Last Updated: November 06, 2025

Managing Long-Running Tasks on SaladCloud with RabbitMQ

Managing long running tasks, such as molecular simulations, LoRA training, and LLM finetuning, presents unique challenges on SaladCloud, due primarily to the interruptible nature of nodes. At the core of all solutions to this problem are a job queue, and progress checkpoints. The job queue is responsible for distributing tasks to workers, and detecting when a worker has been interrupted. Workloads should save checkpoints of their progress and upload it to cloud storage, so that they can be resumed from the last checkpoint in the event of an interruption. Workers should also upload completed artifacts to cloud storage.

Basic Architecture — Basic architecture for long-running tasks on SaladCloud

We will be using RabbitMQ hosted on CloudAMQP as our job queue, and Cloudflare R2, an S3-compatible object storage service, as our cloud storage. We prefer R2 to AWS S3 for many SaladCloud workloads, because R2 does not charge for egress data, which helps reduce costs when distributed workers fetch inputs and upload results. Instrumenting your code to use S3-compatible storage will make it easier to switch storage providers in the future if you choose to do so. For this guide, we will build an application that slowly calculates a sum for n steps, sleeping for 30 seconds between steps to simulate work. We will set up a job queue and related resources, a storage bucket, a checkpoint saving system, and a simple auto-scaling mechanism. You will need a CloudAMQP account, and a Cloudflare account to follow this guide.

The Job Queue: RabbitMQ

RabbitMQ is a highly configurable open-source message broker that implements the Advanced Message Queuing Protocol (AMQP) and has client libraries in many languages. It is a robust and scalable solution for job queues, and is widely used in the industry. You can self-host if desired, but for this guide we will be using CloudAMQP’s hosted RabbitMQ service.

Relevant Limitations

While RabbitMQ itself has no such inherent limitations, The “Sassy Squirrel” plan we’ll be using on CloudAMQP supports a maximum of 1.5k connections, and up to 500 messages per second. This will be more than sufficient for this guide, where we will only be scaling up to 250 workers.
Maximum message size is 512MB, and further limited by the amount of RAM available on the host machine, as messages are held in memory, with optional persistence. The default max message size on CloudAMQP is 128MB. As is true for most job queues, it is recommended to keep large amounts of data in cloud storage, putting only references to the data location in the message itself.
CloudAMQP’s default message timeout is 2 hours, but we can disable this limit entirely, allowing for extremely long-running tasks.
RabbitMQ relies on long-lived connections between the message broker and clients, so it is important to handle reconnections gracefully in your code.
Using RabbitMQ in python in a multi-threaded environment can be a little tricky, however it is required in order to support long lived jobs and quick interruption detection. We will be using the pika library, which does have support for threaded workers.

Setting Up RabbitMQ on CloudAMQP

Once you have your account on CloudAMQP, it’s time to deploy a new instance. We will be using the “Sassy Squirrel” plan for this guide, which is $50/month (billed by the second). You can choose a different plan if you need more or less resources.

Next, you can choose the datacenter and region for your instance. We will be using DigitalOcean’s New York 3 datacenter for this guide, but if you have other application components (besides the worker) in a different cloud, you should consider deploying the broker to the same cloud and region as your other components.

Choosing a datacenter and region — Choosing a Datacenter and Region

Next, select the number of nodes you want to deploy, and the version of RabbitMQ. We will be using a single node, and the latest version of RabbitMQ (4.0.5 as of the time of this writing)

Choosing the number of nodes and RabbitMQ version

Confirm all of your settings on the next page, and deploy your instance. Once deployed, you should see something like this on the CloudAMQP console:

Click the name of the instance to pull up the details page. Later, you will need info from this page to connect to your RabbitMQ instance, but for now, just navigate to the configuration tab on the left-hand navigation bar. Once there, disable the field labeled rabbit.consumer_timeout. This will allow us to have tasks run longer than the default 2-hour timeout.

Save your changes when done, and then navigate to the “RabbitMQ Manager”, which will open a new tab. The RabbitMQ Manager is a web interface for managing your RabbitMQ instance. You can view queues, exchanges, and other RabbitMQ objects, as well as publish and consume messages. There is an HTTP API for this management layer, but for this guide we will be using the web interface.

Deadletter Exchange

A deadletter exchange is an exchange that messages are sent to when they are rejected by a queue. This can happen when a message is not acknowledged by a consumer, typically indicating that the consumer has gone offline, or the message is malformed. We will be creating a deadletter exchange and queue first, so that we can configure our main queue to send messages to it when they are rejected. This will allow us to inspect and requeue messages that have failed to be processed. From the RabbitMQ Manager, navigate to the Exchanges tab, and add a new exchange called “deadletter”.

Make sure to select the non-root virtual host, and set the type to “direct.” Next, navigate to the Queues tab, and add a new queue called “deadletter”. Make sure to select the same virtual host as the exchange.

