Tag Archives: java

Implementing Default Directory Indexes in Amazon S3-backed Amazon CloudFront Origins Using [email protected]

Post Syndicated from Ronnie Eichler original https://aws.amazon.com/blogs/compute/implementing-default-directory-indexes-in-amazon-s3-backed-amazon-cloudfront-origins-using-lambdaedge/

With the recent launch of [email protected], it’s now possible for you to provide even more robust functionality to your static websites. Amazon CloudFront is a content distribution network service. In this post, I show how you can use [email protected] along with the CloudFront origin access identity (OAI) for Amazon S3 and still provide simple URLs (such as www.example.com/about/ instead of www.example.com/about/index.html).

Background

Amazon S3 is a great platform for hosting a static website. You don’t need to worry about managing servers or underlying infrastructure—you just publish your static to content to an S3 bucket. S3 provides a DNS name such as <bucket-name>.s3-website-<AWS-region>.amazonaws.com. Use this name for your website by creating a CNAME record in your domain’s DNS environment (or Amazon Route 53) as follows:

www.example.com -> <bucket-name>.s3-website-<AWS-region>.amazonaws.com

You can also put CloudFront in front of S3 to further scale the performance of your site and cache the content closer to your users. CloudFront can enable HTTPS-hosted sites, by either using a custom Secure Sockets Layer (SSL) certificate or a managed certificate from AWS Certificate Manager. In addition, CloudFront also offers integration with AWS WAF, a web application firewall. As you can see, it’s possible to achieve some robust functionality by using S3, CloudFront, and other managed services and not have to worry about maintaining underlying infrastructure.

One of the key concerns that you might have when implementing any type of WAF or CDN is that you want to force your users to go through the CDN. If you implement CloudFront in front of S3, you can achieve this by using an OAI. However, in order to do this, you cannot use the HTTP endpoint that is exposed by S3’s static website hosting feature. Instead, CloudFront must use the S3 REST endpoint to fetch content from your origin so that the request can be authenticated using the OAI. This presents some challenges in that the REST endpoint does not support redirection to a default index page.

CloudFront does allow you to specify a default root object (index.html), but it only works on the root of the website (such as http://www.example.com > http://www.example.com/index.html). It does not work on any subdirectory (such as http://www.example.com/about/). If you were to attempt to request this URL through CloudFront, CloudFront would do a S3 GetObject API call against a key that does not exist.

Of course, it is a bad user experience to expect users to always type index.html at the end of every URL (or even know that it should be there). Until now, there has not been an easy way to provide these simpler URLs (equivalent to the DirectoryIndex Directive in an Apache Web Server configuration) to users through CloudFront. Not if you still want to be able to restrict access to the S3 origin using an OAI. However, with the release of [email protected], you can use a JavaScript function running on the CloudFront edge nodes to look for these patterns and request the appropriate object key from the S3 origin.

Solution

In this example, you use the compute power at the CloudFront edge to inspect the request as it’s coming in from the client. Then re-write the request so that CloudFront requests a default index object (index.html in this case) for any request URI that ends in ‘/’.

When a request is made against a web server, the client specifies the object to obtain in the request. You can use this URI and apply a regular expression to it so that these URIs get resolved to a default index object before CloudFront requests the object from the origin. Use the following code:

'use strict';
exports.handler = (event, context, callback) => {
    
    // Extract the request from the CloudFront event that is sent to [email protected] 
    var request = event.Records[0].cf.request;

    // Extract the URI from the request
    var olduri = request.uri;

    // Match any '/' that occurs at the end of a URI. Replace it with a default index
    var newuri = olduri.replace(/\/$/, '\/index.html');
    
    // Log the URI as received by CloudFront and the new URI to be used to fetch from origin
    console.log("Old URI: " + olduri);
    console.log("New URI: " + newuri);
    
    // Replace the received URI with the URI that includes the index page
    request.uri = newuri;
    
    // Return to CloudFront
    return callback(null, request);

};

To get started, create an S3 bucket to be the origin for CloudFront:

Create bucket

On the other screens, you can just accept the defaults for the purposes of this walkthrough. If this were a production implementation, I would recommend enabling bucket logging and specifying an existing S3 bucket as the destination for access logs. These logs can be useful if you need to troubleshoot issues with your S3 access.

Now, put some content into your S3 bucket. For this walkthrough, create two simple webpages to demonstrate the functionality:  A page that resides at the website root, and another that is in a subdirectory.

<s3bucketname>/index.html

<!doctype html>
<html>
    <head>
        <meta charset="utf-8">
        <title>Root home page</title>
    </head>
    <body>
        <p>Hello, this page resides in the root directory.</p>
    </body>
</html>

<s3bucketname>/subdirectory/index.html

<!doctype html>
<html>
    <head>
        <meta charset="utf-8">
        <title>Subdirectory home page</title>
    </head>
    <body>
        <p>Hello, this page resides in the /subdirectory/ directory.</p>
    </body>
</html>

When uploading the files into S3, you can accept the defaults. You add a bucket policy as part of the CloudFront distribution creation that allows CloudFront to access the S3 origin. You should now have an S3 bucket that looks like the following:

Root of bucket

Subdirectory in bucket

Next, create a CloudFront distribution that your users will use to access the content. Open the CloudFront console, and choose Create Distribution. For Select a delivery method for your content, under Web, choose Get Started.

On the next screen, you set up the distribution. Below are the options to configure:

  • Origin Domain Name:  Select the S3 bucket that you created earlier.
  • Restrict Bucket Access: Choose Yes.
  • Origin Access Identity: Create a new identity.
  • Grant Read Permissions on Bucket: Choose Yes, Update Bucket Policy.
  • Object Caching: Choose Customize (I am changing the behavior to avoid having CloudFront cache objects, as this could affect your ability to troubleshoot while implementing the Lambda code).
    • Minimum TTL: 0
    • Maximum TTL: 0
    • Default TTL: 0

You can accept all of the other defaults. Again, this is a proof-of-concept exercise. After you are comfortable that the CloudFront distribution is working properly with the origin and Lambda code, you can re-visit the preceding values and make changes before implementing it in production.

CloudFront distributions can take several minutes to deploy (because the changes have to propagate out to all of the edge locations). After that’s done, test the functionality of the S3-backed static website. Looking at the distribution, you can see that CloudFront assigns a domain name:

CloudFront Distribution Settings

Try to access the website using a combination of various URLs:

http://<domainname>/:  Works

› curl -v http://d3gt20ea1hllb.cloudfront.net/
*   Trying 54.192.192.214...
* TCP_NODELAY set
* Connected to d3gt20ea1hllb.cloudfront.net (54.192.192.214) port 80 (#0)
> GET / HTTP/1.1
> Host: d3gt20ea1hllb.cloudfront.net
> User-Agent: curl/7.51.0
> Accept: */*
>
< HTTP/1.1 200 OK
< ETag: "cb7e2634fe66c1fd395cf868087dd3b9"
< Accept-Ranges: bytes
< Server: AmazonS3
< X-Cache: Miss from cloudfront
< X-Amz-Cf-Id: -D2FSRwzfcwyKZKFZr6DqYFkIf4t7HdGw2MkUF5sE6YFDxRJgi0R1g==
< Content-Length: 209
< Content-Type: text/html
< Last-Modified: Wed, 19 Jul 2017 19:21:16 GMT
< Via: 1.1 6419ba8f3bd94b651d416054d9416f1e.cloudfront.net (CloudFront), 1.1 iad6-proxy-3.amazon.com:80 (Cisco-WSA/9.1.2-010)
< Connection: keep-alive
<
<!doctype html>
<html>
    <head>
        <meta charset="utf-8">
        <title>Root home page</title>
    </head>
    <body>
        <p>Hello, this page resides in the root directory.</p>
    </body>
</html>
* Curl_http_done: called premature == 0
* Connection #0 to host d3gt20ea1hllb.cloudfront.net left intact

This is because CloudFront is configured to request a default root object (index.html) from the origin.

http://<domainname>/subdirectory/:  Doesn’t work

› curl -v http://d3gt20ea1hllb.cloudfront.net/subdirectory/
*   Trying 54.192.192.214...
* TCP_NODELAY set
* Connected to d3gt20ea1hllb.cloudfront.net (54.192.192.214) port 80 (#0)
> GET /subdirectory/ HTTP/1.1
> Host: d3gt20ea1hllb.cloudfront.net
> User-Agent: curl/7.51.0
> Accept: */*
>
< HTTP/1.1 200 OK
< ETag: "d41d8cd98f00b204e9800998ecf8427e"
< x-amz-server-side-encryption: AES256
< Accept-Ranges: bytes
< Server: AmazonS3
< X-Cache: Miss from cloudfront
< X-Amz-Cf-Id: Iqf0Gy8hJLiW-9tOAdSFPkL7vCWBrgm3-1ly5tBeY_izU82ftipodA==
< Content-Length: 0
< Content-Type: application/x-directory
< Last-Modified: Wed, 19 Jul 2017 19:21:24 GMT
< Via: 1.1 6419ba8f3bd94b651d416054d9416f1e.cloudfront.net (CloudFront), 1.1 iad6-proxy-3.amazon.com:80 (Cisco-WSA/9.1.2-010)
< Connection: keep-alive
<
* Curl_http_done: called premature == 0
* Connection #0 to host d3gt20ea1hllb.cloudfront.net left intact

If you use a tool such like cURL to test this, you notice that CloudFront and S3 are returning a blank response. The reason for this is that the subdirectory does exist, but it does not resolve to an S3 object. Keep in mind that S3 is an object store, so there are no real directories. User interfaces such as the S3 console present a hierarchical view of a bucket with folders based on the presence of forward slashes, but behind the scenes the bucket is just a collection of keys that represent stored objects.

http://<domainname>/subdirectory/index.html:  Works

› curl -v http://d3gt20ea1hllb.cloudfront.net/subdirectory/index.html
*   Trying 54.192.192.130...
* TCP_NODELAY set
* Connected to d3gt20ea1hllb.cloudfront.net (54.192.192.130) port 80 (#0)
> GET /subdirectory/index.html HTTP/1.1
> Host: d3gt20ea1hllb.cloudfront.net
> User-Agent: curl/7.51.0
> Accept: */*
>
< HTTP/1.1 200 OK
< Date: Thu, 20 Jul 2017 20:35:15 GMT
< ETag: "ddf87c487acf7cef9d50418f0f8f8dae"
< Accept-Ranges: bytes
< Server: AmazonS3
< X-Cache: RefreshHit from cloudfront
< X-Amz-Cf-Id: bkh6opXdpw8pUomqG3Qr3UcjnZL8axxOH82Lh0OOcx48uJKc_Dc3Cg==
< Content-Length: 227
< Content-Type: text/html
< Last-Modified: Wed, 19 Jul 2017 19:21:45 GMT
< Via: 1.1 3f2788d309d30f41de96da6f931d4ede.cloudfront.net (CloudFront), 1.1 iad6-proxy-3.amazon.com:80 (Cisco-WSA/9.1.2-010)
< Connection: keep-alive
<
<!doctype html>
<html>
    <head>
        <meta charset="utf-8">
        <title>Subdirectory home page</title>
    </head>
    <body>
        <p>Hello, this page resides in the /subdirectory/ directory.</p>
    </body>
</html>
* Curl_http_done: called premature == 0
* Connection #0 to host d3gt20ea1hllb.cloudfront.net left intact

This request works as expected because you are referencing the object directly. Now, you implement the [email protected] function to return the default index.html page for any subdirectory. Looking at the example JavaScript code, here’s where the magic happens:

var newuri = olduri.replace(/\/$/, '\/index.html');

You are going to use a JavaScript regular expression to match any ‘/’ that occurs at the end of the URI and replace it with ‘/index.html’. This is the equivalent to what S3 does on its own with static website hosting. However, as I mentioned earlier, you can’t rely on this if you want to use a policy on the bucket to restrict it so that users must access the bucket through CloudFront. That way, all requests to the S3 bucket must be authenticated using the S3 REST API. Because of this, you implement a [email protected] function that takes any client request ending in ‘/’ and append a default ‘index.html’ to the request before requesting the object from the origin.

In the Lambda console, choose Create function. On the next screen, skip the blueprint selection and choose Author from scratch, as you’ll use the sample code provided.

Next, configure the trigger. Choosing the empty box shows a list of available triggers. Choose CloudFront and select your CloudFront distribution ID (created earlier). For this example, leave Cache Behavior as * and CloudFront Event as Origin Request. Select the Enable trigger and replicate box and choose Next.

Lambda Trigger

Next, give the function a name and a description. Then, copy and paste the following code:

'use strict';
exports.handler = (event, context, callback) => {
    
    // Extract the request from the CloudFront event that is sent to [email protected] 
    var request = event.Records[0].cf.request;

    // Extract the URI from the request
    var olduri = request.uri;

    // Match any '/' that occurs at the end of a URI. Replace it with a default index
    var newuri = olduri.replace(/\/$/, '\/index.html');
    
    // Log the URI as received by CloudFront and the new URI to be used to fetch from origin
    console.log("Old URI: " + olduri);
    console.log("New URI: " + newuri);
    
    // Replace the received URI with the URI that includes the index page
    request.uri = newuri;
    
    // Return to CloudFront
    return callback(null, request);

};

Next, define a role that grants permissions to the Lambda function. For this example, choose Create new role from template, Basic Edge Lambda permissions. This creates a new IAM role for the Lambda function and grants the following permissions:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "logs:CreateLogGroup",
                "logs:CreateLogStream",
                "logs:PutLogEvents"
            ],
            "Resource": [
                "arn:aws:logs:*:*:*"
            ]
        }
    ]
}

In a nutshell, these are the permissions that the function needs to create the necessary CloudWatch log group and log stream, and to put the log events so that the function is able to write logs when it executes.

After the function has been created, you can go back to the browser (or cURL) and re-run the test for the subdirectory request that failed previously:

› curl -v http://d3gt20ea1hllb.cloudfront.net/subdirectory/
*   Trying 54.192.192.202...
* TCP_NODELAY set
* Connected to d3gt20ea1hllb.cloudfront.net (54.192.192.202) port 80 (#0)
> GET /subdirectory/ HTTP/1.1
> Host: d3gt20ea1hllb.cloudfront.net
> User-Agent: curl/7.51.0
> Accept: */*
>
< HTTP/1.1 200 OK
< Date: Thu, 20 Jul 2017 21:18:44 GMT
< ETag: "ddf87c487acf7cef9d50418f0f8f8dae"
< Accept-Ranges: bytes
< Server: AmazonS3
< X-Cache: Miss from cloudfront
< X-Amz-Cf-Id: rwFN7yHE70bT9xckBpceTsAPcmaadqWB9omPBv2P6WkIfQqdjTk_4w==
< Content-Length: 227
< Content-Type: text/html
< Last-Modified: Wed, 19 Jul 2017 19:21:45 GMT
< Via: 1.1 3572de112011f1b625bb77410b0c5cca.cloudfront.net (CloudFront), 1.1 iad6-proxy-3.amazon.com:80 (Cisco-WSA/9.1.2-010)
< Connection: keep-alive
<
<!doctype html>
<html>
    <head>
        <meta charset="utf-8">
        <title>Subdirectory home page</title>
    </head>
    <body>
        <p>Hello, this page resides in the /subdirectory/ directory.</p>
    </body>
</html>
* Curl_http_done: called premature == 0
* Connection #0 to host d3gt20ea1hllb.cloudfront.net left intact

You have now configured a way for CloudFront to return a default index page for subdirectories in S3!

Summary

In this post, you used [email protected] to be able to use CloudFront with an S3 origin access identity and serve a default root object on subdirectory URLs. To find out some more about this use-case, see [email protected] integration with CloudFront in our documentation.

If you have questions or suggestions, feel free to comment below. For troubleshooting or implementation help, check out the Lambda forum.

What’s new in HiveMQ 3.3

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

We are pleased to announce the release of HiveMQ 3.3. This version of HiveMQ is the most advanced and user friendly version of HiveMQ ever. A broker is the heart of every MQTT deployment and it’s key to monitor and understand how healthy your system and your connected clients are. Version 3.3 of HiveMQ focuses on observability, usability and advanced administration features and introduces a brand new Web UI. This version is a drop-in replacement for HiveMQ 3.2 and of course supports rolling upgrades for zero-downtime.

HiveMQ 3.3 brings many features that your users, administrators and plugin developers are going to love. These are the highlights:

Web UI

Web UI
The new HiveMQ version has a built-in Web UI for advanced analysis and administrative tasks. A powerful dashboard shows important data about the health of the broker cluster and an overview of the whole MQTT deployment.
With the new Web UI, administrators are able to drill down to specific client information and can perform administrative actions like disconnecting a client. Advanced analytics functionality allows indetifying clients with irregular behavior. It’s easy to identify message-dropping clients as HiveMQ shows detailed statistics of such misbehaving MQTT participants.
Of course all Web UI features work at scale with more than a million connected MQTT clients. Learn more about the Web UI in the documentation.

