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Majority of Canadians Consume Online Content Legally, Survey Finds

Post Syndicated from Andy original https://torrentfreak.com/majority-of-canadians-consume-online-content-legally-survey-finds-180531/

Back in January, a coalition of companies and organizations with ties to the entertainment industries called on local telecoms regulator CRTC to implement a national website blocking regime.

Under the banner of Fairplay Canada, members including Bell, Cineplex, Directors Guild of Canada, Maple Leaf Sports and Entertainment, Movie Theatre Association of Canada, and Rogers Media, spoke of an industry under threat from marauding pirates. But just how serious is this threat?

The results of a new survey commissioned by Innovation Science and Economic Development Canada (ISED) in collaboration with the Department of Canadian Heritage (PCH) aims to shine light on the problem by revealing the online content consumption habits of citizens in the Great White North.

While there are interesting findings for those on both sides of the site-blocking debate, the situation seems somewhat removed from the Armageddon scenario predicted by the entertainment industries.

Carried out among 3,301 Canadians aged 12 years and over, the Kantar TNS study aims to cover copyright infringement in six key content areas – music, movies, TV shows, video games, computer software, and eBooks. Attitudes and behaviors are also touched upon while measuring the effectiveness of Canada’s copyright measures.

General Digital Content Consumption

In its introduction, the report notes that 28 million Canadians used the Internet in the three-month study period to November 27, 2017. Of those, 22 million (80%) consumed digital content. Around 20 million (73%) streamed or accessed content, 16 million (59%) downloaded content, while 8 million (28%) shared content.

Music, TV shows and movies all battled for first place in the consumption ranks, with 48%, 48%, and 46% respectively.

Copyright Infringement

According to the study, the majority of Canadians do things completely by the book. An impressive 74% of media-consuming respondents said that they’d only accessed material from legal sources in the preceding three months.

The remaining 26% admitted to accessing at least one illegal file in the same period. Of those, just 5% said that all of their consumption was from illegal sources, with movies (36%), software (36%), TV shows (34%) and video games (33%) the most likely content to be consumed illegally.

Interestingly, the study found that few demographic factors – such as gender, region, rural and urban, income, employment status and language – play a role in illegal content consumption.

“We found that only age and income varied significantly between consumers who infringed by downloading or streaming/accessing content online illegally and consumers who did not consume infringing content online,” the report reads.

“More specifically, the profile of consumers who downloaded or streamed/accessed infringing content skewed slightly younger and towards individuals with household incomes of $100K+.”

Licensed services much more popular than pirate haunts

It will come as no surprise that Netflix was the most popular service with consumers, with 64% having used it in the past three months. Sites like YouTube and Facebook were a big hit too, visited by 36% and 28% of content consumers respectively.

Overall, 74% of online content consumers use licensed services for content while 42% use social networks. Under a third (31%) use a combination of peer-to-peer (BitTorrent), cyberlocker platforms, or linking sites. Stream-ripping services are used by 9% of content consumers.

“Consumers who reported downloading or streaming/accessing infringing content only are less likely to use licensed services and more likely to use peer-to-peer/cyberlocker/linking sites than other consumers of online content,” the report notes.

Attitudes towards legal consumption & infringing content

In common with similar surveys over the years, the Kantar research looked at the reasons why people consume content from various sources, both legal and otherwise.

Convenience (48%), speed (36%) and quality (34%) were the most-cited reasons for using legal sources. An interesting 33% of respondents said they use legal sites to avoid using illegal sources.

On the illicit front, 54% of those who obtained unauthorized content in the previous three months said they did so due to it being free, with 40% citing convenience and 34% mentioning speed.

Almost six out of ten (58%) said lower costs would encourage them to switch to official sources, with 47% saying they’d move if legal availability was improved.

Canada’s ‘Notice-and-Notice’ warning system

People in Canada who share content on peer-to-peer systems like BitTorrent without permission run the risk of receiving an infringement notice warning them to stop. These are sent by copyright holders via users’ ISPs and the hope is that the shock of receiving a warning will turn consumers back to the straight and narrow.

The study reveals that 10% of online content consumers over the age of 12 have received one of these notices but what kind of effect have they had?

“Respondents reported that receiving such a notice resulted in the following: increased awareness of copyright infringement (38%), taking steps to ensure password protected home networks (27%), a household discussion about copyright infringement (27%), and discontinuing illegal downloading or streaming (24%),” the report notes.

While these are all positives for the entertainment industries, Kantar reports that almost a quarter (24%) of people who receive a notice simply ignore them.

Stream-ripping

Once upon a time, people obtaining music via P2P networks was cited as the music industry’s greatest threat but, with the advent of sites like YouTube, so-called stream-ripping is the latest bogeyman.

According to the study, 11% of Internet users say they’ve used a stream-ripping service. They are most likely to be male (62%) and predominantly 18 to 34 (52%) years of age.

“Among Canadians who have used a service to stream-rip music or entertainment, nearly half (48%) have used stream-ripping sites, one-third have used downloader apps (38%), one-in-seven (14%) have used a stream-ripping plug-in, and one-in-ten (10%) have used stream-ripping software,” the report adds.

Set-Top Boxes and VPNs

Few general piracy studies would be complete in 2018 without touching on set-top devices and Virtual Private Networks and this report doesn’t disappoint.

More than one in five (21%) respondents aged 12+ reported using a VPN, with the main purpose of securing communications and Internet browsing (57%).

A relatively modest 36% said they use a VPN to access free content while 32% said the aim was to access geo-blocked content unavailable in Canada. Just over a quarter (27%) said that accessing content from overseas at a reasonable price was the main motivator.

One in ten (10%) of respondents reported using a set-top box, with 78% stating they use them to access paid-for content. Interestingly, only a small number say they use the devices to infringe.

“A minority use set-top boxes to access other content that is not legal or they are unsure if it is legal (16%), or to access live sports that are not legal or they are unsure if it is legal (11%),” the report notes.

“Individuals who consumed a mix of legal and illegal content online are more likely to use VPN services (42%) or TV set-top boxes (21%) than consumers who only downloaded or streamed/accessed legal content.”

Kantar says that the findings of the report will be used to help policymakers evaluate how Canada’s Copyright Act is coping with a changing market and technological developments.

“This research will provide the necessary information required to further develop copyright policy in Canada, as well as to provide a foundation to assess the effectiveness of the measures to address copyright infringement, should future analysis be undertaken,” it concludes.

The full report can be found here (pdf)

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

C is to low level

Post Syndicated from Robert Graham original https://blog.erratasec.com/2018/05/c-is-too-low-level.html

I’m in danger of contradicting myself, after previously pointing out that x86 machine code is a high-level language, but this article claiming C is a not a low level language is bunk. C certainly has some problems, but it’s still the closest language to assembly. This is obvious by the fact it’s still the fastest compiled language. What we see is a typical academic out of touch with the real world.

The author makes the (wrong) observation that we’ve been stuck emulating the PDP-11 for the past 40 years. C was written for the PDP-11, and since then CPUs have been designed to make C run faster. The author imagines a different world, such as where CPU designers instead target something like LISP as their preferred language, or Erlang. This misunderstands the state of the market. CPUs do indeed supports lots of different abstractions, and C has evolved to accommodate this.


The author criticizes things like “out-of-order” execution which has lead to the Spectre sidechannel vulnerabilities. Out-of-order execution is necessary to make C run faster. The author claims instead that those resources should be spent on having more slower CPUs, with more threads. This sacrifices single-threaded performance in exchange for a lot more threads executing in parallel. The author cites Sparc Tx CPUs as his ideal processor.

But here’s the thing, the Sparc Tx was a failure. To be fair, it’s mostly a failure because most of the time, people wanted to run old C code instead of new Erlang code. But it was still a failure at running Erlang.

Time after time, engineers keep finding that “out-of-order”, single-threaded performance is still the winner. A good example is ARM processors for both mobile phones and servers. All the theory points to in-order CPUs as being better, but all the products are out-of-order, because this theory is wrong. The custom ARM cores from Apple and Qualcomm used in most high-end phones are so deeply out-of-order they give Intel CPUs competition. The same is true on the server front with the latest Qualcomm Centriq and Cavium ThunderX2 processors, deeply out of order supporting more than 100 instructions in flight.

The Cavium is especially telling. Its ThunderX CPU had 48 simple cores which was replaced with the ThunderX2 having 32 complex, deeply out-of-order cores. The performance increase was massive, even on multithread-friendly workloads. Every competitor to Intel’s dominance in the server space has learned the lesson from Sparc Tx: many wimpy cores is a failure, you need fewer beefy cores. Yes, they don’t need to be as beefy as Intel’s processors, but they need to be close.