Once the queue is created, click on it in the list of queues, and create a binding from the deadletter exchange to the deadletter queue. This will ensure that messages sent to the deadletter exchange are routed to the deadletter queue.

Binding the deadletter exchange to the deadletter queue

Main Job Queue

Next, we will create the main job queue. This queue will hold messages that represent tasks to be processed by workers. Navigate back to the Queues tab, and add a new Quorum queue called “my-job-queue”, and set the deadletter exchange to the exchange we created earlier, and setting the delivery limit to 3. This will allow a message to be retried 3 times before being sent to the deadletter exchange.

Cloud Storage: R2

R2 is a cloud storage service from Cloudflare that is compatible with the S3 API. It is a great choice for SaladCloud workloads because it does not charge egress fees, which helps reduce costs when distributed workers fetch inputs and upload results. From the R2 console, navigate to “R2 Object Storage”, and click “Create Bucket”.

Give your bucket a meaningful name, and select an appropriate location. We are going to use the standard storage class, and automatic location.

Once your bucket is created, you will need to create an access key and secret key. Select “Manage API tokens” from the ”{ } API” menu, and click “Create Token”.

Navigate to manage api tokens — You still need an API token to access your bucket

Create a token with “Object Read & Write” permissions, and only grant it access to the bucket we’ve just created. Since secret rotation is outside the scope of this guide, we’re going to use the “forever” TTL. However, it is best practice to user shorter-lived secrets and to have easy automatic mechanisms in place to rotate secrets as needed. Once created you will be given an access key and secret key. Save these somewhere safe, as you will not be able to retrieve them again. The application code will get these keys from environment variables, so you will need to set them in your environment. Also on that page will be the S3 endpoint URL for your bucket. Save this as well, as it will be needed in the application code.

Instrumenting Our Application

We’re going to use the boto3 library to interact with R2, and the pika library to interact with RabbitMQ. You can install it with pip install boto3 pika. First, we need to set up our environment variables. All of the following environment variables will be needed by the application code. There are several ways to do this, but what I’ve done for my development environment is create a file called worker.env in the root of my project, and add the following lines:

AMQP_URL=amqps://your-username:your-password@your-hostname/your-vhost
JOB_QUEUE=my-job-queue
R2_AWS_ACCESS_KEY_ID=your-access-key-id
R2_AWS_SECRET_ACCESS_KEY=your-secret-access-key
R2_S3_ENDPOINT_URL=your-s3-endpoint-url
R2_BUCKET_NAME=your-bucket-name

Then, to source this into my environment when I run my code, I run the following command:

export $(grep -v '^#' worker.env | xargs -d '\n')

Make sure *.env is in your .gitignore. You don’t want to commit your secrets to your repository. Now, create a file called main.py in the root of your project, and add the following code:

import os
import boto3
import pika
import json
import time
import threading
import functools

# Get the environment variables
r2_aws_region = "auto"
r2_aws_access_key_id = os.getenv('R2_AWS_ACCESS_KEY_ID')
r2_aws_secret_access_key = os.getenv('R2_AWS_SECRET_ACCESS_KEY')
r2_s3_endpoint_url = os.getenv('R2_S3_ENDPOINT_URL')
r2_bucket_name = os.getenv('R2_BUCKET_NAME')

amqp_url = os.getenv('AMQP_URL')
job_queue = os.getenv('JOB_QUEUE')

machine_id = os.getenv('SALAD_MACHINE_ID')

# Create the R2 client
r2 = boto3.client('s3',
                  aws_access_key_id=r2_aws_access_key_id,
                  aws_secret_access_key=r2_aws_secret_access_key,
                  region_name=r2_aws_region,
                  endpoint_url=r2_s3_endpoint_url)

Next, let’s take a look at the “work” function, where we will do the actual work for the job. This function will simulate work by sleeping for 30 seconds and incrementing the step and sum in the checkpoint, and saving that checkpoint to R2.

cancel_signal = threading.Event()


def do_the_actual_work(job: dict, checkpoint: dict) -> int | None:
    '''
    Do the actual work for the job. This function will simulate work by
    sleeping for 30 seconds and incrementing the step and sum in the
    checkpoint.

    Parameters:
    - job: dict, the job
    - checkpoint: dict, the checkpoint
    '''
    global cancel_signal
    print(f'Starting job {job["job_id"]}', flush=True)
    print(f"Max steps: {job['steps']}", flush=True)
    print(f"Starting step: {checkpoint['step']}", flush=True)
    while checkpoint['step'] < job['steps'] and not cancel_signal.is_set():
        # Simulate work
        print(
            f"Working on job {job['job_id']}, step {checkpoint['step']}", flush=True)
        time.sleep(30)
        if cancel_signal.is_set():
            # If we were interrupted, we need to return None to indicate that
            # the job was interrupted.
            return None
        # Update the checkpoint.
        checkpoint['step'] += 1
        checkpoint['sum'] += checkpoint['step']
        upload_checkpoint(job['job_id'], checkpoint)

    print(f'Job {job["job_id"]} finished')
    return checkpoint['sum']

