Tag Archives: storage

Intel Does a 360 on Discontinuing VROC Around Sapphire Rapids Server Launch

Post Syndicated from Patrick Kennedy original https://www.servethehome.com/intel-does-a-360-on-discontinuing-vroc-around-sapphire-rapids-server-launch/

Intel did a 360 on VROC support, discontinuing and then reinstating the product bookending the Sapphire Rapids Xeon launch

The post Intel Does a 360 on Discontinuing VROC Around Sapphire Rapids Server Launch appeared first on ServeTheHome.

New – Announcing Amazon EFS Elastic Throughput

Post Syndicated from Veliswa Boya original https://aws.amazon.com/blogs/aws/new-announcing-amazon-efs-elastic-throughput/

Today, we are announcing the availability of Amazon EFS Elastic Throughput, a new throughput mode for Amazon EFS that is designed to provide your applications with as much throughput as they need with pay-as-you-use pricing. This new throughput mode enables you to further simplify running workloads and applications on AWS by providing shared file storage that doesn’t need provisioning or capacity management.

Elastic Throughput is ideal for spiky and unpredictable workloads with performance requirements that are difficult to forecast. When you enable Elastic Throughput on an Amazon EFS file system, you no longer need to think about actively managing your file system performance or over-paying for idle resources in order to ensure performance for your applications. When you enable Elastic Throughput, you don’t specify or provision throughput capacity, Amazon EFS automatically delivers the throughput performance your application needs while you the builder pays only for the amount of data read or written.

Amazon EFS is built to provide serverless, fully elastic file storage that lets you share file data for your cloud-based applications without having to think about provisioning or managing storage capacity and performance. With Elastic Throughput, Amazon EFS now extends its simplicity and elasticity to performance, enabling you to run an even broader range of file workloads on Amazon EFS. Amazon EFS is well suited to support a broad spectrum of use cases that include analytics and data science, machine learning, CI/CD tools, content management and web serving, and SaaS applications.

A Quick Review
As you may already know, Amazon EFS already has the Bursting Throughput mode, which is available as a default and supports bursting to higher levels for up to 12 hours a day. If your application is throughput constrained on Bursting mode (for example, utilizes more than 80 percent of permitted throughput or exhausts burst credits), then you should consider using Provisioned (which we announced in 2018), or the new Elastic Throughput modes.

With this announcement of Elastic Throughput mode, and in addition to the already existing Provisioned Throughput mode, Amazon EFS now offers two options for workloads that require higher levels of throughput performance. You should use Provisioned Throughput if you know your workload’s performance requirements and you expect your workload to consume a higher share (more than 5 percent on average) of your application’s peak throughput capacity. You should use Elastic Throughput if you don’t know your application’s throughput or your application is very spiky.

To access Elastic Throughput mode (or any of the Throughput modes), select Customize (selecting Create instead will create your file system with the default Bursting mode).

Create File system

Create File system

New - Elastic Throughput

New – Elastic Throughput

You can also enable Elastic Throughput for new and existing General Purpose file systems using the Amazon EFS console or programmatically using the Amazon EFS CLI, Amazon EFS API, or AWS CloudFormation.

Elastic Throughput in Action
Once you have enabled Elastic Throughput mode, you will be able to monitor your cost and throughput usage using Amazon CloudWatch and set alerts on unplanned throughput charges using AWS Budgets.

I have a test file system elasticblog that I created previously using the Amazon EFS console, and now I cannot wait to see Elastic Throughput in action.

File system (elasticblog)

File system (elasticblog)

I have provisioned an Amazon Elastic Compute Cloud (Amazon C2) instance which I mounted to my file system. This EC2 instance has data that I will add to the file system.

I have also created CloudWatch Alarms, which will monitor throughput usage and set alarm thresholds (ReadIOBytes, WriteIOBytes, TotalIOBytes, and MetadataIOBytes).

CloudWatch for Throughput Usage

CloudWatch for Throughput Usage

The CloudWatch dashboard for my test file system elasticblog looks like this.

CloudWatch Dashboard - TotalIOBytes for File System

CloudWatch Dashboard – TotalIOBytes for File System

Elastic Throughput allows you to drive throughput up to a limit of 3 GiB/s for read operations and 1 GiB/s for write operations per file system in all Regions.

Available Now
Amazon EFS Elastic Throughput is available in all Regions supporting EFS except for the AWS China Regions.

To learn more, see the Amazon EFS User Guide. Please send feedback to AWS re:Post for Amazon Elastic File System or through your usual AWS support contacts.

Veliswa x

New – Amazon Redshift Support in AWS Backup

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/new-amazon-redshift-support-in-aws-backup/

With Amazon Redshift, you can analyze data in the cloud at any scale. Amazon Redshift offers native data protection capabilities to protect your data using automatic and manual snapshots. This works great by itself, but when you’re using other AWS services, you have to configure more than one tool to manage your data protection policies.

To make this easier, I am happy to share that we added support for Amazon Redshift in AWS Backup. AWS Backup allows you to define a central backup policy to manage data protection of your applications and can now also protect your Amazon Redshift clusters. In this way, you have a consistent experience when managing data protection across all supported services. If you have a multi-account setup, the centralized policies in AWS Backup let you define your data protection policies across all your accounts within your AWS Organizations. To help you meet your regulatory compliance needs, AWS Backup now includes Amazon Redshift in its auditor-ready reports. You also have the option to use AWS Backup Vault Lock to have immutable backups and prevent malicious or inadvertent changes.

Let’s see how this works in practice.

Using AWS Backup with Amazon Redshift
The first step is to turn on the Redshift resource type for AWS Backup. In the AWS Backup console, I choose Settings in the navigation pane and then, in the Service opt-in section, Configure resources. There, I toggle the Redshift resource type on and choose Confirm.

Console screenshot.

Now, I can create or update a backup plan to include the backup of all, or some, of my Redshift clusters. In the backup plan, I can define how often these backups should be taken and for how long they should be kept. For example, I can have daily backups with one week of retention, weekly backups with one month of retention, and monthly backups with one year of retention.

I can also create on-demand backups. Let’s see this with more details. I choose Protected resources in the navigation pane and then Create on-demand backup.

I select Redshift in the Resource type dropdown. In the Cluster identifier, I select one of my clusters. For this workload, I need two weeks of retention. Then, I choose Create on-demand backup.

Console screenshot.

My data warehouse is not huge, so after a few minutes, the backup job has completed.

Console screenshot.

I now see my Redshift cluster in the list of the resources protected by AWS Backup.

Console screenshot.

In the Protected resources list, I choose the Redshift cluster to see the list of the available recovery points.

Console screenshot.

When I choose one of the recovery points, I have the option to restore the full data warehouse or just a table into a new Redshift cluster.

Console screenshot.

I now have the possibility to edit the cluster and database configuration, including security and networking settings. I just update the cluster identifier, otherwise the restore would fail because it must be unique. Then, I choose Restore backup to start the restore job.

After some time, the restore job has completed, and I see the old and the new clusters in the Amazon Redshift console. Using AWS Backup gives me a simple centralized way to manage data protection for Redshift clusters as well as many other resources in my AWS accounts.

Console screenshot.

Availability and Pricing
Amazon Redshift support in AWS Backup is available today in the AWS Regions where both AWS Backup and Amazon Redshift are offered, with the exception of the Regions based in China. You can use this capability via the AWS Management Console, AWS Command Line Interface (CLI), and AWS SDKs.

There is no additional cost for using AWS Backup compared to the native snapshot capability of Amazon Redshift. Your overall costs depend on the amount of storage and retention you need. For more information, see AWS Backup pricing.

Danilo

New for AWS Backup – Protect and Restore Your CloudFormation Stacks

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/new-for-aws-backup-protect-and-restore-your-cloudformation-stacks/

To define the data protection policy of an application, you have to look at its components and find which ones store data that needs to be protected. Those are the stateful components of your application, such as databases and file systems. Other components don’t store data but need to be restored as well in case of issues. These are stateless components, such as containers and their network configurations.

When you manage your application using infrastructure as code (IaC), you have a single repository where all these components are described. Can we use this information to help protect your applications? Yes! AWS Backup now supports attaching an AWS CloudFormation stack to your data protection policies.

When you use CloudFormation as a resource, all stateful components supported by AWS Backup are backed up around the same time. The backup also includes the stateless resources in the stack, such as AWS Identity and Access Management (IAM) roles and Amazon Virtual Private Cloud (Amazon VPC) security groups. This gives you a single recovery point that you can use to recover the application stack or the individual resources you need. In case of recovery, you don’t need to mix automated tools with custom scripts and manual activities to recover and put the whole application stack back together. As you modernize and update an application managed with CloudFormation, AWS Backup automatically keeps track of changes and updates the data protection policies for you.

