Apache SpamAssassin 4.0.0 released

Post Syndicated from original https://lwn.net/Articles/918145/

Version 4.0.0 of the Apache SpamAssassin spam filter has been released.

Apache SpamAssassin 4.0.0 contains numerous tweaks and bug fixes over
the past releases. In particular, it includes major changes that
significantly improve the handling of text in international language.

As with any major release, there are countless functional patches and
improvements to upgrade to 4.0.0. Apache SpamAssassin 4.0.0 includes
several years of fixes that significantly improve classification and
performance. It has been thoroughly tested in production systems. We
strongly recommend upgrading as soon as possible.

OCaml 5.0.0 released

Post Syndicated from original https://lwn.net/Articles/918109/

Version
5.0.0
of the OCaml programming language is out.

The highlight of this new major version of OCaml is the
long-awaited runtime support for shared memory parallelism and
effect handlers. This multicore support is the culmination of more
than 8 years of effort, and required a full rewrite of the OCaml
runtime environment. Consequently, OCaml 5.0.0 is expected to be a
more experimental version of OCaml than the usual OCaml releases.

Friday Squid Blogging: Squid in Concert

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2022/12/friday-squid-blogging-squid-in-concert.html

Squid is performing a concert in London in February.

If you don’t know what their music is like, try this or this or this.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Read my blog posting guidelines here.

Metasploit Weekly Wrap-Up

Post Syndicated from Brendan Watters original https://blog.rapid7.com/2022/12/16/metasploit-weekly-wrap-up-187/

A sack full of cheer from the Hacking Elves of Metasploit

Metasploit Weekly Wrap-Up

It is clear that the Metasploit elves have been busy this season: Five new modules, six new enhancements, nine new bug fixes, and a partridge in a pear tree are headed out this week! (Partridge nor pear tree included.) In this sack of goodies, we have a gift that keeps on giving: Shelby’s Acronis TrueImage Privilege Escalation works wonderfully, even after the software is uninstalled.

If you prefer elf files to holiday elves, we’ve still got you covered

Jan Rude submitted two modules targeting Syncovery for Linux. One takes advantage of an insecure session token generator and allows for the brute-force creation of a token that matches that of a logged-in user, and the other allows an authenticated user to create a job that will run when a user’s profile is run.

New module content (5)

Enhancements and features (6)

  • #17191 from liangjs – This PR fixes a bug where the Windows Subsystem for Linux crashes when using a reverse_tcp x64 stager because of data in the upper bits of the RDI register when the syscall occurs.
  • #17255 from JustAnda7 – The command payloads have been updated to allow specifying the file system path for several of their commands within datastore options. This should allow users to specify these commands locations should they not be contained within the searchable PATH.
  • #17346 from adfoster-r7 – The logic for counting threads within lib/metasploit/framework/spec/threads/suite.rb has been updated to appropriately count and document the known threads that can be left behind when running the rspec test suite. This fixes an intermittent rspec crash.
  • #17355 from adfoster-r7 – The creds command has been updated to show the full SSH key contents when running the creds -v command or when exporting to a file with creds -o output.txt. Previously only a shortened fingerprint string would be shown to the user.
  • #17357 from adfoster-r7 – The docs site has been updated to support mermaid graphs for rendering diagrams to assist with explanations.
  • #17387 from smashery – The hosts, services, vulns and notes command have been updated to support tab expansion in paths using the ~ character when using the -o option to specify the path to the file to write the output to.

Bugs fixed (9)

  • #17345 from adfoster-r7 – A crash has been fixed when using the report API with verbose mode enabled and no active DB.
  • #17350 from smashery – This updates three UAC bypass modules to remove a hard coded delay in favor of using the module’s builtin cleanup method. This results in the user having access to the interactive session without needing to wait.
  • #17351 from smashery – This fixes an issue in the exploit/windows/local/s4u_persistence module where the default value for FREQUENCY would cause an error.
  • #17352 from smashery – A bug has been fixed in the file_version method for Windows Meterpreter, which would cause the session to crash if it was run on a file that did not exist on the target system.
  • #17361 from jmartin-r7 – A bug has been fixed that would cause a crash when running the exit command from within msfconsole when running msfconsole with a 3.1.x release of Ruby.
  • #17366 from zeroSteiner – The upload and download commands used by shell sessions have been updated to handle directory destinations in the same way as the Meterpreter equivalents do, and to fix some bugs when uploading and downloading files that would prevent errors from being displayed and might cause session crashes.
  • #17368 from adfoster-r7 – Fixes a regression issue with msfvenom payload generation for large payloads taking more than 5 minutes to generate when outputting as hex format. Now it takes a few seconds as normal.
  • #17370 from jmartin-r7 – A bug has been fixed in the smb_enumshares.rb whereby if a SMBv1 connection is used a call was made to the net_share_enum_all function on the wrong object. This has since been updated to address this error.
  • #17378 from gwillcox-r7 – A bug has been fixed in the Meterpreter payloads that was preventing Python Meterpreter from being able to utilize its EventLog API properly. Additionally a bug has been fixed in the COFFLoader that prevented BOFLoader from working with some COFF files.

Get it

As always, you can update to the latest Metasploit Framework with msfupdate
and you can get more details on the changes since the last blog post from
GitHub:

If you are a git user, you can clone the Metasploit Framework repo (master branch) for the latest.
To install fresh without using git, you can use the open-source-only Nightly Installers or the
binary installers (which also include the commercial edition).

Architecting your security model in AWS for legacy application migrations

Post Syndicated from Irfan Saleem original https://aws.amazon.com/blogs/architecture/architecting-your-security-model-in-aws-for-legacy-application-migrations/

Application migrations, especially from legacy/mainframe to the cloud, are done in phases that sometimes span multiple years. Each phase migrates a set of applications, data, and other resources to the cloud. During the transition phases, applications might require access to both on-premises and cloud-based resources to perform their function. While working with our customers, we observed that the most common resources that applications require access to are databases, file storage, and shared services.

This blog post includes architecture guidelines for setting up access to commonly used resources by building a security model in Amazon Web Services (AWS). As you move your legacy applications to the cloud, you can apply Zero Trust concepts and security best practices according to your security needs. With AWS, you can build strong identity and access management with centralized control and set up and manage guardrails and fine-grained access controls for your workforce and applications.

In large organizations, on-premises applications rely on mainframe-based security services, an Identity Provider (IdP) platform, or a combination of both.

  • A mainframe-based control facility enables on-premises applications to:
    • Identify and verify users.
    • Establish an authority (authorize users and backend programs to access protected resources) through privileges defined in the control facility.
    • The backend programs use a unique identifier (or surrogate key) and run under the authority defined by the privileges assigned to the unique identifier.This security mechanism needs to be transformed into a role-based security model in AWS as applications are moved to the cloud. You assign permissions to a role, which is assumed by an application to get access to resources in AWS, similar to an authority defined in the legacy environment.
  • An IdP platform (such as Octa or Ping Identify) provides capabilities such as centralized access management and identity federation using SAML 2.0 or OpenID Connect (OIDC), that builds a system of trust between on-premises IdP and AWS. Once the federation is set up, on-premises applications can access AWS resources using AWS Identity and Access Management (IAM) roles, as explained in the next section.

Setting up a scalable security model in AWS

Figure 1 shows an on-premises environment where enterprise identity management is integrated with the mainframe and provides authentication and authorization to applications running off the mainframe. Generally, mainframe-based security controls (users, resources, and profiles) are replicated to the enterprise identity platform and are kept in sync through a change data capture process.

Access to AWS resources from on-premises

Figure 1. Access to AWS resources from on-premises

To enable your on-premises applications to access AWS resources, the applications need valid AWS credentials for making AWS API requests. Avoid using long-term access keys (such as those associated with IAM users) because they remain valid until you remove them. The following two methods can be used to assume an IAM role and get temporary security credentials to gain access to the AWS resources:

  • SAML based Identity federation – AWS supports identity federation with SAML. It allows federated access to users and applications in your organization by assuming an IAM role created for SAML federation to get temporary credentials. This method is helpful:
    • If your application needs to restrict access to AWS resources based on logged in users. You can define attribute mapping and additional attributes as required.
    • If your application uses a service account to manage AWS resource access, regardless of who is logged in.
  • IAM Roles Anywhere – Your on-premises applications will exchange X.509 certificates so that they can assume a role and get temporary credentials. This method is helpful if your application needs access to an AWS resource based on a service account.

In both of these cases, authenticated requests assume an IAM role, get temporary security credentials, and perform certain actions using AWS command line interface (CLI) and AWS SDKs. The IAM role has attached permissions for AWS resources such as Amazon Simple Storage Service (Amazon S3), Amazon DynamoDB, and Amazon Relational Database Service (Amazon RDS).

The temporary credentials expire when the session expires. By default, the session duration is one hour; you can request longer duration and session refresh.

To understand better, let’s consider the use case in Figure 2, where on-premises applications need access to AWS resources.

Access to resources that are created or already migrated to AWS from on-premises

Figure 2. Access to resources that are created or already migrated to AWS from on-premises

Applications can get temporary security credentials through SAML or IAM Roles Anywhere as explained earlier. The next sections explain setting up access to the resources in Figure 2 using temporary credentials.

1. Amazon S3

On-premises applications can access Amazon S3 using the REST API or the AWS SDK to perform certain actions (such as GetObjects or ListObjects):

2. Amazon RDS and Amazon Aurora

AWS Secrets Manager helps you store credentials for Amazon RDS and Amazon Aurora. You can also set up automatic rotation of your database secrets to meet your security and compliance needs. Applications can retrieve secrets using AWS SDKs and AWS CLI.

Additional configuration values can be stored in AWS Systems Manager Parameter Store, which provides secure, hierarchical storage for configuration data management such as passwords, database strings and license codes as parameter values rather than hard coding them in the code.

To access Amazon RDS and Amazon Aurora:

    • You can launch Amazon RDS DB instances into a virtual private cloud (VPC). A client application can access DB instance through the internet or through the private network only using an established connection from on-premises to the AWS environment.
    • On-premises applications can connect to a relational database using a database driver such as Java Database Connectivity (JDBC). The application can retrieve database connection details (such as database URL, port, or credentials) from AWS Secrets Manager and AWS Systems Manager Parameter Store  through API calls and can use them for the database connection.
    • Database admins can access AWS Management Console through an assumed role and can have access to database credentials from AWS Secrets Manager in order to connect directly with the database. For certain administration tasks (such as cluster setup, backup, recovery, maintenance, and management), they will need access to the Amazon RDS management console.
    • Amazon RDS also provides IAM database authentication option for MariaDB, MySQL, and PostgreSQL. You can authenticate without a password when you connect to a DB instance. Instead, you use an authentication token. For more information, go to IAM database authentication.

3. Amazon DynamoDB

Applications can use temporary credentials to invoke certain actions using AWS SDKs for DynamoDB. You can create a VPC endpoint for DynamoDB to access DynamoDB with no exposure to the public internet, then restrict access further by using VPC endpoint and IAM policies.

Conclusion

This blog helps you architect an application security model in AWS to provide on-premises access to commonly used resources in AWS.

