Tag Archives: cases

From Idea to Launch: Getting Your First Customers

2017-06-22 Gleb Budman

Post Syndicated from Gleb Budman original https://www.backblaze.com/blog/how-to-get-your-first-customers/

After deciding to build an unlimited backup service and developing our own storage platform, the next step was to get customers and feedback. Not all customers are created equal. Let’s talk about the types, and when and how to attract them.

How to Get Your First Customers

First Step – Don’t Launch Publicly
Launch when you’re ready for the judgments of people who don’t know you at all. Until then, don’t launch. Sign up users and customers either that you know, those you can trust to cut you some slack (while providing you feedback), or at minimum those for whom you can set expectations. For months the Backblaze website was a single page with no ability to get the product and minimal info on what it would be. This is not to counter the Lean Startup ‘iterate quickly with customer feedback’ advice. Rather, this is an acknowledgement that there are different types of feedback required based on your development stage.

Sign Up Your Friends
We knew all of our first customers; they were friends, family, and previous co-workers. Many knew what we were up to and were excited to help us. No magic marketing or tech savviness was required to reach them – we just asked that they try the service. We asked them to provide us feedback on their experience and collected it through email and conversations. While the feedback wasn’t unbiased, it was nonetheless wide-ranging, real, and often insightful. These people were willing to spend time carefully thinking about their feedback and delving deeper into the conversations.

Broaden to Beta
Unless you’re famous or your service costs $1 million per customer, you’ll probably need to expand quickly beyond your friends to build a business – and to get broader feedback. Our next step was to broaden the customer base to beta users.

Opening up the service in beta provides three benefits:

Air cover for the early warts. There are going to be issues, bugs, unnecessarily complicated user flows, and poorly worded text. Beta tells people, “We don’t consider the product ‘done’ and you should expect some of these issues. Please be patient with us.”
A request for feedback. Some people always provide feedback, but beta communicates that you want it.
An awareness opportunity. Opening up in beta provides an early (but not only) opportunity to have an announcement and build awareness.

Pitching Beta to Press
Not all press cares about, or is even willing to cover, beta products. Much of the mainstream press wants to write about services that are fully live, have scale, and are important in the marketplace. However, there are a number of sites that like to cover the leading edge – and that means covering betas. Techcrunch, Ars Technica, and SimpleHelp covered our initial private beta launch. I’ll go into the details of how to work with the press to cover your announcements in a post next month.

Private vs. Public Beta
Both private and public beta provide all three of the benefits above. The difference between the two is that private betas are much more controlled, whereas public ones bring in more users. But this isn’t an either/or – I recommend doing both.

Private Beta
For our original beta in 2008, we decided that we were comfortable with about 1,000 users subscribing to our service. That would provide us with a healthy amount of feedback and get some early adoption, while not overwhelming us or our server capacity, and equally important not causing cash flow issues from having to buy more equipment. So we decided to limit the sign-up to only the first 1,000 people who signed up; then we would shut off sign-ups for a while.

But how do you even get 1,000 people to sign up for your service? In our case, get some major publications to write about our beta. (Note: In a future post I’ll explain exactly how to find and reach out to writers. Sign up to receive all of the entrepreneurial posts in this series.)

Public Beta
For our original service (computer backup), we did not have a public beta; but when we launched Backblaze B2, we had a private and then a public beta. The private beta allowed us to work out early kinks, while the public beta brought us a more varied set of use cases. In public beta, there is no cap on the number of users that may try the service.

While this is a first-class problem to have, if your service is flooded and stops working, it’s still a problem. Think through what you will do if that happens. In our early days, when our system could get overwhelmed by volume, we had a static web page hosted with a different registrar that wouldn’t let customers sign up but would tell them when our service would be open again. When we reached a critical volume level we would redirect to it in order to at least provide status for when we could accept more customers.

Collect Feedback
Since one of the goals of betas is to get feedback, we made sure that we had our email addresses clearly presented on the site so users could send us thoughts. We were most interested in broad qualitative feedback on users’ experience, so all emails went to an internal mailing list that would be read by everyone at Backblaze.

For our B2 public and private betas, we also added an optional short survey to the sign-up process. In order to be considered for the private beta you had to fill the survey out, though we found that 80% of users continued to fill out the survey even when it was not required. This survey had both closed-end questions (“how much data do you have”) and open-ended ones (“what do you want to use cloud storage for?”).

BTW, despite us getting a lot of feedback now via our support team, Twitter, and marketing surveys, we are always open to more – you can email me directly at gleb.budman {at} backblaze.com.

Don’t Throw Away Users
Initially our backup service was available only on Windows, but we had an email sign-up list for people who wanted it for their Mac. This provided us with a sense of market demand and a ready list of folks who could be beta users and early adopters when we had a Mac version. Have a service targeted at doctors but lawyers are expressing interest? Capture that.

Product Launch

When
The first question is “when” to launch. Presuming your service is in ‘public beta’, what is the advantage of moving out of beta and into a “version 1.0”, “gold”, or “public availability”? That depends on your service and customer base. Some services fly through public beta. Gmail, on the other hand, was (in)famous for being in beta for 5 years, despite having over 100 million users.

The term beta says to users, “give us some leeway, but feel free to use the service”. That’s fine for many consumer apps and will have near zero impact on them. However, services aimed at businesses and government will often not be adopted with a beta label as the enterprise customers want to know the company feels the service is ‘ready’. While Backblaze started out as a purely consumer service, because it was a data backup service, it was important for customers to trust that the service was ready.

No product is bug-free. But from a product readiness perspective, the nomenclature should also be a reflection of the quality of the product. You can launch a product with one feature that works well out of beta. But a product with fifty features on which half the users will bump into problems should likely stay in beta. The customer feedback, surveys, and your own internal testing should guide you in determining this quality during the beta. Be careful about “we’ve only seen that one time” or “I haven’t been able to reproduce that on my machine”; those issues are likely to scale with customers when you launch.

How
Launching out of beta can be as simple as removing the beta label from the website/product. However, this can be a great time to reach out to press, write a blog post, and send an email announcement to your customers.

Consider thanking your beta testers somehow; can they get some feature turned out for free, an extension of their trial, or premium support? If nothing else, remember to thank them for their feedback. Users that signed up during your beta are likely the ones who will propel your service. They had the need and interest to both be early adopters and deal with bugs. They are likely the key to getting 1,000 true fans.

The Beginning
The title of this post was “Getting your first customers”, because getting to launch may feel like the peak of your journey when you’re pre-launch, but it really is just the beginning. It’s a step along the journey of building your business. If your launch is wildly successful, enjoy it, work to build on the momentum, but don’t lose track of building your business. If your launch is a dud, go out for a coffee with your team, say “well that sucks”, and then get back to building your business. You can learn a tremendous amount from your early customers, and they can become your biggest fans, but the success of your business will depend on what you continue to do the months and years after your launch.

The post From Idea to Launch: Getting Your First Customers appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

NSA Insider Security Post-Snowden

2017-06-22 Bruce Schneier

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2017/06/nsa_insider_sec.html

According to a recently declassified report obtained under FOIA, the NSA’s attempts to protect itself against insider attacks aren’t going very well:

The N.S.A. failed to consistently lock racks of servers storing highly classified data and to secure data center machine rooms, according to the report, an investigation by the Defense Department’s inspector general completed in 2016.

[…]

The agency also failed to meaningfully reduce the number of officials and contractors who were empowered to download and transfer data classified as top secret, as well as the number of “privileged” users, who have greater power to access the N.S.A.’s most sensitive computer systems. And it did not fully implement software to monitor what those users were doing.

In all, the report concluded, while the post-Snowden initiative — called “Secure the Net” by the N.S.A. — had some successes, it “did not fully meet the intent of decreasing the risk of insider threats to N.S.A. operations and the ability of insiders to exfiltrate data.”

Marcy Wheeler comments:

The IG report examined seven of the most important out of 40 “Secure the Net” initiatives rolled out since Snowden began leaking classified information. Two of the initiatives aspired to reduce the number of people who had the kind of access Snowden did: those who have privileged access to maintain, configure, and operate the NSA’s computer systems (what the report calls PRIVACs), and those who are authorized to use removable media to transfer data to or from an NSA system (what the report calls DTAs).

But when DOD’s inspectors went to assess whether NSA had succeeded in doing this, they found something disturbing. In both cases, the NSA did not have solid documentation about how many such users existed at the time of the Snowden leak. With respect to PRIVACs, in June 2013 (the start of the Snowden leak), “NSA officials stated that they used a manually kept spreadsheet, which they no longer had, to identify the initial number of privileged users.” The report offered no explanation for how NSA came to no longer have that spreadsheet just as an investigation into the biggest breach thus far at NSA started. With respect to DTAs, “NSA did not know how many DTAs it had because the manually kept list was corrupted during the months leading up to the security breach.”

There seem to be two possible explanations for the fact that the NSA couldn’t track who had the same kind of access that Snowden exploited to steal so many documents. Either the dog ate their homework: Someone at NSA made the documents unavailable (or they never really existed). Or someone fed the dog their homework: Some adversary made these lists unusable. The former would suggest the NSA had something to hide as it prepared to explain why Snowden had been able to walk away with NSA’s crown jewels. The latter would suggest that someone deliberately obscured who else in the building might walk away with the crown jewels. Obscuring that list would be of particular value if you were a foreign adversary planning on walking away with a bunch of files, such as the set of hacking tools the Shadow Brokers have since released, which are believed to have originated at NSA.

Read the whole thing. Securing against insiders, especially those with technical access, is difficult, but I had assumed the NSA did more post-Snowden.

Three Men Sentenced Following £2.5m Internet Piracy Case

2017-06-22 Andy

Post Syndicated from Andy original https://torrentfreak.com/three-men-sentenced-following-2-5m-internet-piracy-case-170622/

While legal action against low-level individual file-sharers is extremely rare in the UK, the country continues to pose a risk for those engaged in larger-scale infringement.

That is largely due to the activities of the Police Intellectual Property Crime Unit and private anti-piracy outfits such as the Federation Against Copyright Theft (FACT). Investigations are often a joint effort which can take many years to complete, but the outcomes can often involve criminal sentences.

That was the profile of another Internet piracy case that concluded in London this week. It involved three men from the UK, Eric Brooks, 43, from Bolton, Mark Valentine, 44, from Manchester, and Craig Lloyd, 33, from Wolverhampton.

The case began when FACT became aware of potentially infringing activity back in February 2011. The anti-piracy group then investigated for more than a year before handing the case to police in March 2012.

On July 4, 2012, officers from City of London Police arrested Eric Brooks’ at his home in Bolton following a joint raid with FACT. Computer equipment was seized containing evidence that Brooks had been running a Netherlands-based server hosting more than £100,000 worth of pirated films, music, games, software and ebooks.

According to police, a spreadsheet on Brooks’ computer revealed he had hundreds of paying customers, all recruited from online forums. Using PayPal or utilizing bank transfers, each paid money to access the server. Police mentioned no group or site names in information released this week.

“Enquiries with PayPal later revealed that [Brooks] had made in excess of £500,000 in the last eight years from his criminal business and had in turn defrauded the film and TV industry alone of more than £2.5 million,” police said.

“As his criminal enterprise affected not only the film and TV but the wider entertainment industry including music, games, books and software it is thought that he cost the wider industry an amount much higher than £2.5 million.”

On the same day police arrested Brooks, Mark Valentine’s home in Manchester had a similar unwelcome visit. A day later, Craig Lloyd’s home in Wolverhampton become the third target for police.

Computer equipment was seized from both addresses which revealed that the pair had been paying for access to Brooks’ servers in order to service their own customers.

“They too had used PayPal as a means of taking payment and had earned thousands of pounds from their criminal actions; Valentine gaining £34,000 and Lloyd making over £70,000,” police revealed.

But after raiding the trio in 2012, it took more than four years to charge the men. In a feature common to many FACT cases, all three were charged with Conspiracy to Defraud rather than copyright infringement offenses. All three men pleaded guilty before trial.

On Monday, the men were sentenced at Inner London Crown Court. Brooks was sentenced to 24 months in prison, suspended for 12 months and ordered to complete 140 hours of unpaid work.

Valentine and Lloyd were each given 18 months in prison, suspended for 12 months. Each was ordered to complete 80 hours unpaid work.

Detective Constable Chris Glover, who led the investigation for the City of London Police, welcomed the sentencing.

“The success of this investigation is a result of co-ordinated joint working between the City of London Police and FACT. Brooks, Valentine and Lloyd all thought that they were operating under the radar and doing something which they thought was beyond the controls of law enforcement,” Glover said.

“Brooks, Valentine and Lloyd will now have time in prison to reflect on their actions and the result should act as deterrent for anyone else who is enticed by abusing the internet to the detriment of the entertainment industry.”

While even suspended sentences are a serious matter, none of the men will see the inside of a cell if they meet the conditions of their sentence for the next 12 months. For a case lasting four years involving such large sums of money, that is probably a disappointing result for FACT and the police.

