Amazon Redshift. This means that we did not need to change our BI tools or query syntax, whether we used complex queries across a single table or joins across multiple tables.

An interesting capability introduced recently is the ability to create a view that spans both Amazon Redshift and Redshift Spectrum external tables. With this feature, you can query frequently accessed data in your Amazon Redshift cluster and less-frequently accessed data in Amazon S3, using a single view.

Leveraging Parquet for higher performance

Parquet is a columnar data format that provides superior performance and allows Redshift Spectrum (or Amazon Athena) to scan significantly less data. With less I/O, queries run faster and we pay less per query. You can read all about Parquet at https://parquet.apache.org/ or https://en.wikipedia.org/wiki/Apache_Parquet.

Lower cost

From a cost perspective, we pay standard rates for our data in Amazon S3, and only small amounts per query to analyze data with Redshift Spectrum. Using the Parquet format, we can significantly reduce the amount of data scanned. Our costs are now lower, and our users get fast results even for large complex queries.

What we learned about Amazon Redshift vs. Redshift Spectrum performance

When we first started looking at Redshift Spectrum, we wanted to put it to the test. We wanted to know how it would compare to Amazon Redshift, so we looked at two key questions:

  1. What is the performance difference between Amazon Redshift and Redshift Spectrum on simple and complex queries?
  2. Does the data format impact performance?

During the migration phase, we had our dataset stored in Amazon Redshift and S3 as CSV/GZIP and as Parquet file formats. We tested three configurations:

  • Amazon Redshift cluster with 28 DC1.large nodes
  • Redshift Spectrum using CSV/GZIP
  • Redshift Spectrum using Parquet

We performed benchmarks for simple and complex queries on one month’s worth of data. We tested how much time it took to perform the query, and how consistent the results were when running the same query multiple times. The data we used for the tests was already partitioned by date and hour. Properly partitioning the data improves performance significantly and reduces query times.

Simple query

First, we tested a simple query aggregating billing data across a month:

SELECT 
  user_id, 
  count(*) AS impressions, 
  SUM(billing)::decimal /1000000 AS billing 
FROM <table_name> 
WHERE 
  date >= '2017-08-01' AND 
  date <= '2017-08-31'  
GROUP BY 
  user_id;

We ran the same query seven times and measured the response times (red marking the longest time and green the shortest time):

Execution Time (seconds)
  Amazon Redshift Redshift Spectrum
CSV
Redshift Spectrum Parquet
Run #1 39.65 45.11 11.92
Run #2 15.26 43.13 12.05
Run #3 15.27 46.47 13.38
Run #4 21.22 51.02 12.74
Run #5 17.27 43.35 11.76
Run #6 16.67 44.23 13.67
Run #7 25.37 40.39 12.75
Average 21.53  44.82 12.61

For simple queries, Amazon Redshift performed better than Redshift Spectrum, as we thought, because the data is local to Amazon Redshift.

What was surprising was that using Parquet data format in Redshift Spectrum significantly beat ‘traditional’ Amazon Redshift performance. For our queries, using Parquet data format with Redshift Spectrum delivered an average 40% performance gain over traditional Amazon Redshift. Furthermore, Redshift Spectrum showed high consistency in execution time with a smaller difference between the slowest run and the fastest run.

Comparing the amount of data scanned when using CSV/GZIP and Parquet, the difference was also significant:

Data Scanned (GB)
CSV (Gzip) 135.49
Parquet 2.83

Because we pay only for the data scanned by Redshift Spectrum, the cost saving of using Parquet is evident and substantial.

Complex query

Next, we compared the same three configurations with a complex query.

Execution Time (seconds)
  Amazon Redshift Redshift Spectrum CSV Redshift Spectrum Parquet
Run #1 329.80 84.20 42.40
Run #2 167.60 65.30 35.10
Run #3 165.20 62.20 23.90
Run #4 273.90 74.90 55.90
Run #5 167.70 69.00 58.40
Average 220.84 71.12 43.14

This time, Redshift Spectrum using Parquet cut the average query time by 80% compared to traditional Amazon Redshift!

Bottom line: For complex queries, Redshift Spectrum provided a 67% performance gain over Amazon Redshift. Using the Parquet data format, Redshift Spectrum delivered an 80% performance improvement over Amazon Redshift. For us, this was substantial.

Optimizing the data structure for different workloads

Because the cost of S3 is relatively inexpensive and we pay only for the data scanned by each query, we believe that it makes sense to keep our data in different formats for different workloads and different analytics engines. It is important to note that we can have any number of tables pointing to the same data on S3. It all depends on how we partition the data and update the table partitions.

Data permutations

For example, we have a process that runs every minute and generates statistics for the last minute of data collected. With Amazon Redshift, this would be done by running the query on the table with something as follows:

SELECT 
  user, 
  COUNT(*) 
FROM 
  events_table 
WHERE 
  ts BETWEEN ‘2017-08-01 14:00:00’ AND ‘2017-08-01 14:00:59’ 
GROUP BY 
  user;

(Assuming ‘ts’ is your column storing the time stamp for each event.)