Additionally, we may want a function that validates the job before starting work on it. In our simple example, we’ll just make sure the basic fields are present.

def validate_job(job: dict) -> bool:
    '''
    Validate the job

    Parameters:
    - job: dict, the job

    Returns:
    - bool, whether the job is valid
    '''
    # This is a very simple function for our very simple application.
    # You should replace this with your actual validation logic.
    return 'job_id' in job and 'steps' in job

We also want functions to upload and download our progress checkpoints, and to upload the result. In this simplified example, we’re going to use a small JSON file for the checkpoint, but the principle is the same no matter what the actual checkpoint is.

def download_checkpoint(job_id: str) -> dict:
    '''
    Download the checkpoint from S3

    Parameters:
    - job_id: str, the job ID

    Returns:
    - checkpoint: dict, the checkpoint
    '''
    try:
        response = r2.get_object(
            Bucket=r2_bucket_name,
            Key=f'{job_id}/checkpoint.json'
        )
    except r2.exceptions.NoSuchKey:
        return None

    checkpoint = json.loads(response['Body'].read())
    return checkpoint


def upload_checkpoint(job_id: str, checkpoint: dict) -> None:
    '''
    Upload the checkpoint to S3

    Parameters:
    - job_id: str, the job ID
    - checkpoint: dict, the checkpoint
    '''
    r2.put_object(
        Bucket=r2_bucket_name,
        Key=f'{job_id}/checkpoint.json',
        Body=json.dumps(checkpoint)
    )
    print(f'Checkpoint uploaded for job {job_id}', flush=True)


def upload_result(job_id: str, result: int) -> None:
    '''
    Upload the result to S3

    Parameters:
    - job_id: str, the job ID
    - result: int, the result
    '''
    r2.put_object(
        Bucket=r2_bucket_name,
        Key=f'{job_id}/result.txt',
        Body=str(result)
    )
    print(f'Result uploaded for job {job_id}', flush=True)

We also are going to make helper functions for acknowledging and rejecting messages from the queue.

def ack_message(channel, delivery_tag):
    '''
    Acknowledge the message, indicating that it has been processed successfully
    '''
    if channel.is_open:
        channel.basic_ack(delivery_tag)
    else:
        # Channel is already closed, so we can't ack this message;
        print("Channel is closed, message not acked")


def nack_message(channel, delivery_tag, requeue=True):
    '''
    Reject the message, indicating that it has not been processed successfully
    '''
    if channel.is_open:
        channel.basic_nack(delivery_tag, requeue=requeue)
    else:
        # Channel is already closed, so we can't nack this message;
        print("Channel is closed, message not nacked")

Now, we put these parts together in a function called process_job.

def process_job(channel, delivery_tag, body):
    job = json.loads(body)
    print(f"Received job {job['job_id']}", flush=True)

    # If there's a checkpoint, we want to use it, but if not, we need to
    # initialize our state.
    checkpoint = download_checkpoint(job['job_id'])
    if checkpoint is None:
        checkpoint = {'step': 0, 'sum': 0}

    # Some jobs may have a validation step. For instance, dreambooth training may have a step
    # that verifies if all inputs have faces. If the validation fails, we should stop the job
    # and not retry it, but instead move it to the DLQ. In this situation, we can
    # be confident that the job will never succeed.
    if not validate_job(job):
        cb = functools.partial(nack_message, channel, delivery_tag, False)
        if channel.is_open:
            channel.connection.add_callback_threadsafe(cb)
        return

    # Now we can do the actual work
    try:
        result = do_the_actual_work(job, checkpoint)
    except Exception as e:
        print(f"Error in job {job['job_id']}: {str(e)}")
        cb = functools.partial(nack_message, channel, delivery_tag)
        if channel.is_open:
            channel.connection.add_callback_threadsafe(cb)
        return

    if result is None:
        # Job was interrupted
        cb = functools.partial(nack_message, channel, delivery_tag)
        if channel.is_open:
            channel.connection.add_callback_threadsafe(cb)
        return

    # Upload the result and ack the message
    upload_result(job['job_id'], result)
    cb = functools.partial(ack_message, channel, delivery_tag)
    if channel.is_open:
        channel.connection.add_callback_threadsafe(cb)

Now, because we need to run our long-running task in a separate thread from the rabbitmq client, we need a function that spawns that worker thread for us.

def on_message(channel, method_frame, header_frame, body, args):
    threads = args
    delivery_tag = method_frame.delivery_tag
    t = threading.Thread(target=process_job, args=(
        channel, delivery_tag, body))
    t.start()
    threads.append(t)

Finally, we need to connect to RabbitMQ and start consuming messages from the queue, taking care to handle all sorts of connection errors that may arise.