Even Intel’s “Xeon Phi” custom chip learned this lesson. This is their GPU-like chip, running 60 cores with 512-bit wide “vector” (sic) instructions, designed for supercomputer applications. Its first version was purely in-order. Its current version is slightly out-of-order. It supports four threads and focuses on basic number crunching, so in-order cores seems to be the right approach, but Intel found in this case that out-of-order processing still provided a benefit. Practice is different than theory.

As an academic, the author of the above article focuses on abstractions. The criticism of C is that it has the wrong abstractions which are hard to optimize, and that if we instead expressed things in the right abstractions, it would be easier to optimize.

This is an intellectually compelling argument, but so far bunk.

The reason is that while the theoretical base language has issues, everyone programs using extensions to the language, like “intrinsics” (C ‘functions’ that map to assembly instructions). Programmers write libraries using these intrinsics, which then the rest of the normal programmers use. In other words, if your criticism is that C is not itself low level enough, it still provides the best access to low level capabilities.

Given that C can access new functionality in CPUs, CPU designers add new paradigms, from SIMD to transaction processing. In other words, while in the 1980s CPUs were designed to optimize C (stacks, scaled pointers), these days CPUs are designed to optimize tasks regardless of language.

The author of that article criticizes the memory/cache hierarchy, claiming it has problems. Yes, it has problems, but only compared to how well it normally works. The author praises the many simple cores/threads idea as hiding memory latency with little caching, but misses the point that caches also dramatically increase memory bandwidth. Intel processors are optimized to read a whopping 256 bits every clock cycle from L1 cache. Main memory bandwidth is orders of magnitude slower.

The author goes onto criticize cache coherency as a problem. C uses it, but other languages like Erlang don’t need it. But that’s largely due to the problems each languages solves. Erlang solves the problem where a large number of threads work on largely independent tasks, needing to send only small messages to each other across threads. The problems C solves is when you need many threads working on a huge, common set of data.

For example, consider the “intrusion prevention system”. Any thread can process any incoming packet that corresponds to any region of memory. There’s no practical way of solving this problem without a huge coherent cache. It doesn’t matter which language or abstractions you use, it’s the fundamental constraint of the problem being solved. RDMA is an important concept that’s moved from supercomputer applications to the data center, such as with memcached. Again, we have the problem of huge quantities (terabytes worth) shared among threads rather than small quantities (kilobytes).

The fundamental issue the author of the the paper is ignoring is decreasing marginal returns. Moore’s Law has gifted us more transistors than we can usefully use. We can’t apply those additional registers to just one thing, because the useful returns we get diminish.

For example, Intel CPUs have two hardware threads per core. That’s because there are good returns by adding a single additional thread. However, the usefulness of adding a third or fourth thread decreases. That’s why many CPUs have only two threads, or sometimes four threads, but no CPU has 16 threads per core.

You can apply the same discussion to any aspect of the CPU, from register count, to SIMD width, to cache size, to out-of-order depth, and so on. Rather than focusing on one of these things and increasing it to the extreme, CPU designers make each a bit larger every process tick that adds more transistors to the chip.

The same applies to cores. It’s why the “more simpler cores” strategy fails, because more cores have their own decreasing marginal returns. Instead of adding cores tied to limited memory bandwidth, it’s better to add more cache. Such cache already increases the size of the cores, so at some point it’s more effective to add a few out-of-order features to each core rather than more cores. And so on.

The question isn’t whether we can change this paradigm and radically redesign CPUs to match some academic’s view of the perfect abstraction. Instead, the goal is to find new uses for those additional transistors. For example, “message passing” is a useful abstraction in languages like Go and Erlang that’s often more useful than sharing memory. It’s implemented with shared memory and atomic instructions, but I can’t help but think it couldn’t better be done with direct hardware support.

Of course, as soon as they do that, it’ll become an intrinsic in C, then added to languages like Go and Erlang.

Summary

Academics live in an ideal world of abstractions, the rest of us live in practical reality. The reality is that vast majority of programmers work with the C family of languages (JavaScript, Go, etc.), whereas academics love the epiphanies they learned using other languages, especially function languages. CPUs are only superficially designed to run C and “PDP-11 compatibility”. Instead, they keep adding features to support other abstractions, abstractions available to C. They are driven by decreasing marginal returns — they would love to add new abstractions to the hardware because it’s a cheap way to make use of additional transitions. Academics are wrong believing that the entire system needs to be redesigned from scratch. Instead, they just need to come up with new abstractions CPU designers can add.

Solving Complex Ordering Challenges with Amazon SQS FIFO Queues

Post Syndicated from Christie Gifrin original https://aws.amazon.com/blogs/compute/solving-complex-ordering-challenges-with-amazon-sqs-fifo-queues/

Contributed by Shea Lutton, AWS Cloud Infrastructure Architect

Amazon Simple Queue Service (Amazon SQS) is a fully managed queuing service that helps decouple applications, distributed systems, and microservices to increase fault tolerance. SQS queues come in two distinct types:

  • Standard SQS queues are able to scale to enormous throughput with at-least-once delivery.
  • FIFO queues are designed to guarantee that messages are processed exactly once in the exact order that they are received and have a default rate of 300 transactions per second.

As customers explore SQS FIFO queues, they often have questions about how the behavior works when messages arrive and are consumed. This post walks through some common situations to identify the exact behavior that you can expect. It also covers the behavior of message groups in depth and explains why message groups are key to understanding how FIFO queues work.

The simple case

Suppose that you run a major auction platform where people buy and sell a wide range of products. Your platform requires that transactions from buyers and sellers get processed in exactly the order received. Here’s how a FIFO queue helps you keep all your transactions in one straight flow.

A seller currently is holding an auction for a laptop, and three different bids are received for the same price. Ties are awarded to the first bidder at that price so it is important to track which arrived first. Your auction platform receives the three bids and sends them to a FIFO queue before they are processed.

Now observe how messages leave the queue. When your consumer asks for a batch of up to 10 messages, SQS starts filling the batch with the oldest message (bid A1). It keeps filling until either the batch is full or the queue is empty. In this case, the batch contains the three messages and the queue is now empty. After a batch has left the queue, SQS considers that batch of messages to be “in-flight” until the consumer either deletes them or the batch’s visibility timer expires.

 

When you have a single consumer, this is easy to envision. The consumer gets a batch of messages (now in-flight), does its processing, and deletes the messages. That consumer is then ready to ask for the next batch of messages.

The critical thing to keep in mind is that SQS won’t release the next batch of messages until the first batch has been deleted. By adding more messages to the queue, you can see more interesting behaviors. Imagine that a burst of 11 bids is sent to your FIFO queue, with two bids for Auction A arriving last.

The FIFO queue now has at least two batches of messages in it. When your single consumer requests the first batch of 10 messages, it receives a batch starting with B1 and ending with A1. Later, after the first batch has been deleted, the consumer can get the second batch of messages containing the final A2 message from the queue.

Adding complexity with multiple message groups

A new challenge arises. Your auction platform is getting busier and your dev team added a number of new features. The combination of increased messages and extra processing time for the new features means that a single consumer is too slow. The solution is to scale to have more consumers and process messages in parallel.

To work in parallel, your team realized that only the messages related to a single auction must be kept in order. All transactions for Auction A need to be kept in order and so do all transactions for Auction B. But the two auctions are independent and it does not matter which auctions transactions are processed first.

FIFO can handle that case with a feature called message groups. Each transaction related to Auction A is placed by your producer into message group A, and so on. In the diagram below, Auction A and Auction B each received three bid transactions, with bid B1 arriving first. The FIFO queue always keeps transactions within a message group in the order in which they arrived.

How is this any different than earlier examples? The consumer now gets the messages ordered by message groups, all the B group messages followed by all the A group messages. Multiple message groups create the possibility of using multiple consumers, which I explain in a moment. If FIFO can’t fill up a batch of messages with a single message group, FIFO can place more than one message group in a batch of messages. But whenever possible, the queue gives you a full batch of messages from the same group.

The order of messages leaving a FIFO queue is governed by three rules:

  1. Return the oldest message where no other message in the same message group is currently in-flight.
  2. Return as many messages from the same message group as possible.
  3. If a message batch is still not full, go back to rule 1.

To see this behavior, add a second consumer and insert many more messages into the queue. For simplicity, the delete message action has been omitted in these diagrams but it is assumed that all messages in a batch are processed successfully by the consumer and the batch is properly deleted immediately after.

