Tag Archives: 3.0

Jailed Streaming Site Operator Hit With Fresh $3m Damages Lawsuit

Post Syndicated from Andy original https://torrentfreak.com/jailed-streaming-site-operator-hit-with-fresh-3m-damages-lawsuit-180207/

After being founded more than half a decade ago, Swefilmer grew to become Sweden’s most popular movie and TV show streaming site. It was only a question of time before authorities stepped in to bring the show to an end.

In 2015, a Swedish operator of the site in his early twenties was raided by local police. A second man, Turkish and in his late twenties, was later arrested in Germany.

The pair, who hadn’t met in person, appeared before the Varberg District Court in January 2017, accused of making more than $1.5m from their activities between November 2013 and June 2015.

The prosecutor described Swefilmer as “organized crime”, painting the then 26-year-old as the main brains behind the site and the 23-year-old as playing a much smaller role. The former was said to have led a luxury lifestyle after benefiting from $1.5m in advertising revenue.

The sentences eventually handed down matched the defendants’ alleged level of participation. While the younger man received probation and community service, the Turk was sentenced to serve three years in prison and ordered to forfeit $1.59m.

Very quickly it became clear there would be an appeal, with plaintiffs represented by anti-piracy outfit RightsAlliance complaining that their 10m krona ($1.25m) claim for damages over the unlawful distribution of local movie Johan Falk: Kodnamn: Lisa had been ruled out by the Court.

With the appeal hearing now just a couple of weeks away, Swedish outlet Breakit is reporting that media giant Bonnier Broadcasting has launched an action of its own against the now 27-year-old former operator of Swefilmer.

According to the publication, Bonnier’s pay-TV company C More, which distributes for Fox, MGM, Paramount, Universal, Sony and Warner, is set to demand around 24m krona ($3.01m) via anti-piracy outfit RightsAlliance.

“This is about organized crime and grossly criminal individuals who earned huge sums on our and others’ content. We want to take every opportunity to take advantage of our rights,” says Johan Gustafsson, Head of Corporate Communications at Bonnier Broadcasting.

C More reportedly filed its lawsuit at the Stockholm District Court on January 30, 2018. At its core are four local movies said to have been uploaded and made available via Swefilmer.

“C More would probably never even have granted a license to [the operator] to make or allow others to make the films available to the public in a similar way as [the operator] did, but if that had happened, the fee would not be less than 5,000,000 krona ($628,350) per film or a total of 20,000,000 krona ($2,513,400),” C More’s claim reads.

Speaking with Breakit, lawyer Ansgar Firsching said he couldn’t say much about C More’s claims against his client.

“I am very surprised that two weeks before the main hearing [C More] comes in with this requirement. If you open another front, we have two trials that are partly about the same thing,” he said.

Firsching said he couldn’t elaborate at this stage but expects his client to deny the claim for damages. C More sees things differently.

“Many people live under the illusion that sites like Swefilmer are driven by idealistic teens in their parents’ basements, which is completely wrong. This is about organized crime where our content is used to generate millions and millions in revenue,” the company notes.

The appeal in the main case is set to go ahead February 20th.

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Success at Apache: A Newbie’s Narrative

Post Syndicated from mikesefanov original https://yahooeng.tumblr.com/post/170536010891

yahoodevelopers:

Kuhu Shukla (bottom center) and team at the 2017 DataWorks Summit


By Kuhu Shukla

This post first appeared here on the Apache Software Foundation blog as part of ASF’s “Success at Apache” monthly blog series.

As I sit at my desk on a rather frosty morning with my coffee, looking up new JIRAs from the previous day in the Apache Tez project, I feel rather pleased. The latest community release vote is complete, the bug fixes that we so badly needed are in and the new release that we tested out internally on our many thousand strong cluster is looking good. Today I am looking at a new stack trace from a different Apache project process and it is hard to miss how much of the exceptional code I get to look at every day comes from people all around the globe. A contributor leaves a JIRA comment before he goes on to pick up his kid from soccer practice while someone else wakes up to find that her effort on a bug fix for the past two months has finally come to fruition through a binding +1.

Yahoo – which joined AOL, HuffPost, Tumblr, Engadget, and many more brands to form the Verizon subsidiary Oath last year – has been at the frontier of open source adoption and contribution since before I was in high school. So while I have no historical trajectories to share, I do have a story on how I found myself in an epic journey of migrating all of Yahoo jobs from Apache MapReduce to Apache Tez, a then-new DAG based execution engine.

Oath grid infrastructure is through and through driven by Apache technologies be it storage through HDFS, resource management through YARN, job execution frameworks with Tez and user interface engines such as Hive, Hue, Pig, Sqoop, Spark, Storm. Our grid solution is specifically tailored to Oath’s business-critical data pipeline needs using the polymorphic technologies hosted, developed and maintained by the Apache community.

On the third day of my job at Yahoo in 2015, I received a YouTube link on An Introduction to Apache Tez. I watched it carefully trying to keep up with all the questions I had and recognized a few names from my academic readings of Yarn ACM papers. I continued to ramp up on YARN and HDFS, the foundational Apache technologies Oath heavily contributes to even today. For the first few weeks I spent time picking out my favorite (necessary) mailing lists to subscribe to and getting started on setting up on a pseudo-distributed Hadoop cluster. I continued to find my footing with newbie contributions and being ever more careful with whitespaces in my patches. One thing was clear – Tez was the next big thing for us. By the time I could truly call myself a contributor in the Hadoop community nearly 80-90% of the Yahoo jobs were now running with Tez. But just like hiking up the Grand Canyon, the last 20% is where all the pain was. Being a part of the solution to this challenge was a happy prospect and thankfully contributing to Tez became a goal in my next quarter.

The next sprint planning meeting ended with me getting my first major Tez assignment – progress reporting. The progress reporting in Tez was non-existent – “Just needs an API fix,”  I thought. Like almost all bugs in this ecosystem, it was not easy. How do you define progress? How is it different for different kinds of outputs in a graph? The questions were many.

I, however, did not have to go far to get answers. The Tez community actively came to a newbie’s rescue, finding answers and posing important questions. I started attending the bi-weekly Tez community sync up calls and asking existing contributors and committers for course correction. Suddenly the team was much bigger, the goals much more chiseled. This was new to anyone like me who came from the networking industry, where the most open part of the code are the RFCs and the implementation details are often hidden. These meetings served as a clean room for our coding ideas and experiments. Ideas were shared, to the extent of which data structure we should pick and what a future user of Tez would take from it. In between the usual status updates and extensive knowledge transfers were made.

Oath uses Apache Pig and Apache Hive extensively and most of the urgent requirements and requests came from Pig and Hive developers and users. Each issue led to a community JIRA and as we started running Tez at Oath scale, new feature ideas and bugs around performance and resource utilization materialized. Every year most of the Hadoop team at Oath travels to the Hadoop Summit where we meet our cohorts from the Apache community and we stand for hours discussing the state of the art and what is next for the project. One such discussion set the course for the next year and a half for me.

We needed an innovative way to shuffle data. Frameworks like MapReduce and Tez have a shuffle phase in their processing lifecycle wherein the data from upstream producers is made available to downstream consumers. Even though Apache Tez was designed with a feature set corresponding to optimization requirements in Pig and Hive, the Shuffle Handler Service was retrofitted from MapReduce at the time of the project’s inception. With several thousands of jobs on our clusters leveraging these features in Tez, the Shuffle Handler Service became a clear performance bottleneck. So as we stood talking about our experience with Tez with our friends from the community, we decided to implement a new Shuffle Handler for Tez. All the conversation points were tracked now through an umbrella JIRA TEZ-3334 and the to-do list was long. I picked a few JIRAs and as I started reading through I realized, this is all new code I get to contribute to and review. There might be a better way to put this, but to be honest it was just a lot of fun! All the whiteboards were full, the team took walks post lunch and discussed how to go about defining the API. Countless hours were spent debugging hangs while fetching data and looking at stack traces and Wireshark captures from our test runs. Six months in and we had the feature on our sandbox clusters. There were moments ranging from sheer frustration to absolute exhilaration with high fives as we continued to address review comments and fixing big and small issues with this evolving feature.

As much as owning your code is valued everywhere in the software community, I would never go on to say “I did this!” In fact, “we did!” It is this strong sense of shared ownership and fluid team structure that makes the open source experience at Apache truly rewarding. This is just one example. A lot of the work that was done in Tez was leveraged by the Hive and Pig community and cross Apache product community interaction made the work ever more interesting and challenging. Triaging and fixing issues with the Tez rollout led us to hit a 100% migration score last year and we also rolled the Tez Shuffle Handler Service out to our research clusters. As of last year we have run around 100 million Tez DAGs with a total of 50 billion tasks over almost 38,000 nodes.

In 2018 as I move on to explore Hadoop 3.0 as our future release, I hope that if someone outside the Apache community is reading this, it will inspire and intrigue them to contribute to a project of their choice. As an astronomy aficionado, going from a newbie Apache contributor to a newbie Apache committer was very much like looking through my telescope - it has endless possibilities and challenges you to be your best.

About the Author:

Kuhu Shukla is a software engineer at Oath and did her Masters in Computer Science at North Carolina State University. She works on the Big Data Platforms team on Apache Tez, YARN and HDFS with a lot of talented Apache PMCs and Committers in Champaign, Illinois. A recent Apache Tez Committer herself she continues to contribute to YARN and HDFS and spoke at the 2017 Dataworks Hadoop Summit on “Tez Shuffle Handler: Shuffling At Scale With Apache Hadoop”. Prior to that she worked on Juniper Networks’ router and switch configuration APIs. She likes to participate in open source conferences and women in tech events. In her spare time she loves singing Indian classical and jazz, laughing, whale watching, hiking and peering through her Dobsonian telescope.

Raspberry Crusoe: how a Pi got lost at sea

Post Syndicated from James Robinson original https://www.raspberrypi.org/blog/lost-high-altitude-balloon/

The tale of the little HAB that could and its three-month journey from Portslade Aldridge Community Academy in the UK to the coast of Denmark.

PACA Computing on Twitter

Where did it land ???? #skypaca #skycademy @pacauk #RaspberryPi

High-altitude ballooning

Some of you may be familiar with Raspberry Pi being used as the flight computer, or tracker, of high-altitude balloon (HAB) payloads. For those who aren’t, high-altitude ballooning is a relatively simple activity (at least in principle) where a tracker is attached to a large weather balloon which is then released into the atmosphere. While the HAB ascends, the tracker takes pictures and data readings the whole time. Eventually (around 30km up) the balloon bursts, leaving the payload free to descend and be recovered. For a better explanation, I’m handing over to the students of UTC Oxfordshire:

Pi in the Sky | UTC Oxfordshire

On Tuesday 2nd May, students launched a Raspberry Pi computer 35,000 metres into the stratosphere as part of an Employer-Led project at UTC Oxfordshire, set by the Raspberry Pi Foundation. The project involved engineering, scientific and communication/publicity skills being developed to create the payload and code to interpret experiments set by the science team.