if __name__ == "__main__":
    # We will be doing all of our work in separate threads, so that rabbitmq's heartbeat
    # can be properly handled.
    threads = []
    while True:
        try:
            # Create the connection and channel, heartbeating every 30 seconds.
            connection = pika.BlockingConnection(
                pika.URLParameters(amqp_url + "?heartbeat=30"))
            channel = connection.channel()

            # We only want 1 job at a time per worker
            channel.basic_qos(prefetch_count=1)

            # Start consuming the messages
            on_message_callback = functools.partial(
                on_message, args=(threads)
            )
            channel.basic_consume(
                queue=job_queue, on_message_callback=on_message_callback, consumer_tag=machine_id)
            channel.start_consuming()
        # Don't recover if connection was closed by broker
        except pika.exceptions.ConnectionClosedByBroker:
            print("Connection closed by broker")
            break
        # Don't recover on channel errors
        except pika.exceptions.AMQPChannelError as e:
            print("Channel error")
            print(str(e))
            break
        # Recover on all other connection errors
        except pika.exceptions.AMQPConnectionError as e:
            print("Connection error, retrying...")
            print(str(e))
            time.sleep(1)
            continue
        except KeyboardInterrupt:
            print("Keyboard interrupt")
            channel.stop_consuming()
            break
        except Exception as e:
            print(f"Error: {str(e)}")
            break

    cancel_signal.set()
    print("Exiting")
    for thread in threads:
        thread.join()
    connection.close()

Completed Example

import os
import boto3
import pika
import json
import time
import threading
import functools

# Get the environment variables
r2_aws_region = "auto"
r2_aws_access_key_id = os.getenv('R2_AWS_ACCESS_KEY_ID')
r2_aws_secret_access_key = os.getenv('R2_AWS_SECRET_ACCESS_KEY')
r2_s3_endpoint_url = os.getenv('R2_S3_ENDPOINT_URL')
r2_bucket_name = os.getenv('R2_BUCKET_NAME')

amqp_url = os.getenv('AMQP_URL')
job_queue = os.getenv('JOB_QUEUE')

machine_id = os.getenv('SALAD_MACHINE_ID')

# Create the R2 client
r2 = boto3.client('s3',
                  aws_access_key_id=r2_aws_access_key_id,
                  aws_secret_access_key=r2_aws_secret_access_key,
                  region_name=r2_aws_region,
                  endpoint_url=r2_s3_endpoint_url)


def download_checkpoint(job_id: str) -> dict:
    '''
    Download the checkpoint from S3

    Parameters:
    - job_id: str, the job ID

    Returns:
    - checkpoint: dict, the checkpoint
    '''
    try:
        response = r2.get_object(
            Bucket=r2_bucket_name,
            Key=f'{job_id}/checkpoint.json'
        )
    except r2.exceptions.NoSuchKey:
        return None

    checkpoint = json.loads(response['Body'].read())
    return checkpoint


def upload_checkpoint(job_id: str, checkpoint: dict) -> None:
    '''
    Upload the checkpoint to S3

    Parameters:
    - job_id: str, the job ID
    - checkpoint: dict, the checkpoint
    '''
    r2.put_object(
        Bucket=r2_bucket_name,
        Key=f'{job_id}/checkpoint.json',
        Body=json.dumps(checkpoint)
    )
    print(f'Checkpoint uploaded for job {job_id}', flush=True)


def validate_job(job: dict) -> bool:
    '''
    Validate the job

    Parameters:
    - job: dict, the job

    Returns:
    - bool, whether the job is valid
    '''
    # This is a very simple function for our very simple application.
    # You should replace this with your actual validation logic.
    return 'job_id' in job and 'steps' in job


cancel_signal = threading.Event()


def do_the_actual_work(job: dict, checkpoint: dict) -> int | None:
    '''
    Do the actual work for the job. This function will simulate work by
    sleeping for 30 seconds and incrementing the step and sum in the
    checkpoint.

    Parameters:
    - job: dict, the job
    - checkpoint: dict, the checkpoint
    '''
    global cancel_signal
    print(f'Starting job {job["job_id"]}', flush=True)
    print(f"Max steps: {job['steps']}", flush=True)
    print(f"Starting step: {checkpoint['step']}", flush=True)
    while checkpoint['step'] < job['steps'] and not cancel_signal.is_set():
        # Simulate work
        print(
            f"Working on job {job['job_id']}, step {checkpoint['step']}", flush=True)
        time.sleep(30)
        if cancel_signal.is_set():
            # If we were interrupted, we need to return None to indicate that
            # the job was interrupted.
            return None
        # Update the checkpoint.
        checkpoint['step'] += 1
        checkpoint['sum'] += checkpoint['step']
        upload_checkpoint(job['job_id'], checkpoint)

    print(f'Job {job["job_id"]} finished')
    return checkpoint['sum']


def upload_result(job_id: str, result: int) -> None:
    '''
    Upload the result to S3