You can apply security best practices and Zero Trust concepts as you move your legacy applications to the cloud. With AWS, you can build identity and access management with centralized and fine-grained access controls for your workforce and applications.

Start building your security model on AWS:

Closing out 2022 with our latest Impact Report

Post Syndicated from Andie Goodwin original https://blog.cloudflare.com/impact-report-2022/

Closing out 2022 with our latest Impact Report

Closing out 2022 with our latest Impact Report

To conclude Impact Week, which has been filled with announcements about new initiatives and features that we are thrilled about, today we are publishing our 2022 Impact Report.

In short, the Impact Report is an annual summary highlighting how we are helping build a better Internet and the progress we are making on our environmental, social, and governance priorities. It is where we showcase successes from Cloudflare Impact programs, celebrate awards and recognitions, and explain our approach to fundamental values like transparency and privacy.

We believe that a better Internet is principled, for everyone, and sustainable; these are the three themes around which we constructed the report. The Impact Report also serves as our repository for disclosures consistent with our commitments for the Global Reporting Initiative (GRI), Sustainability Accounting Standards Board (SASB), and UN Global Compact (UNGC).

Check out the full report to:

  • Explore how we are expanding the value and scope of our Cloudflare Impact programs
  • Review our latest diversity statistics — and our newest employee resource group
  • Understand how we are supporting humanitarian and human rights causes
  • Read quick summaries of Impact Week announcements
  • Examine how we calculate and validate emissions data

As fantastic as 2022 has been for scaling up Cloudflare Impact and making strides toward a better Internet, we are aiming even higher in 2023. To keep up with developments throughout the year, follow us on Twitter and LinkedIn, and keep an eye out for updates on our Cloudflare Impact page.

Everything you might have missed during Cloudflare’s Impact Week 2022

Post Syndicated from Lorraine Bellon original https://blog.cloudflare.com/everything-you-might-have-missed-during-cloudflares-impact-week-2022/

Everything you might have missed during Cloudflare's Impact Week 2022

Everything you might have missed during Cloudflare's Impact Week 2022

And that’s a wrap! Impact Week 2022 has come to a close. Over the last week, Cloudflare announced new commitments in our mission to help build a better Internet, including delivering Zero Trust services for the most vulnerable voices and for critical infrastructure providers. We also announced new products and services, and shared technical deep dives.

Were you able to keep up with everything that was announced? Watch the Impact Week 2022 wrap-up video on Cloudflare TV, or read our recap below for anything you may have missed.

Product announcements

Blog Summary
Cloudflare Zero Trust for Project Galileo and the Athenian Project We are making the Cloudflare One Zero Trust suite available to teams that qualify for Project Galileo or Athenian at no cost. Cloudflare One includes the same Zero Trust security and connectivity solutions used by over 10,000 customers today to connect their users and safeguard their data.
Project Safekeeping – protecting the world’s most vulnerable infrastructure with Zero Trust Under-resourced organizations that are vital to the basic functioning of our global communities (such as community hospitals, water treatment facilities, and local energy providers) face relentless cyber attacks, threatening basic needs for health, safety and security. Cloudflare’s mission is to help make a better Internet. We will help support these vulnerable infrastructure by providing our enterprise-level Zero Trust cybersecurity solution to them at no cost, with no time limit.
Cloudflare achieves FedRAMP authorization to secure more of the public sector We are excited to announce our public sector suite of services, Cloudflare for Government, has achieved FedRAMP Moderate Authorization. The Federal Risk and Authorization Management Program (“FedRAMP”) is a US-government-wide program that provides a standardized approach to security assessment, authorization, and continuous monitoring for cloud products and services.
A new, configurable and scalable version of Geo Key Manager, now available in Closed Beta At Cloudflare, we want to give our customers tools that allow them to maintain compliance in this ever-changing environment. That’s why we’re excited to announce a new version of Geo Key Manager — one that allows customers to define boundaries by country, by region, or by standard.

Everything you might have missed during Cloudflare's Impact Week 2022

Technical deep dives

Blog Summary
Cloudflare is joining the AS112 project to help the Internet deal with misdirected DNS queries Cloudflare is participating in the AS112 project, becoming an operator of the loosely coordinated, distributed sink of the reverse lookup (PTR) queries for RFC 1918 addresses, dynamic DNS updates and other ambiguous addresses.
Measuring BGP RPKI Route Origin Validation The Border Gateway Protocol (BGP) is the glue that keeps the entire Internet together. However, despite its vital function, BGP wasn’t originally designed to protect against malicious actors or routing mishaps. It has since been updated to account for this shortcoming with the Resource Public Key Infrastructure (RPKI) framework, but can we declare it to be safe yet?

Customer stories

Blog Summary
Democratizing access to Zero Trust with Project Galileo Learn how organizations under Project Galileo use Cloudflare Zero Trust to protect their organization from cyberattacks.
Securing the inboxes of democracy Cloudflare email security worked hard in the 2022 U.S. midterm elections to ensure that the email inboxes of those seeking office were secure.
Expanding Area 1 email security to the Athenian Project We are excited to share that we have grown our offering under the Athenian Project to include Cloudflare’s Area 1 email security suite to help state and local governments protect against a broad spectrum of phishing attacks to keep voter data safe and secure.
How Cloudflare helps protect small businesses Large-scale cyber attacks on enterprises and governments make the headlines, but the impacts of cyber conflicts can be felt more profoundly and acutely by small businesses that struggle to keep the lights on during normal times. In this blog, we’ll share new research on how small businesses, including those using our free services, have leveraged Cloudflare services to make their businesses more secure and resistant to disruption.

Internet access

Blog Summary
Cloudflare expands Project Pangea to connect and protect (even) more community networks A year and a half ago, Cloudflare launched Project Pangea to help provide Internet services to underserved communities. Today, we’re sharing what we’ve learned by partnering with community networks, and announcing an expansion of the project.
The US government is working on an “Internet for all” plan. We’re on board. The US government has a $65 billion program to get all Americans on the Internet. It’s a great initiative, and we’re on board.
The Montgomery, Alabama Internet Exchange is making the Internet faster. We’re happy to be there. Internet Exchanges are a critical part of a strong Internet. Here’s the story of one of them.
Partnering with civil society to track Internet shutdowns with Radar Alerts and API We want to tell you more about how we work with civil society organizations to provide tools to track and document the scope of these disruptions. We want to support their critical work and provide the tools they need so they can demand accountability and condemn the use of shutdowns to silence dissent.
How Cloudflare helps next-generation markets At Cloudflare, part of our role is to make sure every person on the planet with an Internet connection has a good experience, whether they’re in a next-generation market or a current-gen market. In this blog we talk about how we define next-generation markets, how we help people in these markets get faster access to the websites and applications they use on a daily basis, and how we make it easy for developers to deploy services geographically close to users in next-generation markets.

Everything you might have missed during Cloudflare's Impact Week 2022

Sustainability

Blog Summary
Independent report shows: moving to Cloudflare can cut your carbon footprint We didn’t start out with the goal to reduce the Internet’s environmental impact. But as the Internet has become an ever larger part of our lives, that has changed. Our mission is to help build a better Internet — and a better Internet needs to be a sustainable one.
A more sustainable end-of-life for your legacy hardware appliances with Cloudflare and Iron Mountain We’re excited to announce an opportunity for Cloudflare customers to make it easier to decommission and dispose of their used hardware appliances in a sustainable way. We’re partnering with Iron Mountain to offer preferred pricing and value-back for Cloudflare customers that recycle or remarket legacy hardware through their service.
How we’re making Cloudflare’s infrastructure more sustainable With the incredible growth of the Internet, and the increased usage of Cloudflare’s network, even linear improvements to sustainability in our hardware today will result in exponential gains in the future. We want to use this post to outline how we think about the sustainability impact of the hardware in our network, and what we’re doing to continually mitigate that impact.
Historical emissions offsets (and Scope 3 sneak preview) Last year, Cloudflare committed to removing or offsetting the historical emissions associated with powering our network by 2025. We are excited to announce our first step toward offsetting our historical emissions by investing in 6,060 MTs’ worth of reforestation carbon offsets as part of the Pacajai Reduction of Emissions from Deforestation and forest Degradation (REDD+) Project in the State of Para, Brazil.
How we redesigned our offices to be more sustainable Cloudflare is working hard to ensure that we’re making a positive impact on the environment around us, with the goal of building the most sustainable network. At the same time, we want to make sure that the positive changes that we are making are also something that our local Cloudflare team members can touch and feel, and know that in each of our actions we are having a positive impact on the environment around us. This is why we make sustainability one of the underlying goals of the design, construction, and operations of our global office spaces.
More bots, more trees Once a year, we pull data from our Bot Fight Mode to determine the number of trees we can donate to our partners at One Tree Planted. It’s part of the commitment we made in 2019 to deter malicious bots online by redirecting them to a challenge page that requires them to perform computationally intensive, but meaningless tasks. While we use these tasks to drive up the bill for bot operators, we account for the carbon cost by planting trees.

Policy

Blog Summary
The Challenges of Sanctioning the Internet As governments continue to use sanctions as a foreign policy tool, we think it’s important that policymakers continue to hear from Internet infrastructure companies about how the legal framework is impacting their ability to support a global Internet. Here are some of the key issues we’ve identified and ways that regulators can help balance the policy goals of sanctions with the need to support the free flow of communications for ordinary citizens around the world.
An Update on Cloudflare’s Assistance to Ukraine On February 24, 2022, when Russia invaded Ukraine, Cloudflare jumped into action to provide services that could help prevent potentially destructive cyber attacks and keep the global Internet flowing. During Impact Week, we want to provide an update on where things currently stand, the role of security companies like Cloudflare, and some of our takeaways from the conflict so far.
Two months later: Internet use in Iran during the Mahsa Amini Protests A series of protests began in Iran on September 16, following the death in custody of Mahsa Amini — a 22 year old who had been arrested for violating Iran’s mandatory hijab law. The protests and civil unrest have continued to this day. But the impact hasn’t just been on the ground in Iran — the impact of the civil unrest can be seen in Internet usage inside the country, as well.
How Cloudflare advocates for a better Internet We thought this week would be a great opportunity to share Cloudflare’s principles and our theories behind policy engagement. Because at its core, a public policy approach needs to reflect who the company is through their actions and rhetoric. And as a company, we believe there is real value in helping governments understand how companies work, and helping our employees understand how governments and law-makers work.
Applying Human Rights Frameworks to our approach to abuse What does it mean to apply human rights frameworks to our response to abuse? As we’ll talk about in more detail, we use human rights concepts like access to fair process, proportionality (the idea that actions should be carefully calibrated to minimize any effect on rights), and transparency.
The Unintended Consequences of blocking IP addresses This blog dives into a discussion of IP blocking: why we see it, what it is, what it does, who it affects, and why it’s such a problematic way to address content online.