Time To Live

TTL
HiveMQ introduces Time to Live (TTL) on various levels of the MQTT lifecycle. Automatic cleanup of expired messages is as well supported as the wiping of abandoned persistent MQTT sessions. In particular, version 3.3 implements the following TTL features:

  • MQTT client session expiration
  • Retained Message expiration
  • MQTT PUBLISH message expiration

Configuring a TTL for MQTT client sessions and retained messages allows freeing system resources without manual administrative intervention as soon as the data is not needed anymore.
Beside global configuration, MQTT PUBLISHES can have individual TTLs based on application specific characteristics. It’s a breeze to change the TTL of particular messages with the HiveMQ plugin system. As soon as a message TTL expires, the broker won’t send out the message anymore, even if the message was previously queued or in-flight. This can save precious bandwidth for mobile connections as unnecessary traffic is avoided for expired messages.

Trace Recordings

Trace Recordings
Debugging specific MQTT clients or groups of MQTT clients can be challenging at scale. HiveMQ 3.3 introduces an innovative Trace Recording mechanism that allows creating detailed recordings of all client interactions with given filters.
It’s possible to filter based on client identifiers, MQTT message types and topics. And the best of all: You can use regular expressions to select multiple MQTT clients at once as well as topics with complex structures. Getting detailed information about the behavior of specific MQTT clients for debugging complex issues was never easier.

Native SSL

Native SSL
The new native SSL integration of HiveMQ brings a performance boost of more than 40% for SSL Handshakes (in terms of CPU usage) by utilizing an integration with BoringSSL. BoringSSL is Google’s fork of OpenSSL which is also used in Google Chrome and Android. Besides the compute and huge memory optimizations (saves up to 60% Java Heap), additional secure state-of-the-art cipher suites are supported by HiveMQ which are not directly available for Java (like ChaCha20-Poly1305).
Most HiveMQ deployments on Linux systems are expected to see decreased CPU load on TLS handshakes with the native SSL integration and huge memory improvements.

New Plugin System Features

New Plugin System Features
The popular and powerful plugin system has received additional services and callbacks which are useful for many existing and future plugins.
Plugin developers can now use a ConnectionAttributeStore and a SessionAttributeStore for storing arbitrary data for the lifetime of a single MQTT connection of a client or for the whole session of a client. The new ClientGroupService allows grouping different MQTT client identifiers by the same key, so it’s easy to address multiple MQTT clients (with the same group) at once.

A new callback was introduced which notifies a plugin when a HiveMQ instance is ready, which means the instance is part of the cluster and all listeners were started successfully. Developers can now react when a MQTT client session is ready and usable in the cluster with a dedicated callback.

Some use cases require modifying a MQTT PUBLISH packet before it’s sent out to a client. This is now possible with a new callback that was introduced for modifying a PUBLISH before sending it out to a individual client.
The offline queue size for persistent clients is now also configurable for individual clients as well as the queue discard strategy.

Additional Features

Additional Features
HiveMQ 3.3 has many additional features designed for power users and professional MQTT deployments. The new version also has the following highlights:

  • OCSP Stapling
  • Event Log for MQTT client connects, disconnects and unusual events (e.g. discarded message due to slow consumption on the client side
  • Throttling of concurrent TLS handshakes
  • Connect Packet overload protection
  • Configuration of Socket send and receive buffer sizes
  • Global System Information like the HiveMQ Home folder can now be set via Environment Variables without changing the run script
  • The internal HTTP server of HiveMQ is now exposed to the holistic monitoring subsystem
  • Many additional useful metrics were exposed to HiveMQ’s monitoring subsystem

 

In order to upgrade to HiveMQ 3.3 from HiveMQ 3.2 or older versions, take a look at our Upgrade Guide.
Don’t forget to learn more about all the new features with our HiveMQ User Guide.

Download HiveMQ 3.3 now

Predict Billboard Top 10 Hits Using RStudio, H2O and Amazon Athena

Post Syndicated from Gopal Wunnava original https://aws.amazon.com/blogs/big-data/predict-billboard-top-10-hits-using-rstudio-h2o-and-amazon-athena/

Success in the popular music industry is typically measured in terms of the number of Top 10 hits artists have to their credit. The music industry is a highly competitive multi-billion dollar business, and record labels incur various costs in exchange for a percentage of the profits from sales and concert tickets.

Predicting the success of an artist’s release in the popular music industry can be difficult. One release may be extremely popular, resulting in widespread play on TV, radio and social media, while another single may turn out quite unpopular, and therefore unprofitable. Record labels need to be selective in their decision making, and predictive analytics can help them with decision making around the type of songs and artists they need to promote.

In this walkthrough, you leverage H2O.ai, Amazon Athena, and RStudio to make predictions on whether a song might make it to the Top 10 Billboard charts. You explore the GLM, GBM, and deep learning modeling techniques using H2O’s rapid, distributed and easy-to-use open source parallel processing engine. RStudio is a popular IDE, licensed either commercially or under AGPLv3, for working with R. This is ideal if you don’t want to connect to a server via SSH and use code editors such as vi to do analytics. RStudio is available in a desktop version, or a server version that allows you to access R via a web browser. RStudio’s Notebooks feature is used to demonstrate the execution of code and output. In addition, this post showcases how you can leverage Athena for query and interactive analysis during the modeling phase. A working knowledge of statistics and machine learning would be helpful to interpret the analysis being performed in this post.

Walkthrough

Your goal is to predict whether a song will make it to the Top 10 Billboard charts. For this purpose, you will be using multiple modeling techniques―namely GLM, GBM and deep learning―and choose the model that is the best fit.

This solution involves the following steps:

  • Install and configure RStudio with Athena
  • Log in to RStudio
  • Install R packages
  • Connect to Athena
  • Create a dataset
  • Create models

Install and configure RStudio with Athena

Use the following AWS CloudFormation stack to install, configure, and connect RStudio on an Amazon EC2 instance with Athena.

Launching this stack creates all required resources and prerequisites:

  • Amazon EC2 instance with Amazon Linux (minimum size of t2.large is recommended)
  • Provisioning of the EC2 instance in an existing VPC and public subnet
  • Installation of Java 8
  • Assignment of an IAM role to the EC2 instance with the required permissions for accessing Athena and Amazon S3
  • Security group allowing access to the RStudio and SSH ports from the internet (I recommend restricting access to these ports)
  • S3 staging bucket required for Athena (referenced within RStudio as ATHENABUCKET)
  • RStudio username and password
  • Setup logs in Amazon CloudWatch Logs (if needed for additional troubleshooting)
  • Amazon EC2 Systems Manager agent, which makes it easy to manage and patch

All AWS resources are created in the US-East-1 Region. To avoid cross-region data transfer fees, launch the CloudFormation stack in the same region. To check the availability of Athena in other regions, see Region Table.

Log in to RStudio

The instance security group has been automatically configured to allow incoming connections on the RStudio port 8787 from any source internet address. You can edit the security group to restrict source IP access. If you have trouble connecting, ensure that port 8787 isn’t blocked by subnet network ACLS or by your outgoing proxy/firewall.

  1. In the CloudFormation stack, choose Outputs, Value, and then open the RStudio URL. You might need to wait for a few minutes until the instance has been launched.
  2. Log in to RStudio with the and password you provided during setup.

Install R packages

Next, install the required R packages from the RStudio console. You can download the R notebook file containing just the code.

#install pacman – a handy package manager for managing installs
if("pacman" %in% rownames(installed.packages()) == FALSE)
{install.packages("pacman")}  
library(pacman)
p_load(h2o,rJava,RJDBC,awsjavasdk)
h2o.init(nthreads = -1)
##  Connection successful!
## 
## R is connected to the H2O cluster: 
##     H2O cluster uptime:         2 hours 42 minutes 
##     H2O cluster version:        3.10.4.6 
##     H2O cluster version age:    4 months and 4 days !!! 
##     H2O cluster name:           H2O_started_from_R_rstudio_hjx881 
##     H2O cluster total nodes:    1 
##     H2O cluster total memory:   3.30 GB 
##     H2O cluster total cores:    4 
##     H2O cluster allowed cores:  4 
##     H2O cluster healthy:        TRUE 
##     H2O Connection ip:          localhost 
##     H2O Connection port:        54321 
##     H2O Connection proxy:       NA 
##     H2O Internal Security:      FALSE 
##     R Version:                  R version 3.3.3 (2017-03-06)
## Warning in h2o.clusterInfo(): 
## Your H2O cluster version is too old (4 months and 4 days)!
## Please download and install the latest version from http://h2o.ai/download/
#install aws sdk if not present (pre-requisite for using Athena with an IAM role)
if (!aws_sdk_present()) {
  install_aws_sdk()
}

load_sdk()
## NULL

Connect to Athena

Next, establish a connection to Athena from RStudio, using an IAM role associated with your EC2 instance. Use ATHENABUCKET to specify the S3 staging directory.

URL <- 'https://s3.amazonaws.com/athena-downloads/drivers/AthenaJDBC41-1.0.1.jar'
fil <- basename(URL)
#download the file into current working directory
if (!file.exists(fil)) download.file(URL, fil)
#verify that the file has been downloaded successfully
list.files()
## [1] "AthenaJDBC41-1.0.1.jar"
drv <- JDBC(driverClass="com.amazonaws.athena.jdbc.AthenaDriver", fil, identifier.quote="'")

con <- jdbcConnection <- dbConnect(drv, 'jdbc:awsathena://athena.us-east-1.amazonaws.com:443/',
                                   s3_staging_dir=Sys.getenv("ATHENABUCKET"),
                                   aws_credentials_provider_class="com.amazonaws.auth.DefaultAWSCredentialsProviderChain")

Verify the connection. The results returned depend on your specific Athena setup.

con
## <JDBCConnection>
dbListTables(con)
##  [1] "gdelt"               "wikistats"           "elb_logs_raw_native"
##  [4] "twitter"             "twitter2"            "usermovieratings"   
##  [7] "eventcodes"          "events"              "billboard"          
## [10] "billboardtop10"      "elb_logs"            "gdelthist"          
## [13] "gdeltmaster"         "twitter"             "twitter3"

Create a dataset

For this analysis, you use a sample dataset combining information from Billboard and Wikipedia with Echo Nest data in the Million Songs Dataset. Upload this dataset into your own S3 bucket. The table below provides a description of the fields used in this dataset.

Field Description
year Year that song was released
songtitle Title of the song
artistname Name of the song artist
songid Unique identifier for the song
artistid Unique identifier for the song artist
timesignature Variable estimating the time signature of the song
timesignature_confidence Confidence in the estimate for the timesignature
loudness Continuous variable indicating the average amplitude of the audio in decibels
tempo Variable indicating the estimated beats per minute of the song
tempo_confidence Confidence in the estimate for tempo
key Variable with twelve levels indicating the estimated key of the song (C, C#, B)
key_confidence Confidence in the estimate for key
energy Variable that represents the overall acoustic energy of the song, using a mix of features such as loudness
pitch Continuous variable that indicates the pitch of the song
timbre_0_min thru timbre_11_min Variables that indicate the minimum values over all segments for each of the twelve values in the timbre vector
timbre_0_max thru timbre_11_max Variables that indicate the maximum values over all segments for each of the twelve values in the timbre vector
top10 Indicator for whether or not the song made it to the Top 10 of the Billboard charts (1 if it was in the top 10, and 0 if not)

Create an Athena table based on the dataset

In the Athena console, select the default database, sampled, or create a new database.

Run the following create table statement.

create external table if not exists billboard
(
year int,
songtitle string,
artistname string,
songID string,
artistID string,
timesignature int,
timesignature_confidence double,
loudness double,
tempo double,
tempo_confidence double,
key int,
key_confidence double,
energy double,
pitch double,
timbre_0_min double,
timbre_0_max double,
timbre_1_min double,
timbre_1_max double,
timbre_2_min double,
timbre_2_max double,
timbre_3_min double,
timbre_3_max double,
timbre_4_min double,
timbre_4_max double,
timbre_5_min double,
timbre_5_max double,
timbre_6_min double,
timbre_6_max double,
timbre_7_min double,
timbre_7_max double,
timbre_8_min double,
timbre_8_max double,
timbre_9_min double,
timbre_9_max double,
timbre_10_min double,
timbre_10_max double,
timbre_11_min double,
timbre_11_max double,
Top10 int
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
STORED AS TEXTFILE
LOCATION 's3://aws-bigdata-blog/artifacts/predict-billboard/data'
;

Inspect the table definition for the ‘billboard’ table that you have created. If you chose a database other than sampledb, replace that value with your choice.

dbGetQuery(con, "show create table sampledb.billboard")
##                                      createtab_stmt
## 1       CREATE EXTERNAL TABLE `sampledb.billboard`(
## 2                                       `year` int,
## 3                               `songtitle` string,
## 4                              `artistname` string,
## 5                                  `songid` string,
## 6                                `artistid` string,
## 7                              `timesignature` int,
## 8                `timesignature_confidence` double,
## 9                                `loudness` double,
## 10                                  `tempo` double,
## 11                       `tempo_confidence` double,
## 12                                       `key` int,
## 13                         `key_confidence` double,
## 14                                 `energy` double,
## 15                                  `pitch` double,
## 16                           `timbre_0_min` double,
## 17                           `timbre_0_max` double,
## 18                           `timbre_1_min` double,
## 19                           `timbre_1_max` double,
## 20                           `timbre_2_min` double,
## 21                           `timbre_2_max` double,
## 22                           `timbre_3_min` double,
## 23                           `timbre_3_max` double,
## 24                           `timbre_4_min` double,
## 25                           `timbre_4_max` double,
## 26                           `timbre_5_min` double,
## 27                           `timbre_5_max` double,
## 28                           `timbre_6_min` double,
## 29                           `timbre_6_max` double,
## 30                           `timbre_7_min` double,
## 31                           `timbre_7_max` double,
## 32                           `timbre_8_min` double,
## 33                           `timbre_8_max` double,
## 34                           `timbre_9_min` double,
## 35                           `timbre_9_max` double,
## 36                          `timbre_10_min` double,
## 37                          `timbre_10_max` double,
## 38                          `timbre_11_min` double,
## 39                          `timbre_11_max` double,
## 40                                     `top10` int)
## 41                             ROW FORMAT DELIMITED 
## 42                         FIELDS TERMINATED BY ',' 
## 43                            STORED AS INPUTFORMAT 
## 44       'org.apache.hadoop.mapred.TextInputFormat' 
## 45                                     OUTPUTFORMAT 
## 46  'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
## 47                                        LOCATION
## 48    's3://aws-bigdata-blog/artifacts/predict-billboard/data'
## 49                                  TBLPROPERTIES (
## 50            'transient_lastDdlTime'='1505484133')

Run a sample query

Next, run a sample query to obtain a list of all songs from Janet Jackson that made it to the Billboard Top 10 charts.

dbGetQuery(con, " SELECT songtitle,artistname,top10   FROM sampledb.billboard WHERE lower(artistname) =     'janet jackson' AND top10 = 1")
##                       songtitle    artistname top10
## 1                       Runaway Janet Jackson     1
## 2               Because Of Love Janet Jackson     1
## 3                         Again Janet Jackson     1
## 4                            If Janet Jackson     1
## 5  Love Will Never Do (Without You) Janet Jackson 1
## 6                     Black Cat Janet Jackson     1
## 7               Come Back To Me Janet Jackson     1
## 8                       Alright Janet Jackson     1
## 9                      Escapade Janet Jackson     1
## 10                Rhythm Nation Janet Jackson     1

Determine how many songs in this dataset are specifically from the year 2010.

dbGetQuery(con, " SELECT count(*)   FROM sampledb.billboard WHERE year = 2010")
##   _col0
## 1   373

The sample dataset provides certain song properties of interest that can be analyzed to gauge the impact to the song’s overall popularity. Look at one such property, timesignature, and determine the value that is the most frequent among songs in the database. Timesignature is a measure of the number of beats and the type of note involved.

Running the query directly may result in an error, as shown in the commented lines below. This error is a result of trying to retrieve a large result set over a JDBC connection, which can cause out-of-memory issues at the client level. To address this, reduce the fetch size and run again.

#t<-dbGetQuery(con, " SELECT timesignature FROM sampledb.billboard")
#Note:  Running the preceding query results in the following error: 
#Error in .jcall(rp, "I", "fetch", stride, block): java.sql.SQLException: The requested #fetchSize is more than the allowed value in Athena. Please reduce the fetchSize and try #again. Refer to the Athena documentation for valid fetchSize values.
# Use the dbSendQuery function, reduce the fetch size, and run again
r <- dbSendQuery(con, " SELECT timesignature     FROM sampledb.billboard")
dftimesignature<- fetch(r, n=-1, block=100)
dbClearResult(r)
## [1] TRUE
table(dftimesignature)
## dftimesignature
##    0    1    3    4    5    7 
##   10  143  503 6787  112   19
nrow(dftimesignature)
## [1] 7574

From the results, observe that 6787 songs have a timesignature of 4.