Nevertheless, the men won’t be allowed to enjoy the financial proceeds of their piracy, if indeed any money is left. City of London Police say the trio will be subject to a future confiscation hearing to seize any proceeds of crime.

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

Protect Web Sites & Services Using Rate-Based Rules for AWS WAF

2017-06-21 Jeff Barr

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/protect-web-sites-services-using-rate-based-rules-for-aws-waf/

AWS WAF (Web Application Firewall) helps to protect your application from many different types of application-layer attacks that involve requests that are malicious or malformed. As I showed you when I first wrote about this service (New – AWS WAF), you can define rules that match cross-site scripting, IP address, SQL injection, size, or content constraints:

When incoming requests match rules, actions are invoked. Actions can either allow, block, or simply count matches.

The existing rule model is powerful and gives you the ability to detect and respond to many different types of attacks. It does not, however, allow you to respond to attacks that simply consist of a large number of otherwise valid requests from a particular IP address. These requests might be a web-layer DDoS attack, a brute-force login attempt, or even a partner integration gone awry.

New Rate-Based Rules
Today we are adding Rate-based Rules to WAF, giving you control of when IP addresses are added to and removed from a blacklist, along with the flexibility to handle exceptions and special cases:

Blacklisting IP Addresses – You can blacklist IP addresses that make requests at a rate that exceeds a configured threshold rate.

IP Address Tracking– You can see which IP addresses are currently blacklisted.

IP Address Removal – IP addresses that have been blacklisted are automatically removed when they no longer make requests at a rate above the configured threshold.

IP Address Exemption – You can exempt certain IP addresses from blacklisting by using an IP address whitelist inside of the a rate-based rule. For example, you might want to allow trusted partners to access your site at a higher rate.

Monitoring & Alarming – You can watch and alarm on CloudWatch metrics that are published for each rule.

You can combine new Rate-based Rules with WAF Conditions to implement sophisticated rate-limiting strategies. For example, you could use a Rate-based Rule and a WAF Condition that matches your login pages. This would allow you to impose a modest threshold on your login pages (to avoid brute-force password attacks) and allow a more generous one on your marketing or system status pages.

Thresholds are defined in terms of the number of incoming requests from a single IP address within a 5 minute period. Once this threshold is breached, additional requests from the IP address are blocked until the request rate falls below the threshold.

Using Rate-Based Rules
Here’s how you would define a Rate-based Rule that protects the /login portion of your site. Start by defining a WAF condition that matches the desired string in the URI of the page:

Then use this condition to define a Rate-based Rule (the rate limit is expressed in terms of requests within a 5 minute interval, but the blacklisting goes in to effect as soon as the limit is breached):

With the condition and the rule in place, create a Web ACL (ProtectLoginACL) to bring it all together and to attach it to the AWS resource (a CloudFront distribution in this case):

Then attach the rule (ProtectLogin) to the Web ACL:

The resource is now protected in accord with the rule and the web ACL. You can monitor the associated CloudWatch metrics (ProtectLogin and ProtectLoginACL in this case). You could even create CloudWatch Alarms and use them to fire Lambda functions when a protection threshold is breached. The code could examine the offending IP address and make a complex, business-driven decision, perhaps adding a whitelisting rule that gives an extra-generous allowance to a trusted partner or to a user with a special payment plan.

Available Now
The new, Rate-based Rules are available now and you can start using them today! Rate-based rules are priced the same as Regular rules; see the WAF Pricing page for more info.

— Jeff;

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

2017-06-20 Tara Van Unen

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

Stephen Liedig, Solutions Architect

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

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

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

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

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

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

Walkthrough

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

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

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

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

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

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

Scaling the order processing nodes

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

Scale-out Alarm

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

Scale-in Alarm

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

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

Scaling the order processing implementation

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

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

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

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

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

            receiveMessageResponse = sqs.ReceiveMessageAsync(receiveMessageRequest);
        }

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

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

For more information, see Amazon SQS Long Polling.

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

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

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

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

Handling Duplicate Messages

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

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

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

There are several strategies you could apply to achieve this:

Create messages that have inherent idempotent characteristics. That is, they are non-transactional in nature and are unique at a specified point in time. Rather than saying “place new order for Customer A,” which adds a duplicate order to the customer, use “place order <orderid> on <timestamp> for Customer A,” which creates a single order no matter how often it is persisted.
Deliver your messages via an Amazon SQS FIFO queue, which provides the benefits of message sequencing, but also mechanisms for content-based deduplication. You can deduplicate using the MessageDeduplicationId property on the SendMessage request or by enabling content-based deduplication on the queue, which generates a hash for MessageDeduplicationId, based on the content of the message, not the attributes.

var sendMessageRequest = new SendMessageRequest
{
    QueueUrl = _queueUrl,
    MessageBody = JsonConvert.SerializeObject(order),
    MessageGroupId = Guid.NewGuid().ToString("N"),
    MessageDeduplicationId = Guid.NewGuid().ToString("N")
};

If using SQS FIFO queues is not an option, keep a message log of all messages attributes processed for a specified period of time, as an alternative to message deduplication on the receiving end. Verifying the existence of the message in the log before processing the message adds additional computational overhead to your processing. This can be minimized through low latency persistence solutions such as Amazon DynamoDB. Bear in mind that this solution is dependent on the successful, distributed transaction of the message and the message log.

Handling exceptions

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

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

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

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

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

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

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

Using SQS to adapt to changing business requirements

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

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

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

Extending the design pattern with Amazon SNS

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

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

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

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

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

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

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

Summary

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

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

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

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

Happy messaging!

Sync vs. Backup vs. Storage

2017-06-20 Yev

Post Syndicated from Yev original https://www.backblaze.com/blog/sync-vs-backup-vs-storage/

Google Drive recently announced their new Backup and Sync feature for Google Drive, which allows users to select folders on their computer that they want to back up to their Google Drive account (note: these files count against your Google Drive storage limit). Whenever new backup services are announced, we get a lot of questions so I thought we should take a minute to review the differences in cloud based services.

What is the Cloud? Sync Vs Backup Vs Storage

There is still a lot of confusion in the space about what exactly the “cloud” is and how different services interact with it. When folks use a syncing and sharing service like Dropbox, Box, Google Drive, OneDrive or any of the others, they often assume those are acting as a cloud backup solution as well. Adding to the confusion, cloud storage services are often the backend for backup and sync services as well as standalone services. To help sort this out, we’ll define some of the terms below as they apply to a traditional computer set-up with a bunch of apps and data.

Cloud Sync (ex. Dropbox, iCloud Drive, OneDrive, Box, Google Drive) – these services sync folders on your computer to folders on other machines or to the cloud – allowing users to work from a folder or directory across devices. Typically these services have tiered pricing, meaning you pay for the amount of data you store with the service. If there is data loss, sometimes these services even have a rollback feature, of course only files that are in the synced folders are available to be recovered.

Cloud Backup (ex. Backblaze Cloud Backup, Mozy, Carbonite) – these services work in the background automatically. The user does not need to take any action like setting up specific folders. Backup services typically back up any new or changed data on your computer to another location. Before the cloud took off, that location was primarily a CD or an external hard drive – but as cloud storage became more readily available it became the most popular storage medium. Typically these services have fixed pricing, and if there is a system crash or data loss, all backed up data is available for restore. In addition, these services have rollback features in case there is data loss / accidental file deletion.

Cloud Storage (ex. Backblaze B2, Amazon S3, Microsoft Azure) – these services are where many online backup and syncing and sharing services store data. Cloud storage providers typically serve as the endpoint for data storage. These services typically provide APIs, CLIs, and access points for individuals and developers to tie in their cloud storage offerings directly. These services are priced “per GB” meaning you pay for the amount of storage that you use. Since these services are designed for high-availability and durability, data can live solely on these services – though we still recommend having multiple copies of your data, just in case.

What Should You Use?

Backblaze strongly believes in a 3-2-1 Backup Strategy. A 3-2-1 strategy means having at least 3 total copies of your data, 2 of which are local but on different mediums (e.g. an external hard drive in addition to your computer’s local drive), and at least 1 copy offsite. The best setup is data on your computer, a copy on a hard drive that lives somewhere not inside your computer, and another copy with a cloud backup provider. Backblaze Cloud Backup is a great compliment to other services, like Time Machine, Dropbox, and even the free-tiers of cloud storage services.

What is The Difference Between Cloud Sync and Backup?

Let’s take a look at some sync setups that we see fairly frequently.

Example 1) Users have one folder on their computer that is designated for Dropbox, Google Drive, OneDrive, or one of the other syncing/sharing services. Users save or place data into those directories when they want them to appear on other devices. Often these users are using the free-tier of those syncing and sharing services and only have a few GB of data uploaded in them.

Example 2) Users are paying for extended storage for Dropbox, Google Drive, OneDrive, etc… and use those folders as the “Documents” folder – essentially working out of those directories. Files in that folder are available across devices, however, files outside of that folder (e.g. living on the computer’s desktop or anywhere else) are not synced or stored by the service.

What both examples are missing however is the backup of photos, movies, videos, and the rest of the data on their computer. That’s where cloud backup providers excel, by automatically backing up user data with little or no set-up, and no need for the dragging-and-dropping of files. Backblaze actually scans your hard drive to find all the data, regardless of where it might be hiding. The results are, all the user’s data is kept in the Backblaze cloud and the portion of the data that is synced is also kept in that provider’s cloud – giving the user another layer of redundancy. Best of all, Backblaze will actually back up your Dropbox, iCloud Drive, Google Drive, and OneDrive folders.

Data Recovery

The most important feature to think about is how easy it is to get your data back from all of these services. With sync and share services, retrieving a lot of data, especially if you are in a high-data tier, can be cumbersome and take awhile. Generally, the sync and share services only allow customers to download files over the Internet. If you are trying to download more than a couple gigabytes of data, the process can take time and can be fraught with errors.

With cloud storage services, you can usually only retrieve data over the Internet as well, and you pay for both the storage and the egress of the data, so retrieving a large amount of data can be both expensive and time consuming.

Cloud backup services will enable you to download files over the internet too and can also suffer from long download times. At Backblaze we never want our customers to feel like we’re holding their data hostage, which is why we have a lot of restore options, including our Restore Return Refund policy, which allows people to restore their data via a USB Hard Drive, and then return that drive to us for a refund. Cloud sync providers do not provide this capability.

One popular data recovery use case we’ve seen when a person has a lot of data to restore is to download just the files that are needed immediately, and then order a USB Hard Drive restore for the remaining files that are not as time sensitive. The user gets all their files back in a few days, and their network is spared the download charges.

The bottom line is that all of these services have merit for different use-cases. Have questions about which is best for you? Sound off in the comments below!

The post Sync vs. Backup vs. Storage appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

casync — A tool for distributing file system images

2017-06-20 Lennart Poettering

Post Syndicated from Lennart Poettering original http://0pointer.net/blog/casync-a-tool-for-distributing-file-system-images.html

Introducing casync

In the past months I have been working on a new project:
casync. casync takes
inspiration from the popular rsync file
synchronization tool as well as the probably even more popular
git revision control system. It combines the
idea of the rsync algorithm with the idea of git-style
content-addressable file systems, and creates a new system for
efficiently storing and delivering file system images, optimized for
high-frequency update cycles over the Internet. Its current focus is
on delivering IoT, container, VM, application, portable service or OS
images, but I hope to extend it later in a generic fashion to become
useful for backups and home directory synchronization as well (but
more about that later).

The basic technological building blocks casync is built from are
neither new nor particularly innovative (at least not anymore),
however the way casync combines them is different from existing tools,
and that’s what makes it useful for a variety of usecases that other
tools can’t cover that well.

Why?

I created casync after studying how today’s popular tools store and
deliver file system images. To very incomprehensively and briefly name
a few: Docker has a layered tarball approach,
OSTree serves the
individual files directly via HTTP and maintains packed deltas to
speed up updates, while other systems operate on the block layer and
place raw squashfs images (or other archival file systems, such as
IS09660) for download on HTTP shares (in the better cases combined
with zsync data).

Neither of these approaches appeared fully convincing to me when used
in high-frequency update cycle systems. In such systems, it is
important to optimize towards a couple of goals:

Most importantly, make updates cheap traffic-wise (for this most tools use image deltas of some form)
Put boundaries on disk space usage on servers (keeping deltas between all version combinations clients might want to run updates between, would suggest keeping an exponentially growing amount of deltas on servers)
Put boundaries on disk space usage on clients
Be friendly to Content Delivery Networks (CDNs), i.e. serve neither too many small nor too many overly large files, and only require the most basic form of HTTP. Provide the repository administrator with high-level knobs to tune the average file size delivered.
Simplicity to use for users, repository administrators and developers

I don’t think any of the tools mentioned above are really good on more
than a small subset of these points.