Everything you might have missed during Cloudflare's Impact Week 2022

Impact

Blog Summary
Closing out 2022 with our latest Impact Report Our Impact Report is an annual summary highlighting how we are trying to build a better Internet and the progress we are making on our environmental, social, and governance priorities.
Working to help the HBCU Smart Cities Challenge The HBCU Smart Cities Challenge invites all HBCUs across the United States to build technological solutions to solve real-world problems.
Introducing Cloudflare’s Third Party Code of Conduct Cloudflare is on a mission to help build a better Internet, and we are committed to doing this with ethics and integrity in everything that we do. This commitment extends beyond our own actions, to third parties acting on our behalf. We are excited to share our Third Party Code of Conduct, specifically formulated with our suppliers, resellers and other partners in mind.
The latest from Cloudflare’s seventeen Employee Resource Groups In this blog post, we highlight a few stories from some of our 17 Employee Resource Groups (ERGs), including the most recent, Persianflare.

What’s next?

That’s it for Impact Week 2022. But let’s keep the conversation going. We want to hear from you!

Visit the Cloudflare Community to share your thoughts about Impact Week 2022, or engage with our team on Facebook, Twitter, LinkedIn, and YouTube.

Or if you’d like to rewatch any Cloudflare TV segments associated with the above stories, visit the Impact Week hub on our website.

Watch on Cloudflare TV

Getting started with AWS Glue Data Quality for ETL Pipelines

Post Syndicated from Deenbandhu Prasad original https://aws.amazon.com/blogs/big-data/getting-started-with-aws-glue-data-quality-for-etl-pipelines/

Today, hundreds of thousands of customers use data lakes for analytics and machine learning. However, data engineers have to cleanse and prepare this data before it can be used. The underlying data has to be accurate and recent for customer to make confident business decisions. Otherwise, data consumers lose trust in the data and make suboptimal or incorrect decisions. It is a common task for data engineers to evaluate whether the data is accurate and recent or not. Today there are various data quality tools. However, common data quality tools usually require manual processes to monitor data quality.

AWS Glue Data Quality is a preview feature of AWS Glue that measures and monitors the data quality of Amazon Simple Storage Service (Amazon S3) data lakes and in AWS Glue extract, transform, and load (ETL) jobs. This is an open preview feature so it is already enabled in your account in the available Regions. You can easily define and measure the data quality checks in AWS Glue Studio console without writing codes. It simplifies your experience of managing data quality.

This post is Part 2 of a four-post series to explain how AWS Glue Data Quality works. Check out the previous post in this series:

Getting started with AWS Glue Data Quality

In this post, we show how to create an AWS Glue job that measures and monitors the data quality of a data pipeline. We also show how to take action based on the data quality results.

Solution overview

Let’s consider an example use case in which a data engineer needs to build a data pipeline to ingest the data from a raw zone to a curated zone in a data lake. As a data engineer, one of your key responsibilities—along with extracting, transforming, and loading data—is validating the quality of data. Identifying data quality issues upfront helps you prevent placing bad data in the curated zone and avoid arduous data corruption incidents.

In this post, you’ll learn how to easily set up built-in and custom data validation checks in your AWS Glue job to prevent bad data from corrupting the downstream high-quality data.

The dataset used for this post is synthetically generated; the following screenshot shows an example of the data.

Set up resources with AWS CloudFormation

This post includes an AWS CloudFormation template for a quick setup. You can review and customize it to suit your needs.

The CloudFormation template generates the following resources:

  • An Amazon Simple Storage Service (Amazon S3) bucket (gluedataqualitystudio-*).
  • The following prefixes and objects in the S3 bucket:
    • datalake/raw/customer/customer.csv
    • datalake/curated/customer/
    • scripts/
    • sparkHistoryLogs/
    • temporary/
  • AWS Identity and Access Management (IAM) users, roles, and policies. The IAM role (GlueDataQualityStudio-*) has permission to read and write from the S3 bucket.
  • AWS Lambda functions and IAM policies required by those functions to create and delete this stack.

To create your resources, complete the following steps:

  1. Sign in to the AWS CloudFormation console in the us-east-1 Region.
  2. Choose Launch Stack:

  3. Select I acknowledge that AWS CloudFormation might create IAM resources.
  4. Choose Create stack and wait for the stack creation step to complete.

Implement the solution

To start configuring your solution, complete the following steps:

  1. On the AWS Glue Studio console, choose Jobs in the navigation pane.
  2. Select Visual with a blank canvas and choose Create.
  3. Choose the Job Details tab to configure the job.
  4. For Name, enter GlueDataQualityStudio.
  5. For IAM Role, choose the role starting with GlueDataQualityStudio-*.
  6. For Glue version, choose Glue 3.0.
  7. For Job bookmark, choose Disable. This allows you to run this job multiple times with the same input dataset.
  8. For Number of retries, enter 0.
  9. In the Advanced properties section, provide the S3 bucket created by the CloudFormation template (starting with gluedataqualitystudio-*).
  10. Choose Save.
  11. After the job is saved, choose the Visual tab and on the Source menu, choose Amazon S3.
  12. On the Data source properties – S3 tab, for S3 source type, select S3 location.
  13. Choose Browse S3 and navigate to prefix /datalake/raw/customer/ in the S3 bucket starting with gluedataqualitystudio-* .
  14. Choose Infer schema.
  15. On the Action menu, choose Evaluate Data Quality.
  16. Choose the Evaluate Data Quality node.

    On the Transform tab, you can now start building data quality rules. The first rule you create is to check if Customer_ID is unique and not null using the isPrimaryKey rule.
  17. On the Rule types tab of the DQDL rule builder, search for isprimarykey and choose the plus sign.
  18. On the Schema tab of the DQDL rule builder, choose the plus sign next to Customer_ID.
  19. In the rule editor, delete id.

    The next rule we add checks that the First_Name column value is present for all the rows.
  20. You can also enter the data quality rules directly in the rule editor. Add a comma (,) and enter IsComplete "First_Name", after the first rule.

    Next, you add a custom rule to validate that no row exists without Telephone or Email.
  21. Enter the following custom rule in the rule editor:
    CustomSql "select count(*) from primary where Telephone is null and Email is null" = 0


    The Evaluate Data Quality feature provides actions to manage the outcome of a job based on the job quality results.

  22. For this post, select Fail job when data quality fails and choose Fail job without loading target data actions. In the Data quality output setting section, choose Browse S3 and navigate to prefix dqresults in the S3 bucket starting with gluedataqualitystudio-*.
  23. On the Target menu, choose Amazon S3.
  24. Choose the Data target – S3 bucket node.
  25. On the Data target properties – S3 tab, for Format, choose Parquet, and for Compression Type, choose Snappy.
  26. For S3 Target Location, choose Browse S3 and navigate to the prefix /datalake/curated/customer/ in the S3 bucket starting with gluedataqualitystudio-*.
  27. Choose Save, then choose Run.
    You can view the job run details on the Runs tab. In our example, the job fails with the error message “AssertionError: The job failed due to failing DQ rules for node: <node>.”
    You can review the data quality result on the Data quality tab. In our example, the custom data quality validation failed because one of the rows in the dataset had no Telephone or Email value.Evaluate Data Quality results is also written to the S3 bucket in JSON format based on the data quality result location parameter of the node.
  28. Navigate to dqresults prefix under the S3 bucket starting gluedataqualitystudio-*. You will see that the data quality result is partitioned by date.

The following is the output of the JSON file. You can use this file output to build custom data quality visualization dashboards.

You can also monitor the Evaluate Data Quality node through Amazon CloudWatch metrics and set alarms to send notifications about data quality results. To learn more on how to set up CloudWatch alarms, refer to Using Amazon CloudWatch alarms.

Clean up

To avoid incurring future charges and to clean up unused roles and policies, delete the resources you created:

  1. Delete the GlueDataQualityStudio job you created as part of this post.
  2. On the AWS CloudFormation console, delete the GlueDataQualityStudio stack.

Conclusion

AWS Glue Data Quality offers an easy way to measure and monitor the data quality of your ETL pipeline. In this post, you learned how to take necessary actions based on the data quality results, which helps you maintain high data standards and make confident business decisions.

To learn more about AWS Glue Data Quality, check out the documentation:


About the Authors

Deenbandhu Prasad is a Senior Analytics Specialist at AWS, specializing in big data services. He is passionate about helping customers build modern data architecture on the AWS Cloud. He has helped customers of all sizes implement data management, data warehouse, and data lake solutions.

Yannis Mentekidis is a Senior Software Development Engineer on the AWS Glue team.

Getting started with AWS Glue Data Quality from the AWS Glue Data Catalog

Post Syndicated from Aniket Jiddigoudar original https://aws.amazon.com/blogs/big-data/getting-started-with-aws-glue-data-quality-from-the-aws-glue-data-catalog/

AWS Glue is a serverless data integration service that makes it simple to discover, prepare, and combine data for analytics, machine learning (ML), and application development. You can use AWS Glue to create, run, and monitor data integration and ETL (extract, transform, and load) pipelines and catalog your assets across multiple data stores.

Hundreds of thousands of customers use data lakes for analytics and machine learning to make data-driven business decisions. Data consumers lose trust in data if it is not accurate and recent, making data quality essential for undertaking optimal and correct decisions.

Evaluation of the accuracy and freshness of data is a common task for engineers. Currently, there are various tools available to evaluate data quality. However, these tools often require manual processes of data discovery and expertise in data engineering and coding.

We are pleased to announce the public preview launch of AWS Glue Data Quality. You can access this feature today without requesting any additional access in the available Regions. AWS Glue Data Quality is a new preview feature of AWS Glue that measures and monitors the data quality of Amazon S3-based data lakes and in AWS Glue ETL jobs. It does not require any expertise in data engineering or coding. It simplifies your experience of monitoring and evaluating the quality of your data.

This is Part 1 of a four-part series of posts to explain how AWS Glue Data Quality works. Check out the next posts in the series:

Getting started with AWS Glue Data Quality

In this post, we will go over the simplicity of using the AWS Glue Data Quality feature by:

  1. Starting data quality recommendations and runs on your data in AWS Glue Data Catalog.
  2. Creating an Amazon CloudWatch alarm for getting notifications when data quality results are below a certain threshold.
  3. Analyzing your AWS Glue Data Quality run results through Amazon Athena.

Set up resources with AWS CloudFormation

The provided CloudFormation script creates the following resources for you:

  1. The IAM role required to run AWS Glue Data Quality runs
  2. An Amazon Simple Storage Service (Amazon S3) bucket to store the NYC Taxi dataset
  3. An S3 bucket to store and analyze the results of AWS Glue Data Quality runs
  4. An AWS Glue database and table created from the NYC Taxi dataset

Steps:

  1. Open the AWS CloudFormation console.
  2. Choose Create stack and then select With new resources (standard).
  3. For Template source, choose Upload a template File, and provide the above attached template file. Then choose Next.
  4. For Stack name, DataQualityDatabase, and DataQualityTable, leave as default. For DataQualityS3BucketName, enter the name of your S3 bucket. Then choose Next.
  5. On the final screen, make sure to acknowledge that this stack would create IAM resources for you, and choose Submit.
  6. Once the stack is successfully created, navigate to the S3 bucket created by the stack and upload the yellow_tripdata_2022-01.parquet file.