With Redshift Spectrum, we pay for the data scanned in each query. If the data is partitioned by the minute instead of the hour, a query looking at one minute would be 1/60th the cost. If we use a temporary table that points only to the data of the last minute, we save that unnecessary cost.

Creating Parquet data efficiently

On the average, we have 800 instances that process our traffic. Each instance sends events that are eventually loaded into Amazon Redshift. When we started three years ago, we would offload data from each server to S3 and then perform a periodic copy command from S3 to Amazon Redshift.

Recently, Amazon Kinesis Firehose added the capability to offload data directly to Amazon Redshift. While this is now a viable option, we kept the same collection process that worked flawlessly and efficiently for three years.

This changed, however, when we incorporated Redshift Spectrum. With Redshift Spectrum, we needed to find a way to:

  • Collect the event data from the instances.
  • Save the data in Parquet format.
  • Partition the data effectively.

To accomplish this, we save the data as CSV and then transform it to Parquet. The most effective method to generate the Parquet files is to:

  1. Send the data in one-minute intervals from the instances to Kinesis Firehose with an S3 temporary bucket as the destination.
  2. Aggregate hourly data and convert it to Parquet using AWS Lambda and AWS Glue.
  3. Add the Parquet data to S3 by updating the table partitions.

With this new process, we had to give more attention to validating the data before we sent it to Kinesis Firehose, because a single corrupted record in a partition fails queries on that partition.

Data validation

To store our click data in a table, we considered the following SQL create table command:

create external TABLE spectrum.blog_clicks (
    user_id varchar(50),
    campaign_id varchar(50),
    os varchar(50),
    ua varchar(255),
    ts bigint,
    billing float
)
partitioned by (date date, hour smallint)  
stored as parquet
location 's3://nuviad-temp/blog/clicks/';

The above statement defines a new external table (all Redshift Spectrum tables are external tables) with a few attributes. We stored ‘ts’ as a Unix time stamp and not as Timestamp, and billing data is stored as float and not decimal (more on that later). We also said that the data is partitioned by date and hour, and then stored as Parquet on S3.

First, we need to get the table definitions. This can be achieved by running the following query:

SELECT 
  * 
FROM 
  svv_external_columns 
WHERE 
  tablename = 'blog_clicks';

This query lists all the columns in the table with their respective definitions:

schemaname tablename columnname external_type columnnum part_key
spectrum blog_clicks user_id varchar(50) 1 0
spectrum blog_clicks campaign_id varchar(50) 2 0
spectrum blog_clicks os varchar(50) 3 0
spectrum blog_clicks ua varchar(255) 4 0
spectrum blog_clicks ts bigint 5 0
spectrum blog_clicks billing double 6 0
spectrum blog_clicks date date 7 1
spectrum blog_clicks hour smallint 8 2

Now we can use this data to create a validation schema for our data:

const rtb_request_schema = {
    "name": "clicks",
    "items": {
        "user_id": {
            "type": "string",
            "max_length": 100
        },
        "campaign_id": {
            "type": "string",
            "max_length": 50
        },
        "os": {
            "type": "string",
            "max_length": 50            
        },
        "ua": {
            "type": "string",
            "max_length": 255            
        },
        "ts": {
            "type": "integer",
            "min_value": 0,
            "max_value": 9999999999999
        },
        "billing": {
            "type": "float",
            "min_value": 0,
            "max_value": 9999999999999
        }
    }
};

Next, we create a function that uses this schema to validate data:

function valueIsValid(value, item_schema) {
    if (schema.type == 'string') {
        return (typeof value == 'string' && value.length <= schema.max_length);
    }
    else if (schema.type == 'integer') {
        return (typeof value == 'number' && value >= schema.min_value && value <= schema.max_value);
    }
    else if (schema.type == 'float' || schema.type == 'double') {
        return (typeof value == 'number' && value >= schema.min_value && value <= schema.max_value);
    }
    else if (schema.type == 'boolean') {
        return typeof value == 'boolean';
    }
    else if (schema.type == 'timestamp') {
        return (new Date(value)).getTime() > 0;
    }
    else {
        return true;
    }
}

Near real-time data loading with Kinesis Firehose

On Kinesis Firehose, we created a new delivery stream to handle the events as follows:

Delivery stream name: events
Source: Direct PUT
S3 bucket: nuviad-events
S3 prefix: rtb/
IAM role: firehose_delivery_role_1
Data transformation: Disabled
Source record backup: Disabled
S3 buffer size (MB): 100
S3 buffer interval (sec): 60
S3 Compression: GZIP
S3 Encryption: No Encryption
Status: ACTIVE
Error logging: Enabled