In this example, there are 11 Group A and 11 Group B transactions arriving in interleaved order and a second consumer has been added. Consumer 1 asks for a group of 10 messages and receives 10 Group A messages. Consumer 2 then asks for 10 messages but SQS knows that Group A is in flight, so it releases 10 Group B messages. The two consumers are now processing two batches of messages in parallel, speeding up throughput and then deleting their batches. When Consumer 1 requests the next batch of messages, it receives the remaining two messages, one from Group A and one from Group B.

Consider this nuanced detail from the example above. What would happen if Consumer 1 was on a faster server and processed its first batch of messages before Consumer 2 could mark its messages for deletion? See if you can predict the behavior before looking at the answer.

If Consumer 2 has not deleted its Group B messages yet when Consumer 1 asks for the next batch, then the FIFO queue considers Group B to still be in flight. It does not release any more Group B messages. Consumer 1 gets only the remaining Group A message. Later, after Consumer 2 has deleted its first batch, the remaining Group B message is released.

Conclusion

I hope this post answered your questions about how Amazon SQS FIFO queues work and why message groups are helpful. If you’re interested in exploring SQS FIFO queues further, here are a few ideas to get you started:

Puerto Rico’s First Raspberry Pi Educator Workshop

Post Syndicated from Dana Augustin original https://www.raspberrypi.org/blog/puerto-rico-raspberry-pi-workshop/

Earlier this spring, an excited group of STEM educators came together to participate in the first ever Raspberry Pi and Arduino workshop in Puerto Rico.

Their three-day digital making adventure was led by MakerTechPR’s José Rullán and Raspberry Pi Certified Educator Alex Martínez. They ran the event as part of the Robot Makers challenge organized by Yees! and sponsored by Puerto Rico’s Department of Economic Development and Trade to promote entrepreneurial skills within Puerto Rico’s education system.

Over 30 educators attended the workshop, which covered the use of the Raspberry Pi 3 as a computer and digital making resource. The educators received a kit consisting of a Raspberry Pi 3 with an Explorer HAT Pro and an Arduino Uno. At the end of the workshop, the educators were able to keep the kit as a demonstration unit for their classrooms. They were enthusiastic to learn new concepts and immerse themselves in the world of physical computing.

In their first session, the educators were introduced to the Raspberry Pi as an affordable technology for robotic clubs. In their second session, they explored physical computing and the coding languages needed to control the Explorer HAT Pro. They started off coding with Scratch, with which some educators had experience, and ended with controlling the GPIO pins with Python. In the final session, they learned how to develop applications using the powerful combination of Arduino and Raspberry Pi for robotics projects. This gave them a better understanding of how they could engage their students in physical computing.

“The Raspberry Pi ecosystem is the perfect solution in the classroom because to us it is very resourceful and accessible.” – Alex Martínez

Computer science and robotics courses are important for many schools and teachers in Puerto Rico. The simple idea of programming a microcontroller from a $35 computer increases the chances of more students having access to more technology to create things.

Puerto Rico’s education system has faced enormous challenges after Hurricane Maria, including economic collapse and the government’s closure of many schools due to the exodus of families from the island. By attending training like this workshop, educators in Puerto Rico are becoming more experienced in fields like robotics in particular, which are key for 21st-century skills and learning. This, in turn, can lead to more educational opportunities, and hopefully the reopening of more schools on the island.

“We find it imperative that our children be taught STEM disciplines and skills. Our goal is to continue this work of spreading digital making and computer science using the Raspberry Pi around Puerto Rico. We want our children to have the best education possible.” – Alex Martínez

After attending Picademy in 2016, Alex has integrated the Raspberry Pi Foundation’s online resources into his classroom. He has also taught small workshops around the island and in the local Puerto Rican makerspace community. José is an electrical engineer, entrepreneur, educator and hobbyist who enjoys learning to use technology and sharing his knowledge through projects and challenges.

The post Puerto Rico’s First Raspberry Pi Educator Workshop appeared first on Raspberry Pi.

A serverless solution for invoking AWS Lambda at a sub-minute frequency

Post Syndicated from Emanuele Menga original https://aws.amazon.com/blogs/architecture/a-serverless-solution-for-invoking-aws-lambda-at-a-sub-minute-frequency/

If you’ve used Amazon CloudWatch Events to schedule the invocation of a Lambda function at regular intervals, you may have noticed that the highest frequency possible is one invocation per minute. However, in some cases, you may need to invoke Lambda more often than that. In this blog post, I’ll cover invoking a Lambda function every 10 seconds, but with some simple math you can change to whatever interval you like.

To achieve this, I’ll show you how to leverage Step Functions and Amazon Kinesis Data Streams.

The Solution

For this example, I’ve created a Step Functions State Machine that invokes our Lambda function 6 times, 10 seconds apart. Such State Machine is then executed once per minute by a CloudWatch Events Rule. This state machine is then executed once per minute by an Amazon CloudWatch Events rule. Finally, the Kinesis Data Stream triggers our Lambda function for each record inserted. The result is our Lambda function being invoked every 10 seconds, indefinitely.

Below is a diagram illustrating how the various services work together.

Step 1: My sampleLambda function doesn’t actually do anything, it just simulates an execution for a few seconds. This is the (Python) code of my dummy function:

import time

import random


def lambda_handler(event, context):

rand = random.randint(1, 3)

print('Running for {} seconds'.format(rand))

time.sleep(rand)

return True

Step 2:

The next step is to create a second Lambda function, that I called Iterator, which has two duties:

  • It keeps track of the current number of iterations, since Step Function doesn’t natively have a state we can use for this purpose.
  • It asynchronously invokes our Lambda function at every loops.

This is the code of the Iterator, adapted from here.

 

import boto3

client = boto3.client('kinesis')

def lambda_handler(event, context):

index = event['iterator']['index'] + 1

response = client.put_record(

StreamName='LambdaSubMinute',

PartitionKey='1',

Data='',

)

return {

'index': index,

'continue': index < event['iterator']['count'],

'count': event['iterator']['count']

}

This function does three things:

  • Increments the counter.
  • Verifies if we reached a count of (in this example) 6.
  • Sends an empty record to the Kinesis Stream.

Now we can create the Step Functions State Machine; the definition is, again, adapted from here.

 

{

"Comment": "Invoke Lambda every 10 seconds",

"StartAt": "ConfigureCount",

"States": {

"ConfigureCount": {

"Type": "Pass",

"Result": {

"index": 0,

"count": 6

},

"ResultPath": "$.iterator",

"Next": "Iterator"

},

"Iterator": {

"Type": "Task",

"Resource": “arn:aws:lambda:REGION:ACCOUNT_ID:function:Iterator",

"ResultPath": "$.iterator",

"Next": "IsCountReached"

},

"IsCountReached": {

"Type": "Choice",

"Choices": [

{

"Variable": "$.iterator.continue",

"BooleanEquals": true,

"Next": "Wait"

}

],

"Default": "Done"

},

"Wait": {

"Type": "Wait",

"Seconds": 10,

"Next": "Iterator"

},

"Done": {

"Type": "Pass",

"End": true

}

}

}

This is how it works:

  1. The state machine starts and sets the index at 0 and the count at 6.
  2. Iterator function is invoked.
  3. If the iterator function reached the end of the loop, the IsCountReached state terminates the execution, otherwise the machine waits for 10 seconds.
  4. The machine loops back to the iterator.

Step 3: Create an Amazon CloudWatch Events rule scheduled to trigger every minute and add the state machine as its target. I’ve actually prepared an Amazon CloudFormation template that creates the whole stack and starts the Lambda invocations, you can find it here.

Performance

Let’s have a look at a sample series of invocations and analyse how precise the timing is. In the following chart I reported the delay (in excess of the expected 10-second-wait) of 30 consecutive invocations of my dummy function, when the Iterator is configured with a memory size of 1024MB.

Invocations Delay

Notice the delay increases by a few hundred milliseconds at every invocation. The good news is it accrues only within the same loop, 6 times; after that, a new CloudWatch Events kicks in and it resets.

This delay  is due to the work that AWS Step Function does outside of the Wait state, the main component of which is the Iterator function itself, that runs synchronously in the state machine and therefore adds up its duration to the 10-second-wait.

As we can easily imagine, the memory size of the Iterator Lambda function does make a difference. Here are the Average and Maximum duration of the function with 256MB, 512MB, 1GB and 2GB of memory.

Average Duration

Maximum Duration


Given those results, I’d say that a memory of 1024MB is a good compromise between costs and performance.