Next, determine the song with the highest tempo.

dbGetQuery(con, " SELECT songtitle,artistname,tempo   FROM sampledb.billboard WHERE tempo = (SELECT max(tempo) FROM sampledb.billboard) ")
##                   songtitle      artistname   tempo
## 1 Wanna Be Startin' Somethin' Michael Jackson 244.307

Create the training dataset

Your model needs to be trained such that it can learn and make accurate predictions. Split the data into training and test datasets, and create the training dataset first.  This dataset contains all observations from the year 2009 and earlier. You may face the same JDBC connection issue pointed out earlier, so this query uses a fetch size.

#BillboardTrain <- dbGetQuery(con, "SELECT * FROM sampledb.billboard WHERE year <= 2009")
#Running the preceding query results in the following error:-
#Error in .verify.JDBC.result(r, "Unable to retrieve JDBC result set for ", : Unable to retrieve #JDBC result set for SELECT * FROM sampledb.billboard WHERE year <= 2009 (Internal error)
#Follow the same approach as before to address this issue.

r <- dbSendQuery(con, "SELECT * FROM sampledb.billboard WHERE year <= 2009")
BillboardTrain <- fetch(r, n=-1, block=100)
dbClearResult(r)
## [1] TRUE
BillboardTrain[1:2,c(1:3,6:10)]
##   year           songtitle artistname timesignature
## 1 2009 The Awkward Goodbye    Athlete             3
## 2 2009        Rubik's Cube    Athlete             3
##   timesignature_confidence loudness   tempo tempo_confidence
## 1                    0.732   -6.320  89.614   0.652
## 2                    0.906   -9.541 117.742   0.542
nrow(BillboardTrain)
## [1] 7201

Create the test dataset

BillboardTest <- dbGetQuery(con, "SELECT * FROM sampledb.billboard where year = 2010")
BillboardTest[1:2,c(1:3,11:15)]
##   year              songtitle        artistname key
## 1 2010 This Is the House That Doubt Built A Day to Remember  11
## 2 2010        Sticks & Bricks A Day to Remember  10
##   key_confidence    energy pitch timbre_0_min
## 1          0.453 0.9666556 0.024        0.002
## 2          0.469 0.9847095 0.025        0.000
nrow(BillboardTest)
## [1] 373

Convert the training and test datasets into H2O dataframes

train.h2o <- as.h2o(BillboardTrain)
## 
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test.h2o <- as.h2o(BillboardTest)
## 
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  |=================================================================| 100%

Inspect the column names in your H2O dataframes.

colnames(train.h2o)
##  [1] "year"                     "songtitle"               
##  [3] "artistname"               "songid"                  
##  [5] "artistid"                 "timesignature"           
##  [7] "timesignature_confidence" "loudness"                
##  [9] "tempo"                    "tempo_confidence"        
## [11] "key"                      "key_confidence"          
## [13] "energy"                   "pitch"                   
## [15] "timbre_0_min"             "timbre_0_max"            
## [17] "timbre_1_min"             "timbre_1_max"            
## [19] "timbre_2_min"             "timbre_2_max"            
## [21] "timbre_3_min"             "timbre_3_max"            
## [23] "timbre_4_min"             "timbre_4_max"            
## [25] "timbre_5_min"             "timbre_5_max"            
## [27] "timbre_6_min"             "timbre_6_max"            
## [29] "timbre_7_min"             "timbre_7_max"            
## [31] "timbre_8_min"             "timbre_8_max"            
## [33] "timbre_9_min"             "timbre_9_max"            
## [35] "timbre_10_min"            "timbre_10_max"           
## [37] "timbre_11_min"            "timbre_11_max"           
## [39] "top10"

Create models

You need to designate the independent and dependent variables prior to applying your modeling algorithms. Because you’re trying to predict the ‘top10’ field, this would be your dependent variable and everything else would be independent.

Create your first model using GLM. Because GLM works best with numeric data, you create your model by dropping non-numeric variables. You only use the variables in the dataset that describe the numerical attributes of the song in the logistic regression model. You won’t use these variables:  “year”, “songtitle”, “artistname”, “songid”, or “artistid”.

y.dep <- 39
x.indep <- c(6:38)
x.indep
##  [1]  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
## [24] 29 30 31 32 33 34 35 36 37 38

Create Model 1: All numeric variables

Create Model 1 with the training dataset, using GLM as the modeling algorithm and H2O’s built-in h2o.glm function.

modelh1 <- h2o.glm( y = y.dep, x = x.indep, training_frame = train.h2o, family = "binomial")
## 
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  |=====                                                            |   8%
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Measure the performance of Model 1, using H2O’s built-in performance function.

h2o.performance(model=modelh1,newdata=test.h2o)
## H2OBinomialMetrics: glm
## 
## MSE:  0.09924684
## RMSE:  0.3150347
## LogLoss:  0.3220267
## Mean Per-Class Error:  0.2380168
## AUC:  0.8431394
## Gini:  0.6862787
## R^2:  0.254663
## Null Deviance:  326.0801
## Residual Deviance:  240.2319
## AIC:  308.2319
## 
## Confusion Matrix (vertical: actual; across: predicted) for F1-optimal threshold:
##          0   1    Error     Rate
## 0      255  59 0.187898  =59/314
## 1       17  42 0.288136   =17/59
## Totals 272 101 0.203753  =76/373
## 
## Maximum Metrics: Maximum metrics at their respective thresholds
##                         metric threshold    value idx
## 1                       max f1  0.192772 0.525000 100
## 2                       max f2  0.124912 0.650510 155
## 3                 max f0point5  0.416258 0.612903  23
## 4                 max accuracy  0.416258 0.879357  23
## 5                max precision  0.813396 1.000000   0
## 6                   max recall  0.037579 1.000000 282
## 7              max specificity  0.813396 1.000000   0
## 8             max absolute_mcc  0.416258 0.455251  23
## 9   max min_per_class_accuracy  0.161402 0.738854 125
## 10 max mean_per_class_accuracy  0.124912 0.765006 155
## 
## Gains/Lift Table: Extract with `h2o.gainsLift(<model>, <data>)` or ` 
h2o.auc(h2o.performance(modelh1,test.h2o)) 
## [1] 0.8431394

The AUC metric provides insight into how well the classifier is able to separate the two classes. In this case, the value of 0.8431394 indicates that the classification is good. (A value of 0.5 indicates a worthless test, while a value of 1.0 indicates a perfect test.)

Next, inspect the coefficients of the variables in the dataset.

dfmodelh1 <- as.data.frame(h2o.varimp(modelh1))
dfmodelh1
##                       names coefficients sign
## 1              timbre_0_max  1.290938663  NEG
## 2                  loudness  1.262941934  POS
## 3                     pitch  0.616995941  NEG
## 4              timbre_1_min  0.422323735  POS
## 5              timbre_6_min  0.349016024  NEG
## 6                    energy  0.348092062  NEG
## 7             timbre_11_min  0.307331997  NEG
## 8              timbre_3_max  0.302225619  NEG
## 9             timbre_11_max  0.243632060  POS
## 10             timbre_4_min  0.224233951  POS
## 11             timbre_4_max  0.204134342  POS
## 12             timbre_5_min  0.199149324  NEG
## 13             timbre_0_min  0.195147119  POS
## 14 timesignature_confidence  0.179973904  POS
## 15         tempo_confidence  0.144242598  POS
## 16            timbre_10_max  0.137644568  POS
## 17             timbre_7_min  0.126995955  NEG
## 18            timbre_10_min  0.123851179  POS
## 19             timbre_7_max  0.100031481  NEG
## 20             timbre_2_min  0.096127636  NEG
## 21           key_confidence  0.083115820  POS
## 22             timbre_6_max  0.073712419  POS
## 23            timesignature  0.067241917  POS
## 24             timbre_8_min  0.061301881  POS
## 25             timbre_8_max  0.060041698  POS
## 26                      key  0.056158445  POS
## 27             timbre_3_min  0.050825116  POS
## 28             timbre_9_max  0.033733561  POS
## 29             timbre_2_max  0.030939072  POS
## 30             timbre_9_min  0.020708113  POS
## 31             timbre_1_max  0.014228818  NEG
## 32                    tempo  0.008199861  POS
## 33             timbre_5_max  0.004837870  POS
## 34                                    NA <NA>

Typically, songs with heavier instrumentation tend to be louder (have higher values in the variable “loudness”) and more energetic (have higher values in the variable “energy”). This knowledge is helpful for interpreting the modeling results.

You can make the following observations from the results:

  • The coefficient estimates for the confidence values associated with the time signature, key, and tempo variables are positive. This suggests that higher confidence leads to a higher predicted probability of a Top 10 hit.
  • The coefficient estimate for loudness is positive, meaning that mainstream listeners prefer louder songs with heavier instrumentation.
  • The coefficient estimate for energy is negative, meaning that mainstream listeners prefer songs that are less energetic, which are those songs with light instrumentation.

These coefficients lead to contradictory conclusions for Model 1. This could be due to multicollinearity issues. Inspect the correlation between the variables “loudness” and “energy” in the training set.

cor(train.h2o$loudness,train.h2o$energy)
## [1] 0.7399067

This number indicates that these two variables are highly correlated, and Model 1 does indeed suffer from multicollinearity. Typically, you associate a value of -1.0 to -0.5 or 1.0 to 0.5 to indicate strong correlation, and a value of 0.1 to 0.1 to indicate weak correlation. To avoid this correlation issue, omit one of these two variables and re-create the models.

You build two variations of the original model:

  • Model 2, in which you keep “energy” and omit “loudness”
  • Model 3, in which you keep “loudness” and omit “energy”

You compare these two models and choose the model with a better fit for this use case.

Create Model 2: Keep energy and omit loudness

colnames(train.h2o)
##  [1] "year"                     "songtitle"               
##  [3] "artistname"               "songid"                  
##  [5] "artistid"                 "timesignature"           
##  [7] "timesignature_confidence" "loudness"                
##  [9] "tempo"                    "tempo_confidence"        
## [11] "key"                      "key_confidence"          
## [13] "energy"                   "pitch"                   
## [15] "timbre_0_min"             "timbre_0_max"            
## [17] "timbre_1_min"             "timbre_1_max"            
## [19] "timbre_2_min"             "timbre_2_max"            
## [21] "timbre_3_min"             "timbre_3_max"            
## [23] "timbre_4_min"             "timbre_4_max"            
## [25] "timbre_5_min"             "timbre_5_max"            
## [27] "timbre_6_min"             "timbre_6_max"            
## [29] "timbre_7_min"             "timbre_7_max"            
## [31] "timbre_8_min"             "timbre_8_max"            
## [33] "timbre_9_min"             "timbre_9_max"            
## [35] "timbre_10_min"            "timbre_10_max"           
## [37] "timbre_11_min"            "timbre_11_max"           
## [39] "top10"
y.dep <- 39
x.indep <- c(6:7,9:38)
x.indep
##  [1]  6  7  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
## [24] 30 31 32 33 34 35 36 37 38
modelh2 <- h2o.glm( y = y.dep, x = x.indep, training_frame = train.h2o, family = "binomial")
## 
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  |                                                                 |   0%
  |                                                                       
  |=======                                                          |  10%
  |                                                                       
  |=================================================================| 100%

Measure the performance of Model 2.

h2o.performance(model=modelh2,newdata=test.h2o)
## H2OBinomialMetrics: glm
## 
## MSE:  0.09922606
## RMSE:  0.3150017
## LogLoss:  0.3228213
## Mean Per-Class Error:  0.2490554
## AUC:  0.8431933
## Gini:  0.6863867
## R^2:  0.2548191
## Null Deviance:  326.0801
## Residual Deviance:  240.8247
## AIC:  306.8247
## 
## Confusion Matrix (vertical: actual; across: predicted) for F1-optimal threshold:
##          0  1    Error     Rate
## 0      280 34 0.108280  =34/314
## 1       23 36 0.389831   =23/59
## Totals 303 70 0.152815  =57/373
## 
## Maximum Metrics: Maximum metrics at their respective thresholds
##                         metric threshold    value idx
## 1                       max f1  0.254391 0.558140  69
## 2                       max f2  0.113031 0.647208 157
## 3                 max f0point5  0.413999 0.596026  22
## 4                 max accuracy  0.446250 0.876676  18
## 5                max precision  0.811739 1.000000   0
## 6                   max recall  0.037682 1.000000 283
## 7              max specificity  0.811739 1.000000   0
## 8             max absolute_mcc  0.254391 0.469060  69
## 9   max min_per_class_accuracy  0.141051 0.716561 131
## 10 max mean_per_class_accuracy  0.113031 0.761821 157
## 
## Gains/Lift Table: Extract with `h2o.gainsLift(<model>, <data>)` or `h2o.gainsLift(<model>, valid=<T/F>, xval=<T/F>)`
dfmodelh2 <- as.data.frame(h2o.varimp(modelh2))
dfmodelh2
##                       names coefficients sign
## 1                     pitch  0.700331511  NEG
## 2              timbre_1_min  0.510270513  POS
## 3              timbre_0_max  0.402059546  NEG
## 4              timbre_6_min  0.333316236  NEG
## 5             timbre_11_min  0.331647383  NEG
## 6              timbre_3_max  0.252425901  NEG
## 7             timbre_11_max  0.227500308  POS
## 8              timbre_4_max  0.210663865  POS
## 9              timbre_0_min  0.208516163  POS
## 10             timbre_5_min  0.202748055  NEG
## 11             timbre_4_min  0.197246582  POS
## 12            timbre_10_max  0.172729619  POS
## 13         tempo_confidence  0.167523934  POS
## 14 timesignature_confidence  0.167398830  POS
## 15             timbre_7_min  0.142450727  NEG
## 16             timbre_8_max  0.093377516  POS
## 17            timbre_10_min  0.090333426  POS
## 18            timesignature  0.085851625  POS
## 19             timbre_7_max  0.083948442  NEG
## 20           key_confidence  0.079657073  POS
## 21             timbre_6_max  0.076426046  POS
## 22             timbre_2_min  0.071957831  NEG
## 23             timbre_9_max  0.071393189  POS
## 24             timbre_8_min  0.070225578  POS
## 25                      key  0.061394702  POS
## 26             timbre_3_min  0.048384697  POS
## 27             timbre_1_max  0.044721121  NEG
## 28                   energy  0.039698433  POS
## 29             timbre_5_max  0.039469064  POS
## 30             timbre_2_max  0.018461133  POS
## 31                    tempo  0.013279926  POS
## 32             timbre_9_min  0.005282143  NEG
## 33                                    NA <NA>

h2o.auc(h2o.performance(modelh2,test.h2o)) 
## [1] 0.8431933

You can make the following observations:

  • The AUC metric is 0.8431933.
  • Inspecting the coefficient of the variable energy, Model 2 suggests that songs with high energy levels tend to be more popular. This is as per expectation.
  • As H2O orders variables by significance, the variable energy is not significant in this model.

You can conclude that Model 2 is not ideal for this use , as energy is not significant.

