Tag Archives: running

Deep Down, Games Pirates Love Enemies Like Denuvo

Post Syndicated from Andy original https://torrentfreak.com/deep-down-games-pirates-love-enemies-like-denuvo-171022/

While there are plenty of people who just want content for free, digging through the last 35 years of piracy reveals an interesting trend. One way or another, people have always secretly admired anti-piracy systems, since they simultaneously relish the prospect of one day subverting their barriers.

In the very early 80s, when the first 8-bit home computers became more readily available, finding ways to pirate games was almost as much fun as playing them. Children, with limited pocket money, would pool their resources and buy a single copy of a cassette-based game, hoping to clone it at home with a twin-deck recorder, to share among their fellow investors.

With significant trial and error (and only pre-Internet schoolyard advice and folklore available) copying eventually became easy. Then the ‘evil’ games companies worked out what was going on and decided to do something about it.

Early protection systems, such as ‘Hyper Loaders‘, threw a wrench in the works for a while but along came software like Lerm (with full page ads in the media) to level the playing field. Anything you can do we can do better, those kids rejoiced.

Unsurprisingly, copiers like Lerm also fell victim to pirates, with all self-respecting red beards owning a copy. But then the next waves of anti-piracy systems would come along, ensuring that working out how to pirate games became a time-consuming hobby in itself. But most pirates were kids – what else did they have to do?

With a young and inexperienced mindset, however, it was sometimes easy to fear that like Denuvo a year or two ago, some things might never be copied. Take the 1983 release of International Soccer for the Commodore 64 home computer, for example. That originally came on a cartridge – who could ever copy one of those?

Of course, someone did, dumping it onto cassette tape complete with a modification that had some players sitting in wheelchairs, others on crutches, instead of running around. By today’s standards that’s both technically trivial and rather insensitive, but at the time it represented a pirate double-whammy.

A game that couldn’t be pirated getting pirated onto another format, plus a ridiculous addition that no game company would ever allow to market? To teenage pirates, that was a supremely delicious not to mention rebellious treat.

As the months and years rolled on, new consoles – such as Commodore’s Amiga – brought 3.5″ floppy disc storage and new copy protection mechanisms to the masses. And, as expected, fresh solutions to thwart them came to market. Tools such as X-Copy Pro went down in history and were universally hailed by pirates. Who immediately pirated them, of course.

Today the situation is somewhat different but somehow just the same. Copy protection mechanisms, such as the now-infamous Denuvo, are so complex that no user-operated tool is available to copy the games protected by it. Yet people, driven by a passion for subverting the system and solving technological puzzles, are dedicating thousands of hours to take them apart.

Just recently, Denuvo was well and truly dismantled. Games are now routinely getting cracked in a day, sometimes just hours, and the excitement in the air is palpable. In many ways, this is the same kind of enthusiasm expressed by the relatively naive kid pirates of the 80s. They too were frustrated by copy protection, they too screamed with glee when it fell from grace.

While gaming has always been fun, the sense of achievement – of subverting the system – has always come a close second to actually playing games for those with an enthusiastic pirate streak. Imagine a world where every game could be easily copied by just about anyone. Now compare that to a war of attrition against the dark forces behind 80s Hyper Loaders and the evil Denuvo of today.

In the end, there’s no doubt. Most dedicated pirates, provided they eventually taste victory, will take the warfare option any day, fighting to the end, fighting for victory.

Let’s be honest. Pirates absolutely need a nemesis like Denuvo. Because – quite simply – it’s only half the fun without one.

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

timeShift(GrafanaBuzz, 1w) Issue 18

Post Syndicated from Blogs on Grafana Labs Blog original https://grafana.com/blog/2017/10/20/timeshiftgrafanabuzz-1w-issue-18/

Welcome to another issue of timeShift. This week we released Grafana 4.6.0-beta2, which includes some fixes for alerts, annotations, the Cloudwatch data source, and a few panel updates. We’re also gearing up for Oredev, one of the biggest tech conferences in Scandinavia, November 7-10. In addition to sponsoring, our very own Carl Bergquist will be presenting “Monitoring for everyone.” Hope to see you there – swing by our booth and say hi!


Latest Release

Grafana 4.6-beta-2 is now available! Grafana 4.6.0-beta2 adds fixes for:

  • ColorPicker display
  • Alerting test
  • Cloudwatch improvements
  • CSV export
  • Text panel enhancements
  • Annotation fix for MySQL

To see more details on what’s in the newest version, please see the release notes.

Download Grafana 4.6.0-beta-2 Now


From the Blogosphere

Screeps and Grafana: Graphing your AI: If you’re unfamiliar with Screeps, it’s a MMO RTS game for programmers, where the objective is to grow your colony through programming your units’ AI. You control your colony by writing JavaScript, which operates 247 in the single persistent real-time world filled by other players. This article walks you through graphing all your game stats with Grafana.

ntopng Grafana Integration: The Beauty of Data Visualization: Our friends at ntop created a tutorial so that you can graph ntop monitoring data in Grafana. He goes through the metrics exposed, configuring the ntopng Data Source plugin, and building your first dashboard. They’ve also created a nice video tutorial of the process.

Installing Graphite and Grafana to Display the Graphs of Centreon: This article, provides a step-by-step guide to getting your Centreon data into Graphite and visualizing the data in Grafana.

Bit v. Byte Episode 3 – Metrics for the Win: Bit v. Byte is a new weekly Podcast about the web industry, tools and techniques upcoming and in use today. This episode dives into metrics, and discusses Grafana, Prometheus and NGINX Amplify.

Code-Quickie: Visualize heating with Grafana: With the winter weather coming, Reinhard wanted to monitor the stats in his boiler room. This article covers not only the visualization of the data, but the different devices and sensors you can use to can use in your own home.

RuuviTag with C.H.I.P – BLE – Node-RED: Following the temperature-monitoring theme from the last article, Tobias writes about his journey of hooking up his new RuuviTag to Grafana to measure temperature, relative humidity, air pressure and more.


Early Bird will be Ending Soon

Early bird discounts will be ending soon, but you still have a few days to lock in the lower price. We will be closing early bird on October 31, so don’t wait until the last minute to take advantage of the discounted tickets!

Also, there’s still time to submit your talk. We’ll accept submissions through the end of October. We’re looking for technical and non-technical talks of all sizes. Submit a CFP now.

Get Your Early Bird Ticket Now


Grafana Plugins

This week we have updates to two panels and a brand new panel that can add some animation to your dashboards. Installing plugins in Grafana is easy; for on-prem Grafana, use the Grafana-cli tool, or with 1 click if you are using Hosted Grafana.

NEW PLUGIN

Geoloop Panel – The Geoloop panel is a simple visualizer for joining GeoJSON to Time Series data, and animating the geo features in a loop. An example of using the panel would be showing the rate of rainfall during a 5-hour storm.

Install Now

UPDATED PLUGIN

Breadcrumb Panel – This plugin keeps track of dashboards you have visited within one session and displays them as a breadcrumb. The latest update fixes some issues with back navigation and url query params.

Update

UPDATED PLUGIN

Influx Admin Panel – The Influx Admin panel duplicates features from the now deprecated Web Admin Interface for InfluxDB and has lots of features like letting you see the currently running queries, which can also be easily killed.

Changes in the latest release:

  • Converted to typescript project based on typescript-template-datasource
  • Select Databases. This only works with PR#8096
  • Added time format options
  • Show tags from response
  • Support template variables in the query

Update


Contribution of the week:

Each week we highlight some of the important contributions from our amazing open source community. Thank you for helping make Grafana better!

The Stockholm Go Meetup had a hackathon this week and sent a PR for letting whitelisted cookies pass through the Grafana proxy. Thanks to everyone who worked on this PR!


Tweet of the Week

We scour Twitter each week to find an interesting/beautiful dashboard and show it off! #monitoringLove

This is awesome – we can’t get enough of these public dashboards!

We Need Your Help!

Do you have a graph that you love because the data is beautiful or because the graph provides interesting information? Please get in touch. Tweet or send us an email with a screenshot, and we’ll tell you about this fun experiment.

Tell Me More


Grafana Labs is Hiring!

We are passionate about open source software and thrive on tackling complex challenges to build the future. We ship code from every corner of the globe and love working with the community. If this sounds exciting, you’re in luck – WE’RE HIRING!

Check out our Open Positions


How are we doing?

Please tell us how we’re doing. Submit a comment on this article below, or post something at our community forum. Help us make these weekly roundups better!

Follow us on Twitter, like us on Facebook, and join the Grafana Labs community.

Federate Database User Authentication Easily with IAM and Amazon Redshift

Post Syndicated from Thiyagarajan Arumugam original https://aws.amazon.com/blogs/big-data/federate-database-user-authentication-easily-with-iam-and-amazon-redshift/

Managing database users though federation allows you to manage authentication and authorization procedures centrally. Amazon Redshift now supports database authentication with IAM, enabling user authentication though enterprise federation. No need to manage separate database users and passwords to further ease the database administration. You can now manage users outside of AWS and authenticate them for access to an Amazon Redshift data warehouse. Do this by integrating IAM authentication and a third-party SAML-2.0 identity provider (IdP), such as AD FS, PingFederate, or Okta. In addition, database users can also be automatically created at their first login based on corporate permissions.

In this post, I demonstrate how you can extend the federation to enable single sign-on (SSO) to the Amazon Redshift data warehouse.

SAML and Amazon Redshift

AWS supports Security Assertion Markup Language (SAML) 2.0, which is an open standard for identity federation used by many IdPs. SAML enables federated SSO, which enables your users to sign in to the AWS Management Console. Users can also make programmatic calls to AWS API actions by using assertions from a SAML-compliant IdP. For example, if you use Microsoft Active Directory for corporate directories, you may be familiar with how Active Directory and AD FS work together to enable federation. For more information, see the Enabling Federation to AWS Using Windows Active Directory, AD FS, and SAML 2.0 AWS Security Blog post.

Amazon Redshift now provides the GetClusterCredentials API operation that allows you to generate temporary database user credentials for authentication. You can set up an IAM permissions policy that generates these credentials for connecting to Amazon Redshift. Extending the IAM authentication, you can configure the federation of AWS access though a SAML 2.0–compliant IdP. An IAM role can be configured to permit the federated users call the GetClusterCredentials action and generate temporary credentials to log in to Amazon Redshift databases. You can also set up policies to restrict access to Amazon Redshift clusters, databases, database user names, and user group.

Amazon Redshift federation workflow

In this post, I demonstrate how you can use a JDBC– or ODBC-based SQL client to log in to the Amazon Redshift cluster using this feature. The SQL clients used with Amazon Redshift JDBC or ODBC drivers automatically manage the process of calling the GetClusterCredentials action, retrieving the database user credentials, and establishing a connection to your Amazon Redshift database. You can also use your database application to programmatically call the GetClusterCredentials action, retrieve database user credentials, and connect to the database. I demonstrate these features using an example company to show how different database users accounts can be managed easily using federation.

The following diagram shows how the SSO process works:

  1. JDBC/ODBC
  2. Authenticate using Corp Username/Password
  3. IdP sends SAML assertion
  4. Call STS to assume role with SAML
  5. STS Returns Temp Credentials
  6. Use Temp Credentials to get Temp cluster credentials
  7. Connect to Amazon Redshift using temp credentials

Walkthrough

Example Corp. is using Active Directory (idp host:demo.examplecorp.com) to manage federated access for users in its organization. It has an AWS account: 123456789012 and currently manages an Amazon Redshift cluster with the cluster ID “examplecorp-dw”, database “analytics” in us-west-2 region for its Sales and Data Science teams. It wants the following access:

  • Sales users can access the examplecorp-dw cluster using the sales_grp database group
  • Sales users access examplecorp-dw through a JDBC-based SQL client
  • Sales users access examplecorp-dw through an ODBC connection, for their reporting tools
  • Data Science users access the examplecorp-dw cluster using the data_science_grp database group.
  • Partners access the examplecorp-dw cluster and query using the partner_grp database group.
  • Partners are not federated through Active Directory and are provided with separate IAM user credentials (with IAM user name examplecorpsalespartner).
  • Partners can connect to the examplecorp-dw cluster programmatically, using language such as Python.
  • All users are automatically created in Amazon Redshift when they log in for the first time.
  • (Optional) Internal users do not specify database user or group information in their connection string. It is automatically assigned.
  • Data warehouse users can use SSO for the Amazon Redshift data warehouse using the preceding permissions.

Step 1:  Set up IdPs and federation

The Enabling Federation to AWS Using Windows Active Directory post demonstrated how to prepare Active Directory and enable federation to AWS. Using those instructions, you can establish trust between your AWS account and the IdP and enable user access to AWS using SSO.  For more information, see Identity Providers and Federation.

For this walkthrough, assume that this company has already configured SSO to their AWS account: 123456789012 for their Active Directory domain demo.examplecorp.com. The Sales and Data Science teams are not required to specify database user and group information in the connection string. The connection string can be configured by adding SAML Attribute elements to your IdP. Configuring these optional attributes enables internal users to conveniently avoid providing the DbUser and DbGroup parameters when they log in to Amazon Redshift.

The user-name attribute can be set up as follows, with a user ID (for example, nancy) or an email address (for example. [email protected]):

<Attribute Name="https://redshift.amazon.com/SAML/Attributes/DbUser">  
  <AttributeValue>user-name</AttributeValue>
</Attribute>

The AutoCreate attribute can be defined as follows:

<Attribute Name="https://redshift.amazon.com/SAML/Attributes/AutoCreate">
    <AttributeValue>true</AttributeValue>
</Attribute>

The sales_grp database group can be included as follows:

<Attribute Name="https://redshift.amazon.com/SAML/Attributes/DbGroups">
    <AttributeValue>sales_grp</AttributeValue>
</Attribute>

For more information about attribute element configuration, see Configure SAML Assertions for Your IdP.