Start an AWS Glue Data Quality run on your data in AWS Glue Data Catalog

In this first section, we will generate data quality rule recommendations from the AWS Glue Data Quality service. Using these recommendations, we will then run a data quality task against our dataset to obtain an analysis of our data.

To get started, complete the following steps:

  1. Open AWS Glue console.
  2. Choose Tables under Data Catalog.
  3. Select the DataQualityTable table created via the CloudFormation stack.
  4. Select the Data quality tab.
  5. Choose Recommend ruleset.
  6. On the Recommend data quality rules page, check Save recommended rules as a ruleset. This will allow us to save the recommended rules in a ruleset automatically, for use in the next steps.
  7. For IAM Role, choose the IAM role that was created from the CloudFormation stack.
  8. For Additional configurations -optional, leave the default number of workers and timeout.
  9. Choose Recommend ruleset. This will start a data quality recommendation run, with the given number of workers.
  10. Wait for the ruleset to be completed.
  11. Once completed, navigate back to the Rulesets tab. You should see a successful recommendation run and a ruleset created.

Understand AWS Glue Data Quality recommendations

AWS Glue Data Quality recommendations are suggestions generated by the AWS Glue Data Quality service and are based on the shape of your data. These recommendations automatically take into account aspects like RowCounts, Mean, Standard Deviation etc. of your data, and generate a set of rules, for you to use as a starting point.

The dataset used here was the NYC Taxi dataset. Based on this, the columns in this dataset, and the values of those columns, AWS Glue Data Quality recommends a set of rules. In total, the recommendation service automatically took into consideration all the columns of the dataset, and recommended 55 rules.

Some of these rules are:

  • “RowCount between <> and <> ” → Expect a count of number of rows based on the data it saw
  • “ColumnValues “VendorID” in [ ] → Expect the ”VendorID“ column to be within a specific set of values
  • IsComplete “VendorID” → Expect the “VendorID” to be a non-null value

How do I use the recommended AWS Glue Data Quality rules?

  1. From the Rulesets section, you should see your generated ruleset. Select the generated ruleset, and choose Evaluate ruleset.
    • If you didn’t check the box to Save recommended rules as a ruleset when you ran the recommendation, you can still click on the recommendation task run and copy the rules to create a new ruleset
  2. For Data quality actions under Data quality properties, select Publish metrics to Amazon CloudWatch. If this box isn’t checked, the data quality run will not publish metrics to Amazon CloudWatch.
  3. For IAM role, select the GlueDataQualityBlogRole created in the AWS CloudFormation stack.
  4. For Requested number of workers under Advanced properties, leave as default. 
  5. For Data quality results location, select the value of the GlueDataQualityResultsS3Bucket location that was created via the AWS CloudFormation stack
  6. Choose Evaluate ruleset.
  7. Once the run begins, you can see the status of the run on the Data quality results tab.
  8. After the run reaches a successful stage, select the completed data quality task run, and view the data quality results shown in Run results.

Our recommendation service suggested that we enforce 55 rules, based on the column values and the data within our NYC Taxi dataset. We then converted the collection of 55 rules into a RuleSet. Then, we ran a Data Quality Evaluation task run using our RuleSet against our dataset. In our results above, we see the status of each within the RuleSet.

You can also utilize the AWS Glue Data Quality APIs to carry out these steps.

Get Amazon SNS notifications for my failing data quality runs through Amazon CloudWatch alarms

Each AWS Glue Data Quality evaluation run from the Data Catalog, emits a pair of metrics named glue.data.quality.rules.passed (indicating a number of rules that passed) and glue.data.quality.rules.failed (indicating the number of failed rules) per data quality run. This emitted metric can be used to create alarms to alert users if a given data quality run falls below a threshold.

To get started with setting up an alarm that would send an email via an Amazon SNS notification, follow the steps below:

  1. Open Amazon CloudWatch console.
  2. Choose All metrics under Metrics. You will see an additional namespace under Custom namespaces titled Glue Data Quality.

Note: When starting an AWS Glue Data Quality run, make sure the Publish metrics to Amazon CloudWatch checkbox is enabled, as shown below. Otherwise, metrics for that particular run will not be published to Amazon CloudWatch.

  1. Under the Glue Data Quality namespace, you should be able to see metrics being emitted per table, per ruleset. For the purpose of our blog, we shall be using the glue.data.quality.rules.failed rule and alarm, if this value goes over 1 (indicating that, if we see a number of failed rule evaluations greater than 1, we would like to be notified).
  2. In order to create the alarm, choose All alarms under Alarms.
  3. Choose Create alarm.
  4. Choose Select metric.
  5. Select the glue.data.quality.rules.failed metric corresponding to the table you’ve created, then choose Select metric.
  6. Under the Specify metric and conditions tab, under the Metrics section:
    1. For Statistic, select Sum.
    2. For Period, select 1 minute.
  7. Under the Conditions section:
    1. For Threshold type, choose Static.
    2. For Whenever glue.data.quality.rules.failed is…, select Greater/Equal.
    3. For than…, enter 1 as the threshold value.
    4. Expand the Additional configurations dropdown and select Treat missing data as good

These selections imply that if the glue.data.quality.rules.failed metric emits a value greater than or equal to 1, we will trigger an alarm. However, if there is no data, we will treat it as acceptable.

  1. Choose Next.
  2. On Configure actions:
    1. For the Alarm state trigger section, select In alarm .
    2. For Send a notification to the following SNS topic, choose Create a new topic to send a notification via a new SNS topic.
    3. For Email endpoints that will receive the notification…, enter your email address. Choose Next.
  3. For Alarm name, enter myFirstDQAlarm, then choose Next.
  4. Finally, you should see a summary of all the selections on the Preview and create screen. Choose Create alarm at the bottom.
  5. You should now be able to see the alarm being created from the Amazon CloudWatch alarms dashboard.

In order to demonstrate AWS Glue Data Quality alarms, we are going to go over a real-world scenario where we have corrupted data being ingested, and how we could use the AWS Glue Data Quality service to get notified of this, using the alarm we created in the previous steps. For this purpose, we will use the provided file malformed_yellow_taxi.parquet that contains data that has been tweaked on purpose.

  1. Navigate to the S3 location DataQualityS3BucketName mentioned in the CloudFormation template supplied at the beginning of the blog post.
  2. Upload the malformed_yellow_tripdata.parquet file to this location. This will help us simulate a flow where we have a file with poor data quality coming into our data lakes via our ETL processes.
  3. Navigate to the AWS Glue Data Catalog console, select the demo_nyc_taxi_data_input that was created via the provided AWS CloudFormation template and then navigate to the Data quality tab.
  4. Select the RuleSet we had created in the first section. Then select Evaluate ruleset.
  5. From the Evaluate data quality screen:
    1. Check the box to Publish metrics to Amazon CloudWatch. This checkbox is needed to ensure that the failure metrics are emitted to Amazon CloudWatch.
    2. Select the IAM role created via the AWS CloudFormation template.
    3. Optionally, select an S3 location to publish your AWS Glue Data Quality results.
    4. Select Evaluate ruleset.
  6.  Navigate to the Data Quality results tab. You should now see two runs, one from the previous steps of this blog and one that we currently triggered. Wait for the current run to complete.
  7. As you see, we have a failed AWS Glue Data Quality run result, with only 52 of our original 55 rules passing. These failures are attributed to the new file we uploaded to S3.
  8. Navigate to the Amazon CloudWatch console and select the alarm we created at the beginning of this section.
  9. As you can see, we configured the alarm to fire every time the glue.data.quality.rules.failed metric crosses a threshold of 1. After the above AWS Glue Data Quality run, we see 3 rules failing, which triggered the alarm. Further, you also should have gotten an email detailing the alarm’s firing.

We have thus demonstrated an example where incoming malformed data, coming into our data lakes can be identified via the AWS Glue Data Quality rules, and subsequent alerting mechanisms can be created to notify appropriate personas.

Analyze your AWS Glue Data Quality run results through Amazon Athena

In scenarios where you have multiple AWS Glue Data Quality run results against a dataset, over a period of time, you might want to track the trends of the dataset’s quality over a period of time. To achieve this, we can export our AWS Glue Data Quality run results to S3, and use Amazon Athena to run analytical queries against the exported run. The results can then be further used in Amazon QuickSight to build dashboards to have a graphical representation of your data quality trends

In the third part of this post, we will see the steps needed to start tracking data on your dataset’s quality:

  1. For our data quality runs that we set up in the previous sections, we set the Data quality results location parameter to the bucket location specified by the AWS CloudFormation stack.
  2. After each successful run, you should see a single JSONL file being exported to your selected S3 location, corresponding to that particular run.
  3. Open the Amazon Athena console.
  4. In the query editor, run the following CREATE TABLE statement (replace the <my_table_name> with a relevant value, and <GlueDataQualityResultsS3Bucket_from_cfn> section with the GlueDataQualityResultsS3Bucket value from the provided AWS CloudFormation template):
    CREATE EXTERNAL TABLE `<my_table_name>`(
    `catalogid` string,
    `databasename` string,
    `tablename` string,
    `dqrunid` string,
    `evaluationstartedon` timestamp,
    `evaluationcompletedon` timestamp,
    `rule` string,
    `outcome` string,
    `failurereason` string,
    `evaluatedmetrics` string)
    PARTITIONED BY (
    `year` string,
    `month` string,
    `day` string)
    ROW FORMAT SERDE
    'org.openx.data.jsonserde.JsonSerDe'
    WITH SERDEPROPERTIES (
    'paths'='catalogId,databaseName,dqRunId,evaluatedMetrics,evaluationCompletedOn,evaluationStartedOn,failureReason,outcome,rule,tableName')
    STORED AS INPUTFORMAT
    'org.apache.hadoop.mapred.TextInputFormat'
    OUTPUTFORMAT
    'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
    LOCATION
    's3://<GlueDataQualityResultsS3Bucket_from_cfn>/'
    TBLPROPERTIES (
    'classification'='json',
    'compressionType'='none',
    'typeOfData'='file')
    
    MSCK REPAIR TABLE `<my_table_name>`

  5. Once the above table is created, you should be able to run queries to analyze your data quality results.

For example, consider the following query that shows me the failed AWS Glue Data Quality runs against my table demo_nyc_taxi_data_input within a time window:

SELECT * from "<my_table_name>"
WHERE "outcome" = 'Failed'
AND "tablename" = '<my_source_table>'
AND "evaluationcompletedon" between
parse_datetime('2022-12-05 17:00:00:000', 'yyyy-MM-dd HH:mm:ss:SSS') AND parse_datetime('2022-12-05 20:00:00:000', 'yyyy-MM-dd HH:mm:ss:SSS');

The output of the above query shows me details about all the runs with “outcome” = ‘Failed’ that ran against my NYC Taxi dataset table ( “tablename” = ‘demo_nyc_taxi_data_input’ ). The output also gives me information about the failure reason ( failurereason ) and the values it was evaluated against ( evaluatedmetrics ).