Skycademy

Over the past few years, we’ve seen schools and their students explore the possibilities that high-altitude ballooning offers, and back in 2015 and 2016 we ran Skycademy. The programme was simple enough: get a bunch of educators together in the same space, show them how to launch a balloon flight, and then send them back to their students to try and repeat what they’ve learned. Since the first Skycademy event, a number of participants have carried out launches, and we are extremely proud of each and every one of them.

The case of the vanishing PACA HAB

Not every launch has been a 100% success though. There are many things that can and do go wrong during HAB flights, and watching each launch from the comfort of our office can be a nerve-wracking experience. We had such an experience back in July 2017, during the launch performed by Skycademy graduate and Raspberry Pi Certified Educator Dave Hartley and his students from Portslade Aldridge Community Academy (PACA).

Dave and his team had been working on their payload for some time, and were awaiting suitable weather conditions. Early one Wednesday in July, everything aligned: they had a narrow window of good weather and so set their launch plan in motion. Soon they had assembled the payload in the school grounds and all was ready for the launch.

Dave Hartley on Twitter

Launch day! @pacauk #skycademy #skypaca #raspberrypi

Just before 11:00, they’d completed their final checks and released their payload into the atmosphere. Over the course of 64 minutes, the HAB steadily rose to an altitude of 25647m, where it captured some amazing pictures before the balloon burst and a rapid descent began.

Portslade Aldridge Community Academy Skycademy Raspberry Pi
Portslade Aldridge Community Academy Skycademy Raspberry Pi

Soon after the payload began to descend, the team noticed something worrying: their predicted descent path took the payload dangerously far south — it was threatening to land in the sea. As the payload continued to lose altitude, their calculated results kept shifting, alternately predicting a landing on the ground or out to sea. Eventually it became clear that the payload would narrowly overshoot the land, and it finally landed about 2 km out to sea.

Portslade Aldridge Community Academy Skycademy Raspberry Pi High Altitude Ballooning

The path of the balloon

It’s not uncommon for a HAB payload to get lost. There are many ways this can happen, particularly in a narrow country with a prevailing easterly wind like the UK. Payloads can get lost at sea, land somewhere inaccessible, or simply run out of power before they are located and retrieved. So normally, this would be the end of the story for the PACA students — even if the team had had a speedboat to hand, their payload was surely lost for good.

A message from Denmark

However, this is not the end of our story! A couple of months later, I arrived at work and saw this tweet from a colleague:

Raspberry Pi on Twitter

Anyone lost a Raspberry Pi HAB? Someone found this one on a beach in south western Denmark yesterday #UKHAS https://t.co/7lBzFiemgr

Good Samaritan Henning Hansen had found a Raspberry Pi washed up on a remote beach in Denmark! While walking a stretch of coast to collect plastic debris for an environmental monitoring project, he came across something unusual near the shore at 55°04’53.0″N and 8°38’46.9″E.

This of course piqued my interest, and we began to investigate the image he had shared on Facebook.

Portslade Aldridge Community Academy Skycademy Raspberry Pi High Altitude Ballooning

Inspecting the photo closely, we noticed a small asset label — the kind of label that, over a year earlier, we’d stuck to each and every bit of Skycademy field kit. We excitedly claimed the kit on behalf of Dave and his students, and contacted Henning to arrange the recovery of the payload. He told us it must have been carried ashore with the tide some time between 21 and 27 September, and probably on 21 September, since that day had the highest tide over the period. This meant the payload must have spent over two months at sea!

From the photo we could tell that the Raspberry Pi had suffered significant corrosion, having been exposed to salt water for so long, and so we felt pessimistic about the chances that there would be any recoverable data on it. However, Henning said that he’d been able to read some files from the FAT partition of the SD card, so all hope was not lost.

After a few weeks and a number of complications around dispatch and delivery (thank you, Henning, for your infinite patience!), Helen collected the HAB from a local Post Office.

Portslade Aldridge Community Academy Skycademy Raspberry Pi High Altitude Ballooning

SUCCESS!

We set about trying to read the data from the SD card, and eventually became disheartened: despite several attempts, we were unable to read its contents.

In a last-ditch effort, we gave the SD card to Jonathan, one of our engineers, who initially laughed at the prospect of recovering any data from it. But ten minutes later, he returned with news of success!

Portslade Aldridge Community Academy Skycademy Raspberry Pi
Portslade Aldridge Community Academy Skycademy Raspberry Pi
Portslade Aldridge Community Academy Skycademy Raspberry Pi
Portslade Aldridge Community Academy Skycademy Raspberry Pi

Since then, we’ve been able to reunite the payload with the PACA launch team, and the students sent us the perfect message to end this story:

Portslade Aldridge Community Academy Skycademy Raspberry Pi High Altitude Ballooning

The post Raspberry Crusoe: how a Pi got lost at sea appeared first on Raspberry Pi.

Wine 3.0 released

Post Syndicated from corbet original https://lwn.net/Articles/744741/rss

Version 3.0 of the
Wine Windows emulation layer has been released. “This release
represents a year of development effort and over 6,000 individual
changes.
” Most of the improvements seem to be around Direct3D
graphics, but it also now possible to package up Wine as an Android app;
see the release notes for
details.

MusE 3.0.0 released

Post Syndicated from ris original https://lwn.net/Articles/743598/rss

Three years after the last stable release, version 3.0 of the MusE
MIDI/Audio sequencer is now available. As you might expect there many
changes since the last release including a switch to Qt5, a new Plugin Path
editor in Global Settings, a mixer makeover with lots of fixes, a
system-wide move to double precision of all audio paths, and much more.

Modding Legends Team-Xecuter Announce “Future-Proof” Nintendo Switch Hack

Post Syndicated from Andy original https://torrentfreak.com/modding-legends-team-xecuter-announce-future-proof-nintendo-switch-hack-180104/

Since the advent of the first truly mass-market videogames consoles, people have dreamed about removing the protection mechanisms that prevent users from tinkering with their machines.

These modifications – which are software, hardware, or combination of the two – facilitate the running of third-party or “homebrew” code. On this front, a notable mention must go to XBMC (now known as Kodi) which ran on the original Xbox after its copy protection mechanisms had been removed.

However, these same modifications regularly open the door to mass-market piracy too, with mod-chips (hardware devices) or soft-mods (software solutions) opening up machines so that consumers can run games obtained from the Internet or elsewhere.

For the Nintendo Switch, that prospect edged closer at the end of December when Wololo reported that hackers Plutoo, Derrek, and Naehrwert had given a long presentation (video) at the 34C3 hacking conference in Germany, revealing their kernel hack for the Nintendo Switch.

While this in itself is an exciting development, fresh news from a veteran hacking group suggests that Nintendo could be in big trouble on the piracy front in the not-too-distant future.

“In the light of a recent presentation at the Chaos Communication Congress in Germany we’ve decided to come out of the woodwork and tease you all a bit with our latest upcoming product,” the legendary Team-Xecutor just announced.

While the hack announced in December requires Switch firmware 3.0 (and a copy of Pokken Tournament DX), Team-Xecutor say that their product will be universal, something which tends to suggest a fundamental flaw in the Switch system.

“This solution will work on ANY Nintendo Switch console regardless of the currently installed firmware, and will be completely future proof,” the team explain.

Xecutor say that their solution opens up the possibility of custom firmware (CFW) on Nintendo’s console. In layman’s terms, this means that those with the technical ability will be able to dictate, at least to a point, how the console functions.

“We want to move the community forward and provide a persistent, stable and fast method of running your own code and custom firmware patches on Nintendo’s latest flagship product. And we think we’ve succeeded!” the team add.

The console-modding community thrives on rumors, with various parties claiming to have made progress here and there, on this console and that, so it’s natural for people to greet this kind of announcement with a degree of skepticism. That being said, Team-Xecutor is no regular group.

With a long history of console-based meddling, Team-Xecutor’s efforts include hardware solutions for the original Playstation and Playstation 2, an array of hacks for the original Xbox (Enigmah and various Xecuter-branded solutions), plus close involvement in prominent Xbox360 mods. Their pedigree is definitely not up for debate.

For now, the team isn’t releasing any more details on the nature of the hack but they have revealed when the public can expect to get their hands on it.

“Spring 2018 or there around,” they conclude.

Team-Xecutor demo

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Random with care

Post Syndicated from Eevee original https://eev.ee/blog/2018/01/02/random-with-care/

Hi! Here are a few loose thoughts about picking random numbers.

A word about crypto

DON’T ROLL YOUR OWN CRYPTO

This is all aimed at frivolous pursuits like video games. Hell, even video games where money is at stake should be deferring to someone who knows way more than I do. Otherwise you might find out that your deck shuffles in your poker game are woefully inadequate and some smartass is cheating you out of millions. (If your random number generator has fewer than 226 bits of state, it can’t even generate every possible shuffling of a deck of cards!)

Use the right distribution

Most languages have a random number primitive that spits out a number uniformly in the range [0, 1), and you can go pretty far with just that. But beware a few traps!

Random pitches

Say you want to pitch up a sound by a random amount, perhaps up to an octave. Your audio API probably has a way to do this that takes a pitch multiplier, where I say “probably” because that’s how the only audio API I’ve used works.

Easy peasy. If 1 is unchanged and 2 is pitched up by an octave, then all you need is rand() + 1. Right?

No! Pitch is exponential — within the same octave, the “gap” between C and C♯ is about half as big as the gap between B and the following C. If you pick a pitch multiplier uniformly, you’ll have a noticeable bias towards the higher pitches.

One octave corresponds to a doubling of pitch, so if you want to pick a random note, you want 2 ** rand().

Random directions

For two dimensions, you can just pick a random angle with rand() * TAU.

If you want a vector rather than an angle, or if you want a random direction in three dimensions, it’s a little trickier. You might be tempted to just pick a random point where each component is rand() * 2 - 1 (ranging from −1 to 1), but that’s not quite right. A direction is a point on the surface (or, equivalently, within the volume) of a sphere, and picking each component independently produces a point within the volume of a cube; the result will be a bias towards the corners of the cube, where there’s much more extra volume beyond the sphere.

No? Well, just trust me. I don’t know how to make a diagram for this.