    Parameters:
    - job_id: str, the job ID
    - result: int, the result
    '''
    r2.put_object(
        Bucket=r2_bucket_name,
        Key=f'{job_id}/result.txt',
        Body=str(result)
    )
    print(f'Result uploaded for job {job_id}', flush=True)


def ack_message(channel, delivery_tag):
    '''
    Acknowledge the message, indicating that it has been processed successfully
    '''
    if channel.is_open:
        channel.basic_ack(delivery_tag)
    else:
        # Channel is already closed, so we can't ack this message;
        print("Channel is closed, message not acked")


def nack_message(channel, delivery_tag, requeue=True):
    '''
    Reject the message, indicating that it has not been processed successfully
    '''
    if channel.is_open:
        channel.basic_nack(delivery_tag, requeue=requeue)
    else:
        # Channel is already closed, so we can't nack this message;
        print("Channel is closed, message not nacked")


def process_job(channel, delivery_tag, body):
    job = json.loads(body)
    print(f"Received job {job['job_id']}", flush=True)

    # If there's a checkpoint, we want to use it, but if not, we need to
    # initialize our state.
    checkpoint = download_checkpoint(job['job_id'])
    if checkpoint is None:
        checkpoint = {'step': 0, 'sum': 0}

    # Some jobs may have a validation step. For instance, dreambooth training may have a step
    # that verifies if all inputs have faces. If the validation fails, we should stop the job
    # and not retry it, but instead move it to the DLQ. In this situation, we can
    # be confident that the job will never succeed.
    if not validate_job(job):
        cb = functools.partial(nack_message, channel, delivery_tag, False)
        if channel.is_open:
            channel.connection.add_callback_threadsafe(cb)
        return

    # Now we can do the actual work
    try:
        result = do_the_actual_work(job, checkpoint)
    except Exception as e:
        print(f"Error in job {job['job_id']}: {str(e)}")
        cb = functools.partial(nack_message, channel, delivery_tag)
        if channel.is_open:
            channel.connection.add_callback_threadsafe(cb)
        return

    if result is None:
        # Job was interrupted
        cb = functools.partial(nack_message, channel, delivery_tag)
        if channel.is_open:
            channel.connection.add_callback_threadsafe(cb)
        return

    # Upload the result and ack the message
    upload_result(job['job_id'], result)
    cb = functools.partial(ack_message, channel, delivery_tag)
    if channel.is_open:
        channel.connection.add_callback_threadsafe(cb)


def on_message(channel, method_frame, header_frame, body, args):
    threads = args
    delivery_tag = method_frame.delivery_tag
    t = threading.Thread(target=process_job, args=(
        channel, delivery_tag, body))
    t.start()
    threads.append(t)


if __name__ == "__main__":
    # We will be doing all of our work in separate threads, so that rabbitmq's heartbeat
    # can be properly handled.
    threads = []
    while True:
        try:
            # Create the connection and channel, heartbeating every 30 seconds.
            connection = pika.BlockingConnection(
                pika.URLParameters(amqp_url + "?heartbeat=30"))
            channel = connection.channel()

            # We only want 1 job at a time per worker
            channel.basic_qos(prefetch_count=1)

            # Start consuming the messages
            on_message_callback = functools.partial(
                on_message, args=(threads)
            )
            channel.basic_consume(
                queue=job_queue, on_message_callback=on_message_callback, consumer_tag=machine_id)
            channel.start_consuming()
        # Don't recover if connection was closed by broker
        except pika.exceptions.ConnectionClosedByBroker:
            print("Connection closed by broker")
            break
        # Don't recover on channel errors
        except pika.exceptions.AMQPChannelError as e:
            print("Channel error")
            print(str(e))
            break
        # Recover on all other connection errors
        except pika.exceptions.AMQPConnectionError as e:
            print("Connection error, retrying...")
            print(str(e))
            time.sleep(1)
            continue
        except KeyboardInterrupt:
            print("Keyboard interrupt")
            channel.stop_consuming()
            break
        except Exception as e:
            print(f"Error: {str(e)}")
            break

    cancel_signal.set()
    print("Exiting")
    for thread in threads:
        thread.join()
    connection.close()

Submitting Jobs to the Queue

Next, we need a way to submit jobs to the queue. We’re going to use the pika library for this as well, with the same AMQP_URL and JOB_QUEUE from worker.env. I’ve saved mine in a file called submitter.env, and I’m going to source them into my environment with the following command:

export $(grep -v '^#' submitter.env | xargs -d '\n')

Suppose we have a csv with our 10,000 jobs, and we want to submit them all. Our CSV (data.csv) looks like this, with 10,000 rows.