CloudFormation support for AWS Backup also helps you prove compliance of your data protection policies. You can monitor your application resources in AWS Backup Audit Manager, a feature of AWS Backup that enables you to audit and report on the compliance of data protection policies. You can also use AWS Backup Vault Lock to manage the immutability of your backups as required by your compliance obligations.

Let’s see how this works in practice.

Using AWS Backup Support for CloudFormation Stacks
First, I need to turn on the CloudFormation resource type for AWS Backup. In the AWS Backup console, I choose Settings in the navigation pane and then, in the Service opt-in section, Configure resources. There, I toggle the CloudFormation resource type on and choose Confirm.

Console screenshot.

Now that CloudFormation support is enabled, I choose Dashboard in the navigation pane and then Create backup plan. I select the Start with a template option and then the Daily-35day-Retention template. As the name suggests, this template creates daily backups that are kept for 35 days before being automatically deleted. I enter a name for the backup plan and choose Create plan.

Console screenshot.

Now I can assign resources to my backup plan. I enter a resource assignment name and use the default IAM role that is automatically created with the correct permissions.

Console screenshot.

In the Resource selection, I can select Include all resource types to automatically protect all resource types that are enabled in my account. Because I’d like to show how CloudFormation support works, I select Include specific resource types and then CloudFormation in the Select resource types dropdown menu. In the Choose resources menu, I can use the All supported CloudFormation stacks option to have all my stacks protected. For simplicity, I choose to protect only one stack, the my-app stack.

Console screenshot.

I leave the other options at their default values and choose Assign resources. That’s all! Now the CloudFormation stack that I selected will be backed up daily with 35 days of retention. What does that mean? Let’s have a look at what happens when I create an on-demand backup of a CloudFormation stack.

Creating On-Demand Backups for CloudFormation Stacks
I choose Protected resources in the navigation pane and then Create on-demand backup. The next steps are similar to what I did before when assigning resources to a backup plan. I select the CloudFormation resource type and the my-app stack. I use the Create backup now option to start the backup within one hour. I choose 7 days of retention and the Default backup vault. Backup vaults are logical containers that store and organize your backups. I select the default IAM role and choose Create on-demand backup.

Console screenshot.

Within a few minutes, the backup job is running. I expand the Backup job ID in the Backup jobs list to see the resources being backed up. The stateful resources (such as Amazon DynamoDB tables and Amazon Relational Database Service (RDS) databases) are listed with the current state of the backup job. The stateless resources in my stack (such as IAM roles, AWS Lambda functions, and VPC configurations) are backed up by the job with the CloudFormation resource type.

Console screenshot.

When the backup job has completed, I go back to the Protected resources page to see the list of resources that I can now restore. In the list, I see the IDs of the stateful resources (in this case, two DynamoDB tables and an Aurora database) and of the CloudFormation stack. If I choose each of the stateful resources, I see the available recovery points corresponding to the different points in time when that resource has been backed up.

Console screenshot.

If I choose the CloudFormation stack, I get a list of composite recovery points. Each composite recovery point includes all stateless and stateful resources in the stack. More specifically, the stateless resources are included in the CloudFormation template recovery point (the last one in the following screenshot).

Console screenshot.

Restoring a CloudFormation Backup
Inside the composite recovery point, I select the recovery point of the CloudFormation stack and choose Restore. Restoring a CloudFormation stack backup creates a new stack with a change set that represents the backup. I enter the new stack and change set names and choose Restore backup. After a few minutes, the restore job is completed.

In the CloudFormation console, the new stack is under review. I need to apply the change set.

Console screenshot.

I choose the new stack and select the change set created by the restore job to apply the change set.

Console screenshot.

After some time, the resources in my original stack have been recreated in the new stack. The stateful resources have been recreated empty. To recover the stateful resources, I can go back to the list of recovery points, select the recovery point I need, and initiate a restore.

Availability and Pricing
AWS Backup support for CloudFormation stacks is available today using the console, AWS Command Line Interface (CLI), and AWS SDKs in all AWS Regions where AWS Backup is offered. There is no additional cost for the stateless resources backed up and restored by AWS Backup. You only pay for the stateful resources such as databases, storage volumes, or file systems. For more information, see AWS Backup pricing.

You now have an automated solution to create and restore your applications with a simplified experience, eliminating the need to manage custom scripts.

Danilo

Our guide to AWS Compute at re:Invent 2022

Post Syndicated from Sheila Busser original https://aws.amazon.com/blogs/compute/our-guide-to-aws-compute-at-reinvent-2022/

This blog post is written by Shruti Koparkar, Senior Product Marketing Manager, Amazon EC2.

AWS re:Invent is the most transformative event in cloud computing and it is starting on November 28, 2022. AWS Compute team has many exciting sessions planned for you covering everything from foundational content, to technology deep dives, customer stories, and even hands on workshops. To help you build out your calendar for this year’s re:Invent, let’s look at some highlights from the AWS Compute track in this blog. Please visit the session catalog for a full list of AWS Compute sessions.

Learn what powers AWS Compute

AWS offers the broadest and deepest functionality for compute. Amazon Elastic Cloud Compute (Amazon EC2) offers granular control for managing your infrastructure with the choice of processors, storage, and networking.

The AWS Nitro System is the underlying platform for our all our modern EC2 instances. It enables AWS to innovate faster, further reduce cost for our customers, and deliver added benefits like increased security and new instance types.

Discover the benefits of AWS Silicon

AWS has invested years designing custom silicon optimized for the cloud. This investment helps us deliver high performance at lower costs for a wide range of applications and workloads using AWS services.

  • Explore the AWS journey into silicon innovation with our “CMP201: Silicon Innovation at AWS” session. We will cover some of the thought processes, learnings, and results from our experience building silicon for AWS Graviton, AWS Nitro System, and AWS Inferentia.
  • To learn about customer-proven strategies to help you make the move to AWS Graviton quickly and confidently while minimizing uncertainty and risk, attend “CMP410: Framework for adopting AWS Graviton-based instances”.

 Explore different use cases

Amazon EC2 provides secure and resizable compute capacity for several different use-cases including general purpose computing for cloud native and enterprise applications, and accelerated computing for machine learning and high performance computing (HPC) applications.

High performance computing

  • HPC on AWS can help you design your products faster with simulations, predict the weather, detect seismic activity with greater precision, and more. To learn how to solve world’s toughest problems with extreme-scale compute come join us for “CMP205: HPC on AWS: Solve complex problems with pay-as-you-go infrastructure”.
  • Single on-premises general-purpose supercomputers can fall short when solving increasingly complex problems. Attend “CMP222: Redefining supercomputing on AWS” to learn how AWS is reimagining supercomputing to provide scientists and engineers with more access to world-class facilities and technology.
  • AWS offers many solutions to design, simulate, and verify the advanced semiconductor devices that are the foundation of modern technology. Attend “CMP320: Accelerating semiconductor design, simulation, and verification” to hear from ARM and Marvel about how they are using AWS to accelerate EDA workloads.

Machine Learning

Cost Optimization

Hear from our customers

We have several sessions this year where AWS customers are taking the stage to share their stories and details of exciting innovations made possible by AWS.

Get started with hands-on sessions

Nothing like a hands-on session where you can learn by doing and get started easily with AWS compute. Our speakers and workshop assistants will help you every step of the way. Just bring your laptop to get started!

You’ll get to meet the global cloud community at AWS re:Invent and get an opportunity to learn, get inspired, and rethink what’s possible. So build your schedule in the re:Invent portal and get ready to hit the ground running. We invite you to stop by the AWS Compute booth and chat with our experts. We look forward to seeing you in Las Vegas!

Making static sites dynamic with Cloudflare D1

Post Syndicated from Kristian Freeman original https://blog.cloudflare.com/making-static-sites-dynamic-with-cloudflare-d1/

Making static sites dynamic with Cloudflare D1

Introduction

Making static sites dynamic with Cloudflare D1

There are many ways to store data in your applications. For example, in Cloudflare Workers applications, we have Workers KV for key-value storage and Durable Objects for real-time, coordinated storage without compromising on consistency. Outside the Cloudflare ecosystem, you can also plug in other tools like NoSQL and graph databases.

But sometimes, you want SQL. Indexes allow us to retrieve data quickly. Joins enable us to describe complex relationships between different tables. SQL declaratively describes how our application’s data is validated, created, and performantly queried.

D1 was released today in open alpha, and to celebrate, I want to share my experience building apps with D1: specifically, how to get started, and why I’m excited about D1 joining the long list of tools you can use to build apps on Cloudflare.

Making static sites dynamic with Cloudflare D1

D1 is remarkable because it’s an instant value-add to applications without needing new tools or stepping out of the Cloudflare ecosystem. Using wrangler, we can do local development on our Workers applications, and with the addition of D1 in wrangler, we can now develop proper stateful applications locally as well. Then, when it’s time to deploy the application, wrangler allows us to both access and execute commands to your D1 database, as well as your API itself.