As you can see, we are able to get detailed information about our AWS Glue Data Quality runs, via the run results uploaded to S3, perform more detailed analysis and build dashboards on top of the data.

Clean up

  • Navigate to the Amazon Athena console and delete the table created for data quality analysis.
  • Navigate to the Amazon CloudWatch console and delete the alarms created.
  • If you deployed the sample CloudFormation stack, delete the CloudFormation stack via the AWS CloudFormation console. You will need to empty the S3 bucket before you delete the bucket.
  • If you have enabled your AWS Glue Data Quality runs to output to S3, empty those buckets as well.

Conclusion

In this post, we talked about the ease and speed of incorporating data quality rules using the AWS Glue Data Quality feature, into your AWS Glue Data Catalog tables. We also talked about how to run recommendations and evaluate data quality against your tables. We then discussed analyzing the data quality results via Amazon Athena, and the process for setting up alarms via Amazon CloudWatch in order to notify users of failed data quality.

To dive into the AWS Glue Data Quality APIs, take a look at the AWS Glue Data Quality API documentation
To learn more about AWS Glue Data Quality, check out the AWS Glue Data Quality Developer Guide


About the authors

Aniket Jiddigoudar is a Big Data Architect on the AWS Glue team.

Joseph Barlan is a Frontend Engineer at AWS Glue. He has over 5 years of experience helping teams build reusable UI components and is passionate about frontend design systems. In his spare time, he enjoys pencil drawing and binge watching tv shows.

AWS Marketplace Seller Insights team uses Amazon QuickSight Embedded to empower sellers with actionable business insights

Post Syndicated from Snigdha Sahi original https://aws.amazon.com/blogs/big-data/aws-marketplace-seller-insights-team-uses-amazon-quicksight-embedded-to-empower-sellers-with-actionable-business-insights/

AWS Marketplace enables independent software vendors (ISVs), data providers, and consulting partners to sell software, services, and data to millions of AWS customers. Working in partnership with the AWS Partner Network (APN), AWS Marketplace helps ISVs and partners build, market and sell their AWS offerings by providing crucial business, technical, and marketing support.

The AWS Marketplace Seller Insights team helps AWS sellers scale their businesses by providing reports, data feeds, and insights. We share this data directly with sellers through the AWS Marketplace Management Portal (AMMP). In 2013, we launched the first version of reporting insights, delivered monthly via static CSV files. To improve the customer experience, the next iteration of reporting upgrades was launched in 2020 and provided more granular data, refreshed daily and delivered via feeds.

We’ve spent the past few months working closely with the QuickSight team and are excited to now offer AWS Marketplace sellers public preview access to two new Amazon QuickSight dashboards. QuickSight improves the reporting experience for AWS Marketplace sellers by making it easy for users to monitor key metrics, perform ad hoc analysis, and quickly get business insights from data at any time and on any device.

In this post, we discuss our decision to implement QuickSight Embedded, as well as some of the benefits to AWS Marketplace sellers.

Data agility is a key differentiator

Making informed decisions is a business-critical function for successful organizations. Being able to quickly view performance trends and adjust strategies accordingly can make all the difference between hitting or missing business goals. Investing time, effort, and resources in technical data integrations, or creating pivot tables and charts from downloaded raw data, means a slower response rate to analyze shifts in performance trends. The more agile we can be in providing fast, efficient access to key performance indicators, the better positioned AWS Marketplace sellers can be to take action based on what their data tells them.

After reviewing customer feedback on their reporting experience, several trends emerged in what would be most helpful to them. First, sellers wanted a visual representation of their data. Second, though some wanted data feeds available to integrate AWS Marketplace data into their own business intelligence tools, others wanted to be able to access and review data without needing to invest technical business intelligence bandwidth in integrating feeds to create more user-friendly reports. Finally, they wanted the ability to easily filter the data, as well as the option to download it. In researching options to provide the reporting experience AWS Marketplace sellers wanted, we found that QuickSight was a perfect fit.

Doing more with data

Billed Revenue and the Collections & Disbursements are two new QuickSight dashboards embedded directly into the AWS Marketplace Management Portal (AMMP), accessed via the Insights tab. These dashboards—pre-built with 10+ controls or filters, 15+ visualizations, and powered by daily refreshed data—provide a visual reporting experience for revenue recognition and disbursements tracking.

The following screenshot shows what the dashboards look like in the Finance operations section on the Insights tab within the AMMP.

The dashboards are divided into several sections:

  • Controls provides filters to refine your dashboard data.
  • Date Range Filter provides the ability to filter dashboard data based on the dates.
  • Metrics, Trends, and Breakdowns all provide detailed analytics to understand business performance.
  • Granular data provides the option to download the raw data from the dashboard by clicking on the table and choosing the Export to CSV option.

For quick help, sellers can select the Info button to view the side navigation panel with tips on how to use the dashboard. They can also reach out to our team by selecting Contact us from the Info panel.

Improving the customer experience

The Billed Revenue dashboard now reflects changes within 24 hours of a customer being billed or refunded—a major improvement over the 45 days it once took to access this data from the legacy AMMP Billed Revenue report. Similarly, the Collections & Disbursements dashboard provides disbursement information within 24 hours of AWS sending funds to sellers, whereas it used to take up to 4 days from the legacy AMMP Disbursement Report.

Our team’s decision to go with QuickSight was the direct result of a clear alignment between what our sellers told us they wanted and what QuickSight offered. With QuickSight Embedded dashboards, AWS Marketplace sellers now have a visual representation of their data that doesn’t require time or resources dedicated to technical integration implementations, and they can quickly and easily manipulate the data via filters, or they can download it to a CSV if that’s their preference. Embedded dashboards simplify the viewing, analyzing, and tracking of key business metrics and trends related to financial operations. Being able to easily show each seller only their relevant data (using row-level security) provides us with the flexibility we need, with the peace of mind of knowing everything is secure. AWS Marketplace data is hosted in an Amazon Redshift cluster; QuickSight’s seamless integration into Amazon Redshift made it a fantastic choice.

Data-driven decisions with QuickSight

Embedding these new QuickSight dashboards into the AMMP has enabled us to provide at-a-glance metrics to AWS Marketplace sellers in a faster, more efficient, and far more user-friendly way than ever before. QuickSight has made a significant impact on how quickly sellers can access their data, which helps them respond faster to shifting trends.

To learn more about how you can embed customized data visuals, interactive dashboards, and natural language querying into any application, visit Amazon QuickSight Embedded.


About the Authors

Snigdha Sahi is a Senior Product Manager (Technical) with Amazon Web Services (AWS) Marketplace. She has diverse work experience across product management, product development and management consulting. A technology enthusiast, she loves brainstorming and building products that are intuitive and easy to use. In her spare time, she enjoys hiking, solving Sudoku and listening to music.

Vincent Larchet is a Principal Software Development Engineer on the AWS Marketplace team based in Vancouver, BC. Outside of work, he is a passionate wood worker and DIYer.

Organize your AWS Serverless code to prevent merge conflicts

Post Syndicated from Mark Curtis original https://aws.amazon.com/blogs/devops/organize-your-aws-serverless-code-to-prevent-merge-conflicts/

How do you prevent the most common merge conflicts when your team is working on a Serverless application? How do you make sure that your team stays productive and avoids large merge issues while trying to update the same crucial files simultaneously? –The answer to both questions is code organization! You can use cfn-include and swagger-cli to organize, collaborate, and maintain a large serverless application as well as support a large or decentralized development team.

Real life inspiration

WRAP Technologies Inc. (WRAP) creates advanced technologies for the protection and security of public safety. Their WRAP Reality product allows law enforcement agencies to train their officers using virtual reality-based scenarios.

Too many cooks in the kitchen

When multiple developers collaborate on a serverless architecture built with AWS CloudFormation, and its extensions such as the AWS Serverless Application Model (SAM), the nature of specifying resources in both the template.yaml and the optional OpenAPI.yaml specification for Amazon API Gateway leads to merge conflicts, such as the one demonstrated in the following figure  where two developers are adding different API endpoints at the same time. These conflicts detract from the developer’s time and agility. Furthermore, navigating and maintaining the long template files required for a larger serverless architecture slows development  as the developer scans large files to find a particular resource definition.

Figure 1. The frustrating merge conflicts.

Figure 1. The frustrating merge conflicts.

By refactoring and organizing the CloudFormation and OpenAPI files, your development team can realize several benefits:

  • Improve developer efficiency by decomposing large, hard-to-manage files into a series of well-organized and single-purpose files.
  • Enhance developer productivity by allowing each developer to have ownership of their own code, thereby reducing the need to coordinate merges with teammates.
  • Eliminate potential merge issues for files that generate the most conflicts during the development of a typical Serverless API application.

Rapid development

WRAP partnered with AWS to develop and host the backend for their new officer training management platform. This entirely new platform was developed, completed, and available for use in a matter of months. Moreover, it’s a collaboration of developers spread across multiple teams worldwide, all contributing to the same code base. By instituting the norms and techniques of this post, WRAP created a large and maintainable serverless application with minimal developer code collisions.

Development of the WRAP Reality training management system was accomplished using CloudFormation for defining Infrastructure as Code (IaC), and an Amazon API Gateway OpenAPI specification for defining API contracts. The development team for the WRAP Reality training management service leveraged agile development for expediency, including the GitHub Flow branching strategy. However, since project contributors were not co-located, several considerations were put in place to make sure of consistency and speed of code development:

  • The API specifications and contracts were defined in OpenAPI (Swagger) specifications early in the development process, clearly defining the project structure up front, and allowing developers to independently build infrastructure components.
  • The two code assets central to the entire project – the CloudFormation template and the OpenAPI Specification – were decomposed into small, easily manageable components. This enabled components to be organized in a way that enhanced development productivity and practically eliminated the inevitable merge conflicts that come with large source code files that are being modified on a daily basis.

The development process was accelerated by utilizing OpenAPI integrations with AWS Services, as well as techniques for managing the OpenAPI specification and Cloudformation Template files.

Sample project

To demonstrate these techniques, we’ll explore the following sample project comprised of API endpoints for “widget” management, available on GitHub. This project provides the following end points:

  • /widget PUT: Creation of a new widget
  • /widget GET: Retrieval of a new widget
  • /reports/color GET: Retrieval of a set of widgets based on the widget color
  • /reports/filterpage GET: Retrieval of widgets based on specified filters

The overall architecture of the application is shown in the following diagram:

Figure 2. Architecture Diagram

Figure 2. Architecture Diagram

The application comprises:

  • Amazon API Gateway is a fully-managed service that makes it easy for developers to create, publish, maintain, monitor, and secure APIs at any scale. In this example, API Gateway serves as the web service for the API endpoints. The mapping of data to and from the API endpoints to the Lambda functions is formally defined by an OpenAPI specification file.
  • AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers, creating workload-aware cluster scaling logic, maintaining event integrations, or managing runtimes. In this example, four Lambda functions are used to service each of the four API calls.
  • Amazon DynamoDB is a key-value and document database that delivers single-digit millisecond performance at any scale. DynamoDB is used as a persistent data store for widgets and associated properties.