Step 2: Create IAM roles for access to the Amazon Redshift cluster

The next step is to create IAM policies with permissions to call GetClusterCredentials and provide authorization for Amazon Redshift resources. To grant a SQL client the ability to retrieve the cluster endpoint, region, and port automatically, include the redshift:DescribeClusters action with the Amazon Redshift cluster resource in the IAM role.  For example, users can connect to the Amazon Redshift cluster using a JDBC URL without the need to hardcode the Amazon Redshift endpoint:

Previous:  jdbc:redshift://endpoint:port/database

Current:  jdbc:redshift:iam://clustername:region/dbname

Use IAM to create the following policies. You can also use an existing user or role and assign these policies. For example, if you already created an IAM role for IdP access, you can attach the necessary policies to that role. Here is the policy created for sales users for this example:

Sales_DW_IAM_Policy

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "redshift:DescribeClusters"
            ],
            "Resource": [
                "arn:aws:redshift:us-west-2:123456789012:cluster:examplecorp-dw"
            ]
        },
        {
            "Effect": "Allow",
            "Action": [
                "redshift:GetClusterCredentials"
            ],
            "Resource": [
                "arn:aws:redshift:us-west-2:123456789012:cluster:examplecorp-dw",
                "arn:aws:redshift:us-west-2:123456789012:dbuser:examplecorp-dw/${redshift:DbUser}"
            ],
            "Condition": {
                "StringEquals": {
                    "aws:userid": "AIDIODR4TAW7CSEXAMPLE:${redshift:DbUser}@examplecorp.com"
                }
            }
        },
        {
            "Effect": "Allow",
            "Action": [
                "redshift:CreateClusterUser"
            ],
            "Resource": [
                "arn:aws:redshift:us-west-2:123456789012:dbuser:examplecorp-dw/${redshift:DbUser}"
            ]
        },
        {
            "Effect": "Allow",
            "Action": [
                "redshift:JoinGroup"
            ],
            "Resource": [
                "arn:aws:redshift:us-west-2:123456789012:dbgroup:examplecorp-dw/sales_grp"
            ]
        }
    ]
}

The policy uses the following parameter values:

  • Region: us-west-2
  • AWS Account: 123456789012
  • Cluster name: examplecorp-dw
  • Database group: sales_grp
  • IAM role: AIDIODR4TAW7CSEXAMPLE
Policy Statement Description
{
"Effect":"Allow",
"Action":[
"redshift:DescribeClusters"
],
"Resource":[
"arn:aws:redshift:us-west-2:123456789012:cluster:examplecorp-dw"
]
}

Allow users to retrieve the cluster endpoint, region, and port automatically for the Amazon Redshift cluster examplecorp-dw. This specification uses the resource format arn:aws:redshift:region:account-id:cluster:clustername. For example, the SQL client JDBC can be specified in the format jdbc:redshift:iam://clustername:region/dbname.

For more information, see Amazon Resource Names.

{
"Effect":"Allow",
"Action":[
"redshift:GetClusterCredentials"
],
"Resource":[
"arn:aws:redshift:us-west-2:123456789012:cluster:examplecorp-dw",
"arn:aws:redshift:us-west-2:123456789012:dbuser:examplecorp-dw/${redshift:DbUser}"
],
"Condition":{
"StringEquals":{
"aws:userid":"AIDIODR4TAW7CSEXAMPLE:${redshift:DbUser}@examplecorp.com"
}
}
}

Generates a temporary token to authenticate into the examplecorp-dw cluster. “arn:aws:redshift:us-west-2:123456789012:dbuser:examplecorp-dw/${redshift:DbUser}” restricts the corporate user name to the database user name for that user. This resource is specified using the format: arn:aws:redshift:region:account-id:dbuser:clustername/dbusername.

The Condition block enforces that the AWS user ID should match “AIDIODR4TAW7CSEXAMPLE:${redshift:DbUser}@examplecorp.com”, so that individual users can authenticate only as themselves. The AIDIODR4TAW7CSEXAMPLE role has the Sales_DW_IAM_Policy policy attached.

{
"Effect":"Allow",
"Action":[
"redshift:CreateClusterUser"
],
"Resource":[
"arn:aws:redshift:us-west-2:123456789012:dbuser:examplecorp-dw/${redshift:DbUser}"
]
}
Automatically creates database users in examplecorp-dw, when they log in for the first time. Subsequent logins reuse the existing database user.
{
"Effect":"Allow",
"Action":[
"redshift:JoinGroup"
],
"Resource":[
"arn:aws:redshift:us-west-2:123456789012:dbgroup:examplecorp-dw/sales_grp"
]
}
Allows sales users to join the sales_grp database group through the resource “arn:aws:redshift:us-west-2:123456789012:dbgroup:examplecorp-dw/sales_grp” that is specified in the format arn:aws:redshift:region:account-id:dbgroup:clustername/dbgroupname.

Similar policies can be created for Data Science users with access to join the data_science_grp group in examplecorp-dw. You can now attach the Sales_DW_IAM_Policy policy to the role that is mapped to IdP application for SSO.
 For more information about how to define the claim rules, see Configuring SAML Assertions for the Authentication Response.

Because partners are not authorized using Active Directory, they are provided with IAM credentials and added to the partner_grp database group. The Partner_DW_IAM_Policy is attached to the IAM users for partners. The following policy allows partners to log in using the IAM user name as the database user name.

Partner_DW_IAM_Policy

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "redshift:DescribeClusters"
            ],
            "Resource": [
                "arn:aws:redshift:us-west-2:123456789012:cluster:examplecorp-dw"
            ]
        },
        {
            "Effect": "Allow",
            "Action": [
                "redshift:GetClusterCredentials"
            ],
            "Resource": [
                "arn:aws:redshift:us-west-2:123456789012:cluster:examplecorp-dw",
                "arn:aws:redshift:us-west-2:123456789012:dbuser:examplecorp-dw/${redshift:DbUser}"
            ],
            "Condition": {
                "StringEquals": {
                    "redshift:DbUser": "${aws:username}"
                }
            }
        },
        {
            "Effect": "Allow",
            "Action": [
                "redshift:CreateClusterUser"
            ],
            "Resource": [
                "arn:aws:redshift:us-west-2:123456789012:dbuser:examplecorp-dw/${redshift:DbUser}"
            ]
        },
        {
            "Effect": "Allow",
            "Action": [
                "redshift:JoinGroup"
            ],
            "Resource": [
                "arn:aws:redshift:us-west-2:123456789012:dbgroup:examplecorp-dw/partner_grp"
            ]
        }
    ]
}

redshift:DbUser“: “${aws:username}” forces an IAM user to use the IAM user name as the database user name.

With the previous steps configured, you can now establish the connection to Amazon Redshift through JDBC– or ODBC-supported clients.

Step 3: Set up database user access

Before you start connecting to Amazon Redshift using the SQL client, set up the database groups for appropriate data access. Log in to your Amazon Redshift database as superuser to create a database group, using CREATE GROUP.

Log in to examplecorp-dw/analytics as superuser and create the following groups and users:

CREATE GROUP sales_grp;
CREATE GROUP datascience_grp;
CREATE GROUP partner_grp;

Use the GRANT command to define access permissions to database objects (tables/views) for the preceding groups.

Step 4: Connect to Amazon Redshift using the JDBC SQL client

Assume that sales user “nancy” is using the SQL Workbench client and JDBC driver to log in to the Amazon Redshift data warehouse. The following steps help set up the client and establish the connection:

  1. Download the latest Amazon Redshift JDBC driver from the Configure a JDBC Connection page
  2. Build the JDBC URL with the IAM option in the following format:
    jdbc:redshift:iam://examplecorp-dw:us-west-2/sales_db

Because the redshift:DescribeClusters action is assigned to the preceding IAM roles, it automatically resolves the cluster endpoints and the port. Otherwise, you can specify the endpoint and port information in the JDBC URL, as described in Configure a JDBC Connection.

Identify the following JDBC options for providing the IAM credentials (see the “Prepare your environment” section) and configure in the SQL Workbench Connection Profile:

plugin_name=com.amazon.redshift.plugin.AdfsCredentialsProvider 
idp_host=demo.examplecorp.com (The name of the corporate identity provider host)
idp_port=443  (The port of the corporate identity provider host)
user=examplecorp\nancy(corporate user name)
password=***(corporate user password)

The SQL workbench configuration looks similar to the following screenshot:

Now, “nancy” can connect to examplecorp-dw by authenticating using the corporate Active Directory. Because the SAML attributes elements are already configured for nancy, she logs in as database user nancy and is assigned the sales_grp. Similarly, other Sales and Data Science users can connect to the examplecorp-dw cluster. A custom Amazon Redshift ODBC driver can also be used to connect using a SQL client. For more information, see Configure an ODBC Connection.

Step 5: Connecting to Amazon Redshift using JDBC SQL Client and IAM Credentials

This optional step is necessary only when you want to enable users that are not authenticated with Active Directory. Partners are provided with IAM credentials that they can use to connect to the examplecorp-dw Amazon Redshift clusters. These IAM users are attached to Partner_DW_IAM_Policy that assigns them to be assigned to the public database group in Amazon Redshift. The following JDBC URLs enable them to connect to the Amazon Redshift cluster:

jdbc:redshift:iam//examplecorp-dw/analytics?AccessKeyID=XXX&SecretAccessKey=YYY&DbUser=examplecorpsalespartner&DbGroup= partner_grp&AutoCreate=true

The AutoCreate option automatically creates a new database user the first time the partner logs in. There are several other options available to conveniently specify the IAM user credentials. For more information, see Options for providing IAM credentials.

Step 6: Connecting to Amazon Redshift using an ODBC client for Microsoft Windows

Assume that another sales user “uma” is using an ODBC-based client to log in to the Amazon Redshift data warehouse using Example Corp Active Directory. The following steps help set up the ODBC client and establish the Amazon Redshift connection in a Microsoft Windows operating system connected to your corporate network:

  1. Download and install the latest Amazon Redshift ODBC driver.
  2. Create a system DSN entry.
    1. In the Start menu, locate the driver folder or folders:
      • Amazon Redshift ODBC Driver (32-bit)
      • Amazon Redshift ODBC Driver (64-bit)
      • If you installed both drivers, you have a folder for each driver.
    2. Choose ODBC Administrator, and then type your administrator credentials.
    3. To configure the driver for all users on the computer, choose System DSN. To configure the driver for your user account only, choose User DSN.
    4. Choose Add.
  3. Select the Amazon Redshift ODBC driver, and choose Finish. Configure the following attributes:
    Data Source Name =any friendly name to identify the ODBC connection 
    Database=analytics
    user=uma(corporate user name)
    Auth Type-Identity Provider: AD FS
    password=leave blank (Windows automatically authenticates)
    Cluster ID: examplecorp-dw
    idp_host=demo.examplecorp.com (The name of the corporate IdP host)

This configuration looks like the following:

  1. Choose OK to save the ODBC connection.
  2. Verify that uma is set up with the SAML attributes, as described in the “Set up IdPs and federation” section.

The user uma can now use this ODBC connection to establish the connection to the Amazon Redshift cluster using any ODBC-based tools or reporting tools such as Tableau. Internally, uma authenticates using the Sales_DW_IAM_Policy  IAM role and is assigned the sales_grp database group.

Step 7: Connecting to Amazon Redshift using Python and IAM credentials

To enable partners, connect to the examplecorp-dw cluster programmatically, using Python on a computer such as Amazon EC2 instance. Reuse the IAM users that are attached to the Partner_DW_IAM_Policy policy defined in Step 2.

The following steps show this set up on an EC2 instance:

  1. Launch a new EC2 instance with the Partner_DW_IAM_Policy role, as described in Using an IAM Role to Grant Permissions to Applications Running on Amazon EC2 Instances. Alternatively, you can attach an existing IAM role to an EC2 instance.
  2. This example uses Python PostgreSQL Driver (PyGreSQL) to connect to your Amazon Redshift clusters. To install PyGreSQL on Amazon Linux, use the following command as the ec2-user:
    sudo easy_install pip
    sudo yum install postgresql postgresql-devel gcc python-devel
    sudo pip install PyGreSQL

  1. The following code snippet demonstrates programmatic access to Amazon Redshift for partner users:
    #!/usr/bin/env python
    """
    Usage:
    python redshift-unload-copy.py <config file> <region>
    
    * Copyright 2014, Amazon.com, Inc. or its affiliates. All Rights Reserved.
    *
    * Licensed under the Amazon Software License (the "License").
    * You may not use this file except in compliance with the License.
    * A copy of the License is located at
    *
    * http://aws.amazon.com/asl/
    *
    * or in the "license" file accompanying this file. This file is distributed
    * on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
    * express or implied. See the License for the specific language governing
    * permissions and limitations under the License.
    """
    
    import sys
    import pg
    import boto3
    
    REGION = 'us-west-2'
    CLUSTER_IDENTIFIER = 'examplecorp-dw'
    DB_NAME = 'sales_db'
    DB_USER = 'examplecorpsalespartner'
    
    options = """keepalives=1 keepalives_idle=200 keepalives_interval=200
                 keepalives_count=6"""
    
    set_timeout_stmt = "set statement_timeout = 1200000"
    
    def conn_to_rs(host, port, db, usr, pwd, opt=options, timeout=set_timeout_stmt):
        rs_conn_string = """host=%s port=%s dbname=%s user=%s password=%s
                             %s""" % (host, port, db, usr, pwd, opt)
        print "Connecting to %s:%s:%s as %s" % (host, port, db, usr)
        rs_conn = pg.connect(dbname=rs_conn_string)
        rs_conn.query(timeout)
        return rs_conn
    
    def main():
        # describe the cluster and fetch the IAM temporary credentials
        global redshift_client
        redshift_client = boto3.client('redshift', region_name=REGION)
        response_cluster_details = redshift_client.describe_clusters(ClusterIdentifier=CLUSTER_IDENTIFIER)
        response_credentials = redshift_client.get_cluster_credentials(DbUser=DB_USER,DbName=DB_NAME,ClusterIdentifier=CLUSTER_IDENTIFIER,DurationSeconds=3600)
        rs_host = response_cluster_details['Clusters'][0]['Endpoint']['Address']
        rs_port = response_cluster_details['Clusters'][0]['Endpoint']['Port']
        rs_db = DB_NAME
        rs_iam_user = response_credentials['DbUser']
        rs_iam_pwd = response_credentials['DbPassword']
        # connect to the Amazon Redshift cluster
        conn = conn_to_rs(rs_host, rs_port, rs_db, rs_iam_user,rs_iam_pwd)
        # execute a query
        result = conn.query("SELECT sysdate as dt")
        # fetch results from the query
        for dt_val in result.getresult() :
            print dt_val
        # close the Amazon Redshift connection
        conn.close()
    
    if __name__ == "__main__":
        main()

You can save this Python program in a file (redshiftscript.py) and execute it at the command line as ec2-user:

python redshiftscript.py

Now partners can connect to the Amazon Redshift cluster using the Python script, and authentication is federated through the IAM user.

Summary

In this post, I demonstrated how to use federated access using Active Directory and IAM roles to enable single sign-on to an Amazon Redshift cluster. I also showed how partners outside an organization can be managed easily using IAM credentials.  Using the GetClusterCredentials API action, now supported by Amazon Redshift, lets you manage a large number of database users and have them use corporate credentials to log in. You don’t have to maintain separate database user accounts.

Although this post demonstrated the integration of IAM with AD FS and Active Directory, you can replicate this solution across with your choice of SAML 2.0 third-party identity providers (IdP), such as PingFederate or Okta. For the different supported federation options, see Configure SAML Assertions for Your IdP.

If you have questions or suggestions, please comment below.


Additional Reading

Learn how to establish federated access to your AWS resources by using Active Directory user attributes.