Caveats

As mentioned, in our Amazon CloudWatch Events documentation, in rare cases a rule can be triggered twice, causing two parallel executions of the state machine. If that is a concern, we can add a task state at the beginning of the state machine that checks if any other executions are currently running. If the outcome is positive, then a choice state can immediately terminate the flow. Since the state machine is invoked every 60 seconds and runs for about 50, it is safe to assume that executions should all be sequential and any parallel executions should be treated as duplicates. The task state that checks for current running executions can be a Lambda function similar to the following:

 

import boto3

client = boto3.client('stepfunctions')

def lambda_handler(event, context):

response = client.list_executions(

stateMachineArn='arn:aws:states:REGION:ACCOUNTID:stateMachine:LambdaSubMinute',

statusFilter='RUNNING'

)

return {

'alreadyRunning': len(response['executions']) > 0

}

About the Author

Emanuele Menga, Cloud Support Engineer

 

Analyze Apache Parquet optimized data using Amazon Kinesis Data Firehose, Amazon Athena, and Amazon Redshift

Post Syndicated from Roy Hasson original https://aws.amazon.com/blogs/big-data/analyzing-apache-parquet-optimized-data-using-amazon-kinesis-data-firehose-amazon-athena-and-amazon-redshift/

Amazon Kinesis Data Firehose is the easiest way to capture and stream data into a data lake built on Amazon S3. This data can be anything—from AWS service logs like AWS CloudTrail log files, Amazon VPC Flow Logs, Application Load Balancer logs, and others. It can also be IoT events, game events, and much more. To efficiently query this data, a time-consuming ETL (extract, transform, and load) process is required to massage and convert the data to an optimal file format, which increases the time to insight. This situation is less than ideal, especially for real-time data that loses its value over time.

To solve this common challenge, Kinesis Data Firehose can now save data to Amazon S3 in Apache Parquet or Apache ORC format. These are optimized columnar formats that are highly recommended for best performance and cost-savings when querying data in S3. This feature directly benefits you if you use Amazon Athena, Amazon Redshift, AWS Glue, Amazon EMR, or any other big data tools that are available from the AWS Partner Network and through the open-source community.

Amazon Connect is a simple-to-use, cloud-based contact center service that makes it easy for any business to provide a great customer experience at a lower cost than common alternatives. Its open platform design enables easy integration with other systems. One of those systems is Amazon Kinesis—in particular, Kinesis Data Streams and Kinesis Data Firehose.

What’s really exciting is that you can now save events from Amazon Connect to S3 in Apache Parquet format. You can then perform analytics using Amazon Athena and Amazon Redshift Spectrum in real time, taking advantage of this key performance and cost optimization. Of course, Amazon Connect is only one example. This new capability opens the door for a great deal of opportunity, especially as organizations continue to build their data lakes.

Amazon Connect includes an array of analytics views in the Administrator dashboard. But you might want to run other types of analysis. In this post, I describe how to set up a data stream from Amazon Connect through Kinesis Data Streams and Kinesis Data Firehose and out to S3, and then perform analytics using Athena and Amazon Redshift Spectrum. I focus primarily on the Kinesis Data Firehose support for Parquet and its integration with the AWS Glue Data Catalog, Amazon Athena, and Amazon Redshift.

Solution overview

Here is how the solution is laid out:

 

 

The following sections walk you through each of these steps to set up the pipeline.

1. Define the schema

When Kinesis Data Firehose processes incoming events and converts the data to Parquet, it needs to know which schema to apply. The reason is that many times, incoming events contain all or some of the expected fields based on which values the producers are advertising. A typical process is to normalize the schema during a batch ETL job so that you end up with a consistent schema that can easily be understood and queried. Doing this introduces latency due to the nature of the batch process. To overcome this issue, Kinesis Data Firehose requires the schema to be defined in advance.

To see the available columns and structures, see Amazon Connect Agent Event Streams. For the purpose of simplicity, I opted to make all the columns of type String rather than create the nested structures. But you can definitely do that if you want.

The simplest way to define the schema is to create a table in the Amazon Athena console. Open the Athena console, and paste the following create table statement, substituting your own S3 bucket and prefix for where your event data will be stored. A Data Catalog database is a logical container that holds the different tables that you can create. The default database name shown here should already exist. If it doesn’t, you can create it or use another database that you’ve already created.

CREATE EXTERNAL TABLE default.kfhconnectblog (
  awsaccountid string,
  agentarn string,
  currentagentsnapshot string,
  eventid string,
  eventtimestamp string,
  eventtype string,
  instancearn string,
  previousagentsnapshot string,
  version string
)
STORED AS parquet
LOCATION 's3://your_bucket/kfhconnectblog/'
TBLPROPERTIES ("parquet.compression"="SNAPPY")

That’s all you have to do to prepare the schema for Kinesis Data Firehose.

2. Define the data streams

Next, you need to define the Kinesis data streams that will be used to stream the Amazon Connect events.  Open the Kinesis Data Streams console and create two streams.  You can configure them with only one shard each because you don’t have a lot of data right now.

3. Define the Kinesis Data Firehose delivery stream for Parquet

Let’s configure the Data Firehose delivery stream using the data stream as the source and Amazon S3 as the output. Start by opening the Kinesis Data Firehose console and creating a new data delivery stream. Give it a name, and associate it with the Kinesis data stream that you created in Step 2.

As shown in the following screenshot, enable Record format conversion (1) and choose Apache Parquet (2). As you can see, Apache ORC is also supported. Scroll down and provide the AWS Glue Data Catalog database name (3) and table names (4) that you created in Step 1. Choose Next.

To make things easier, the output S3 bucket and prefix fields are automatically populated using the values that you defined in the LOCATION parameter of the create table statement from Step 1. Pretty cool. Additionally, you have the option to save the raw events into another location as defined in the Source record S3 backup section. Don’t forget to add a trailing forward slash “ / “ so that Data Firehose creates the date partitions inside that prefix.

On the next page, in the S3 buffer conditions section, there is a note about configuring a large buffer size. The Parquet file format is highly efficient in how it stores and compresses data. Increasing the buffer size allows you to pack more rows into each output file, which is preferred and gives you the most benefit from Parquet.

Compression using Snappy is automatically enabled for both Parquet and ORC. You can modify the compression algorithm by using the Kinesis Data Firehose API and update the OutputFormatConfiguration.

Be sure to also enable Amazon CloudWatch Logs so that you can debug any issues that you might run into.

Lastly, finalize the creation of the Firehose delivery stream, and continue on to the next section.

4. Set up the Amazon Connect contact center

After setting up the Kinesis pipeline, you now need to set up a simple contact center in Amazon Connect. The Getting Started page provides clear instructions on how to set up your environment, acquire a phone number, and create an agent to accept calls.

After setting up the contact center, in the Amazon Connect console, choose your Instance Alias, and then choose Data Streaming. Under Agent Event, choose the Kinesis data stream that you created in Step 2, and then choose Save.

At this point, your pipeline is complete.  Agent events from Amazon Connect are generated as agents go about their day. Events are sent via Kinesis Data Streams to Kinesis Data Firehose, which converts the event data from JSON to Parquet and stores it in S3. Athena and Amazon Redshift Spectrum can simply query the data without any additional work.

So let’s generate some data. Go back into the Administrator console for your Amazon Connect contact center, and create an agent to handle incoming calls. In this example, I creatively named mine Agent One. After it is created, Agent One can get to work and log into their console and set their availability to Available so that they are ready to receive calls.

To make the data a bit more interesting, I also created a second agent, Agent Two. I then made some incoming and outgoing calls and caused some failures to occur, so I now have enough data available to analyze.

5. Analyze the data with Athena

Let’s open the Athena console and run some queries. One thing you’ll notice is that when we created the schema for the dataset, we defined some of the fields as Strings even though in the documentation they were complex structures.  The reason for doing that was simply to show some of the flexibility of Athena to be able to parse JSON data. However, you can define nested structures in your table schema so that Kinesis Data Firehose applies the appropriate schema to the Parquet file.

Let’s run the first query to see which agents have logged into the system.

The query might look complex, but it’s fairly straightforward:

WITH dataset AS (
  SELECT 
    from_iso8601_timestamp(eventtimestamp) AS event_ts,
    eventtype,
    -- CURRENT STATE
    json_extract_scalar(
      currentagentsnapshot,
      '$.agentstatus.name') AS current_status,
    from_iso8601_timestamp(
      json_extract_scalar(
        currentagentsnapshot,
        '$.agentstatus.starttimestamp')) AS current_starttimestamp,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.firstname') AS current_firstname,
    json_extract_scalar(
      currentagentsnapshot,
      '$.configuration.lastname') AS current_lastname,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.username') AS current_username,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.routingprofile.defaultoutboundqueue.name') AS               current_outboundqueue,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.routingprofile.inboundqueues[0].name') as current_inboundqueue,
    -- PREVIOUS STATE
    json_extract_scalar(
      previousagentsnapshot, 
      '$.agentstatus.name') as prev_status,
    from_iso8601_timestamp(
      json_extract_scalar(
        previousagentsnapshot, 
       '$.agentstatus.starttimestamp')) as prev_starttimestamp,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.firstname') as prev_firstname,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.lastname') as prev_lastname,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.username') as prev_username,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.routingprofile.defaultoutboundqueue.name') as current_outboundqueue,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.routingprofile.inboundqueues[0].name') as prev_inboundqueue
  from kfhconnectblog
  where eventtype <> 'HEART_BEAT'
)
SELECT
  current_status as status,
  current_username as username,
  event_ts
FROM dataset
WHERE eventtype = 'LOGIN' AND current_username <> ''
ORDER BY event_ts DESC

The query output looks something like this:

Here is another query that shows the sessions each of the agents engaged with. It tells us where they were incoming or outgoing, if they were completed, and where there were missed or failed calls.