What we’re building

In this blog post, I’ll show you how to use D1 to add comments to a static blog site. To do this, we’ll construct a new D1 database and build a simple JSON API that allows the creation and retrieval of comments.

As I mentioned, separating D1 from the app itself – an API and database that remains separate from the static site – allows us to abstract the static and dynamic pieces of our website from each other. It also makes it easier to deploy our application: we will deploy the frontend to Cloudflare Pages, and the D1-powered API to Cloudflare Workers.

Building a new application

First, we’ll add a basic API in Workers. Create a new directory and in it a new wrangler project inside it:

$ mkdir d1-example && d1-example
$ wrangler init

In this example, we’ll use Hono, an Express.js-style framework, to rapidly build our API. To use Hono in this project, install it using NPM:

$ npm install hono

Then, in src/index.ts, we’ll initialize a new Hono app, and define a few endpoints – GET /API/posts/:slug/comments, and POST /get/api/:slug/comments.

import { Hono } from 'hono'
import { cors } from 'hono/cors'

const app = new Hono()

app.get('/api/posts/:slug/comments', async c => {
  // do something
})

app.post('/api/posts/:slug/comments', async c => {
  // do something
})

export default app

Now we’ll create a D1 database. In Wrangler 2, there is support for the wrangler d1 subcommand, which allows you to create and query your D1 databases directly from the command line. So, for example, we can create a new database with a single command:

$ wrangler d1 create d1-example

With our created database, we can take the database name ID and associate it with a binding inside of wrangler.toml, wrangler’s configuration file. Bindings allow us to access Cloudflare resources, like D1 databases, KV namespaces, and R2 buckets, using a simple variable name in our code. Below, we’ll create the binding DB and use it to represent our new database:

[[ d1_databases ]]
binding = "DB" # i.e. available in your Worker on env.DB
database_name = "d1-example"
database_id = "4e1c28a9-90e4-41da-8b4b-6cf36e5abb29"

Note that this directive, the [[d1_databases]] field, currently requires a beta version of wrangler. You can install this for your project using the command npm install -D wrangler/beta.

With the database configured in our wrangler.toml, we can start interacting with it from the command line and inside our Workers function.

First, you can issue direct SQL commands using wrangler d1 execute:

$ wrangler d1 execute d1-example --command "SELECT name FROM sqlite_schema WHERE type ='table'"
Executing on d1-example:
┌─────────────────┐
│ name │
├─────────────────┤
│ sqlite_sequence │
└─────────────────┘

You can also pass a SQL file – perfect for initial data seeding in a single command. Create src/schema.sql, which will create a new comments table for our project:

drop table if exists comments;
create table comments (
  id integer primary key autoincrement,
  author text not null,
  body text not null,
  post_slug text not null
);
create index idx_comments_post_id on comments (post_slug);

-- Optionally, uncomment the below query to create data

-- insert into comments (author, body, post_slug)
-- values ("Kristian", "Great post!", "hello-world");

With the file created, execute the schema file against the D1 database by passing it with the flag --file:

$ wrangler d1 execute d1-example --file src/schema.sql

We’ve created a SQL database with just a few commands and seeded it with initial data. Now we can add a route to our Workers function to retrieve data from that database. Based on our wrangler.toml config, the D1 database is now accessible via the DB binding. In our code, we can use the binding to prepare SQL statements and execute them, for instance, to retrieve comments:

app.get('/api/posts/:slug/comments', async c => {
  const { slug } = c.req.param()
  const { results } = await c.env.DB.prepare(`
    select * from comments where post_slug = ?
  `).bind(slug).all()
  return c.json(results)
})

In this function, we accept a slug URL query parameter and set up a new SQL statement where we select all comments with a matching post_slug value to our query parameter. We can then return it as a simple JSON response.

So far, we’ve built read-only access to our data. But “inserting” values to SQL is, of course, possible as well. So let’s define another function that allows POST-ing to an endpoint to create a new comment:

app.post('/API/posts/:slug/comments', async c => {
  const { slug } = c.req.param()
  const { author, body } = await c.req.json<Comment>()

  if (!author) return c.text("Missing author value for new comment")
  if (!body) return c.text("Missing body value for new comment")

  const { success } = await c.env.DB.prepare(`
    insert into comments (author, body, post_slug) values (?, ?, ?)
  `).bind(author, body, slug).run()

  if (success) {
    c.status(201)
    return c.text("Created")
  } else {
    c.status(500)
    return c.text("Something went wrong")
  }
})

In this example, we built a comments API for powering a blog. To see the source for this D1-powered comments API, you can visit cloudflare/templates/worker-d1-api.

Making static sites dynamic with Cloudflare D1

Conclusion

One of the things most exciting about D1 is the opportunity to augment existing applications or websites with dynamic, relational data. As a former Ruby on Rails developer, one of the things I miss most about that framework in the world of JavaScript and serverless development tools is the ability to rapidly spin up full data-driven applications without needing to be an expert in managing database infrastructure. With D1 and its easy onramp to SQL-based data, we can build true data-driven applications without compromising on performance or developer experience.

This shift corresponds nicely with the advent of static sites in the last few years, using tools like Hugo or Gatsby. A blog built with a static site generator like Hugo is incredibly performant – it will build in seconds with small asset sizes.

But by trading a tool like WordPress for a static site generator, you lose the opportunity to add dynamic information to your site. Many developers have patched over this problem by adding more complexity to their build processes: fetching and retrieving data and generating pages using that data as part of the build.

This addition of complexity in the build process attempts to fix the lack of dynamism in applications, but it still isn’t genuinely dynamic. Instead of being able to retrieve and display new data as it’s created, the application rebuilds and redeploys whenever data changes so that it appears to be a live, dynamic representation of data. Your application can remain static, and the dynamic data will live geographically close to the users of your site, accessible via a queryable and expressive API.

Easy Postgres integration on Cloudflare Workers with Neon.tech

Post Syndicated from Erwin van der Koogh original https://blog.cloudflare.com/neon-postgres-database-from-workers/

Easy Postgres integration on Cloudflare Workers with Neon.tech

Easy Postgres integration on Cloudflare Workers with Neon.tech

It’s no wonder that Postgres is one of the world’s favorite databases. It’s easy to learn, a pleasure to use, and can scale all the way up from your first database in an early-stage startup to the system of record for giant organizations. Postgres has been an integral part of Cloudflare’s journey, so we know this fact well. But when it comes to connecting to Postgres from environments like Cloudflare Workers, there are unfortunately a bunch of challenges, as we mentioned in our Relational Database Connector post.

Neon.tech not only solves these problems; it also has other cool features such as branching databases — being able to branch your database in exactly the same way you branch your code: instant, cheap and completely isolated.

How to use it

It’s easy to get started. Neon’s client library @neondatabase/serverless is a drop-in replacement for node-postgres, the npm pg package with which you may already be familiar. After going through the getting started process to set up your Neon database, you can easily create a Worker to ask Postgres for the current time like so:

  1. Create a new Worker — Run npx wrangler init neon-cf-demo and accept all the defaults. Enter the new folder with cd neon-cf-demo.
  2. Install the Neon package — Run npm install @neondatabase/serverless.
  3. Provide connection details — For deployment, run npx wrangler secret put DATABASE_URL and paste in your connection string when prompted (you’ll find this in your Neon dashboard: something like postgres://user:[email protected]/main). For development, create a new file .dev.vars with the contents DATABASE_URL= plus the same connection string.
  4. Write the code — Lastly, replace src/index.ts with the following code:

import { Client } from '@neondatabase/serverless';
interface Env { DATABASE_URL: string; }

export default {
  async fetch(request: Request, env: Env, ctx: ExecutionContext) {
    const client = new Client(env.DATABASE_URL);
    await client.connect();
    const { rows: [{ now }] } = await client.query('select now();');
    ctx.waitUntil(client.end());  // this doesn’t hold up the response
    return new Response(now);
  }
}

To try this locally, type npm start. To deploy it around the globe, type npx wrangler publish.

You can also check out the source for a slightly more complete demo app. This shows your nearest UNESCO World Heritage sites using IP geolocation in Cloudflare Workers and nearest-neighbor sorting in PostGIS.

Easy Postgres integration on Cloudflare Workers with Neon.tech

How does this work? In this case, we take the coordinates supplied to our Worker in request.cf.longitude and request.cf.latitude. We then feed these coordinates to a SQL query that uses the PostGIS distance operator <-> to order our results:

const { longitude, latitude } = request.cf
const { rows } = await client.query(`
  select 
    id_no, name_en, category,
    st_makepoint($1, $2) <-> location as distance
  from whc_sites_2021
  order by distance limit 10`,
  [longitude, latitude]
);

Since we created a spatial index on the location column, the query is blazing fast. The result (rows) looks like this:

[{
  "id_no": 308,
  "name_en": "Yosemite National Park",
  "category": "Natural",
  "distance": 252970.14782223428
},
{
  "id_no": 134,
  "name_en": "Redwood National and State Parks",
  "category": "Natural",
  "distance": 416334.3926827573
},
/* … */
]

For even lower latencies, we could cache these results at a slightly coarser geographical resolution — rounding, say, to one sixtieth of a degree (one arc minute) of longitude and latitude, which is a little under a mile.