About the Author

Thiyagarajan Arumugam is a Big Data Solutions Architect at Amazon Web Services and designs customer architectures to process data at scale. Prior to AWS, he built data warehouse solutions at Amazon.com. In his free time, he enjoys all outdoor sports and practices the Indian classical drum mridangam.

 

N O D E’s Handheld Linux Terminal

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/n-o-d-es-handheld-linux-terminal/

Fit an entire Raspberry Pi-based laptop into your pocket with N O D E’s latest Handheld Linux Terminal build.

The Handheld Linux Terminal Version 3 (Portable Pi 3)

Hey everyone. Today I want to show you the new version 3 of the Handheld Linux Terminal. It’s taken a long time, but I’m finally finished. This one takes all the things I’ve learned so far, and improves on many of the features from the previous iterations.

N O D E

With interests in modding tech, exploring the boundaries of the digital world, and open source, YouTuber N O D E has become one to watch within the digital maker world. He maintains a channel focused on “the transformative power of technology.”

“Understanding that electronics isn’t voodoo is really powerful”, he explains in his Patreon video. “And learning how to build your own stuff opens up so many possibilities.”

NODE Youtube channel logo - Handheld Linux Terminal v3

The topics of his videos range from stripped-down devices, upgraded tech, and security upgrades, to the philosophy behind technology. He also provides weekly roundups of, and discussions about, new releases.

Essentially, if you like technology, you’ll like N O D E.

Handheld Linux Terminal v3

Subscribers to N O D E’s YouTube channel, of whom there are currently over 44000, will have seen him documenting variations of this handheld build throughout the last year. By stripping down a Raspberry Pi 3, and incorporating a Zero W, he’s been able to create interesting projects while always putting functionality first.

Handheld Linux Terminal v3

With the third version of his terminal, N O D E has taken experiences gained from previous builds to create something of which he’s obviously extremely proud. And so he should be. The v3 handheld is impressively small considering he managed to incorporate a fully functional keyboard with mouse, a 3.5″ screen, and a fan within the 3D-printed body.

Handheld Linux Terminal v3

“The software side of things is where it really shines though, and the Pi 3 is more than capable of performing most non-intensive tasks,” N O D E goes on to explain. He demonstrates various applications running on Raspbian, plus other operating systems he has pre-loaded onto additional SD cards:

“I have also installed Exagear Desktop, which allows it to run x86 apps too, and this works great. I have x86 apps such as Sublime Text and Spotify running without any problems, and it’s technically possible to use Wine to also run Windows apps on the device.”

We think this is an incredibly neat build, and we can’t wait to see where N O D E takes it next!

The post N O D E’s Handheld Linux Terminal appeared first on Raspberry Pi.

Using AWS Step Functions State Machines to Handle Workflow-Driven AWS CodePipeline Actions

Post Syndicated from Marcilio Mendonca original https://aws.amazon.com/blogs/devops/using-aws-step-functions-state-machines-to-handle-workflow-driven-aws-codepipeline-actions/

AWS CodePipeline is a continuous integration and continuous delivery service for fast and reliable application and infrastructure updates. It offers powerful integration with other AWS services, such as AWS CodeBuildAWS CodeDeployAWS CodeCommit, AWS CloudFormation and with third-party tools such as Jenkins and GitHub. These services make it possible for AWS customers to successfully automate various tasks, including infrastructure provisioning, blue/green deployments, serverless deployments, AMI baking, database provisioning, and release management.

Developers have been able to use CodePipeline to build sophisticated automation pipelines that often require a single CodePipeline action to perform multiple tasks, fork into different execution paths, and deal with asynchronous behavior. For example, to deploy a Lambda function, a CodePipeline action might first inspect the changes pushed to the code repository. If only the Lambda code has changed, the action can simply update the Lambda code package, create a new version, and point the Lambda alias to the new version. If the changes also affect infrastructure resources managed by AWS CloudFormation, the pipeline action might have to create a stack or update an existing one through the use of a change set. In addition, if an update is required, the pipeline action might enforce a safety policy to infrastructure resources that prevents the deletion and replacement of resources. You can do this by creating a change set and having the pipeline action inspect its changes before updating the stack. Change sets that do not conform to the policy are deleted.

This use case is a good illustration of workflow-driven pipeline actions. These are actions that run multiple tasks, deal with async behavior and loops, need to maintain and propagate state, and fork into different execution paths. Implementing workflow-driven actions directly in CodePipeline can lead to complex pipelines that are hard for developers to understand and maintain. Ideally, a pipeline action should perform a single task and delegate the complexity of dealing with workflow-driven behavior associated with that task to a state machine engine. This would make it possible for developers to build simpler, more intuitive pipelines and allow them to use state machine execution logs to visualize and troubleshoot their pipeline actions.

In this blog post, we discuss how AWS Step Functions state machines can be used to handle workflow-driven actions. We show how a CodePipeline action can trigger a Step Functions state machine and how the pipeline and the state machine are kept decoupled through a Lambda function. The advantages of using state machines include:

  • Simplified logic (complex tasks are broken into multiple smaller tasks).
  • Ease of handling asynchronous behavior (through state machine wait states).
  • Built-in support for choices and processing different execution paths (through state machine choices).
  • Built-in visualization and logging of the state machine execution.

The source code for the sample pipeline, pipeline actions, and state machine used in this post is available at https://github.com/awslabs/aws-codepipeline-stepfunctions.

Overview

This figure shows the components in the CodePipeline-Step Functions integration that will be described in this post. The pipeline contains two stages: a Source stage represented by a CodeCommit Git repository and a Prod stage with a single Deploy action that represents the workflow-driven action.

This action invokes a Lambda function (1) called the State Machine Trigger Lambda, which, in turn, triggers a Step Function state machine to process the request (2). The Lambda function sends a continuation token back to the pipeline (3) to continue its execution later and terminates. Seconds later, the pipeline invokes the Lambda function again (4), passing the continuation token received. The Lambda function checks the execution state of the state machine (5,6) and communicates the status to the pipeline. The process is repeated until the state machine execution is complete. Then the Lambda function notifies the pipeline that the corresponding pipeline action is complete (7). If the state machine has failed, the Lambda function will then fail the pipeline action and stop its execution (7). While running, the state machine triggers various Lambda functions to perform different tasks. The state machine and the pipeline are fully decoupled. Their interaction is handled by the Lambda function.

The Deploy State Machine

The sample state machine used in this post is a simplified version of the use case, with emphasis on infrastructure deployment. The state machine will follow distinct execution paths and thus have different outcomes, depending on:

  • The current state of the AWS CloudFormation stack.
  • The nature of the code changes made to the AWS CloudFormation template and pushed into the pipeline.

If the stack does not exist, it will be created. If the stack exists, a change set will be created and its resources inspected by the state machine. The inspection consists of parsing the change set results and detecting whether any resources will be deleted or replaced. If no resources are being deleted or replaced, the change set is allowed to be executed and the state machine completes successfully. Otherwise, the change set is deleted and the state machine completes execution with a failure as the terminal state.

Let’s dive into each of these execution paths.

Path 1: Create a Stack and Succeed Deployment

The Deploy state machine is shown here. It is triggered by the Lambda function using the following input parameters stored in an S3 bucket.

Create New Stack Execution Path

{
    "environmentName": "prod",
    "stackName": "sample-lambda-app",
    "templatePath": "infra/Lambda-template.yaml",
    "revisionS3Bucket": "codepipeline-us-east-1-418586629775",
    "revisionS3Key": "StepFunctionsDrivenD/CodeCommit/sjcmExZ"
}

Note that some values used here are for the use case example only. Account-specific parameters like revisionS3Bucket and revisionS3Key will be different when you deploy this use case in your account.

These input parameters are used by various states in the state machine and passed to the corresponding Lambda functions to perform different tasks. For example, stackName is used to create a stack, check the status of stack creation, and create a change set. The environmentName represents the environment (for example, dev, test, prod) to which the code is being deployed. It is used to prefix the name of stacks and change sets.

With the exception of built-in states such as wait and choice, each state in the state machine invokes a specific Lambda function.  The results received from the Lambda invocations are appended to the state machine’s original input. When the state machine finishes its execution, several parameters will have been added to its original input.

The first stage in the state machine is “Check Stack Existence”. It checks whether a stack with the input name specified in the stackName input parameter already exists. The output of the state adds a Boolean value called doesStackExist to the original state machine input as follows:

{
  "doesStackExist": true,
  "environmentName": "prod",
  "stackName": "sample-lambda-app",
  "templatePath": "infra/lambda-template.yaml",
  "revisionS3Bucket": "codepipeline-us-east-1-418586629775",
  "revisionS3Key": "StepFunctionsDrivenD/CodeCommit/sjcmExZ",
}

The following stage, “Does Stack Exist?”, is represented by Step Functions built-in choice state. It checks the value of doesStackExist to determine whether a new stack needs to be created (doesStackExist=true) or a change set needs to be created and inspected (doesStackExist=false).

If the stack does not exist, the states illustrated in green in the preceding figure are executed. This execution path creates the stack, waits until the stack is created, checks the status of the stack’s creation, and marks the deployment successful after the stack has been created. Except for “Stack Created?” and “Wait Stack Creation,” each of these stages invokes a Lambda function. “Stack Created?” and “Wait Stack Creation” are implemented by using the built-in choice state (to decide which path to follow) and the wait state (to wait a few seconds before proceeding), respectively. Each stage adds the results of their Lambda function executions to the initial input of the state machine, allowing future stages to process them.

Path 2: Safely Update a Stack and Mark Deployment as Successful

Safely Update a Stack and Mark Deployment as Successful Execution Path

If the stack indicated by the stackName parameter already exists, a different path is executed. (See the green states in the figure.) This path will create a change set and use wait and choice states to wait until the change set is created. Afterwards, a stage in the execution path will inspect  the resources affected before the change set is executed.

The inspection procedure represented by the “Inspect Change Set Changes” stage consists of parsing the resources affected by the change set and checking whether any of the existing resources are being deleted or replaced. The following is an excerpt of the algorithm, where changeSetChanges.Changes is the object representing the change set changes:

...
var RESOURCES_BEING_DELETED_OR_REPLACED = "RESOURCES-BEING-DELETED-OR-REPLACED";
var CAN_SAFELY_UPDATE_EXISTING_STACK = "CAN-SAFELY-UPDATE-EXISTING-STACK";
for (var i = 0; i < changeSetChanges.Changes.length; i++) {
    var change = changeSetChanges.Changes[i];
    if (change.Type == "Resource") {
        if (change.ResourceChange.Action == "Delete") {
            return RESOURCES_BEING_DELETED_OR_REPLACED;
        }
        if (change.ResourceChange.Action == "Modify") {
            if (change.ResourceChange.Replacement == "True") {
                return RESOURCES_BEING_DELETED_OR_REPLACED;
            }
        }
    }
}
return CAN_SAFELY_UPDATE_EXISTING_STACK;

The algorithm returns different values to indicate whether the change set can be safely executed (CAN_SAFELY_UPDATE_EXISTING_STACK or RESOURCES_BEING_DELETED_OR_REPLACED). This value is used later by the state machine to decide whether to execute the change set and update the stack or interrupt the deployment.

The output of the “Inspect Change Set” stage is shown here.

{
  "environmentName": "prod",
  "stackName": "sample-lambda-app",
  "templatePath": "infra/lambda-template.yaml",
  "revisionS3Bucket": "codepipeline-us-east-1-418586629775",
  "revisionS3Key": "StepFunctionsDrivenD/CodeCommit/sjcmExZ",
  "doesStackExist": true,
  "changeSetName": "prod-sample-lambda-app-change-set-545",
  "changeSetCreationStatus": "complete",
  "changeSetAction": "CAN-SAFELY-UPDATE-EXISTING-STACK"
}

At this point, these parameters have been added to the state machine’s original input:

  • changeSetName, which is added by the “Create Change Set” state.
  • changeSetCreationStatus, which is added by the “Get Change Set Creation Status” state.
  • changeSetAction, which is added by the “Inspect Change Set Changes” state.

The “Safe to Update Infra?” step is a choice state (its JSON spec follows) that simply checks the value of the changeSetAction parameter. If the value is equal to “CAN-SAFELY-UPDATE-EXISTING-STACK“, meaning that no resources will be deleted or replaced, the step will execute the change set by proceeding to the “Execute Change Set” state. The deployment is successful (the state machine completes its execution successfully).

"Safe to Update Infra?": {
      "Type": "Choice",
      "Choices": [
        {
          "Variable": "$.taskParams.changeSetAction",
          "StringEquals": "CAN-SAFELY-UPDATE-EXISTING-STACK",
          "Next": "Execute Change Set"
        }
      ],
      "Default": "Deployment Failed"
 }

Path 3: Reject Stack Update and Fail Deployment

Reject Stack Update and Fail Deployment Execution Path

If the changeSetAction parameter is different from “CAN-SAFELY-UPDATE-EXISTING-STACK“, the state machine will interrupt the deployment by deleting the change set and proceeding to the “Deployment Fail” step, which is a built-in Fail state. (Its JSON spec follows.) This state causes the state machine to stop in a failed state and serves to indicate to the Lambda function that the pipeline deployment should be interrupted in a fail state as well.

 "Deployment Failed": {
      "Type": "Fail",
      "Cause": "Deployment Failed",
      "Error": "Deployment Failed"
    }

In all three scenarios, there’s a state machine’s visual representation available in the AWS Step Functions console that makes it very easy for developers to identify what tasks have been executed or why a deployment has failed. Developers can also inspect the inputs and outputs of each state and look at the state machine Lambda function’s logs for details. Meanwhile, the corresponding CodePipeline action remains very simple and intuitive for developers who only need to know whether the deployment was successful or failed.

The State Machine Trigger Lambda Function

The Trigger Lambda function is invoked directly by the Deploy action in CodePipeline. The CodePipeline action must pass a JSON structure to the trigger function through the UserParameters attribute, as follows:

{
  "s3Bucket": "codepipeline-StepFunctions-sample",
  "stateMachineFile": "state_machine_input.json"
}

The s3Bucket parameter specifies the S3 bucket location for the state machine input parameters file. The stateMachineFile parameter specifies the file holding the input parameters. By being able to specify different input parameters to the state machine, we make the Trigger Lambda function and the state machine reusable across environments. For example, the same state machine could be called from a test and prod pipeline action by specifying a different S3 bucket or state machine input file for each environment.