This delivery stream aggregates event data every minute, or up to 100 MB, and writes the data to an S3 bucket as a CSV/GZIP compressed file. Next, after we have the data validated, we can safely send it to our Kinesis Firehose API:

if (validated) {
    let itemString = item.join('|')+'\n'; //Sending csv delimited by pipe and adding new line

    let params = {
        DeliveryStreamName: 'events',
        Record: {
            Data: itemString
        }
    };

    firehose.putRecord(params, function(err, data) {
        if (err) {
            console.error(err, err.stack);        
        }
        else {
            // Continue to your next step 
        }
    });
}

Now, we have a single CSV file representing one minute of event data stored in S3. The files are named automatically by Kinesis Firehose by adding a UTC time prefix in the format YYYY/MM/DD/HH before writing objects to S3. Because we use the date and hour as partitions, we need to change the file naming and location to fit our Redshift Spectrum schema.

Automating data distribution using AWS Lambda

We created a simple Lambda function triggered by an S3 put event that copies the file to a different location (or locations), while renaming it to fit our data structure and processing flow. As mentioned before, the files generated by Kinesis Firehose are structured in a pre-defined hierarchy, such as:

S3://your-bucket/your-prefix/2017/08/01/20/events-4-2017-08-01-20-06-06-536f5c40-6893-4ee4-907d-81e4d3b09455.gz

All we need to do is parse the object name and restructure it as we see fit. In our case, we did the following (the event is an object received in the Lambda function with all the data about the object written to S3):

/*
	object key structure in the event object:
your-prefix/2017/08/01/20/event-4-2017-08-01-20-06-06-536f5c40-6893-4ee4-907d-81e4d3b09455.gz
	*/

let key_parts = event.Records[0].s3.object.key.split('/'); 

let event_type = key_parts[0];
let date = key_parts[1] + '-' + key_parts[2] + '-' + key_parts[3];
let hour = key_parts[4];
if (hour.indexOf('0') == 0) {
 		hour = parseInt(hour, 10) + '';
}
    
let parts1 = key_parts[5].split('-');
let minute = parts1[7];
if (minute.indexOf('0') == 0) {
        minute = parseInt(minute, 10) + '';
}

Now, we can redistribute the file to the two destinations we need—one for the minute processing task and the other for hourly aggregation:

    copyObjectToHourlyFolder(event, date, hour, minute)
        .then(copyObjectToMinuteFolder.bind(null, event, date, hour, minute))
        .then(addPartitionToSpectrum.bind(null, event, date, hour, minute))
        .then(deleteOldMinuteObjects.bind(null, event))
        .then(deleteStreamObject.bind(null, event))        
        .then(result => {
            callback(null, { message: 'done' });            
        })
        .catch(err => {
            console.error(err);
            callback(null, { message: err });            
        }); 

Kinesis Firehose stores the data in a temporary folder. We copy the object to another folder that holds the data for the last processed minute. This folder is connected to a small Redshift Spectrum table where the data is being processed without needing to scan a much larger dataset. We also copy the data to a folder that holds the data for the entire hour, to be later aggregated and converted to Parquet.

Because we partition the data by date and hour, we created a new partition on the Redshift Spectrum table if the processed minute is the first minute in the hour (that is, minute 0). We ran the following:

ALTER TABLE 
  spectrum.events 
ADD partition
  (date='2017-08-01', hour=0) 
  LOCATION 's3://nuviad-temp/events/2017-08-01/0/';

After the data is processed and added to the table, we delete the processed data from the temporary Kinesis Firehose storage and from the minute storage folder.

Migrating CSV to Parquet using AWS Glue and Amazon EMR

The simplest way we found to run an hourly job converting our CSV data to Parquet is using Lambda and AWS Glue (and thanks to the awesome AWS Big Data team for their help with this).

Creating AWS Glue jobs

What this simple AWS Glue script does:

  • Gets parameters for the job, date, and hour to be processed
  • Creates a Spark EMR context allowing us to run Spark code
  • Reads CSV data into a DataFrame
  • Writes the data as Parquet to the destination S3 bucket
  • Adds or modifies the Redshift Spectrum / Amazon Athena table partition for the table
import sys
import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.job import Job
import boto3

## @params: [JOB_NAME]
args = getResolvedOptions(sys.argv, ['JOB_NAME','day_partition_key', 'hour_partition_key', 'day_partition_value', 'hour_partition_value' ])