Anyway, you could use the Pythagorean theorem a few times and make a huge mess of things, or it turns out there’s a really easy way that even works for two or four or any number of dimensions. You pick each coordinate from a Gaussian (normal) distribution, then normalize the resulting vector. In other words, using Python’s random module:

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def random_direction():
    x = random.gauss(0, 1)
    y = random.gauss(0, 1)
    z = random.gauss(0, 1)
    r = math.sqrt(x*x + y*y + z*z)
    return x/r, y/r, z/r

Why does this work? I have no idea!

Note that it is possible to get zero (or close to it) for every component, in which case the result is nonsense. You can re-roll all the components if necessary; just check that the magnitude (or its square) is less than some epsilon, which is equivalent to throwing away a tiny sphere at the center and shouldn’t affect the distribution.

Beware Gauss

Since I brought it up: the Gaussian distribution is a pretty nice one for choosing things in some range, where the middle is the common case and should appear more frequently.

That said, I never use it, because it has one annoying drawback: the Gaussian distribution has no minimum or maximum value, so you can’t really scale it down to the range you want. In theory, you might get any value out of it, with no limit on scale.

In practice, it’s astronomically rare to actually get such a value out. I did a hundred million trials just to see what would happen, and the largest value produced was 5.8.

But, still, I’d rather not knowingly put extremely rare corner cases in my code if I can at all avoid it. I could clamp the ends, but that would cause unnatural bunching at the endpoints. I could reroll if I got a value outside some desired range, but I prefer to avoid rerolling when I can, too; after all, it’s still (astronomically) possible to have to reroll for an indefinite amount of time. (Okay, it’s really not, since you’ll eventually hit the period of your PRNG. Still, though.) I don’t bend over backwards here — I did just say to reroll when picking a random direction, after all — but when there’s a nicer alternative I’ll gladly use it.

And lo, there is a nicer alternative! Enter the beta distribution. It always spits out a number in [0, 1], so you can easily swap it in for the standard normal function, but it takes two “shape” parameters α and β that alter its behavior fairly dramatically.

With α = β = 1, the beta distribution is uniform, i.e. no different from rand(). As α increases, the distribution skews towards the right, and as β increases, the distribution skews towards the left. If α = β, the whole thing is symmetric with a hump in the middle. The higher either one gets, the more extreme the hump (meaning that value is far more common than any other). With a little fiddling, you can get a number of interesting curves.

Screenshots don’t really do it justice, so here’s a little Wolfram widget that lets you play with α and β live:

Note that if α = 1, then 1 is a possible value; if β = 1, then 0 is a possible value. You probably want them both greater than 1, which clamps the endpoints to zero.

Also, it’s possible to have either α or β or both be less than 1, but this creates very different behavior: the corresponding endpoints become poles.

Anyway, something like α = β = 3 is probably close enough to normal for most purposes but already clamped for you. And you could easily replicate something like, say, NetHack’s incredibly bizarre rnz function.

Random frequency

Say you want some event to have an 80% chance to happen every second. You (who am I kidding, I) might be tempted to do something like this:

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if random() < 0.8 * dt:
    do_thing()

In an ideal world, dt is always the same and is equal to 1 / f, where f is the framerate. Replace that 80% with a variable, say P, and every tic you have a P / f chance to do the… whatever it is.

Each second, f tics pass, so you’ll make this check f times. The chance that any check succeeds is the inverse of the chance that every check fails, which is \(1 – \left(1 – \frac{P}{f}\right)^f\).

For P of 80% and a framerate of 60, that’s a total probability of 55.3%. Wait, what?

Consider what happens if the framerate is 2. On the first tic, you roll 0.4 twice — but probabilities are combined by multiplying, and splitting work up by dt only works for additive quantities. You lose some accuracy along the way. If you’re dealing with something that multiplies, you need an exponent somewhere.

But in this case, maybe you don’t want that at all. Each separate roll you make might independently succeed, so it’s possible (but very unlikely) that the event will happen 60 times within a single second! Or 200 times, if that’s someone’s framerate.

If you explicitly want something to have a chance to happen on a specific interval, you have to check on that interval. If you don’t have a gizmo handy to run code on an interval, it’s easy to do yourself with a time buffer:

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timer += dt
# here, 1 is the "every 1 seconds"
while timer > 1:
    timer -= 1
    if random() < 0.8:
        do_thing()

Using while means rolls still happen even if you somehow skipped over an entire second.

(For the curious, and the nerds who already noticed: the expression \(1 – \left(1 – \frac{P}{f}\right)^f\) converges to a specific value! As the framerate increases, it becomes a better and better approximation for \(1 – e^{-P}\), which for the example above is 0.551. Hey, 60 fps is pretty accurate — it’s just accurately representing something nowhere near what I wanted. Er, you wanted.)

Rolling your own

Of course, you can fuss with the classic [0, 1] uniform value however you want. If I want a bias towards zero, I’ll often just square it, or multiply two of them together. If I want a bias towards one, I’ll take a square root. If I want something like a Gaussian/normal distribution, but with clearly-defined endpoints, I might add together n rolls and divide by n. (The normal distribution is just what you get if you roll infinite dice and divide by infinity!)

It’d be nice to be able to understand exactly what this will do to the distribution. Unfortunately, that requires some calculus, which this post is too small to contain, and which I didn’t even know much about myself until I went down a deep rabbit hole while writing, and which in many cases is straight up impossible to express directly.

Here’s the non-calculus bit. A source of randomness is often graphed as a PDF — a probability density function. You’ve almost certainly seen a bell curve graphed, and that’s a PDF. They’re pretty nice, since they do exactly what they look like: they show the relative chance that any given value will pop out. On a bog standard bell curve, there’s a peak at zero, and of course zero is the most common result from a normal distribution.

(Okay, actually, since the results are continuous, it’s vanishingly unlikely that you’ll get exactly zero — but you’re much more likely to get a value near zero than near any other number.)

For the uniform distribution, which is what a classic rand() gives you, the PDF is just a straight horizontal line — every result is equally likely.


If there were a calculus bit, it would go here! Instead, we can cheat. Sometimes. Mathematica knows how to work with probability distributions in the abstract, and there’s a free web version you can use. For the example of squaring a uniform variable, try this out:

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PDF[TransformedDistribution[u^2, u \[Distributed] UniformDistribution[{0, 1}]], u]

(The \[Distributed] is a funny tilde that doesn’t exist in Unicode, but which Mathematica uses as a first-class operator. Also, press shiftEnter to evaluate the line.)

This will tell you that the distribution is… \(\frac{1}{2\sqrt{u}}\). Weird! You can plot it:

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Plot[%, {u, 0, 1}]

(The % refers to the result of the last thing you did, so if you want to try several of these, you can just do Plot[PDF[…], u] directly.)

The resulting graph shows that numbers around zero are, in fact, vastly — infinitely — more likely than anything else.

What about multiplying two together? I can’t figure out how to get Mathematica to understand this, but a great amount of digging revealed that the answer is -ln x, and from there you can plot them both on Wolfram Alpha. They’re similar, though squaring has a much better chance of giving you high numbers than multiplying two separate rolls — which makes some sense, since if either of two rolls is a low number, the product will be even lower.

What if you know the graph you want, and you want to figure out how to play with a uniform roll to get it? Good news! That’s a whole thing called inverse transform sampling. All you have to do is take an integral. Good luck!


This is all extremely ridiculous. New tactic: Just Simulate The Damn Thing. You already have the code; run it a million times, make a histogram, and tada, there’s your PDF. That’s one of the great things about computers! Brute-force numerical answers are easy to come by, so there’s no excuse for producing something like rnz. (Though, be sure your histogram has sufficiently narrow buckets — I tried plotting one for rnz once and the weird stuff on the left side didn’t show up at all!)

By the way, I learned something from futzing with Mathematica here! Taking the square root (to bias towards 1) gives a PDF that’s a straight diagonal line, nothing like the hyperbola you get from squaring (to bias towards 0). How do you get a straight line the other way? Surprise: \(1 – \sqrt{1 – u}\).

Okay, okay, here’s the actual math

I don’t claim to have a very firm grasp on this, but I had a hell of a time finding it written out clearly, so I might as well write it down as best I can. This was a great excuse to finally set up MathJax, too.

Say \(u(x)\) is the PDF of the original distribution and \(u\) is a representative number you plucked from that distribution. For the uniform distribution, \(u(x) = 1\). Or, more accurately,

$$
u(x) = \begin{cases}
1 & \text{ if } 0 \le x \lt 1 \\
0 & \text{ otherwise }
\end{cases}
$$

Remember that \(x\) here is a possible outcome you want to know about, and the PDF tells you the relative probability that a roll will be near it. This PDF spits out 1 for every \(x\), meaning every number between 0 and 1 is equally likely to appear.

We want to do something to that PDF, which creates a new distribution, whose PDF we want to know. I’ll use my original example of \(f(u) = u^2\), which creates a new PDF \(v(x)\).

The trick is that we need to work in terms of the cumulative distribution function for \(u\). Where the PDF gives the relative chance that a roll will be (“near”) a specific value, the CDF gives the relative chance that a roll will be less than a specific value.

The conventions for this seem to be a bit fuzzy, and nobody bothers to explain which ones they’re using, which makes this all the more confusing to read about… but let’s write the CDF with a capital letter, so we have \(U(x)\). In this case, \(U(x) = x\), a straight 45° line (at least between 0 and 1). With the definition I gave, this should make sense. At some arbitrary point like 0.4, the value of the PDF is 1 (0.4 is just as likely as anything else), and the value of the CDF is 0.4 (you have a 40% chance of getting a number from 0 to 0.4).

Calculus ahoy: the PDF is the derivative of the CDF, which means it measures the slope of the CDF at any point. For \(U(x) = x\), the slope is always 1, and indeed \(u(x) = 1\). See, calculus is easy.

Okay, so, now we’re getting somewhere. What we want is the CDF of our new distribution, \(V(x)\). The CDF is defined as the probability that a roll \(v\) will be less than \(x\), so we can literally write:

$$V(x) = P(v \le x)$$

(This is why we have to work with CDFs, rather than PDFs — a PDF gives the chance that a roll will be “nearby,” whatever that means. A CDF is much more concrete.)

What is \(v\), exactly? We defined it ourselves; it’s the do something applied to a roll from the original distribution, or \(f(u)\).

$$V(x) = P\!\left(f(u) \le x\right)$$

Now the first tricky part: we have to solve that inequality for \(u\), which means we have to do something, backwards to \(x\).

$$V(x) = P\!\left(u \le f^{-1}(x)\right)$$

Almost there! We now have a probability that \(u\) is less than some value, and that’s the definition of a CDF!

$$V(x) = U\!\left(f^{-1}(x)\right)$$

Hooray! Now to turn these CDFs back into PDFs, all we need to do is differentiate both sides and use the chain rule. If you never took calculus, don’t worry too much about what that means!

$$v(x) = u\!\left(f^{-1}(x)\right)\left|\frac{d}{dx}f^{-1}(x)\right|$$

Wait! Where did that absolute value come from? It takes care of whether \(f(x)\) increases or decreases. It’s the least interesting part here by far, so, whatever.

