Generative AI is a fantastic tool to use to quickly create images based on prompts.

One of the issues with some of these platforms is that they don’t actually store the images in a way that makes them easier to retrieve after they’ve been created. Oftentimes you have to make sure to save it immediately after the process is completed, otherwise, it's gone. Luckily, Stability offers an API that can be used to programmatically generate images, and Tigris is the perfect solution to store those images for retrieval.

In this article, you’ll learn how to deploy an app to Fly.io that will allow you to generate images using the Stability API and automatically store them in a Tigris bucket.

The Stability AI Tigris Database​

Let’s take a look at what you’ll be deploying. There are several key components of the project:

• A Next.js app that the user interacts with
• An API endpoint (which is part of the Next app) that processes jobs.
• A background job that periodically polls for new jobs.
• A Postgres database to store jobs
• A Tigris bucket to store the generated images.

The Next.js app​

The first part of the project is a Next.js project that contains a single page that users will interact with. There is a simple form that accepts a prompt and image dimensions. This form uses server actions to store the request in the jobs table of a Fly Postgres database. Each grid item will periodically poll the table to check on the execution status of each job.

The API processing endpoint​

The Next project also contains a single API endpoint that is used to execute jobs against the Stability API before storing the results in a Tigris bucket. This allows for a queue-like structure where jobs can be processed asynchronously.

This endpoint does much of the heavy lifting to make this app possible. Let’s step through what happens when it’s called.

It will start by checking to see if there are any jobs with a status of pending (0):

let res = await db
  .select()
  .from(jobs)
  .where(eq(jobs.status, 0))
  .limit(1)
  .execute();

If a job is found, the status is set to in progress (1). This prevents other executions from processing a job twice.

await db.update(jobs).set({ status: 1 }).where(eq(jobs.id, job.id)).execute();

Next, the prompt and image dimensions are sent to the Stability API for generating an image. The base64 encoded image is returned in the response from Stability.

const engineId = "stable-diffusion-v1-6";
const apiHost = process.env.API_HOST ?? "https://api.stability.ai";

// Request an image from Stability
const stabilityRes = await fetch(
  `${apiHost}/v1/generation/${engineId}/text-to-image`,
  {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      Accept: "application/json",
      Authorization: `Bearer ${process.env.STABILITY_API_KEY}`,
    },
    body: JSON.stringify({
      text_prompts: [
        {
          text: job.prompt,
        },
      ],
      cfg_scale: 7,
      height: job.height,
      width: job.width,
      steps: 30,
      samples: 1,
    }),
  }
);

let rb = await stabilityRes.json();
if (!rb.artifacts) {
  throw new Error(`${rb.name} -- ${rb.message} -- ${rb.details}`);
}

Then we can take that image and upload it to Tigris using the AWS SDK before setting the job to done (2.

let artifact = rb.artifacts[0];
if (artifact.finishReason == "SUCCESS") {
  let imgdata = artifact.base64;
  var buf = Buffer.from(imgdata, "base64");
  const upload = new Upload({
    params: {
      Bucket: process.env.BUCKET_NAME,
      Key: `${job.id}.png`,
      Body: buf,
    },
    client: S3,
    queueSize: 3,
  });

  // Upload the file to Tigris
  await upload.done();

  await db.update(jobs).set({ status: 2 }).where(eq(jobs.id, job.id)).execute();
}

The background job​

Using node-cron , a simple background job is used to poll the API endpoint from the Next.js app. When polled, that endpoint will handle the next job in the list. This is run as a secondary process in Fly using concurrently to avoid needing unnecessary infrastructure, keeping the project isolated to a single container. The following diagram demonstrates what’s performed in the background job:

1. The background job polls the API endpoint in Next regularly.
2. When a job is detected, the API will set the status to in progress.
3. Next will then dispatch a message to the Stability API, which will respond with a base64 encoded image when processing is complete.
4. That image will be stored in a Tigris bucket.
5. The database record is set to complete.