Sign up to Neon using the invite code serverless and try the @neondatabase/serverless driver with Cloudflare Workers.

Why we did it

Cloudflare Workers has enormous potential to improve back-end development and deployment. It’s cost-effective, admin-free, and radically scalable.

The use of V8 isolates means Workers are now fast and lightweight enough for nearly any use case. But it has a key drawback: Cloudflare Workers don’t yet support raw TCP communication, which has made database connections a challenge.

Even when Workers eventually support raw TCP communication, we will not have fully solved our problem, because database connections are expensive to set up and also have quite a bit of memory overhead.

This is what the solution looks like:

Easy Postgres integration on Cloudflare Workers with Neon.tech

It consists of three parts:

  1. Connection pooling built into the platform — Given Neon’s serverless compute model, splitting storage and compute operations, it is not recommended to rely on a one-to-one mapping between external clients and Postgres connections. Instead, you can turn on connection pooling simply by flicking a switch (it’s in the Settings area of your Neon dashboard).
  2. WebSocket proxy — We deploy our own WebSocket-to-TCP proxy, written in Go. The proxy simply accepts WebSocket connections from Cloudflare Worker clients, relays message payloads to a requested (Neon-only) host over plain TCP, and relays back the responses.
  3. Client library — Our driver library is based on node-postgres but provides the necessary shims for Node.js features that aren’t present in Cloudflare Workers. Crucially, we replace Node’s net.Socket and tls.connect with code that redirects network reads and writes via the WebSocket connection. To support end-to-end TLS encryption between Workers and the database, we compile WolfSSL to WebAssembly with emscripten. Then we use esbuild to bundle it all together into an easy-to-use npm package.

The @neondatabase/serverless package is currently in public beta. We have plans to improve, extend, and explain it further in the near future on the Neon blog. In line with our commitment to open source, you can configure our serverless driver and/or run our WebSocket proxy to provide access to Postgres databases hosted anywhere — just see the respective repos for details.

So try Neon using invite code serverless, sign up and connect to it with Cloudflare Workers, and you’ll have a fully flexible back-end service running in next to no time.

Build applications of any size on Cloudflare with the Queues open beta

Post Syndicated from Rob Sutter original https://blog.cloudflare.com/cloudflare-queues-open-beta/

Build applications of any size on Cloudflare with the Queues open beta

Build applications of any size on Cloudflare with the Queues open beta

Message queues are a fundamental building block of cloud applications—and today the Cloudflare Queues open beta brings queues to every developer building for Region: Earth. Cloudflare Queues follows Cloudflare Workers and Cloudflare R2 in a long line of innovative services built for the Workers Developer Platform, enabling developers to build more complex applications without configuring networks, choosing regions, or estimating capacity. Best of all, like many other Cloudflare services, there are no egregious egress charges!

Build applications of any size on Cloudflare with the Queues open beta

If you’ve ever purchased something online and seen a message like “you will receive confirmation of your order shortly,” you’ve interacted with a queue. When you completed your order, your shopping cart and information were stored and the order was placed into a queue. At some later point, the order fulfillment service picks and packs your items and hands it off to the shipping service—again, via a queue. Your order may sit for only a minute, or much longer if an item is out of stock or a warehouse is busy, and queues enable all of this functionality.

Message queues are great at decoupling components of applications, like the checkout and order fulfillment services for an ecommerce site. Decoupled services are easier to reason about, deploy, and implement, allowing you to ship features that delight your customers without worrying about synchronizing complex deployments.

Queues also allow you to batch and buffer calls to downstream services and APIs. This post shows you how to enroll in the open beta, walks you through a practical example of using Queues to build a log sink, and tells you how we built Queues using other Cloudflare services. You’ll also learn a bit about the roadmap for the open beta.

Getting started

Enrolling in the open beta

Open the Cloudflare dashboard and navigate to the Workers section. Select Queues from the Workers navigation menu and choose Enable Queues Beta.

Review your order and choose Proceed to Payment Details.

Note: If you are not already subscribed to a Workers Paid Plan, one will be added to your order automatically.

Enter your payment details and choose Complete Purchase. That’s it – you’re enrolled in the open beta! Choose Return to Queues on the confirmation page to return to the Cloudflare Queues home page.

Creating your first queue

After enabling the open beta, open the Queues home page and choose Create Queue. Name your queue `my-first-queue` and choose Create queue. That’s all there is to it!

The dash displays a confirmation message along with a list of all the queues in your account.

Build applications of any size on Cloudflare with the Queues open beta

Note: As of the writing of this blog post each account is limited to ten queues. We intend to raise this limit as we build towards general availability.

Managing your queues with Wrangler

You can also manage your queues from the command line using Wrangler, the CLI for Cloudflare Workers. In this section, you build a simple but complete application implementing a log aggregator or sink to learn how to integrate Workers, Queues, and R2.

Setting up resources
To create this application, you need access to a Cloudflare Workers account with a subscription plan, access to the Queues open beta, and an R2 plan.

Install and authenticate Wrangler then run wrangler queues create log-sink from the command line to create a queue for your application.

Run wrangler queues list and note that Wrangler displays your new queue.

Note: The following screenshots use the jq utility to format the JSON output of wrangler commands. You do not need to install jq to complete this application.

Build applications of any size on Cloudflare with the Queues open beta

Finally, run wrangler r2 bucket create log-sink to create an R2 bucket to store your aggregated logs. After the bucket is created, run wrangler r2 bucket list to see your new bucket.

Build applications of any size on Cloudflare with the Queues open beta

Creating your Worker
Next, create a Workers application with two handlers: a fetch() handler to receive individual incoming log lines and a queue() handler to aggregate a batch of logs and write the batch to R2.

In an empty directory, run wrangler init to create a new Cloudflare Workers application. When prompted:

  • Choose “y” to create a new package.json
  • Choose “y” to use TypeScript
  • Choose “Fetch handler” to create a new Worker at src/index.ts
Build applications of any size on Cloudflare with the Queues open beta

Open wrangler.toml and replace the contents with the following:

wrangler.toml

name = "queues-open-beta"
main = "src/index.ts"
compatibility_date = "2022-11-03"
 
 
[[queues.producers]]
 queue = "log-sink"
 binding = "BUFFER"
 
[[queues.consumers]]
 queue = "log-sink"
 max_batch_size = 100
 max_batch_timeout = 30
 
[[r2_buckets]]
 bucket_name = "log-sink"
 binding = "LOG_BUCKET"

The [[queues.producers]] section creates a producer binding for the Worker at src/index.ts called BUFFER that refers to the log-sink queue. This Worker can place messages onto the log-sink queue by calling await env.BUFFER.send(log);

The [[queues.consumers]] section creates a consumer binding for the log-sink queue for your Worker. Once the log-sink queue has a batch ready to be processed (or consumed), the Workers runtime will look for the queue() event handler in src/index.ts and invoke it, passing the batch as an argument. The queue() function signature looks as follows:

async queue(batch: MessageBatch<Error>, env: Environment): Promise<void> {

The final binding in your wrangler.toml creates a binding for the log-sink R2 bucket that makes the bucket available to your Worker via env.LOG_BUCKET.

src/index.ts

Open src/index.ts and replace the contents with the following code:

export interface Env {
 BUFFER: Queue;
 LOG_BUCKET: R2Bucket;
}
 
export default {
 async fetch(request: Request, env: Environment): Promise<Response> {
   let log = await request.json();
   await env.BUFFER.send(log);
   return new Response("Success!");
 },
 async queue(batch: MessageBatch<Error>, env: Environment): Promise<void> {
   const logBatch = await JSON.stringify(batch.messages);
   await env.LOG_BUCKET.put(`logs/${Date.now()}.log.json`, logBatch);
 },
};

The export interface Env section exposes the two bindings you defined in wrangler.toml: a queue named BUFFER and an R2 bucket named LOG_BUCKET.

The fetch() handler transforms the request body into JSON, adds the body to the BUFFER queue, then returns an HTTP 200 response with the message Success!

The `queue()` handler receives a batch of messages that each contain log entries, iterates through concatenating each log into a string buffer, then writes that buffer to the LOG_BUCKET R2 bucket using the current timestamp as the filename.

Publishing and running your application
To publish your log sink application, run wrangler publish. Wrangler packages your application and its dependencies and deploys it to Cloudflare’s global network.

Build applications of any size on Cloudflare with the Queues open beta

Note that the output of wrangler publish includes the BUFFER queue binding, indicating that this Worker is a producer and can place messages onto the queue. The final line of output also indicates that this Worker is a consumer for the log-sink queue and can read and remove messages from the queue.