There’s one more magical part here when using the uniform distribution — \(u(\dots)\) is always equal to 1, so that entire term disappears! (Note that this only works for a uniform distribution with a width of 1; PDFs are scaled so the entire area under them sums to 1, so if you had a rand() that could spit out a number between 0 and 2, the PDF would be \(u(x) = \frac{1}{2}\).)

$$v(x) = \left|\frac{d}{dx}f^{-1}(x)\right|$$

So for the specific case of modifying the output of rand(), all we have to do is invert, then differentiate. The inverse of \(f(u) = u^2\) is \(f^{-1}(x) = \sqrt{x}\) (no need for a ± since we’re only dealing with positive numbers), and differentiating that gives \(v(x) = \frac{1}{2\sqrt{x}}\). Done! This is also why square root comes out nicer; inverting it gives \(x^2\), and differentiating that gives \(2x\), a straight line.

Incidentally, that method for turning a uniform distribution into any distribution — inverse transform sampling — is pretty much the same thing in reverse: integrate, then invert. For example, when I saw that taking the square root gave \(v(x) = 2x\), I naturally wondered how to get a straight line going the other way, \(v(x) = 2 – 2x\). Integrating that gives \(2x – x^2\), and then you can use the quadratic formula (or just ask Wolfram Alpha) to solve \(2x – x^2 = u\) for \(x\) and get \(f(u) = 1 – \sqrt{1 – u}\).

Multiply two rolls is a bit more complicated; you have to write out the CDF as an integral and you end up doing a double integral and wow it’s a mess. The only thing I’ve retained is that you do a division somewhere, which then gets integrated, and that’s why it ends up as \(-\ln x\).

And that’s quite enough of that! (Okay but having math in my blog is pretty cool and I will definitely be doing more of this, sorry, not sorry.)

Random vs varied

Sometimes, random isn’t actually what you want. We tend to use the word “random” casually to mean something more like chaotic, i.e., with no discernible pattern. But that’s not really random. In fact, given how good humans can be at finding incidental patterns, they aren’t all that unlikely! Consider that when you roll two dice, they’ll come up either the same or only one apart almost half the time. Coincidence? Well, yes.

If you ask for randomness, you’re saying that any outcome — or series of outcomes — is acceptable, including five heads in a row or five tails in a row. Most of the time, that’s fine. Some of the time, it’s less fine, and what you really want is variety. Here are a couple examples and some fairly easy workarounds.

NPC quips

The nature of games is such that NPCs will eventually run out of things to say, at which point further conversation will give the player a short brush-off quip — a slight nod from the designer to the player that, hey, you hit the end of the script.

Some NPCs have multiple possible quips and will give one at random. The trouble with this is that it’s very possible for an NPC to repeat the same quip several times in a row before abruptly switching to another one. With only a few options to choose from, getting the same option twice or thrice (especially across an entire game, which may have numerous NPCs) isn’t all that unlikely. The notion of an NPC quip isn’t very realistic to start with, but having someone repeat themselves and then abruptly switch to something else is especially jarring.

The easy fix is to show the quips in order! Paradoxically, this is more consistently varied than choosing at random — the original “order” is likely to be meaningless anyway, and it already has the property that the same quip can never appear twice in a row.

If you like, you can shuffle the list of quips every time you reach the end, but take care here — it’s possible that the last quip in the old order will be the same as the first quip in the new order, so you may still get a repeat. (Of course, you can just check for this case and swap the first quip somewhere else if it bothers you.)

That last behavior is, in fact, the canonical way that Tetris chooses pieces — the game simply shuffles a list of all 7 pieces, gives those to you in shuffled order, then shuffles them again to make a new list once it’s exhausted. There’s no avoidance of duplicates, though, so you can still get two S blocks in a row, or even two S and two Z all clumped together, but no more than that. Some Tetris variants take other approaches, such as actively avoiding repeats even several pieces apart or deliberately giving you the worst piece possible.

Random drops

Random drops are often implemented as a flat chance each time. Maybe enemies have a 5% chance to drop health when they die. Legally speaking, over the long term, a player will see health drops for about 5% of enemy kills.

Over the short term, they may be desperate for health and not survive to see the long term. So you may want to put a thumb on the scale sometimes. Games in the Metroid series, for example, have a somewhat infamous bias towards whatever kind of drop they think you need — health if your health is low, missiles if your missiles are low.

I can’t give you an exact approach to use, since it depends on the game and the feeling you’re going for and the variables at your disposal. In extreme cases, you might want to guarantee a health drop from a tough enemy when the player is critically low on health. (Or if you’re feeling particularly evil, you could go the other way and deny the player health when they most need it…)

The problem becomes a little different, and worse, when the event that triggers the drop is relatively rare. The pathological case here would be something like a raid boss in World of Warcraft, which requires hours of effort from a coordinated group of people to defeat, and which has some tiny chance of dropping a good item that will go to only one of those people. This is why I stopped playing World of Warcraft at 60.

Dialing it back a little bit gives us Enter the Gungeon, a roguelike where each room is a set of encounters and each floor only has a dozen or so rooms. Initially, you have a 1% chance of getting a reward after completing a room — but every time you complete a room and don’t get a reward, the chance increases by 9%, up to a cap of 80%. Once you get a reward, the chance resets to 1%.

The natural question is: how frequently, exactly, can a player expect to get a reward? We could do math, or we could Just Simulate The Damn Thing.

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from collections import Counter
import random

histogram = Counter()

TRIALS = 1000000
chance = 1
rooms_cleared = 0
rewards_found = 0
while rewards_found < TRIALS:
    rooms_cleared += 1
    if random.random() * 100 < chance:
        # Reward!
        rewards_found += 1
        histogram[rooms_cleared] += 1
        rooms_cleared = 0
        chance = 1
    else:
        chance = min(80, chance + 9)

for gaps, count in sorted(histogram.items()):
    print(f"{gaps:3d} | {count / TRIALS * 100:6.2f}%", '#' * (count // (TRIALS // 100)))
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  1 |   0.98%
  2 |   9.91% #########
  3 |  17.00% ################
  4 |  20.23% ####################
  5 |  19.21% ###################
  6 |  15.05% ###############
  7 |   9.69% #########
  8 |   5.07% #####
  9 |   2.09% ##
 10 |   0.63%
 11 |   0.12%
 12 |   0.03%
 13 |   0.00%
 14 |   0.00%
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We’ve got kind of a hilly distribution, skewed to the left, which is up in this histogram. Most of the time, a player should see a reward every three to six rooms, which is maybe twice per floor. It’s vanishingly unlikely to go through a dozen rooms without ever seeing a reward, so a player should see at least one per floor.

Of course, this simulated a single continuous playthrough; when starting the game from scratch, your chance at a reward always starts fresh at 1%, the worst it can be. If you want to know about how many rewards a player will get on the first floor, hey, Just Simulate The Damn Thing.

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  0 |   0.01%
  1 |  13.01% #############
  2 |  56.28% ########################################################
  3 |  27.49% ###########################
  4 |   3.10% ###
  5 |   0.11%
  6 |   0.00%

Cool. Though, that’s assuming exactly 12 rooms; it might be worth changing that to pick at random in a way that matches the level generator.

(Enter the Gungeon does some other things to skew probability, which is very nice in a roguelike where blind luck can make or break you. For example, if you kill a boss without having gotten a new gun anywhere else on the floor, the boss is guaranteed to drop a gun.)

Critical hits

I suppose this is the same problem as random drops, but backwards.

Say you have a battle sim where every attack has a 6% chance to land a devastating critical hit. Presumably the same rules apply to both the player and the AI opponents.

Consider, then, that the AI opponents have exactly the same 6% chance to ruin the player’s day. Consider also that this gives them an 0.4% chance to critical hit twice in a row. 0.4% doesn’t sound like much, but across an entire playthrough, it’s not unlikely that a player might see it happen and find it incredibly annoying.

Perhaps it would be worthwhile to explicitly forbid AI opponents from getting consecutive critical hits.

In conclusion

An emerging theme here has been to Just Simulate The Damn Thing. So consider Just Simulating The Damn Thing. Even a simple change to a random value can do surprising things to the resulting distribution, so unless you feel like differentiating the inverse function of your code, maybe test out any non-trivial behavior and make sure it’s what you wanted. Probability is hard to reason about.

OWASP Dependency Check Maven Plugin – a Must-Have

Post Syndicated from Bozho original https://techblog.bozho.net/owasp-dependency-check-maven-plugin-must/

I have to admit with a high degree of shame that I didn’t know about the OWASP dependency check maven plugin. And seems to have been around since 2013. And apparently a thousand projects on GitHub are using it already.

In the past I’ve gone manually through dependencies to check them against vulnerability databases, or in many cases I was just blissfully ignorant about any vulnerabilities that my dependencies had.

The purpose of this post is just that – to recommend the OWASP dependency check maven plugin as a must-have in practically every maven project. (There are dependency-check tools for other build systems as well).

When you add the plugin it generates a report. Initially you can go and manually upgrade the problematic dependencies (I upgraded two of those in my current project), or suppress the false positives (e.g. the cassandra library is marked as vulnerable, whereas the actual vulnerability is that Cassandra binds an unauthenticated RMI endpoint, which I’ve addressed via my stack setup, so the library isn’t an issue).

Then you can configure a threshold for vulnerabilities and fail the build if new ones appear – either by you adding a vulnerable dependency, or in case a vulnerability is discovered in an existing dependency.

All of that is shown in the examples page and is pretty straightforward. I’d suggest adding the plugin immediately, it’s a must-have:

<plugin>
	<groupId>org.owasp</groupId>
	<artifactId>dependency-check-maven</artifactId>
	<version>3.0.2</version>
	<executions>
		<execution>
			<goals>
				<goal>check</goal>
			</goals>
		</execution>
	</executions>
</plugin>

Now, checking dependencies for vulnerabilities is just one small aspect of having your software secure and it shouldn’t give you a false sense of security (a sort-of “I have my dependencies checked, therefore my system is secure” fallacy). But it’s an important aspect. And having that check automated is a huge gain.

The post OWASP Dependency Check Maven Plugin – a Must-Have appeared first on Bozho's tech blog.

Managing AWS Lambda Function Concurrency

Post Syndicated from Chris Munns original https://aws.amazon.com/blogs/compute/managing-aws-lambda-function-concurrency/

One of the key benefits of serverless applications is the ease in which they can scale to meet traffic demands or requests, with little to no need for capacity planning. In AWS Lambda, which is the core of the serverless platform at AWS, the unit of scale is a concurrent execution. This refers to the number of executions of your function code that are happening at any given time.