#day_partition_key = "partition_0"
#hour_partition_key = "partition_1"
#day_partition_value = "2017-08-01"
#hour_partition_value = "0"

day_partition_key = args['day_partition_key']
hour_partition_key = args['hour_partition_key']
day_partition_value = args['day_partition_value']
hour_partition_value = args['hour_partition_value']

print("Running for " + day_partition_value + "/" + hour_partition_value)

sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args['JOB_NAME'], args)

df = spark.read.option("delimiter","|").csv("s3://nuviad-temp/events/"+day_partition_value+"/"+hour_partition_value)
df.registerTempTable("data")

df1 = spark.sql("select _c0 as user_id, _c1 as campaign_id, _c2 as os, _c3 as ua, cast(_c4 as bigint) as ts, cast(_c5 as double) as billing from data")

df1.repartition(1).write.mode("overwrite").parquet("s3://nuviad-temp/parquet/"+day_partition_value+"/hour="+hour_partition_value)

client = boto3.client('athena', region_name='us-east-1')

response = client.start_query_execution(
    QueryString='alter table parquet_events add if not exists partition(' + day_partition_key + '=\'' + day_partition_value + '\',' + hour_partition_key + '=' + hour_partition_value + ')  location \'s3://nuviad-temp/parquet/' + day_partition_value + '/hour=' + hour_partition_value + '\'' ,
    QueryExecutionContext={
        'Database': 'spectrumdb'
    },
    ResultConfiguration={
        'OutputLocation': 's3://nuviad-temp/convertresults'
    }
)

response = client.start_query_execution(
    QueryString='alter table parquet_events partition(' + day_partition_key + '=\'' + day_partition_value + '\',' + hour_partition_key + '=' + hour_partition_value + ') set location \'s3://nuviad-temp/parquet/' + day_partition_value + '/hour=' + hour_partition_value + '\'' ,
    QueryExecutionContext={
        'Database': 'spectrumdb'
    },
    ResultConfiguration={
        'OutputLocation': 's3://nuviad-temp/convertresults'
    }
)

job.commit()

Note: Because Redshift Spectrum and Athena both use the AWS Glue Data Catalog, we could use the Athena client to add the partition to the table.

Here are a few words about float, decimal, and double. Using decimal proved to be more challenging than we expected, as it seems that Redshift Spectrum and Spark use them differently. Whenever we used decimal in Redshift Spectrum and in Spark, we kept getting errors, such as:

S3 Query Exception (Fetch). Task failed due to an internal error. File 'https://s3-external-1.amazonaws.com/nuviad-temp/events/2017-08-01/hour=2/part-00017-48ae5b6b-906e-4875-8cde-bc36c0c6d0ca.c000.snappy.parquet has an incompatible Parquet schema for column 's3://nuviad-events/events.lat'. Column type: DECIMAL(18, 8), Parquet schema:\noptional float lat [i:4 d:1 r:0]\n (https://s3-external-1.amazonaws.com/nuviad-temp/events/2017-08-01/hour=2/part-00017-48ae5b6b-906e-4875-8cde-bc36c0c6d0ca.c000.snappy.parq

We had to experiment with a few floating-point formats until we found that the only combination that worked was to define the column as double in the Spark code and float in Spectrum. This is the reason you see billing defined as float in Spectrum and double in the Spark code.

Creating a Lambda function to trigger conversion

Next, we created a simple Lambda function to trigger the AWS Glue script hourly using a simple Python code:

import boto3
import json
from datetime import datetime, timedelta
 
client = boto3.client('glue')
 
def lambda_handler(event, context):
    last_hour_date_time = datetime.now() - timedelta(hours = 1)
    day_partition_value = last_hour_date_time.strftime("%Y-%m-%d") 
    hour_partition_value = last_hour_date_time.strftime("%-H") 
    response = client.start_job_run(
    JobName='convertEventsParquetHourly',
    Arguments={
         '--day_partition_key': 'date',
         '--hour_partition_key': 'hour',
         '--day_partition_value': day_partition_value,
         '--hour_partition_value': hour_partition_value
         }
    )

Using Amazon CloudWatch Events, we trigger this function hourly. This function triggers an AWS Glue job named ‘convertEventsParquetHourly’ and runs it for the previous hour, passing job names and values of the partitions to process to AWS Glue.

Redshift Spectrum and Node.js

Our development stack is based on Node.js, which is well-suited for high-speed, light servers that need to process a huge number of transactions. However, a few limitations of the Node.js environment required us to create workarounds and use other tools to complete the process.

Node.js and Parquet

The lack of Parquet modules for Node.js required us to implement an AWS Glue/Amazon EMR process to effectively migrate data from CSV to Parquet. We would rather save directly to Parquet, but we couldn’t find an effective way to do it.

One interesting project in the works is the development of a Parquet NPM by Marc Vertes called node-parquet (https://www.npmjs.com/package/node-parquet). It is not in a production state yet, but we think it would be well worth following the progress of this package.

Timestamp data type

According to the Parquet documentation, Timestamp data are stored in Parquet as 64-bit integers. However, JavaScript does not support 64-bit integers, because the native number type is a 64-bit double, giving only 53 bits of integer range.

The result is that you cannot store Timestamp correctly in Parquet using Node.js. The solution is to store Timestamp as string and cast the type to Timestamp in the query. Using this method, we did not witness any performance degradation whatsoever.

Lessons learned

You can benefit from our trial-and-error experience.