job_id,steps
job-0,600
job-1,600
job-2,600
job-3,600

It is pretty straightforward to submit these jobs to the queue. Here’s an example script that does just that:

import csv
import json
import pika
import os

amqp_url = os.getenv('AMQP_URL')
job_queue = os.getenv('JOB_QUEUE')

if __name__ == '__main__':
    connection = pika.BlockingConnection(pika.URLParameters(amqp_url))
    channel = connection.channel()
    with open("data.csv") as f:
        reader = csv.DictReader(f)
        for row in reader:
            job = {
                "job_id": row["job_id"],
                "steps": int(row["steps"])
            }
            channel.basic_publish(
                exchange='',
                routing_key=job_queue,
                body=json.dumps(job)
            )
            print(f'Job {job["job_id"]} submitted')
    connection.close()

Running the Job Submitter

Run the job submitter with python submit-jobs.py. It will read the csv file and submit all the jobs to the queue. Once that has run, we can see in the RabbitMQ management interface

RabbitMQ Management Interface showing a full queue

Containerize the Worker Application

Now that we have our worker application and our job submitter, we can package our worker in a docker container, and run it on a SaladCloud Container Group. First, let’s make sure our dependencies are documented in requirements.txt.

boto3
pika

Now, create a new file called Dockerfile. Our application is simple, so a basic python base image should be fine.

FROM python:3.10.12-slim-buster

WORKDIR /app

COPY requirements.txt .
RUN pip install -r requirements.txt

COPY main.py .

CMD ["python", "main.py"]

Now, build the docker image, and use a tag that makes sense for you.

docker build -t saladtechnologies/lrt-worker-examples:rabbitmq .

Now, we can test it locally to make sure it works, before we deploy it to SaladCloud.

docker run -it --rm  --env-file worker.env saladtechnologies/lrt-worker-examples:rabbitmq

You should see it start up and begin processing a job. Once this is working, you can go ahead and terminate the container with Ctrl+C. Now, we can push the image to Docker Hub.

docker push saladtechnologies/lrt-worker-examples:rabbitmq

Deploying the Worker to SaladCloud

To deploy our worker to SaladCloud, we need to create a new Container Group. This can be done via the API, SDKs, or the Portal. We’re going to use the Portal. We’re going to create a new Container Group, and we’re going to use the image we just pushed to Docker Hub. We’re going to request 100 replicas (the max via the portal), and we’re going to set all of our environment variables from worker.env.

Our application is extremely simple, so we’re going to only request 1 vCPU, 1 GB of RAM, and no GPU. Your hardware requirements are likely significantly higher than this.

All CPU-only jobs are prioritized as “Lowest”, and we don’t need any additional storage for this particular application.

Setting the job priority and storage requirements

We do not need the container gateway, as our application pulls its work from a queue. We also do not need health probes, as those are primarily for services accessed via Container Gateway. Go ahead and hit deploy, and you’ll be taken to the container group page, where you can see its status. First, it will prepare by pulling the container image into our high-performance cache.

Once it’s prepared, it will start allocating replicas, and downloading the container image to those replicas.

After a minute or so, we should see our instances up and running.

Validating That It Works

Now that our cluster is up and running, we can go to the RabbitMQ management console, and see that we have in-flight messages now.

In-flight messages in the queue — In-flight messages in our queue

From the R2 console, we can see that our bucket is being filled with checkpoints and results.

Checkpoints and results in the R2 bucket

Autoscaling

Now that we have our worker running, we can set up some simple autoscaling to automatically scale the number of replicas up and down based on the number of messages in the queue. There are many ways to implement autoscaling, but for simplicity, we are going to use a scheduled task that runs every 5 minutes, and sets the number of replicas to be equal to the number of messages in the queue, limited to 250 replicas (the maximum in the API). To implement this, we’re going to use Cloudflare Workers with a schedule trigger. Navigate to the Cloudflare portal, and select the “Compute (Workers)” tab from the left navigation bar. Click “Create”, and then choose the “Hello World” template. Go ahead and deploy the default, we’re going to edit it in the next step.

Now, we’re going to edit the worker code to set the number of replicas to be equal to the number of messages in the queue. First, we need some helper functions to interact with the SaladCloud API.

const saladBaseUrl = 'https://api.salad.com/api/public'

async function getContainerGroup(org, project, containerGroupName, saladApiKey) {
  const url = `${saladBaseUrl}/organizations/${org}/projects/${project}/containers/${containerGroupName}`
  const resp = await fetch(url, {
    method: 'GET',
    headers: {
      'Salad-Api-Key': saladApiKey,
    },
  })
  if (!resp.ok) {
    throw new Error(`Failed to fetch container group: ${resp.statusText}`)
  }
  return resp.json()
}

async function startContainerGroup(org, project, containerGroupName, saladApiKey) {
  console.log('Starting container group')
  const url = `${saladBaseUrl}/organizations/${org}/projects/${project}/containers/${containerGroupName}/start`
  const resp = await fetch(url, {
    method: 'POST',
    headers: {
      'Salad-Api-Key': saladApiKey,
    },
  })
  if (!resp.ok) {
    throw new Error(`Failed to start container group: ${resp.statusText}`)
  }
}