WITH src AS (
  SELECT
     eventid,
     json_extract_scalar(currentagentsnapshot, '$.configuration.username') as username,
     cast(json_extract(currentagentsnapshot, '$.contacts') AS ARRAY(JSON)) as c,
     cast(json_extract(previousagentsnapshot, '$.contacts') AS ARRAY(JSON)) as p
  from kfhconnectblog
),
src2 AS (
  SELECT *
  FROM src CROSS JOIN UNNEST (c, p) AS contacts(c_item, p_item)
),
dataset AS (
SELECT 
  eventid,
  username,
  json_extract_scalar(c_item, '$.contactid') as c_contactid,
  json_extract_scalar(c_item, '$.channel') as c_channel,
  json_extract_scalar(c_item, '$.initiationmethod') as c_direction,
  json_extract_scalar(c_item, '$.queue.name') as c_queue,
  json_extract_scalar(c_item, '$.state') as c_state,
  from_iso8601_timestamp(json_extract_scalar(c_item, '$.statestarttimestamp')) as c_ts,
  
  json_extract_scalar(p_item, '$.contactid') as p_contactid,
  json_extract_scalar(p_item, '$.channel') as p_channel,
  json_extract_scalar(p_item, '$.initiationmethod') as p_direction,
  json_extract_scalar(p_item, '$.queue.name') as p_queue,
  json_extract_scalar(p_item, '$.state') as p_state,
  from_iso8601_timestamp(json_extract_scalar(p_item, '$.statestarttimestamp')) as p_ts
FROM src2
)
SELECT 
  username,
  c_channel as channel,
  c_direction as direction,
  p_state as prev_state,
  c_state as current_state,
  c_ts as current_ts,
  c_contactid as id
FROM dataset
WHERE c_contactid = p_contactid
ORDER BY id DESC, current_ts ASC

The query output looks similar to the following:

6. Analyze the data with Amazon Redshift Spectrum

With Amazon Redshift Spectrum, you can query data directly in S3 using your existing Amazon Redshift data warehouse cluster. Because the data is already in Parquet format, Redshift Spectrum gets the same great benefits that Athena does.

Here is a simple query to show querying the same data from Amazon Redshift. Note that to do this, you need to first create an external schema in Amazon Redshift that points to the AWS Glue Data Catalog.

SELECT 
  eventtype,
  json_extract_path_text(currentagentsnapshot,'agentstatus','name') AS current_status,
  json_extract_path_text(currentagentsnapshot, 'configuration','firstname') AS current_firstname,
  json_extract_path_text(currentagentsnapshot, 'configuration','lastname') AS current_lastname,
  json_extract_path_text(
    currentagentsnapshot,
    'configuration','routingprofile','defaultoutboundqueue','name') AS current_outboundqueue,
FROM default_schema.kfhconnectblog

The following shows the query output:

Summary

In this post, I showed you how to use Kinesis Data Firehose to ingest and convert data to columnar file format, enabling real-time analysis using Athena and Amazon Redshift. This great feature enables a level of optimization in both cost and performance that you need when storing and analyzing large amounts of data. This feature is equally important if you are investing in building data lakes on AWS.

 


Additional Reading

If you found this post useful, be sure to check out Analyzing VPC Flow Logs with Amazon Kinesis Firehose, Amazon Athena, and Amazon QuickSight and Work with partitioned data in AWS Glue.


About the Author

Roy Hasson is a Global Business Development Manager for AWS Analytics. He works with customers around the globe to design solutions to meet their data processing, analytics and business intelligence needs. Roy is big Manchester United fan cheering his team on and hanging out with his family.

 

 

 

Spring 2018 AWS SOC Reports are Now Available with 11 Services Added in Scope

Post Syndicated from Chris Gile original https://aws.amazon.com/blogs/security/spring-2018-aws-soc-reports-are-now-available-with-11-services-added-in-scope/

Since our last System and Organization Control (SOC) audit, our service and compliance teams have been working to increase the number of AWS Services in scope prioritized based on customer requests. Today, we’re happy to report 11 services are newly SOC compliant, which is a 21 percent increase in the last six months.

With the addition of the following 11 new services, you can now select from a total of 62 SOC-compliant services. To see the full list, go to our Services in Scope by Compliance Program page:

• Amazon Athena
• Amazon QuickSight
• Amazon WorkDocs
• AWS Batch
• AWS CodeBuild
• AWS Config
• AWS OpsWorks Stacks
• AWS Snowball
• AWS Snowball Edge
• AWS Snowmobile
• AWS X-Ray

Our latest SOC 1, 2, and 3 reports covering the period from October 1, 2017 to March 31, 2018 are now available. The SOC 1 and 2 reports are available on-demand through AWS Artifact by logging into the AWS Management Console. The SOC 3 report can be downloaded here.

Finally, prospective customers can read our SOC 1 and 2 reports by reaching out to AWS Compliance.

Want more AWS Security news? Follow us on Twitter.

Virginia Beach Police Want Encrypted Radios

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/05/virginia_beach_.html

This article says that the Virginia Beach police are looking to buy encrypted radios.

Virginia Beach police believe encryption will prevent criminals from listening to police communications. They said officer safety would increase and citizens would be better protected.

Someone should ask them if they want those radios to have a backdoor.

How AWS Meets a Physical Separation Requirement with a Logical Separation Approach

Post Syndicated from Min Hyun original https://aws.amazon.com/blogs/security/how-aws-meets-a-physical-separation-requirement-with-a-logical-separation-approach/

We have a new resource available to help you meet a requirement for physically-separated infrastructure using logical separation in the AWS cloud. Our latest guide, Logical Separation: An Evaluation of the U.S. Department of Defense Cloud Security Requirements for Sensitive Workloads outlines how AWS meets the U.S. Department of Defense’s (DoD) stringent physical separation requirement by pioneering a three-pronged logical separation approach that leverages virtualization, encryption, and deploying compute to dedicated hardware.

This guide will help you understand logical separation in the cloud and demonstrates its advantages over a traditional physical separation model. Embracing this approach can help organizations confidently meet or exceed security requirements found in traditional on-premises environments, while also providing increased security control and flexibility.

Logical Separation is the second guide in the AWS Government Handbook Series, which examines cybersecurity policy initiatives and identifies best practices.

If you have questions or want to learn more, contact your account executive or AWS Support.

What’s new in HiveMQ 3.4

Post Syndicated from The HiveMQ Team original https://www.hivemq.com/whats-new-in-hivemq-3-4

We are pleased to announce the release of HiveMQ 3.4. This version of HiveMQ is the most resilient and advanced version of HiveMQ ever. The main focus in this release was directed towards addressing the needs for the most ambitious MQTT deployments in the world for maximum performance and resilience for millions of concurrent MQTT clients. Of course, deployments of all sizes can profit from the improvements in the latest and greatest HiveMQ.

This version is a drop-in replacement for HiveMQ 3.3 and of course supports rolling upgrades with zero-downtime.

HiveMQ 3.4 brings many features that your users, administrators and plugin developers are going to love. These are the highlights:

 

New HiveMQ 3.4 features at a glance

Cluster

HiveMQ 3.4 brings various improvements in terms of scalability, availability, resilience and observability for the cluster mechanism. Many of the new features remain under the hood, but several additions stand out:

Cluster Overload Protection

The new version has a first-of-its-kind Cluster Overload Protection. The whole cluster is able to spot MQTT clients that cause overload on nodes or the cluster as a whole and protects itself from the overload. This mechanism also protects the deployment from cascading failures due to slow or failing underlying hardware (as sometimes seen on cloud providers). This feature is enabled by default and you can learn more about the mechanism in our documentation.