Thinking about concurrent executions as a unit of scale is a fairly unique concept. In this post, I dive deeper into this and talk about how you can make use of per function concurrency limits in Lambda.

Understanding concurrency in Lambda

Instead of diving right into the guts of how Lambda works, here’s an appetizing analogy: a magical pizza.
Yes, a magical pizza!

This magical pizza has some unique properties:

  • It has a fixed maximum number of slices, such as 8.
  • Slices automatically re-appear after they are consumed.
  • When you take a slice from the pizza, it does not re-appear until it has been completely consumed.
  • One person can take multiple slices at a time.
  • You can easily ask to have the number of slices increased, but they remain fixed at any point in time otherwise.

Now that the magical pizza’s properties are defined, here’s a hypothetical situation of some friends sharing this pizza.

Shawn, Kate, Daniela, Chuck, Ian and Avleen get together every Friday to share a pizza and catch up on their week. As there is just six of them, they can easily all enjoy a slice of pizza at a time. As they finish each slice, it re-appears in the pizza pan and they can take another slice again. Given the magical properties of their pizza, they can continue to eat all they want, but with two very important constraints:

  • If any of them take too many slices at once, the others may not get as much as they want.
  • If they take too many slices, they might also eat too much and get sick.

One particular week, some of the friends are hungrier than the rest, taking two slices at a time instead of just one. If more than two of them try to take two pieces at a time, this can cause contention for pizza slices. Some of them would wait hungry for the slices to re-appear. They could ask for a pizza with more slices, but then run the same risk again later if more hungry friends join than planned for.

What can they do?

If the friends agreed to accept a limit for the maximum number of slices they each eat concurrently, both of these issues are avoided. Some could have a maximum of 2 of the 8 slices, or other concurrency limits that were more or less. Just so long as they kept it at or under eight total slices to be eaten at one time. This would keep any from going hungry or eating too much. The six friends can happily enjoy their magical pizza without worry!

Concurrency in Lambda

Concurrency in Lambda actually works similarly to the magical pizza model. Each AWS Account has an overall AccountLimit value that is fixed at any point in time, but can be easily increased as needed, just like the count of slices in the pizza. As of May 2017, the default limit is 1000 “slices” of concurrency per AWS Region.

Also like the magical pizza, each concurrency “slice” can only be consumed individually one at a time. After consumption, it becomes available to be consumed again. Services invoking Lambda functions can consume multiple slices of concurrency at the same time, just like the group of friends can take multiple slices of the pizza.

Let’s take our example of the six friends and bring it back to AWS services that commonly invoke Lambda:

  • Amazon S3
  • Amazon Kinesis
  • Amazon DynamoDB
  • Amazon Cognito

In a single account with the default concurrency limit of 1000 concurrent executions, any of these four services could invoke enough functions to consume the entire limit or some part of it. Just like with the pizza example, there is the possibility for two issues to pop up:

  • One or more of these services could invoke enough functions to consume a majority of the available concurrency capacity. This could cause others to be starved for it, causing failed invocations.
  • A service could consume too much concurrent capacity and cause a downstream service or database to be overwhelmed, which could cause failed executions.

For Lambda functions that are launched in a VPC, you have the potential to consume the available IP addresses in a subnet or the maximum number of elastic network interfaces to which your account has access. For more information, see Configuring a Lambda Function to Access Resources in an Amazon VPC. For information about elastic network interface limits, see Network Interfaces section in the Amazon VPC Limits topic.

One way to solve both of these problems is applying a concurrency limit to the Lambda functions in an account.

Configuring per function concurrency limits

You can now set a concurrency limit on individual Lambda functions in an account. The concurrency limit that you set reserves a portion of your account level concurrency for a given function. All of your functions’ concurrent executions count against this account-level limit by default.

If you set a concurrency limit for a specific function, then that function’s concurrency limit allocation is deducted from the shared pool and assigned to that specific function. AWS also reserves 100 units of concurrency for all functions that don’t have a specified concurrency limit set. This helps to make sure that future functions have capacity to be consumed.

Going back to the example of the consuming services, you could set throttles for the functions as follows:

Amazon S3 function = 350
Amazon Kinesis function = 200
Amazon DynamoDB function = 200
Amazon Cognito function = 150
Total = 900

With the 100 reserved for all non-concurrency reserved functions, this totals the account limit of 1000.

Here’s how this works. To start, create a basic Lambda function that is invoked via Amazon API Gateway. This Lambda function returns a single “Hello World” statement with an added sleep time between 2 and 5 seconds. The sleep time simulates an API providing some sort of capability that can take a varied amount of time. The goal here is to show how an API that is underloaded can reach its concurrency limit, and what happens when it does.
To create the example function

  1. Open the Lambda console.
  2. Choose Create Function.
  3. For Author from scratch, enter the following values:
    1. For Name, enter a value (such as concurrencyBlog01).
    2. For Runtime, choose Python 3.6.
    3. For Role, choose Create new role from template and enter a name aligned with this function, such as concurrencyBlogRole.
  4. Choose Create function.
  5. The function is created with some basic example code. Replace that code with the following:

import time
from random import randint
seconds = randint(2, 5)

def lambda_handler(event, context):
time.sleep(seconds)
return {"statusCode": 200,
"body": ("Hello world, slept " + str(seconds) + " seconds"),
"headers":
{
"Access-Control-Allow-Headers": "Content-Type,X-Amz-Date,Authorization,X-Api-Key,X-Amz-Security-Token",
"Access-Control-Allow-Methods": "GET,OPTIONS",
}}

  1. Under Basic settings, set Timeout to 10 seconds. While this function should only ever take up to 5-6 seconds (with the 5-second max sleep), this gives you a little bit of room if it takes longer.

  1. Choose Save at the top right.

At this point, your function is configured for this example. Test it and confirm this in the console:

  1. Choose Test.
  2. Enter a name (it doesn’t matter for this example).
  3. Choose Create.
  4. In the console, choose Test again.
  5. You should see output similar to the following:

Now configure API Gateway so that you have an HTTPS endpoint to test against.

  1. In the Lambda console, choose Configuration.
  2. Under Triggers, choose API Gateway.
  3. Open the API Gateway icon now shown as attached to your Lambda function:

  1. Under Configure triggers, leave the default values for API Name and Deployment stage. For Security, choose Open.
  2. Choose Add, Save.

API Gateway is now configured to invoke Lambda at the Invoke URL shown under its configuration. You can take this URL and test it in any browser or command line, using tools such as “curl”:


$ curl https://ofixul557l.execute-api.us-east-1.amazonaws.com/prod/concurrencyBlog01
Hello world, slept 2 seconds

Throwing load at the function

Now start throwing some load against your API Gateway + Lambda function combo. Right now, your function is only limited by the total amount of concurrency available in an account. For this example account, you might have 850 unreserved concurrency out of a full account limit of 1000 due to having configured a few concurrency limits already (also the 100 concurrency saved for all functions without configured limits). You can find all of this information on the main Dashboard page of the Lambda console:

For generating load in this example, use an open source tool called “hey” (https://github.com/rakyll/hey), which works similarly to ApacheBench (ab). You test from an Amazon EC2 instance running the default Amazon Linux AMI from the EC2 console. For more help with configuring an EC2 instance, follow the steps in the Launch Instance Wizard.

After the EC2 instance is running, SSH into the host and run the following:


sudo yum install go
go get -u github.com/rakyll/hey

“hey” is easy to use. For these tests, specify a total number of tests (5,000) and a concurrency of 50 against the API Gateway URL as follows(replace the URL here with your own):


$ ./go/bin/hey -n 5000 -c 50 https://ofixul557l.execute-api.us-east-1.amazonaws.com/prod/concurrencyBlog01

The output from “hey” tells you interesting bits of information:


$ ./go/bin/hey -n 5000 -c 50 https://ofixul557l.execute-api.us-east-1.amazonaws.com/prod/concurrencyBlog01

Summary:
Total: 381.9978 secs
Slowest: 9.4765 secs
Fastest: 0.0438 secs
Average: 3.2153 secs
Requests/sec: 13.0891
Total data: 140024 bytes
Size/request: 28 bytes

Response time histogram:
0.044 [1] |
0.987 [2] |
1.930 [0] |
2.874 [1803] |∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎
3.817 [1518] |∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎
4.760 [719] |∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎
5.703 [917] |∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎
6.647 [13] |
7.590 [14] |
8.533 [9] |
9.477 [4] |

Latency distribution:
10% in 2.0224 secs
25% in 2.0267 secs
50% in 3.0251 secs
75% in 4.0269 secs
90% in 5.0279 secs
95% in 5.0414 secs
99% in 5.1871 secs

Details (average, fastest, slowest):
DNS+dialup: 0.0003 secs, 0.0000 secs, 0.0332 secs
DNS-lookup: 0.0000 secs, 0.0000 secs, 0.0046 secs
req write: 0.0000 secs, 0.0000 secs, 0.0005 secs
resp wait: 3.2149 secs, 0.0438 secs, 9.4472 secs
resp read: 0.0000 secs, 0.0000 secs, 0.0004 secs

Status code distribution:
[200] 4997 responses
[502] 3 responses

You can see a helpful histogram and latency distribution. Remember that this Lambda function has a random sleep period in it and so isn’t entirely representational of a real-life workload. Those three 502s warrant digging deeper, but could be due to Lambda cold-start timing and the “second” variable being the maximum of 5, causing the Lambda functions to time out. AWS X-Ray and the Amazon CloudWatch logs generated by both API Gateway and Lambda could help you troubleshoot this.

Configuring a concurrency reservation

Now that you’ve established that you can generate this load against the function, I show you how to limit it and protect a backend resource from being overloaded by all of these requests.

  1. In the console, choose Configure.
  2. Under Concurrency, for Reserve concurrency, enter 25.

  1. Click on Save in the top right corner.

You could also set this with the AWS CLI using the Lambda put-function-concurrency command or see your current concurrency configuration via Lambda get-function. Here’s an example command:


$ aws lambda get-function --function-name concurrencyBlog01 --output json --query Concurrency
{
"ReservedConcurrentExecutions": 25
}

Either way, you’ve set the Concurrency Reservation to 25 for this function. This acts as both a limit and a reservation in terms of making sure that you can execute 25 concurrent functions at all times. Going above this results in the throttling of the Lambda function. Depending on the invoking service, throttling can result in a number of different outcomes, as shown in the documentation on Throttling Behavior. This change has also reduced your unreserved account concurrency for other functions by 25.