OpenAPI and AWS service integration

When using API Gateway, developers have the option of using proxy Lambda integrations, or formally defining the API interface in an OpenAPI yaml file. The OpenAPI specification can be leveraged to document the API prior to development, and the example/mock features of the OpenAPI specification facilitates concurrent development by quickly establishing a working infrastructure to build upon. Furthermore, API documentation can be automatically generated from the OpenAPI specification.

As the number of endpoints increases, the OpenAPI specification file can grow in size, reaching thousands of lines of code that must be updated and maintained regularly by multiple developers. To aid in management and usability, the OpenAPI file can be decomposed into separate files for endpoints, responses, fields, and schemas.

Start with a “skeleton” file as an entry point for the OpenAPI definition, and then add a separate file for the definition of each endpoint or construct. For example, the sample project entry point is api/apiSkeleton.yaml, which contains the global definitions and effectively defines a simple list of endpoints and the reference ($ref) file path to each endpoint’s definition.

The application comprises:

/reports/color:
    $ref: './paths/reports/reportsColor.yaml'

  /reports/filterpage:
    $ref: './paths/reports/reportsFilterPage.yaml'

Diving into a file referenced by an endpoint, we see that it contains all of the specification details for that endpoint. Looking at the reportsColor.yaml file reveals the full endpoint specification for /reports/color:

get:
  description: Get widgets by color
  parameters:
    - in: path
      $ref: '../../requestParameters/color.yaml'
  responses:
    200:
      description: Get All the Widgets of a color
      content:
        application/json:
          schema:
            $ref: '../../schemas/widgetList.yaml'
    . . .

In turn, this endpoint specification can include further references to yaml files defining common parameters, schemas, and even full gateway responses. For example, color.yaml defines the color path variable:

  type: string
    description: "The widget's color"
    example: "Red"

To paraphrase a common catch phrase, “With a great many files, comes a great responsibility for organization.” To this end, we offer the following organizational structure as a start. Place all of the related API specifications in an “api” subfolder of your project. Have child subfolders for field, metadata, and gateway response definition files. Then, create child subfolder trees for each branch of your endpoints that mirror the endpoint paths. This will result in a highly-organized directory structure, as seen in the sample project:

├── api
│   ├── apiSkeleton.yaml
│   ├── fields
│   │   ├── color.yaml
│   │   ├── metadata
│   │   │   ├── count.yaml
│   │   │   ├── message.yaml
│   │   └── widgetname.yaml
│   ├── gatewayResponses
│   │   ├── error.yaml
│   │   └── notFound.yaml
│   ├── paths
│   │   ├── reports
│   │   │   ├── reportsColor.yaml
│   │   │   └── reportsFilterPage.yaml
│   │   └── widget
│   │       ├── widgetPut.yaml
│   │       └── widgetWidgetnameGet.yaml

We still need a consolidated single OpenAPI file to provide to CloudFormation during deployment to AWS. Therefore, the multiple files are combined and validated using the swagger-cli bundle command, resulting in a single file for deployment. The bundle command must be executed before a CloudFormation build. This command can also be included as a shortcut in the Makefile as the “buildOpenApi” command:

swagger-cli bundle -o api/api.yaml --dereference --t yaml  api/apiSkeleton.yaml

or

make buildOpenApi

Once compiled, api/api.yaml is then used normally for API Gateway integrations and as a Postman  API Collection import. As api/api.yaml is dynamically compiled, it’s included in .gitignore and not checked in to AWS CodeCommit.

cfn-include and nested stacks

The CloudFormation template that defines the infrastructure for even a simple service can grow to considerable length, perhaps thousands of lines. This presents challenges from a support and continued development perspective, as specific code locations become difficult to find and merge conflicts become commonplace.

CloudFormation Nested Stacks are a method of breaking a large CloudFormation template into separate templates. When there are clear delineations between groups of resources in a stack breaking it into separate nested stacks makes sense. There is also a 500 resource limit in a single CloudFormation stack and in order to go above that nested or separate stacks are necessary. Depending on the complexity of the architecture and frequency of updates however, the Nested Stacks can also become large. Furthermore, in a serverless architecture, the logical separation of architecture layers into separate stacks may not be direct, for example when a Lambda function is triggered by an event sent to an EventBridge event bus, then that Lambda function sends a different event back to the same event bus.

In these cases, CloudFormation templates can be decomposed to further leverage cfn-include . With this technique, the top-level CloudFormation template becomes a skeleton file which contains the stack parameters, global specifications, a list of resource names without properties, and the outputs. The properties of each resource are contained in separate files, referenced by an ‘include’ directive.
CloudFormation template organization

To organize your CloudFormation template, deconstruct the template into one-file-per-resource, with one main “skeleton” file as the main entry point. This skeleton file contains the full parameters, global section, conditions, and output specification. The resources are specified by resource name in this skeleton file, and then an ‘include’ directive points to the file that contains the body of the resource declaration. See the following example of the main skeleton file with two resources:

AWSTemplateFormatVersion: '2010-09-09'
Transform: AWS::Serverless-2016-10-31
Description: >
  Widget API Service
Globals:
  Function:
    Handler: app.lambda_handler
    Runtime: python3.8
Resources:

    WidgetApi:
        !Include ./resources/apigw/widgetApiGW.yaml

    WidgetDdbTable:
        !Include ./resources/dynamodb/widgetDdbTable.yaml

Then, the resource files contain the properties of that specific resource. For example, widgetApiGW.yaml defines an API Gateway:

Type: AWS::Serverless::Api
    Properties:
      DefinitionBody:
        Fn::Transform:
          Name: AWS::Include
          Parameters:
            Location: api/api.yaml
      EndpointConfiguration:
        Type: REGIONAL
      StageName: prod
      TracingEnabled: true

This approach has the benefit of breaking the CloudFormation template into multiple small files, while still maintaining a top-level holistic view. The resource definitions, which normally comprise the majority of the content and can cause merge conflicts, are moved out of the main template.

For organization, you can create a directory in your project to contain the CloudFormation scripts. This directory also contains the entry-point skeleton file. Create further sub-folders for resources, and then further folders by resource type and architecture. We found that placing applicable AWS Identity and Access Management (IAM) role resource definitions in the same folder with the applied resource facilitated easier navigation. For example:

├── cloudformation
│   ├── resources
│   │   ├── apigw
│   │   │   └── widgetApiGW.yaml
│   │   ├── dynamodb
│   │   │   └── widgetDdbTable.yaml
│   │   └── lambda
│   │       ├── layers
│   │       │   └── lambdaDDBEnv.yaml
│   │       ├── reports
│   │       │   ├── reportsColorLambda.yaml
│   │       │   └── reportsColorLambdaRole.yaml
│   │       └── widget
│   │           ├── widgetGetLambda.yaml
│   │           └── widgetGetLambdaRole.yaml
│   └── templateSkeleton.yaml

The files must be reconstituted to a single template.yaml for CloudFormation build and deployment. This is accomplished with the cfn-include command. A convenience command can optionally be included in the Makefile.

cfn-include --yaml  cloudFormation/templateSkeleton.yaml > template.yaml

or

make buildTemplate

As the final template.yaml file is dynamically compiled, it’s included in .gitignore and not checked in to CodeCommit.

Conclusion

This post demonstrates techniques used by WRAP and AWS to rapidly develop and maintain key files in an Serverless architecture. The techniques discussed in this post allowed the WRAP and AWS team to do the following:

  • Improve developer efficiency by decomposing large, hard-to-manage files into a series of well-organized and single purpose files.
  • Enhance developer productivity by allowing each developer to have ownership of their own piece of the code without having to coordinate with teammates.
  • Eliminate potential merge issues on the files that typically generate the most conflicts during the development of a typical Serverless API application.

Applying these techniques was one of the key factors in the rapid development of the WRAP Reality training framework.

About the Authors:

 Tom Romano

Tom Romano is a Solutions Architect from Tampa, FL. Tom is a member the Service Creation team for the World Wide Public Sector, who assists GovTech and EdTech customers as they create new solutions that are cloud-native, event-driven, and serverless. He is an enthusiastic Python programmer for both application development and data analytics. In his free time, Tom flies remote control model airplanes and enjoys vacationing around Florida.

Robert Maefs

Robert Maefs is a lead technologist currently working with Wrap, Inc. developing innovative Virtual Reality training simulations for law enforcement and corrections. He is a repeat entrepreneur with expertise bringing mature technologies to under-served industries. In his personal life, Robert nerds out with board games and 3D printing.

Mark Curtis

Mark Curtis is a Senior Solutions Architect at AWS. At AWS he helps EdTech and GovTech customers architect and modernize their applications using cloud native serverless services. Prior to joining AWS, he spent 18 years developing scalable applications for both EdTech and Government customers.

Juan Peredo

Juan Peredo is a Cloud Application Architect at AWS Professional Services. He enjoys working with customers to design, migrate, and optimize cloud native applications. He is a problem solver at heart who likes using emerging technologies to solve interesting problems.

Training teachers and empowering students in Machakos, Kenya

Post Syndicated from Wariara Waireri original https://www.raspberrypi.org/blog/computing-education-machakos-kenya-edtech-hub-launch/

Over the past months, we’ve been working with two partner organisations, Team4Tech and Kenya Connect, to support computing education across the rural county of Machakos, Kenya.

Working in rural Kenya

In line with our 2025 strategy, we have started work to improve computing education for young people in Kenya and South Africa. We are especially eager to support communities that experience educational disadvantage. One of our projects in this area is in partnership with Team4Tech and Kenya Connect. Together we have set up the Dr Isaac Minae EdTech Hub in the community Kenya Connect supports in the rural county of Machakos, and we are training teachers so they can equip their learners with coding and physical computing skills.

“Watching teachers and students find joy and excitement in learning has been tremendous! The Raspberry Pi Foundation’s hands-on approach is helping learners make connections through seeing how technology can be used for innovation to solve problems. We are excited to be partnering with Raspberry Pi Foundation and Team4Tech in bringing technology to our rural community.”

– Sharon Runge, Executive Director, Kenya Connect

We are providing the Wamunyu community with the hardware and the skills and knowledge training they need to use digital technology to create solutions to problems they see. The training will make sure that teachers across Machakos can sustain the EdTech Hub and computing education activities independently. This is important because we want the community to be empowered to solve problems that matter to them and for all the local young people to have opportunities that are open to their peers in Nairobi, Kisumu, Mombasa, and other cities in Kenya.

Launching the Dr Isaac Minae EdTech Hub in Wamunyu

In October this year, we travelled to Wamunyu to help Kenya Connect set up and launch the Dr Isaac Minae EdTech Hub, for which we provided hardware including Raspberry Pi 400 computers and physical computing kits with Raspberry Pi Pico microcontrollers, LEDs, buzzers, buttons, motors and more. We also held a teacher training session to start setting up the local educators with the skills and knowledge they need to teach coding and physical computing. In the training, educators brought a range of experiences with using computers. Some were unfamiliar with computer hardware, but at the end of the training session, they all had designed and created physical computing projects using electronic circuits and code. It was hugely inspiring to work with these teachers and see their enthusiasm and commitment to learning.