Dynamic Replicates

HiveMQ’s sophisticated cluster mechanism is able to scale in a linear fashion due to extremely efficient and true data distribution mechanics based on a configured replication factor. The most important aspect of every cluster is availability, which is achieved by having eventual consistency functions in place for edge cases. The 3.4 version adds dynamic replicates to the cluster so even the most challenging edge cases involving network splits don’t lead to the sacrifice of consistency for the most important MQTT operations.

Node Stress Level Metrics

All MQTT cluster nodes are now aware of their own stress level and the stress levels of other cluster members. While all stress mitigation is handled internally by HiveMQ, experienced operators may want to monitor the individual node’s stress level (e.g with Grafana) in order to start investigating what caused the increase of load.

WebUI

Operators worldwide love the HiveMQ WebUI introduced with HiveMQ 3.3. We gathered all the fantastic feedback from our users and polished the WebUI, so it’s even more useful for day-to-day broker operations and remote debugging of MQTT clients. The most important changes and additions are:

Trace Recording Download

The unique Trace Recordings functionality is without doubt a lifesaver when the behavior of individual MQTT clients needs further investigation as all interactions with the broker can be traced — at runtime and at scale! Huge production deployments may accumulate multiple gigabytes of trace recordings. HiveMQ now offers a convenient way to collect all trace recordings from all nodes, zips them and allows the download via a simple button on the WebUI. Remote debugging was never easier!

Additional Client Detail Information in WebUI

The mission of the HiveMQ WebUI is to provide easy insights to the whole production MQTT cluster for operators and administrators. Individual MQTT client investigations are a piece of cake, as all available information about clients can be viewed in detail. We further added the ability to view the restrictions a concrete client has:

  • Maximum Inflight Queue Size
  • Client Offline Queue Messages Size
  • Client Offline Message Drop Strategy

Session Invalidation

MQTT persistent sessions are one of the outstanding features of the MQTT protocol specification. Sessions which do not expire but are never reused unnecessarily consume disk space and memory. Administrators can now invalidate individual session directly in the HiveMQ WebUI for client sessions, which can be deleted safely. HiveMQ 3.4 will take care and release the resources on all cluster nodes after a session was invalidated

Web UI Polishing

Most texts on the WebUI were revisited and are now clearer and crisper. The help texts also received a major overhaul and should now be more, well, helpful. In addition, many small improvements were added, which are most of the time invisible but are here to help when you need them most. For example, the WebUI now displays a warning if cluster nodes with old versions are in the cluster (which may happen if a rolling upgrade was not finished properly)

Plugin System

One of the most popular features of HiveMQ is the extensive Plugin System, which virtually enables the integration of HiveMQ to any system and allows hooking into all aspects of the MQTT lifecycle. We listened to the feedback and are pleased to announce many improvements, big and small, for the Plugin System:

Client Session Time-to-live for individual clients

HiveMQ 3.3 offered a global configuration for setting the Time-To-Live for MQTT sessions. With the advent of HiveMQ 3.4, users can now programmatically set Time-To-Live values for individual MQTT clients and can discard a MQTT session immediately.

Individual Inflight Queues

While the Inflight Queue configuration is typically sufficient in the HiveMQ default configuration, there are some use cases that require the adjustment of this configuration. It’s now possible to change the Inflight Queue size for individual clients via the Plugin System.
 
 

Plugin Service Overload Protection

The HiveMQ Plugin System is a power-user tool and it’s possible to do unbelievably useful modifications as well as putting major stress on the system as a whole if the programmer is not careful. In order to protect the HiveMQ instances from accidental overload, a Plugin Service Overload Protection can be configured. This rate limits the Plugin Service usage and gives feedback to the application programmer in case the rate limit is exceeded. This feature is disabled by default but we strongly recommend updating your plugins to profit from this feature.

Session Attribute Store putIfNewer

This is one of the small bits you almost never need but when you do, you’re ecstatic for being able to use it. The Session Attribute Store now offers methods to put values, if the values you want to put are newer or fresher than the values already written. This is extremely useful, if multiple cluster nodes want to write to the Session Attribute Store simultaneously, as this guarantees that outdated values can no longer overwrite newer values.
 
 
 
 

Disconnection Timestamp for OnDisconnectCallback

As the OnDisconnectCallback is executed asynchronously, the client might already be gone when the callback is executed. It’s now easy to obtain the exact timestamp when a MQTT client disconnected, even if the callback is executed later on. This feature might be very interesting for many plugin developers in conjunction with the Session Attribute Store putIfNewer functionality.

Operations

We ❤️ Operators and we strive to provide all the tools needed for operating and administrating a MQTT broker cluster at scale in any environment. A key strategy for successful operations of any system is monitoring. We added some interesting new metrics you might find useful.

System Metrics

In addition to JVM Metrics, HiveMQ now also gathers Operating System Metrics for Linux Systems. So HiveMQ is able to see for itself how the operating system views the process, including native memory, the real CPU usage, and open file usage. These metrics are particularly useful, if you don’t have a monitoring agent for Linux systems setup. All metrics can be found here.

Client Disconnection Metrics

The reality of many MQTT scenarios is that not all clients are able to disconnect gracefully by sending MQTT DISCONNECT messages. HiveMQ now also exposes metrics about clients that disconnected by closing the TCP connection instead of sending a DISCONNECT packet first. This is especially useful for monitoring, if you regularly deal with clients that don’t have a stable connection to the MQTT brokers.

 

JMX enabled by default

JMX, the Java Monitoring Extension, is now enabled by default. Many HiveMQ operators use Application Performance Monitoring tools, which are able to hook into the metrics via JMX or use plain JMX for on-the-fly debugging. While we recommend to use official off-the-shelf plugins for monitoring, it’s now easier than ever to just use JMX if other solutions are not available to you.

Other notable improvements

The 3.4 release of HiveMQ is full of hidden gems and improvements. While it would be too much to highlight all small improvements, these notable changes stand out and contribute to the best HiveMQ release ever.

Topic Level Distribution Configuration

Our recommendation for all huge deployments with millions of devices is: Start with separate topic prefixes by bringing the dynamic topic parts directly to the beginning. The reality is that many customers have topics that are constructed like the following: “devices/{deviceId}/status”. So what happens is that all topics in this example start with a common prefix, “devices”, which is the first topic level. Unfortunately the first topic level doesn’t include a dynamic topic part. In order to guarantee the best scalability of the cluster and the best performance of the topic tree, customers can now configure how many topic levels are used for distribution. In the example outlined here, a topic level distribution of 2 would be perfect and guarantees the best scalability.

Mass disconnect performance improvements

Mass disconnections of MQTT clients can happen. This might be the case when e.g. a load balancer in front of the MQTT broker cluster drops the connections or if a mobile carrier experiences connectivity problems. Prior to HiveMQ 3.4, mass disconnect events caused stress on the cluster. Mass disconnect events are now massively optimized and even tens of millions of connection losses at the same time won’t bring the cluster into stress situations.

 
 
 
 
 
 

Replication Performance Improvements

Due to the distributed nature of a HiveMQ, data needs to be replicated across the cluster in certain events, e.g. when cluster topology changes occur. There are various internal improvements in HiveMQ version 3.4, which increase the replication performance significantly. Our engineers put special love into the replication of Queued Messages, which is now faster than ever, even for multiple millions of Queued Messages that need to be transferred across the cluster.

Updated Native SSL Libraries

The Native SSL Integration of HiveMQ was updated to the newest BoringSSL version. This results in better performance and increased security. In case you’re using SSL and you are not yet using the native SSL integration, we strongly recommend to give it a try, more than 40% performance improvement can be observed for most deployments.

 
 

Improvements for Java 9

While Java 9 was already supported for older HiveMQ versions, HiveMQ 3.4 has full-blown Java 9 support. The minimum Java version still remains Java 7, although we strongly recommend to use Java 8 or newer for the best performance of HiveMQ.

Infamous ‘Kodi Box’ Case Sees Man Pay Back Just £1 to the State

Post Syndicated from Andy original https://torrentfreak.com/infamous-kodi-box-case-sees-man-pay-back-just-1-to-the-state-180507/

In 2015, Middlesbrough-based shopkeeper Brian ‘Tomo’ Thompson shot into the headlines after being raided by police and Trading Standards in the UK.

Thompson had been selling “fully-loaded” piracy-configured Kodi boxes from his shop but didn’t think he’d done anything wrong.

“All I want to know is whether I am doing anything illegal. I know it’s a gray area but I want it in black and white,” he said.

Thompson started out with a particularly brave tone. He insisted he’d take the case to Crown Court and even to the European Court. His mission was show what was legal and what wasn’t, he said.

Very quickly, Thompson’s case took on great importance, with observers everywhere reporting on a potential David versus Goliath copyright battle for the ages. But Thompson’s case wasn’t straightforward.