Lesson #1: Data validation is critical

As mentioned earlier, a single corrupt entry in a partition can fail queries running against this partition, especially when using Parquet, which is harder to edit than a simple CSV file. Make sure that you validate your data before scanning it with Redshift Spectrum.

Lesson #2: Structure and partition data effectively

One of the biggest benefits of using Redshift Spectrum (or Athena for that matter) is that you don’t need to keep nodes up and running all the time. You pay only for the queries you perform and only for the data scanned per query.

Keeping different permutations of your data for different queries makes a lot of sense in this case. For example, you can partition your data by date and hour to run time-based queries, and also have another set partitioned by user_id and date to run user-based queries. This results in faster and more efficient performance of your data warehouse.

Storing data in the right format

Use Parquet whenever you can. The benefits of Parquet are substantial. Faster performance, less data to scan, and much more efficient columnar format. However, it is not supported out-of-the-box by Kinesis Firehose, so you need to implement your own ETL. AWS Glue is a great option.

Creating small tables for frequent tasks

When we started using Redshift Spectrum, we saw our Amazon Redshift costs jump by hundreds of dollars per day. Then we realized that we were unnecessarily scanning a full day’s worth of data every minute. Take advantage of the ability to define multiple tables on the same S3 bucket or folder, and create temporary and small tables for frequent queries.

Lesson #3: Combine Athena and Redshift Spectrum for optimal performance

Moving to Redshift Spectrum also allowed us to take advantage of Athena as both use the AWS Glue Data Catalog. Run fast and simple queries using Athena while taking advantage of the advanced Amazon Redshift query engine for complex queries using Redshift Spectrum.

Redshift Spectrum excels when running complex queries. It can push many compute-intensive tasks, such as predicate filtering and aggregation, down to the Redshift Spectrum layer, so that queries use much less of your cluster’s processing capacity.

Lesson #4: Sort your Parquet data within the partition

We achieved another performance improvement by sorting data within the partition using sortWithinPartitions(sort_field). For example:

df.repartition(1).sortWithinPartitions("campaign_id")…

Conclusion

We were extremely pleased with using Amazon Redshift as our core data warehouse for over three years. But as our client base and volume of data grew substantially, we extended Amazon Redshift to take advantage of scalability, performance, and cost with Redshift Spectrum.

Redshift Spectrum lets us scale to virtually unlimited storage, scale compute transparently, and deliver super-fast results for our users. With Redshift Spectrum, we store data where we want at the cost we want, and have the data available for analytics when our users need it with the performance they expect.


About the Author

With 7 years of experience in the AdTech industry and 15 years in leading technology companies, Rafi Ton is the founder and CEO of NUVIAD. He enjoys exploring new technologies and putting them to use in cutting edge products and services, in the real world generating real money. Being an experienced entrepreneur, Rafi believes in practical-programming and fast adaptation of new technologies to achieve a significant market advantage.

 

 

AWS Media Services – Process, Store, and Monetize Cloud-Based Video

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-media-services-process-store-and-monetize-cloud-based-video/

Do you remember what web video was like in the early days? Standalone players, video no larger than a postage stamp, slow & cantankerous connections, overloaded servers, and the ever-present buffering messages were the norm less than two decades ago.

Today, thanks to technological progress and a broad array of standards, things are a lot better. Video consumers are now in control. They use devices of all shapes, sizes, and vintages to enjoy live and recorded content that is broadcast, streamed, or sent over-the-top (OTT, as they say), and expect immediate access to content that captures and then holds their attention. Meeting these expectations presents a challenge for content creators and distributors. Instead of generating video in a one-size-fits-all format, they (or their media servers) must be prepared to produce video that spans a broad range of sizes, formats, and bit rates, taking care to be ready to deal with planned or unplanned surges in demand. In the face of all of this complexity, they must backstop their content with a monetization model that supports the content and the infrastructure to deliver it.

New AWS Media Services
Today we are launching an array of broadcast-quality media services, each designed to address one or more aspects of the challenge that I outlined above. You can use them together to build a complete end-to-end video solution or you can use one or more in building-block style. In true AWS fashion, you can spend more time innovating and less time setting up and running infrastructure, leaving you ready to focus on creating, delivering, and monetizing your content. The services are all elastic, allowing you to ramp up processing power, connections, and storage and giving you the ability to handle million-user (and beyond) spikes with ease.

Here are the services (all accessible from a set of interactive consoles as well as through a comprehensive set of APIs):

AWS Elemental MediaConvert – File-based transcoding for OTT, broadcast, or archiving, with support for a long list of formats and codecs. Features include multi-channel audio, graphic overlays, closed captioning, and several DRM options.

AWS Elemental MediaLive – Live encoding to deliver video streams in real time to both televisions and multiscreen devices. Allows you to deploy highly reliable live channels in minutes, with full control over encoding parameters. It supports ad insertion, multi-channel audio, graphic overlays, and closed captioning.

