georgeguimaraes/elixir-agent-tools/oban

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Build and debug durable background jobs with Oban and Oban Pro. Use for workers, job argument serialization, retries, scheduled or recurring jobs, uniqueness, batches, and workflows. Use otp for in-memory tasks and process supervision.

Install

npx vskill@latest install georgeguimaraes/elixir-agent-tools/oban

SKILL.md

Oban

Handle durable jobs, serialization, failures, and workflow composition with Oban.


Part 1: Oban (Non-Pro)

The Iron Law: JSON Serialization

JOB ARGS ARE JSON. ATOMS BECOME STRINGS.

This single fact causes most Oban debugging headaches.

elixir
# Creating - atom keys are fine
MyWorker.new(%{user_id: 123})
# Processing - must use string keys (JSON converted atoms to strings)
def perform(%Oban.Job{args: %{"user_id" => user_id}}) do
# ...
end

Error Handling: Let It Crash

Don't catch errors in Oban jobs. Let them bubble up to Oban for proper handling.

Why?
  1. Automatic logging: Oban logs the full error with stacktrace
  2. Automatic retries: Jobs retry with exponential backoff
  3. Visibility: Failed jobs appear in Oban Web dashboard
  4. Consistency: Error states are tracked in the database
Anti-Pattern
elixir
# Bad: Swallowing errors
def perform(%Oban.Job{} = job) do
case do_work(job.args) do
{:ok, result} -> {:ok, result}
{:error, reason} ->
Logger.error("Failed: #{reason}")
{:ok, :failed} # Silently marks as complete!
end
end
Correct Pattern
elixir
# Good: Let errors propagate
def perform(%Oban.Job{} = job) do
result = do_work!(job.args) # Raises on failure
{:ok, result}
end
# Or return error tuple - Oban treats as failure
def perform(%Oban.Job{} = job) do
case do_work(job.args) do
{:ok, result} -> {:ok, result}
{:error, reason} -> {:error, reason} # Oban will retry
end
end
When to Catch Errors

Only catch errors when you need custom retry logic or want to mark a job as permanently failed:

elixir
def perform(%Oban.Job{} = job) do
case external_api_call(job.args) do
{:ok, result} -> {:ok, result}
{:error, :not_found} -> {:cancel, :resource_not_found} # Don't retry
{:error, :rate_limited} -> {:snooze, 60} # Retry in 60 seconds
{:error, _} -> {:error, :will_retry} # Normal retry
end
end

Snoozing for Polling

Use {:snooze, seconds} for polling external state instead of manual retry logic:

elixir
def perform(%Oban.Job{} = job) do
if external_thing_finished?(job.args) do
{:ok, :done}
else
{:snooze, 5} # Check again in 5 seconds
end
end

Simple Job Chaining

For simple sequential chains (JobA → JobB → JobC), have each job enqueue the next:

elixir
def perform(%Oban.Job{} = job) do
result = do_work(job.args)
# Enqueue next job on success
NextWorker.new(%{data: result}) |> Oban.insert()
{:ok, result}
end

Don't reach for Oban Pro Workflows for linear chains.

Unique Jobs

Prevent duplicate jobs with the unique option:

elixir
use Oban.Worker,
queue: :default,
unique: [period: 60] # Only one job with same args per 60 seconds
# Or scope uniqueness to specific fields
unique: [period: 300, keys: [:user_id]]

Gotcha: Uniqueness is checked on insert, not execution. Two identical jobs inserted 61 seconds apart will both run.

High Throughput: Chunking

For millions of records, chunk work into batches rather than one job per item:

elixir
# Bad: One job per contact (millions of jobs = database strain)
Enum.each(contacts, &ContactWorker.new(%{id: &1.id}) |> Oban.insert())
# Good: Chunk into batches
contacts
|> Enum.chunk_every(100)
|> Enum.each(&BatchWorker.new(%{contact_ids: Enum.map(&1, fn c -> c.id end)}) |> Oban.insert())

Use bulk inserts without uniqueness constraints for maximum throughput.