CreateModel 3: Keep loudness but omit energy

colnames(train.h2o)
##  [1] "year"                     "songtitle"               
##  [3] "artistname"               "songid"                  
##  [5] "artistid"                 "timesignature"           
##  [7] "timesignature_confidence" "loudness"                
##  [9] "tempo"                    "tempo_confidence"        
## [11] "key"                      "key_confidence"          
## [13] "energy"                   "pitch"                   
## [15] "timbre_0_min"             "timbre_0_max"            
## [17] "timbre_1_min"             "timbre_1_max"            
## [19] "timbre_2_min"             "timbre_2_max"            
## [21] "timbre_3_min"             "timbre_3_max"            
## [23] "timbre_4_min"             "timbre_4_max"            
## [25] "timbre_5_min"             "timbre_5_max"            
## [27] "timbre_6_min"             "timbre_6_max"            
## [29] "timbre_7_min"             "timbre_7_max"            
## [31] "timbre_8_min"             "timbre_8_max"            
## [33] "timbre_9_min"             "timbre_9_max"            
## [35] "timbre_10_min"            "timbre_10_max"           
## [37] "timbre_11_min"            "timbre_11_max"           
## [39] "top10"
y.dep <- 39
x.indep <- c(6:12,14:38)
x.indep
##  [1]  6  7  8  9 10 11 12 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
## [24] 30 31 32 33 34 35 36 37 38
modelh3 <- h2o.glm( y = y.dep, x = x.indep, training_frame = train.h2o, family = "binomial")
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perfh3<-h2o.performance(model=modelh3,newdata=test.h2o)
perfh3
## H2OBinomialMetrics: glm
## 
## MSE:  0.0978859
## RMSE:  0.3128672
## LogLoss:  0.3178367
## Mean Per-Class Error:  0.264925
## AUC:  0.8492389
## Gini:  0.6984778
## R^2:  0.2648836
## Null Deviance:  326.0801
## Residual Deviance:  237.1062
## AIC:  303.1062
## 
## Confusion Matrix (vertical: actual; across: predicted) for F1-optimal threshold:
##          0  1    Error     Rate
## 0      286 28 0.089172  =28/314
## 1       26 33 0.440678   =26/59
## Totals 312 61 0.144772  =54/373
## 
## Maximum Metrics: Maximum metrics at their respective thresholds
##                         metric threshold    value idx
## 1                       max f1  0.273799 0.550000  60
## 2                       max f2  0.125503 0.663265 155
## 3                 max f0point5  0.435479 0.628931  24
## 4                 max accuracy  0.435479 0.882038  24
## 5                max precision  0.821606 1.000000   0
## 6                   max recall  0.038328 1.000000 280
## 7              max specificity  0.821606 1.000000   0
## 8             max absolute_mcc  0.435479 0.471426  24
## 9   max min_per_class_accuracy  0.173693 0.745763 120
## 10 max mean_per_class_accuracy  0.125503 0.775073 155
## 
## Gains/Lift Table: Extract with `h2o.gainsLift(<model>, <data>)` or `h2o.gainsLift(<model>, valid=<T/F>, xval=<T/F>)`
dfmodelh3 <- as.data.frame(h2o.varimp(modelh3))
dfmodelh3
##                       names coefficients sign
## 1              timbre_0_max 1.216621e+00  NEG
## 2                  loudness 9.780973e-01  POS
## 3                     pitch 7.249788e-01  NEG
## 4              timbre_1_min 3.891197e-01  POS
## 5              timbre_6_min 3.689193e-01  NEG
## 6             timbre_11_min 3.086673e-01  NEG
## 7              timbre_3_max 3.025593e-01  NEG
## 8             timbre_11_max 2.459081e-01  POS
## 9              timbre_4_min 2.379749e-01  POS
## 10             timbre_4_max 2.157627e-01  POS
## 11             timbre_0_min 1.859531e-01  POS
## 12             timbre_5_min 1.846128e-01  NEG
## 13 timesignature_confidence 1.729658e-01  POS
## 14             timbre_7_min 1.431871e-01  NEG
## 15            timbre_10_max 1.366703e-01  POS
## 16            timbre_10_min 1.215954e-01  POS
## 17         tempo_confidence 1.183698e-01  POS
## 18             timbre_2_min 1.019149e-01  NEG
## 19           key_confidence 9.109701e-02  POS
## 20             timbre_7_max 8.987908e-02  NEG
## 21             timbre_6_max 6.935132e-02  POS
## 22             timbre_8_max 6.878241e-02  POS
## 23            timesignature 6.120105e-02  POS
## 24                      key 5.814805e-02  POS
## 25             timbre_8_min 5.759228e-02  POS
## 26             timbre_1_max 2.930285e-02  NEG
## 27             timbre_9_max 2.843755e-02  POS
## 28             timbre_3_min 2.380245e-02  POS
## 29             timbre_2_max 1.917035e-02  POS
## 30             timbre_5_max 1.715813e-02  POS
## 31                    tempo 1.364418e-02  NEG
## 32             timbre_9_min 8.463143e-05  NEG
## 33                                    NA <NA>
h2o.sensitivity(perfh3,0.5)
## Warning in h2o.find_row_by_threshold(object, t): Could not find exact
## threshold: 0.5 for this set of metrics; using closest threshold found:
## 0.501855569251422. Run `h2o.predict` and apply your desired threshold on a
## probability column.
## [[1]]
## [1] 0.2033898
h2o.auc(perfh3)
## [1] 0.8492389

You can make the following observations:

  • The AUC metric is 0.8492389.
  • From the confusion matrix, the model correctly predicts that 33 songs will be top 10 hits (true positives). However, it has 26 false positives (songs that the model predicted would be Top 10 hits, but ended up not being Top 10 hits).
  • Loudness has a positive coefficient estimate, meaning that this model predicts that songs with heavier instrumentation tend to be more popular. This is the same conclusion from Model 2.
  • Loudness is significant in this model.

Overall, Model 3 predicts a higher number of top 10 hits with an accuracy rate that is acceptable. To choose the best fit for production runs, record labels should consider the following factors:

  • Desired model accuracy at a given threshold
  • Number of correct predictions for top10 hits
  • Tolerable number of false positives or false negatives

Next, make predictions using Model 3 on the test dataset.

predict.regh <- h2o.predict(modelh3, test.h2o)
## 
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print(predict.regh)
##   predict        p0          p1
## 1       0 0.9654739 0.034526052
## 2       0 0.9654748 0.034525236
## 3       0 0.9635547 0.036445318
## 4       0 0.9343579 0.065642149
## 5       0 0.9978334 0.002166601
## 6       0 0.9779949 0.022005078
## 
## [373 rows x 3 columns]
predict.regh$predict
##   predict
## 1       0
## 2       0
## 3       0
## 4       0
## 5       0
## 6       0
## 
## [373 rows x 1 column]
dpr<-as.data.frame(predict.regh)
#Rename the predicted column 
colnames(dpr)[colnames(dpr) == 'predict'] <- 'predict_top10'
table(dpr$predict_top10)
## 
##   0   1 
## 312  61

The first set of output results specifies the probabilities associated with each predicted observation.  For example, observation 1 is 96.54739% likely to not be a Top 10 hit, and 3.4526052% likely to be a Top 10 hit (predict=1 indicates Top 10 hit and predict=0 indicates not a Top 10 hit).  The second set of results list the actual predictions made.  From the third set of results, this model predicts that 61 songs will be top 10 hits.

Compute the baseline accuracy, by assuming that the baseline predicts the most frequent outcome, which is that most songs are not Top 10 hits.

table(BillboardTest$top10)
## 
##   0   1 
## 314  59

Now observe that the baseline model would get 314 observations correct, and 59 wrong, for an accuracy of 314/(314+59) = 0.8418231.

It seems that Model 3, with an accuracy of 0.8552, provides you with a small improvement over the baseline model. But is this model useful for record labels?

View the two models from an investment perspective:

  • A production company is interested in investing in songs that are more likely to make it to the Top 10. The company’s objective is to minimize the risk of financial losses attributed to investing in songs that end up unpopular.
  • How many songs does Model 3 correctly predict as a Top 10 hit in 2010? Looking at the confusion matrix, you see that it predicts 33 top 10 hits correctly at an optimal threshold, which is more than half the number
  • It will be more useful to the record label if you can provide the production company with a list of songs that are highly likely to end up in the Top 10.
  • The baseline model is not useful, as it simply does not label any song as a hit.

Considering the three models built so far, you can conclude that Model 3 proves to be the best investment choice for the record label.

GBM model

H2O provides you with the ability to explore other learning models, such as GBM and deep learning. Explore building a model using the GBM technique, using the built-in h2o.gbm function.

Before you do this, you need to convert the target variable to a factor for multinomial classification techniques.

train.h2o$top10=as.factor(train.h2o$top10)
gbm.modelh <- h2o.gbm(y=y.dep, x=x.indep, training_frame = train.h2o, ntrees = 500, max_depth = 4, learn_rate = 0.01, seed = 1122,distribution="multinomial")
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perf.gbmh<-h2o.performance(gbm.modelh,test.h2o)
perf.gbmh
## H2OBinomialMetrics: gbm
## 
## MSE:  0.09860778
## RMSE:  0.3140188
## LogLoss:  0.3206876
## Mean Per-Class Error:  0.2120263
## AUC:  0.8630573
## Gini:  0.7261146
## 
## Confusion Matrix (vertical: actual; across: predicted) for F1-optimal threshold:
##          0  1    Error     Rate
## 0      266 48 0.152866  =48/314
## 1       16 43 0.271186   =16/59
## Totals 282 91 0.171582  =64/373
## 
## Maximum Metrics: Maximum metrics at their respective thresholds
##                       metric threshold    value idx
## 1                     max f1  0.189757 0.573333  90
## 2                     max f2  0.130895 0.693717 145
## 3               max f0point5  0.327346 0.598802  26
## 4               max accuracy  0.442757 0.876676  14
## 5              max precision  0.802184 1.000000   0
## 6                 max recall  0.049990 1.000000 284
## 7            max specificity  0.802184 1.000000   0
## 8           max absolute_mcc  0.169135 0.496486 104
## 9 max min_per_class_accuracy  0.169135 0.796610 104
## 10 max mean_per_class_accuracy  0.169135 0.805948 104
## 
## Gains/Lift Table: Extract with `h2o.gainsLift(<model>, <data>)` or `
h2o.sensitivity(perf.gbmh,0.5)
## Warning in h2o.find_row_by_threshold(object, t): Could not find exact
## threshold: 0.5 for this set of metrics; using closest threshold found:
## 0.501205344484314. Run `h2o.predict` and apply your desired threshold on a
## probability column.
## [[1]]
## [1] 0.1355932
h2o.auc(perf.gbmh)
## [1] 0.8630573

This model correctly predicts 43 top 10 hits, which is 10 more than the number predicted by Model 3. Moreover, the AUC metric is higher than the one obtained from Model 3.

As seen above, H2O’s API provides the ability to obtain key statistical measures required to analyze the models easily, using several built-in functions. The record label can experiment with different parameters to arrive at the model that predicts the maximum number of Top 10 hits at the desired level of accuracy and threshold.

H2O also allows you to experiment with deep learning models. Deep learning models have the ability to learn features implicitly, but can be more expensive computationally.

Now, create a deep learning model with the h2o.deeplearning function, using the same training and test datasets created before. The time taken to run this model depends on the type of EC2 instance chosen for this purpose.  For models that require more computation, consider using accelerated computing instances such as the P2 instance type.

system.time(
  dlearning.modelh <- h2o.deeplearning(y = y.dep,
                                      x = x.indep,
                                      training_frame = train.h2o,
                                      epoch = 250,
                                      hidden = c(250,250),
                                      activation = "Rectifier",
                                      seed = 1122,
                                      distribution="multinomial"
  )
)
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##    user  system elapsed 
##   1.216   0.020 166.508
perf.dl<-h2o.performance(model=dlearning.modelh,newdata=test.h2o)
perf.dl
## H2OBinomialMetrics: deeplearning
## 
## MSE:  0.1678359
## RMSE:  0.4096778
## LogLoss:  1.86509
## Mean Per-Class Error:  0.3433013
## AUC:  0.7568822
## Gini:  0.5137644
## 
## Confusion Matrix (vertical: actual; across: predicted) for F1-optimal threshold:
##          0  1    Error     Rate
## 0      290 24 0.076433  =24/314
## 1       36 23 0.610169   =36/59
## Totals 326 47 0.160858  =60/373
## 
## Maximum Metrics: Maximum metrics at their respective thresholds
##                       metric threshold    value idx
## 1                     max f1  0.826267 0.433962  46
## 2                     max f2  0.000000 0.588235 239
## 3               max f0point5  0.999929 0.511811  16
## 4               max accuracy  0.999999 0.865952  10
## 5              max precision  1.000000 1.000000   0
## 6                 max recall  0.000000 1.000000 326
## 7            max specificity  1.000000 1.000000   0
## 8           max absolute_mcc  0.999929 0.363219  16
## 9 max min_per_class_accuracy  0.000004 0.662420 145
## 10 max mean_per_class_accuracy  0.000000 0.685334 224
## 
## Gains/Lift Table: Extract with `h2o.gainsLift(<model>, <data>)` or `h2o.gainsLift(<model>, valid=<T/F>, xval=<T/F>)`
h2o.sensitivity(perf.dl,0.5)
## Warning in h2o.find_row_by_threshold(object, t): Could not find exact
## threshold: 0.5 for this set of metrics; using closest threshold found:
## 0.496293348880151. Run `h2o.predict` and apply your desired threshold on a
## probability column.
## [[1]]
## [1] 0.3898305
h2o.auc(perf.dl)
## [1] 0.7568822

The AUC metric for this model is 0.7568822, which is less than what you got from the earlier models. I recommend further experimentation using different hyper parameters, such as the learning rate, epoch or the number of hidden layers.

H2O’s built-in functions provide many key statistical measures that can help measure model performance. Here are some of these key terms.

Metric Description
Sensitivity Measures the proportion of positives that have been correctly identified. It is also called the true positive rate, or recall.
Specificity Measures the proportion of negatives that have been correctly identified. It is also called the true negative rate.
Threshold Cutoff point that maximizes specificity and sensitivity. While the model may not provide the highest prediction at this point, it would not be biased towards positives or negatives.
Precision The fraction of the documents retrieved that are relevant to the information needed, for example, how many of the positively classified are relevant
AUC

Provides insight into how well the classifier is able to separate the two classes. The implicit goal is to deal with situations where the sample distribution is highly skewed, with a tendency to overfit to a single class.

0.90 – 1 = excellent (A)

0.8 – 0.9 = good (B)

0.7 – 0.8 = fair (C)

.6 – 0.7 = poor (D)

0.5 – 0.5 = fail (F)

Here’s a summary of the metrics generated from H2O’s built-in functions for the three models that produced useful results.

Metric Model 3 GBM Model Deep Learning Model

Accuracy

(max)

0.882038

(t=0.435479)

0.876676

(t=0.442757)

0.865952

(t=0.999999)

Precision

(max)

1.0

(t=0.821606)

1.0

(t=0802184)

1.0

(t=1.0)

Recall

(max)

1.0 1.0

1.0

(t=0)

Specificity

(max)

1.0 1.0

1.0

(t=1)

Sensitivity

 

0.2033898 0.1355932

0.3898305

(t=0.5)

AUC 0.8492389 0.8630573 0.756882

Note: ‘t’ denotes threshold.

Your options at this point could be narrowed down to Model 3 and the GBM model, based on the AUC and accuracy metrics observed earlier.  If the slightly lower accuracy of the GBM model is deemed acceptable, the record label can choose to go to production with the GBM model, as it can predict a higher number of Top 10 hits.  The AUC metric for the GBM model is also higher than that of Model 3.

Record labels can experiment with different learning techniques and parameters before arriving at a model that proves to be the best fit for their business. Because deep learning models can be computationally expensive, record labels can choose more powerful EC2 instances on AWS to run their experiments faster.

Conclusion

In this post, I showed how the popular music industry can use analytics to predict the type of songs that make the Top 10 Billboard charts. By running H2O’s scalable machine learning platform on AWS, data scientists can easily experiment with multiple modeling techniques and interactively query the data using Amazon Athena, without having to manage the underlying infrastructure. This helps record labels make critical decisions on the type of artists and songs to promote in a timely fashion, thereby increasing sales and revenue.

If you have questions or suggestions, please comment below.


Additional Reading

Learn how to build and explore a simple geospita simple GEOINT application using SparkR.


About the Authors

gopalGopal Wunnava is a Partner Solution Architect with the AWS GSI Team. He works with partners and customers on big data engagements, and is passionate about building analytical solutions that drive business capabilities and decision making. In his spare time, he loves all things sports and movies related and is fond of old classics like Asterix, Obelix comics and Hitchcock movies.

 

 

Bob Strahan, a Senior Consultant with AWS Professional Services, contributed to this post.

 

 

Pirate Bay is Mining Cryptocurrency Again, No Opt Out

Post Syndicated from Ernesto original https://torrentfreak.com/pirate-bay-is-mining-cryptocurrency-again-no-opt-out-171011/

Last month The Pirate Bay caused some uproar by adding a Javascript-based cryptocurrency miner to its website.

The miner utilizes CPU power from visitors to generate Monero coins for the site, providing an extra source of revenue.

The Pirate Bay only tested the option briefly, but that was enough to inspire many others to follow suit. Now, a few weeks later, Pirate Bay has also turned on the miners again.

The miner is not directly embedded in the site’s core code but runs through an ad script. Many ad blockers and anti-malware tools are stopping these request, but people who don’t use any will see a clear spike in CPU usage when they access the site.

The Pirate Bay team previously said that they were testing the miner to see if it can replace ads. While there is some real revenue potential, for now, it’s running in addition to the regular banners. It’s unclear whether the current mining period is another test or if it will run permanently from now on.

The miner does appear to be throttled to a certain degree, so most users might not even notice that it’s running.

Pirate Bay load requests

Running a cryptocurrency miner such as the Coin-Hive script TPB is currently using is not without risk. Aside from user complaints, there is an issue that may make it harder for the site to operate in the future.

Last week we reported that CDN provider Cloudflare had suspended the account of torrent proxy site ProxyBunker, flagging its coin miner as malware. This means that The Pirate Bay now risks losing the Cloudflare service, which they rely on for DDoS protection, among other things.