Specifically: Docker’s layered tarball approach dumps the “delta”
question onto the feet of the image creators: the best way to make
your image downloads minimal is basing your work on an existing image
clients might already have, and inherit its resources, maintaing full
history. Here, revision control (a tool for the developer) is
intermingled with update management (a concept for optimizing
production delivery). As container histories grow individual deltas
are likely to stay small, but on the other hand a brand-new deployment
usually requires downloading the full history onto the deployment
system, even though there’s no use for it there, and likely requires
substantially more disk space and download sizes.

OSTree’s serving of individual files is unfriendly to CDNs (as many
small files in file trees cause an explosion of HTTP GET
requests). To counter that OSTree supports placing pre-calculated
delta images between selected revisions on the delivery servers, which
means a certain amount of revision management, that leaks into the
clients.

Delivering direct squashfs (or other file system) images is almost
beautifully simple, but of course means every update requires a full
download of the newest image, which is both bad for disk usage and
generated traffic. Enhancing it with zsync makes this a much better
option, as it can reduce generated traffic substantially at very
little cost of history/metadata (no explicit deltas between a large
number of versions need to be prepared server side). On the other hand
server requirements in disk space and functionality (HTTP Range
requests) are minus points for the usecase I am interested in.

(Note: all the mentioned systems have great properties, and it’s not
my intention to badmouth them. They only point I am trying to make is
that for the use case I care about — file system image delivery with
high high frequency update-cycles — each system comes with certain
drawbacks.)

Security & Reproducability

Besides the issues pointed out above I wasn’t happy with the security
and reproducability properties of these systems. In today’s world
where security breaches involving hacking and breaking into connected
systems happen every day, an image delivery system that cannot make
strong guarantees regarding data integrity is out of
date. Specifically, the tarball format is famously undeterministic:
the very same file tree can result in any number of different
valid serializations depending on the tool used, its version and the
underlying OS and file system. Some tar implementations attempt to
correct that by guaranteeing that each file tree maps to exactly
one valid serialization, but such a property is always only specific
to the tool used. I strongly believe that any good update system must
guarantee on every single link of the chain that there’s only one
valid representatin of the data to deliver, that can easily be
verified.

What casync Is

So much about the background why I created casync. Now, let’s have a
look what casync actually is like, and what it does. Here’s the brief
technical overview:

Encoding: Let’s take a large linear data stream, split it into
variable-sized chunks (the size of each being a function of the
chunk’s contents), and store these chunks in individual, compressed
files in some directory, each file named after a strong hash value of
its contents, so that the hash value may be used to as key for
retrieving the full chunk data. Let’s call this directory a “chunk
store”. At the same time, generate a “chunk index” file that lists
these chunk hash values plus their respective chunk sizes in a simple
linear array. The chunking algorithm is supposed to create variable,
but similarly sized chunks from the data stream, and do so in a way
that the same data results in the same chunks even if placed at
varying offsets. For more information see this blog
story.

Decoding: Let’s take the chunk index file, and reassemble the large
linear data stream by concatenating the uncompressed chunks retrieved
from the chunk store, keyed by the listed chunk hash values.

As an extra twist, we introduce a well-defined, reproducible,
random-access serialization format for file trees (think: a more
modern tar), to permit efficient, stable storage of complete file
trees in the system, simply by serializing them and then passing them
into the encoding step explained above.

Finally, let’s put all this on the network: for each image you want to
deliver, generate a chunk index file and place it on an HTTP
server. Do the same with the chunk store, and share it between the
various index files you intend to deliver.

Why bother with all of this? Streams with similar contents will result
in mostly the same chunk files in the chunk store. This means it is
very efficient to store many related versions of a data stream in the
same chunk store, thus minimizing disk usage. Moreover, when
transferring linear data streams chunks already known on the receiving
side can be made use of, thus minimizing network traffic.

Why is this different from rsync or OSTree, or similar tools? Well,
one major difference between casync and those tools is that we
remove file boundaries before chunking things up. This means that
small files are lumped together with their siblings and large files
are chopped into pieces, which permits us to recognize similarities in
files and directories beyond file boundaries, and makes sure our chunk
sizes are pretty evenly distributed, without the file boundaries
affecting them.

The “chunking” algorithm is based on a the buzhash rolling hash
function. SHA256 is used as strong hash function to generate digests
of the chunks. xz is used to compress the individual chunks.

Here’s a diagram, hopefully explaining a bit how the encoding process
works, wasn’t it for my crappy drawing skills:

The diagram shows the encoding process from top to bottom. It starts
with a block device or a file tree, which is then serialized and
chunked up into variable sized blocks. The compressed chunks are then
placed in the chunk store, while a chunk index file is written listing
the chunk hashes in order. (The original SVG of this graphic may be
found here.

Details

Note that casync operates on two different layers, depending on the
usecase of the user:

You may use it on the block layer. In this case the raw block data
on disk is taken as-is, read directly from the block device, split
into chunks as described above, compressed, stored and delivered.
You may use it on the file system layer. In this case, the
file tree serialization format mentioned above comes into play:
the file tree is serialized depth-first (much like tar would do
it) and then split into chunks, compressed, stored and delivered.

The fact that it may be used on both the block and file system layer
opens it up for a variety of different usecases. In the VM and IoT
ecosystems shipping images as block-level serializations is more
common, while in the container and application world file-system-level
serializations are more typically used.

Chunk index files referring to block-layer serializations carry the
.caibx suffix, while chunk index files referring to file system
serializations carry the .caidx suffix. Note that you may also use
casync as direct tar replacement, i.e. without the chunking, just
generating the plain linear file tree serialization. Such files
carry the .catar suffix. Internally .caibx are identical to
.caidx files, the only difference is semantical: .caidx files
describe a .catar file, while .caibx files may describe any other
blob. Finally, chunk stores are directories carrying the .castr
suffix.

Features

Here are a couple of other features casync has:

When downloading a new image you may use casync‘s --seed=
feature: each block device, file, or directory specified is processed
using the same chunking logic described above, and is used as
preferred source when putting together the downloaded image locally,
avoiding network transfer of it. This of course is useful whenever
updating an image: simply specify one or more old versions as seed and
only download the chunks that truly changed since then. Note that
using seeds requires no history relationship between seed and the new
image to download. This has major benefits: you can even use it to
speed up downloads of relatively foreign and unrelated data. For
example, when downloading a container image built using Ubuntu you can
use your Fedora host OS tree in /usr as seed, and casync will
automatically use whatever it can from that tree, for example timezone
and locale data that tends to be identical between
distributions. Example: casync extract http://example.com/myimage.caibx --seed=/dev/sda1 /dev/sda2. This
will place the block-layer image described by the indicated URL in the
/dev/sda2 partition, using the existing /dev/sda1 data as seeding
source. An invocation like this could be typically used by IoT systems
with an A/B partition setup. Example 2: casync extract http://example.com/mycontainer-v3.caidx --seed=/srv/container-v1 --seed=/srv/container-v2 /src/container-v3, is very similar but
operates on the file system layer, and uses two old container versions
to seed the new version.
When operating on the file system level, the user has fine-grained
control on the metadata included in the serialization. This is
relevant since different usecases tend to require a different set of
saved/restored metadata. For example, when shipping OS images, file
access bits/ACLs and ownership matter, while file modification times
hurt. When doing personal backups OTOH file ownership matters little
but file modification times are important. Moreover different backing
file systems support different feature sets, and storing more
information than necessary might make it impossible to validate a tree
against an image if the metadata cannot be replayed in full. Due to
this, casync provides a set of --with= and --without= parameters
that allow fine-grained control of the data stored in the file tree
serialization, including the granularity of modification times and
more. The precise set of selected metadata features is also always
part of the serialization, so that seeding can work correctly and
automatically.
casync tries to be as accurate as possible when storing file
system metadata. This means that besides the usual baseline of file
metadata (file ownership and access bits), and more advanced features
(extended attributes, ACLs, file capabilities) a number of more exotic
data is stored as well, including Linux
chattr(1) file attributes, as
well as FAT file
attributes
(you may wonder why the latter? — EFI is FAT, and /efi is part of
the comprehensive serialization of any host). In the future I intend
to extend this further, for example storing btrfs subvolume
information where available. Note that as described above every single
type of metadata may be turned off and on individually, hence if you
don’t need FAT file bits (and I figure it’s pretty likely you don’t),
then they won’t be stored.
The user creating .caidx or .caibx files may control the desired
average chunk length (before compression) freely, using the
--chunk-size= parameter. Smaller chunks increase the number of
generated files in the chunk store and increase HTTP GET load on the
server, but also ensure that sharing between similar images is
improved, as identical patterns in the images stored are more likely
to be recognized. By default casync will use a 64K average chunk
size. Tweaking this can be particularly useful when adapting the
system to specific CDNs, or when delivering compressed disk images
such as squashfs (see below).
Emphasis is placed on making all invocations reproducible,
well-defined and strictly deterministic. As mentioned above this is a
requirement to reach the intended security guarantees, but is also
useful for many other usecases. For example, the casync digest
command may be used to calculate a hash value identifying a specific
directory in all desired detail (use --with= and --without to pick
the desired detail). Moreover the casync mtree command may be used
to generate a BSD mtree(5) compatible manifest of a directory tree,
.caidx or .catar file.
The file system serialization format is nicely composable. By this
I mean that the serialization of a file tree is the concatenation of
the serializations of all files and file subtrees located at the
top of the tree, with zero metadata references from any of these
serializations into the others. This property is essential to ensure
maximum reuse of chunks when similar trees are serialized.
When extracting file trees or disk image files, casync
will automatically create
reflinks
from any specified seeds if the underlying file system supports it
(such as btrfs, ocfs, and future xfs). After all, instead of
copying the desired data from the seed, we can just tell the file
system to link up the relevant blocks. This works both when extracting
.caidx and .caibx files — the latter of course only when the
extracted disk image is placed in a regular raw image file on disk,
rather than directly on a plain block device, as plain block devices
do not know the concept of reflinks.
Optionally, when extracting file trees, casync can
create traditional UNIX hardlinks for identical files in specified
seeds (--hardlink=yes). This works on all UNIX file systems, and can
save substantial amounts of disk space. However, this only works for
very specific usecases where disk images are considered read-only
after extraction, as any changes made to one tree will propagate to
all other trees sharing the same hardlinked files, as that’s the
nature of hardlinks. In this mode, casync exposes OSTree-like
behaviour, which is built heavily around read-only hardlink trees.
casync tries to be smart when choosing what to include in file
system images. Implicitly, file systems such as procfs and sysfs are
exluded from serialization, as they expose API objects, not real
files. Moreover, the “nodump” (+d)
chattr(1) flag is honoured by
default, permitting users to mark files to exclude from serialization.
When creating and extracting file trees casync may apply am
automatic or explicit UID/GID shift. This is particularly useful when
transferring container image for use with Linux user namespacing.
In addition to local operation, casync currently supports HTTP,
HTTPS, FTP and ssh natively for downloading chunk index files and
chunks (the ssh mode requires installing casync on the remote host,
though, but an sftp mode not requiring that should be easy to
add). When creating index files or chunks, only ssh is supported as
remote backend.
When operating on block-layer images, you may expose locally or
remotely stored images as local block devices. Example: casync mkdev http://example.com/myimage.caibx exposes the disk image described by
the indicated URL as local block device in /dev, which you then may
use the usual block device tools on, such as mount or fdisk (only
read-only though). Chunks are downloaded on access with high priority,
and at low priority when idle in the background. Note that in this
mode, casync also plays a role similar to “dm-verity”, as all blocks
are validated against the strong digests in the chunk index file
before passing them on to the kernel’s block layer. This feature is
implemented though Linux’ NBD kernel facility.
Similar, when operating on file-system-layer images, you may mount
locally or remotely stored images as regular file systems. Example:
casync mount http://example.com/mytree.caidx /srv/mytree mounts the
file tree image described by the indicated URL as a local directory
/srv/mytree. This feature is implemented though Linux’ FUSE kernel
facility. Note that special care is taken that the images exposed this
way can be packed up again with casync make and are guaranteed to
return the bit-by-bit exact same serialization again that it was
mounted from. No data is lost or changed while passing thrings through
FUSE (OK, strictly speaking this is a lie, we do lose ACLs, but that’s
hopefully just a temporary gap to be fixed soon).
In IoT A/B fixed size partition setups the file systems placed in
the two partitions are usually much shorter than the partition size,
in order to keep some room for later, larger updates. casync is able
to analyze the superblock of a number of common file systems in order
to determine the actual size of a file system stored on a block
device, so that writing a file system to such a partition and reading
it back again will result in reproducible data. Moreover this speeds
up the seeding process, as there’s little point in seeding the
whitespace after the file system within the partition.