async function stopContainerGroup(org, project, containerGroupName, saladApiKey) {
  console.log('Stopping container group')
  const url = `${saladBaseUrl}/organizations/${org}/projects/${project}/containers/${containerGroupName}/stop`
  const resp = await fetch(url, {
    method: 'POST',
    headers: {
      'Salad-Api-Key': saladApiKey,
    },
  })
  if (!resp.ok) {
    throw new Error(`Failed to stop container group: ${resp.statusText}`)
  }
}

async function setReplicas(org, project, containerGroupName, replicas, saladApiKey) {
  console.log(`Setting replicas to ${replicas}`)
  const url = `${saladBaseUrl}/organizations/${org}/projects/${project}/containers/${containerGroupName}`
  const resp = await fetch(url, {
    method: 'PATCH',
    headers: {
      'Salad-Api-Key': saladApiKey,
      'Content-Type': 'application/merge-patch+json',
    },
    body: JSON.stringify({ replicas }),
  })
  if (!resp.ok) {
    throw new Error(`Failed to set replicas: ${resp.statusText}`)
  }
}

Next, we need a helper function to get metadata about the queue, including the number of messages it contains. This can be done via the RabbitMQ Management API.

async function getQueueInfo(baseUrl, username, password, vHost, queueName) {
  const url = `${baseUrl}/api/queues/${vHost}/${queueName}`
  const resp = await fetch(url, {
    method: 'GET',
    headers: {
      Authorization: `Basic ${btoa(`${username}:${password}`)}`,
    },
  })
  if (!resp.ok) {
    throw new Error(`Failed to fetch queue info: ${resp.statusText}`)
  }
  return resp.json()
}

Now, we can put it all together in a scheduled event listener. Note all of the values that we will provide via environment variables.

export default {
  async scheduled(event, env, ctx) {
    const {
      min_replicas,
      max_replicas,
      org,
      project,
      container_group_name,
      salad_api_key,
      rabbitmq_url,
      rabbitmq_username,
      rabbitmq_password,
      vhost,
      queue_name,
    } = env

    const queueInfo = await getQueueInfo(rabbitmq_url, rabbitmq_username, rabbitmq_password, vhost, queue_name)
    const numMessages = queueInfo.messages
    console.log(`Queue ${queue_name} has ${numMessages} messages`)

    const desiredReplicas = Math.min(Math.max(parseInt(min_replicas), numMessages), parseInt(max_replicas))

    const containerGroup = await getContainerGroup(org, project, container_group_name, salad_api_key)
    const currentReplicas = containerGroup.replicas
    const currentState = containerGroup.current_state.status
    console.log(
      `Current replicas: ${currentReplicas}, current state: ${currentState}, desired replicas: ${desiredReplicas}`,
    )
    if (currentState === 'stopped' && desiredReplicas > 0) {
      await startContainerGroup(org, project, container_group_name, salad_api_key)
    }
    if (currentState === 'running' && desiredReplicas === 0) {
      await stopContainerGroup(org, project, container_group_name, salad_api_key)
    }
    if (currentReplicas !== desiredReplicas) {
      await setReplicas(org, project, container_group_name, desiredReplicas, salad_api_key)
    }
  },
}

Click Deploy, and find your way to the settings tab for the worker function. Here, we are going to disable the domains and routes that point to our function, since it doesn’t even have an http handler. We will also fill in all of the environment variables that we used in our function. Make sure to use the type “Secret” for sensitive values like your rabbitmq password and your Salad API key.

Disabling the domains and routes, and setting the environment variables

Finally, we can set a trigger for the function to run every 5 minutes.

Setting the trigger to run every 5 minutes

Now, if we’ve done everything correctly, we should see our worker scale up and down based on the number of messages in the queue. You can live-tail the logs via the “Logs” tab in the Cloudflare Workers console, or with the wrangler cli.

Completed Example

const saladBaseUrl = 'https://api.salad.com/api/public'

async function getContainerGroup(org, project, containerGroupName, saladApiKey) {
  const url = `${saladBaseUrl}/organizations/${org}/projects/${project}/containers/${containerGroupName}`
  const resp = await fetch(url, {
    method: 'GET',
    headers: {
      'Salad-Api-Key': saladApiKey,
    },
  })
  if (!resp.ok) {
    throw new Error(`Failed to fetch container group: ${resp.statusText}`)
  }
  return resp.json()
}

async function startContainerGroup(org, project, containerGroupName, saladApiKey) {
  console.log('Starting container group')
  const url = `${saladBaseUrl}/organizations/${org}/projects/${project}/containers/${containerGroupName}/start`
  const resp = await fetch(url, {
    method: 'POST',
    headers: {
      'Salad-Api-Key': saladApiKey,
    },
  })
  if (!resp.ok) {
    throw new Error(`Failed to start container group: ${resp.statusText}`)
  }
}