Rerun the same load generation as before and see what happens. Previously, you tested at 50 concurrency, which worked just fine. By limiting the Lambda functions to 25 concurrency, you should see rate limiting kick in. Run the same test again:


$ ./go/bin/hey -n 5000 -c 50 https://ofixul557l.execute-api.us-east-1.amazonaws.com/prod/concurrencyBlog01

While this test runs, refresh the Monitoring tab on your function detail page. You see the following warning message:

This is great! It means that your throttle is working as configured and you are now protecting your downstream resources from too much load from your Lambda function.

Here is the output from a new “hey” command:


$ ./go/bin/hey -n 5000 -c 50 https://ofixul557l.execute-api.us-east-1.amazonaws.com/prod/concurrencyBlog01
Summary:
Total: 379.9922 secs
Slowest: 7.1486 secs
Fastest: 0.0102 secs
Average: 1.1897 secs
Requests/sec: 13.1582
Total data: 164608 bytes
Size/request: 32 bytes

Response time histogram:
0.010 [1] |
0.724 [3075] |∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎
1.438 [0] |
2.152 [811] |∎∎∎∎∎∎∎∎∎∎∎
2.866 [11] |
3.579 [566] |∎∎∎∎∎∎∎
4.293 [214] |∎∎∎
5.007 [1] |
5.721 [315] |∎∎∎∎
6.435 [4] |
7.149 [2] |

Latency distribution:
10% in 0.0130 secs
25% in 0.0147 secs
50% in 0.0205 secs
75% in 2.0344 secs
90% in 4.0229 secs
95% in 5.0248 secs
99% in 5.0629 secs

Details (average, fastest, slowest):
DNS+dialup: 0.0004 secs, 0.0000 secs, 0.0537 secs
DNS-lookup: 0.0002 secs, 0.0000 secs, 0.0184 secs
req write: 0.0000 secs, 0.0000 secs, 0.0016 secs
resp wait: 1.1892 secs, 0.0101 secs, 7.1038 secs
resp read: 0.0000 secs, 0.0000 secs, 0.0005 secs

Status code distribution:
[502] 3076 responses
[200] 1924 responses

This looks fairly different from the last load test run. A large percentage of these requests failed fast due to the concurrency throttle failing them (those with the 0.724 seconds line). The timing shown here in the histogram represents the entire time it took to get a response between the EC2 instance and API Gateway calling Lambda and being rejected. It’s also important to note that this example was configured with an edge-optimized endpoint in API Gateway. You see under Status code distribution that 3076 of the 5000 requests failed with a 502, showing that the backend service from API Gateway and Lambda failed the request.

Other uses

Managing function concurrency can be useful in a few other ways beyond just limiting the impact on downstream services and providing a reservation of concurrency capacity. Here are two other uses:

  • Emergency kill switch
  • Cost controls

Emergency kill switch

On occasion, due to issues with applications I’ve managed in the past, I’ve had a need to disable a certain function or capability of an application. By setting the concurrency reservation and limit of a Lambda function to zero, you can do just that.

With the reservation set to zero every invocation of a Lambda function results in being throttled. You could then work on the related parts of the infrastructure or application that aren’t working, and then reconfigure the concurrency limit to allow invocations again.

Cost controls

While I mentioned how you might want to use concurrency limits to control the downstream impact to services or databases that your Lambda function might call, another resource that you might be cautious about is money. Setting the concurrency throttle is another way to help control costs during development and testing of your application.

You might want to prevent against a function performing a recursive action too quickly or a development workload generating too high of a concurrency. You might also want to protect development resources connected to this function from generating too much cost, such as APIs that your Lambda function calls.

Conclusion

Concurrent executions as a unit of scale are a fairly unique characteristic about Lambda functions. Placing limits on how many concurrency “slices” that your function can consume can prevent a single function from consuming all of the available concurrency in an account. Limits can also prevent a function from overwhelming a backend resource that isn’t as scalable.

Unlike monolithic applications or even microservices where there are mixed capabilities in a single service, Lambda functions encourage a sort of “nano-service” of small business logic directly related to the integration model connected to the function. I hope you’ve enjoyed this post and configure your concurrency limits today!

Looking Forward to 2018

Post Syndicated from Let's Encrypt - Free SSL/TLS Certificates original https://letsencrypt.org//2017/12/07/looking-forward-to-2018.html

Let’s Encrypt had a great year in 2017. We more than doubled the number of active (unexpired) certificates we service to 46 million, we just about tripled the number of unique domains we service to 61 million, and we did it all while maintaining a stellar security and compliance track record. Most importantly though, the Web went from 46% encrypted page loads to 67% according to statistics from Mozilla – a gain of 21% in a single year – incredible. We’re proud to have contributed to that, and we’d like to thank all of the other people and organizations who also worked hard to create a more secure and privacy-respecting Web.

While we’re proud of what we accomplished in 2017, we are spending most of the final quarter of the year looking forward rather than back. As we wrap up our own planning process for 2018, I’d like to share some of our plans with you, including both the things we’re excited about and the challenges we’ll face. We’ll cover service growth, new features, infrastructure, and finances.

Service Growth

We are planning to double the number of active certificates and unique domains we service in 2018, to 90 million and 120 million, respectively. This anticipated growth is due to continuing high expectations for HTTPS growth in general in 2018.

Let’s Encrypt helps to drive HTTPS adoption by offering a free, easy to use, and globally available option for obtaining the certificates required to enable HTTPS. HTTPS adoption on the Web took off at an unprecedented rate from the day Let’s Encrypt launched to the public.

One of the reasons Let’s Encrypt is so easy to use is that our community has done great work making client software that works well for a wide variety of platforms. We’d like to thank everyone involved in the development of over 60 client software options for Let’s Encrypt. We’re particularly excited that support for the ACME protocol and Let’s Encrypt is being added to the Apache httpd server.

Other organizations and communities are also doing great work to promote HTTPS adoption, and thus stimulate demand for our services. For example, browsers are starting to make their users more aware of the risks associated with unencrypted HTTP (e.g. Firefox, Chrome). Many hosting providers and CDNs are making it easier than ever for all of their customers to use HTTPS. Government agencies are waking up to the need for stronger security to protect constituents. The media community is working to Secure the News.

New Features

We’ve got some exciting features planned for 2018.

First, we’re planning to introduce an ACME v2 protocol API endpoint and support for wildcard certificates along with it. Wildcard certificates will be free and available globally just like our other certificates. We are planning to have a public test API endpoint up by January 4, and we’ve set a date for the full launch: Tuesday, February 27.

Later in 2018 we plan to introduce ECDSA root and intermediate certificates. ECDSA is generally considered to be the future of digital signature algorithms on the Web due to the fact that it is more efficient than RSA. Let’s Encrypt will currently sign ECDSA keys from subscribers, but we sign with the RSA key from one of our intermediate certificates. Once we have an ECDSA root and intermediates, our subscribers will be able to deploy certificate chains which are entirely ECDSA.

Infrastructure

Our CA infrastructure is capable of issuing millions of certificates per day with multiple redundancy for stability and a wide variety of security safeguards, both physical and logical. Our infrastructure also generates and signs nearly 20 million OCSP responses daily, and serves those responses nearly 2 billion times per day. We expect issuance and OCSP numbers to double in 2018.

Our physical CA infrastructure currently occupies approximately 70 units of rack space, split between two datacenters, consisting primarily of compute servers, storage, HSMs, switches, and firewalls.

When we issue more certificates it puts the most stress on storage for our databases. We regularly invest in more and faster storage for our database servers, and that will continue in 2018.

We’ll need to add a few additional compute servers in 2018, and we’ll also start aging out hardware in 2018 for the first time since we launched. We’ll age out about ten 2u compute servers and replace them with new 1u servers, which will save space and be more energy efficient while providing better reliability and performance.

We’ll also add another infrastructure operations staff member, bringing that team to a total of six people. This is necessary in order to make sure we can keep up with demand while maintaining a high standard for security and compliance. Infrastructure operations staff are systems administrators responsible for building and maintaining all physical and logical CA infrastructure. The team also manages a 24/7/365 on-call schedule and they are primary participants in both security and compliance audits.

Finances

We pride ourselves on being an efficient organization. In 2018 Let’s Encrypt will secure a large portion of the Web with a budget of only $3.0M. For an overall increase in our budget of only 13%, we will be able to issue and service twice as many certificates as we did in 2017. We believe this represents an incredible value and that contributing to Let’s Encrypt is one of the most effective ways to help create a more secure and privacy-respecting Web.

Our 2018 fundraising efforts are off to a strong start with Platinum sponsorships from Mozilla, Akamai, OVH, Cisco, Google Chrome and the Electronic Frontier Foundation. The Ford Foundation has renewed their grant to Let’s Encrypt as well. We are seeking additional sponsorship and grant assistance to meet our full needs for 2018.

We had originally budgeted $2.91M for 2017 but we’ll likely come in under budget for the year at around $2.65M. The difference between our 2017 expenses of $2.65M and the 2018 budget of $3.0M consists primarily of the additional infrastructure operations costs previously mentioned.

Support Let’s Encrypt

We depend on contributions from our community of users and supporters in order to provide our services. If your company or organization would like to sponsor Let’s Encrypt please email us at [email protected]. We ask that you make an individual contribution if it is within your means.

We’re grateful for the industry and community support that we receive, and we look forward to continuing to create a more secure and privacy-respecting Web!