The Trigger Lambda function performs two main tasks: triggering the state machine and checking the execution state of the state machine. Its core logic is shown here:

exports.index = function (event, context, callback) {
    try {
        console.log("Event: " + JSON.stringify(event));
        console.log("Context: " + JSON.stringify(context));
        console.log("Environment Variables: " + JSON.stringify(process.env));
        if (Util.isContinuingPipelineTask(event)) {
            monitorStateMachineExecution(event, context, callback);
        }
        else {
            triggerStateMachine(event, context, callback);
        }
    }
    catch (err) {
        failure(Util.jobId(event), callback, context.invokeid, err.message);
    }
}

Util.isContinuingPipelineTask(event) is a utility function that checks if the Trigger Lambda function is being called for the first time (that is, no continuation token is passed by CodePipeline) or as a continuation of a previous call. In its first execution, the Lambda function will trigger the state machine and send a continuation token to CodePipeline that contains the state machine execution ARN. The state machine ARN is exposed to the Lambda function through a Lambda environment variable called stateMachineArn. Here is the code that triggers the state machine:

function triggerStateMachine(event, context, callback) {
    var stateMachineArn = process.env.stateMachineArn;
    var s3Bucket = Util.actionUserParameter(event, "s3Bucket");
    var stateMachineFile = Util.actionUserParameter(event, "stateMachineFile");
    getStateMachineInputData(s3Bucket, stateMachineFile)
        .then(function (data) {
            var initialParameters = data.Body.toString();
            var stateMachineInputJSON = createStateMachineInitialInput(initialParameters, event);
            console.log("State machine input JSON: " + JSON.stringify(stateMachineInputJSON));
            return stateMachineInputJSON;
        })
        .then(function (stateMachineInputJSON) {
            return triggerStateMachineExecution(stateMachineArn, stateMachineInputJSON);
        })
        .then(function (triggerStateMachineOutput) {
            var continuationToken = { "stateMachineExecutionArn": triggerStateMachineOutput.executionArn };
            var message = "State machine has been triggered: " + JSON.stringify(triggerStateMachineOutput) + ", continuationToken: " + JSON.stringify(continuationToken);
            return continueExecution(Util.jobId(event), continuationToken, callback, message);
        })
        .catch(function (err) {
            console.log("Error triggering state machine: " + stateMachineArn + ", Error: " + err.message);
            failure(Util.jobId(event), callback, context.invokeid, err.message);
        })
}

The Trigger Lambda function fetches the state machine input parameters from an S3 file, triggers the execution of the state machine using the input parameters and the stateMachineArn environment variable, and signals to CodePipeline that the execution should continue later by passing a continuation token that contains the state machine execution ARN. In case any of these operations fail and an exception is thrown, the Trigger Lambda function will fail the pipeline immediately by signaling a pipeline failure through the putJobFailureResult CodePipeline API.

If the Lambda function is continuing a previous execution, it will extract the state machine execution ARN from the continuation token and check the status of the state machine, as shown here.

function monitorStateMachineExecution(event, context, callback) {
    var stateMachineArn = process.env.stateMachineArn;
    var continuationToken = JSON.parse(Util.continuationToken(event));
    var stateMachineExecutionArn = continuationToken.stateMachineExecutionArn;
    getStateMachineExecutionStatus(stateMachineExecutionArn)
        .then(function (response) {
            if (response.status === "RUNNING") {
                var message = "Execution: " + stateMachineExecutionArn + " of state machine: " + stateMachineArn + " is still " + response.status;
                return continueExecution(Util.jobId(event), continuationToken, callback, message);
            }
            if (response.status === "SUCCEEDED") {
                var message = "Execution: " + stateMachineExecutionArn + " of state machine: " + stateMachineArn + " has: " + response.status;
                return success(Util.jobId(event), callback, message);
            }
            // FAILED, TIMED_OUT, ABORTED
            var message = "Execution: " + stateMachineExecutionArn + " of state machine: " + stateMachineArn + " has: " + response.status;
            return failure(Util.jobId(event), callback, context.invokeid, message);
        })
        .catch(function (err) {
            var message = "Error monitoring execution: " + stateMachineExecutionArn + " of state machine: " + stateMachineArn + ", Error: " + err.message;
            failure(Util.jobId(event), callback, context.invokeid, message);
        });
}

If the state machine is in the RUNNING state, the Lambda function will send the continuation token back to the CodePipeline action. This will cause CodePipeline to call the Lambda function again a few seconds later. If the state machine has SUCCEEDED, then the Lambda function will notify the CodePipeline action that the action has succeeded. In any other case (FAILURE, TIMED-OUT, or ABORT), the Lambda function will fail the pipeline action.

This behavior is especially useful for developers who are building and debugging a new state machine because a bug in the state machine can potentially leave the pipeline action hanging for long periods of time until it times out. The Trigger Lambda function prevents this.

Also, by having the Trigger Lambda function as a means to decouple the pipeline and state machine, we make the state machine more reusable. It can be triggered from anywhere, not just from a CodePipeline action.

The Pipeline in CodePipeline

Our sample pipeline contains two simple stages: the Source stage represented by a CodeCommit Git repository and the Prod stage, which contains the Deploy action that invokes the Trigger Lambda function. When the state machine decides that the change set created must be rejected (because it replaces or deletes some the existing production resources), it fails the pipeline without performing any updates to the existing infrastructure. (See the failed Deploy action in red.) Otherwise, the pipeline action succeeds, indicating that the existing provisioned infrastructure was either created (first run) or updated without impacting any resources. (See the green Deploy stage in the pipeline on the left.)

The Pipeline in CodePipeline

The JSON spec for the pipeline’s Prod stage is shown here. We use the UserParameters attribute to pass the S3 bucket and state machine input file to the Lambda function. These parameters are action-specific, which means that we can reuse the state machine in another pipeline action.

{
  "name": "Prod",
  "actions": [
      {
          "inputArtifacts": [
              {
                  "name": "CodeCommitOutput"
              }
          ],
          "name": "Deploy",
          "actionTypeId": {
              "category": "Invoke",
              "owner": "AWS",
              "version": "1",
              "provider": "Lambda"
          },
          "outputArtifacts": [],
          "configuration": {
              "FunctionName": "StateMachineTriggerLambda",
              "UserParameters": "{\"s3Bucket\": \"codepipeline-StepFunctions-sample\", \"stateMachineFile\": \"state_machine_input.json\"}"
          },
          "runOrder": 1
      }
  ]
}

Conclusion

In this blog post, we discussed how state machines in AWS Step Functions can be used to handle workflow-driven actions. We showed how a Lambda function can be used to fully decouple the pipeline and the state machine and manage their interaction. The use of a state machine greatly simplified the associated CodePipeline action, allowing us to build a much simpler and cleaner pipeline while drilling down into the state machine’s execution for troubleshooting or debugging.

Here are two exercises you can complete by using the source code.

Exercise #1: Do not fail the state machine and pipeline action after inspecting a change set that deletes or replaces resources. Instead, create a stack with a different name (think of blue/green deployments). You can do this by creating a state machine transition between the “Safe to Update Infra?” and “Create Stack” stages and passing a new stack name as input to the “Create Stack” stage.

Exercise #2: Add wait logic to the state machine to wait until the change set completes its execution before allowing the state machine to proceed to the “Deployment Succeeded” stage. Use the stack creation case as an example. You’ll have to create a Lambda function (similar to the Lambda function that checks the creation status of a stack) to get the creation status of the change set.

Have fun and share your thoughts!

About the Author

Marcilio Mendonca is a Sr. Consultant in the Canadian Professional Services Team at Amazon Web Services. He has helped AWS customers design, build, and deploy best-in-class, cloud-native AWS applications using VMs, containers, and serverless architectures. Before he joined AWS, Marcilio was a Software Development Engineer at Amazon. Marcilio also holds a Ph.D. in Computer Science. In his spare time, he enjoys playing drums, riding his motorcycle in the Toronto GTA area, and spending quality time with his family.

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.

Amazon Redshift Dense Compute (DC2) Nodes Deliver Twice the Performance as DC1 at the Same Price

Post Syndicated from Quaseer Mujawar original https://aws.amazon.com/blogs/big-data/amazon-redshift-dense-compute-dc2-nodes-deliver-twice-the-performance-as-dc1-at-the-same-price/

Amazon Redshift makes analyzing exabyte-scale data fast, simple, and cost-effective. It delivers advanced data warehousing capabilities, including parallel execution, compressed columnar storage, and end-to-end encryption as a fully managed service, for less than $1,000/TB/year. With Amazon Redshift Spectrum, you can run SQL queries directly against exabytes of unstructured data in Amazon S3 for $5/TB scanned.

Today, we are making our Dense Compute (DC) family faster and more cost-effective with new second-generation Dense Compute (DC2) nodes at the same price as our previous generation DC1. DC2 is designed for demanding data warehousing workloads that require low latency and high throughput. DC2 features powerful Intel E5-2686 v4 (Broadwell) CPUs, fast DDR4 memory, and NVMe-based solid state disks.

We’ve tuned Amazon Redshift to take advantage of the better CPU, network, and disk on DC2 nodes, providing up to twice the performance of DC1 at the same price. Our DC2.8xlarge instances now provide twice the memory per slice of data and an optimized storage layout with 30 percent better storage utilization.

Customer successes

Several flagship customers, ranging from fast growing startups to large Fortune 100 companies, previewed the new DC2 node type. In their tests, DC2 provided up to twice the performance as DC1. Our preview customers saw faster ETL (extract, transform, and load) jobs, higher query throughput, better concurrency, faster reports, and shorter data-to-insights—all at the same cost as DC1. DC2.8xlarge customers also noted that their databases used up to 30 percent less disk space due to our optimized storage format, reducing their costs.

4Cite Marketing, one of America’s fastest growing private companies, uses Amazon Redshift to analyze customer data and determine personalized product recommendations for retailers. “Amazon Redshift’s new DC2 node is giving us a 100 percent performance increase, allowing us to provide faster insights for our retailers, more cost-effectively, to drive incremental revenue,” said Jim Finnerty, 4Cite’s senior vice president of product.

BrandVerity, a Seattle-based brand protection and compliance‎ company, provides solutions to monitor, detect, and mitigate online brand, trademark, and compliance abuse. “We saw a 70 percent performance boost with the DC2 nodes for running Redshift Spectrum queries. As a result, we can analyze far more data for our customers and deliver results much faster,” said Hyung-Joon Kim, principal software engineer at BrandVerity.

“Amazon Redshift is at the core of our operations and our marketing automation tools,” said Jarno Kartela, head of analytics and chief data scientist at DNA Plc, one of the leading Finnish telecommunications groups and Finland’s largest cable operator and pay TV provider. “We saw a 52 percent performance gain in moving to Amazon Redshift’s DC2 nodes. We can now run queries in half the time, allowing us to provide more analytics power and reduce time-to-insight for our analytics and marketing automation users.”

You can read about their experiences on our Customer Success page.

Get started

You can try the new node type using our getting started guide. Just choose dc2.large or dc2.8xlarge in the Amazon Redshift console:

If you have a DC1.large Amazon Redshift cluster, you can restore to a new DC2.large cluster using an existing snapshot. To migrate from DS2.xlarge, DS2.8xlarge, or DC1.8xlarge Amazon Redshift clusters, you can use the resize operation to move data to your new DC2 cluster. For more information, see Clusters and Nodes in Amazon Redshift.

To get the latest Amazon Redshift feature announcements, check out our What’s New page, and subscribe to the RSS feed.

How to Compete with Giants

Post Syndicated from Gleb Budman original https://www.backblaze.com/blog/how-to-compete-with-giants/

How to Compete with Giants

This post by Backblaze’s CEO and co-founder Gleb Budman is the sixth in a series about entrepreneurship. You can choose posts in the series from the list below:

  1. How Backblaze got Started: The Problem, The Solution, and the Stuff In-Between
  2. Building a Competitive Moat: Turning Challenges Into Advantages
  3. From Idea to Launch: Getting Your First Customers
  4. How to Get Your First 1,000 Customers
  5. Surviving Your First Year
  6. How to Compete with Giants

Use the Join button above to receive notification of new posts in this series.

Perhaps your business is competing in a brand new space free from established competitors. Most of us, though, start companies that compete with existing offerings from large, established companies. You need to come up with a better mousetrap — not the first mousetrap.

That’s the challenge Backblaze faced. In this post, I’d like to share some of the lessons I learned from that experience.

Backblaze vs. Giants

Competing with established companies that are orders of magnitude larger can be daunting. How can you succeed?

I’ll set the stage by offering a few sets of giants we compete with:

  • When we started Backblaze, we offered online backup in a market where companies had been offering “online backup” for at least a decade, and even the newer entrants had raised tens of millions of dollars.
  • When we built our storage servers, the alternatives were EMC, NetApp, and Dell — each of which had a market cap of over $10 billion.
  • When we introduced our cloud storage offering, B2, our direct competitors were Amazon, Google, and Microsoft. You might have heard of them.

What did we learn by competing with these giants on a bootstrapped budget? Let’s take a look.

Determine What Success Means

For a long time Apple considered Apple TV to be a hobby, not a real product worth focusing on, because it did not generate a billion in revenue. For a $10 billion per year revenue company, a new business that generates $50 million won’t move the needle and often isn’t worth putting focus on. However, for a startup, getting to $50 million in revenue can be the start of a wildly successful business.

Lesson Learned: Don’t let the giants set your success metrics.

The Advantages Startups Have

The giants have a lot of advantages: more money, people, scale, resources, access, etc. Following their playbook and attacking head-on means you’re simply outgunned. Common paths to failure are trying to build more features, enter more markets, outspend on marketing, and other similar approaches where scale and resources are the primary determinants of success.

But being a startup affords many advantages most giants would salivate over. As a nimble startup you can leverage those to succeed. Let’s breakdown nine competitive advantages we’ve used that you can too.

1. Drive Focus

It’s hard to build a $10 billion revenue business doing just one thing, and most giants have a broad portfolio of businesses, numerous products for each, and targeting a variety of customer segments in multiple markets. That adds complexity and distributes management attention.

Startups get the benefit of having everyone in the company be extremely focused, often on a singular mission, product, customer segment, and market. While our competitors sell everything from advertising to Zantac, and are investing in groceries and shipping, Backblaze has focused exclusively on cloud storage. This means all of our best people (i.e. everyone) is focused on our cloud storage business. Where is all of your focus going?

Lesson Learned: Align everyone in your company to a singular focus to dramatically out-perform larger teams.

2. Use Lack-of-Scale as an Advantage

You may have heard Paul Graham say “Do things that don’t scale.” There are a host of things you can do specifically because you don’t have the same scale as the giants. Use that as an advantage.

When we look for data center space, we have more options than our largest competitors because there are simply more spaces available with room for 100 cabinets than for 1,000 cabinets. With some searching, we can find data center space that is better/cheaper.

When a flood in Thailand destroyed factories, causing the world’s supply of hard drives to plummet and prices to triple, we started drive farming. The giants certainly couldn’t. It was a bit crazy, but it let us keep prices unchanged for our customers.