Example Command Lines

Here’s how to use casync, explained with a few examples:

$ casync make foobar.caidx /some/directory

This will create a chunk index file foobar.caidx in the local
directory, and populate the chunk store directory default.castr
located next to it with the chunks of the serialization (you can
change the name for the store directory with --store= if you
like). This command operates on the file-system level. A similar
command operating on the block level:

$ casync make foobar.caibx /dev/sda1

This command creates a chunk index file foobar.caibx in the local
directory describing the current contents of the /dev/sda1 block
device, and populates default.castr in the same way as above. Note
that you may as well read a raw disk image from a file instead of a
block device:

$ casync make foobar.caibx myimage.raw

To reconstruct the original file tree from the .caidx file and
the chunk store of the first command, use:

$ casync extract foobar.caidx /some/other/directory

And similar for the block-layer version:

$ casync extract foobar.caibx /dev/sdb1

or, to extract the block-layer version into a raw disk image:

$ casync extract foobar.caibx myotherimage.raw

The above are the most basic commands, operating on local data
only. Now let’s make this more interesting, and reference remote
resources:

$ casync extract http://example.com/images/foobar.caidx /some/other/directory

This extracts the specified .caidx onto a local directory. This of
course assumes that foobar.caidx was uploaded to the HTTP server in
the first place, along with the chunk store. You can use any command
you like to accomplish that, for example scp or
rsync. Alternatively, you can let casync do this directly when
generating the chunk index:

$ casync make ssh.example.com:images/foobar.caidx /some/directory

This will use ssh to connect to the ssh.example.com server, and then
places the .caidx file and the chunks on it. Note that this mode of
operation is “smart”: this scheme will only upload chunks currently
missing on the server side, and not retransmit what already is
available.

Note that you can always configure the precise path or URL of the
chunk store via the --store= option. If you do not do that, then the
store path is automatically derived from the path or URL: the last
component of the path or URL is replaced by default.castr.

Of course, when extracting .caidx or .caibx files from remote sources,
using a local seed is advisable:

$ casync extract http://example.com/images/foobar.caidx --seed=/some/exising/directory /some/other/directory

Or on the block layer:

$ casync extract http://example.com/images/foobar.caibx --seed=/dev/sda1 /dev/sdb2

When creating chunk indexes on the file system layer casync will by
default store metadata as accurately as possible. Let’s create a chunk
index with reduced metadata:

$ casync make foobar.caidx --with=sec-time --with=symlinks --with=read-only /some/dir

This command will create a chunk index for a file tree serialization
that has three features above the absolute baseline supported: 1s
granularity timestamps, symbolic links and a single read-only bit. In
this mode, all the other metadata bits are not stored, including
nanosecond timestamps, full unix permission bits, file ownership or
even ACLs or extended attributes.

Now let’s make a .caidx file available locally as a mounted file
system, without extracting it:

$ casync mount http://example.comf/images/foobar.caidx /mnt/foobar

And similar, let’s make a .caibx file available locally as a block device:

$ casync mkdev http://example.comf/images/foobar.caidx

This will create a block device in /dev and print the used device
node path to STDOUT.

As mentioned, casync is big about reproducability. Let’s make use of
that to calculate the a digest identifying a very specific version of
a file tree:

$ casync digest .

This digest will include all metadata bits casync and the underlying
file system know about. Usually, to make this useful you want to
configure exactly what metadata to include:

$ casync digest --with=unix .

This makes use of the --with=unix shortcut for selecting metadata
fields. Specifying --with-unix= selects all metadata that
traditional UNIX file systems support. It is a shortcut for writing out:
--with=16bit-uids --with=permissions --with=sec-time --with=symlinks --with=device-nodes --with=fifos --with=sockets.

Note that when calculating digests or creating chunk indexes you may
also use the negative --without= option to remove specific features
but start from the most precise:

$ casync digest --without=flag-immutable

This generates a digest with the most accurate metadata, but leaves
one feature out: chattr(1)‘s
immutable (+i) file flag.

To list the contents of a .caidx file use a command like the following:

$ casync list http://example.com/images/foobar.caidx

$ casync mtree http://example.com/images/foobar.caidx

The former command will generate a brief list of files and
directories, not too different from tar t or ls -al in its
output. The latter command will generate a BSD
mtree(5) compatible
manifest. Note that casync actually stores substantially more file
metadata than mtree files can express, though.

What casync isn’t

casync is not an attempt to minimize serialization and downloaded
deltas to the extreme. Instead, the tool is supposed to find a good
middle ground, that is good on traffic and disk space, but not at the
price of convenience or requiring explicit revision control. If you
care about updates that are absolutely minimal, there are binary delta
systems around that might be an option for you, such as Google’s
Courgette.
casync is not a replacement for rsync, or git or zsync or
anything like that. They have very different usecases and
semantics. For example, rsync permits you to directly synchronize two
file trees remotely. casync just cannot do that, and it is unlikely
it every will.

Where next?

casync is supposed to be a generic synchronization tool. Its primary
focus for now is delivery of OS images, but I’d like to make it useful
for a couple other usecases, too. Specifically:

To make the tool useful for backups, encryption is missing. I have
pretty concrete plans how to add that. When implemented, the tool
would might become an alternative to
restic or
tarsnap.
Right now, if you want to deploy casync in real-life, you still
need to validate the downloaded .caidx or .caibx file yourself, for
example with some gpg signature. It is my intention to integrate with
gpg in a minimal way so that signing and verifying chunk index files
is done automatically.
In the longer run, I’d like to build an automatic synchronizer for
$HOME between systems from this. Each $HOME instance would be
stored automatically in regular intervals in the cloud using casync,
and conflicts would be resolved locally.
casync is written in a shared library style, but it is not yet
built as one. Specifically this means that almost all of casync‘s
functionality is supposed to be available as C API soon, and
applications can process casync files on every level. It is my
intention to make this library useful enough so that it will be easy
to write a module for GNOME’s gvfs subsystem in order to make remote
or local .caidx files directly available to applications (as an
alternative to casync mount). In fact the idea is to make this all
flexible enough that even the remoting backends can be replaced
easily, for example to replace casync‘s default HTTP/HTTPS backends
built on CURL with GNOME’s own HTTP implementation, in order to share
cookies, certificates, … There’s also an alternative method to
integrate with casync in place already: simply invoke casync as a
subprocess. casync will inform you about a certain set of state
changes using a mechanism compatible with
sd_notify(3). In
future it will also propagate progress data this way and more.
I intend to a add a new seeding back-end that sources chunks from
the local network. After downloading the new .caidx file off the
Internet casync would then search for the listed chunks on the local
network first before retrieving them from the Internet. This should
speed things up on all installations that have multiple similar
systems deployed in the same network.

Further plans are listed tersely in the
TODO file.

FAQ:

Is this a systemd project? — casync is hosted under the
github systemd umbrella, and the
projects share the same coding style. However, the codebases are
distinct and without interdependencies, and casync works fine both
on systemd systems and systems without it.
Is casync portable? — At the moment: no. I only run Linux and
that’s what I code for. That said, I am open to accepting portability
patches (unlike for systemd, which doesn’t really make sense on
non-Linux systems), as long as they don’t interfere too much with the
way casync works. Specifically this means that I am not too
enthusiastic about merging portability patches for OSes lacking the
openat(2) family
of APIs.
Does casync require reflink-capable file systems to work, such
as btrfs? No it doesn’t. The reflink magic in casync is
employed when the file system permits it, and it’s good to have it,
but it’s not a requirement, and casync will implicitly fall back to
copying when it isn’t available. Note that casync supports a number
of file system features on a variety of file systems that aren’t
available everywhere, for example FAT’s system/hidden file flags or
xfs‘s projinherit file flag.
Is casync stable? — I just tagged the first, initial
release. While I have been working on it since quite some time and it
is quite featureful, this is the first time I advertise it publicly,
and it hence received very little testing outside of its own test
suite. I am also not fully ready to commit to the stability of the
current serialization or chunk index format. I don’t see any breakages
coming for it though. casync is pretty light on documentation right
now, and does not even have a man page. I also intend to correct that
soon.
Are the .caidx/.caibx and .catar file formats open and
documented? — casync is Open Source, so if you want to know the
precise format, have a look at the sources for now. It’s definitely my
intention to add comprehensive docs for both formats however. Don’t
forget this is just the initial version right now.
casync is just like $SOMEOTHERTOOL! Why are you reinventing
the wheel (again)? — Well, because casync isn’t “just like” some
other tool. I am pretty sure I did my homework, and that there is no
tool just like casync right now. The tools coming closest are probably
rsync, zsync, tarsnap, restic, but they are quite different beasts
each.
Why did you invent your own serialization format for file trees?
Why don’t you just use tar? That’s a good question, and other
systems — most prominently tarsnap — do that. However, as mentioned
above tar doesn’t enforce reproducability. It also doesn’t really do
random access: if you want to access some specific file you need to
read every single byte stored before it in the tar archive to find
it, which is of course very expensive. The serialization casync
implements places a focus on reproducability, random access, and
metadata control. Much like traditional tar it can still be
generated and extracted in a stream fashion though.
Does casync save/restore SELinux/SMACK file labels? At the
moment not. That’s not because I wouldn’t want it to, but simply
because I am not a guru of either of these systems, and didn’t want to
implement something I do not fully grok nor can test. If you look at
the sources you’ll find that there’s already some definitions in place
that keep room for them though. I’d be delighted to accept a patch
implementing this fully.
What about delivering squashfs images? How well does chunking
work on compressed serializations? – That’s a very good point!
Usually, if you apply the a chunking algorithm to a compressed data
stream (let’s say a tar.gz file), then changing a single bit at the
front will propagate into the entire remainder of the file, so that
minimal changes will explode into major changes. Thankfully this
doesn’t apply that strictly to squashfs images, as it provides
random access to files and directories and thus breaks up the
compression streams in regular intervals to make seeking easy. This
fact is beneficial for systems employing chunking, such as casync as
this means single bit changes might affect their vicinity but will not
explode in an unbounded fashion. In order achieve best results when
delivering squashfs images through casync the block sizes of
squashfs and the chunks sizes of casync should be matched up (using
casync‘s --chunk-size= option). How precisely to choose both
values is left to reasearch by the user, for now.
What does the name casync mean? – It’s a synchronizing
tool, hence the -sync suffix, following rsync‘s naming. It makes
use of the content-addressable concept of git hence the ca-
prefix.
Where can I get this stuff? Is it already packaged? – Check
out the sources on GitHub. I
just tagged the first
version. Martin
Pitt has packaged casync for
Ubuntu. There
is also an ArchLinux
package.

Should you care? Is this a tool for you?

Well, that’s up to you really. If you are involved with projects that
need to deliver IoT, VM, container, application or OS images, then
maybe this is a great tool for you — but other options exist, some of
which are linked above.

Note that casync is an Open Source project: if it doesn’t do exactly
what you need, prepare a patch that adds what you need, and we’ll
consider it.

If you are interested in the project and would like to talk about this
in person, I’ll be presenting casync soon at Kinvolk’s Linux
Technologies
Meetup
in Berlin, Germany. You are invited. I also intend to talk about it at
All Systems Go!, also in Berlin.

The Pirate Bay Isn’t Affected By Adverse Court Rulings – Everyone Else Is

2017-06-18 Andy

Post Syndicated from Andy original https://torrentfreak.com/the-pirate-bay-isnt-affected-by-adverse-court-rulings-everyone-else-is-170618/

For more than a decade The Pirate Bay has been the world’s most controversial site. Delivering huge quantities of copyrighted content to the masses, the platform is revered and reviled across the copyright spectrum.

Its reputation is one of a defiant Internet swashbuckler, but due to changes in how the site has been run in more recent times, its current philosophy is more difficult to gauge. What has never been in doubt, however, is the site’s original intent to be as provocative as possible.

Through endless publicity stunts, some real, some just for the ‘lulz’, The Pirate Bay managed to attract a massive audience, all while incurring the wrath of every major copyright holder in the world.

Make no mistake, they all queued up to strike back, but every subsequent rightsholder action was met by a Pirate Bay middle finger, two fingers, or chin flick, depending on the mood of the day. This only served to further delight the masses, who happily spread the word while keeping their torrents flowing.

This vicious circle of being targeted by the entertainment industries, mocking them, and then reaping the traffic benefits, developed into the cheapest long-term marketing campaign the Internet had ever seen. But nothing is ever truly for free and there have been consequences.

After taunting Hollywood and the music industry with its refusals to capitulate, endless legal action that the site would have ordinarily been forced to participate in largely took place without The Pirate Bay being present. It doesn’t take a law degree to work out what happened in each and every one of those cases, whatever complex route they took through the legal system. No defense, no win.

For example, the web-blocking phenomenon across the UK, Europe, Asia and Australia was driven by the site’s absolute resilience and although there would clearly have been other scapegoats had The Pirate Bay disappeared, the site was the ideal bogeyman the copyright lobby required to move forward.

Filing blocking lawsuits while bringing hosts, advertisers, and ISPs on board for anti-piracy initiatives were also made easier with the ‘evil’ Pirate Bay still online. Immune from every anti-piracy technique under the sun, the existence of the platform in the face of all onslaughts only strengthened the cases of those arguing for even more drastic measures.