See it in action​

When a user provides a prompt, a new grid item will appear with an hourglass icon, indicating that it is waiting to be processed.

When the background job picks up the new request, the status will be updated in the database and the grid item will change to a spinner to show that it’s currently being processed.

Once the job is completed and the image is available, hovering over the thumbnail will show you the original prompt, as well as provide options to download the image or copy the pre-signed URL to your clipboard for sharing.

Create a Stability API key​

Before you can deploy the application, you’ll need to create an API key that will allow you to programmatically generate images using the Stability API. Start by heading to https://platform.stability.ai and create an account.

Once your account is created, you’ll be able to access your profile where you can create an API key. To do this, click on your avatar in the upper right.

Then click the Create API Key button.

Take note of the API key that is generated as you’ll need it in a later step.

Deploy to Fly.io​

Start the deployment process by cloning the repository to your computer. Open a terminal and run the following command to do so:

git clone https://github.com/bmorrisondev/sd-tigris-database.git

Navigate into the sd-tigris-database directory. Since all apps on Fly.io require globally unique names, you’ll need to customize the name of the app in the fly.toml file. You can set it to something manually, or you can run the following command to automatically customize the name:

npm install node rename.mjs
## Output:
## App name changed to sd-tigris-database-65b013f6fc

Next, run the following to deploy the application and database to Fly.io:

fly launch

Since a fly.toml is stored with the repository, it should contain all of the necessary configurations to launch the app. When asked if you want to copy the configuration, type y to do so.

Next, you’ll be asked to review the app that will be launched:

Organization:
  YOUR ORGANIZATION                                                    (fly
  launch defaults to the personal org)
Name:
  sd-tigris-database-65b013f6fc                                        (from
  your fly.toml)
Region:
  Ashburn, Virginia (US)                                               (from
  your fly.toml)
App Machines:
  shared-cpu-1x, 1GB RAM                                               (from
  your fly.toml)
Postgres:
  (Fly Postgres) 1 Node, shared-cpu-1x, 256MB RAM (1GB RAM), 10GB disk
  (determined from app source)
Redis:
  <none>                                                               (not
  requested)
Sentry:
  false                                                                (not
  requested)

When asked if you want to tweak the settings, type n to proceed. The main part of your app will start deploying. Wait until the deployment is finished and take note of the URL at the end:

Visit your newly deployed app at https://sd-tigris-database-65b013f6fc.fly.dev/

Configure the Postgres database​

A Postgres database will be configured as part of the deployment, but you’ll need to create the schema for the application before it will function properly. This will be done using drizzle-kit and the provided schema.ts file.

Scroll up through the output of the deployment and locate the value for DATABASE_URL. It should look something like this:

DATABASE_URL=postgres://sd_tigris_database_65b013f6fc:Eb2tnGHch9m9u90@sd-tigris-database-65b013f6fc-db.flycast:5432/sd_tigris_database_65b013f6fc?sslmode=disable

As it is now, this connection string won’t work locally, but we can tweak it a bit before configuring a proxy using the Fly.io CLI tool. Create a file in the root of the project named .env.local and paste the connection string in it. Replace the hostname with 127.0.0.1. It should look similar to this, but with different credentials:

DATABASE_URL=postgres://sd_tigris_database_65b013f6fc:Eb2tnGHch9m9u90@127.0.0.1:5432/sd_tigris_database_65b013f6fc?sslmode=disable

In the terminal, run fly apps ls to get a list of your applications. Take note of the name ending in -db as this is the app that is the Postgres cluster you’ll need to create the proxy to.