Our Chief Cloud Officer, Tim, used to work at Adobe. Because of their size, any new product needed to always launch in a multitude of languages and in global markets. Once launched, they had scale. But getting any new product launched was incredibly challenging.

Lesson Learned: Use lack-of-scale to exploit opportunities that are closed to giants.

3. Build a Better Product

This one is probably obvious. If you’re going to provide the same product, at the same price, to the same customers — why do it? Remember that better does not always mean more features. Here’s one way we built a better product that didn’t require being a bigger company.

All online backup services required customers to choose what to include in their backup. We found that this was complicated for users since they often didn’t know what needed to be backed up. We flipped the model to back up everything and allow users to exclude if they wanted to, but it was not required. This reduced the number of features/options, while making it easier and better for the user.

This didn’t require the resources of a huge company; it just required understanding customers a bit deeper and thinking about the solution differently. Building a better product is the most classic startup competitive advantage.

Lesson Learned: Dig deep with your customers to understand and deliver a better mousetrap.

4. Provide Better Service

How can you provide better service? Use your advantages. Escalations from your customer care folks to engineering can go through fewer hoops. Fixing an issue and shipping can be quicker. Access to real answers on Twitter or Facebook can be more effective.

A strategic decision we made was to have all customer support people as full-time employees in our headquarters. This ensures they are in close contact to the whole company for feedback to quickly go both ways.

Having a smaller team and fewer layers enables faster internal communication, which increases customer happiness. And the option to do things that don’t scale — such as help a customer in a unique situation — can go a long way in building customer loyalty.

Lesson Learned: Service your customers better by establishing clear internal communications.

5. Remove The Unnecessary

After determining that the industry standard EMC/NetApp/Dell storage servers would be too expensive to build our own cloud storage upon, we decided to build our own infrastructure. Many said we were crazy to compete with these multi-billion dollar companies and that it would be impossible to build a lower cost storage server. However, not only did it prove to not be impossible — it wasn’t even that hard.

One key trick? Remove the unnecessary. While EMC and others built servers to sell to other companies for a wide variety of use cases, Backblaze needed servers that only Backblaze would run, and for a single use case. As a result we could tailor the servers for our needs by removing redundancy from each server (since we would run redundant servers), and using lower-performance components (since we would get high-performance by running parallel servers).

What do your customers and use cases not need? This can trim costs and complexity while often improving the product for your use case.

Lesson Learned: Don’t think “what can we add” to what the giants offer — think “what can we remove.”

6. Be Easy

How many times have you visited a large company website, particularly one that’s not consumer-focused, only to leave saying, “Huh? I don’t understand what you do.” Keeping your website clear, and your product and pricing simple, will dramatically increase conversion and customer satisfaction. If you’re able to make it 2x easier and thus increasing your conversion by 2x, you’ve just allowed yourself to spend ½ as much acquiring a customer.

Providing unlimited data backup wasn’t specifically about providing more storage — it was about making it easier. Since users didn’t know how much data they needed to back up, charging per gigabyte meant they wouldn’t know the cost. Providing unlimited data backup meant they could just relax.

Customers love easy — and being smaller makes easy easier to deliver. Use that as an advantage in your website, marketing materials, pricing, product, and in every other customer interaction.

Lesson Learned: Ease-of-use isn’t a slogan: it’s a competitive advantage. Treat it as seriously as any other feature of your product

7. Don’t Be Afraid of Risk

Obviously unnecessary risks are unnecessary, and some risks aren’t worth taking. However, large companies that have given guidance to Wall Street with a $0.01 range on their earning-per-share are inherently going to be very risk-averse. Use risk-tolerance to open up opportunities, and adjust your tolerance level as you scale. In your first year, there are likely an infinite number of ways your business may vaporize; don’t be too worried about taking a risk that might have a 20% downside when the upside is hockey stick growth.

Using consumer-grade hard drives in our servers may have caused pain and suffering for us years down-the-line, but they were priced at approximately 50% of enterprise drives. Giants wouldn’t have considered the option. Turns out, the consumer drives performed great for us.

Lesson Learned: Use calculated risks as an advantage.

8. Be Open

The larger a company grows, the more it wants to hide information. Some of this is driven by regulatory requirements as a public company. But most of this is cultural. Sharing something might cause a problem, so let’s not. All external communication is treated as a critical press release, with rounds and rounds of editing by multiple teams and approvals. However, customers are often desperate for information. Moreover, sharing information builds trust, understanding, and advocates.

I started blogging at Backblaze before we launched. When we blogged about our Storage Pod and open-sourced the design, many thought we were crazy to share this information. But it was transformative for us, establishing Backblaze as a tech thought leader in storage and giving people a sense of how we were able to provide our service at such a low cost.

Over the years we’ve developed a culture of being open internally and externally, on our blog and with the press, and in communities such as Hacker News and Reddit. Often we’ve been asked, “why would you share that!?” — but it’s the continual openness that builds trust. And that culture of openness is incredibly challenging for the giants.

Lesson Learned: Overshare to build trust and brand where giants won’t.

9. Be Human

As companies scale, typically a smaller percent of founders and executives interact with customers. The people who build the company become more hidden, the language feels “corporate,” and customers start to feel they’re interacting with the cliche “faceless, nameless corporation.” Use your humanity to your advantage. From day one the Backblaze About page listed all the founders, and my email address. While contacting us shouldn’t be the first path for a customer support question, I wanted it to be clear that we stand behind the service we offer; if we’re doing something wrong — I want to know it.

To scale it’s important to have processes and procedures, but sometimes a situation falls outside of a well-established process. While we want our employees to follow processes, they’re still encouraged to be human and “try to do the right thing.” How to you strike this balance? Simon Sinek gives a good talk about it: make your employees feel safe. If employees feel safe they’ll be human.

If your customer is a consumer, they’ll appreciate being treated as a human. Even if your customer is a corporation, the purchasing decision-makers are still people.

Lesson Learned: Being human is the ultimate antithesis to the faceless corporation.

Build Culture to Sustain Your Advantages at Scale

Presumably the goal is not to always be competing with giants, but to one day become a giant. Does this mean you’ll lose all of these advantages? Some, yes — but not all. Some of these advantages are cultural, and if you build these into the culture from the beginning, and fight to keep them as you scale, you can keep them as you become a giant.

Tesla still comes across as human, with Elon Musk frequently interacting with people on Twitter. Apple continues to provide great service through their Genius Bar. And, worst case, if you lose these at scale, you’ll still have the other advantages of being a giant such as money, people, scale, resources, and access.

Of course, some new startup will be gunning for you with grand ambitions, so just be sure not to get complacent. 😉

The post How to Compete with Giants appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

More Raspberry Pi labs in West Africa

Post Syndicated from Rachel Churcher original https://www.raspberrypi.org/blog/pi-based-ict-west-africa/

Back in May 2013, we heard from Dominique Laloux about an exciting project to bring Raspberry Pi labs to schools in rural West Africa. Until 2012, 75 percent of teachers there had never used a computer. The project has been very successful, and Dominique has been in touch again to bring us the latest news.

A view of the inside of the new Pi lab building

Preparing the new Pi labs building in Kuma Tokpli, Togo

Growing the project

Thanks to the continuing efforts of a dedicated team of teachers, parents and other supporters, the Centre Informatique de Kuma, now known as INITIC (from the French ‘INItiation aux TIC’), runs two Raspberry Pi labs in schools in Togo, and plans to open a third in December. The second lab was opened last year in Kpalimé, a town in the Plateaux Region in the west of the country.

Student using a Raspberry Pi computer

Using the new Raspberry Pi labs in Kpalimé, Togo

More than 400 students used the new lab intensively during the last school year. Dominique tells us more:

“The report made in early July by the seven teachers who accompanied the students was nothing short of amazing: the young people covered a very impressive number of concepts and skills, from the GUI and the file system, to a solid introduction to word processing and spreadsheets, and many other skills. The lab worked exactly as expected. Its 21 Raspberry Pis worked flawlessly, with the exception of a couple of SD cards that needed re-cloning, and a couple of old screens that needed to be replaced. All the Raspberry Pis worked without a glitch. They are so reliable!”

The teachers and students have enjoyed access to a range of software and resources, all running on Raspberry Pi 2s and 3s.

“Our current aim is to introduce the students to ICT using the Raspberry Pis, rather than introducing them to programming and electronics (a step that will certainly be considered later). We use Ubuntu Mate along with a large selection of applications, from LibreOffice, Firefox, GIMP, Audacity, and Calibre, to special maths, science, and geography applications. There are also special applications such as GnuCash and GanttProject, as well as logic games including PyChess. Since December, students also have access to a local server hosting Kiwix, Wiktionary (a local copy of Wikipedia in four languages), several hundred videos, and several thousand books. They really love it!”

Pi lab upgrade

This summer, INITIC upgraded the equipment in their Pi lab in Kuma Adamé, which has been running since 2014. 21 older model Raspberry Pis were replaced with Pi 2s and 3s, to bring this lab into line with the others, and encourage co-operation between the different locations.

“All 21 first-generation Raspberry Pis worked flawlessly for three years, despite the less-than-ideal conditions in which they were used — tropical conditions, dust, frequent power outages, etc. I brought them all back to Brussels, and they all still work fine. The rationale behind the upgrade was to bring more computing power to the lab, and also to have the same equipment in our two Raspberry Pi labs (and in other planned installations).”

Students and teachers using the upgraded Pi labs in Kuma Adamé

Students and teachers using the upgraded Pi lab in Kuma Adamé

An upgrade of the organisation’s first lab, installed in 2012 in Kuma Tokpli, will be completed in December. This lab currently uses ‘retired’ laptops, which will be replaced with Raspberry Pis and peripherals. INITIC, in partnership with the local community, is also constructing a new building to house the upgraded technology, and the organisation’s third Raspberry Pi lab.

Reliable tech

Dominique has been very impressed with the performance of the Raspberry Pis since 2014.

“Our experience of three years, in two very different contexts, clearly demonstrates that the Raspberry Pi is a very convincing alternative to more ‘conventional’ computers for introducing young students to ICT where resources are scarce. I wish I could convince more communities in the world to invest in such ‘low cost, low consumption, low maintenance’ infrastructure. It really works!”

He goes on to explain that:

“Our goal now is to build at least one new Raspberry Pi lab in another Togolese school each year. That will, of course, depend on how successful we are at gathering the funds necessary for each installation, but we are confident we can convince enough friends to give us the financial support needed for our action.”

A desk with Raspberry Pis and peripherals

Reliable Raspberry Pis in the labs at Kpalimé

Get involved

We are delighted to see the Raspberry Pi being used to bring information technology to new teachers, students, and communities in Togo – it’s wonderful to see this project becoming established and building on its achievements. The mission of the Raspberry Pi Foundation is to put the power of digital making into the hands of people all over the world. Therefore, projects like this, in which people use our tech to fulfil this mission in places with few resources, are wonderful to us.

More information about INITIC and its projects can be found on its website. If you are interested in helping the organisation to meet its goals, visit the How to help page. And if you are involved with a project like this, bringing ICT, computer science, and coding to new places, please tell us about it in the comments below.

The post More Raspberry Pi labs in West Africa appeared first on Raspberry Pi.

Abandon Proactive Copyright Filters, Huge Coalition Tells EU Heavyweights

Post Syndicated from Andy original https://torrentfreak.com/abandon-proactive-copyright-filters-huge-coalition-tells-eu-heavyweights-171017/

Last September, EU Commission President Jean-Claude Juncker announced plans to modernize copyright law in Europe.

The proposals (pdf) are part of the Digital Single Market reforms, which have been under development for the past several years.

One of the proposals is causing significant concern. Article 13 would require some online service providers to become ‘Internet police’, proactively detecting and filtering allegedly infringing copyright works, uploaded to their platforms by users.

Currently, users are generally able to share whatever they like but should a copyright holder take exception to their upload, mechanisms are available for that content to be taken down. It’s envisioned that proactive filtering, whereby user uploads are routinely scanned and compared to a database of existing protected content, will prevent content becoming available in the first place.

These proposals are of great concern to digital rights groups, who believe that such filters will not only undermine users’ rights but will also place unfair burdens on Internet platforms, many of which will struggle to fund such a program. Yesterday, in the latest wave of opposition to Article 13, a huge coalition of international rights groups came together to underline their concerns.

Headed up by Civil Liberties Union for Europe (Liberties) and European Digital Rights (EDRi), the coalition is formed of dozens of influential groups, including Electronic Frontier Foundation (EFF), Human Rights Watch, Reporters without Borders, and Open Rights Group (ORG), to name just a few.

In an open letter to European Commission President Jean-Claude Juncker, President of the European Parliament Antonio Tajani, President of the European Council Donald Tusk and a string of others, the groups warn that the proposals undermine the trust established between EU member states.

“Fundamental rights, justice and the rule of law are intrinsically linked and constitute
core values on which the EU is founded,” the letter begins.

“Any attempt to disregard these values undermines the mutual trust between member states required for the EU to function. Any such attempt would also undermine the commitments made by the European Union and national governments to their citizens.”

Those citizens, the letter warns, would have their basic rights undermined, should the new proposals be written into EU law.

“Article 13 of the proposal on Copyright in the Digital Single Market include obligations on internet companies that would be impossible to respect without the imposition of excessive restrictions on citizens’ fundamental rights,” it notes.

A major concern is that by placing new obligations on Internet service providers that allow users to upload content – think YouTube, Facebook, Twitter and Instagram – they will be forced to err on the side of caution. Should there be any concern whatsoever that content might be infringing, fair use considerations and exceptions will be abandoned in favor of staying on the right side of the law.

“Article 13 appears to provoke such legal uncertainty that online services will have no other option than to monitor, filter and block EU citizens’ communications if they are to have any chance of staying in business,” the letter warns.

But while the potential problems for service providers and users are numerous, the groups warn that Article 13 could also be illegal since it contradicts case law of the Court of Justice.

According to the E-Commerce Directive, platforms are already required to remove infringing content, once they have been advised it exists. The new proposal, should it go ahead, would force the monitoring of uploads, something which goes against the ‘no general obligation to monitor‘ rules present in the Directive.

“The requirement to install a system for filtering electronic communications has twice been rejected by the Court of Justice, in the cases Scarlet Extended (C70/10) and Netlog/Sabam (C 360/10),” the rights groups warn.

“Therefore, a legislative provision that requires internet companies to install a filtering system would almost certainly be rejected by the Court of Justice because it would contravene the requirement that a fair balance be struck between the right to intellectual property on the one hand, and the freedom to conduct business and the right to freedom of expression, such as to receive or impart information, on the other.”

Specifically, the groups note that the proactive filtering of content would violate freedom of expression set out in Article 11 of the Charter of Fundamental Rights. That being the case, the groups expect national courts to disapply it and the rule to be annulled by the Court of Justice.