Use your favorite API client, like curl, httpie, or Postman, to send JSON log entries to the published URL for your Worker via HTTP POST requests. Navigate to your log-sink R2 bucket in the Cloudflare dashboard and note that the logs prefix is now populated with aggregated logs from your request.

Build applications of any size on Cloudflare with the Queues open beta

Download and open one of the logfiles to view the JSON array inside. That’s it – with fewer than 45 lines of code and config, you’ve built a log aggregator to ingest and store data in R2!

Build applications of any size on Cloudflare with the Queues open beta

Buffering R2 writes with Queues in the real world

In the previous example, you create a simple Workers application that buffers data into batches before writing the batches to R2. This reduces the number of calls to the downstream service, reducing load on the service and saving you money.

UUID.rocks, the fastest UUIDv4-as-a-service, wanted to confirm whether their API truly generates unique IDs on every request. With 80,000 requests per day, it wasn’t trivial to find out. They decided to write every generated UUID to R2 to compare IDs across the entire population. However, writing directly to R2 at the rate UUIDs are generated is inefficient and expensive.

To reduce writes and costs, UUID.rocks introduced Cloudflare Queues into their UUID generation workflow. Each time a UUID is requested, a Worker places the value of the UUID into a queue. Once enough messages have been received, the buffered batch of JSON objects is written to R2. This avoids invoking an R2 write on every API call, saving costs and making the data easier to process later.

The uuid-queue application consists of a single Worker with three event handlers:

  1. A fetch handler that receives a JSON object representing the generated UUID and writes it to a Cloudflare Queue.
  2. A queue handler that writes batches of JSON objects to R2 in CSV format.
  3. A scheduled handler that combines batches from the previous hour into a single file for future processing.

To view the source or deploy this application into your own account, visit the repository on GitHub.

How we built Cloudflare Queues

Like many of the Cloudflare services you use and love, we built Queues by composing other Cloudflare services like Workers and Durable Objects. This enabled us to rapidly solve two difficult challenges: securely invoking your Worker from our own service and maintaining a strongly consistent state at scale. Several recent Cloudflare innovations helped us overcome these challenges.

Securely invoking your Worker

In the Before Times (early 2022), invoking one Worker from another Worker meant a fresh HTTP call from inside your script. This was a brittle experience, requiring you to know your downstream endpoint at deployment time. Nested invocations ran as HTTP calls, passing all the way through the Cloudflare network a second time and adding latency to your request. It also meant security was on you – if you wanted to control how that second Worker was invoked, you had to create and implement your own authentication and authorization scheme.

Worker to Worker requests
During Platform Week in May 2022, Service Worker Bindings entered general availability. With Service Worker Bindings, your Worker code has a binding to another Worker in your account that you invoke directly, avoiding the network penalty of a nested HTTP call. This removes the performance and security barriers discussed previously, but it still requires that you hard-code your nested Worker at compile time. You can think of this setup as “static dispatch,” where your Worker has a static reference to another Worker where it can dispatch events.

Dynamic dispatch
As Service Worker Bindings entered general availability, we also launched a closed beta of Workers for Platforms, our tool suite to help make any product programmable. With Workers for Platforms, software as a service (SaaS) and platform providers can allow users to upload their own scripts and run them safely via Cloudflare Workers. User scripts are not known at compile time, but are dynamically dispatched at runtime.

Workers for Platforms entered general availability during GA week in September 2022, and is available for all customers to build with today.

With dynamic dispatch generally available, we now have the ability to discover and invoke Workers at runtime without the performance penalty of HTTP traffic over the network. We use dynamic dispatch to invoke your queue’s consumer Worker whenever a message or batch of messages is ready to be processed.

Consistent stateful data with Durable Objects

Another challenge we faced was storing messages durably without sacrificing performance. We took the design goal of ensuring that all messages were persisted to disk in multiple locations before we confirmed receipt of the message to the user. Again, we turned to an existing Cloudflare product—Durable Objects—which entered general availability nearly one year ago today.

Durable Objects are named instances of JavaScript classes that are guaranteed to be unique across Cloudflare’s entire network. Durable Objects process messages in-order and on a single-thread, allowing for coordination across messages and provide a strongly consistent storage API for key-value pairs. Offloading the hard problem of storing data durably in a distributed environment to Distributed Objects allowed us to reduce the time to build Queues and prepare it for open beta.

Open beta roadmap

Our open beta process empowers you to influence feature prioritization and delivery. We’ve set ambitious goals for ourselves on the path to general availability, most notably supporting unlimited throughput while maintaining 100% durability. We also have many other great features planned, like first-in first-out (FIFO) message processing and API compatibility layers to ease migrations, but we need your feedback to build what you need most, first.

Conclusion

Cloudflare Queues is a global message queue for the Workers developer. Building with Queues makes your applications more performant, resilient, and cost-effective—but we’re not done yet. Join the Open Beta today and share your feedback to help shape the Queues roadmap as we deliver application integration services for the next generation cloud.

Cloudflare Workers scale too well and broke our infrastructure, so we are rebuilding it on Workers

Post Syndicated from Jonathan Norris (Guest Blogger) original https://blog.cloudflare.com/devcycle-customer-story/

Cloudflare Workers scale too well and broke our infrastructure, so we are rebuilding it on Workers

Cloudflare Workers scale too well and broke our infrastructure, so we are rebuilding it on Workers

While scaling our new Feature Flagging product DevCycle, we’ve encountered an interesting challenge: our Cloudflare Workers-based infrastructure can handle way more instantaneous load than our traditional AWS infrastructure. This led us to rethink how we design our infrastructure to always use Cloudflare Workers for everything.

The origin of DevCycle

For almost 10 years, Taplytics has been a leading provider of no-code A/B testing and feature flagging solutions for product and marketing teams across a wide range of use cases for some of the largest consumer-facing companies in the world. So when we applied ourselves to build a new engineering-focused feature management product, DevCycle, we built upon our experience using Workers which have served over 140 billion requests for Taplytics customers.

The inspiration behind DevCycle is to build a focused feature management tool for engineering teams, empowering them to build their software more efficiently and deploy it faster. Helping engineering teams reach their goals, whether it be continuous deployment, lower change failure rate, or a faster recovery time. DevCycle is the culmination of our vision of how teams should use Feature Management to build high-quality software faster. We’ve used DevCycle to build DevCycle, enabling us to implement continuous deployment successfully.

DevCycle architecture

One of the first things we asked ourselves when ideating DevCycle was how we could get out of the business of managing 1000’s of vCPUs worth of AWS instances and move our core business logic closer to our end-user devices. Based on our experience with Cloudflare Workers at Taplytics we knew we wanted it to be a core part of our future infrastructure for DevCycle.

By using the global computing power of Workers and moving as much logic to the SDKs as possible with our local bucketing server-side SDKs, we were able to massively reduce or eliminate the latency of fetching feature flag configurations for our users. In addition, we used a shared WASM library across our Workers and local bucketing SDKs to dramatically reduce the amount of code we need to maintain per SDK, and increase the consistency of our platform. This architecture has also fundamentally changed our business’s cost structure to easily serve any customer of any scale.

The core architecture of DevCycle revolves around publishing and consuming JSON configuration files per project environment. The publishing side is managed in our AWS services, while Cloudflare manages the consumption of these config files at scale. This split in responsibilities allows for all high-scale requests to be managed by Cloudflare, while keeping our AWS services simple and low-scale.

Cloudflare Workers scale too well and broke our infrastructure, so we are rebuilding it on Workers

“Workers are breaking our events pipeline”

One of the primary challenges as a feature management platform is that we don’t have direct control over the load from our customers’ applications using our SDKs; our systems need the ability to scale instantly to match their load. For example, we have a couple of large customers whose mobile traffic is primarily driven by push notifications, which causes massive instantaneous spikes in traffic to our APIs in the range of 10x increases in load. As you can imagine, any traditional auto-scaled API service and the load balancer cannot manage that type of increase in load. Thus, our choices are to dramatically increase the minimum size of our cluster and load balancer to handle these unknown load spikes, accept that some requests will be rate-limited, or move to an architecture that can handle this load.

Cloudflare Workers scale too well and broke our infrastructure, so we are rebuilding it on Workers

Given that all our SDK API requests are already served with Workers, they have no problem scaling instantly to 10x+ their base load. Sadly we can’t say the same about the traditional parts of our infrastructure.

For each feature flag configuration request to a Worker, a corresponding events request is sent to our AWS events infrastructure. The events are received by our events API container in Kubernetes, where they are then published to Kafka and eventually ingested by Snowflake. While Cloudflare Workers have no problem handling instantaneous spikes in feature flag requests, the events system can’t keep up. Our cluster and events API containers need to be scaled up faster to prevent the existing instances from being overwhelmed. Even the load balancer has issues accepting the sudden increase. Cloudflare Workers just work too well in comparison to EC2 instances + EKS.