Cloudflare’s suspension of ProxyBunker occurred even though the site provided users with an option to disable the miner. This functionality was implemented by Coinhive after the script was misused by some sites, which ran it without alerting their users.

The Pirate Bay currently has no opt-out option, nor has it informed users about the latest mining efforts. This could lead to another problem since Coinhive said it would crack down on customers who failed to keep users in the loop.

“We will verify this opt-in on our servers and will implement it in a way that it can not be circumvented. We will pledge to keep the opt-in intact at all times, without exceptions,” the Coinhive team previously noted.

The Pirate Bay team has not commented on the issue thus far. In theory, it’s possible that a rogue advertiser is responsible for the latest mining efforts. If that’s the case it will be disabled soon enough.

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

Weekly roundup: Slow start

Post Syndicated from Eevee original https://eev.ee/dev/2017/10/08/weekly-roundup-slow-start/

Getting back up to speed, finishing getting my computer back how it was, etc. Also we got a SNES Classic and Stardew Valley so, those have been things. But between all that, I somehow found time to do a microscopic amount of actual work!

  • art: Sketched some stuff! It wasn’t very good. Need to do this more often.

  • fox flux: Finally, after a great many attempts, I drew a pixel art bush I’m fairly happy with. And yet, I can already see ways to improve it! But hey I’m learning stuff and that’s really cool. I’ve been working on a much larger pixel art forest background, too, which is proving a little harder to figure out.

  • blog: After a long period of silence, I wrote about how JavaScript has gotten a bit better lately. More words to come, probably!

I’ve got some high aspirations for the month, so I’m gonna get to it and definitely not go visit my video game chickens.

JavaScript got better while I wasn’t looking

Post Syndicated from Eevee original https://eev.ee/blog/2017/10/07/javascript-got-better-while-i-wasnt-looking/

IndustrialRobot has generously donated in order to inquire:

In the last few years there seems to have been a lot of activity with adding emojis to Unicode. Has there been an equal effort to add ‘real’ languages/glyph systems/etc?

And as always, if you don’t have anything to say on that topic, feel free to choose your own. :p

Yes.

I mean, each release of Unicode lists major new additions right at the top — Unicode 10, Unicode 9, Unicode 8, etc. They also keep fastidious notes, so you can also dig into how and why these new scripts came from, by reading e.g. the proposal for the addition of Zanabazar Square. I don’t think I have much to add here; I’m not a real linguist, I only play one on TV.

So with that out of the way, here’s something completely different!

A brief history of JavaScript

JavaScript was created in seven days, about eight thousand years ago. It was pretty rough, and it stayed rough for most of its life. But that was fine, because no one used it for anything besides having a trail of sparkles follow your mouse on their Xanga profile.

Then people discovered you could actually do a handful of useful things with JavaScript, and it saw a sharp uptick in usage. Alas, it stayed pretty rough. So we came up with polyfills and jQuerys and all kinds of miscellaneous things that tried to smooth over the rough parts, to varying degrees of success.

And… that’s it. That’s pretty much how things stayed for a while.


I have complicated feelings about JavaScript. I don’t hate it… but I certainly don’t enjoy it, either. It has some pretty neat ideas, like prototypical inheritance and “everything is a value”, but it buries them under a pile of annoying quirks and a woefully inadequate standard library. The DOM APIs don’t make things much better — they seem to be designed as though the target language were Java, rarely taking advantage of any interesting JavaScript features. And the places where the APIs overlap with the language are a hilarious mess: I have to check documentation every single time I use any API that returns a set of things, because there are at least three totally different conventions for handling that and I can’t keep them straight.

The funny thing is that I’ve been fairly happy to work with Lua, even though it shares most of the same obvious quirks as JavaScript. Both languages are weakly typed; both treat nonexistent variables and keys as simply false values, rather than errors; both have a single data structure that doubles as both a list and a map; both use 64-bit floating-point as their only numeric type (though Lua added integers very recently); both lack a standard object model; both have very tiny standard libraries. Hell, Lua doesn’t even have exceptions, not really — you have to fake them in much the same style as Perl.

And yet none of this bothers me nearly as much in Lua. The differences between the languages are very subtle, but combined they make a huge impact.

  • Lua has separate operators for addition and concatenation, so + is never ambiguous. It also has printf-style string formatting in the standard library.

  • Lua’s method calls are syntactic sugar: foo:bar() just means foo.bar(foo). Lua doesn’t even have a special this or self value; the invocant just becomes the first argument. In contrast, JavaScript invokes some hand-waved magic to set its contextual this variable, which has led to no end of confusion.

  • Lua has an iteration protocol, as well as built-in iterators for dealing with list-style or map-style data. JavaScript has a special dedicated Array type and clumsy built-in iteration syntax.

  • Lua has operator overloading and (surprisingly flexible) module importing.

  • Lua allows the keys of a map to be any value (though non-scalars are always compared by identity). JavaScript implicitly converts keys to strings — and since there’s no operator overloading, there’s no way to natively fix this.

These are fairly minor differences, in the grand scheme of language design. And almost every feature in Lua is implemented in a ridiculously simple way; in fact the entire language is described in complete detail in a single web page. So writing JavaScript is always frustrating for me: the language is so close to being much more ergonomic, and yet, it isn’t.

Or, so I thought. As it turns out, while I’ve been off doing other stuff for a few years, browser vendors have been implementing all this pie-in-the-sky stuff from “ES5” and “ES6”, whatever those are. People even upgrade their browsers now. Lo and behold, the last time I went to write JavaScript, I found out that a number of papercuts had actually been solved, and the solutions were sufficiently widely available that I could actually use them in web code.

The weird thing is that I do hear a lot about JavaScript, but the feature I’ve seen raved the most about by far is probably… built-in types for working with arrays of bytes? That’s cool and all, but not exactly the most pressing concern for me.

Anyway, if you also haven’t been keeping tabs on the world of JavaScript, here are some things we missed.

let

MDN docs — supported in Firefox 44, Chrome 41, IE 11, Safari 10

I’m pretty sure I first saw let over a decade ago. Firefox has supported it for ages, but you actually had to opt in by specifying JavaScript version 1.7. Remember JavaScript versions? You know, from back in the days when people actually suggested you write stuff like this:

1
<SCRIPT LANGUAGE="JavaScript1.2" TYPE="text/javascript">

Yikes.

Anyway, so, let declares a variable — but scoped to the immediately containing block, unlike var, which scopes to the innermost function. The trouble with var was that it was very easy to make misleading:

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// foo exists here
while (true) {
    var foo = ...;
    ...
}
// foo exists here too

If you reused the same temporary variable name in a different block, or if you expected to be shadowing an outer foo, or if you were trying to do something with creating closures in a loop, this would cause you some trouble.

But no more, because let actually scopes the way it looks like it should, the way variable declarations do in C and friends. As an added bonus, if you refer to a variable declared with let outside of where it’s valid, you’ll get a ReferenceError instead of a silent undefined value. Hooray!

There’s one other interesting quirk to let that I can’t find explicitly documented. Consider:

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let closures = [];
for (let i = 0; i < 4; i++) {
    closures.push(function() { console.log(i); });
}
for (let j = 0; j < closures.length; j++) {
    closures[j]();
}

If this code had used var i, then it would print 4 four times, because the function-scoped var i means each closure is sharing the same i, whose final value is 4. With let, the output is 0 1 2 3, as you might expect, because each run through the loop gets its own i.

But wait, hang on.

The semantics of a C-style for are that the first expression is only evaluated once, at the very beginning. So there’s only one let i. In fact, it makes no sense for each run through the loop to have a distinct i, because the whole idea of the loop is to modify i each time with i++.

I assume this is simply a special case, since it’s what everyone expects. We expect it so much that I can’t find anyone pointing out that the usual explanation for why it works makes no sense. It has the interesting side effect that for no longer de-sugars perfectly to a while, since this will print all 4s:

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closures = [];
let i = 0;
while (i < 4) {
    closures.push(function() { console.log(i); });
    i++;
}
for (let j = 0; j < closures.length; j++) {
    closures[j]();
}

This isn’t a problem — I’m glad let works this way! — it just stands out to me as interesting. Lua doesn’t need a special case here, since it uses an iterator protocol that produces values rather than mutating a visible state variable, so there’s no problem with having the loop variable be truly distinct on each run through the loop.

Classes

MDN docs — supported in Firefox 45, Chrome 42, Safari 9, Edge 13

Prototypical inheritance is pretty cool. The way JavaScript presents it is a little bit opaque, unfortunately, which seems to confuse a lot of people. JavaScript gives you enough functionality to make it work, and even makes it sound like a first-class feature with a property outright called prototype… but to actually use it, you have to do a bunch of weird stuff that doesn’t much look like constructing an object or type.

The funny thing is, people with almost any background get along with Python just fine, and Python uses prototypical inheritance! Nobody ever seems to notice this, because Python tucks it neatly behind a class block that works enough like a Java-style class. (Python also handles inheritance without using the prototype, so it’s a little different… but I digress. Maybe in another post.)

The point is, there’s nothing fundamentally wrong with how JavaScript handles objects; the ergonomics are just terrible.

Lo! They finally added a class keyword. Or, rather, they finally made the class keyword do something; it’s been reserved this entire time.

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class Vector {
    constructor(x, y) {
        this.x = x;
        this.y = y;
    }

    get magnitude() {
        return Math.sqrt(this.x * this.x + this.y * this.y);
    }

    dot(other) {
        return this.x * other.x + this.y * other.y;
    }
}

This is all just sugar for existing features: creating a Vector function to act as the constructor, assigning a function to Vector.prototype.dot, and whatever it is you do to make a property. (Oh, there are properties. I’ll get to that in a bit.)

The class block can be used as an expression, with or without a name. It also supports prototypical inheritance with an extends clause and has a super pseudo-value for superclass calls.

It’s a little weird that the inside of the class block has its own special syntax, with function omitted and whatnot, but honestly you’d have a hard time making a class block without special syntax.

One severe omission here is that you can’t declare values inside the block, i.e. you can’t just drop a bar = 3; in there if you want all your objects to share a default attribute. The workaround is to just do this.bar = 3; inside the constructor, but I find that unsatisfying, since it defeats half the point of using prototypes.

Properties

MDN docs — supported in Firefox 4, Chrome 5, IE 9, Safari 5.1

JavaScript historically didn’t have a way to intercept attribute access, which is a travesty. And by “intercept attribute access”, I mean that you couldn’t design a value foo such that evaluating foo.bar runs some code you wrote.

Exciting news: now it does. Or, rather, you can intercept specific attributes, like in the class example above. The above magnitude definition is equivalent to:

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Object.defineProperty(Vector.prototype, 'magnitude', {
    configurable: true,
    enumerable: true,
    get: function() {
        return Math.sqrt(this.x * this.x + this.y * this.y);
    },
});

Beautiful.

And what even are these configurable and enumerable things? It seems that every single key on every single object now has its own set of three Boolean twiddles:

  • configurable means the property itself can be reconfigured with another call to Object.defineProperty.
  • enumerable means the property appears in for..in or Object.keys().
  • writable means the property value can be changed, which only applies to properties with real values rather than accessor functions.

The incredibly wild thing is that for properties defined by Object.defineProperty, configurable and enumerable default to false, meaning that by default accessor properties are immutable and invisible. Super weird.

Nice to have, though. And luckily, it turns out the same syntax as in class also works in object literals.

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Vector.prototype = {
    get magnitude() {
        return Math.sqrt(this.x * this.x + this.y * this.y);
    },
    ...
};

Alas, I’m not aware of a way to intercept arbitrary attribute access.

Another feature along the same lines is Object.seal(), which marks all of an object’s properties as non-configurable and prevents any new properties from being added to the object. The object is still mutable, but its “shape” can’t be changed. And of course you can just make the object completely immutable if you want, via setting all its properties non-writable, or just using Object.freeze().

I have mixed feelings about the ability to irrevocably change something about a dynamic runtime. It would certainly solve some gripes of former Haskell-minded colleagues, and I don’t have any compelling argument against it, but it feels like it violates some unwritten contract about dynamic languages — surely any structural change made by user code should also be able to be undone by user code?

Slurpy arguments

MDN docs — supported in Firefox 15, Chrome 47, Edge 12, Safari 10

Officially this feature is called “rest parameters”, but that’s a terrible name, no one cares about “arguments” vs “parameters”, and “slurpy” is a good word. Bless you, Perl.

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function foo(a, b, ...args) {
    // ...
}

Now you can call foo with as many arguments as you want, and every argument after the second will be collected in args as a regular array.

You can also do the reverse with the spread operator:

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let args = [];
args.push(1);
args.push(2);
args.push(3);
foo(...args);

It even works in array literals, even multiple times:

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let args2 = [...args, ...args];
console.log(args2);  // [1, 2, 3, 1, 2, 3]

Apparently there’s also a proposal for allowing the same thing with objects inside object literals.

Default arguments

MDN docs — supported in Firefox 15, Chrome 49, Edge 14, Safari 10

Yes, arguments can have defaults now. It’s more like Sass than Python — default expressions are evaluated once per call, and later default expressions can refer to earlier arguments. I don’t know how I feel about that but whatever.

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function foo(n = 1, m = n + 1, list = []) {
    ...
}

Also, unlike Python, you can have an argument with a default and follow it with an argument without a default, since the default default (!) is and always has been defined as undefined. Er, let me just write it out.

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function bar(a = 5, b) {
    ...
}

Arrow functions

MDN docs — supported in Firefox 22, Chrome 45, Edge 12, Safari 10

Perhaps the most humble improvement is the arrow function. It’s a slightly shorter way to write an anonymous function.

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(a, b, c) => { ... }
a => { ... }
() => { ... }

An arrow function does not set this or some other magical values, so you can safely use an arrow function as a quick closure inside a method without having to rebind this. Hooray!

Otherwise, arrow functions act pretty much like regular functions; you can even use all the features of regular function signatures.

Arrow functions are particularly nice in combination with all the combinator-style array functions that were added a while ago, like Array.forEach.

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[7, 8, 9].forEach(value => {
    console.log(value);
});

Symbol

MDN docs — supported in Firefox 36, Chrome 38, Edge 12, Safari 9

This isn’t quite what I’d call an exciting feature, but it’s necessary for explaining the next one. It’s actually… extremely weird.

symbol is a new kind of primitive (like number and string), not an object (like, er, Number and String). A symbol is created with Symbol('foo'). No, not new Symbol('foo'); that throws a TypeError, for, uh, some reason.

The only point of a symbol is as a unique key. You see, symbols have one very special property: they can be used as object keys, and will not be stringified. Remember, only strings can be keys in JavaScript — even the indices of an array are, semantically speaking, still strings. Symbols are a new exception to this rule.

Also, like other objects, two symbols don’t compare equal to each other: Symbol('foo') != Symbol('foo').

The result is that symbols solve one of the problems that plauges most object systems, something I’ve talked about before: interfaces. Since an interface might be implemented by any arbitrary type, and any arbitrary type might want to implement any number of arbitrary interfaces, all the method names on an interface are effectively part of a single global namespace.

I think I need to take a moment to justify that. If you have IFoo and IBar, both with a method called method, and you want to implement both on the same type… you have a problem. Because most object systems consider “interface” to mean “I have a method called method, with no way to say which interface’s method you mean. This is a hard problem to avoid, because IFoo and IBar might not even come from the same library. Occasionally languages offer a clumsy way to “rename” one method or the other, but the most common approach seems to be for interface designers to avoid names that sound “too common”. You end up with redundant mouthfuls like IFoo.foo_method.

This incredibly sucks, and the only languages I’m aware of that avoid the problem are the ML family and Rust. In Rust, you define all the methods for a particular trait (interface) in a separate block, away from the type’s “own” methods. It’s pretty slick. You can still do obj.method(), and as long as there’s only one method among all the available traits, you’ll get that one. If not, there’s syntax for explicitly saying which trait you mean, which I can’t remember because I’ve never had to use it.

Symbols are JavaScript’s answer to this problem. If you want to define some interface, you can name its methods with symbols, which are guaranteed to be unique. You just have to make sure you keep the symbol around somewhere accessible so other people can actually use it. (Or… not?)

The interesting thing is that JavaScript now has several of its own symbols built in, allowing user objects to implement features that were previously reserved for built-in types. For example, you can use the Symbol.hasInstance symbol — which is simply where the language is storing an existing symbol and is not the same as Symbol('hasInstance')! — to override instanceof:

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// oh my god don't do this though
class EvenNumber {
    static [Symbol.hasInstance](obj) {
        return obj % 2 == 0;
    }
}
console.log(2 instanceof EvenNumber);  // true
console.log(3 instanceof EvenNumber);  // false

Oh, and those brackets around Symbol.hasInstance are a sort of reverse-quoting — they indicate an expression to use where the language would normally expect a literal identifier. I think they work as object keys, too, and maybe some other places.