The latest protests against Article 13 come in the wake of large-scale objections earlier in the year, voicing similar concerns. However, despite the groups’ fears, they have powerful adversaries, each determined to stop the flood of copyrighted content currently being uploaded to the Internet.

Front and center in support of Article 13 is the music industry and its current hot-topic, the so-called Value Gap(1,2,3). The industry feels that platforms like YouTube are able to avoid paying expensive licensing fees (for music in particular) by exploiting the safe harbor protections of the DMCA and similar legislation.

They believe that proactively filtering uploads would significantly help to diminish this problem, which may very well be the case. But at what cost to the general public and the platforms they rely upon? Citizens and scholars feel that freedoms will be affected and it’s likely the outcry will continue.

The ball is now with the EU, whose members will soon have to make what could be the most important decision in recent copyright history. The rights groups, who are urging for Article 13 to be deleted, are clear where they stand.

The full letter is available here (pdf)

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

Amazon Lightsail Update – Launch and Manage Windows Virtual Private Servers

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/amazon-lightsail-update-launch-and-manage-windows-virtual-private-servers/

I first told you about Amazon Lightsail last year in my blog post, Amazon Lightsail – the Power of AWS, the Simplicity of a VPS. Since last year’s launch, thousands of customers have used Lightsail to get started with AWS, launching Linux-based Virtual Private Servers.

Today we are adding support for Windows-based Virtual Private Servers. You can launch a VPS that runs Windows Server 2012 R2, Windows Server 2016, or Windows Server 2016 with SQL Server 2016 Express and be up and running in minutes. You can use your VPS to build, test, and deploy .NET or Windows applications without having to set up or run any infrastructure. Backups, DNS management, and operational metrics are all accessible with a click or two.

Servers are available in five sizes, with 512 MB to 8 GB of RAM, 1 or 2 vCPUs, and up to 80 GB of SSD storage. Prices (including software licenses) start at $10 per month:

You can try out a 512 MB server for one month (up to 750 hours) at no charge.

Launching a Windows VPS
To launch a Windows VPS, log in to Lightsail , click on Create instance, and select the Microsoft Windows platform. Then click on Apps + OS if you want to run SQL Server 2016 Express, or OS Only if Windows is all you need:

If you want to use a Powershell script to customize your instance after it launches for the first time, click on Add launch script and enter the script:

Choose your instance plan, enter a name for your instance(s), and select the quantity to be launched, then click on Create:

Your instance will be up and running within a minute or so:

Click on the instance, and then click on Connect using RDP:

This will connect using a built-in, browser-based RDP client (you can also use the IP address and the credentials with another client):

Available Today
This feature is available today in the US East (Northern Virginia), US East (Ohio), US West (Oregon), EU (London), EU (Ireland), EU (Frankfurt), Asia Pacific (Singapore), Asia Pacific (Mumbai), Asia Pacific (Sydney), and Asia Pacific (Tokyo) Regions.

Jeff;

 

Some notes on the KRACK attack

Post Syndicated from Robert Graham original http://blog.erratasec.com/2017/10/some-notes-on-krack-attack.html

This is my interpretation of the KRACK attacks paper that describes a way of decrypting encrypted WiFi traffic with an active attack.

tl;dr: Wow. Everyone needs to be afraid. (Well, worried — not panicked.) It means in practice, attackers can decrypt a lot of wifi traffic, with varying levels of difficulty depending on your precise network setup. My post last July about the DEF CON network being safe was in error.

Details

This is not a crypto bug but a protocol bug (a pretty obvious and trivial protocol bug).
When a client connects to the network, the access-point will at some point send a random “key” data to use for encryption. Because this packet may be lost in transmission, it can be repeated many times.
What the hacker does is just repeatedly sends this packet, potentially hours later. Each time it does so, it resets the “keystream” back to the starting conditions. The obvious patch that device vendors will make is to only accept the first such packet it receives, ignore all the duplicates.
At this point, the protocol bug becomes a crypto bug. We know how to break crypto when we have two keystreams from the same starting position. It’s not always reliable, but reliable enough that people need to be afraid.
Android, though, is the biggest danger. Rather than simply replaying the packet, a packet with key data of all zeroes can be sent. This allows attackers to setup a fake WiFi access-point and man-in-the-middle all traffic.
In a related case, the access-point/base-station can sometimes also be attacked, affecting the stream sent to the client.
Not only is sniffing possible, but in some limited cases, injection. This allows the traditional attack of adding bad code to the end of HTML pages in order to trick users into installing a virus.

This is an active attack, not a passive attack, so in theory, it’s detectable.

Who is vulnerable?

Everyone, pretty much.
The hacker only needs to be within range of your WiFi. Your neighbor’s teenage kid is going to be downloading and running the tool in order to eavesdrop on your packets.
The hacker doesn’t need to be logged into your network.
It affects all WPA1/WPA2, the personal one with passwords that we use in home, and the enterprise version with certificates we use in enterprises.
It can’t defeat SSL/TLS or VPNs. Thus, if you feel your laptop is safe surfing the public WiFi at airports, then your laptop is still safe from this attack. With Android, it does allow running tools like sslstrip, which can fool many users.
Your home network is vulnerable. Many devices will be using SSL/TLS, so are fine, like your Amazon echo, which you can continue to use without worrying about this attack. Other devices, like your Phillips lightbulbs, may not be so protected.

How can I defend myself?

Patch.
More to the point, measure your current vendors by how long it takes them to patch. Throw away gear by those vendors that took a long time to patch and replace it with vendors that took a short time.
High-end access-points that contains “WIPS” (WiFi Intrusion Prevention Systems) features should be able to detect this and block vulnerable clients from connecting to the network (once the vendor upgrades the systems, of course). Even low-end access-points, like the $30 ones you get for home, can easily be updated to prevent packet sequence numbers from going back to the start (i.e. from the keystream resetting back to the start).
At some point, you’ll need to run the attack against yourself, to make sure all your devices are secure. Since you’ll be constantly allowing random phones to connect to your network, you’ll need to check their vulnerability status before connecting them. You’ll need to continue doing this for several years.
Of course, if you are using SSL/TLS for everything, then your danger is mitigated. This is yet another reason why you should be using SSL/TLS for internal communications.
Most security vendors will add things to their products/services to defend you. While valuable in some cases, it’s not a defense. The defense is patching the devices you know about, and preventing vulnerable devices from attaching to your network.
If I remember correctly, DEF CON uses Aruba. Aruba contains WIPS functionality, which means by the time DEF CON roles around again next year, they should have the feature to deny vulnerable devices from connecting, and specifically to detect an attack in progress and prevent further communication.
However, for an attacker near an Android device using a low-powered WiFi, it’s likely they will be able to conduct man-in-the-middle without any WIPS preventing them.

Coaxing 2D platforming out of Unity

Post Syndicated from Eevee original https://eev.ee/blog/2017/10/13/coaxing-2d-platforming-out-of-unity/

An anonymous donor asked a question that I can’t even begin to figure out how to answer, but they also said anything else is fine, so here’s anything else.

I’ve been avoiding writing about game physics, since I want to save it for ✨ the book I’m writing ✨, but that book will almost certainly not touch on Unity. Here, then, is a brief run through some of the brick walls I ran into while trying to convince Unity to do 2D platforming.

This is fairly high-level — there are no blocks of code or helpful diagrams. I’m just getting this out of my head because it’s interesting. If you want more gritty details, I guess you’ll have to wait for ✨ the book ✨.

The setup

I hadn’t used Unity before. I hadn’t even used a “real” physics engine before. My games so far have mostly used LÖVE, a Lua-based engine. LÖVE includes box2d bindings, but for various reasons (not all of them good), I opted to avoid them and instead write my own physics completely from scratch. (How, you ask? ✨ Book ✨!)

I was invited to work on a Unity project, Chaos Composer, that someone else had already started. It had basic movement already implemented; I taught myself Unity’s physics system by hacking on it. It’s entirely possible that none of this is actually the best way to do anything, since I was really trying to reproduce my own homegrown stuff in Unity, but it’s the best I’ve managed to come up with.

Two recurring snags were that you can’t ask Unity to do multiple physics updates in a row, and sometimes getting the information I wanted was difficult. Working with my own code spoiled me a little, since I could invoke it at any time and ask it anything I wanted; Unity, on the other hand, is someone else’s black box with a rigid interface on top.

Also, wow, Googling for a lot of this was not quite as helpful as expected. A lot of what’s out there is just the first thing that works, and often that’s pretty hacky and imposes severe limits on the game design (e.g., “this won’t work with slopes”). Basic movement and collision are the first thing you do, which seems to me like the worst time to be locking yourself out of a lot of design options. I tried very (very, very, very) hard to minimize those kinds of constraints.

Problem 1: Movement

When I showed up, movement was already working. Problem solved!

Like any good programmer, I immediately set out to un-solve it. Given a “real” physics engine like Unity prominently features, you have two options: ⓐ treat the player as a physics object, or ⓑ don’t. The existing code went with option ⓑ, like I’d done myself with LÖVE, and like I’d seen countless people advise. Using a physics sim makes for bad platforming.

But… why? I believed it, but I couldn’t concretely defend it. I had to know for myself. So I started a blank project, drew some physics boxes, and wrote a dozen-line player controller.

Ah! Immediate enlightenment.

If the player was sliding down a wall, and I tried to move them into the wall, they would simply freeze in midair until I let go of the movement key. The trouble is that the physics sim works in terms of forces — moving the player involves giving them a nudge in some direction, like a giant invisible hand pushing them around the level. Surprise! If you press a real object against a real wall with your real hand, you’ll see the same effect — friction will cancel out gravity, and the object will stay in midair..

Platformer movement, as it turns out, doesn’t make any goddamn physical sense. What is air control? What are you pushing against? Nothing, really; we just have it because it’s nice to play with, because not having it is a nightmare.

I looked to see if there were any common solutions to this, and I only really found one: make all your walls frictionless.

Game development is full of hacks like this, and I… don’t like them. I can accept that minor hacks are necessary sometimes, but this one makes an early and widespread change to a fundamental system to “fix” something that was wrong in the first place. It also imposes an “invisible” requirement, something I try to avoid at all costs — if you forget to make a particular wall frictionless, you’ll never know unless you happen to try sliding down it.

And so, I swiftly returned to the existing code. It wasn’t too different from what I’d come up with for LÖVE: it applied gravity by hand, tracked the player’s velocity, computed the intended movement each frame, and moved by that amount. The interesting thing was that it used MovePosition, which schedules a movement for the next physics update and stops the movement if the player hits something solid.

It’s kind of a nice hybrid approach, actually; all the “physics” for conscious actors is done by hand, but the physics engine is still used for collision detection. It’s also used for collision rejection — if the player manages to wedge themselves several pixels into a solid object, for example, the physics engine will try to gently nudge them back out of it with no extra effort required on my part. I still haven’t figured out how to get that to work with my homegrown stuff, which is built to prevent overlap rather than to jiggle things out of it.

But wait, what about…

Our player is a dynamic body with rotation lock and no gravity. Why not just use a kinematic body?

I must be missing something, because I do not understand the point of kinematic bodies. I ran into this with Godot, too, which documented them the same way: as intended for use as players and other manually-moved objects. But by default, they don’t even collide with other kinematic bodies or static geometry. What? There’s a checkbox to turn this on, which I enabled, but then I found out that MovePosition doesn’t stop kinematic bodies when they hit something, so I would’ve had to cast along the intended path of movement to figure out when to stop, thus duplicating the same work the physics engine was about to do.

But that’s impossible anyway! Static geometry generally wants to be made of edge colliders, right? They don’t care about concave/convex. Imagine the player is standing on the ground near a wall and tries to move towards the wall. Both the ground and the wall are different edges from the same edge collider.

If you try to cast the player’s hitbox horizontally, parallel to the ground, you’ll only get one collision: the existing collision with the ground. Casting doesn’t distinguish between touching and hitting. And because Unity only reports one collision per collider, and because the ground will always show up first, you will never find out about the impending wall collision.

So you’re forced to either use raycasts for collision detection or decomposed polygons for world geometry, both of which are slightly worse tools for no real gain.

I ended up sticking with a dynamic body.


Oh, one other thing that doesn’t really fit anywhere else: keep track of units! If you’re adding something called “velocity” directly to something called “position”, something has gone very wrong. Acceleration is distance per time squared; velocity is distance per time; position is distance. You must multiply or divide by time to convert between them.

I never even, say, add a constant directly to position every frame; I always phrase it as velocity and multiply by Δt. It keeps the units consistent: time is always in seconds, not in tics.

Problem 2: Slopes

Ah, now we start to get off in the weeds.

A sort of pre-problem here was detecting whether we’re on a slope, which means detecting the ground. The codebase originally used a manual physics query of the area around the player’s feet to check for the ground, which seems to be somewhat common, but that can’t tell me the angle of the detected ground. (It’s also kind of error-prone, since “around the player’s feet” has to be specified by hand and may not stay correct through animations or changes in the hitbox.)

I replaced that with what I’d eventually settled on in LÖVE: detect the ground by detecting collisions, and looking at the normal of the collision. A normal is a vector that points straight out from a surface, so if you’re standing on the ground, the normal points straight up; if you’re on a 10° incline, the normal points 10° away from straight up.

Not all collisions are with the ground, of course, so I assumed something is ground if the normal pointed away from gravity. (I like this definition more than “points upwards”, because it avoids assuming anything about the direction of gravity, which leaves some interesting doors open for later on.) That’s easily detected by taking the dot product — if it’s negative, the collision was with the ground, and I now have the normal of the ground.

Actually doing this in practice was slightly tricky. With my LÖVE engine, I could cram this right into the middle of collision resolution. With Unity, not quite so much. I went through a couple iterations before I really grasped Unity’s execution order, which I guess I will have to briefly recap for this to make sense.

Unity essentially has two update cycles. It performs physics updates at fixed intervals for consistency, and updates everything else just before rendering. Within a single frame, Unity does as many fixed physics updates as it has spare time for (which might be zero, one, or more), then does a regular update, then renders. User code can implement either or both of Update, which runs during a regular update, and FixedUpdate, which runs just before Unity does a physics pass.

So my solution was:

  • At the very end of FixedUpdate, clear the actor’s “on ground” flag and ground normal.

  • During OnCollisionEnter2D and OnCollisionStay2D (which are called from within a physics pass), if there’s a collision that looks like it’s with the ground, set the “on ground” flag and ground normal. (If there are multiple ground collisions, well, good luck figuring out the best way to resolve that! At the moment I’m just taking the first and hoping for the best.)

That means there’s a brief window between the end of FixedUpdate and Unity’s physics pass during which a grounded actor might mistakenly believe it’s not on the ground, which is a bit of a shame, but there are very few good reasons for anything to be happening in that window.

Okay! Now we can do slopes.

Just kidding! First we have to do sliding.