Over a decade, this has meant a significant tightening of the sharing and streaming climate. Without any big legislative changes but plenty of case law against The Pirate Bay, web-blocking is now a walk in the park, ad hoc domain seizures are a fairly regular occurrence, and few companies want to host sharing sites. Advertisers and brands are also hesitant over where they place their ads. It’s a very different world to the one of 10 years ago.

While it would be wrong to attribute every tightening of the noose to the actions of The Pirate Bay, there’s little doubt that the site and its chaotic image played a huge role in where copyright enforcement is today. The platform set out to provoke and succeeded in every way possible, gaining supporters in their millions. It could also be argued it kicked a hole in a hornets’ nest, releasing the hell inside.

But perhaps the site’s most amazing achievement is the way it has managed to stay online, despite all the turmoil.

This week yet another ruling, this time from the powerful European Court of Justice, found that by offering links in the manner it does, The Pirate Bay and other sites are liable for communicating copyright works to the public. Of course, this prompted the usual swathe of articles claiming that this could be the final nail in the site’s coffin.

Wrong.

In common with every ruling, legal defeat, and legislative restriction put in place due to the site’s activities, this week’s decision from the ECJ will have zero effect on the Pirate Bay’s availability. For right or wrong, the site was breaking the law long before this ruling and will continue to do so until it decides otherwise.

What we have instead is a further tightened legal landscape that will have a lasting effect on everything BUT the site, including weaker torrent sites, Internet users, and user-uploaded content sites such as YouTube.

With The Pirate Bay carrying on regardless, that is nothing short of remarkable.

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

Comodo DNS Blocks TorrentFreak Over “Hacking and Warez “

2017-06-17 Ernesto

Post Syndicated from Ernesto original https://torrentfreak.com/comodo-dns-blocks-torrentfreak-over-hacking-and-warez-170617/

Website blocking has become one of the go-to methods for reducing online copyright infringement.

In addition to court-ordered blockades, various commercial vendors also offer a broad range of blocking tools. This includes Comodo, which offers a free DNS service that keeps people away from dangerous sites.

The service labeled SecureDNS is part of the Comodo Internet Security bundle but can be used by the general public as well, without charge. Just change the DNS settings on your computer or any other device, and you’re ready to go.

“As a leading provider of computer security solutions, Comodo is keenly aware of the dangers that plague the Internet today. SecureDNS helps users keep safe online with its malware domain filtering feature,” the company explains.

Aside from malware and spyware, Comodo also blocks access to sites that offer access to pirated content. Or put differently, they try to do this. But it’s easier said than done.

This week we were alerted to the fact that Comodo blocks direct access to TorrentFreak. Those who try to access our news site get an ominous warning instead, suggesting that we might share pirated content.

“This website has been blocked temporarily because of the following reason(s): Hacking/Warez: Site may offer illegal sharing of copyrighted software or media,” the warning reads, adding that several users also reported the site to be unsafe.

TorrentFreak blocked

People can still access the site by clicking on a big red cross, although that’s something Comodo doesn’t recommend. However, it is quite clear that new readers will be pretty spooked by the alarming message.

We assume that TorrentFreak was added to Comodo’s blocklist by mistake. And while mistakes can happen everywhere, this once again show that overblocking is a serious concern.

We are lucky enough that readers alerted us to the problem, but in other cases, it could easily go unnoticed.

Interestingly, the ‘piracy’ blocklist is not as stringent as the above would suggest. While we replicated the issue, we also checked several other known ‘pirate’ sites including The Pirate Bay, RARBG, GoMovies, and Pubfilm. These could all be accessed through SecureDNS without any warning.

TorrentFreak contacted Comodo for a comment on their curious blocking efforts, but we have yet to hear back from the company. In the meantime, Comodo SecureDNS users may want to consider switching to a more open DNS provider.

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

“Kodi Boxes Are a Fire Risk”: Awful Timing or Opportunism?

2017-06-17 Andy

Post Syndicated from Andy original https://torrentfreak.com/kodi-boxes-are-a-fire-risk-awful-timing-or-opportunism-170618/

Anyone who saw the pictures this week couldn’t have failed to be moved by the plight of Londoners caught up in the Grenfell Tower inferno. The apocalyptic images are likely to stay with people for years to come and the scars for those involved may never heal.

As the building continued to smolder and the death toll increased, UK tabloids provided wall-to-wall coverage of the disaster. On Thursday, however, The Sun took a short break to put out yet another sensationalized story about Kodi. Given the week’s events, it was bound to raise eyebrows.

“HOT GOODS: Kodi boxes are a fire hazard because thousands of IPTV devices nabbed by customs ‘failed UK electrical standards’,” the headline reads.

Another sensational ‘Kodi’ headline

“It’s estimated that thousands of Brits have bought so-called Kodi boxes which can be connected to telly sets to stream pay-per-view sport and films for free,” the piece continued.

“But they could be a fire hazard, according to the Federation Against Copyright Theft (FACT), which has been nabbing huge deliveries of the devices as they arrive in the UK.”

As the image below shows, “Kodi box” fire hazard claims appeared next to images from other news articles about the huge London fire. While all separate stories, the pairing is not a great look.

A ‘Kodi Box’, as depicted in The Sun

FACT chief executive Kieron Sharp told The Sun that his group had uncovered two parcels of 2,000 ‘Kodi’ boxes and found that they “failed electrical safety standards”, making them potentially dangerous. While that may well be the case, the big question is all about timing.

It’s FACT’s job to reduce copyright infringement on behalf of clients such as The Premier League so it’s no surprise that they’re making a sustained effort to deter the public from buying these devices. That being said, it can’t have escaped FACT or The Sun that fire and death are extremely sensitive topics this week.

That leaves us with a few options including unfortunate opportunism or perhaps terrible timing, but let’s give the benefit of the doubt for a moment.

There’s a good argument that FACT and The Sun brought a valid issue to the public’s attention at a time when fire safety is on everyone’s lips. So, to give credit where it’s due, providing people with a heads-up about potentially dangerous devices is something that most people would welcome.

However, it’s difficult to offer congratulations on the PSA when the story as it appears in The Sun does nothing – absolutely nothing – to help people stay safe.

If some boxes are a risk (and that’s certainly likely given the level of Far East imports coming into the UK) which ones are dangerous? Where were they manufactured? Who sold them? What are the serial numbers? Which devices do people need to get out of their houses?

Sadly, none of these questions were answered or even addressed in the article, making it little more than scaremongering. Only making matters worse, the piece notes that it isn’t even clear how many of the seized devices are indeed a fire risk and that more tests need to be done. Is this how we should tackle such an important issue during an extremely sensitive week?

Timing and lack of useful information aside, one then has to question the terminology employed in the article.

As a piece of computer software, Kodi cannot catch fire. So, what we’re actually talking about here is small computers coming into the country without passing safety checks. The presence of Kodi on the devices – if indeed Kodi was even installed pre-import – is absolutely irrelevant.

Anti-piracy groups warning people of the dangers associated with their piracy habits is nothing new. For years, Internet users have been told that their computers will become malware infested if they share files or stream infringing content. While in some cases that may be true, there’s rarely any effort by those delivering the warnings to inform people on how to stay safe.

A classic example can be found in the numerous reports put out by the Digital Citizens Alliance in the United States. The DCA has produced several and no doubt expensive reports which claim to highlight the risks Internet users are exposed to on ‘pirate’ sites.

The DCA claims to do this in the interests of consumers but the group offers no practical advice on staying safe nor does it provide consumers with risk reduction strategies. Like many high-level ‘drug prevention’ documents shuffled around government, it could be argued that on a ‘street’ level their reports are next to useless.

Demonizing piracy is a well-worn and well-understood strategy but if warnings are to be interpreted as representing genuine concern for the welfare of people, they have to be a lot more substantial than mere scaremongering.

Anyone concerned about potentially dangerous devices can check out these useful guides from Electrical Safety First (pdf) and the Electrical Safety Council (pdf)

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

Open Sourcing Bullet, Yahoo’s Forward-Looking Query Engine for Streaming Data

2017-06-15 mikesefanov

Post Syndicated from mikesefanov original https://yahooeng.tumblr.com/post/161855616651

By Michael Natkovich, Akshai Sarma, Nathan Speidel, Marcus Svedman, and Cat Utah

Big Data is no longer just Apache server logs. Nowadays, the data may be user engagement data, performance metrics, IoT (Internet of Things) data, or something else completely atypical. Regardless of the size of the data, or the type of querying patterns on it (exploratory, ad-hoc, periodic, long-term, etc.), everyone wants queries to be as fast as possible and cheap to run in terms of resources. Data can be broadly split into two kinds: the streaming (generally real-time) kind or the batched-up-over-a-time-interval (e.g., hourly or daily) kind. The batch version is typically easier to query since it is stored somewhere like a data warehouse that has nice SQL-like interfaces or an easy to use UI provided by tools such as Tableau, Looker, or Superset. Running arbitrary queries on streaming data quickly and cheaply though, is generally much harder… until now. Today, we are pleased to share our newly open sourced, forward-looking general purpose query engine, called Bullet, with the community on GitHub.

With Bullet, you can:

Run queries on real-time streaming data with the same convenience and flexibility as batch data. You get an API and a UI.
Execute forward-looking queries on typed data with arbitrary schemas.
Run queries that support

Powerful and nested filtering
Fetching raw data records
Aggregating data using Group Bys (Sum, Count, Average, etc.), Count Distincts, Top Ks
Getting distributions of fields like Percentiles or Frequency histograms

One of the key differences between how Bullet queries data and the standard querying paradigm is that Bullet does not store any data. In most other systems where you have a persistence layer (including in-memory storage), you are doing a look-back when you query the layer. Instead, Bullet operates on data flowing through the system after the query is started – it’s a look-forward system that doesn’t need persistence. On a real-time data stream, this means that Bullet is querying data after the query is submitted. This also means that Bullet does not query any data that has already passed through the stream. The fact that Bullet does not rely on a persistence layer is exactly what makes it extremely lightweight and cheap to run.

To see why this is better for the kinds of use cases Bullet is meant for – such as quickly looking at some metric, checking some assumption, iterating on a query, checking the status of something right now, etc. – consider the following: if you had a 1000 queries in a traditional query system that operated on the same data, these query systems would most likely scan the data 1000 times each. By the very virtue of it being forward looking, 1000 queries in Bullet scan the data only once because the arrival of the query determines and fixes the data that it will see. Essentially, the data is coming to the queries instead of the queries being farmed out to where the data is. When the conditions of the query are satisfied (usually a time window or a number of events), the query terminates and returns you the result.

A Brief Architecture Overview

High Level Bullet Architecture

The Bullet architecture is multi-tenant, can scale linearly for more queries and/or more data, and has been tested to handle 700+ simultaneous queries on a data stream that had up to 1.5 million records per second, or 5-6 GB/s. Bullet is currently implemented on top of Storm and can be extended to support other stream processing engines as well, like Spark Streaming or Flink. Bullet is pluggable, so you can plug in any source of data that can be read in Storm by implementing a simple data container interface to let Bullet work with it.

The UI, web service, and the backend layers constitute your standard three-tier architecture. The Bullet backend can be split into three main subsystems:

Request Processor – receives queries, adds metadata, and sends it to the rest of the system
Data Processor – reads data from an input stream, converts it to a unified data format, and matches it against queries
Combiner – combines results for different queries, performs final aggregations, and returns results

The web service can be deployed on any servlet container, like Jetty. The UI is a Node-based Ember application that runs in the client browser. Our full documentation contains all the details on exactly how we perform computationally-intractable queries like Count Distincts on fields with cardinality in the millions, etc. (DataSketches).

Usage at Yahoo

An instance of Bullet is currently running at Yahoo in production against a small subset of Yahoo’s user engagement data stream. This data is roughly 100,000 records per second and is about 130 MB/s compressed. Bullet queries this with about 100 CPU Virtual Cores and 120 GB of RAM. This fits on less than 2 of our (64 Virtual Cores, 256 GB RAM each) test Storm cluster machines.

One of the most popular use cases at Yahoo is to use Bullet to manually validate the instrumentation of an app or web application. Instrumentation produces user engagement data like clicks, views, swipes, etc. Since this data powers everything we do from analytics to personalization to targeting, it is absolutely critical that the data is correct. The usage pattern is generally to:

Submit a Bullet query to obtain data associated with your mobile device or browser (filter on a cookie value or mobile device ID)
Open and use the application to generate the data while the Bullet query is running
Go back to Bullet and inspect the data

In addition, Bullet is also used programmatically in continuous delivery pipelines for functional testing instrumentation on product releases. Product usage is simulated, then data is generated and validated in seconds using Bullet. Bullet is orders of magnitude faster to use for this kind of validation and for general data exploration use cases, as opposed to waiting for the data to be available in Hive or other systems. The Bullet UI supports pivot tables and a multitude of charting options that may speed up analysis further compared to other querying options.

We also use Bullet to do a bunch of other interesting things, including instances where we dynamically compute cardinalities (using a Count Distinct Bullet query) of fields as a check to protect systems that can’t support extremely high cardinalities for fields like Druid.