AWS Elemental MediaPackage – Video origination and just-in-time packaging. Starting from a single input, produces output for multiple devices representing a long list of current and legacy formats. Supports multiple monetization models, time-shifted live streaming, ad insertion, DRM, and blackout management.

AWS Elemental MediaStore – Media-optimized storage that enables high performance and low latency applications such as live streaming, while taking advantage of the scale and durability of Amazon Simple Storage Service (S3).

AWS Elemental MediaTailor – Monetization service that supports ad serving and server-side ad insertion, a broad range of devices, transcoding, and accurate reporting of server-side and client-side ad insertion.

Instead of listing out all of the features in the sections below, I’ve simply included as many screen shots as possible with the expectation that this will give you a better sense of the rich set of features, parameters, and settings that you get with this set of services.

AWS Elemental MediaConvert
MediaConvert allows you to transcode content that is stored in files. You can process individual files or entire media libraries, or anything in-between. You simply create a conversion job that specifies the content and the desired outputs, and submit it to MediaConvert. There’s no software to install or patch and the service scales to meet your needs without affecting turnaround time or performance.

The MediaConvert Console lets you manage Output presets, Job templates, Queues, and Jobs:

You can use a built-in system preset or you can make one of your own. You have full control over the settings when you make your own:

Jobs templates are named, and produce one or more output groups. You can add a new group to a template with a click:

When everything is ready to go, you create a job and make some final selections, then click on Create:

Each account starts with a default queue for jobs, where incoming work is processed in parallel using all processing resources available to the account. Adding queues does not add processing resources, but does cause them to be apportioned across queues. You can temporarily pause one queue in order to devote more resources to the others. You can submit jobs to paused queues and you can also cancel any that have yet to start.

Pricing for this service is based on the amount of video that you process and the features that you use.

AWS Elemental MediaLive
This service is for live encoding, and can be run 24×7. MediaLive channels are deployed on redundant resources distributed in two physically separated Availability Zones in order to provide the reliability expected by our customers in the broadcast industry. You can specify your inputs and define your channels in the MediaLive Console:

After you create an Input, you create a Channel and attach it to the Input:

You have full control over the settings for each channel:

 

AWS Elemental MediaPackage
This service lets you deliver video to many devices from a single source. It focuses on protection and just-in-time packaging, giving you the ability to provide your users with the desired content on the device of their choice. You simply create a channel to get started:

Then you add one or more endpoints. Once again, plenty of options and full control, including a startover window and a time delay:

You find the input URL, user name, and password for your channel and route your live video stream to it for packaging:

AWS Elemental MediaStore
MediaStore offers the performance, consistency, and latency required for live and on-demand media delivery. Objects are written and read into a new “temporal” tier of object storage for a limited amount of time, then move silently into S3 for long-lived durability. You simply create a storage container to group your media content:

The container is available within a minute or so:

Like S3 buckets, MediaStore containers have access policies and no limits on the number of objects or storage capacity.

MediaStore helps you to take full advantage of S3 by managing the object key names so as to maximize storage and retrieval throughput, in accord with the Request Rate and Performance Considerations.

AWS Elemental MediaTailor
This service takes care of server-side ad insertion while providing a broadcast-quality viewer experience by transcoding ad assets on the fly. Your customer’s video player asks MediaTailor for a playlist. MediaTailor, in turn, calls your Ad Decision Server and returns a playlist that references the origin server for your original video and the ads recommended by the Ad Decision Server. The video player makes all of its requests to a single endpoint in order to ensure that client-side ad-blocking is ineffective. You simply create a MediaTailor Configuration:

Context information is passed to the Ad Decision Server in the URL:

Despite the length of this post I have barely scratched the surface of the AWS Media Services. Once AWS re:Invent is in the rear view mirror I hope to do a deep dive and show you how to use each of these services.

Available Now
The entire set of AWS Media Services is available now and you can start using them today! Pricing varies by service, but is built around a pay-as-you-go model.

Jeff;

Rightscorp: Revenue From Piracy Settlements Down 48% in 2017

Post Syndicated from Andy original https://torrentfreak.com/rightscorp-revenue-from-piracy-settlements-down-48-in-2017-171125/

For the past several years, anti-piracy outfit Rightscorp has been trying to turn piracy into profit. The company monitors BitTorrent networks, captures IP addresses, then attempts to force ISPs to forward cash settlement demands to its subscribers.

Unlike other companies operating in the same area, Rightscorp has adopted a “speeding fine” type model, where it asks for $20 to $30 to make a supposed lawsuit go away, instead of the many hundreds demanded by its rivals. To date, this has resulted in the company closing more than 230,000 cases of infringement.

But despite the high numbers, the company doesn’t seem to be able to make it pay. Rightscorp’s latest set of financial results covering the three months ended September 30, 2017, show how bad things have got on the settlement front.

During the period in question, Rightscorp generated copyright settlement revenues of $45,848, an average of just $15,282 per month. That represents a decrease of 67% when compared to the $139,834 generated during the same period in 2016.