When I first looked at this code, it didn’t apply gravity while the player was on the ground. I think I may have had some problems with detecting the ground as result, since the player was no longer pushing down against it? Either way, it seemed like a silly special case, so I made gravity always apply.

Lo! I was a fool. The player could no longer move.

Why? Because MovePosition does exactly what it promises. If the player collides with something, they’ll stop moving. Applying gravity means that the player is trying to move diagonally downwards into the ground, and so MovePosition stops them immediately.

Hence, sliding. I don’t want the player to actually try to move into the ground. I want them to move the unblocked part of that movement. For flat ground, that means the horizontal part, which is pretty much the same as discarding gravity. For sloped ground, it’s a bit more complicated!

Okay but actually it’s less complicated than you’d think. It can be done with some cross products fairly easily, but Unity makes it even easier with a couple casts. There’s a Vector3.ProjectOnPlane function that projects an arbitrary vector on a plane given by its normal — exactly the thing I want! So I apply that to the attempted movement before passing it along to MovePosition. I do the same thing with the current velocity, to prevent the player from accelerating infinitely downwards while standing on flat ground.

One other thing: I don’t actually use the detected ground normal for this. The player might be touching two ground surfaces at the same time, and I’d want to project on both of them. Instead, I use the player body’s GetContacts method, which returns contact points (and normals!) for everything the player is currently touching. I believe those contact points are tracked by the physics engine anyway, so asking for them doesn’t require any actual physics work.

(Looking at the code I have, I notice that I still only perform the slide for surfaces facing upwards — but I’d want to slide against sloped ceilings, too. Why did I do this? Maybe I should remove that.)

(Also, I’m pretty sure projecting a vector on a plane is non-commutative, which raises the question of which order the projections should happen in and what difference it makes. I don’t have a good answer.)

(I note that my LÖVE setup does something slightly different: it just tries whatever the movement ought to be, and if there’s a collision, then it projects — and tries again with the remaining movement. But I can’t ask Unity to do multiple moves in one physics update, alas.)

Okay! Now, slopes. But actually, with the above work done, slopes are most of the way there already.

One obvious problem is that the player tries to move horizontally even when on a slope, and the easy fix is to change their movement from speed * Vector2.right to speed * new Vector2(ground.y, -ground.x) while on the ground. That’s the ground normal rotated a quarter-turn clockwise, so for flat ground it still points to the right, and in general it points rightwards along the ground. (Note that it assumes the ground normal is a unit vector, but as far as I’m aware, that’s true for all the normals Unity gives you.)

Another issue is that if the player stands motionless on a slope, gravity will cause them to slowly slide down it — because the movement from gravity will be projected onto the slope, and unlike flat ground, the result is no longer zero. For conscious actors only, I counter this by adding the opposite factor to the player’s velocity as part of adding in their walking speed. This matches how the real world works, to some extent: when you’re standing on a hill, you’re exerting some small amount of effort just to stay in place.

(Note that slope resistance is not the same as friction. Okay, yes, in the real world, virtually all resistance to movement happens as a result of friction, but bracing yourself against the ground isn’t the same as being passively resisted.)

From here there are a lot of things you can do, depending on how you think slopes should be handled. You could make the player unable to walk up slopes that are too steep. You could make walking down a slope faster than walking up it. You could make jumping go along the ground normal, rather than straight up. You could raise the player’s max allowed speed while running downhill. Whatever you want, really. Armed with a normal and awareness of dot products, you can do whatever you want.

But first you might want to fix a few aggravating side effects.

Problem 3: Ground adherence

I don’t know if there’s a better name for this. I rarely even see anyone talk about it, which surprises me; it seems like it should be a very common problem.

The problem is: if the player runs up a slope which then abruptly changes to flat ground, their momentum will carry them into the air. For very fast players going off the top of very steep slopes, this makes sense, but it becomes visible even for relatively gentle slopes. It was a mild nightmare in the original release of our game Lunar Depot 38, which has very “rough” ground made up of lots of shallow slopes — so the player is very frequently slightly off the ground, which meant they couldn’t jump, for seemingly no reason. (I even had code to fix this, but I disabled it because of a silly visual side effect that I never got around to fixing.)

Anyway! The reason this is a problem is that game protagonists are generally not boxes sliding around — they have legs. We don’t go flying off the top of real-world hilltops because we put our foot down until it touches the ground.

Simulating this footfall is surprisingly fiddly to get right, especially with someone else’s physics engine. It’s made somewhat easier by Cast, which casts the entire hitbox — no matter what shape it is — in a particular direction, as if it had moved, and tells you all the hypothetical collisions in order.

So I cast the player in the direction of gravity by some distance. If the cast hits something solid with a ground-like collision normal, then the player must be close to the ground, and I move them down to touch it (and set that ground as the new ground normal).

There are some wrinkles.

Wrinkle 1: I only want to do this if the player is off the ground now, but was on the ground last frame, and is not deliberately moving upwards. That latter condition means I want to skip this logic if the player jumps, for example, but also if the player is thrust upwards by a spring or abducted by a UFO or whatever. As long as external code goes through some interface and doesn’t mess with the player’s velocity directly, that shouldn’t be too hard to track.

Wrinkle 2: When does this logic run? It needs to happen after the player moves, which means after a Unity physics pass… but there’s no callback for that point in time. I ended up running it at the beginning of FixedUpdate and the beginning of Update — since I definitely want to do it before rendering happens! That means it’ll sometimes happen twice between physics updates. (I could carefully juggle a flag to skip the second run, but I… didn’t do that. Yet?)

Wrinkle 3: I can’t move the player with MovePosition! Remember, MovePosition schedules a movement, it doesn’t actually perform one; that means if it’s called twice before the physics pass, the first call is effectively ignored. I can’t easily combine the drop with the player’s regular movement, for various fiddly reasons. I ended up doing it “by hand” using transform.Translate, which I think was the “old way” to do manual movement before MovePosition existed. I’m not totally sure if it activates triggers? For that matter, I’m not sure it even notices collisions — but since I did a full-body Cast, there shouldn’t be any anyway.

Wrinkle 4: What, exactly, is “some distance”? I’ve yet to find a satisfying answer for this. It seems like it ought to be based on the player’s current speed and the slope of the ground they’re moving along, but every time I’ve done that math, I’ve gotten totally ludicrous answers that sometimes exceed the size of a tile. But maybe that’s not wrong? Play around, I guess, and think about when the effect should “break” and the player should go flying off the top of a hill.

Wrinkle 5: It’s possible that the player will launch off a slope, hit something, and then be adhered to the ground where they wouldn’t have hit it. I don’t much like this edge case, but I don’t see a way around it either.

This problem is surprisingly awkward for how simple it sounds, and the solution isn’t entirely satisfying. Oh, well; the results are much nicer than the solution. As an added bonus, this also fixes occasional problems with running down a hill and becoming detached from the ground due to precision issues or whathaveyou.

Problem 4: One-way platforms

Ah, what a nightmare.

It took me ages just to figure out how to define one-way platforms. Only block when the player is moving downwards? Nope. Only block when the player is above the platform? Nuh-uh.

Well, okay, yes, those approaches might work for convex players and flat platforms. But what about… sloped, one-way platforms? There’s no reason you shouldn’t be able to have those. If Super Mario World can do it, surely Unity can do it almost 30 years later.

The trick is, again, to look at the collision normal. If it faces away from gravity, the player is hitting a ground-like surface, so the platform should block them. Otherwise (or if the player overlaps the platform), it shouldn’t.

Here’s the catch: Unity doesn’t have conditional collision. I can’t decide, on the fly, whether a collision should block or not. In fact, I think that by the time I get a callback like OnCollisionEnter2D, the physics pass is already over.

I could go the other way and use triggers (which are non-blocking), but then I have the opposite problem: I can’t stop the player on the fly. I could move them back to where they hit the trigger, but I envision all kinds of problems as a result. What if they were moving fast enough to activate something on the other side of the platform? What if something else moved to where I’m trying to shove them back to in the meantime? How does this interact with ground detection and listing contacts, which would rightly ignore a trigger as non-blocking?

I beat my head against this for a while, but the inability to respond to collision conditionally was a huge roadblock. It’s all the more infuriating a problem, because Unity ships with a one-way platform modifier thing. Unfortunately, it seems to have been implemented by someone who has never played a platformer. It’s literally one-way — the player is only allowed to move straight upwards through it, not in from the sides. It also tries to block the player if they’re moving downwards while inside the platform, which invokes clumsy rejection behavior. And this all seems to be built into the physics engine itself somehow, so I can’t simply copy whatever they did.

Eventually, I settled on the following. After calculating attempted movement (including sliding), just at the end of FixedUpdate, I do a Cast along the movement vector. I’m not thrilled about having to duplicate the physics engine’s own work, but I do filter to only things on a “one-way platform” physics layer, which should at least help. For each object the cast hits, I use Physics2D.IgnoreCollision to either ignore or un-ignore the collision between the player and the platform, depending on whether the collision was ground-like or not.

(A lot of people suggested turning off collision between layers, but that can’t possibly work — the player might be standing on one platform while inside another, and anyway, this should work for all actors!)

Again, wrinkles! But fewer this time. Actually, maybe just one: handling the case where the player already overlaps the platform. I can’t just check for that with e.g. OverlapCollider, because that doesn’t distinguish between overlapping and merely touching.

I came up with a fairly simple fix: if I was going to un-ignore the collision (i.e. make the platform block), and the cast distance is reported as zero (either already touching or overlapping), I simply do nothing instead. If I’m standing on the platform, I must have already set it blocking when I was approaching it from the top anyway; if I’m overlapping it, I must have already set it non-blocking to get here in the first place.

I can imagine a few cases where this might go wrong. Moving platforms, especially, are going to cause some interesting issues. But this is the best I can do with what I know, and it seems to work well enough so far.

Oh, and our player can deliberately drop down through platforms, which was easy enough to implement; I just decide the platform is always passable while some button is held down.

Problem 5: Pushers and carriers

I haven’t gotten to this yet! Oh boy, can’t wait. I implemented it in LÖVE, but my way was hilariously invasive; I’m hoping that having a physics engine that supports a handwaved “this pushes that” will help. Of course, you also have to worry about sticking to platforms, for which the recommended solution is apparently to parent the cargo to the platform, which sounds goofy to me? I guess I’ll find out when I throw myself at it later.

Overall result

I ended up with a fairly pleasant-feeling system that supports slopes and one-way platforms and whatnot, with all the same pieces as I came up with for LÖVE. The code somehow ended up as less of a mess, too, but it probably helps that I’ve been down this rabbit hole once before and kinda knew what I was aiming for this time.

Animation of a character running smoothly along the top of an irregular dinosaur skeleton

Sorry that I don’t have a big block of code for you to copy-paste into your project. I don’t think there are nearly enough narrative discussions of these fundamentals, though, so hopefully this is useful to someone. If not, well, look forward to ✨ my book, that I am writing ✨!

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)
## 
  |                                                                       
  |                                                                 |   0%
  |                                                                       
  |=================================================================| 100%
test.h2o <- as.h2o(BillboardTest)
## 
  |                                                                       
  |                                                                 |   0%
  |                                                                       
  |=================================================================| 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")
## 
  |                                                                       
  |                                                                 |   0%
  |                                                                       
  |=====                                                            |   8%
  |                                                                       
  |=================================================================| 100%

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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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.

 

 

Impersonating iOS Password Prompts

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2017/10/impersonating_i.html

This is an interesting security vulnerability: because it is so easy to impersonate iOS password prompts, a malicious app can steal your password just by asking.

Why does this work?

iOS asks the user for their iTunes password for many reasons, the most common ones are recently installed iOS operating system updates, or iOS apps that are stuck during installation.

As a result, users are trained to just enter their Apple ID password whenever iOS prompts you to do so. However, those popups are not only shown on the lock screen, and the home screen, but also inside random apps, e.g. when they want to access iCloud, GameCenter or In-App-Purchases.

This could easily be abused by any app, just by showing an UIAlertController, that looks exactly like the system dialog.

Even users who know a lot about technology have a hard time detecting that those alerts are phishing attacks.

The essay proposes some solutions, but I’m not sure they’ll work. We’re all trained to trust our computers and the applications running on them.

Introducing Email Templates and Bulk Sending

Post Syndicated from Brent Meyer original https://aws.amazon.com/blogs/ses/introducing-email-templates-and-bulk-sending/

The Amazon SES team is excited to announce our latest update, which includes two related features that help you send personalized emails to large groups of customers. This post discusses these features, and provides examples that you can follow to start using these features right away.

Email templates

You can use email templates to create the structure of an email that you plan to send to multiple recipients, or that you will use again in the future. Each template contains a subject line, a text part, and an HTML part. Both the subject and the email body can contain variables that are automatically replaced with values specific to each recipient. For example, you can include a {{name}} variable in the body of your email. When you send the email, you specify the value of {{name}} for each recipient. Amazon SES then automatically replaces the {{name}} variable with the recipient’s first name.

Creating a template

To create a template, you use the CreateTemplate API operation. To use this operation, pass a JSON object with four properties: a template name (TemplateName), a subject line (SubjectPart), a plain text version of the email body (TextPart), and an HTML version of the email body (HtmlPart). You can include variables in the subject line or message body by enclosing the variable names in two sets of curly braces. The following example shows the structure of this JSON object.

{
  "TemplateName": "MyTemplate",
  "SubjectPart": "Greetings, {{name}}!",
  "TextPart": "Dear {{name}},\r\nYour favorite animal is {{favoriteanimal}}.",
  "HtmlPart": "<h1>Hello {{name}}</h1><p>Your favorite animal is {{favoriteanimal}}.</p>"
}

Use this example to create your own template, and save the resulting file as mytemplate.json. You can then use the AWS Command Line Interface (AWS CLI) to create your template by running the following command: aws ses create-template --cli-input-json mytemplate.json

Sending an email created with a template

Now that you have created a template, you’re ready to send email that uses the template. You can use the SendTemplatedEmail API operation to send email to a single destination using a template. Like the CreateTemplate operation, this operation accepts a JSON object with four properties. For this operation, the properties are the sender’s email address (Source), the name of an existing template (Template), an object called Destination that contains the recipient addresses (and, optionally, any CC or BCC addresses) that will receive the email, and a property that refers to the values that will be replaced in the email (TemplateData). The following example shows the structure of the JSON object used by the SendTemplatedEmail operation.

{
  "Source": "[email protected]",
  "Template": "MyTemplate",
  "Destination": {
    "ToAddresses": [ "[email protected]" ]
  },
  "TemplateData": "{ \"name\":\"Alejandro\", \"favoriteanimal\": \"zebra\" }"
}

Customize this example to fit your needs, and then save the resulting file as myemail.json. One important note: in the TemplateData property, you must use a blackslash (\) character to escape the quotes within this object, as shown in the preceding example.