To solve this issue we are moving towards a new events Cloudflare Worker which will be able to handle the instantaneous events load from these requests and make use of the Kinesis Data Firehose to write events to our existing S3 bucket which is ingested by Snowflake. In the future, we look forward to testing out Cloudflare Queues writing to R2 once a Snowflake connector has been created. This architecture should allow us to ingest events at almost any scale and withstand instantaneous traffic spikes with a predictable and efficient cost structure.

Cloudflare Workers scale too well and broke our infrastructure, so we are rebuilding it on Workers

Building without a database next to your code

Workers provide many benefits, including fast response times, infinite scalability, serverless architecture, and excellent up-time performance. However, if you want to see all these benefits, you need to architect your Workers to assume that you don’t have direct access to a centralized SQL / NoSQL database (or D1) like you would with a traditional API service. For example, suppose you build your workers to require reaching out to a database to fetch and update user data every time a request is made to your Workers. In that case, your request latency will be tied to the geographic distance between your Worker and the database plus the latency of the database. In addition, your Workers will be able to scale significantly beyond the number of database connections you can support, and your uptime will be tied to the uptime of your external database. Therefore, when architecting your systems to use Workers, we advise relying primarily on data sent as part of the API request and cacheable data on Cloudflare’s global network.

Cloudflare provides multiple products and services to help with data on their global network:

  • KV: “global, low-latency, key-value data store.”
  • However, the lowest latency way of retrieving data from within a Worker is limited by a minimum 60-second TTL. So you’ll need to be ok with cached data that is 60 seconds stale.
  • Durable Objects: “provide low-latency coordination and consistent storage for the Workers platform through two features: global uniqueness and a transactional storage API.”
  • Ability to store user-level information closer to the end user.
  • Unfamiliar worker interface for accessing data for developers with SQL / NoSQL experience.
  • R2: “store large amounts of unstructured data.”
  • Ability to store arbitrarily large amounts of unstructured data using familiar S3 APIs.
  • Cloudflare’s cache can be used to provide low-latency access within workers.
  • D1: “serverless SQLite database”

Each of these tools that Cloudflare provides has made building APIs far more accessible than when Workers launched initially; however, each service has aspects which need to be accounted for when architecting your systems. Being an open platform, you can also access any publically available database you want from a Worker. For example, we are making use of Macrometa for our EdgeDB product built into our Workers to help customers access their user data.

The predictable cost structure of Workers

One of the greatest advantages of moving most of our workloads towards Cloudflare Workers is the predictable cost structure that can scale 1:1 with our request loads and can be easily mapped to usage-based billing for our customers. In addition, we no longer have to run excess EC2 instances to handle random spikes in load, just in case they happen.

Too many SaaS services have opaque billing based on max usage or other metrics that don’t relate directly to their costs. Moving from our legacy AWS architecture with high fixed costs like databases and caching layers to Workers has resulted in our infrastructure spending is directly tied to using our APIs and SDKs. For DevCycle, this architecture has been over ~5x more cost-efficient to operate.

The future of DevCycle and Cloudflare

With DevCycle we will continue to invest in leveraging serverless computing and moving our core business logic as close to our users as possible, either on Cloudflare’s global network or locally within our SDKs. We’re excited to integrate even more deeply with the Cloudflare developer platform as new services evolve. We already see future use cases for R2, Queues and Durable Objects and look forward to what’s coming next from Cloudflare.

AWS Week in Review – October 10, 2022

Post Syndicated from Marcia Villalba original https://aws.amazon.com/blogs/aws/aws-week-in-review-october-10-2022/

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

I had an amazing start to the week last week as I was speaking at the AWS Community Day NL. This event had 500 attendees and over 70 speakers, and Dr. Werner Vogels, Amazon CTO, delivered the keynote. AWS Community Days are community-led conferences organized by local communities, with a variety of workshops and sessions. I recommend checking your region for any of these events.

Community Day NL

Last Week’s Launches
Here are some launches that got my attention during the previous week.

Amazon S3 Object Lambda now supports using your own code to change the results of HEAD and LIST requests, besides GET (which we launched last year). This feature now enables more capabilities for what you can do with S3 Object Lambda. Danilo made a Twitter thread with lots of use cases for this new launch.

Amazon SageMaker Clarify now can provide near real-time explanations for ML predictions. SageMaker Clarify is a service that provides explainability by ML models individual predictions. These explanations are important for developers to get visibility into their training data and models to identify potential bias.

AWS Storage Gateway now supports 15 TiB tapes. It increased the maximum supported virtual tape size on Tape Gateway from 5 TiB to 15 TiB, so you can store more data on a single virtual tape, and you can reduce the number of tapes you need to manage.

Amazon Aurora Serverless v2 now supports AWS CloudFormation. Early this year, we announced the general availability of Aurora Serverless v2, and now you can use AWS CloudFormation Templates to deploy and change the database along with the rest of your infrastructure.

AWS Config now supports 15 new resource types, including AWS DataSync, Amazon GuardDuty, Amazon Simple Email Service (Amazon SES), AWS AppSync, AWS Cloud Map, Amazon EC2, and AWS AppConfig. With this launch, you can use AWS Config to monitor configuration data for the supported resource types in your AWS account, and you can see how the configuration changes.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
Some other updates and news that you may have missed:

This week an article about how AWS is leading a pilot project to turn the Greek island of Naxos into a smart island caught my attention. The project introduces smart solutions for mobility, primary healthcare, and the transport of goods. The solution has been built based on four pillars that were important for the island: sustainability, telehealth, leisure, and digital skills. Check out the whole article to learn what they are doing.

Podcast Charlas Técnicas de AWS – If you understand Spanish, this podcast is for you. Podcast Charlas Técnicas is one of the official AWS podcasts in Spanish, and every other week there is a new episode. The podcast is meant for builders, and it shares stories about how customers implemented and learned AWS services, how to architect applications, and how to use new services. You can listen to all the episodes directly from your favorite podcast app or at AWS Podcasts en español.

AWS open-source news and updates – This is a newsletter curated by my colleague Ricardo to bring you the latest open-source projects, posts, events, and more.

Upcoming AWS Events
Check your calendars and sign up for these AWS events:

AWS re:Invent reserved seating opens on October 11. If you are planning to attend, book a spot in advance for your favorite sessions. AWS re:Invent is our biggest conference of the year, it happens in Las Vegas from November 28 to December 2, and registrations are open. Many writers of this blog have sessions at re:Invent, and you can search the event agenda using our names.

I started the post talking about AWS Community Days, and there is one in Warsaw, Poland, on October 14. If you are around Warsaw during this week, you can first check out the AWS Pop-up Hub in Warsaw that runs October 10-14 and then join for the Community Day.

On October 20, there is a virtual event for modernizing .NET workloads with Windows containers on AWS, You can register for free.

That’s all for this week. Check back next Monday for another Week in Review!

— Marcia

Amazon File Cache – A High Performance Cache On AWS For Your On-Premises File Systems

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/amazon-file-cache-a-high-performance-cache-on-aws-for-your-on-premises-file-systems/

I am pleased to announce today the availability of Amazon File Cache, a new high-speed cache service on AWS designed for processing file data stored in disparate locations—including on premises. File Cache accelerates and simplifies your most demanding cloud bursting and hybrid workflows by giving your applications access to files using a fast and familiar POSIX interface, no matter if the original files live on premises on any file system that can be accessed through NFS v3 or on Amazon Simple Storage Service (Amazon S3).

Imagine you have a large data set on on-premises storage infrastructure, and your end-of-month reporting typically takes two to three days to run. You want to move that occasional workload to the cloud to run it on larger machines with more CPU and memory to reduce the processing time. But you’re not ready to move the data set to the cloud yet.

Imagine another scenario where you have access to a large data set on Amazon Simple Storage Service (Amazon S3), spread across multiple Regions. Your application that wants to exploit this data set is coded for traditional (POSIX) file system access and uses command line tools like awk, sed, pipes, and so on. Your application requires file access with sub-millisecond latencies. You cannot update the source code to use the S3 API.

File Cache helps to address these use cases and many others, think about management and transformation of video files, AI/ML data sets, and so on. File Cache creates a file system–based cache in front of either NFS v3 file systems or S3 buckets in one or more Regions. It transparently loads file content and metadata (such as the file name, size, and permissions) from the origin and presents it to your applications as a traditional file system. File Cache automatically releases the less recently used cached files to ensure the most active files are available in the cache for your applications.

You can link up to eight NFS file systems or eight S3 buckets to a cache, and they will be exposed as a unified set of files and directories. You can access the cache from a variety of AWS compute services, such as virtual machines or containers. The connection between File Cache and your on-premises infrastructure uses your existing network connection, based on AWS Direct Connect and/or Site-to-Site VPN.