The equivalent in Python is to implement a method called __instancecheck__, a name which is not special in any way except that Python has reserved all method names of the form __foo__. That’s great for Python, but doesn’t really help user code. JavaScript has actually outclassed (ho ho) Python here.

Of course, obj[BobNamespace.some_method]() is not the prettiest way to call an interface method, so it’s not perfect. I imagine this would be best implemented in user code by exposing a polymorphic function, similar to how Python’s len(obj) pretty much just calls obj.__len__().

I only bring this up because it’s the plumbing behind one of the most incredible things in JavaScript that I didn’t even know about until I started writing this post. I’m so excited oh my gosh. Are you ready? It’s:

Iteration protocol

MDN docs — supported in Firefox 27, Chrome 39, Safari 10; still experimental in Edge

Yes! Amazing! JavaScript has first-class support for iteration! I can’t even believe this.

It works pretty much how you’d expect, or at least, how I’d expect. You give your object a method called Symbol.iterator, and that returns an iterator.

What’s an iterator? It’s an object with a next() method that returns the next value and whether the iterator is exhausted.

Wait, wait, wait a second. Hang on. The method is called next? Really? You didn’t go for Symbol.next? Python 2 did exactly the same thing, then realized its mistake and changed it to __next__ in Python 3. Why did you do this?

Well, anyway. My go-to test of an iterator protocol is how hard it is to write an equivalent to Python’s enumerate(), which takes a list and iterates over its values and their indices. In Python it looks like this:

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for i, value in enumerate(['one', 'two', 'three']):
    print(i, value)
# 0 one
# 1 two
# 2 three

It’s super nice to have, and I’m always amazed when languages with “strong” “support” for iteration don’t have it. Like, C# doesn’t. So if you want to iterate over a list but also need indices, you need to fall back to a C-style for loop. And if you want to iterate over a lazy or arbitrary iterable but also need indices, you need to track it yourself with a counter. Ridiculous.

Here’s my attempt at building it in JavaScript.

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function enumerate(iterable) {
    // Return a new iter*able* object with a Symbol.iterator method that
    // returns an iterator.
    return {
        [Symbol.iterator]: function() {
            let iterator = iterable[Symbol.iterator]();
            let i = 0;

            return {
                next: function() {
                    let nextval = iterator.next();
                    if (! nextval.done) {
                        nextval.value = [i, nextval.value];
                        i++;
                    }
                    return nextval;
                },
            };
        },
    };
}
for (let [i, value] of enumerate(['one', 'two', 'three'])) {
    console.log(i, value);
}
// 0 one
// 1 two
// 2 three

Incidentally, for..of (which iterates over a sequence, unlike for..in which iterates over keys — obviously) is finally supported in Edge 12. Hallelujah.

Oh, and let [i, value] is destructuring assignment, which is also a thing now and works with objects as well. You can even use the splat operator with it! Like Python! (And you can use it in function signatures! Like Python! Wait, no, Python decided that was terrible and removed it in 3…)

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let [x, y, ...others] = ['apple', 'orange', 'cherry', 'banana'];

It’s a Halloween miracle. 🎃

Generators

MDN docs — supported in Firefox 26, Chrome 39, Edge 13, Safari 10

That’s right, JavaScript has goddamn generators now. It’s basically just copying Python and adding a lot of superfluous punctuation everywhere. Not that I’m complaining.

Also, generators are themselves iterable, so I’m going to cut to the chase and rewrite my enumerate() with a generator.

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function enumerate(iterable) {
    return {
        [Symbol.iterator]: function*() {
            let i = 0;
            for (let value of iterable) {
                yield [i, value];
                i++;
            }
        },
    };
}
for (let [i, value] of enumerate(['one', 'two', 'three'])) {
    console.log(i, value);
}
// 0 one
// 1 two
// 2 three

Amazing. function* is a pretty strange choice of syntax, but whatever? I guess it also lets them make yield only act as a keyword inside a generator, for ultimate backwards compatibility.

JavaScript generators support everything Python generators do: yield* yields every item from a subsequence, like Python’s yield from; generators can return final values; you can pass values back into the generator if you iterate it by hand. No, really, I wasn’t kidding, it’s basically just copying Python. It’s great. You could now built asyncio in JavaScript!

In fact, they did that! JavaScript now has async and await. An async function returns a Promise, which is also a built-in type now. Amazing.

Sets and maps

MDN docs for MapMDN docs for Set — supported in Firefox 13, Chrome 38, IE 11, Safari 7.1

I did not save the best for last. This is much less exciting than generators. But still exciting.

The only data structure in JavaScript is the object, a map where the strings are keys. (Or now, also symbols, I guess.) That means you can’t readily use custom values as keys, nor simulate a set of arbitrary objects. And you have to worry about people mucking with Object.prototype, yikes.

But now, there’s Map and Set! Wow.

Unfortunately, because JavaScript, Map couldn’t use the indexing operators without losing the ability to have methods, so you have to use a boring old method-based API. But Map has convenient methods that plain objects don’t, like entries() to iterate over pairs of keys and values. In fact, you can use a map with for..of to get key/value pairs. So that’s nice.

Perhaps more interesting, there’s also now a WeakMap and WeakSet, where the keys are weak references. I don’t think JavaScript had any way to do weak references before this, so that’s pretty slick. There’s no obvious way to hold a weak value, but I guess you could substitute a WeakSet with only one item.

Template literals

MDN docs — supported in Firefox 34, Chrome 41, Edge 12, Safari 9

Template literals are JavaScript’s answer to string interpolation, which has historically been a huge pain in the ass because it doesn’t even have string formatting in the standard library.

They’re just strings delimited by backticks instead of quotes. They can span multiple lines and contain expressions.

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console.log(`one plus
two is ${1 + 2}`);

Someone decided it would be a good idea to allow nesting more sets of backticks inside a ${} expression, so, good luck to syntax highlighters.

However, someone also had the most incredible idea ever, which was to add syntax allowing user code to do the interpolation — so you can do custom escaping, when absolutely necessary, which is virtually never, because “escaping” means you’re building a structured format by slopping strings together willy-nilly instead of using some API that works with the structure.

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// OF COURSE, YOU SHOULDN'T BE DOING THIS ANYWAY; YOU SHOULD BUILD HTML WITH
// THE DOM API AND USE .textContent FOR LITERAL TEXT.  BUT AS AN EXAMPLE:
function html(literals, ...values) {
    let ret = [];
    literals.forEach((literal, i) => {
        if (i > 0) {
            // Is there seriously still not a built-in function for doing this?
            // Well, probably because you SHOULDN'T BE DOING IT
            ret.push(values[i - 1]
                .replace(/&/g, '&amp;')
                .replace(/</g, '&lt;')
                .replace(/>/g, '&gt;')
                .replace(/"/g, '&quot;')
                .replace(/'/g, '&apos;'));
        }
        ret.push(literal);
    });
    return ret.join('');
}
let username = 'Bob<script>';
let result = html`<b>Hello, ${username}!</b>`;
console.log(result);
// <b>Hello, Bob&lt;script&gt;!</b>

It’s a shame this feature is in JavaScript, the language where you are least likely to need it.

Trailing commas

Remember how you couldn’t do this for ages, because ass-old IE considered it a syntax error and would reject the entire script?

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{
    a: 'one',
    b: 'two',
    c: 'three',  // <- THIS GUY RIGHT HERE
}

Well now it’s part of the goddamn spec and if there’s anything in this post you can rely on, it’s this. In fact you can use AS MANY GODDAMN TRAILING COMMAS AS YOU WANT. But only in arrays.

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[1, 2, 3,,,,,,,,,,,,,,,,,,,,,,,,,]

Apparently that has the bizarre side effect of reserving extra space at the end of the array, without putting values there.

And more, probably

Like strict mode, which makes a few silent “errors” be actual errors, forces you to declare variables (no implicit globals!), and forbids the completely bozotic with block.

Or String.trim(), which trims whitespace off of strings.

Or… Math.sign()? That’s new? Seriously? Well, okay.

Or the Proxy type, which lets you customize indexing and assignment and calling. Oh. I guess that is possible, though this is a pretty weird way to do it; why not just use symbol-named methods?

You can write Unicode escapes for astral plane characters in strings (or identifiers!), as \u{XXXXXXXX}.

There’s a const now? I extremely don’t care, just name it in all caps and don’t reassign it, come on.

There’s also a mountain of other minor things, which you can peruse at your leisure via MDN or the ECMAScript compatibility tables (note the links at the top, too).

That’s all I’ve got. I still wouldn’t say I’m a big fan of JavaScript, but it’s definitely making an effort to clean up some goofy inconsistencies and solve common problems. I think I could even write some without yelling on Twitter about it now.

On the other hand, if you’re still stuck supporting IE 10 for some reason… well, er, my condolences.

Cloudflare Bans Sites For Using Cryptocurrency Miners

Post Syndicated from Andy original https://torrentfreak.com/cloudflare-bans-sites-for-using-cryptocurrency-miners-171004/

After years of accepting donations via Bitcoin, last month various ‘pirate’ sites began to generate digital currency revenues in a brand new way.

It all began with The Pirate Bay, which quietly added a Javascript cryptocurrency miner to its main site, something that first manifested itself as a large spike in CPU utilization on the machines of visitors.

The stealth addition to the platform, which its operators later described as a test, was extremely controversial. While many thought of the miner as a cool and innovative way to generate revenue in a secure fashion, a vocal majority expressed a preference for permission being requested first, in case they didn’t want to participate in the program.

Over the past couple of weeks, several other sites have added similar miners, some which ask permission to run and others that do not. While the former probably aren’t considered problematic, the latter are now being viewed as a serious problem by an unexpected player in the ecosystem.

TorrentFreak has learned that popular CDN service Cloudflare, which is often criticized for not being harsh enough on ‘pirate’ sites, is actively suspending the accounts of sites that deploy cryptocurrency miners on their platforms.

“Cloudflare kicked us from their service for using a Coinhive miner,” the operator of ProxyBunker.online informed TF this morning.

ProxyBunker is a site that that links to several other domains that offer unofficial proxy services for the likes of The Pirate Bay, RARBG, KickassTorrents, Torrentz2, and dozens of other sites. It first tested a miner for four days starting September 23. Official implementation began October 1 but was ended last evening, abruptly.

“Late last night, all our domains got deleted off Cloudflare without warning so I emailed Cloudflare to ask what was going on,” the operator explained.

Bye bye

As the email above shows, Cloudflare cited only a “possible” terms of service violation. Further clarification was needed to get to the root of the problem.

So, just a few minutes later, the site operator contacted Cloudflare, acknowledging the suspension but pointing out that the notification email was somewhat vague and didn’t give a reason for the violation. A follow-up email from Cloudflare certainly put some meat on the bones.

“Multiple domains in your account were injecting Coinhive mining code without
notifying users and without any option to disabling [sic] the mining,” wrote Justin Paine, Head of Trust & Safety at Cloudflare.

“We consider this to be malware, and as such the account was suspended, and all domains removed from Cloudflare.”

Cloudflare: Unannounced miners are malware

ProxyBunker’s operator wrote back to Cloudflare explaining that the Coinhive miner had been running on his domains but that his main domain had a way of disabling mining, as per new code made available from Coinhive.

“We were running the miner on our proxybunker.online domain using Coinhive’s new Javacode Simple Miner UI that lets the user stop the miner at anytime and set the CPU speed it mines at,” he told TF.

Nevertheless, some element of the configuration appears to have fallen short of Cloudflare’s standards. So, shortly after Cloudflare’s explanation, the site operator asked if he could be reinstated if he completely removed the miner from his site. The response was a ‘yes’ but with a stern caveat attached.

“We will remove the account suspension, however do note you’ll need to re-sign up the domains as they were removed as a result of the account suspension. Please note — if we discover similar activity again the domains and account will be permanently blocked,” Cloudflare’s Justin warned.

ProxyBunker’s operator says that while he sees the value in cryptocurrency miners, he can understand why people might be opposed to them too. That being said, he would appreciate it if services like Cloudflare published clear guidelines on what is and is not acceptable.

“We do understand that most users will not like the miner using up a bit of their CPU but we do see the full potential as a new revenue stream,” he explains.

“I think third-party services need to post clear information that they’re not allowed on their services, if that’s the case.”

At time of publication, Cloudflare had not responded to TorrentFreak’s requests for comment.

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

Iran Arrests Six Movie Pirates After Rival ‘Licensed’ Pirates Complain

Post Syndicated from Andy original https://torrentfreak.com/iran-arrests-six-movie-pirates-after-rival-licensed-pirates-complain-171003/

Article 23 of Iran’s Copyright law is quite clear. Anyone who publishes, distributes or broadcasts another person’s work without permission “shall be condemned to corrective imprisonment for a period of time not less than six months and not more than three years.”

That being said, not all content receives protection. Since there are no copyright agreements between Iran and the United States, for example, US content is pirated almost at will in the country. Even the government itself has run ‘warez’ servers in the past.

That makes the arrest late last month of six men tied to movie piracy site TinyMoviez all the more unusual. At first view (translated image below), the site looks just like any other streaming portal offering Hollywood movies.

TinyMoviez

Indeed, much of the content comes from abroad, augmented with local Farsi-language subtitles or audio voiceovers.

However, according to a source cited by the Center for Human Rights in Iran (CHRI), the site was targeted because rival pirate sites (which had been licensed to ‘pirate’ by the Iranian government) complained about its unlicensed status.

“In July and August [2017], there was a meeting between a number of Iranian start-up companies and [current Telecommunications Minister Mohammad Javad Azari] Jahromi, who was asked by film and TV series distributors as well as video game developers to help shut down and monitor unlicensed rivals,” a film distributor in Tehran told CHRI.

“The start-ups made the request because they could not compete with a site like TinyMovies,” the source added. “After that meeting, Jahromi was nicknamed the ‘Start-Up Tsar’ because of his supportive comments. They were happy that he became the minister.”

That being said, the announcement from the authorities suggested broader issues, including that the site offered movies (none are singled out) that may be unacceptable by Iranian standards.

“Tehran’s prosecutor, after referral of the case to the Cyberspace corruption and prostitution department, said that the defendants in the case, of whom six were currently detained, produced vagabond and pornographic films and sold them in cyberspace,” Tehran Prosecutor Abbas Jafari Dowlatabadi said in an announcement.

“This gang illegally operated the largest source for downloading Hollywood movies and over the past three years, has distributed 18,000 foreign films and series after dubbing, many of which were indecent and immoral, and thus facilitated by illegitimate funds.”

While the authorities say that TinyMoviez has been taken down, various URLs (including Tinyz.us, ironically) now divert to a new domain, Timoviez2.net. However, at least for the moment, download links seem to be disabled.

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

Backing Up WordPress

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/backing-up-wordpress/

WordPress cloud backup
WordPress logo

WordPress is the most popular CMS (Content Management System) for websites, with almost 30% of all websites in the world using WordPress. That’s a lot of sites — over 350 million!

In this post we’ll talk about the different approaches to keeping the data on your WordPress website safe.


Stop the Presses! (Or the Internet!)

As we were getting ready to publish this post, we received news from UpdraftPlus, one of the biggest WordPress plugin developers, that they are supporting Backblaze B2 as a storage solution for their backup plugin. They shipped the update (1.13.9) this week. This is great news for Backblaze customers! UpdraftPlus is also offering a 20% discount to Backblaze customers wishing to purchase or upgrade to UpdraftPlus Premium. The complete information is below.

UpdraftPlus joins backup plugin developer XCloner — Backup and Restore in supporting Backblaze B2. A third developer, BlogVault, also announced their intent to support Backblaze B2. Contact your favorite WordPress backup plugin developer and urge them to support Backblaze B2, as well.

Now, back to our post…


Your WordPress website data is on a web server that’s most likely located in a large data center. You might wonder why it is necessary to have a backup of your website if it’s in a data center. Website data can be lost in a number of ways, including mistakes by the website owner (been there), hacking, or even domain ownership dispute (I’ve seen it happen more than once). A website backup also can provide a history of changes you’ve made to the website, which can be useful. As an overall strategy, it’s best to have a backup of any data that you can’t afford to lose for personal or business reasons.

Your web hosting company might provide backup services as part of your hosting plan. If you are using their service, you should know where and how often your data is being backed up. You don’t want to find out too late that your backup plan was not adequate.

Sites on WordPress.com are automatically backed up by VaultPress (Automattic), which also is available for self-hosted WordPress installations. If you don’t want the work or decisions involved in managing the hosting for your WordPress site, WordPress.com will handle it for you. You do, however, give up some customization abilities, such as the option to add plugins of your own choice.

Very large and active websites might consider WordPress VIP by Automattic, or another premium WordPress hosting service such as Pagely.com.