Looking Forward to 2018

Post Syndicated from Let's Encrypt - Free SSL/TLS Certificates original https://letsencrypt.org/2017/12/07/looking-forward-to-2018.html

<p>Let’s Encrypt had a great year in 2017. We more than doubled the number of active (unexpired) certificates we service to 46 million, we just about tripled the number of unique domains we service to 61 million, and we did it all while maintaining a stellar security and compliance track record. Most importantly though, <a href="https://letsencrypt.org/stats/">the Web went from 46% encrypted page loads to 67%</a> according to statistics from Mozilla – a gain of 21 percentage points in a single year – incredible. We’re proud to have contributed to that, and we’d like to thank all of the other people and organizations who also worked hard to create a more secure and privacy-respecting Web.</p>

<p>While we’re proud of what we accomplished in 2017, we are spending most of the final quarter of the year looking forward rather than back. As we wrap up our own planning process for 2018, I’d like to share some of our plans with you, including both the things we’re excited about and the challenges we’ll face. We’ll cover service growth, new features, infrastructure, and finances.</p>

<h1 id="service-growth">Service Growth</h1>

<p>We are planning to double the number of active certificates and unique domains we service in 2018, to 90 million and 120 million, respectively. This anticipated growth is due to continuing high expectations for HTTPS growth in general in 2018.</p>

<p>Let’s Encrypt helps to drive HTTPS adoption by offering a free, easy to use, and globally available option for obtaining the certificates required to enable HTTPS. HTTPS adoption on the Web took off at an unprecedented rate from the day Let’s Encrypt launched to the public.</p>

<p>One of the reasons Let’s Encrypt is so easy to use is that our community has done great work making client software that works well for a wide variety of platforms. We’d like to thank everyone involved in the development of over 60 <a href="https://letsencrypt.org/docs/client-options/">client software options for Let’s Encrypt</a>. We’re particularly excited that support for the ACME protocol and Let’s Encrypt is <a href="https://letsencrypt.org/2017/10/17/acme-support-in-apache-httpd.html">being added to the Apache httpd server</a>.</p>

<p>Other organizations and communities are also doing great work to promote HTTPS adoption, and thus stimulate demand for our services. For example, browsers are starting to make their users more aware of the risks associated with unencrypted HTTP (e.g. <a href="https://blog.mozilla.org/security/2017/01/20/communicating-the-dangers-of-non-secure-http/">Firefox</a>, <a href="https://security.googleblog.com/2017/04/next-steps-toward-more-connection.html">Chrome</a>). Many hosting providers and CDNs are making it easier than ever for all of their customers to use HTTPS. <a href="https://https.cio.gov/">Government</a> <a href="https://www.canada.ca/en/treasury-board-secretariat/services/information-technology/strategic-plan-2017-2021.html#toc8-3-2">agencies</a> are waking up to the need for stronger security to protect constituents. The media community is working to <a href="https://securethe.news/">Secure the News</a>.</p>

<h1 id="new-features">New Features</h1>

<p>We’ve got some exciting features planned for 2018.</p>

<p>First, we’re planning to introduce an ACME v2 protocol API endpoint and <a href="https://letsencrypt.org/2017/07/06/wildcard-certificates-coming-jan-2018.html">support for wildcard certificates</a> along with it. Wildcard certificates will be free and available globally just like our other certificates. We are planning to have a public test API endpoint up by January 4, and we’ve set a date for the full launch: Tuesday, February 27.</p>

<p>Later in 2018 we plan to introduce ECDSA root and intermediate certificates. ECDSA is generally considered to be the future of digital signature algorithms on the Web due to the fact that it is more efficient than RSA. Let’s Encrypt will currently sign ECDSA keys from subscribers, but we sign with the RSA key from one of our intermediate certificates. Once we have an ECDSA root and intermediates, our subscribers will be able to deploy certificate chains which are entirely ECDSA.</p>

<h1 id="infrastructure">Infrastructure</h1>

<p>Our CA infrastructure is capable of issuing millions of certificates per day with multiple redundancy for stability and a wide variety of security safeguards, both physical and logical. Our infrastructure also generates and signs nearly 20 million OCSP responses daily, and serves those responses nearly 2 billion times per day. We expect issuance and OCSP numbers to double in 2018.</p>

<p>Our physical CA infrastructure currently occupies approximately 70 units of rack space, split between two datacenters, consisting primarily of compute servers, storage, HSMs, switches, and firewalls.</p>

<p>When we issue more certificates it puts the most stress on storage for our databases. We regularly invest in more and faster storage for our database servers, and that will continue in 2018.</p>

<p>We’ll need to add a few additional compute servers in 2018, and we’ll also start aging out hardware in 2018 for the first time since we launched. We’ll age out about ten 2u compute servers and replace them with new 1u servers, which will save space and be more energy efficient while providing better reliability and performance.</p>

<p>We’ll also add another infrastructure operations staff member, bringing that team to a total of six people. This is necessary in order to make sure we can keep up with demand while maintaining a high standard for security and compliance. Infrastructure operations staff are systems administrators responsible for building and maintaining all physical and logical CA infrastructure. The team also manages a 24/7/365 on-call schedule and they are primary participants in both security and compliance audits.</p>

<h1 id="finances">Finances</h1>

<p>We pride ourselves on being an efficient organization. In 2018 Let’s Encrypt will secure a large portion of the Web with a budget of only $3.0M. For an overall increase in our budget of only 13%, we will be able to issue and service twice as many certificates as we did in 2017. We believe this represents an incredible value and that contributing to Let’s Encrypt is one of the most effective ways to help create a more secure and privacy-respecting Web.</p>

<p>Our 2018 fundraising efforts are off to a strong start with Platinum sponsorships from Mozilla, Akamai, OVH, Cisco, Google Chrome and the Electronic Frontier Foundation. The Ford Foundation has renewed their grant to Let’s Encrypt as well. We are seeking additional sponsorship and grant assistance to meet our full needs for 2018.</p>

<p>We had originally budgeted $2.91M for 2017 but we’ll likely come in under budget for the year at around $2.65M. The difference between our 2017 expenses of $2.65M and the 2018 budget of $3.0M consists primarily of the additional infrastructure operations costs previously mentioned.</p>

<h1 id="support-let-s-encrypt">Support Let’s Encrypt</h1>

<p>We depend on contributions from our community of users and supporters in order to provide our services. If your company or organization would like to <a href="https://letsencrypt.org/become-a-sponsor/">sponsor</a> Let’s Encrypt please email us at <a href="mailto:[email protected]">[email protected]</a>. We ask that you make an <a href="https://letsencrypt.org/donate/">individual contribution</a> if it is within your means.</p>

<p>We’re grateful for the industry and community support that we receive, and we look forward to continuing to create a more secure and privacy-respecting Web!</p>

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!

The post Stretch for PCs and Macs, and a Raspbian update appeared first on Raspberry Pi.

H1 Instances – Fast, Dense Storage for Big Data Applications

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-h1-instances-fast-dense-storage-for-big-data-applications/

The scale of AWS and the diversity of our customer base gives us the opportunity to create EC2 instance types that are purpose-built for many different types of workloads. For example, a number of popular big data use cases depend on high-speed, sequential access to multiple terabytes of data. Our customers want to build and run very large MapReduce clusters, host distributed file systems, use Apache Kafka to process voluminous log files, and so forth.

New H1 Instances
The new H1 instances are designed specifically for this use case. In comparison to the existing D2 (dense storage) instances, the H1 instances provide more vCPUs and more memory per terabyte of local magnetic storage, along with increased network bandwidth, giving you the power to address more complex challenges with a nicely balanced mix of resources.

The instances are based on Intel Xeon E5-2686 v4 processors running at a base clock frequency of 2.3 GHz and come in four instance sizes (all VPC-only and HVM-only):

Instance Name vCPUs
RAM
Local Storage Network Bandwidth
h1.2xlarge 8 32 GiB 2 TB Up to 10 Gbps
h1.4xlarge 16 64 GiB 4 TB Up to 10 Gbps
h1.8xlarge 32 128 GiB 8 TB 10 Gbps
h1.16xlarge 64 256 GiB 16 TB 25 Gbps

The two largest sizes support Intel Turbo and CPU power management, with all-core Turbo at 2.7 GHz and single-core Turbo at 3.0 GHz.

Local storage is optimized to deliver high throughput for sequential I/O; you can expect to transfer up to 1.15 gigabytes per second if you use a 2 megabyte block size. The storage is encrypted at rest using 256-bit XTS-AES and one-time keys.

Moving large amounts of data on and off of these instances is facilitated by the use of Enhanced Networking, giving you up to 25 Gbps of network bandwith within Placement Groups.

Launch One Today
H1 instances are available today in the US East (Northern Virginia), US West (Oregon), US East (Ohio), and EU (Ireland) Regions. You can launch them in On-Demand or Spot Form. Dedicated Hosts, Dedicated Instances, and Reserved Instances (both 1-year and 3-year) are also available.

Jeff;

Don Jr.: I’ll bite

Post Syndicated from Robert Graham original http://blog.erratasec.com/2017/11/don-jr-ill-bite.html

So Don Jr. tweets the following, which is an excellent troll. So I thought I’d bite. The reason is I just got through debunk Democrat claims about NetNeutrality, so it seems like a good time to balance things out and debunk Trump nonsense.

The issue here is not which side is right. The issue here is whether you stand for truth, or whether you’ll seize any factoid that appears to support your side, regardless of the truthfulness of it. The ACLU obviously chose falsehoods, as I documented. In the following tweet, Don Jr. does the same.

It’s a preview of the hyperpartisan debates are you are likely to have across the dinner table tomorrow, which each side trying to outdo the other in the false-hoods they’ll claim.

What we see in this number is a steady trend of these statistics since the Great Recession, with no evidence in the graphs showing how Trump has influenced these numbers, one way or the other.

Stock markets at all time highs

This is true, but it’s obviously not due to Trump. The stock markers have been steadily rising since the Great Recession. Trump has done nothing substantive to change the market trajectory. Also, he hasn’t inspired the market to change it’s direction.
To be fair to Don Jr., we’ve all been crediting (or blaming) presidents for changes in the stock market despite the fact they have almost no influence over it. Presidents don’t run the economy, it’s an inappropriate conceit. The most influence they’ve had is in harming it.

Lowest jobless claims since 73

Again, let’s graph this:

As we can see, jobless claims have been on a smooth downward trajectory since the Great Recession. It’s difficult to see here how President Trump has influenced these numbers.

6 Trillion added to the economy

What he’s referring to is that assets have risen in value, like the stock market, homes, gold, and even Bitcoin.
But this is a well known fallacy known as Mercantilism, believing the “economy” is measured by the value of its assets. This was debunked by Adam Smith in his book “The Wealth of Nations“, where he showed instead the the “economy” is measured by how much it produces (GDP – Gross Domestic Product) and not assets.
GDP has grown at 3.0%, which is pretty good compared to the long term trend, and is better than Europe or Japan (though not as good as China). But Trump doesn’t deserve any credit for this — today’s rise in GDP is the result of stuff that happened years ago.
Assets have risen by $6 trillion, but that’s not a good thing. After all, when you sell your home for more money, the buyer has to pay more. So one person is better off and one is worse off, so the net effect is zero.
Actually, such asset price increase is a worrisome indicator — we are entering into bubble territory. It’s the result of a loose monetary policy, low interest rates and “quantitative easing” that was designed under the Obama administration to stimulate the economy. That’s why all assets are rising in value. Normally, a rise in one asset means a fall in another, like selling gold to pay for houses. But because of loose monetary policy, all assets are increasing in price. The amazing rise in Bitcoin over the last year is as much a result of this bubble growing in all assets as it is to an exuberant belief in Bitcoin.
When this bubble collapses, which may happen during Trump’s term, it’ll really be the Obama administration who is to blame. I mean, if Trump is willing to take credit for the asset price bubble now, I’m willing to give it to him, as long as he accepts the blame when it crashes.