When looking at settlement revenues year to date, Rightscorp generated $184,362 in 2017, a decrease of 48% when compared to $354,160 generated during the same nine-month period in 2016.

But as bleak as these figures are, things get much worse. Out of these top-line revenues, Rightscorp has to deal with a whole bunch of costs before it can put anything into its own pockets. For example, in exchange for the right to pursue pirates, Rightscorp agrees to pay around 50% of everything it generates from settlements back to copyright holders.

So, for the past three months when it collected $45,848 from BitTorrent users, it must pay out $22,924 to copyright holders. Last year, in the same period, it paid them $69,143. For the year to date (nine months ended September 30, 2017), the company paid $92,181 to copyright holders, that’s versus $174,878 for the same period last year.

Whichever way you slice it, Rightscorp settlement model appears to be failing. With revenues from settlements down by almost half thus far this year, one has to question where this is all going, especially with BitTorrent piracy volumes continuing to fall in favor of other less traceable methods such as streaming.

However, Rightscorp does have a trick up its sleeve that is helping to keep the company afloat. As previously reported, the company has amassed a lot of intelligence on pirate activity which clearly has some value to copyright holders.

That data is currently being utilized by both BMG and the RIAA, who are using it as evidence in copyright liability lawsuits filed against ISPs Cox and Grande Communications, where each stand accused of failing to disconnect repeat infringers.

This selling of ‘pirate’ data is listed by Rightscorp in its financial reports as “consulting services” and thus far at least, it’s proving to be a crucial source of income.

“During the three months ended September 30, 2017, we generated revenues of $76,666 from consulting services rendered under service arrangements with prominent trade organizations,” Rightscorp reports.

“Under the agreements, the Company is providing certain data and consultation regarding copyright infringements on such organizations’ respective properties. During the three months ended September 30, 2016, we had no consulting services revenue.”

Year to date, the numbers begin to add up. In the nine months ended September 30, 2017, Rightscorp generated revenues of $224,998 from this facet of their business, that’s versus zero revenue in 2016.

It’s clear that without this “consulting” revenue, Rightscorp would be in an even worse situation than it is today. In fact, it appears that these services, provided to the likes of the RIAA, are now preventing the company from falling into the abyss. All that being said, there’s no guarantee that won’t happen anyway.

To the nine months ended September 30, 2017, Rightscorp recorded a net loss of $1,448,899, which is even more than the $1,380,698 it lost during the same period last year. As a result, the company had just $3,147 left in cash at the end of September. That crisis was eased by issuing 2.5 million shares to an investor for a purchase price just $50,000. But to keep going, Rightscorp will need more money – much more.

“Management believes that the Company will need an additional $250,000 to $500,000 in 2017 to fund operations based on our current operating plans,” it reports, noting that there is “substantial doubt” whether Rightscorp can continue as a going concern.

But despite all the bad news, Rightscorp manages to survive and at least in the short-term, the piracy data it has amassed holds value, beyond basic cash settlement letters. The question is, for how long?

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

The Problem Solver

Post Syndicated from Bozho original https://techblog.bozho.net/the-problem-solver/

I’ll start this post with a quote:

Good developers are good problem solvers. They turn each task into a series of problems they have to solve. They don’t necessarily know how to solve them in advance, but they have their toolbox of approaches, shortcuts and other tricks that lead to the solution. I have outlined one such set of steps for identifying problems, but you can’t easily formalize the problem-solving approach.

But is really turning a task into a set of problems a good idea? Programming can be seen as a creative exercise, rather than a problem solving one – you think, you ponder, you deliberate, then you make something out of nothing and it’s beautiful, because it works. And sometimes programming is that, but that is almost always interrupted by a series of problems that stop you from getting the task completed. That process is best visualized with the following short video:

That’s because most things in software break. They either break because there are unknowns, or because of a lot of unsuspected edge cases, or because the abstraction that we use leaks, or because the tools that we use are poorly documented or have poor APIs/UIs, or simply because of bugs. Or in many cases – all of the above.

So inevitably, we have to learn to solve problems. And solving them quickly and properly is in fact, one might argue, the most important skill when doing software. One should learn, though, not to just patch things up with duct tape, but to come up with the best possible solution with the constraints at hand. The library that you are using is missing a feature you really need? Ideally, you should propose the feature and wait for it to be implemented. Too often that’s not an option. Quick and dirty fix – copy-paste a bunch of code. Proper, elegant solution – use design patterns to adapt the library to your needs, or come up with a generic (but not time-wasting) way of patching libraries. Or there’s a memory leak? Just launch a bigger instance? No. Spend a week live-profiling the application? To slow. Figure out how to simulate the leaking scenario in a local setup and fix it in a day? Sounds ideal, but it’s not trivial.