> fly app ls
NAME                                    OWNER           STATUS          LATEST DEPLOY
fly-builder-young-water-4407            personal        deployed
sd-tigris-database-65b013f6fc           personal        suspended       8m31s ago
sd-tigris-database-65b013f6fc-db        personal        deployed

Next, create a proxy using the following command, but replace the sd-tigris-database-65b013f6fc-db with the name of your cluster:

fly proxy -a sd-tigris-database-65b013f6fc-db 5432

The fly proxy will prevent any further commands in that terminal window while it’s running, so open another terminal at the root of the project and run the following command to apply the schema changes:

npm run db:push

You should see a list of changes that will be made to the database, confirm these changes.

> sd-tigris-database@0.1.0 db:push
> drizzle-kit push:pg --config ./drizzle.config.ts

drizzle-kit: v0.20.14
drizzle-orm: v0.30.4

Custom config path was provided, using './drizzle.config.ts'
Reading config file '/Users/brian/Repos/sd-tigris-database/sd-tigris-database/drizzle.config.ts'
postgres://sd_tigris_database_65b013f6fc:Eb2tnGHch9m9u90@127.0.0.1:5432/sd_tigris_database_65b013f6fc?sslmode=disable

Warning  You are about to execute current statements:

CREATE TABLE IF NOT EXISTS "jobs" (
        "id" serial PRIMARY KEY NOT NULL,
        "prompt" text NOT NULL,
        "height" integer DEFAULT 500 NOT NULL,
        "width" integer DEFAULT 500 NOT NULL,
        "status" integer DEFAULT 0 NOT NULL,
        "error" text,
        "meta" json
);

  No, abort
❯ Yes, I want to execute all statements

You can now close the proxy if needed.

Configure the Tigris bucket​

Next up, you’ll need to create the Tigris bucket that will be used to store the generated images. To do this, run the following command:

fly storage create

You can leave the prompt blank to generate a name automatically. This command will automatically update the environment variables of the app in Fly to use the bucket, meaning no further action is required.

Add Stability Key environment variable​

The last step is to use the Stability API key you generated earlier in this guide and set it as an environment variable in Fly. Once set, Fly will automatically restart the underlying containers so they receive the newest set of variables.

fly secrets set STABILITY_API_KEY={YOUR_KEY_HERE}

After adding the environment variables, you should be able to access the app using the URL you grabbed earlier.

Conclusion​

Creating AI-generated images and storing them is just one excellent use case for Tigris. To learn more about what Tigris can do, check out the documentation portal for a more complete list of features!

Tigris is a globally distributed S3-compatible object storage solution available that can easily be hosted on Fly.io. In this article, we'll explore how Tigris fits into the existing slate of object storage options and why you might choose one over the other.

You don't need a CDN​

Probably the most exciting aspect of Tigris is its globally distributed nature. But what does that actually mean?

First, consider a common setup: you want to quickly deliver assets to users from your object storage, so typically you’d need to make use of a content delivery network (CDN) to cache your data in multiple regions, which helps reduce latency. When using Amazon S3, Cloudfront is the CDN most often used.

With Tigris, you don’t need a CDN at all because your data is available in all regions from the start. Global distribution means that the same bucket can store data in many different locations. You can effectively have a CDN-like system for delivering data to any region in a matter of minutes. Unlike a CDN, updates to data can occur in any region, allowing users in that region to see changes faster.

Streamlined developer experience​

Getting started with Tigris is as simple as running a single command to create a bucket:

fly storage create

When run in the directory of a deployed Fly App, it will create a new private Tigris bucket and set the following secrets on your app:

BUCKET_NAME
AWS_ENDPOINT_URL_S3
AWS_ACCESS_KEY_ID
AWS_SECRET_ACCESS_KEY

If you were already using the S3 API in your app, there's no need to change your code aside from updating the values of your environment variables/secrets.

Streamlined authorization​

Tigris buckets are private by default but can be made public to allow anyone to read their content. Additionally, Tigris supports Role-Based-Access-Control (RBAC), with a few predefined roles: Admin, Editor, and ReadOnly. A complete list of the permitted operations for each of these roles can be found here. You can use the predefined roles mentioned above, or you can define your own roles using custom IAM policies.