1.5 million fewer people on food stamps

As you’d expect, I’m going to debunk this with a graph: the numbers have been falling since the great recession. Indeed, in the previous period under Obama, 1.9 fewer people got off food stamps, so Trump’s performance is slight ahead rather than behind Obama. Of course, neither president is really responsible.

Consumer confidence through the roof

Again we are going to graph this number:

Again we find nothing in the graph that suggests President Trump is responsible for any change — it’s been improving steadily since the Great Recession.

One thing to note is that, technically, it’s not “through the roof” — it still quite a bit below the roof set during the dot-com era.

Lowest Unemployment rate in 17 years

Again, let’s simply graph it over time and look for Trump’s contribution. as we can see, there doesn’t appear to be anything special Trump has done — unemployment has steadily been improving since the Great Recession.
But here’s the thing, the “unemployment rate” only measures those looking for work, not those who have given up. The number that concerns people more is the “labor force participation rate”. The Great Recession kicked a lot of workers out of the economy.
Mostly this is because Baby Boomer are now retiring an leaving the workforce, and some have chosen to retire early rather than look for another job. But there are still some other problems in our economy that cause this. President Trump has nothing particular in order to solve these problems.

Conclusion

As we see, Don Jr’s tweet is a troll. When we look at the graphs of these indicators going back to the Great Recession, we don’t see how President Trump has influenced anything. The improvements this year are in line with the improvements last year, which are in turn inline with the improvements in the previous year.
To be fair, all parties credit their President with improvements during their term. President Obama’s supporters did the same thing. But at least right now, with these numbers, we can see that there’s no merit to anything in Don Jr’s tweet.
The hyperpartisan rancor in this country is because neither side cares about the facts. We should care. We should care that these numbers suck, even if we are Republicans. Conversely, we should care that those NetNeutrality claims by Democrats suck, even if we are Democrats.

170 ‘Pirate’ IPTV Vendors Throw in the Towel Facing Legal Pressure

Post Syndicated from Ernesto original https://torrentfreak.com/170-pirate-iptv-vendors-throw-the-in-the-towel-facing-legal-pressure-171121/

Pirate streaming boxes are all the rage this year. Not just among the dozens of millions of users, they are on top of the anti-piracy agenda as well.

Dubbed Piracy 3.0 by the MPAA, copyright holders are trying their best to curb this worrisome trend. In the Netherlands local anti-piracy group BREIN is leading the charge.

Backed by the major film studios, the organization booked a significant victory earlier this year against Filmspeler. In this case, the European Court of Justice ruled that selling or using devices pre-configured to obtain copyright-infringing content is illegal.

Paired with the earlier GS Media ruling, which held that companies with a for-profit motive can’t knowingly link to copyright-infringing material, this provides a powerful enforcement tool.

With these decisions in hand, BREIN previously pressured hundreds of streaming box vendors to halt sales of hardware with pirate addons, but it didn’t stop there. This week the group also highlighted its successes against vendors of unauthorized IPTV services.

“BREIN has already stopped 170 illegal providers of illegal media players and/or IPTV subscriptions. Even providers that only offer illegal IPTV subscriptions are being dealt with,” BREIN reports.

In addition to shutting down the trade in IPTV services, the anti-piracy group also removed 375 advertisements for such services from various marketplaces.

“This is illegal commerce. If you wait until you are warned, you are too late,” BREIN director Tim Kuik says.

“You can be held personally liable. You can also be charged and criminally prosecuted. Willingly committing commercial copyright infringement can lead to a 82,000 euro fine and 4 years imprisonment,” he adds.

While most pirate IPTV vendors threw in the towel voluntarily, some received an extra incentive. Twenty signed a settlement with BREIN for varying amounts, up to tens of thousands of euros. They all face further penalties if they continue to sell pirate subscriptions.

In some cases, the courts were involved. This includes the recent lawsuit against MovieStreamer, that was ordered to stop its IPTV hyperlinking activities immediately. Failure to do so will result in a 5,000 euro per day fine. In addition, the vendor was also ordered to pay legal costs of 17,527 euros.

While BREIN has booked plenty of successes already, as exampled here, the pirate streaming box problem is far from solved. The anti-piracy group currently has one case pending in court, but more are likely to follow in the near future.

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

MPAA Lobbies US Congress on Streaming Piracy Boxes

Post Syndicated from Ernesto original https://torrentfreak.com/mpaa-lobbies-us-congress-on-streaming-piracy-boxes-171112/

As part of its quest to reduce piracy, the MPAA continues to spend money on its lobbying activities, hoping to sway lawmakers in its direction.

While the lobbying talks take place behind closed doors, quarterly disclosure reports provide some insight into the items under discussion.

The MPAA’s most recent lobbying disclosure form features several new topics that weren’t on the agenda last year.

Among other issues, the Hollywood group lobbied the U.S. Senate and the U.S. House of Representatives on set-top boxes, preloaded streaming piracy devices, and streaming piracy in general.

The details of these discussions remain behind closed doors. The only thing we know for sure is what Hollywood is lobbying on, but it doesn’t take much imagination to take an educated guess on the ‘why’ part.

Just over a year ago streaming piracy boxes were hardly mentioned in anti-piracy circles, but today they are on the top of the enforcement list. The MPAA is reporting these concerns to lawmakers, to see whether they can be of assistance in curbing this growing threat.

Some of the lobbying topics

It’s clear that pirate streaming players are a prime concern for Hollywood. MPA boss Stan McCoy recently characterized the use of these devices as “Piracy 3.0” and a coalition of industry players sued a US-based seller of streaming boxes earlier this month.

The lobbying efforts themselves are nothing new of course. Every year the MPAA spends around $4 million to influence the decisions of lawmakers, both directly and through external lobbying firms such as Covington & Burling, Capitol Tax Partners, and Sentinel Worldwide.

While piracy streaming boxes are new on the agenda this year, they are not the only topics under discussion. Other items include trade deals such as the TPP, TTIP, and NAFTA, voluntary domain name initiatives, EU digital single market proposals, and cybersecurity.

TorrentFreak reached out to the MPAA for more information on the streaming box lobbying efforts, but we have yet to hear back.

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

Sky: People Can’t Pirate Live Soccer in the UK Anymore

Post Syndicated from Andy original https://torrentfreak.com/sky-people-cant-pirate-live-soccer-in-the-uk-anymore-171108/

The commotion over the set-top box streaming phenomenon is showing no signs of dying down and if day one at the Cable and Satellite Broadcasting Association of Asia (CASBAA) Conference 2017 was anything to go by, things are only heating up.

Held at Studio City in Macau, the conference has a strong anti-piracy element and was opened by Joe Welch, CASBAA Board Chairman and SVP Public Affairs Asia, 21st Century Fox. He began Tuesday by noting the important recent launch of a brand new anti-piracy initiative.

“CASBAA recently launched the Coalition Against Piracy, funded by 18 of the region’s content players and distribution partners,” he said.

TF reported on the formation of the coalition mid-October. It includes heavyweights such as Disney, Fox, HBO, NBCUniversal and BBC Worldwide, and will have a strong focus on the illicit set-top box market.

Illegal streaming devices (or ISDs, as the industry calls them), were directly addressed in a segment yesterday afternoon titled Face To Face. Led by Dr. Ros Lynch, Director of Copyright & IP Enforcement at the UK Intellectual Property Office, the session detailed the “onslaught of online piracy” and the rise of ISDs that is apparently “shaking the market”.

Given the apparent gravity of those statements, the following will probably come as a surprise. According to Lynch, the UK IPO sought the opinion of UK-based rightsholders about the pirate box phenomenon a while back after being informed of their popularity in the East. The response was that pirate boxes weren’t an issue. It didn’t take long, however, for things to blow up.

“The UKIPO provides intelligence and evidence to industry and the Police Intellectual Property Crime Unit (PIPCU) in London who then take enforcement actions,” Lynch explained.

“We first heard about the issues with ISDs from [broadcaster] TVB in Hong Kong and we then consulted the UK rights holders who responded that it wasn’t a problem. Two years later the issue just exploded.”

The evidence of that in the UK isn’t difficult to find. In addition to millions of devices with both free Kodi addon and subscription-based systems deployed, the app market has bloomed too, offering free or near to free content to all.

This caught the eye of the Premier League who this year obtained two pioneering injunctions (1,2) to tackle live streams of football games. Streams are blocked by local ISPs in real-time, making illicit online viewing a more painful experience than it ever has been. No doubt progress has been made on this front, with thousands of streams blocked, but according to broadcaster Sky, the results are unprecedented.

“Site-blocking has moved the goalposts significantly,” said Matthew Hibbert, head of litigation at Sky UK.

“In the UK you cannot watch pirated live Premier League content anymore,” he said.

While progress has been good, the statement is overly enthusiastic. TF sources have been monitoring the availability of pirate streams on around dozen illicit sites and services every Saturday (when it is actually illegal to broadcast matches in the UK) and service has been steady on around half of them and intermittent at worst on the rest.

There are hundreds of other platforms available so while many are definitely affected by Premier League blocking, it’s safe to assume that live football piracy hasn’t been wiped out. Nevertheless, it would be wrong to suggest that no progress has been made, in this and other related areas.

Kevin Plumb, Director of Legal Services at The Premier League, said that pubs showing football from illegal streams had also massively dwindled in numbers.

“In the past 18 months the illegal broadcasting of live Premier League matches in pubs in the UK has been decimated,” he said.

This result is almost certainly down to prosecutions taken in tandem with the Federation Against Copyright Theft (FACT), that have seen several landlords landed with large fines. Indeed, both sides of the market have been tackled, with both licensed premises and IPTV device sellers being targeted.

“The most successful thing we’ve done to combat piracy has been to undertake criminal prosecutions against ISD piracy,” said FACT chief Kieron Sharp yesterday. “Everyone is pleading guilty to these offenses.”

Most if not all of FACT-led prosecutions target device and subscription sellers under fraud legislation but that could change in the future, Lynch of the Intellectual Property Office said.

“While the UK works to update its legislation, we can’t wait for the new legislation to take enforcement actions and we rely heavily on ‘conspiracy to defraud’ charges, and have successfully prosecuted a number of ISD retailers,” she said.

Finally, information provided yesterday by network company CISCO shine light on what it costs to run a subscription-based pirate IPTV operation.

Director of Intelligence & Security Operations Avigail Gutman said a pirate IPTV server offering 1,000 channels to around 1,000 subscribers can cost as little as 2,000 euros per month to run but can generate 12,000 euros in revenue during the same period.

“In April of 2017, ten major paid TV and content providers had relinquished 3.09 million euros per month to 285 ISD-based streaming pirate syndicates,” she said.

There’s little doubt that IPTV piracy, both paid and free, is here to stay. The big question is how it will be tackled short and long-term and whether any changes in legislation will have any unintended knock-on effects.

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