This post is about backing up self-hosted WordPress sites, so we’ll focus on those options.

WordPress Backup

Backup strategies for WordPress can be divided into broad categories depending on 1) what you back up, 2) when you back up, and 3) where the data is backed up.

With server data, such as with a WordPress installation, you should plan to have three copies of the data (the 3-2-1 backup strategy). The first is the active data on the WordPress web server, the second is a backup stored on the web server or downloaded to your local computer, and the third should be in another location, such as the cloud.

We’ll talk about the different approaches to backing up WordPress, but we recommend using a WordPress plugin to handle your backups. A backup plugin can automate the task, optimize your backup storage space, and alert you of problems with your backups or WordPress itself. We’ll cover plugins in more detail, below.

What to Back Up?

The main components of your WordPress installation are:

You should decide which of these elements you wish to back up. The database is the top priority, as it contains all your website posts and pages (exclusive of media). Your current theme is important, as it likely contains customizations you’ve made. Following those in priority are any other files you’ve customized or made changes to.

You can choose to back up the WordPress core installation and plugins, if you wish, but these files can be downloaded again if necessary from the source, so you might not wish to include them. You likely have all the media files you use on your website on your local computer (which should be backed up), so it is your choice whether to back these up from the server as well.

If you wish to be able to recreate your entire website easily in case of data loss or disaster, you might choose to back up everything, though on a large website this could be a lot of data.

Generally, you should 1) prioritize any file that you’ve customized that you can’t afford to lose, and 2) decide whether you need a copy of everything in order to get your site back up quickly. These choices will determine your backup method and the amount of storage you need.

A good backup plugin for WordPress enables you to specify which files you wish to back up, and even to create separate backups and schedules for different backup contents. That’s another good reason to use a plugin for backing up WordPress.

When to Back Up?

You can back up manually at any time by using the Export tool in WordPress. This is handy if you wish to do a quick backup of your site or parts of it. Since it is manual, however, it is not a part of a dependable backup plan that should be done regularly. If you wish to use this tool, go to Tools, Export, and select what you wish to back up. The output will be an XML file that uses the WordPress Extended RSS format, also known as WXR. You can create a WXR file that contains all of the information on your site or just portions of the site, such as posts or pages by selecting: All content, Posts, Pages, or Media.
Note: You can use WordPress’s Export tool for sites hosted on WordPress.com, as well.

Export instruction for WordPress

Many of the backup plugins we’ll be discussing later also let you do a manual backup on demand in addition to regularly scheduled or continuous backups.

Note:  Another use of the WordPress Export tool and the WXR file is to transfer or clone your website to another server. Once you have exported the WXR file from the website you wish to transfer from, you can import the WXR file from the Tools, Import menu on the new WordPress destination site. Be aware that there are file size limits depending on the settings on your web server. See the WordPress Codex entry for more information. To make this job easier, you may wish to use one of a number of WordPress plugins designed specifically for this task.

You also can manually back up the WordPress MySQL database using a number of tools or a plugin. The WordPress Codex has good information on this. All WordPress plugins will handle this for you and do it automatically. They also typically include tools for optimizing the database tables, which is just good housekeeping.

A dependable backup strategy doesn’t rely on manual backups, which means you should consider using one of the many backup plugins available either free or for purchase. We’ll talk more about them below.

Which Format To Back Up In?

In addition to the WordPress WXR format, plugins and server tools will use various file formats and compression algorithms to store and compress your backup. You may get to choose between zip, tar, tar.gz, tar.gz2, and others. See The Most Common Archive File Formats for more information on these formats.

Select a format that you know you can access and unarchive should you need access to your backup. All of these formats are standard and supported across operating systems, though you might need to download a utility to access the file.

Where To Back Up?

Once you have your data in a suitable format for backup, where do you back it up to?

We want to have multiple copies of our active website data, so we’ll choose more than one destination for our backup data. The backup plugins we’ll discuss below enable you to specify one or more possible destinations for your backup. The possible destinations for your backup include:

A backup folder on your web server
A backup folder on your web server is an OK solution if you also have a copy elsewhere. Depending on your hosting plan, the size of your site, and what you include in the backup, you may or may not have sufficient disk space on the web server. Some backup plugins allow you to configure the plugin to keep only a certain number of recent backups and delete older ones, saving you disk space on the server.
Email to you
Because email servers have size limitations, the email option is not the best one to use unless you use it to specifically back up just the database or your main theme files.
FTP, SFTP, SCP, WebDAV
FTP, SFTP, SCP, and WebDAV are all widely-supported protocols for transferring files over the internet and can be used if you have access credentials to another server or supported storage device that is suitable for storing a backup.
Sync service (Dropbox, SugarSync, Google Drive, OneDrive)
A sync service is another possible server storage location though it can be a pricier choice depending on the plan you have and how much you wish to store.
Cloud storage (Backblaze B2, Amazon S3, Google Cloud, Microsoft Azure, Rackspace)
A cloud storage service can be an inexpensive and flexible option with pay-as-you go pricing for storing backups and other data.

A good website backup strategy would be to have multiple backups of your website data: one in a backup folder on your web hosting server, one downloaded to your local computer, and one in the cloud, such as with Backblaze B2.

If I had to choose just one of these, I would choose backing up to the cloud because it is geographically separated from both your local computer and your web host, it uses fault-tolerant and redundant data storage technologies to protect your data, and it is available from anywhere if you need to restore your site.

Backup Plugins for WordPress

Probably the easiest and most common way to implement a solid backup strategy for WordPress is to use one of the many backup plugins available for WordPress. Fortunately, there are a number of good ones and are available free or in “freemium” plans in which you can use the free version and pay for more features and capabilities only if you need them. The premium options can give you more flexibility in configuring backups or have additional options for where you can store the backups.

How to Choose a WordPress Backup Plugin

screenshot of WordPress plugins search

When considering which plugin to use, you should take into account a number of factors in making your choice.

Is the plugin actively maintained and up-to-date? You can determine this from the listing in the WordPress Plugin Repository. You also can look at reviews and support comments to get an idea of user satisfaction and how well issues are resolved.

Does the plugin work with your web hosting provider? Generally, well-supported plugins do, but you might want to check to make sure there are no issues with your hosting provider.

Does it support the cloud service or protocol you wish to use? This can be determined from looking at the listing in the WordPress Plugin Repository or on the developer’s website. Developers often will add support for cloud services or other backup destinations based on user demand, so let the developer know if there is a feature or backup destination you’d like them to add to their plugin.

Other features and options to consider in choosing a backup plugin are:

  • Whether encryption of your backup data is available
  • What are the options for automatically deleting backups from the storage destination?
  • Can you globally exclude files, folders, and specific types of files from the backup?
  • Do the options for scheduling automatic backups meet your needs for frequency?
  • Can you exclude/include specific database tables (a good way to save space in your backup)?

WordPress Backup Plugins Review

Let’s review a few of the top choices for WordPress backup plugins.

UpdraftPlus

UpdraftPlus is one of the most popular backup plugins for WordPress with over one million active installations. It is available in both free and Premium versions.

UpdraftPlus just released support for Backblaze B2 Cloud Storage in their 1.13.9 update on September 25. According to the developer, support for Backblaze B2 was the most frequent request for a new storage option for their plugin. B2 support is available in their Premium plugin and as a stand-alone update to their standard product.

Note: The developers of UpdraftPlus are offering a special 20% discount to Backblaze customers on the purchase of UpdraftPlus Premium by using the coupon code backblaze20. The discount is valid until the end of Friday, October 6th, 2017.

screenshot of Backblaze B2 cloud backup for WordPress in UpdraftPlus

XCloner — Backup and Restore

XCloner — Backup and Restore is a useful open-source plugin with many options for backing up WordPress.

XCloner supports B2 Cloud Storage in their free plugin.

screenshot of XCloner WordPress Backblaze B2 backup settings

BlogVault

BlogVault describes themselves as a “complete WordPress backup solution.” They offer a free trial of their paid WordPress backup subscription service that features real-time backups of changes to your WordPress site, as well as many other features.

BlogVault has announced their intent to support Backblaze B2 Cloud Storage in a future update.

screenshot of BlogValut WordPress Backup settings

BackWPup

BackWPup is a popular and free option for backing up WordPress. It supports a number of options for storing your backup, including the cloud, FTP, email, or on your local computer.

screenshot of BackWPup WordPress backup settings

WPBackItUp

WPBackItUp has been around since 2012 and is highly rated. It has both free and paid versions.

screenshot of WPBackItUp WordPress backup settings

VaultPress

VaultPress is part of Automattic’s well-known WordPress product, JetPack. You will need a JetPack subscription plan to use VaultPress. There are different pricing plans with different sets of features.

screenshot of VaultPress backup settings

Backup by Supsystic

Backup by Supsystic supports a number of options for backup destinations, encryption, and scheduling.

screenshot of Backup by Supsystic backup settings

BackupWordPress

BackUpWordPress is an open-source project on Github that has a popular and active following and many positive reviews.

screenshot of BackupWordPress WordPress backup settings

BackupBuddy

BackupBuddy, from iThemes, is the old-timer of backup plugins, having been around since 2010. iThemes knows a lot about WordPress, as they develop plugins, themes, utilities, and provide training in WordPress.

BackupBuddy’s backup includes all WordPress files, all files in the WordPress Media library, WordPress themes, and plugins. BackupBuddy generates a downloadable zip file of the entire WordPress website. Remote storage destinations also are supported.

screenshot of BackupBuddy settings

WordPress and the Cloud

Do you use WordPress and back up to the cloud? We’d like to hear about it. We’d also like to hear whether you are interested in using B2 Cloud Storage for storing media files served by WordPress. If you are, we’ll write about it in a future post.

In the meantime, keep your eye out for new plugins supporting Backblaze B2, or better yet, urge them to support B2 if they’re not already.

The Best Backup Strategy is the One You Use

There are other approaches and tools for backing up WordPress that you might use. If you have an approach that works for you, we’d love to hear about it in the comments.

The post Backing Up WordPress appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Firefox takes a Quantum leap forward with new developer edition (ars technica)

Post Syndicated from ris original https://lwn.net/Articles/734831/rss

Ars technica takes
a look
at the Firefox 57 developer edition. “More important, but less immediately visible, is that Firefox 57 has received a ton of performance enhancement. Project Quantum has several strands to it: Mozilla has developed a new CSS engine, Stylo, that parses CSS files, applies the styling rules to elements on the page, and calculates object sizes and positions. There is also a new rendering engine, WebRender, that uses the GPU to draw the (styled) elements of the page. Compositor combines the individual rendered elements and builds them into a complete page, while Quantum DOM changes how JavaScript runs, especially in background tabs. As well as this new development, there’s a final part, Quantum Flow, which has focused on fixing bugs and adding optimizations to those parts of the browser that aren’t being redeveloped.

WebRender is due to arrive in Firefox 59, but the rest of Quantum is part of Firefox 57.”

Cryptocurrency Miner Targeted by Anti-Virus and Adblock Tools

Post Syndicated from Ernesto original https://torrentfreak.com/cryptocurrency-miner-targeted-by-anti-virus-and-adblock-tools-170926/

Earlier this month The Pirate Bay caused some uproar by adding a Javascript-based cryptocurrency miner to its website.

The miner utilizes CPU power from visitors to generate Monero coins for the site, providing an extra revenue source.

While Pirate Bay only tested the option briefly, it inspired many others to follow suit. Streaming related sites such as Alluc, Vidoza, and Rapidvideo jumped on board, and torrent site Demonoid also ran some tests.

During the weekend, Coinhive’s miner code even appeared on the official website of Showtime. The code was quickly removed and it’s still unclear how it got there, as the company refuses to comment. It’s clear, though, that miners are a hot topic thanks to The Pirate Bay.

The revenue potential is also real. TorrentFreak spoke to Vidoza who say that with 30,000 online users throughout the day (2M unique visitors), they can make between $500 and $600. That’s when the miner is throttled at 50%. Although ads can bring in more, it’s not insignificant.

That said, all the uproar about cryptocurrency miners and their possible abuse has also attracted the attention of ad-blockers. Some people have coded new browser add-ons to block miners specifically and the popular uBlock Origin added Coinhive to its default blocklist as well. And that’s just after a few days.

Needless to say, this limits the number of miners, and thus the money that comes in. And there’s another problem with a similar effect.

In addition to ad-blockers, anti-virus tools are also flagging Coinhive. Malwarebytes is one of the companies that lists it as a malicious activity, warning users about the threat.

The anti-virus angle is one of the issues that worries Demonoid’s operator. The site is used to ad-blockers, but getting flagged by anti-virus companies is of a different order.

“The problem I see there and the reason we will likely discontinue [use of the miner] is that some anti-virus programs block it, and that might get the site on their blacklists,” Deimos informs TorrentFreak.

Demonoid’s miner announcement

Vidoza operator Eugene sees all the blocking as an unwelcome development and hopes that Coinhive will tackle it. Coinhive may want to come out in public and start to discuss the issue with ad-blockers and anti-virus companies, he says.

“They should find out under what conditions all these guys will stop blocking the script,” he notes.

The other option would be to circumvent the blocking through proxies and circumvention tools, but that might not be the best choice in the long run.

Coinhive, meanwhile, has chimed in as well. The company says that it wasn’t properly prepared for the massive attention and understands why some ad-blockers have put them on the blacklist.

“Providing a real alternative to ads and users who block them turned out to be a much harder problem. Coinhive, too, is now blocked by many ad-block browser extensions, which – we have to admit – is reasonable at this point.”

Most complaints have been targeted at sites that implemented the miner without the user’s consent. Coinhive doesn’t like this either and will take steps to prevent it in future.

“We’re a bit saddened to see that some of our customers integrate Coinhive into their pages without disclosing to their users what’s going on, let alone asking for their permission,” the Coinhive team notes.

The crypto miner provider is working on a new implementation that requires explicit consent from website visitors in order to run. This should deal with most of the negative responses.

If users start mining voluntarily, then ad-blockers and anti-virus companies should no longer have a reason to block the script. Nor will it be easy for malware peddlers to abuse it.

To be continued.

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

Are Cryptocurrency Miners The Future for Pirate Sites?

Post Syndicated from Ernesto original https://torrentfreak.com/are-cryptocurrency-miners-the-future-for-pirate-sites-170921/

Last weekend The Pirate Bay surprised friend and foe by adding a Javascript-based cryptocurrency miner to its website.

The miner utilizes CPU power from visitors to generate Monero coins for the site, providing an extra revenue source.

Initially, this caused the CPUs of visitors to max out due to a configuration error, but it was later adjusted to be less demanding. Still, there was plenty of discussion on the move, with greatly varying opinions.

Some criticized the site for “hijacking” their computer resources for personal profit, without prior warning. However, there are also people who are happy to give something back to TPB, especially if it can help the site to remain online.

Aside from the configuration error, there was another major mistake everyone agreed on. The Pirate Bay team should have alerted its visitors to this change beforehand, and not after the fact, as they did last weekend.

Despite the sensitivities, The Pirate Bay’s move has inspired others to follow suit. Pirate linking site Alluc.ee is one of the first. While they use the same mining service, their implementation is more elegant.

Alluc shows how many hashes are mined and the site allows users to increase or decrease the CPU load, or turn the miner off completely.

Alluc.ee miner

Putting all the controversy aside for a minute, the idea to let visitors mine coins is a pretty ingenious idea. The Pirate Bay said it was testing the feature to see if it’s possible as a replacement for ads, which might be much needed in the future.

In recent years many pirate sites have struggled to make a decent income. Not only are more people using ad-blockers now, the ad-quality is also dropping as copyright holders actively go after this revenue source, trying to dry up the funds of pirate sites. And with Chrome planning to add a default ad-blocker to its browser, the outlook is grim.

A cryptocurrency miner might alleviate this problem. That is, as long as ad-blockers don’t start to interfere with this revenue source as well.

Interestingly, this would also counter one of the main anti-piracy talking points. Increasingly, industry groups are using the “public safety” argument as a reason to go after pirate sites. They point to malicious advertisements as a great danger, hoping that this will further their calls for tougher legislation and enforcement.

If The Pirate Bay and other pirate sites can ditch the ads, they would be less susceptible to these and other anti-piracy pushes. Of course, copyright holders could still go after the miner revenues, but this might not be easy.

TorrentFreak spoke to Coinhive, the company that provides the mining service to The Pirate Bay, and they don’t seem eager to take action without a court order.

“We don’t track where users come from. We are just providing servers and a script to submit hashes for the Monero blockchain. We don’t see it as our responsibility to determine if a website is ‘valid’ and we don’t have the technical capabilities to do so,” a Coinhive representative says.

We also contacted several site owners and thus far the response has been mixed. Some like the idea and would consider adding a miner, if it doesn’t affect visitors too much. Others are more skeptical and don’t believe that the extra revenue is worth the trouble.