Migrating is easy (without being locked in)​

Migrating is made easier by the use of shadow buckets. Tigris shadow buckets allow you to incrementally migrate the data from an existing S3 or a compatible bucket without incurring egress costs or downtime. And if you migrate, you won't be locked in – Tigris doesn't charge for egress. 😀 💸

Conclusion​

Tigris is revolutionizing object storage for global apps. For developers seeking an efficient, scalable storage solution without the burden of extra costs or technical overhead, Tigris offers a compelling alternative. Give it a try today on your next app on Fly.io!

Eighteen years ago today, Amazon completely changed how developers work with data storage by giving us Simple Storage Service (S3).

S3 rewrote the rules of storage and propelled us into a new era of cloud computing. Traditional storage solutions were cumbersome and costly, and they shackled developers to the limitations of the hardware. With S3, Amazon introduced a shift towards Storage as a Service, liberating developers from the burdensome tasks of purchasing, provisioning, and managing physical storage. No longer were they bound by the precarious dance of capacity planning, where overestimating meant wasted resources and underestimating spelled disaster for uptime.

Hello, world! We're Tigris Data, and today we're announcing the public beta of Tigris. Tigris is a globally distributed object storage service that provides low latency anywhere in the world, enabling developers like you to store and access any amount of data using the S3 libraries you're already using in production. Today, we're launching our public beta on top of Fly.io.

Tigris is a globally available, S3 compatible object storage service.

Tigris uses FoundationDB's transactional key-value interface as its underlying storage engine. In our blog post Skipping the boring parts of building a database using FoundationDB we went into the details of why we chose to build on FoundationDB. To recap, FoundationDB is an ordered, transactional, key-value store with native support for multi-key strictly serializable transactions across its entire keyspace. We leverage FoundationDB to handle the hard problems of durability, replication, sharding, transaction isolation, and load balancing so we can focus on higher-level concerns.

We are starting a series of blog posts that go into the details of how Tigris has been implemented. In the first post of the series, we will share the details of how we have built the metadata storage layer on top of FoundationDB. We will cover the topics of data layout, and schema management.

Tigris being S3 compatible, offers S3-supported authentication and authorization methods. AWS S3 evolved in offerings for their authentication and authorization needs. We support what AWS S3 recommends to their users as of now for the AuthN and AuthZ needs.

I did a presentation at the FoundationDB Monthly Meetup. The talk is about, how building secondary indexes for a database is always about balance. A balance between a system that scales and is easy to manage and an API that is intuitive and delightful for a developer to use. Recently at Tigris Data, we have been adding secondary indexes to our metadata store and have been working hard to achieve a good balance between scale and developer delight. Tigris is a storage platform that leverages FoundationDB as one of its core components. In this talk I cover four aspects we had to balance:

1. Handling schema changes automatically in our secondary indexes.
2. The trade-off between auto-indexing all fields and indexing select fields.
3. Changes we made after performance testing.
4. How we plan build indexes in the background with minimal conflicts.

Client-side encryption refers to the practice of encrypting data on the client side (user's device) before it is transmitted to a server or stored in a remote location. This approach enhances the security and privacy of user data by ensuring that sensitive information is encrypted locally, and only the encrypted form is transferred or stored on the server.

Here's how client-side encryption typically works:

We are running FoundationDB with the official kubernetes operator. FoundationDB supports logical backups (with backup_agent) and disaster recovery (with dr_agent) through copying the database/streaming changes logically. It also supports binary backups through disk snapshots.

In this blog post, we will describe how to make a backup of FoundationDB via backup_agent. The FoundationDB operator supports making logical backups via backup_agent, but it does not support running DR with dr_agent. We decided to run backup_agent as a separate deployment to allow a symmetric setup with dr_agent.

In computer systems, username and password has become a widely used way to authenticate users. The evolution of password storage and authentication methods has gone through various stages to enhance security and protect user credentials.