What you do with Bullet is entirely determined by the data you put it on. If you put it on data that is essentially some set of performance metrics (data center statistics for example), you could be running a lot of queries that find the 95th and 99th percentile of a metric. If you put it on user engagement data, you could be validating instrumentation and mostly looking at raw data.

We hope you will find Bullet interesting and tell us how you use it. If you find something you want to change, improve, or fix, your contributions and ideas are always welcome! You can contact us here.

Helpful Links

Pirate Bay Ruling is Bad News For Google & YouTube, Experts Says

2017-06-15 Andy

Post Syndicated from Andy original https://torrentfreak.com/pirate-bay-ruling-is-bad-news-for-google-youtube-experts-says-170615/

After years of legal wrangling, yesterday the European Court of Justice handed down a decision in the case between Dutch anti-piracy outfit BREIN and ISPs Ziggo and XS4ALL.

BREIN had demanded that the ISPs block The Pirate Bay, but both providers dug in their heels, forcing the case through the Supreme Court and eventually the ECJ.

For BREIN, yesterday’s decision will have been worth the wait. Although The Pirate Bay does not provide the content that’s ultimately downloaded and shared by its users, the ECJ said that it plays an important role in how that content is presented.

“Whilst it accepts that the works in question are placed online by the users, the Court highlights the fact that the operators of the platform play an essential role in making those works available,” the Court said.

With that established the all-important matter is whether by providing such a platform, the operators of The Pirate Bay are effectively engaging in a “communication to the public” of copyrighted works. According to the ECJ, that’s indeed the case.

“The Court holds that the making available and management of an online sharing platform must be considered to be an act of communication for the purposes of the directive,” the ECJ said.

Add into the mix that The Pirate Bay generates profit from its activities and there’s a potent case for copyright liability.

While the case was about The Pirate Bay, ECJ rulings tend to have an effect far beyond individual cases. That’s certainly the opinion of Enzo Mazza, chief at Italian anti-piracy group FIMI.

“The ruling will have a major impact on the way that entities like Google operate, because it will expose them to a greater and more direct responsibility,” Mazza told La Repubblica.

“So far, Google has worked against piracy by eliminating illegal content after it gets reported. But that is not enough. It is a fairly ineffective intervention.”

Mazza says that platforms like Google, YouTube, and thousands of similar sites that help to organize and curate user-uploaded content are somewhat similar to The Pirate Bay. In any event, they are not neutral intermediaries, he insists.

The conclusion that the decision is bad for platforms like YouTube is shared by Fulvio Sarzana, a lawyer with Sarzana and Partners, a law firm specializing in Internet and copyright disputes.

“In the ruling, the Court has in fact attributed, for the first time, secondary liability to sharing platforms due to the violation of copyrights carried out by the users of a platform,” Sarzana informs TF.

“This will have consequences for video-sharing platforms and user-generated content sites like YouTube, but it excludes responsibility for platforms that play a purely passive role, without affecting users’ content. This the case with cyberlockers, for example.”

Sarzana says that “unfortunate judgments” like this should be expected, until the approval of a new European copyright law. Enzo Mazza, on the other hand, feels that the copyright reform debate should take account of this ruling when formulating legislation to stop platforms like YouTube exploiting copyright works without an appropriate license.

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

New – Auto Scaling for Amazon DynamoDB

2017-06-15 Jeff Barr

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-auto-scaling-for-amazon-dynamodb/

Amazon DynamoDB has more than one hundred thousand customers, spanning a wide range of industries and use cases. These customers depend on DynamoDB’s consistent performance at any scale and presence in 16 geographic regions around the world. A recent trend we’ve been observing is customers using DynamoDB to power their serverless applications. This is a good match: with DynamoDB, you don’t have to think about things like provisioning servers, performing OS and database software patching, or configuring replication across availability zones to ensure high availability – you can simply create tables and start adding data, and let DynamoDB handle the rest.

DynamoDB provides a provisioned capacity model that lets you set the amount of read and write capacity required by your applications. While this frees you from thinking about servers and enables you to change provisioning for your table with a simple API call or button click in the AWS Management Console, customers have asked us how we can make managing capacity for DynamoDB even easier.

Today we are introducing Auto Scaling for DynamoDB to help automate capacity management for your tables and global secondary indexes. You simply specify the desired target utilization and provide upper and lower bounds for read and write capacity. DynamoDB will then monitor throughput consumption using Amazon CloudWatch alarms and then will adjust provisioned capacity up or down as needed. Auto Scaling will be on by default for all new tables and indexes, and you can also configure it for existing ones.

Even if you’re not around, DynamoDB Auto Scaling will be monitoring your tables and indexes to automatically adjust throughput in response to changes in application traffic. This can make it easier to administer your DynamoDB data, help you maximize availability for your applications, and help you reduce your DynamoDB costs.

Let’s see how it works…

Using Auto Scaling
The DynamoDB Console now proposes a comfortable set of default parameters when you create a new table. You can accept them as-is or you can uncheck Use default settings and enter your own parameters:

Here’s how you enter your own parameters:

Target utilization is expressed in terms of the ratio of consumed capacity to provisioned capacity. The parameters above would allow for sufficient headroom to allow consumed capacity to double due to a burst in read or write requests (read Capacity Unit Calculations to learn more about the relationship between DynamoDB read and write operations and provisioned capacity). Changes in provisioned capacity take place in the background.

Auto Scaling in Action
In order to see this important new feature in action, I followed the directions in the Getting Started Guide. I launched a fresh EC2 instance, installed (sudo pip install boto3) and configured (aws configure) the AWS SDK for Python. Then I used the code in the Python and DynamoDB section to create and populate a table with some data, and manually configured the table for 5 units each of read and write capacity.

I took a quick break in order to have clean, straight lines for the CloudWatch metrics so that I could show the effect of Auto Scaling. Here’s what the metrics look like before I started to apply a load:

I modified the code in Step 3 to continually issue queries for random years in the range of 1920 to 2007, ran a single copy of the code, and checked the read metrics a minute or two later:

The consumed capacity is higher than the provisioned capacity, resulting in a large number of throttled reads. Time for Auto Scaling!

I returned to the console and clicked on the Capacity tab for my table. Then I clicked on Read capacity, accepted the default values, and clicked on Save:

DynamoDB created a new IAM role (DynamoDBAutoscaleRole) and a pair of CloudWatch alarms to manage the Auto Scaling of read capacity:

DynamoDB Auto Scaling will manage the thresholds for the alarms, moving them up and down as part of the scaling process. The first alarm was triggered and the table state changed to Updating while additional read capacity was provisioned:

The change was visible in the read metrics within minutes:

I started a couple of additional copies of my modified query script and watched as additional capacity was provisioned, as indicated by the red line:

I killed all of the scripts and turned my attention to other things while waiting for the scale-down alarm to trigger. Here’s what I saw when I came back:

The next morning I checked my Scaling activities and saw that the alarm had triggered several more times overnight:

This was also visible in the metrics:

Until now, you would prepare for this situation by setting your read capacity well about your expected usage, and pay for the excess capacity (the space between the blue line and the red line). Or, you might set it too low, forget to monitor it, and run out of capacity when traffic picked up. With Auto Scaling you can get the best of both worlds: an automatic response when an increase in demand suggests that more capacity is needed, and another automated response when the capacity is no longer needed.

Things to Know
DynamoDB Auto Scaling is designed to accommodate request rates that vary in a somewhat predictable, generally periodic fashion. If you need to accommodate unpredictable bursts of read activity, you should use Auto Scaling in combination with DAX (read Amazon DynamoDB Accelerator (DAX) – In-Memory Caching for Read-Intensive Workloads to learn more). Also, the AWS SDKs will detect throttled read and write requests and retry them after a suitable delay.

I mentioned the DynamoDBAutoscaleRole earlier. This role provides Auto Scaling with the privileges that it needs to have in order for it to be able to scale your tables and indexes up and down. To learn more about this role and the permissions that it uses, read Grant User Permissions for DynamoDB Auto Scaling.

Auto Scaling has complete CLI and API support, including the ability to enable and disable the Auto Scaling policies. If you have some predictable, time-bound spikes in traffic, you can programmatically disable an Auto Scaling policy, provision higher throughput for a set period of time, and then enable Auto Scaling again later.

As noted on the Limits in DynamoDB page, you can increase provisioned capacity as often as you would like and as high as you need (subject to per-account limits that we can increase on request). You can decrease capacity up to nine times per day for each table or global secondary index.

You pay for the capacity that you provision, at the regular DynamoDB prices. You can also purchase DynamoDB Reserved Capacity to further savings.

Available Now
This feature is available now in all regions and you can start using it today!

— Jeff;

US Opposes Kim Dotcom’s Supreme Court Petition Over Seized Millions

2017-06-13 Ernesto

Post Syndicated from Ernesto original https://torrentfreak.com/us-opposes-kim-dotcoms-supreme-court-petition-over-seized-millions-170613/

Following the 2012 raid on Megaupload and Kim Dotcom, U.S. and New Zealand authorities seized millions of dollars in cash and other property.

Claiming the assets were obtained through copyright and money laundering crimes, the U.S. government launched a separate civil action in which it asked the court to forfeit the bank accounts, cars, and other seized possessions of the Megaupload defendants.

The U.S. branded Dotcom and his colleagues as “fugitives” and won their case. Dotcom’s legal team quickly appealed this verdict, but lost once more at the Fourth Circuit appeals court.

However, Dotcom didn’t give up and petitioned the US Supreme Court to hear the case. Together with the other defendants, he wants the Supreme Court to overturn the “fugitive disentitlement” ruling and the forfeiture of his assets.

The crux of the case is whether or not the District Court’s order to forfeit an estimated $67 million in assets was right. The defense argues that Dotcom and the other Megaupload defendants were wrongfully labeled as fugitives by the Department of Justice.

“If left undisturbed, the Fourth Circuit’s decision enables the Government to obtain civil forfeiture of every penny of a foreign citizen’s foreign assets based on unproven allegations of the most novel, dubious United States crimes,” Dotcom’s legal team wrote.

The United States Government disagrees with this assessment. In their opposition brief (pdf), submitted late last week and picked up by ARS, the Department of Justice asks the Supreme Court not to take on the case.

According to the US, the decision to label Dotcom and his colleagues as fugitives is how Congress intended the relevant section of the law to work. In addition, the current rulings are not incompatible with previous court decisions in similar cases.

“Petitioners also seek review of the court of appeals’ holding that they qualify as ‘fugitives’ under the federal fugitive-disentitlement statute […] because they declined to enter the United States with the specific intent to avoid prosecution,” DoJ writes in its brief.

“That contention does not warrant review. The court of appeals correctly construed Section 2466 in light of its text and purpose. Its holding applying the statute to the facts here does not conflict with any decision of another circuit,” the brief adds.

The full opposition brief responds in detail to the petition of Dotcom and his colleagues, with the US ultimately concluding that the Supreme Court should deny the request.

Dotcom and his legal team have previously stated that they need more resources to mount a proper defense against the criminal complaint. The case has been ongoing for more than half a decade and is being fought in several courts, which has proven to be rather expensive.

Whether the Supreme Court accepts or denies the case will likely be decided in the weeks to come. Until then, the waiting continues.

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

Balancing Convenience and Privacy

2017-06-13 Ahin Thomas

Post Syndicated from Ahin Thomas original https://www.backblaze.com/blog/privacy-vs-convenience/

In early January of this year, in a conference room with a few other colleagues, we were at a point where we needed to decide how to balance convenience and privacy for our customers. The context being our team earnestly finalizing and prioritizing the launch features of our revamped Business Backup product. In the process, we introduced a piece of functionality that we call “Groups.” A Group is a mechanism that centralizes payment and simplifies management for multiple Backblaze users in a given organization or business. As with many services there were tradeoffs, but this one proved thornier than most.

The Trade-off Between Convenience and Privacy

The problem started as we considered the possibility of having a “Managed” Group. The concept is simple enough: Centralized billing is good, but there are clear use cases where a user would like to have someone act on their behalf. For instance, a business may want a System Administrator to create/manage restores on behalf of a group of employees. We have had many instances of someone from the home office ordering a hard drive restore for an employee in the field. Similarly, a Managed Service Provider (MSP) might provide, and potentially charge for, the service of creating/managing restores for their customers. In short, the idea of having an Administrator manage a defined collection of users (i.e. a Group) was compelling and added a level of convenience.

Great. It’s decided then, we need to introduce the concept of a Managed Group. And we’ll also have Unmanaged Groups. You can have infinite Groups of either kind, we’ll let the user decide!

Here’s the problem: The Managed Group feature could have easily been used for evil. For example, an overeager Administrator could restore an employee’s files, at anytime, for any reason – legitimate or nefarious. This felt wrong as we’re a backup company, not spyware company.

This is when the discussion got more interesting. By adding a convenience feature, we realized that there was potential for user privacy to be violated. As we worked through the use cases, we faced potential conflict between two of our guiding principles:

Make backup astonishingly easy. Whether you are a individual, family, or business (or some combination), we want to make your life easier.
Don’t be evil. With great data storage comes great responsibility. We are the custodians of sensitive data and take that seriously.