The shopkeeper wasn’t charged with basic “making available” under the Copyrights, Designs and Patents Acts that would have found him guilty under the earlier BREIN v Filmspeler case. Instead, he stood accused of two offenses under section 296ZB of the Copyright, Designs and Patents Act, which deals with devices and services designed to “circumvent technological measures”.

In the end it was all moot. After entering his official ‘not guilty’ plea, last year Thompson suddenly changed his tune. He accepted the prosecution’s version of events, throwing himself at the mercy of the court with a guilty plea.

In October 2017, Teeside Crown Court heard that Thompson cost Sky around £200,000 in lost subscriptions while the shopkeeper made around £38,500 from selling the devices. But despite the fairly big numbers, Judge Peter Armstrong decided to go reasonably light on the 55-year-old, handing him an 18-month prison term, suspended for two years.

“I’ve come to the conclusion that in all the circumstances an immediate custodial sentence is not called for. But as a warning to others in future, they may not be so lucky,” the Judge said.

But things wouldn’t end there for Thompson.

In the UK, people who make money or obtain assets from criminal activity can be forced to pay back their profits, which are then confiscated by the state under the Proceeds of Crime Act (pdf). Almost anything can be taken, from straight cash to cars, jewellery and houses.

However, it appears that whatever cash Thompson earned from Kodi Box activities has long since gone.

During a Proceeds of Crime hearing reported on by Gazette Live, the Court heard that Thompson has no assets whatsoever so any confiscation order would have to be a small one.

In the end, Judge Simon Hickey decided that Thompson should forfeit a single pound, an amount that could increase if the businessman got lucky moving forward.

“If anything changes in the future, for instance if you win the lottery, it might come back,” the Judge said.

With that seeming particularly unlikely, perhaps this will be the end for Thompson. Considering the gravity and importance placed on his case, zero jail time and just a £1 to pay back will probably be acceptable to the 55-year-old and also a lesson to the authorities, who have gotten very little out of this expensive case.

Who knows, perhaps they might sum up the outcome using the same eight-letter word that Thompson can be seen half-covering in this photograph.

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

Video Deters People From Pirate Sites…Or Encourages Them to Start One?

Post Syndicated from Andy original https://torrentfreak.com/video-deters-people-from-pirate-sites-or-encourages-them-to-start-one-180505/

There are almost as many anti-piracy strategies as there are techniques for downloading.

Litigation and education are probably the two most likely to be seen by the public, who are often directly targeted by the entertainment industries.

Over the years this has led to many campaigns, one of which famously stated that piracy is a crime while equating it to the physical theft of a car, a handbag, a television, or a regular movie DVD. It’s debatable whether these campaigns have made much difference but they have raised awareness and some of the responses have been hilarious.

While success remains hard to measure, it hasn’t stopped these PSAs from being made. The latest efforts come out of Sweden, where the country’s Patent and Registration Office (PRV) was commissioned by the government to increase public awareness of copyright and help change attitudes surrounding streaming and illegal downloading.

“The purpose is, among other things, to reduce the use of illegal streaming sites and make it easier and safer to find and choose legal options,” PRV says.

“Every year, criminal networks earn millions of dollars from illegal streaming. This money comes from advertising on illegal sites and is used for other criminal activities. The purpose of our film is to inform about this.”

The series of videos show pirates in their supposed natural habitats of beautiful mansions, packed with luxurious items such as indoor pools, fancy staircases, and stacks of money. For some reason (perhaps to depict anonymity, perhaps to suggest something more sinister) the pirates are all dressed in animal masks, such as this one enjoying his Dodge Viper.

The clear suggestion here is that people who visit pirate sites and stream unlicensed content are helping to pay for this guy’s bright green car. The same holds true for his indoor swimming pool, jet bike, and gold chains in the next clip.

While some might have a problem with pirates getting rich from their clicks, it can’t have escaped the targets of these videos that they too are benefiting from the scheme. Granted, hyena-man gets the pool and the Viper, but they get the latest movies. It seems unlikely that pirate streamers refused to watch the copy of Black Panther that leaked onto the web this week (a month before its retail release) on the basis that someone else was getting rich from it.

That being said, most people will probably balk at elements of the full PSA, which suggests that revenue from illegal streaming goes on to fuel other crimes, such as prescription drug offenses.

After reporting piracy cases for more than twelve years, no one at TF has ever seen evidence of this happening with any torrent or streaming site operators. Still, it makes good drama for the full video, embedded below.

“In the film we follow a fictional occupational criminal who gives us a tour of his beautiful villa. He proudly shows up his multi-criminal activity, which was made possible by means of advertising money from his illegal streaming services,” PRV explains.

The dark tone and creepy masks are bound to put some people off but one has to question the effect this kind of video could have on younger people. Do pirates really make mountains of money so huge that they can only be counted by machine? If they do, then it’s a lot less risky than almost any other crime that yields this claimed level of profit.

With that in mind, will this video deter the public or simply encourage people to get involved for some of that big money? We sent a link to the operator of a large pirate site for his considered opinion.

“WTF,” he responded.

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

Danish Traffic to Pirate Sites Increases 67% in Just a Year

Post Syndicated from Andy original https://torrentfreak.com/danish-traffic-to-pirate-sites-increases-67-in-just-a-year-180501/

For close to 20 years, rightsholders have tried to stem the tide of mainstream Internet piracy. Yet despite increasingly powerful enforcement tools, infringement continues on a grand scale.

While the problem is global, rightsholder groups often zoom in on their home turf, to see how the fight is progressing locally. Covering Denmark, the Rights Alliance Data Report 2017 paints a fairly pessimistic picture.

Published this week, the industry study – which uses SimilarWeb and MarkMonitor data – finds that Danes visited 2,000 leading pirate sites 596 million times in 2017. That represents a 67% increase over the 356 million visits to unlicensed platforms made by citizens during 2016.

The report notes that, at least in part, this explosive growth can be attributed to mobile-compatible sites and services, which make it easier than ever to consume illicit content on the move, as well as at home.

In a sea of unauthorized streaming sites, Rights Alliance highlights one platform above all the others as a particularly bad influence in 2017 – 123movies (also known as GoMovies and GoStream, among others).

“The popularity of this service rose sharply in 2017 from 40 million visits in 2016 to 175 million visits in 2017 – an increase of 337 percent, of which most of the traffic originates from mobile devices,” the report notes.

123movies recently announced its closure but before that the platform was subjected to web-blocking in several jurisdictions.

Rights Alliance says that Denmark has one of the most effective blocking systems in the world but that still doesn’t stop huge numbers of people from consuming pirate content from sites that aren’t yet blocked.

“Traffic to infringing sites is overwhelming, and therefore blocking a few sites merely takes the top of the illegal activities,” Rights Alliance chief Maria Fredenslund informs TorrentFreak.

“Blocking is effective by stopping 75% of traffic to blocked sites but certainly, an upscaled effort is necessary.”

Rights Alliance also views the promotion of legal services as crucial to its anti-piracy strategy so when people visit a blocked site, they’re also directed towards legitimate platforms.

“That is why we are working at the moment with Denmark’s Ministry of Culture and ISPs on a campaign ‘Share With Care 2′ which promotes legal services e.g. by offering a search function for legal services which will be placed in combination with the signs that are put on blocked websites,” the anti-piracy group notes.

But even with such measures in place, the thirst for unlicensed content is great. In 2017 alone, 500 of the most popular films and TV shows were downloaded from P2P networks like BitTorrent more than 15 million times from Danish IP addresses, that’s up from 11.9 million in 2016.

Given the dramatic rise in visits to pirate sites overall, the suggestion is that plenty of consumers are still getting through. Rights Alliance says that the number of people being restricted is also hampered by people who don’t use their ISP’s DNS service, which is the method used to block sites in Denmark.

Additionally, interest in VPNs and similar anonymization and bypass-capable technologies is on the increase. Between 3.5% and 5% of Danish Internet users currently use a VPN, a number that’s expected to go up. Furthermore, Rights Alliance reports greater interest in “closed” pirate communities.

“The data is based on closed [BitTorrent] networks. We also address the challenges with private communities on Facebook and other [social media] platforms,” Fredenslund explains.

“Due to the closed doors of these platforms it is not possible for us to say anything precisely about the amount of infringing activities there. However, we receive an increasing number of notices from our members who discover that their products are distributed illegally and also we do an increased monitoring of these platforms.”

But while more established technologies such as torrents and regular web-streaming continue in considerable volumes, newer IPTV-style services accessible via apps and dedicated platforms are also gaining traction.

“The volume of visitors to these services’ websites has been sharply rising in 2017 – an increase of 84 percent from January to December,” Rights Alliance notes.