Through our two-year partnership with Kenya Connect, we aim to reach at least 1000 learners between the ages of 9 to 14 from 62 schools in Machakos county. We will work with at least 150 teachers to build their knowledge, skills, and confidence to teach coding, digital making, and robotics, and to run after-school Code Clubs. We’ll help teachers offer learning experiences based on our established learning paths to their students, and these experiences will include basic coding skills aligned to Kenya’s Competency Based Curriculum (CBC). We are putting particular focus on adapting our learning content so that teachers in Machakos can offer culturally relevant educational activities in their community. 

“Our partnership with the Raspberry Pi Foundation will open up new avenues for teachers to learn coding and physical computing. This is in line with the current Competency Based Curriculum that requires students to start learning coding at an early age. Though coding is entrenched in the curriculum, teachers are ill-prepared and schools lack devices. We are so grateful to the Raspberry Pi Foundation for providing teachers and students access to devices and the Raspberry Pi learning paths.”

– Patrick Munguti, Director of Education and Technology, Kenya Connect

Looking to the future

Next up for our work on this project is to continue supporting Kenya Connect to scale the program in the county.

A group of learners and educators pose together in rural Kenya.

In all our work in Sub-Saharan Africa, we are committed to strengthening and growing our partnerships with locally led youth and community organisations, the private sector, and the public sector, in line with our mission to open up more opportunities for young people to realise their full potential through the power of computing and digital technologies.

Our work in Sub-Saharan Africa is generously funded by the Ezra Charitable Trust.

The post Training teachers and empowering students in Machakos, Kenya appeared first on Raspberry Pi.

The unintended consequences of blocking IP addresses

Post Syndicated from Alissa Starzak original https://blog.cloudflare.com/consequences-of-ip-blocking/

The unintended consequences of blocking IP addresses

The unintended consequences of blocking IP addresses

In late August 2022, Cloudflare’s customer support team began to receive complaints about sites on our network being down in Austria. Our team immediately went into action to try to identify the source of what looked from the outside like a partial Internet outage in Austria. We quickly realized that it was an issue with local Austrian Internet Service Providers.

But the service disruption wasn’t the result of a technical problem. As we later learned from media reports, what we were seeing was the result of a court order. Without any notice to Cloudflare, an Austrian court had ordered Austrian Internet Service Providers (ISPs) to block 11 of Cloudflare’s IP addresses.

In an attempt to block 14 websites that copyright holders argued were violating copyright, the court-ordered IP block rendered thousands of websites inaccessible to ordinary Internet users in Austria over a two-day period. What did the thousands of other sites do wrong? Nothing. They were a temporary casualty of the failure to build legal remedies and systems that reflect the Internet’s actual architecture.

Today, we are going to dive into a discussion of IP blocking: why we see it, what it is, what it does, who it affects, and why it’s such a problematic way to address content online.

Collateral effects, large and small

The craziest thing is that this type of blocking happens on a regular basis, all around the world. But unless that blocking happens at the scale of what happened in Austria, or someone decides to highlight it, it is typically invisible to the outside world. Even Cloudflare, with deep technical expertise and understanding about how blocking works, can’t routinely see when an IP address is blocked.

For Internet users, it’s even more opaque. They generally don’t know why they can’t connect to a particular website, where the connection problem is coming from, or how to address it. They simply know they cannot access the site they were trying to visit. And that can make it challenging to document when sites have become inaccessible because of IP address blocking.

Blocking practices are also wide-spread. In their Freedom on the Net report, Freedom House recently reported that 40 out of the 70 countries that they examined – which vary from countries like Russia, Iran and Egypt to Western democracies like the United Kingdom and Germany –  did some form of website blocking. Although the report doesn’t delve into exactly how those countries block, many of them use forms of IP blocking, with the same kind of potential effects for a partial Internet shutdown that we saw in Austria.

Although it can be challenging to assess the amount of collateral damage from IP blocking, we do have examples where organizations have attempted to quantify it. In conjunction with a case before the European Court of Human Rights, the European Information Society Institute, a Slovakia-based nonprofit, reviewed Russia’s regime for website blocking in 2017. Russia exclusively used IP addresses to block content. The European Information Society Institute concluded that IP blocking led to “collateral website blocking on a massive scale” and noted that as of June 28, 2017, “6,522,629 Internet resources had been blocked in Russia, of which 6,335,850 – or 97% – had been blocked collaterally, that is to say, without legal justification.”

In the UK, overbroad blocking prompted the non-profit Open Rights Group to create the website Blocked.org.uk. The website has a tool enabling users and site owners to report on overblocking and request that ISPs remove blocks. The group also has hundreds of individual stories about the effect of blocking on those whose websites were inappropriately blocked, from charities to small business owners. Although it’s not always clear what blocking methods are being used, the fact that the site is necessary at all conveys the amount of overblocking. Imagine a dressmaker, watchmaker or car dealer looking to advertise their services and potentially gain new customers with their website. That doesn’t work if local users can’t access the site.

One reaction might be, “Well, just make sure there are no restricted sites sharing an address with unrestricted sites.” But as we’ll discuss in more detail, this ignores the large difference between the number of possible domain names and the number of available IP addresses, and runs counter to the very technical specifications that empower the Internet. Moreover, the definitions of restricted and unrestricted differ across nations, communities, and organizations. Even if it were possible to know all the restrictions, the designs of the protocols — of the Internet, itself — mean that it is simply infeasible, if not impossible, to satisfy every agency’s constraints.

Overblocking websites is not only a problem for users; it has legal implications. Because of the effect it can have on ordinary citizens looking to exercise their rights online, government entities (both courts and regulatory bodies) have a legal obligation to make sure that their orders are necessary and proportionate, and don’t unnecessarily affect those who are not contributing to the harm.

It would be hard to imagine, for example, that a court in response to alleged wrongdoing would blindly issue a search warrant or an order based solely on a street address without caring if that address was for a single family home, a six-unit condo building, or a high rise with hundreds of separate units. But those sorts of practices with IP addresses appear to be rampant.

In 2020, the European Court of Human Rights (ECHR) – the court overseeing the implementation of the Council of Europe’s European Convention on Human Rights – considered a case involving a website that was blocked in Russia not because it had been targeted by the Russian government, but because it shared an IP address with a blocked website. The website owner brought suit over the block. The ECHR concluded that the indiscriminate blocking was impermissible, ruling that the block on the lawful content of the site “amounts to arbitrary interference with the rights of owners of such websites.” In other words, the ECHR ruled that it was improper for a government to issue orders that resulted in the blocking of sites that were not targeted.

Using Internet infrastructure to address content challenges

Ordinary Internet users don’t think a lot about how the content they are trying to access online is delivered to them. They assume that when they type a domain name into their browser, the content will automatically pop up. And if it doesn’t, they tend to assume the website itself is having problems unless their entire Internet connection seems to be broken. But those basic assumptions ignore the reality that connections to a website are often used to limit access to content online.

Why do countries block connections to websites? Maybe they want to limit their own citizens from accessing what they believe to be illegal content – like online gambling or explicit material – that is permissible elsewhere in the world. Maybe they want to prevent the viewing of a foreign news source that they believe to be primarily disinformation. Or maybe they want to support copyright holders seeking to block access to a website to limit viewing of content that they believe infringes their intellectual property.

To be clear, blocking access is not the same thing as removing content from the Internet. There are a variety of legal obligations and authorities designed to permit actual removal of illegal content. Indeed, the legal expectation in many countries is that blocking is a matter of last resort, after attempts have been made to remove content at the source.

Blocking just prevents certain viewers – those whose Internet access depends on the ISP that is doing the blocking – from being able to access websites. The site itself continues to exist online and is accessible by everyone else. But when the content originates from a different place and can’t be easily removed, a country may see blocking as their best or only approach.

We recognize the concerns that sometimes drive countries to implement blocking. But fundamentally, we believe it’s important for users to know when the websites they are trying to access have been blocked, and, to the extent possible, who has blocked them from view and why. And it’s critical that any restrictions on content should be as limited as possible to address the harm, to avoid infringing on the rights of others.

Brute force IP address blocking doesn’t allow for those things. It’s fully opaque to Internet users. The practice has unintended, unavoidable consequences on other content. And the very fabric of the Internet means that there is no good way to identify what other websites might be affected either before or during an IP block.

To understand what happened in Austria and what happens in many other countries around the world that seek to block content with the bluntness of IP addresses, we have to understand what is going on behind the scenes. That means diving into some technical details.

Identity is attached to names, never addresses

Before we even get started describing the technical realities of blocking, it’s important to stress that the first and best option to deal with content is at the source. A website owner or hosting provider has the option of removing content at a granular level, without having to take down an entire website. On the more technical side, a domain name registrar or registry can potentially withdraw a domain name, and therefore a website, from the Internet altogether.

But how do you block access to a website, if for whatever reason the content owner or content source is unable or unwilling to remove it from the Internet?  There are only three possible control points.

The first is via the Domain Name System (DNS), which translates domain names into IP addresses so that the site can be found. Instead of returning a valid IP address for a domain name, the DNS resolver could lie and respond with a code, NXDOMAIN, meaning that “there is no such name.” A better approach would be to use one of the honest error numbers standardized in 2020, including error 15 for blocked, error 16 for censored, 17 for filtered, or 18 for prohibited, although these are not widely used currently.

Interestingly, the precision and effectiveness of DNS as a control point depends on whether the DNS resolver is private or public. Private or ‘internal’ DNS resolvers are operated by ISPs and enterprise environments for their own known clients, which means that operators can be precise in applying content restrictions. By contrast, that level of precision is unavailable to open or public resolvers, not least because routing and addressing is global and ever-changing on the Internet map, and in stark contrast to addresses and routes on a fixed postal or street map. For example, private DNS resolvers may be able to block access to websites within specified geographic regions with at least some level of accuracy in a way that public DNS resolvers cannot, which becomes profoundly important given the disparate (and inconsistent) blocking regimes around the world.

The second approach is to block individual connection requests to a restricted domain name. When a user or client wants to visit a website, a connection is initiated from the client to a server name, i.e. the domain name. If a network or on-path device is able to observe the server name, then the connection can be terminated. Unlike DNS, there is no mechanism to communicate to the user that access to the server name was blocked, or why.

The third approach is to block access to an IP address where the domain name can be found. This is a bit like blocking the delivery of all mail to a physical address. Consider, for example, if that address is a skyscraper with its many unrelated and independent occupants. Halting delivery of mail to the address of the skyscraper causes collateral damage by invariably affecting all parties at that address. IP addresses work the same way.

Notably, the IP address is the only one of the three options that has no attachment to the domain name. The website domain name is not required for routing and delivery of data packets; in fact it is fully ignored. A website can be available on any IP address, or even on many IP addresses, simultaneously. And the set of IP addresses that a website is on can change at any time. The set of IP addresses cannot definitively be known by querying DNS, which has been able to return any valid address at any time for any reason, since 1995.

The idea that an address is representative of an identity is anathema to the Internet’s design, because the decoupling of address from name is deeply embedded in the Internet standards and protocols, as is explained next.