So how best to balance a feature that customers clearly want while enabling sane protections for all users? It was an interesting question internally – one where a fair amount of meetings, hallway conversations, and email exchanges were conducted in order to get it right.

Enabling Administration While Safeguarding Team Privacy

Management can be turned on for any Group at the time of Group Creation. As mentioned above, one Administrator can have as many Groups as desired and those Groups can be a mix of Managed and Unmanaged.

But there’s an interesting wrinkle – if Management is enabled, potential members of that Group are told that the feature is enabled before they join the Group.

We’ve, in plain terms, disclosed what is happening before the person starts backing up. If you read that and choose to start backing up, then you have been armed with full information.

Unfortunately, life isn’t that cut and dry. What if your company selected Backblaze and insists that everyone join the Group? Sure, you were told there are Administrators. Fine, my Administrator is supposed to act in the constructive interest of the Group. But what if the Admin is, as the saying goes, “for badness”?

Our solution, while seemingly innocuous, felt like it introduced a level of transparency and auditability that made us comfortable moving forward. Before an Administrator can do a restore on a Group Member’s behalf, the Admin is presented with a pop up that looks like this:

If the Admin is going to create a restore on a user’s behalf, then that user will be notified of the activity. A less than well intentioned Admin will have some reluctance if he knows the user will receive an email. Since permission for this type of activity was granted when the individual joined the Group, we do allow the Admin to proceed with the restore operation without further approval (convenience).

However, the user will get notified and can raise any questions or concerns as desired. There are no false positives, if the user gets an email, that means an Admin was going to restore data from the user’s account. In addition, because the mechanism is email, it creates an audit trail for the company. If there are users that don’t want the alerts, we recommend simply creating an email filter rule and putting them into a folder (in case some day you did want them).

Customer Adoption

The struggle for us was to strike the right balance between privacy and convenience. Specifically, we wanted to empower our users to set the mix where it is appropriate for them. In the case of Groups, it’s been interesting to see that 93% of Groups are of the “Managed” variety.

More importantly to us, we get consistently good feedback about the notification mechanisms in place. Even for organizations where one Admin may be taking a number of legitimate actions, we’re told that the notifications are appreciated in the spirit that they are intended. We’ll continue to solicit feedback and analyze usage to find ways to improve all of our features. But hearing and seeing customer satisfaction is a positive indicator that we’ve struck the appropriate balance between convenience and privacy.

The late 20th century philosopher, Judge Smails, once posited “the most important decision you can make right now is what do you stand for…? Goodness… or badness?”

We choose goodness. How do you think we did?

The post Balancing Convenience and Privacy appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Global Entertainment Giants Form Massive Anti-Piracy Coalition

2017-06-13 Andy

Post Syndicated from Andy original https://torrentfreak.com/global-entertainment-giants-form-massive-anti-piracy-coalition-170613/

It’s not unusual for companies within the same area of business to collaborate in order to combat piracy. The studios and labels that form the MPAA and RIAA, for example, have doing just that for decades.

Today, however, an unprecedented number of global content creators and distribution platforms have announced the formation of a brand new coalition to collaboratively fight Internet piracy on a global scale.

The Alliance for Creativity and Entertainment (ACE) is a coalition of 30 companies that reads like a who’s who of the global entertainment market. In alphabetical order the members are:

Amazon, AMC Networks, BBC Worldwide, Bell Canada and Bell Media, Canal+ Group, CBS Corporation, Constantin Film, Foxtel, Grupo Globo, HBO, Hulu, Lionsgate, Metro-Goldwyn-Mayer (MGM), Millennium Media, NBCUniversal, Netflix, Paramount Pictures, SF Studios, Sky, Sony Pictures Entertainment, Star India, Studio Babelsberg, STX Entertainment, Telemundo, Televisa, Twentieth Century Fox, Univision Communications Inc., Village Roadshow, The Walt Disney Company, and Warner Bros. Entertainment Inc.

In a joint announcement today, ACE notes that there are now more than 480 services available for consumers to watch films and TV programs online. However, despite that abundance of content, piracy continues to pose a threat to creators and the economy.

“Films and television shows can often be found on pirate sites within days – and in many cases hours – of release,” ACE said in a statement.

“Last year, there were an estimated 5.4 billion downloads of pirated wide release films and primetime television and VOD shows using peer-to-peer protocols worldwide. There were also an estimated 21.4 billion total visits to streaming piracy sites worldwide across both desktops and mobile devices in 2016.”

Rather than the somewhat fragmented anti-piracy approach currently employed by ACE members separately, the coalition will present a united front of all major content creators and distributors, with a mission to cooperate and expand in order to minimize the threat.

At the center of the alliance appears to be the MPAA. ACE reports that the anti-piracy resources of the Hollywood group will be used “in concert” with the existing anti-piracy departments of the member companies.

Unprecedented scale aside, ACE’s modus operandi will be a familiar one.

The coalition says it will work closely with law enforcement to shut down pirate sites and services, file civil litigation, and forge new relationships with other content protection groups. It will also strive to reach voluntary anti-piracy agreements with other interested parties across the Internet.

MPAA chief Chris Dodd, whose group will play a major role in ACE, welcomed the birth of the alliance.

“ACE, with its broad coalition of creators from around the world, is designed, specifically, to leverage the best possible resources to reduce piracy,” Dodd said.

“For decades, the MPAA has been the gold standard for antipiracy enforcement. We are proud to provide the MPAA’s worldwide antipiracy resources and the deep expertise of our antipiracy unit to support ACE and all its initiatives.”

The traditionally non-aggressive BBC described ACE as “hugely important” in the fight against “theft and illegal distribution”, with Netflix noting that even its creative strategies for dealing with piracy are in need of assistance.

“While we’re focused on providing a great consumer experience that ultimately discourages piracy, there are still bad players around the world trying to profit off the hard work of others,” said Netflix General Counsel, David Hyman.

“By joining ACE, we will work together, share knowledge, and leverage the group’s combined anti-piracy resources to address the global online piracy problem.”

It’s likely that the creation of ACE will go down as a landmark moment in the fight against piracy. Never before has such a broad coalition promised to pool resources on such a grand and global scale. That being said, with great diversity comes the potential for greatly diverging opinions, so only time will tell if this coalition can really hold together.

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

Kodi Turmoil Continues as TVAddons Mysteriously Disappears

2017-06-13 Ernesto

Post Syndicated from Ernesto original https://torrentfreak.com/kodi-turmoil-continues-as-tvaddons-mysteriously-disappears-170613/

Last week we broke the news that third-party Kodi add-on ZemTV and the TVAddons library were being sued in a federal court in Texas.

Since then, the ‘pirate’ Kodi community has been in turmoil. Several popular Kodi addons decided to shut down, and now TVAddons itself appears to be in trouble as well.

TVAddons is pne of the largest repositories of Kodi add-ons, many of which allow users to watch pirated content. The site has grown massively in recent years and reported that nearly 40 million unique users connected to the site’s servers in March.

Since yesterday, however, these millions of users can no longer access the site. Without prior warning or a public explanation, TVAddons’ domain name stopped responding. The domain’s DNS entries have been removed which means that it’s no longer accessible to the public.

Those who try to access the site either get a browser error message, or are redirected to a page of TVAddons’ domain name registrar Uniregistry.com (in some cases people may still see the site, if the DNS entries are cached).

TVAddons.ag can’t be reached

For now, it’s unclear who removed the DNS entries and why. The registrar could have taken this action, but TVAddons may have done it themselves too.

TorrentFreak reached out to TVAddons a few times over the past several days but without response. The site’s spokesperson was previously quick to reply, but after the Dish lawsuit became public this changed.

In response to our latest email inquiry, we received an error message, suggesting that the site’s official email addresses are no longer functioning due to the domain troubles.

TVAddons has also gone quiet on social media, where TVAddons has been very active in the past. However, the last updates on Twitter and Facebook date back more than a week ago. In fact, a few hours ago TVAddons’ Facebook page disappeared completely.

Facebook page unavailable

Based on the current downtime issues, it’s no surprise that people are getting worried. If TVAddons doesn’t return, the Kodi-addon community has lost what’s arguably its biggest player.

The site’s extensive library listed 1,500 different add-ons, of which the community-maintained Exodus addon was one of the most popular. Now that the site is no longer available, people may run into issues while updating these.

That said, it’s best not to jump to conclusions without an official explanation from the team. If we find out more, this article will be updated accordingly.

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

More notes on US-CERTs IOCs

2017-06-13 Robert Graham

Post Syndicated from Robert Graham original http://blog.erratasec.com/2017/06/more-notes-on-us-certs-iocs.html

Yet another Russian attack against the power grid, and yet more bad IOCs from the DHS US-CERT.

IOCs are “indicators of compromise“, things you can look for in order to order to see if you, too, have been hacked by the same perpetrators. There are several types of IOCs, ranging from the highly specific to the uselessly generic.

A uselessly generic IOC would be like trying to identify bank robbers by the fact that their getaway car was “white” in color. It’s worth documenting, so that if the police ever show up in a suspected cabin in the woods, they can note that there’s a “white” car parked in front.

But if you work bank security, that doesn’t mean you should be on the lookout for “white” cars. That would be silly.

This is what happens with US-CERT’s IOCs. They list some potentially useful things, but they also list a lot of junk that waste’s people’s times, with little ability to distinguish between the useful and the useless.

An example: a few months ago was the GRIZZLEYBEAR report published by US-CERT. Among other things, it listed IP addresses used by hackers. There was no description which would be useful IP addresses to watch for, and which would be useless.

Some of these IP addresses were useful, pointing to servers the group has been using a long time as command-and-control servers. Other IP addresses are more dubious, such as Tor exit nodes. You aren’t concerned about any specific Tor exit IP address, because it changes randomly, so has no relationship to the attackers. Instead, if you cared about those Tor IP addresses, what you should be looking for is a dynamically updated list of Tor nodes updated daily.

And finally, they listed IP addresses of Yahoo, because attackers passed data through Yahoo servers. No, it wasn’t because those Yahoo servers had been compromised, it’s just that everyone passes things though them, like email.

A Vermont power-plant blindly dumped all those IP addresses into their sensors. As a consequence, the next morning when an employee checked their Yahoo email, the sensors triggered. This resulted in national headlines about the Russians hacking the Vermont power grid.

Today, the US-CERT made similar mistakes with CRASHOVERRIDE. They took a report from Dragos Security, then mutilated it. Dragos’s own IOCs focused on things like hostile strings and file hashes of the hostile files. They also included filenames, but similar to the reason you’d noticed a white car — because it happened, not because you should be on the lookout for it. In context, there’s nothing wrong with noting the file name.

But the US-CERT pulled the filenames out of context. One of those filenames was, humorously, “svchost.exe”. It’s the name of an essential Windows service. Every Windows computer is running multiple copies of “svchost.exe”. It’s like saying “be on the lookout for Windows”.

Yes, it’s true that viruses use the same filenames as essential Windows files like “svchost.exe”. That’s, generally, something you should be aware of. But that CRASHOVERRIDE did this is wholly meaningless.

What Dragos Security was actually reporting was that a “svchost.exe” with the file hash of 79ca89711cdaedb16b0ccccfdcfbd6aa7e57120a was the virus — it’s the hash that’s the important IOC. Pulling the filename out of context is just silly.

Luckily, the DHS also provides some of the raw information provided by Dragos. But even then, there’s problems: they provide it in formatted form, for HTML, PDF, or Excel documents. This corrupts the original data so that it’s no longer machine readable. For example, from their webpage, they have the following:

import “pe”
import “hash”

Among the problems are the fact that the quote marks have been altered, probably by Word’s “smart quotes” feature. In other cases, I’ve seen PDF documents get confused by the number 0 and the letter O, as if the raw data had been scanned in from a printed document and OCRed.

If this were a “threat intel” company, we’d call this snake oil. The US-CERT is using Dragos Security’s reports to promote itself, but ultimate providing negative value, mutilating the content.

This, ultimately, causes a lot of harm. The press trusted their content. So does the network of downstream entities, like municipal power grids. There are tens of thousands of such consumers of these reports, often with less expertise than even US-CERT. There are sprinklings of smart people in these organizations, I meet them at hacker cons, and am fascinated by their stories. But institutionally, they are dumbed down the same level as these US-CERT reports, with the smart people marginalized.

There are two solutions to this problem. The first is that when the stupidity of what you do causes everyone to laugh at you, stop doing it. The second is to value technical expertise, empowering those who know what they are doing. Examples of what not to do are giving power to people like Obama’s cyberczar, Michael Daniels, who once claimed his lack of technical knowledge was a bonus, because it allowed him to see the strategic picture instead of getting distracted by details.