When using File Cache, your applications benefit from consistent, sub-millisecond latencies, up to hundreds of GB/s of throughput, and up to millions of operations per second. Just like with other storage services, such as Amazon Elastic Block Store (Amazon EBS), the performance depends on the size of the cache. The cache size can be expanded to petabyte scale, with a minimum size of 1.2 TiB.

Let’s See How It Works
To show you how it works, I create a file cache on top of two existing Amazon FSx for OpenZFS file systems. In a real-world scenario, it is likely you will create caches on top of on-premises file systems. I choose FSx for OpenZFS for the demo because I don’t have an on-premises data center at hand (I should maybe invest in seb-west-1). Both demo OpenZFS file systems are accessible from a private subnet in my AWS account. Finally, I access the cache from an EC2 Linux instance.

I open my browser and navigate to the AWS Management Console. I search for “Amazon FSx” in the console search bar and click on Caches in the left navigation menu. Alternatively, I go directly to the File Cache section of the console. To get started, I select Create cache.

Amazon File Cache consoleI enter a Cache name for my cache (AWSNewsBlog for this demo) and a Cache storage capacity. The storage capacity is expressed in tebibytes. The minimum value is 1.2 TiB or increments of 2.4 TiB. Notice that the Throughput capacity increases as you choose large cache sizes.

Amazon File Cache create 1I check and accept the default values provided for Networking and Encryption. For networking, I might select a VPC, subnet, and security group to associate with my cache network interface. It is recommended to deploy the cache in the same subnet as your compute service to minimize the latency when accessing files. For encryption, I might use an AWS KMS-managed key (the default) or select my own.

Then, I create Data Repository Association. This is the link between the cache and a data source. A data source might be an NFS file system or an S3 bucket or prefix. I might create up to eight data repository associations for one cache. All Data Repository Associations for a cache have the same type: they are all NFS v3 or all S3. If you need both, you can create two caches.

In this demo, I choose to link two OpenZFS file systems on my AWS account. You can link to any NFS v3 servers, including the ones you already have on premises. Cache path allows you to choose where the source file system will be mounted in the cache. The Data repository path is the URL to your NFS v3 or S3 data repository. The format is nfs://hostname/path or s3://bucketname/path.

The DNS server IP addresses allows File Cache to resolve the DNS name of your NFS server. This is useful when DNS resolution is private, like in my example. When you are associating NFS v3 servers deployed in a VPC, and when using the AWS-provided DNS server, the DNS server IP address of your VPC is the VPC Range + two. In my example, my VPC CIDR range is 172.31.0.0, hence the DNS server IP address is 172.31.0.2.

Do not forget to click on the Add button! Otherwise, your input is ignored. You can repeat the operation to add more data repositories.

File Cache - Create new Data Repository Association- dataset one File Cache - Create new Data Repository Association- dataset two

Once I have entered my two data repositories, I select Next, and I review my choices. When I am ready, I select Create cache.

File Cache - review choices

After a few minutes, the cache status becomes ✅ Available.

Amazon File cache status is available

The last part is to mount the cache on the machine where my workload is deployed. File Cache uses Lustre behind the scene. I have to install the Lustre client for Linux first, as explained in our documentation. Once done, I select the Attach button on the console to receive the instructions to download and install the Lustre client and to mount the cache file system.File Cache Attach To do so, I connect to an EC2 instance running in the same VPC. Then I type:

sudo mount -t lustre -o relatime,flock [email protected]:/mountname /mnt

This command mounts my cache with two options:

  • relatime – Maintains atime (inode access times) data, but not for each time that a file is accessed. With this option enabled, atime data is written to disk only if the file has been modified since the atime data was last updated (mtime) or if the file was last accessed more than a certain amount of time ago (one day by default). relatime is required for automatic cache eviction to work properly.
  • flock – Enables file locking for your cache. If you don’t want file locking enabled, use the mount command without flock.

Once mounted, processes running on my EC2 instance can access files in the cache as usual. As I defined at cache creation time, the first ZFS file system is available inside the cache at /dataset1, and the second ZFS file system is available as /dataset2.

$ echo "Hello File Cache World" > /mnt/zsf1/greetings

$ sudo mount -t lustre -o relatime,flock [email protected]:/r3xxxxxx /mnt/cache

$ ls -al /mnt/cache
total 98
drwxr-xr-x 5 root root 33280 Sep 21 14:37 .
drwxr-xr-x 2 root root 33280 Sep 21 14:33 dataset1
drwxr-xr-x 2 root root 33280 Sep 21 14:37 dataset2

$ cat /mnt/cache/dataset1/greetings
Hello File Cache World

I can observe and measure the activity and the health of my caches using Amazon CloudWatch metrics and AWS CloudTrail log monitoring.

CloudWatch metrics for a File Cache resource are organized into three categories:

  • Front-end I/O metrics
  • Backend I/O metrics
  • Cache front-end utilization metrics

As usual, I can create dashboards or define alarms to be informed when metrics reach thresholds that I defined.

Things To Keep In Mind
There are a couple of key points to keep in mind when using or planning to use File Cache.

First, File Cache encrypts data at rest and supports encryption of data in transit. Your data is always encrypted at rest using keys managed in AWS Key Management Service (AWS KMS). You can use either service-owned keys or your own keys (customer-managed CMKs).

Second, File Cache provides two options for importing data from your data repositories to the cache: lazy load and preload. Lazy load imports data on demand if it’s not already cached, and preload imports data at user request before you start your workload. Lazy loading is the default. It makes sense for most workloads since it allows your workload to start without waiting for metadata and data to be imported to the cache. Pre loading is helpful when your access pattern is sensitive to first-byte latencies.

Pricing and Availability
There are no upfront or fixed-price costs when using File Cache. You are billed for the provisioned cache storage capacity and metadata storage capacity. The pricing page has the details. In addition to File Cache itself, you pay for S3 request costs, AWS Direct Connect charges, and the usual data transfer charges for inter-AZ, inter-Region, and internet egress traffic between File Cache and the data sources.

File Cache is available in US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (Ireland), and Europe (London).

Now go build and create your first file cache today!

— seb

Store and retrieve your logs on R2

Post Syndicated from Shelley Jones original https://blog.cloudflare.com/store-and-retrieve-logs-on-r2/

Store and retrieve your logs on R2

Store and retrieve your logs on R2

Following today’s announcement of General Availability of Cloudflare R2 object storage, we’re excited to announce that customers can also store and retrieve their logs on R2.

Cloudflare’s Logging and Analytics products provide vital insights into customers’ applications. Though we have a breadth of capabilities, logs in particular play a pivotal role in understanding what occurs at a granular level; we produce detailed logs containing metadata generated by Cloudflare products via events flowing through our network, and they are depended upon to illustrate or investigate anything (and everything) from the general performance or health of applications to closely examining security incidents.

Until today, we have only provided customers with the ability to export logs to 3rd-party destinations – to both store and perform analysis. However, with Log Storage on R2 we are able to offer customers a cost-effective solution to store event logs for any of our products.

The cost conundrum

We’ve unpacked the commercial impact in a previous blog post, but to recap, the cost of storage can vary broadly depending on the volume of requests Internet properties receive. On top of that – and specifically pertaining to logs – there’s usually more expensive fees to access that data whenever the need arises. This can be incredibly problematic, especially when customers are having to balance their budget with the need to access their logs – whether it’s to mitigate a potential catastrophe or just out of curiosity.

With R2, not only do we not charge customers egress costs, but we also provide the opportunity to make further operational savings by centralizing storage and retrieval. Though, most of all, we just want to make it easy and convenient for customers to access their logs via our Retrieval API – all you need to do is provide a time range!

Logs on R2: get started!

Why would you want to store your logs on Cloudflare R2? First, R2 is S3 API compatible, so your existing tooling will continue to work as is. Second, not only is R2 cost-effective for storage, we also do not charge any egress fees if you want to get your logs out of Cloudflare to be ingested into your own systems. You can store logs for any Cloudflare product, and you can also store what you need for as long as you need; retention is completely within your control.

Storing Logs on R2

To create Logpush jobs pushing to R2, you can use either the dashboard or Cloudflare API. Using the dashboard, you can create a job and select R2 as the destination during configuration:

Store and retrieve your logs on R2

To use the Cloudflare API to create the job, do something like:

curl -s -X POST 'https://api.cloudflare.com/client/v4/zones/<ZONE_ID>/logpush/jobs' \
-H "X-Auth-Email: <EMAIL>" \
-H "X-Auth-Key: <API_KEY>" \
-d '{
 "name":"<DOMAIN_NAME>",
"destination_conf":"r2://<BUCKET_PATH>/{DATE}?account-id=<ACCOUNT_ID>&access-key-id=<R2_ACCESS_KEY_ID>&secret-access-key=<R2_SECRET_ACCESS_KEY>",
 "dataset": "http_requests",
"logpull_options":"fields=ClientIP,ClientRequestHost,ClientRequestMethod,ClientRequestURI,EdgeEndTimestamp,EdgeResponseBytes,EdgeResponseStatus,EdgeStartTimestamp,RayID&timestamps=rfc3339",
 "kind":"edge"
}' | jq .