The Internet is a set of protocols, not a policy or perspective

Many people still incorrectly assume that an IP address represents a single website. We’ve previously stated that the association between names and addresses is understandable given that the earliest connected components of the Internet appeared as one computer, one interface, one address, and one name. This one-to-one association was an artifact of the ecosystem in which the Internet Protocol was deployed, and satisfied the needs of the time.

Despite the one-to-one naming practice of the early Internet, it has always been possible to assign more than one name to a server (or ‘host’). For example, a server was (and is still) often configured with names to reflect its service offerings such as mail.example.com and www.example.com, but these shared a base domain name.  There were few reasons to have completely different domain names until the need to colocate completely different websites onto a single server. That practice was made easier in 1997 by the Host header in HTTP/1.1, a feature preserved by the SNI field in a TLS extension in 2003.

Throughout these changes, the Internet Protocol and, separately, the DNS protocol, have not only kept pace, but have remained fundamentally unchanged. They are the very reason that the Internet has been able to scale and evolve, because they are about addresses, reachability, and arbitrary name to IP address relationships.

The designs of IP and DNS are also entirely independent, which only reinforces that names are separate from addresses. A closer inspection of the protocols’ design elements illuminates the misperceptions of policies that lead to today’s common practice of controlling access to content by blocking IP addresses.

By design, IP is for reachability and nothing else

Much like large public civil engineering projects rely on building codes and best practice, the Internet is built using a set of open standards and specifications informed by experience and agreed by international consensus. The Internet standards that connect hardware and applications are published by the Internet Engineering Task Force (IETF) in the form of “Requests for Comment” or RFCs — so named not to suggest incompleteness, but to reflect that standards must be able to evolve with knowledge and experience. The IETF and its RFCs are cemented in the very fabric of communications, for example, with the first RFC 1 published in 1969. The Internet Protocol (IP) specification reached RFC status in 1981.

Alongside the standards organizations, the Internet’s success has been helped by a core idea known as the end-to-end (e2e) principle, codified also in 1981, based on years of trial and error experience. The end-to-end principle is a powerful abstraction that, despite taking many forms, manifests a core notion of the Internet Protocol specification: the network’s only responsibility is to establish reachability, and every other possible feature has a cost or a risk.

The idea of “reachability” in the Internet Protocol is also enshrined in the design of IP addresses themselves. Looking at the Internet Protocol specification, RFC 791, the following excerpt from Section 2.3 is explicit about IP addresses having no association with names, interfaces, or anything else.

Addressing

    A distinction is made between names, addresses, and routes [4].   A
    name indicates what we seek.  An address indicates where it is.  A
    route indicates how to get there.  The internet protocol deals
    primarily with addresses.  It is the task of higher level (i.e.,
    host-to-host or application) protocols to make the mapping from
    names to addresses.   The internet module maps internet addresses to
    local net addresses.  It is the task of lower level (i.e., local net
    or gateways) procedures to make the mapping from local net addresses
    to routes.
                            [ RFC 791, 1981 ]

Just like postal addresses for skyscrapers in the physical world, IP addresses are no more than street addresses written on a piece of paper. And just like a street address on paper, one can never be confident about the entities or organizations that exist behind an IP address. In a network like Cloudflare’s, any single IP address represents thousands of servers, and can have even more websites and services — in some cases numbering into the millions — expressly because the Internet Protocol is designed to enable it.

Here’s an interesting question: could we, or any content service provider, ensure that every IP address matches to one and only one name? The answer is an unequivocal no, and here too, because of a protocol design — in this case, DNS.

The number of names in DNS always exceeds the available addresses

A one-to-one relationship between names and addresses is impossible given the Internet specifications for the same reasons that it is infeasible in the physical world. Ignore for a moment that people and organizations can change addresses. Fundamentally, the number of people and organizations on the planet exceeds the number of postal addresses. We not only want, but need for the Internet to accommodate more names than addresses.

The difference in magnitude between names and addresses is also codified in the specifications. IPv4 addresses are 32 bits, and IPv6 addresses are 128 bits. The size of a domain name that can be queried by DNS is as many as 253 octets, or 2,024 bits (from Section 2.3.4 in RFC 1035, published 1987). The table below helps to put those differences into perspective:

The unintended consequences of blocking IP addresses

On November 15, 2022, the United Nations announced the population of the Earth surpassed eight billion people. Intuitively, we know that there cannot be anywhere near as many postal addresses. The difference between the number of possible names on the planet, and similarly on the Internet, does and must exceed the number of available addresses.

The proof is in the pudding names!

Now that those two relevant principles about IP addresses and DNS names in the international standards are understood – that IP address and domain names serve distinct purposes and there is no one to one relationship between the two – an examination of a recent case of content blocking using IP addresses can help to see the reasons it is problematic. Take, for example, the IP blocking incident in Austria late August 2022. The goal was to restrict access to 14 target domains, by blocking 11 IP addresses (source: RTR.Telekom. Post via the Internet Archive) — the mismatch between those two numbers should have been a warning flag that IP blocking might not have the desired effect.

Analogies and international standards may explain the reasons that IP blocking should be avoided, but we can see the scale of the problem by looking at Internet-scale data. To better understand and explain the severity of IP blocking, we decided to generate a global view of domain names and IP addresses (thanks are due to a PhD research intern, Sudheesh Singanamalla, for the effort). In September 2022, we used the authoritative zone files for the top-level domains (TLDs) .com, .net, .info, and .org, together with top-1M website lists, to find a total of 255,315,270 unique names. We then queried DNS from each of five regions and recorded the set of IP addresses returned. The table below summarizes our findings:

The unintended consequences of blocking IP addresses

The table above makes clear that it takes no more than 10.7 million addresses to reach 255,315,270 million names from any region on the planet, and the total set of IP addresses for those names from everywhere is about 16 million — the ratio of names to IP addresses is nearly 24x in Europe and 16x globally.

There is one more worthwhile detail about the numbers above: The IP addresses are the combined totals of both IPv4 and IPv6 addresses, meaning that far fewer addresses are needed to reach all 255M websites.

We’ve also inspected the data a few different ways to find some interesting observations. For example, the figure below shows the cumulative distribution (CDF) of the proportion of websites that can be visited with each additional IP address. On the y-axis is the proportion of websites that can be reached given some number of IP addresses. On the x-axis, the 16M IP addresses are ranked from the most domains on the left, to the least domains on the right. Note that any IP address in this set is a response from DNS and so it must have at least one domain name, but the highest numbers of domains on IP addresses in the set number are in the 8-digit millions.

The unintended consequences of blocking IP addresses

By looking at the CDF there are a few eye-watering observations:

  • Fewer than 10 IP addresses are needed to reach 20% of, or approximately 51 million, domains in the set;
  • 100 IPs are enough to reach almost 50% of domains;
  • 1000 IPs are enough to reach 60% of domains;
  • 10,000 IPs are enough to reach 80%, or about 204 million, domains.

In fact, from the total set of 16 million addresses, fewer than half, 7.1M (43.7%), of the addresses in the dataset had one name. On this ‘one’ point we must be additionally clear: we are unable to ascertain if there was only one and no other names on those addresses because there are many more domain names than those contained only in .com, .org, .info., and .net — there might very well be other names on those addresses.

In addition to having a number of domains on a single IP address, any IP address may change over time for any of those domains.  Changing IP addresses periodically can be helpful with certain security, performance, and to improve reliability for websites. One common example in use by many operations is load balancing. This means DNS queries may return different IP addresses over time, or in different places, for the same websites. This is a further, and separate, reason why blocking based on IP addresses will not serve its intended purpose over time.

Ultimately, there is no reliable way to know the number of domains on an IP address without inspecting all names in the DNS, from every location on the planet, at every moment in time — an entirely infeasible proposition.

Any action on an IP address must, by the very definitions of the protocols that rule and empower the Internet, be expected to have collateral effects.

Lack of transparency with IP blocking

So if we have to expect that the blocking of an IP address will have collateral effects, and it’s generally agreed that it’s inappropriate or even legally impermissible to overblock by blocking IP addresses that have multiple domains on them, why does it still happen? That’s hard to know for sure, so we can only speculate. Sometimes it reflects a lack of technical understanding about the possible effects, particularly from entities like judges who are not technologists. Sometimes governments just ignore the collateral damage – as they do with Internet shutdowns – because they see the blocking as in their interest. And when there is collateral damage, it’s not usually obvious to the outside world, so there can be very little external pressure to have it addressed.

It’s worth stressing that point. When an IP is blocked, a user just sees a failed connection. They don’t know why the connection failed, or who caused it to fail. On the other side, the server acting on behalf of the website doesn’t even know it’s been blocked until it starts getting complaints about the fact that it is unavailable. There is virtually no transparency or accountability for the overblocking. And it can be challenging, if not impossible, for a website owner to challenge a block or seek redress for being inappropriately blocked.

Some governments, including Austria, do publish active block lists, which is an important step for transparency. But for all the reasons we’ve discussed, publishing an IP address does not reveal all the sites that may have been blocked unintentionally. And it doesn’t give those affected a means to challenge the overblocking. Again, in the physical world example, it’s hard to imagine a court order on a skyscraper that wouldn’t be posted on the door, but we often seem to jump over such due process and notice requirements in virtual space.

We think talking about the problematic consequences of IP blocking is more important than ever as an increasing number of countries push to block content online. Unfortunately, ISPs often use IP blocks to implement those requirements. It may be that the ISP is newer or less robust than larger counterparts, but larger ISPs engage in the practice, too, and understandably so because IP blocking takes the least effort and is readily available in most equipment.

And as more and more domains are included on the same number of IP addresses, the problem is only going to get worse.

Next steps

So what can we do?

We believe the first step is to improve transparency around the use of IP blocking. Although we’re not aware of any comprehensive way to document the collateral damage caused by IP blocking, we believe there are steps we can take to expand awareness of the practice. We are committed to working on new initiatives that highlight those insights, as we’ve done with the Cloudflare Radar Outage Center.

We also recognize that this is a whole Internet problem, and therefore has to be part of a broader effort. The significant likelihood that blocking by IP address will result in restricting access to a whole series of unrelated (and untargeted) domains should make it a non-starter for everyone. That’s why we’re engaging with civil society partners and like-minded companies to lend their voices to challenge the use of blocking IP addresses as a way of addressing content challenges and to point out collateral damage when they see it.

To be clear, to address the challenges of illegal content online, countries need legal mechanisms that enable the removal or restriction of content in a rights-respecting way. We believe that addressing the content at the source is almost always the best and the required first step. Laws like the EU’s new Digital Services Act or the Digital Millennium Copyright Act provide tools that can be used to address illegal content at the source, while respecting important due process principles. Governments should focus on building and applying legal mechanisms in ways that least affect other people’s rights, consistent with human rights expectations.

Very simply, these needs cannot be met by blocking IP addresses.

We’ll continue to look for new ways to talk about network activity and disruption, particularly when it results in unnecessary limitations on access. Check out Cloudflare Radar for more insights about what we see online.

The collective thoughts of the interwebz