async function stopContainerGroup(org, project, containerGroupName, saladApiKey) {
  console.log('Stopping container group')
  const url = `${saladBaseUrl}/organizations/${org}/projects/${project}/containers/${containerGroupName}/stop`
  const resp = await fetch(url, {
    method: 'POST',
    headers: {
      'Salad-Api-Key': saladApiKey,
    },
  })
  if (!resp.ok) {
    throw new Error(`Failed to stop container group: ${resp.statusText}`)
  }
}

async function setReplicas(org, project, containerGroupName, replicas, saladApiKey) {
  console.log(`Setting replicas to ${replicas}`)
  const url = `${saladBaseUrl}/organizations/${org}/projects/${project}/containers/${containerGroupName}`
  const resp = await fetch(url, {
    method: 'PATCH',
    headers: {
      'Salad-Api-Key': saladApiKey,
      'Content-Type': 'application/merge-patch+json',
    },
    body: JSON.stringify({ replicas }),
  })
  if (!resp.ok) {
    throw new Error(`Failed to set replicas: ${resp.statusText}`)
  }
}

async function getQueueInfo(baseUrl, username, password, vHost, queueName) {
  const url = `${baseUrl}/api/queues/${vHost}/${queueName}`
  const resp = await fetch(url, {
    method: 'GET',
    headers: {
      Authorization: `Basic ${btoa(`${username}:${password}`)}`,
    },
  })
  if (!resp.ok) {
    throw new Error(`Failed to fetch queue info: ${resp.statusText}`)
  }
  return resp.json()
}

export default {
  async scheduled(event, env, ctx) {
    const {
      min_replicas,
      max_replicas,
      org,
      project,
      container_group_name,
      salad_api_key,
      rabbitmq_url,
      rabbitmq_username,
      rabbitmq_password,
      vhost,
      queue_name,
    } = env

    const queueInfo = await getQueueInfo(rabbitmq_url, rabbitmq_username, rabbitmq_password, vhost, queue_name)
    const numMessages = queueInfo.messages
    console.log(`Queue ${queue_name} has ${numMessages} messages`)

    const desiredReplicas = Math.min(Math.max(parseInt(min_replicas), numMessages), parseInt(max_replicas))

    const containerGroup = await getContainerGroup(org, project, container_group_name, salad_api_key)
    const currentReplicas = containerGroup.replicas
    const currentState = containerGroup.current_state.status
    console.log(
      `Current replicas: ${currentReplicas}, current state: ${currentState}, desired replicas: ${desiredReplicas}`,
    )
    if (currentState === 'stopped' && desiredReplicas > 0) {
      await startContainerGroup(org, project, container_group_name, salad_api_key)
    }
    if (currentState === 'running' && desiredReplicas === 0) {
      await stopContainerGroup(org, project, container_group_name, salad_api_key)
    }
    if (currentReplicas !== desiredReplicas) {
      await setReplicas(org, project, container_group_name, desiredReplicas, salad_api_key)
    }
  },
}

Conclusion

In this guide, we’ve built a simple worker application that processes jobs from a RabbitMQ queue, and we’ve deployed it to SaladCloud. We’ve also implemented autoscaling for our worker using a scheduled Cloudflare worker function, so that it can automatically scale up and down based on the number of messages in the queue.

Explanation

Tutorials

How-to Guides

Storage

Reference

RabbitMQ and SaladCloud

Managing Long-Running Tasks on SaladCloud with RabbitMQ

The Job Queue: RabbitMQ

Relevant Limitations

Setting Up RabbitMQ on CloudAMQP

Deadletter Exchange

Main Job Queue

Cloud Storage: R2

Instrumenting Our Application

Completed Example

Submitting Jobs to the Queue

Running the Job Submitter

Containerize the Worker Application

Deploying the Worker to SaladCloud

Validating That It Works

Autoscaling

Completed Example

Conclusion

​Managing Long-Running Tasks on SaladCloud with RabbitMQ

​The Job Queue: RabbitMQ

​Relevant Limitations

​Setting Up RabbitMQ on CloudAMQP

​Deadletter Exchange

​Main Job Queue

​Cloud Storage: R2

​Instrumenting Our Application

​Completed Example

​Submitting Jobs to the Queue

​Running the Job Submitter

​Containerize the Worker Application

​Deploying the Worker to SaladCloud

​Validating That It Works

​Autoscaling

​Completed Example

​Conclusion

Managing Long-Running Tasks on SaladCloud with RabbitMQ

The Job Queue: RabbitMQ

Relevant Limitations

Setting Up RabbitMQ on CloudAMQP

Deadletter Exchange

Main Job Queue

Cloud Storage: R2

Instrumenting Our Application

Completed Example

Submitting Jobs to the Queue

Running the Job Submitter

Containerize the Worker Application

Deploying the Worker to SaladCloud

Validating That It Works

Autoscaling

Completed Example

Conclusion