“Even though the number of visitors does not say anything about actual consumption, as users usually only visit pages one time to download the program, the number gives an indication that the interest in IPTV is increasing.”

To combat this growth market, Rights Alliance says it wants to establish web-blockades against sites hosting the software applications.

Also on the up are visits to platforms offering live sports illegally. In 2017, Danish IP addresses made 2.96 million visits to these services, corresponding to almost 250,000 visits per month and representing an annual increase of 28%.

Rights Alliance informs TF that in future a ‘live’ blocking mechanism similar to the one used by the Premier League in the UK could be deployed in Denmark.

“We already have a dynamic blocking system, and we see an increasing demand for illegal TV products, so this could be a natural next step,” Fredenslund explains.

Another small but perhaps significant detail is how users are accessing pirate sites. According to the report, large volumes of people are now visiting platforms directly, with more than 50% doing so in preference to referrals from search engines such as Google.

In terms of deterrence, the Rights Alliance report sticks to the tried-and-tested approaches seen so often in the anti-piracy arena.

Firstly, the group notes that it’s increasingly encountering people who are paying for legal services such as Netflix and Spotify so believe that allows them to grab something extra from a pirate site. However, in common with similar organizations globally, the group counters that pirate sites can serve malware or have other nefarious business interests behind the scenes, so people should stay away.

Whether significant volumes will heed this advice will remain to be seen but if a 67% increase last year is any predictor of the future, piracy is here to stay – and then some. Rights Alliance says it is ready for the challenge but will need some assistance to achieve its goals.

“As it is evident from the traffic data, criminal activities are not something that we, private companies (right holders in cooperation with ISPs), can handle alone,” Fredenslund says.

“Therefore, we are very pleased that DK Government recently announced that the IP taskforce which was set down as a trial period has now been made permanent. In that regard it is important and necessary that the police will also obtain the authority to handle blocking of massively infringing websites. Police do not have the authority to carry out blocking as it is today.”

The full report is available here (Danish, pdf)

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

Ransomware Update: Viruses Targeting Business IT Servers

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/ransomware-update-viruses-targeting-business-it-servers/

Ransomware warning message on computer

As ransomware attacks have grown in number in recent months, the tactics and attack vectors also have evolved. While the primary method of attack used to be to target individual computer users within organizations with phishing emails and infected attachments, we’re increasingly seeing attacks that target weaknesses in businesses’ IT infrastructure.

How Ransomware Attacks Typically Work

In our previous posts on ransomware, we described the common vehicles used by hackers to infect organizations with ransomware viruses. Most often, downloaders distribute trojan horses through malicious downloads and spam emails. The emails contain a variety of file attachments, which if opened, will download and run one of the many ransomware variants. Once a user’s computer is infected with a malicious downloader, it will retrieve additional malware, which frequently includes crypto-ransomware. After the files have been encrypted, a ransom payment is demanded of the victim in order to decrypt the files.

What’s Changed With the Latest Ransomware Attacks?

In 2016, a customized ransomware strain called SamSam began attacking the servers in primarily health care institutions. SamSam, unlike more conventional ransomware, is not delivered through downloads or phishing emails. Instead, the attackers behind SamSam use tools to identify unpatched servers running Red Hat’s JBoss enterprise products. Once the attackers have successfully gained entry into one of these servers by exploiting vulnerabilities in JBoss, they use other freely available tools and scripts to collect credentials and gather information on networked computers. Then they deploy their ransomware to encrypt files on these systems before demanding a ransom. Gaining entry to an organization through its IT center rather than its endpoints makes this approach scalable and especially unsettling.

SamSam’s methodology is to scour the Internet searching for accessible and vulnerable JBoss application servers, especially ones used by hospitals. It’s not unlike a burglar rattling doorknobs in a neighborhood to find unlocked homes. When SamSam finds an unlocked home (unpatched server), the software infiltrates the system. It is then free to spread across the company’s network by stealing passwords. As it transverses the network and systems, it encrypts files, preventing access until the victims pay the hackers a ransom, typically between $10,000 and $15,000. The low ransom amount has encouraged some victimized organizations to pay the ransom rather than incur the downtime required to wipe and reinitialize their IT systems.

The success of SamSam is due to its effectiveness rather than its sophistication. SamSam can enter and transverse a network without human intervention. Some organizations are learning too late that securing internet-facing services in their data center from attack is just as important as securing endpoints.

The typical steps in a SamSam ransomware attack are:

1
Attackers gain access to vulnerable server
Attackers exploit vulnerable software or weak/stolen credentials.
2
Attack spreads via remote access tools
Attackers harvest credentials, create SOCKS proxies to tunnel traffic, and abuse RDP to install SamSam on more computers in the network.
3
Ransomware payload deployed
Attackers run batch scripts to execute ransomware on compromised machines.
4
Ransomware demand delivered requiring payment to decrypt files
Demand amounts vary from victim to victim. Relatively low ransom amounts appear to be designed to encourage quick payment decisions.

What all the organizations successfully exploited by SamSam have in common is that they were running unpatched servers that made them vulnerable to SamSam. Some organizations had their endpoints and servers backed up, while others did not. Some of those without backups they could use to recover their systems chose to pay the ransom money.

Timeline of SamSam History and Exploits

Since its appearance in 2016, SamSam has been in the news with many successful incursions into healthcare, business, and government institutions.

March 2016
SamSam appears

SamSam campaign targets vulnerable JBoss servers
Attackers hone in on healthcare organizations specifically, as they’re more likely to have unpatched JBoss machines.

April 2016
SamSam finds new targets

SamSam begins targeting schools and government.
After initial success targeting healthcare, attackers branch out to other sectors.

April 2017
New tactics include RDP

Attackers shift to targeting organizations with exposed RDP connections, and maintain focus on healthcare.
An attack on Erie County Medical Center costs the hospital $10 million over three months of recovery.
Erie County Medical Center attacked by SamSam ransomware virus

January 2018
Municipalities attacked

• Attack on Municipality of Farmington, NM.
• Attack on Hancock Health.
Hancock Regional Hospital notice following SamSam attack
• Attack on Adams Memorial Hospital
• Attack on Allscripts (Electronic Health Records), which includes 180,000 physicians, 2,500 hospitals, and 7.2 million patients’ health records.

February 2018
Attack volume increases

• Attack on Davidson County, NC.
• Attack on Colorado Department of Transportation.
SamSam virus notification

March 2018
SamSam shuts down Atlanta

• Second attack on Colorado Department of Transportation.
• City of Atlanta suffers a devastating attack by SamSam.
The attack has far-reaching impacts — crippling the court system, keeping residents from paying their water bills, limiting vital communications like sewer infrastructure requests, and pushing the Atlanta Police Department to file paper reports.
Atlanta Ransomware outage alert
• SamSam campaign nets $325,000 in 4 weeks.
Infections spike as attackers launch new campaigns. Healthcare and government organizations are once again the primary targets.

How to Defend Against SamSam and Other Ransomware Attacks

The best way to respond to a ransomware attack is to avoid having one in the first place. If you are attacked, making sure your valuable data is backed up and unreachable by ransomware infection will ensure that your downtime and data loss will be minimal or none if you ever suffer an attack.

In our previous post, How to Recover From Ransomware, we listed the ten ways to protect your organization from ransomware.

  1. Use anti-virus and anti-malware software or other security policies to block known payloads from launching.
  2. Make frequent, comprehensive backups of all important files and isolate them from local and open networks. Cybersecurity professionals view data backup and recovery (74% in a recent survey) by far as the most effective solution to respond to a successful ransomware attack.
  3. Keep offline backups of data stored in locations inaccessible from any potentially infected computer, such as disconnected external storage drives or the cloud, which prevents them from being accessed by the ransomware.
  4. Install the latest security updates issued by software vendors of your OS and applications. Remember to patch early and patch often to close known vulnerabilities in operating systems, server software, browsers, and web plugins.
  5. Consider deploying security software to protect endpoints, email servers, and network systems from infection.
  6. Exercise cyber hygiene, such as using caution when opening email attachments and links.
  7. Segment your networks to keep critical computers isolated and to prevent the spread of malware in case of attack. Turn off unneeded network shares.
  8. Turn off admin rights for users who don’t require them. Give users the lowest system permissions they need to do their work.
  9. Restrict write permissions on file servers as much as possible.
  10. Educate yourself, your employees, and your family in best practices to keep malware out of your systems. Update everyone on the latest email phishing scams and human engineering aimed at turning victims into abettors.

Please Tell Us About Your Experiences with Ransomware

Have you endured a ransomware attack or have a strategy to avoid becoming a victim? Please tell us of your experiences in the comments.

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