The Pirate Bay itself, meanwhile, has completed its test run and has removed the miner from the site. They will now analyze the results before deciding whether or not it’s “the future” for them.

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

SecureLogin For Java Web Applications

Post Syndicated from Bozho original https://techblog.bozho.net/securelogin-java-web-applications/

No, there is not a missing whitespace in the title. It’s not about any secure login, it’s about the SecureLogin protocol developed by Egor Homakov, a security consultant, who became famous for committing to master in the Rails project without having permissions.

The SecureLogin protocol is very interesting, as it does not rely on any central party (e.g. OAuth providers like Facebook and Twitter), thus avoiding all the pitfalls of OAuth (which Homakov has often criticized). It is not a password manager either. It is just a client-side software that performs a bit of crypto in order to prove to the server that it is indeed the right user. For that to work, two parts are key:

  • Using a master password to generate a private key. It uses a key-derivation function, which guarantees that the produced private key has sufficient entropy. That way, using the same master password and the same email, you will get the same private key everytime you use the password, and therefore the same public key. And you are the only one who can prove this public key is yours, by signing a message with your private key.
  • Service providers (websites) identify you by your public key by storing it in the database when you register and then looking it up on each subsequent login

The client-side part is performed ideally by a native client – a browser plugin (one is available for Chrome) or a OS-specific application (including mobile ones). That may sound tedious, but it’s actually quick and easy and a one-time event (and is easier than password managers).

I have to admit – I like it, because I’ve been having a similar idea for a while. In my “biometric identification” presentation (where I discuss the pitfalls of using biometrics-only identification schemes), I proposed (slide 23) an identification scheme that uses biometrics (e.g. scanned with your phone) + a password to produce a private key (using a key-derivation function). And the biometric can easily be added to SecureLogin in the future.

It’s not all roses, of course, as one issue isn’t fully resolved yet – revocation. In case someone steals your master password (or you suspect it might be stolen), you may want to change it and notify all service providers of that change so that they can replace your old public key with a new one. That has two implications – first, you may not have a full list of sites that you registered on, and since you may have changed devices, or used multiple devices, there may be websites that never get to know about your password change. There are proposed solutions (points 3 and 4), but they are not intrinsic to the protocol and rely on centralized services. The second issue is – what if the attacker changes your password first? To prevent that, service providers should probably rely on email verification, which is neither part of the protocol, nor is encouraged by it. But you may have to do it anyway, as a safeguard.

Homakov has not only defined a protocol, but also provided implementations of the native clients, so that anyone can start using it. So I decided to add it to a project I’m currently working on (the login page is here). For that I needed a java implementation of the server verification, and since no such implementation existed (only ruby and node.js are provided for now), I implemented it myself. So if you are going to use SecureLogin with a Java web application, you can use that instead of rolling out your own. While implementing it, I hit a few minor issues that may lead to protocol changes, so I guess backward compatibility should also be somehow included in the protocol (through versioning).

So, how does the code look like? On the client side you have a button and a little javascript:

<!-- get the latest sdk.js from the GitHub repo of securelogin
   or include it from https://securelogin.pw/sdk.js -->
<script src="js/securelogin/sdk.js"></script>
....
<p class="slbutton" id="securelogin">&#9889; SecureLogin</p>
$("#securelogin").click(function() {
  SecureLogin(function(sltoken){
	// TODO: consider adding csrf protection as in the demo applications
        // Note - pass as request body, not as param, as the token relies 
        // on url-encoding which some frameworks mess with
	$.post('/app/user/securelogin', sltoken, function(result) {
            if(result == 'ok') {
		 window.location = "/app/";
            } else {
                 $.notify("Login failed, try again later", "error");
            }
	});
  });
  return false;
});

A single button can be used for both login and signup, or you can have a separate signup form, if it has to include additional details rather than just an email. Since I added SecureLogin in addition to my password-based login, I kept the two forms.

On the server, you simply do the following:

@RequestMapping(value = "/securelogin/register", method = RequestMethod.POST)
@ResponseBody
public String secureloginRegister(@RequestBody String token, HttpServletResponse response) {
    try {
        SecureLogin login = SecureLogin.verify(request.getSecureLoginToken(), Options.create(websiteRootUrl));
        UserDetails details = userService.getUserDetailsByEmail(login.getEmail());
        if (details == null || !login.getRawPublicKey().equals(details.getSecureLoginPublicKey())) {
            return "failure";
        }
        // sets the proper cookies to the response
        TokenAuthenticationService.addAuthentication(response, login.getEmail(), secure));
        return "ok";
    } catch (SecureLoginVerificationException e) {
        return "failure";
    }
}

This is spring-mvc, but it can be any web framework. You can also incorporate that into a spring-security flow somehow. I’ve never liked spring-security’s complexity, so I did it manually. Also, instead of strings, you can return proper status codes. Note that I’m doing a lookup by email and only then checking the public key (as if it’s a password). You can do the other way around if you have the proper index on the public key column.

I wouldn’t suggest having a SecureLogin-only system, as the project is still in an early stage and users may not be comfortable with it. But certainly adding it as an option is a good idea.

The post SecureLogin For Java Web Applications appeared first on Bozho's tech blog.

The Pirate Bay Website Runs a Cryptocurrency Miner

Post Syndicated from Ernesto original https://torrentfreak.com/the-pirate-bay-website-runs-a-cryptocurrency-miner-170916/

Four years ago many popular torrent sites added an option to donate via Bitcoin. The Pirate Bay was one of the first to jump on board and still lists its address on the website.

While there’s nothing wrong with using Bitcoin as a donation tool, adding a Javascript cryptocurrency miner to a site is of a totally different order.

A few hours ago many Pirate Bay users began noticing that their CPU usage increased dramatically when they browsed certain Pirate Bay pages. Upon closer inspection, this spike appears to have been caused by a Bitcoin miner embedded on the site.

The code in question is tucked away in the site’s footer and uses a miner provided by Coinhive. This service offers site owners the option to convert the CPU power of users into Monero coins.

The miner does indeed appear to increase CPU usage quite a bit. It is throttled at different rates (we’ve seen both 0.6 and 0.8) but the increase in resources is immediately noticeable.

The miner is not enabled site-wide. When we checked, it appeared in the search results and category listings, but not on the homepage or individual torrent pages.

There has been no official comment from the site operators on the issue (update, see below), but many users have complained about it. In the official site forums, TPB supermoderator Sid is clearly not in agreement with the site’s latest addition.

“That really is serious, so hopefully we can get some action on it quickly. And perhaps get some attention for the uploading and commenting bugs while they’re at it,” Sid writes.

Like many others, he also points out that blocking or disabling Javascript can stop the automatic mining. This can be done via browser settings or through script blocker addons such as NoScript and ScriptBlock. Alternatively, people can block the miner URL with an ad-blocker.

Whether the miner is a new and permanent tool, or perhaps triggered by an advertiser, is unknown at the point. When we hear more this article will be updated accordingly.

Update: We were told that the miner is being tested for a short period as a new way to generate revenue. This could eventually replace the ads on the site. More info may be revealed later.

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

[$] Mongoose OS for IoT prototyping

Post Syndicated from jake original https://lwn.net/Articles/733297/rss

Mongoose OS is an open-source
operating system for tiny embedded systems. It is designed to run on
devices such as microcontrollers, which are often constrained with memory on the
order of tens of kilobytes, while exposing a programming interface that
provides access to modern APIs normally found on more powerful devices. A
device running Mongoose OS has access to operating system functionality
such as filesystems and networking, plus higher-level software such as a
JavaScript engine and cloud access APIs.

Make your own game with CoderDojo’s new book

Post Syndicated from Nuala McHale original https://www.raspberrypi.org/blog/coderdojo-nano/

The first official CoderDojo book, CoderDojo Nano: Build Your Own Website, was a resounding success: thousands of copies have been bought by aspiring CoderDojo Ninjas, and it‘s available in ten languages, including Bulgarian, Czech, Dutch, Lithuanian, Latvian, Portuguese, Spanish, and Slovakian. Now we are delighted to announce the release of the second book in our Create with Code trilogy, titled CoderDojo Nano: Make Your Own Game.

Cover of CoderDojo Nano Make your own game

The paperback book will be available in English from Thursday 7 September (with English flexibound and Dutch versions scheduled to follow in the coming months), enabling young people and adults to learn creative and fun coding skills!

What will you learn?

The new book explains the fundamentals of the JavaScript language in a clear, logical way while supporting you to create your very own computer game.

Pixel image of laptop displaying a jump-and-run game

You will learn how to animate characters, create a world for your game, and use the physics of movement within it. The book is full of clear step-by-step instructions and illustrated screenshots to make reviewing your code easy. Additionally, challenges and open-ended prompts at the end of each section will encourage you to get creative while making your game.

This book is the perfect first step towards understanding game development, particularly for those of you who do not (yet) have a local Dojo. Regardless of where you live, using our books you too can learn to ‘Create with Code’!

Tried and tested

As always, CoderDojo Ninjas from all around the world tested our book, and their reactions have been hugely positive. Here is a selection of their thoughts:

“The book is brilliant. The [game] is simple yet innovative. I personally love it, and want to get stuck in making it right away!”

“What I really like is that, unlike most books on coding, this one properly explains what’s happening, and what each piece of code does and where it comes from.”

“I found the book most enjoyable. The layout is great, with lots of colour, and I found the information very easy to follow. The Ninja Tips are a great help in case you get a bit stuck. I liked that the book represents a mix of boy and girl Ninjas — it really makes coding fun for all.”

“The book is a great guide for both beginners and people who want to do something creative with their knowledge of code. Even people who cannot go to a CoderDojo can learn code using this book!”

Writer Jurie Horneman

Author of CoderDojo Nano: Make Your Own Game Jurie Horneman has been working in the game development industry for more than 15 years.

stuffed toy rabbit wearing glasses

Jurie would get on well with Babbage, I think.

He shares how he got into coding, and what he has learnt while creating this awesome book:

“I’ve been designing and programming games since 1991, starting with ancient home computers, and now I’m working with PCs and consoles. As a game designer, it’s my job to teach players the rules of the game in a fun and playful manner — that gave me some useful experience for writing the book.

I believe that, if you want to understand something properly, you have to teach it to others. Therefore, writing this book was very educational for me, as I hope reading it will be for learners.”

Asked what his favorite thing about the book is, Jurie said he loves the incredible pixel art design: “The artist (Gary J Lucken, Army of Trolls) did a great job to help explain some of the abstract concepts in the book.”

Pixel image of a landscape with an East Asian temple on a lonely mountain

Gary’s art is also just gorgeous.

How can you get your copy?

You can pre-order CoderDojo Nano: Make Your Own Game here. Its initial pricing is £9.99 (around €11), and discounted copies with free international delivery are available here.

The post Make your own game with CoderDojo’s new book appeared first on Raspberry Pi.

Five Must-Watch Software Engineering Talks

Post Syndicated from Bozho original https://techblog.bozho.net/five-must-watch-software-engineering-talks/

We’ve all watched dozens of talks online. And we probably don’t remember many of them. But some do stick in our heads and we eventually watch them again (and again) because we know they are good and we want to remember the things that were said there. So I decided to compile a small list of talks that I find very insightful, useful and that have, in a way, shaped my software engineering practice or expanded my understanding of the software world.

1. How To Design A Good API and Why it Matters by Joshua Bloch – this is a must-watch (well, obviously all are). And don’t skip it because “you are not writing APIs” – everyone is writing APIs. Maybe not used by hundreds of other developers, but used by at least several, and that’s a good enough reason. Having watched this talk I ended up buying and reading one of the few software books that I have actually read end-to-end – “Effective Java” (the talk uses Java as an example, but the principles aren’t limited to Java)

2. How to write clean, testable code by Miško Hevery. Maybe there are tons of talks about testing code, maybe Uncle Bob has a more popular one, but I found this one particularly practical and the the point – that writing testable code is a skill, and that testable code is good code. (By the way, the speaker then wrote AngularJS)

3. Back to basics: the mess we’ve made of our fundamental data types by Jon Skeet. The title says it all, and it’s nice to be reminded of how fragile even the basics of programming languages are.

4. The Danger of Software Patents by Richard Stallman. That goes a little bit away from writing software, but puts software in legal context – how do legislation loopholes affect code reuse and business practices related it. It’s a bit long, but I think worth it.

5. Does my ESB look big in this? by Martin Fowler and Jim Webber. It’s about bloated enterprise architecture and how to actually do enterprise architecture without complex and expensive middleware. (Unfortunately it’s not on YouTube, so no embedding).

Although this is not a “ranking”, I’d like to add a few honourable mentions: The famous “WAT” lightning talk, showing some quirks of ruby and javascript, “The future of programming” by Bret Victor, “You suck at Excel” by Joel Spolsky, which isn’t really about creating software, but it’s cool. And a tiny shameless plug with my “Common sense driven development talk”

I hope the compilation is useful and enlightening. Enjoy.

The post Five Must-Watch Software Engineering Talks appeared first on Bozho's tech blog.

How Aussie ecommerce stores can compete with the retail giant Amazon

Post Syndicated from chris desantis original https://www.anchor.com.au/blog/2017/08/aussie-ecommerce-stores-vs-amazon/

The powerhouse Amazon retail store is set to launch in Australia toward the end of 2018 and Aussie ecommerce retailers need to ready themselves for the competition storm ahead.

2018 may seem a while away but getting your ecommerce site in tip top shape and ready to compete can take time. Check out these helpful hints from the Anchor crew.

Speed kills

If you’ve ever heard of the tale of the tortoise and the hare, the moral is that “slow and steady wins the race”. This is definitely not the place for that phrase, because if your site loads as slowly as a 1995 dial up connection, your ecommerce store will not, I repeat, will not win the race.

Site speed can be impacted by a number of factors and getting the balance right between a site that loads at lightning speed and delivering engaging content to your audience. There are many ways to check the performance of your site including Anchor’s free hosting check up or pingdom.

Taking action can boost the performance of your site:

Here’s an interesting blog from the WebCEO team about site speed’s impact on conversion rates on-page, or check out our previous blog on maximising site performance.

Show me the money

As an ecommerce store, getting credit card details as fast as possible is probably at the top of your list, but it’s important to remember that it’s an actual person that needs to hand over the details.

Consider the customer’s experience whilst checking out. Making people log in to their account before checkout, can lead to abandoned carts as customers try to remember the vital details. Similarly, making a customer enter all their details before displaying shipping costs is more of an annoyance than a benefit.

Built for growth

Before you blast out a promo email to your entire database or spend up big on PPC, consider what happens when this 5 fold increase in traffic, all jumps onto your site at around the same time.

Will your site come screeching to a sudden halt with a 504 or 408 error message, or ride high on the wave of increased traffic? If you have fixed infrastructure such as a dedicated server, or are utilising a VPS, then consider the maximum concurrent users that your site can handle.

Consider this. Amazon.com.au will be built on the scalable cloud infrastructure of Amazon Web Services and will utilise all the microservices and data mining technology to offer customers a seamless, personalised shopping experience. How will your business compete?

Search ready

Being found online is important for any business, but for ecommerce sites, it’s essential. Gaining results from SEO practices can take time so beware of ‘quick fix guarantees’ from outsourced agencies.

Search Engine Optimisation (SEO) practices can have lasting effects. Good practices can ensure your site is found via organic search without huge advertising budgets, on the other hand ‘black hat’ practices can push your ecommerce store into search oblivion.

SEO takes discipline and focus to get right. Here are some of our favourite hints for SEO greatness from those who live and breathe SEO:

  • Optimise your site for mobile
  • Use Meta Tags wisely
  • Leverage Descriptive alt tags and image file names
  • Create content for people, not bots (keyword stuffing is a no no!)

SEO best practices are continually evolving, but creating a site that is designed to give users a great experience and give them the content they expect to find.

Google My Business is a free service that EVERY business should take advantage of. It is a listing service where your business can provide details such as address, phone number, website, and trading hours. It’s easy to update and manage, you can add photos, a physical address (if applicable), and display shopper reviews.

Get your site ship shape

Overwhelmed by these starter tips? If you are ready to get your site into tip top shape–get in touch. We work with awesome partners like eWave who can help create a seamless online shopping experience.

 

The post How Aussie ecommerce stores can compete with the retail giant Amazon appeared first on AWS Managed Services by Anchor.

Security updates for Wednesday

Post Syndicated from ris original https://lwn.net/Articles/732396/rss

Security updates have been issued by Debian (libgcrypt20, poppler, and wordpress), Fedora (cvs, java-1.8.0-openjdk-aarch32, and postgresql), Mageia (gstreamer0.10-plugins-base, gstreamer1.0-plugins-base and libgit2), openSUSE (exim), Red Hat (instack-undercloud, openvswitch, and poppler), Scientific Linux (poppler), SUSE (kernel and quagga), and Ubuntu (linux-lts-trusty).