Teaching tech

2017-06-11 Eevee

Post Syndicated from Eevee original https://eev.ee/blog/2017/06/10/teaching-tech/

A sponsored post from Manishearth:

I would kinda like to hear about any thoughts you have on technical teaching or technical writing. Pedagogy is something I care about. But I don’t know how much you do, so feel free to ignore this suggestion 🙂

Good news: I care enough that I’m trying to write a sorta-kinda-teaching book!

Ironically, one of the biggest problems I’ve had with writing the introduction to that book is that I keep accidentally rambling on for pages about problems and difficulties with teaching technical subjects. So maybe this is a good chance to get it out of my system.

Phaser

I recently tried out a new thing. It was Phaser, but this isn’t a dig on them in particular, just a convenient example fresh in my mind. If anything, they’re better than most.

As you can see from Phaser’s website, it appears to have tons of documentation. Two of the six headings are “LEARN” and “EXAMPLES”, which seems very promising. And indeed, Phaser offers:

Several getting-started walkthroughs
Possibly hundreds of examples
A news feed that regularly links to third-party tutorials
Thorough API docs

Perfect. Beautiful. Surely, a dream.

Well, almost.

The examples are all microscopic, usually focused around a single tiny feature — many of them could be explained just as well with one line of code. There are a few example games, but they’re short aimless demos. None of them are complete games, and there’s no showcase either. Games sometimes pop up in the news feed, but most of them don’t include source code, so they’re not useful for learning from.

Likewise, the API docs are just API docs, leading to the sorts of problems you might imagine. For example, in a few places there’s a mention of a preUpdate stage that (naturally) happens before update. You might rightfully wonder what kinds of things happen in preUpdate — and more importantly, what should you put there, and why?

Let’s check the API docs for Phaser.Group.preUpdate:

The core preUpdate – as called by World.

Okay, that didn’t help too much, but let’s check what Phaser.World has to say:

The core preUpdate – as called by World.

Ah. Hm. It turns out World is a subclass of Group and inherits this method — and thus its unaltered docstring — from Group.

I did eventually find some brief docs attached to Phaser.Stage (but only by grepping the source code). It mentions what the framework uses preUpdate for, but not why, and not when I might want to use it too.

The trouble here is that there’s no narrative documentation — nothing explaining how the library is put together and how I’m supposed to use it. I get handed some brief primers and a massive reference, but nothing in between. It’s like buying an O’Reilly book and finding out it only has one chapter followed by a 500-page glossary.

API docs are great if you know specifically what you’re looking for, but they don’t explain the best way to approach higher-level problems, and they don’t offer much guidance on how to mesh nicely with the design of a framework or big library. Phaser does a decent chunk of stuff for you, off in the background somewhere, so it gives the strong impression that it expects you to build around it in a particular way… but it never tells you what that way is.

Tutorials

Ah, but this is what tutorials are for, right?

I confess I recoil whenever I hear the word “tutorial”. It conjures an image of a uniquely useless sort of post, which goes something like this:

Look at this cool thing I made! I’ll teach you how to do it too.
Press all of these buttons in this order. Here’s a screenshot, which looks nothing like what you have, because I’ve customized the hell out of everything.
You did it!

The author is often less than forthcoming about why they made any of the decisions they did, where you might want to try something else, or what might go wrong (and how to fix it).

And this is to be expected! Writing out any of that stuff requires far more extensive knowledge than you need just to do the thing in the first place, and you need to do a good bit of introspection to sort out something coherent to say.

In other words, teaching is hard. It’s a skill, and it takes practice, and most people blogging are not experts at it. Including me!

With Phaser, I noticed that several of the third-party tutorials I tried to look at were 404s — sometimes less than a year after they were linked on the site. Pretty major downside to relying on the community for teaching resources.

But I also notice that… um…

Okay, look. I really am not trying to rag on this author. I’m not. They tried to share their knowledge with the world, and that’s a good thing, something worthy of praise. I’m glad they did it! I hope it helps someone.

But for the sake of example, here is the most recent entry in Phaser’s list of community tutorials. I have to link it, because it’s such a perfect example. Consider:

The post itself is a bulleted list of explanation followed by a single contiguous 250 lines of source code. (Not that there’s anything wrong with bulleted lists, mind you.) That code contains zero comments and zero blank lines.
This is only part two in what I think is a series aimed at beginners, yet the title and much of the prose focus on object pooling, a performance hack that’s easy to add later and that’s almost certainly unnecessary for a game this simple. There is no explanation of why this is done; the prose only says you’ll understand why it’s critical once you add a lot more game objects.
It turns out I only have two things to say here so I don’t know why I made this a bulleted list.

In short, it’s not really a guided explanation; it’s “look what I did”.

And that’s fine, and it can still be interesting. I’m not sure English is even this person’s first language, so I’m hardly going to criticize them for not writing a novel about platforming.

The trouble is that I doubt a beginner would walk away from this feeling very enlightened. They might be closer to having the game they wanted, so there’s still value in it, but it feels closer to having someone else do it for them. And an awful lot of tutorials I’ve seen — particularly of the “post on some blog” form (which I’m aware is the genre of thing I’m writing right now) — look similar.

This isn’t some huge social problem; it’s just people writing on their blog and contributing to the corpus of written knowledge. It does become a bit stickier when a large project relies on these community tutorials as its main set of teaching aids.

Again, I’m not ragging on Phaser here. I had a slightly frustrating experience with it, coming in knowing what I wanted but unable to find a description of the semantics anywhere, but I do sympathize. Teaching is hard, writing documentation is hard, and programmers would usually rather program than do either of those things. For free projects that run on volunteer work, and in an industry where anything other than programming is a little undervalued, getting good docs written can be tricky.

(Then again, Phaser sells books and plugins, so maybe they could hire a documentation writer. Or maybe the whole point is for you to buy the books?)

Some pretty good docs

Python has pretty good documentation. It introduces the language with a tutorial, then documents everything else in both a library and language reference.

This sounds an awful lot like Phaser’s setup, but there’s some considerable depth in the Python docs. The tutorial is highly narrative and walks through quite a few corners of the language, stopping to mention common pitfalls and possible use cases. I clicked an arbitrary heading and found a pleasant, informative read that somehow avoids being bewilderingly dense.

The API docs also take on a narrative tone — even something as humble as the collections module offers numerous examples, use cases, patterns, recipes, and hints of interesting ways you might extend the existing types.

I’m being a little vague and hand-wavey here, but it’s hard to give specific examples without just quoting two pages of Python documentation. Hopefully you can see right away what I mean if you just take a look at them. They’re good docs, Bront.

I’ve likewise always enjoyed the SQLAlchemy documentation, which follows much the same structure as the main Python documentation. SQLAlchemy is a database abstraction layer plus ORM, so it can do a lot of subtly intertwined stuff, and the complexity of the docs reflects this. Figuring out how to do very advanced things correctly, in particular, can be challenging. But for the most part it does a very thorough job of introducing you to a large library with a particular philosophy and how to best work alongside it.

I softly contrast this with, say, the Perl documentation.

It’s gotten better since I first learned Perl, but Perl’s docs are still a bit of a strange beast. They exist as a flat collection of manpage-like documents with terse names like perlootut. The documentation is certainly thorough, but much of it has a strange… allocation of detail.

For example, perllol — the explanation of how to make a list of lists, which somehow merits its own separate documentation — offers no fewer than nine similar variations of the same code for reading a file into a nested lists of words on each line. Where Python offers examples for a variety of different problems, Perl shows you a lot of subtly different ways to do the same basic thing.

A similar problem is that Perl’s docs sometimes offer far too much context; consider the references tutorial, which starts by explaining that references are a powerful “new” feature in Perl 5 (first released in 1994). It then explains why you might want to nest data structures… from a Perl 4 perspective, thus explaining why Perl 5 is so much better.

Some stuff I’ve tried

I don’t claim to be a great teacher. I like to talk about stuff I find interesting, and I try to do it in ways that are accessible to people who aren’t lugging around the mountain of context I already have. This being just some blog, it’s hard to tell how well that works, but I do my best.

I also know that I learn best when I can understand what’s going on, rather than just seeing surface-level cause and effect. Of course, with complex subjects, it’s hard to develop an understanding before you’ve seen the cause and effect a few times, so there’s a balancing act between showing examples and trying to provide an explanation. Too many concrete examples feel like rote memorization; too much abstract theory feels disconnected from anything tangible.

The attempt I’m most pleased with is probably my post on Perlin noise. It covers a fairly specific subject, which made it much easier. It builds up one step at a time from scratch, with visualizations at every point. It offers some interpretations of what’s going on. It clearly explains some possible extensions to the idea, but distinguishes those from the core concept.

It is a little math-heavy, I grant you, but that was hard to avoid with a fundamentally mathematical topic. I had to be economical with the background information, so I let the math be a little dense in places.

But the best part about it by far is that I learned a lot about Perlin noise in the process of writing it. In several places I realized I couldn’t explain what was going on in a satisfying way, so I had to dig deeper into it before I could write about it. Perhaps there’s a good guideline hidden in there: don’t try to teach as much as you know?

I’m also fairly happy with my series on making Doom maps, though they meander into tangents a little more often. It’s hard to talk about something like Doom without meandering, since it’s a convoluted ecosystem that’s grown organically over the course of 24 years and has at least three ways of doing anything.

And finally there’s the book I’m trying to write, which is sort of about game development.

One of my biggest grievances with game development teaching in particular is how often it leaves out important touches. Very few guides will tell you how to make a title screen or menu, how to handle death, how to get a Mario-style variable jump height. They’ll show you how to build a clearly unfinished demo game, then leave you to your own devices.

I realized that the only reliable way to show how to build a game is to build a real game, then write about it. So the book is laid out as a narrative of how I wrote my first few games, complete with stumbling blocks and dead ends and tiny bits of polish.

I have no idea how well this will work, or whether recapping my own mistakes will be interesting or distracting for a beginner, but it ought to be an interesting experiment.

Nintendo & BREIN Target Seller of ‘Pirate’ Retro Gaming System

2017-06-10 Andy

Post Syndicated from Andy original https://torrentfreak.com/nintendo-brein-target-seller-of-pirate-retro-gaming-system-170610/

As millions of often younger gamers immerse themselves in the latest 3D romp-fests from the world’s leading games developers, huge numbers of people are reliving their youth through the wonders of emulation.

The majority of old gaming systems can be emulated on a decent PC these days, opening up the possibility of reanimating thousands of the greatest games to ever grace the planet. While that’s a great prospect, the good news doesn’t stop there. The games are all free – if you don’t mind pirating them.

While many people go the do-it-yourself route by downloading emulators and ROMs (the games) from the Internet, increasingly people are saving time by buying systems ready-made online. Some of these are hugely impressive, housed in full-size arcade machine cabinets and packing many thousands of games. They also have sizeable price tags to match, running in some cases to thousands of dollars. But there are other options.

The rise of affordable compact computers has opened up emulation and retro gaming to a whole new audience and inevitable some people have taken to selling these devices online with the games pre-bundled on SD cards. These systems can be obtained relatively cheaply but despite the games being old, companies like Nintendo still take a dim view of their sale.

That’s also the case in the Netherlands, where Nintendo and other companies are taking action against people involved in the sale of what are effectively pirate gaming systems. In a recent case, Dutch anti-piracy outfit BREIN took action against the operator of the Retrospeler (Retro Player) site, an outlet selling a ready-made retro gaming system.

Retro Player site (translated from Dutch)

As seen from the image above, for a little under 110 euros the player can buy a games machine with classics like Super Mario, Street Fighter, and Final Fantasy pre-installed. Add a TV via an HDMI lead and a joypad or two, and yesteryear gaming becomes reality today. Unfortunately, the fun didn’t last long and it was soon “Game Over” for Retro Player.

Speaking with TorrentFreak, BREIN chief Tim Kuik says that the system sold by Retro Player was based on the popular Raspberry Pi single-board computer. Although small and relatively cheap, the Pi is easily capable of running retro games via software such as RetroPie, but it’s unclear which product was installed on the version sold by Retro Player.

What is clear is that the device came pre-installed with a lot of games. The now-defunct Retro Player site listed 6,500 titles for a wide range of classic gaming systems, including Gameboy, Super Nintendo, Nintendo 64, Megadrive and Playstation. Kuik didn’t provide precise numbers but said that the machine came packaged with “a couple of thousand” titles.

BREIN says in this particular case it was acting on behalf of Nintendo, among others. However, it doesn’t appear that the case will be going to court. Like many other cases handled by the anti-piracy group, BREIN says it has reached a settlement with the operator of the Retro Player site for an unspecified amount.

The debate and controversy surrounding retro gaming and emulation is one that has been running for years. The thriving community sees little wrong with reanimating games for long-dead systems and giving them new life among a new audience. On the other hand, copyright holders such as Nintendo view their titles as their property, to be exploited in a time, place and manner of their choosing.

While that friction will continue for a long time to come, there will be few if any legal problems for those choosing to pursue their emulation fantasies in the privacy of their own home. Retro gaming is here to stay and as long as computing power continues to increase, the experience is only likely to improve.

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

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