Containers – Serverless Jobs

Containers – Serverless Jobs

Use Terraform to provision and manage serverless container jobs on Verda. Serverless jobs are designed for finite, on-demand workloads that run to completion and automatically release resources when finished.

Unlike long-running containers or services, serverless jobs do not stay active after execution. This makes them ideal for batch processing, scheduled tasks, and one-off compute workloads where you only pay for execution time.

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Typical use cases

Serverless jobs are well suited for:

• Batch data processing

• ETL and data transformation pipelines

• Model evaluation or offline inference

• Scheduled or event-driven tasks

• One-off administrative or maintenance jobs

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What this page covers

• Creating serverless jobs with Terraform

• Configuring container images and commands

• Passing environment variables and parameters

• Understanding job execution behavior

• Importing existing jobs into Terraform state

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Basic example

terraform {
  required_providers {
    verda = {
      source  = "verda-cloud/verda"
      version = "~> 1.0"
    }
  }
}

provider "verda" {}

resource "verda_serverless_job" "example" {
  name  = "daily-etl-job"
  image = "ghcr.io/example/data-pipeline:1.0.0"

  command = [
    "python",
    "run_etl.py"
  ]

  cpu    = 2
  memory = 4096

  env = {
    ENVIRONMENT = "production"
    LOG_LEVEL   = "info"
  }
}

output "job_id" {
  value = verda_serverless_job.example.id
}

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Key concepts

Job identity

The name field is a human-readable identifier for the job.

Always reference jobs using the Terraform-managed id when integrating with automation, monitoring, or external systems.

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Container image and command

Serverless jobs are created from OCI-compatible container images.

Best practices:

• Use immutable image tags (avoid latest)

• Ensure the container exits cleanly when the job completes

• Define job behavior explicitly using command, rather than relying on image defaults

If your image registry requires authentication, see Containers – Registry Credentials.

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Resource configuration

Serverless jobs allow you to specify resource limits such as:

• CPU allocation

• Memory limits

• (Optional) GPU resources, if supported

Choose resource values carefully to balance performance and cost. Over-allocating resources can increase execution cost without improving runtime.

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Environment variables

Environment variables allow you to parameterize job behavior without rebuilding images.

Example:

env = {
  DATASET_PATH = "/data/input"
  MODE         = "batch"
}

For sensitive values, use Terraform variables or secret management rather than hardcoding values.

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Job execution behavior

Serverless jobs:

• Start when triggered by the platform or API

• Run until the container process exits

• Automatically stop and release resources upon completion

Jobs are not restarted automatically unless explicitly re-triggered. Design job logic to be idempotent whenever possible.

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Updating jobs safely

Most changes to a serverless job configuration (image, command, resources, or environment variables) will cause Terraform to recreate the job definition.

Best practices:

• Pin image versions explicitly

• Test changes in non-production environments

• Review terraform plan carefully before applying

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Import an existing job

If a serverless job already exists in Verda, you can import it into Terraform:

terraform import verda_serverless_job.example <job-id>

Then run:

terraform plan

Update your Terraform configuration until the plan shows no changes.

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Troubleshooting

Job fails immediately Verify the container image exists, the command is correct, and required environment variables are set.

Unexpected job recreation Changes to image tags, commands, CPU, memory, or environment variables typically force replacement.

Image pull errors Ensure registry credentials are configured correctly (see Containers – Registry Credentials).