When you’re ready to send the email, run the following command: aws ses send-templated-email --cli-input-json myemail.json

Bulk email sending

In most cases, you should use email templates to send personalized emails to several customers at the same time. The SendBulkTemplatedEmail API operation helps you do that. This operation also accepts a JSON object. At a minimum, you must supply a sender email address (Source), a reference to an existing template (Template), a list of recipients in an array called Destinations (within which you specify the recipient’s email address, and the variable values for that recipient), and a list of fallback values for the variables in the template (DefaultTemplateData). The following example shows the structure of this JSON object.

{
  "Source":"[email protected]",
  "ConfigurationSetName":"ConfigSet",
  "Template":"MyTemplate",
  "Destinations":[
    {
      "Destination":{
        "ToAddresses":[
          "[email protected]"
        ]
      },
      "ReplacementTemplateData":"{ \"name\":\"Anaya\", \"favoriteanimal\":\"yak\" }"
    },
    {
      "Destination":{ 
        "ToAddresses":[
          "[email protected]"
        ]
      },
      "ReplacementTemplateData":"{ \"name\":\"Liu\", \"favoriteanimal\":\"water buffalo\" }"
    },
    {
      "Destination":{
        "ToAddresses":[
          "[email protected]"
        ]
      },
      "ReplacementTemplateData":"{ \"name\":\"Shirley\", \"favoriteanimal\":\"vulture\" }"
    },
    {
      "Destination":{
        "ToAddresses":[
          "[email protected]"
        ]
      },
      "ReplacementTemplateData":"{}"
    }
  ],
  "DefaultTemplateData":"{ \"name\":\"friend\", \"favoriteanimal\":\"unknown\" }"
}

This example sends unique emails to Anaya ([email protected]), Liu ([email protected]), Shirley ([email protected]), and a fourth recipient ([email protected]), whose name and favorite animal we didn’t specify. Anaya, Liu, and Shirley will see their names in place of the {{name}} tag in the template (which, in this example, is present in both the subject line and message body), as well as their favorite animals in place of the {{favoriteanimal}} tag in the message body. The DefaultTemplateData property determines what happens if you do not specify the ReplacementTemplateData property for a recipient. In this case, the fourth recipient will see the word “friend” in place of the {{name}} tag, and “unknown” in place of the {{favoriteanimal}} tag.

Use the example to create your own list of recipients, and save the resulting file as mybulkemail.json. When you’re ready to send the email, run the following command: aws ses send-bulk-templated-email --cli-input-json mybulkemail.json

Other considerations

There are a few limits and other considerations when using these features:

  • You can create up to 10,000 email templates per Amazon SES account.
  • Each template can be up to 10 MB in size.
  • You can include an unlimited number of replacement variables in each template.
  • You can send email to up to 50 destinations in each call to the SendBulkTemplatedEmail operation. A destination includes a list of recipients, as well as CC and BCC recipients. Note that the number of destinations you can contact in a single call to the API may be limited by your account’s maximum sending rate. For more information, see Managing Your Amazon SES Sending Limits in the Amazon SES Developer Guide.

We look forward to seeing the amazing things you create with these new features. If you have any questions, please leave a comment on this post, or let us know in the Amazon SES forum.

AWS Developer Tools Expands Integration to Include GitHub

Post Syndicated from Balaji Iyer original https://aws.amazon.com/blogs/devops/aws-developer-tools-expands-integration-to-include-github/

AWS Developer Tools is a set of services that include AWS CodeCommit, AWS CodePipeline, AWS CodeBuild, and AWS CodeDeploy. Together, these services help you securely store and maintain version control of your application’s source code and automatically build, test, and deploy your application to AWS or your on-premises environment. These services are designed to enable developers and IT professionals to rapidly and safely deliver software.

As part of our continued commitment to extend the AWS Developer Tools ecosystem to third-party tools and services, we’re pleased to announce AWS CodeStar and AWS CodeBuild now integrate with GitHub. This will make it easier for GitHub users to set up a continuous integration and continuous delivery toolchain as part of their release process using AWS Developer Tools.

In this post, I will walk through the following:

Prerequisites:

You’ll need an AWS account, a GitHub account, an Amazon EC2 key pair, and administrator-level permissions for AWS Identity and Access Management (IAM), AWS CodeStar, AWS CodeBuild, AWS CodePipeline, Amazon EC2, Amazon S3.

 

Integrating GitHub with AWS CodeStar

AWS CodeStar enables you to quickly develop, build, and deploy applications on AWS. Its unified user interface helps you easily manage your software development activities in one place. With AWS CodeStar, you can set up your entire continuous delivery toolchain in minutes, so you can start releasing code faster.

When AWS CodeStar launched in April of this year, it used AWS CodeCommit as the hosted source repository. You can now choose between AWS CodeCommit or GitHub as the source control service for your CodeStar projects. In addition, your CodeStar project dashboard lets you centrally track GitHub activities, including commits, issues, and pull requests. This makes it easy to manage project activity across the components of your CI/CD toolchain. Adding the GitHub dashboard view will simplify development of your AWS applications.

In this section, I will show you how to use GitHub as the source provider for your CodeStar projects. I’ll also show you how to work with recent commits, issues, and pull requests in the CodeStar dashboard.

Sign in to the AWS Management Console and from the Services menu, choose CodeStar. In the CodeStar console, choose Create a new project. You should see the Choose a project template page.

CodeStar Project

Choose an option by programming language, application category, or AWS service. I am going to choose the Ruby on Rails web application that will be running on Amazon EC2.

On the Project details page, you’ll now see the GitHub option. Type a name for your project, and then choose Connect to GitHub.

Project details

You’ll see a message requesting authorization to connect to your GitHub repository. When prompted, choose Authorize, and then type your GitHub account password.

Authorize

This connects your GitHub identity to AWS CodeStar through OAuth. You can always review your settings by navigating to your GitHub application settings.

Installed GitHub Apps

You’ll see AWS CodeStar is now connected to GitHub:

Create project

You can choose a public or private repository. GitHub offers free accounts for users and organizations working on public and open source projects and paid accounts that offer unlimited private repositories and optional user management and security features.

In this example, I am going to choose the public repository option. Edit the repository description, if you like, and then choose Next.

Review your CodeStar project details, and then choose Create Project. On Choose an Amazon EC2 Key Pair, choose Create Project.

Key Pair

On the Review project details page, you’ll see Edit Amazon EC2 configuration. Choose this link to configure instance type, VPC, and subnet options. AWS CodeStar requires a service role to create and manage AWS resources and IAM permissions. This role will be created for you when you select the AWS CodeStar would like permission to administer AWS resources on your behalf check box.

Choose Create Project. It might take a few minutes to create your project and resources.

Review project details

When you create a CodeStar project, you’re added to the project team as an owner. If this is the first time you’ve used AWS CodeStar, you’ll be asked to provide the following information, which will be shown to others:

  • Your display name.
  • Your email address.

This information is used in your AWS CodeStar user profile. User profiles are not project-specific, but they are limited to a single AWS region. If you are a team member in projects in more than one region, you’ll have to create a user profile in each region.

User settings

User settings

Choose Next. AWS CodeStar will create a GitHub repository with your configuration settings (for example, https://github.com/biyer/ruby-on-rails-service).

When you integrate your integrated development environment (IDE) with AWS CodeStar, you can continue to write and develop code in your preferred environment. The changes you make will be included in the AWS CodeStar project each time you commit and push your code.

IDE

After setting up your IDE, choose Next to go to the CodeStar dashboard. Take a few minutes to familiarize yourself with the dashboard. You can easily track progress across your entire software development process, from your backlog of work items to recent code deployments.

Dashboard

After the application deployment is complete, choose the endpoint that will display the application.

Pipeline

This is what you’ll see when you open the application endpoint:

The Commit history section of the dashboard lists the commits made to the Git repository. If you choose the commit ID or the Open in GitHub option, you can use a hotlink to your GitHub repository.

Commit history

Your AWS CodeStar project dashboard is where you and your team view the status of your project resources, including the latest commits to your project, the state of your continuous delivery pipeline, and the performance of your instances. This information is displayed on tiles that are dedicated to a particular resource. To see more information about any of these resources, choose the details link on the tile. The console for that AWS service will open on the details page for that resource.

Issues

You can also filter issues based on their status and the assigned user.

Filter

AWS CodeBuild Now Supports Building GitHub Pull Requests

CodeBuild is a fully managed build service that compiles source code, runs tests, and produces software packages that are ready to deploy. With CodeBuild, you don’t need to provision, manage, and scale your own build servers. CodeBuild scales continuously and processes multiple builds concurrently, so your builds are not left waiting in a queue. You can use prepackaged build environments to get started quickly or you can create custom build environments that use your own build tools.

We recently announced support for GitHub pull requests in AWS CodeBuild. This functionality makes it easier to collaborate across your team while editing and building your application code with CodeBuild. You can use the AWS CodeBuild or AWS CodePipeline consoles to run AWS CodeBuild. You can also automate the running of AWS CodeBuild by using the AWS Command Line Interface (AWS CLI), the AWS SDKs, or the AWS CodeBuild Plugin for Jenkins.

AWS CodeBuild

In this section, I will show you how to trigger a build in AWS CodeBuild with a pull request from GitHub through webhooks.

Open the AWS CodeBuild console at https://console.aws.amazon.com/codebuild/. Choose Create project. If you already have a CodeBuild project, you can choose Edit project, and then follow along. CodeBuild can connect to AWS CodeCommit, S3, BitBucket, and GitHub to pull source code for builds. For Source provider, choose GitHub, and then choose Connect to GitHub.

Configure

After you’ve successfully linked GitHub and your CodeBuild project, you can choose a repository in your GitHub account. CodeBuild also supports connections to any public repository. You can review your settings by navigating to your GitHub application settings.

GitHub Apps

On Source: What to Build, for Webhook, select the Rebuild every time a code change is pushed to this repository check box.

Note: You can select this option only if, under Repository, you chose Use a repository in my account.

Source

In Environment: How to build, for Environment image, select Use an image managed by AWS CodeBuild. For Operating system, choose Ubuntu. For Runtime, choose Base. For Version, choose the latest available version. For Build specification, you can provide a collection of build commands and related settings, in YAML format (buildspec.yml) or you can override the build spec by inserting build commands directly in the console. AWS CodeBuild uses these commands to run a build. In this example, the output is the string “hello.”

Environment

On Artifacts: Where to put the artifacts from this build project, for Type, choose No artifacts. (This is also the type to choose if you are just running tests or pushing a Docker image to Amazon ECR.) You also need an AWS CodeBuild service role so that AWS CodeBuild can interact with dependent AWS services on your behalf. Unless you already have a role, choose Create a role, and for Role name, type a name for your role.

Artifacts

In this example, leave the advanced settings at their defaults.

If you expand Show advanced settings, you’ll see options for customizing your build, including:

  • A build timeout.
  • A KMS key to encrypt all the artifacts that the builds for this project will use.
  • Options for building a Docker image.
  • Elevated permissions during your build action (for example, accessing Docker inside your build container to build a Dockerfile).
  • Resource options for the build compute type.
  • Environment variables (built-in or custom). For more information, see Create a Build Project in the AWS CodeBuild User Guide.

Advanced settings

You can use the AWS CodeBuild console to create a parameter in Amazon EC2 Systems Manager. Choose Create a parameter, and then follow the instructions in the dialog box. (In that dialog box, for KMS key, you can optionally specify the ARN of an AWS KMS key in your account. Amazon EC2 Systems Manager uses this key to encrypt the parameter’s value during storage and decrypt during retrieval.)

Create parameter

Choose Continue. On the Review page, either choose Save and build or choose Save to run the build later.

Choose Start build. When the build is complete, the Build logs section should display detailed information about the build.

Logs

To demonstrate a pull request, I will fork the repository as a different GitHub user, make commits to the forked repo, check in the changes to a newly created branch, and then open a pull request.

Pull request

As soon as the pull request is submitted, you’ll see CodeBuild start executing the build.

Build

GitHub sends an HTTP POST payload to the webhook’s configured URL (highlighted here), which CodeBuild uses to download the latest source code and execute the build phases.

Build project

If you expand the Show all checks option for the GitHub pull request, you’ll see that CodeBuild has completed the build, all checks have passed, and a deep link is provided in Details, which opens the build history in the CodeBuild console.

Pull request

Summary:

In this post, I showed you how to use GitHub as the source provider for your CodeStar projects and how to work with recent commits, issues, and pull requests in the CodeStar dashboard. I also showed you how you can use GitHub pull requests to automatically trigger a build in AWS CodeBuild — specifically, how this functionality makes it easier to collaborate across your team while editing and building your application code with CodeBuild.


About the author:

Balaji Iyer is an Enterprise Consultant for the Professional Services Team at Amazon Web Services. In this role, he has helped several customers successfully navigate their journey to AWS. His specialties include architecting and implementing highly scalable distributed systems, serverless architectures, large scale migrations, operational security, and leading strategic AWS initiatives. Before he joined Amazon, Balaji spent more than a decade building operating systems, big data analytics solutions, mobile services, and web applications. In his spare time, he enjoys experiencing the great outdoors and spending time with his family.

 

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.

5 Reasons Why AWS Leads the Cloud Market

Post Syndicated from Chris De Santis original https://www.anchor.com.au/blog/2017/10/5-reasons-aws-leads-cloud/

There is no doubt that in the cloud computing market, there is a lot of competition, but there is also a clear market leader. Amazon Web Services (AWS) leads the charge among other web services from similar tech giants such as Microsoft, IBM, and Google, but how did they get there and what’s taking so long for someone of the likes of Google to knock them off their pedestal?

5 Reasons Why AWS Leads the Cloud Market

Credit: Synergy Research Group

Recent research from Synergy Research shows that Amazon has a seemingly unbeatable lead. John Dinsdale, chief analyst at Synergy Research, told TechCrunch that, on paper, AWS is too far ahead of any competitor trying to gain a short-term advantage. The reason behind their spectacular lead is simple:

AWS was first.

If you start the race before everyone else and keep at the pace they’re running, you’re going to win, and that’s exactly what Amazon are doing. Yet, instead of sitting on their colossal market share like a throne, they’re continuing to rapidly innovate and differentiate.

Dinsdale continues to explain that AWS does five things continuously that allows them to stay on top of the cloud market:

  1. Invest considerable amounts in infrastructure
  2. Expand their fleet of services
  3. Execute it all well
  4. Grow its business with enterprises
  5. Has the full long-term backing of Amazon

What can we take from this?

Well, according to Dinsdale, the Amazon formula involves:

  • Investing in your innovation
  • Constantly broadening your product/service range
  • Perform with minimal error
  • Aim for the high-profile customers
  • Look to receive stable funding and support

The post 5 Reasons Why AWS Leads the Cloud Market appeared first on AWS Managed Services by Anchor.