Sometimes there are not too many problems and development goes smoothly. Then the good problem solver identifies problems proactively – this implementation is slow, this is too memory-consuming, this is overcomplicated and should be refactored. And these can (and should) be small steps that don’t interfere with the development process, leaving you 2 days in deep refactoring for no apparent reason. The skill is to know the limit between gradual improvement and spotting problems before they occur, and wasting time in problems that don’t exist or you’ll never hit.

And finally, solving problems is not a solo exercise. In fact I think one of the most important aspects of problem solving is answering questions. If you want to be a good developer, you have to answer the questions of others. Your colleagues in most cases, but sometimes – total strangers on Stackoverflow. I myself found that answering stackoverflow questions actually turned me into a better problem solver – I could solve others problems in a limited time, with limited information. So in many case I was the go-to person on the team when a problem arises, even though I wasn’t the most senior or the most familiar with the project. But one could reasonably expect that I’ll be able to figure out a proper solution quickly. And then the loop goes on – you answer more questions and get better at problem solving, and so on, and so forth. By the way, we shouldn’t assume we are good unless we are able to solver others’ problems in addition to ours.

Problem-solving is a transferable skill. We might not be developers forever, but our approach to problems, the tenacity in fixing them, and the determination to get things done properly, is useful in many contexts. You could, in fact, view each task, not just programming ones, as a problem-solving exercise. And having the confidence that you can fix it, even though you have never encountered it before, is often priceless.

What’s my ultimate point? We should see ourselves as problem solvers and constantly improve our problem solving toolbox. Which, among other things, includes helping others. Otherwise we are tied to our knowledge of a particular technology or stack, and that’s frankly boring.

The post The Problem Solver appeared first on Bozho's tech blog.

Swiss Copyright Law Proposals: Good News for Pirates, Bad For Pirate Sites

Post Syndicated from Andy original https://torrentfreak.com/swiss-copyright-law-proposals-good-news-for-pirates-bad-for-pirate-sites-171124/

While Switzerland sits geographically in the heart of Europe, the country is not part of the European Union, meaning that its copyright laws are often out of touch with those of the countries encircling it.

For years this has meant heavy criticism from the United States, whose trade representative has put Switzerland on the Watch List, citing weaknesses in the country’s ability to curb online copyright infringement.

“The decision to place Switzerland on the Watch List this year is premised on U.S. concerns regarding specific difficulties in Switzerland’s system of online copyright protection and enforcement,” the USTR wrote in 2016.

Things didn’t improve in 2017. Referencing the so-called Logistep Decision, which found that collecting infringers’ IP addresses is unlawful, the USTR said that Switzerland had effectively deprived copyright holders of the means to enforce their rights online.

All of this criticism hasn’t fallen on deaf ears. For the past several years, Switzerland has been deeply involved in consultations that aim to shape future copyright law. Negotiations have been prolonged, however, with the Federal Council aiming to improve the situation for creators without impairing the position of consumers.

A new draft compromise tabled Wednesday is somewhat of a mixed bag, one that is unlikely to please the United States overall but could prove reasonably acceptable to the public.

First of all, people will still be able to ‘pirate’ as much copyrighted material as they like, as long as that content is consumed privately and does not include videogames or software, which are excluded. Any supposed losses accrued by the entertainment industries will be compensated via a compulsory tax of 13 Swiss francs ($13), levied on media playback devices including phones and tablets.

This freedom only applies to downloading and streaming, meaning that any uploading (distribution) is explicitly ruled out. So, while grabbing some streaming content via a ‘pirate’ Kodi addon is just fine, using BitTorrent to achieve the same is ruled out.

Indeed, rightsholders will be able to capture IP addresses of suspected infringers in order to file a criminal complaint with authorities. That being said, there will no system of warning notices targeting file-sharers.

But while the authorization of unlicensed downloads will only frustrate an already irritated United States, the other half of the deal is likely to be welcomed.

Under the recommendations, Internet services will not only be required to remove infringing content from their platforms, they’ll also be compelled to prevent that same content from reappearing. Failure to comply will result in prosecution. It’s a standard that copyright holders everywhere are keen for governments to adopt.

Additionally, the spotlight will fall on datacenters and webhosts that have a reputation for being popular with pirate sites. It’s envisioned that such providers will be prevented from offering services to known pirate sites, with the government clearly stating that services with piracy at the heart of their business models will be ripe for action.

But where there’s a plus for copyright holders, the Swiss have another minus. Previously it was proposed that in serious cases authorities should be able to order the ISP blocking of “obviously illegal content or sources.” That proposal has now been dropped, meaning no site-blocking will be allowed.

Other changes in the draft envision an extension of the copyright term from 50 to 70 years and improved protection for photographic works. The proposals also feature increased freedoms for researchers and libraries, who will be able to use copyrighted works without obtaining permission from rightsholders.

Overall the proposals are a pretty mixed bag but as Minister of Justice Simonetta Sommaruga said Wednesday, if no one is prepared to compromise, no one will get anything.

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