Please see Logpush over R2 docs for more information.

Log Retrieval on R2

If you have your logs pushed to R2, you could use the Cloudflare API to retrieve logs in specific time ranges like the following:

curl -s -g -X GET 'https://api.cloudflare.com/client/v4/accounts/<ACCOUNT_ID>/logs/retrieve?start=2022-09-25T16:00:00Z&end=2022-09-25T16:05:00Z&bucket=<YOUR_BUCKET>&prefix=<YOUR_FILE_PREFIX>/{DATE}' \
-H "X-Auth-Email: <EMAIL>" \
-H "X-Auth-Key: <API_KEY>" \ 
-H "R2-Access-Key-Id: R2_ACCESS_KEY_ID" \
-H "R2-Secret-Access-Key: R2_SECRET_ACCESS_KEY" | jq .

See Log Retrieval API for more details.

Now that you have critical logging infrastructure on Cloudflare, you probably want to be able to monitor the health of these Logpush jobs as well as get relevant alerts when something needs your attention.

Looking forward

While we have a vision to build out log analysis and forensics capabilities on top of R2 – and a roadmap to get us there – we’d still love to hear your thoughts on any improvements we can make, particularly to our retrieval options.

Get setup on R2 to start pushing logs today! If your current plan doesn’t include Logpush, storing logs on R2 is another great reason to upgrade!

Using Cloudflare R2 as an apt/yum repository

Post Syndicated from Sudarsan Reddy original https://blog.cloudflare.com/using-cloudflare-r2-as-an-apt-yum-repository/

Using Cloudflare R2 as an apt/yum repository

Using Cloudflare R2 as an apt/yum repository

In this blog post, we’re going to talk about how we use Cloudflare R2 as an apt/yum repository to bring cloudflared (the Cloudflare Tunnel daemon) to your Debian/Ubuntu and CentOS/RHEL systems and how you can do it for your own distributable in a few easy steps!

I work on Cloudflare Tunnel, a product which enables customers to quickly connect their private networks and services through the Cloudflare global network without needing to expose any public IPs or ports through their firewall. Cloudflare Tunnel is managed for users by cloudflared, a tool that runs on the same network as the private services. It proxies traffic for these services via Cloudflare, and users can then access these services securely through the Cloudflare network.

Our connector, cloudflared, was designed to be lightweight and flexible enough to be effectively deployed on a Raspberry Pi, a router, your laptop, or a server running on a data center with applications ranging from IoT control to private networking. Naturally, this means cloudflared comes built for a myriad of operating systems, architectures and package distributions: You could download the appropriate package from our GitHub releases, brew install it or apt/yum install it (https://pkg.cloudflare.com).

In the rest of this blog post, I’ll use cloudflared as an example of how to create an apt/yum repository backed by Cloudflare’s object storage service R2. Note that this can be any binary/distributable. I simply use cloudflared as an example because this is something we recently did and actually use in production.

How apt-get works

Let’s see what happens when you run something like this on your terminal.

$ apt-get install cloudflared

Let’s also assume that apt sources were already added like so:

  $ echo 'deb [signed-by=/usr/share/keyrings/cloudflare-main.gpg] https://pkg.cloudflare.com/cloudflared buster main' | sudo tee /etc/apt/sources.list.d/cloudflared.list


$ apt-get update

From the source.list above, apt first looks up the Release file (or InRelease if it’s a signed package like cloudflared, but we will ignore this for the sake of simplicity).

A Release file contains a list of supported architectures and their md5, sha1 and sha256 checksums. It looks something like this:

$ curl https://pkg.cloudflare.com/cloudflared/dists/buster/Release
Origin: cloudflared
Label: cloudflared
Codename: buster
Date: Thu, 11 Aug 2022 08:40:18 UTC
Architectures: amd64 386 arm64 arm armhf
Components: main
Description: apt repository for cloudflared - buster
MD5Sum:
 c14a4a1cbe9437d6575ae788008a1ef4 549 main/binary-amd64/Packages
 6165bff172dd91fa658ca17a9556f3c8 374 main/binary-amd64/Packages.gz
 9cd622402eabed0b1b83f086976a8e01 128 main/binary-amd64/Release
 5d2929c46648ea8dbeb1c5f695d2ef6b 545 main/binary-386/Packages
 7419d40e4c22feb19937dce49b0b5a3d 371 main/binary-386/Packages.gz
 1770db5634dddaea0a5fedb4b078e7ef 126 main/binary-386/Release
 b0f5ccfe3c3acee38ba058d9d78a8f5f 549 main/binary-arm64/Packages
 48ba719b3b7127de21807f0dfc02cc19 376 main/binary-arm64/Packages.gz
 4f95fe1d9afd0124a32923ddb9187104 128 main/binary-arm64/Release
 8c50559a267962a7da631f000afc6e20 545 main/binary-arm/Packages
 4baed71e49ae3a5d895822837bead606 372 main/binary-arm/Packages.gz
 e472c3517a0091b30ab27926587c92f9 126 main/binary-arm/Release
 bb6d18be81e52e57bc3b729cbc80c1b5 549 main/binary-armhf/Packages
 31fd71fec8acc969a6128ac1489bd8ee 371 main/binary-armhf/Packages.gz
 8fbe2ff17eb40eacd64a82c46114d9e4 128 main/binary-armhf/Release
SHA1:
…
SHA256:
…

Depending on your system’s architecture, Debian picks the appropriate Packages location. A Packages file contains metadata about the binary apt wants to install, including location and its checksum. Let’s say it’s an amd64 machine. This means we’ll go here next:

$ curl https://pkg.cloudflare.com/cloudflared/dists/buster/main/binary-amd64/Packages
Package: cloudflared
Version: 2022.8.0
License: Apache License Version 2.0
Vendor: Cloudflare
Architecture: amd64
Maintainer: Cloudflare <[email protected]>
Installed-Size: 30736
Homepage: https://github.com/cloudflare/cloudflared
Priority: extra
Section: default
Filename: pool/main/c/cloudflared/cloudflared_2022.8.0_amd64.deb
Size: 15286808
SHA256: c47ca10a3c60ccbc34aa5750ad49f9207f855032eb1034a4de2d26916258ccc3
SHA1: 1655dd22fb069b8438b88b24cb2a80d03e31baea
MD5sum: 3aca53ccf2f9b2f584f066080557c01e
Description: Cloudflare Tunnel daemon

Notice the Filename field. This is where apt gets the deb from before running a dpkg command on it. What all of this means is the apt repository (and the yum repositories too) is basically a structured file system of mostly plaintext files and a deb.

The deb file is a Debian software package that contains two things: installable data (in our case, the cloudflared binary) and metadata about the installable.

Building our own apt repository

Now that we know what happens when an apt-get install runs, let’s work our way backwards to construct the repository.

Create a deb file out of the binary

Note: It is optional but recommended one signs the packages. See the section about how apt verifies packages here.

Debian files can be built by the dpkg-buildpackage tool in a linux or Debian environment. We use a handy command line tool called fpm (https://fpm.readthedocs.io/en/v1.13.1/) to do this because it works for both rpm and deb.

$ fpm -s <dir> -t deb -C /path/to/project -name <project_name> –version <version>

This yields a .deb file.

Create plaintext files needed by apt to lookup downloads

Again, these files can be built by hand, but there are multiple tools available to generate this:

We use reprepro.

Using it is as simple as

$ reprepro buster includedeb <path/to/the/deb>

reprepro neatly creates a bunch of folders in the structure we looked into above.

Upload them to Cloudflare R2

We use Cloudflare R2 to now be the host for this structured file system. R2 lets us upload and serve objects in this structured format. This means, we just need to upload the files in the same structure reprepro created them.

Here is a copyable example of how we do it for cloudflared.

Serve them from an R2 worker

For fine-grained control, we’ll write a very lightweight Cloudflare Worker to be the service we talk to and serve as the front-end API for apt to talk to. For an apt repository, we only need it to perform the GET function.

Here’s an example on how-to do this: https://developers.cloudflare.com/r2/examples/demo-worker/

Putting it all together

Here is a handy script we use to push cloudflared to R2 ready for apt/yum downloads and includes signing and publishing the pubkey.

And that’s it! You now have your own apt/yum repo without needing a server that needs to be maintained, there are no egress fees for downloads, and it is on the Cloudflare global network, protecting it against high request volumes. You can automate many of these steps to make it part of a release process.

Today, this is how cloudflared is distributed on the apt and yum repositories: https://pkg.cloudflare.com/