Part 2: Oban Pro

Cascade Context: Erlang Term Serialization

Unlike regular job args, cascade context preserves atoms:

elixir
# Creating - atom keys
Workflow.put_context(%{score_run_id: id})
# Processing - atom keys still work!
def my_cascade(%{score_run_id: id}) do
# ...
end
# Dot notation works too
def later_step(context) do
context.score_run_id
context.previous_result
end
Serialization Summary
CreatingProcessing
Regular jobsatoms okstrings only
Cascade contextatoms okatoms ok

When to Use Workflows

Reserve Workflows for:

  • Complex dependency graphs (not just linear chains)
  • Fan-out/fan-in patterns
  • When you need recorded values across steps
  • Conditional branching based on runtime state

Don't use Workflows for simple A → B → C chains.

Workflow Composition with Graft

When you need a parent workflow to wait for a sub-workflow to complete before continuing, use add_graft instead of add_workflow.

Key Differences
MethodSub-workflow completes before deps run?Output accessible?
add_workflowNo - just inserts jobsNo
add_graftYes - waits for all jobsYes, via recorded values
Pattern: Composing Independent Concerns

Don't couple unrelated concerns (e.g., notifications) to domain-specific workflows (e.g., scoring). Instead, create a higher-level orchestrator:

elixir
# Bad: Notification logic buried in AggregateScores
defmodule AggregateScores do
def workflow(score_run_id) do
Workflow.new()
|> Workflow.add(:aggregate, AggregateJob.new(...))
|> Workflow.add(:send_notification, SendEmail.new(...), deps: :aggregate) # Wrong place!
end
end
# Good: Higher-level workflow composes scoring + notification
defmodule FullRunWithNotifications do
def workflow(site_url, opts) do
notification_opts = build_notification_opts(opts)
Workflow.new()
|> Workflow.put_context(%{notification_opts: notification_opts})
|> Workflow.add_graft(:scoring, &graft_full_run/1)
|> Workflow.add_cascade(:send_notification, &send_notification/1, deps: :scoring)
end
defp graft_full_run(context) do
# Sub-workflow doesn't know about notifications
FullRun.workflow(context.site_url, context.opts)
|> Workflow.apply_graft()
|> Oban.insert_all()
end
end
Recording Values for Dependent Steps

For a grafted workflow's output to be available to dependent steps, the final job must use recorded: true:

elixir
defmodule FinalJob do
use Oban.Pro.Worker, queue: :default, recorded: true
def perform(%Oban.Job{} = job) do
# Return value becomes available in context
{:ok, %{score_run_id: score_run_id, composite_score: score}}
end
end

Dynamic Workflow Appending

Add jobs to a running workflow with Workflow.append/2:

elixir
def perform(%Oban.Job{} = job) do
if needs_extra_step?(job.args) do
job
|> Workflow.append()
|> Workflow.add(:extra, ExtraWorker.new(%{}), deps: [:current_step])
|> Oban.insert_all()
end
{:ok, :done}
end

Caveat: Cannot override context or add dependencies to already-running jobs. For complex dynamic scenarios, check external state in the job itself.

Fan-Out/Fan-In with Batches

To run a final job after multiple paginated workflows complete, use Batch callbacks:

elixir
# Wrap workflows in a shared batch
batch_id = "import-#{import_id}"
pages
|> Enum.each(fn page ->
PageWorkflow.workflow(page)
|> Batch.from_workflow(batch_id: batch_id)
|> Oban.insert_all()
end)
# Add completion callback
Batch.new(batch_id: batch_id)
|> Batch.add_callback(:completed, CompletionWorker)
|> Oban.insert()

Tip: Include pagination workers in the batch to prevent premature completion.

Testing Workflows

Don't use inline testing mode - workflows need database interaction.

elixir
# Use run_workflow/1 for integration tests
assert %{completed: 3} =
Workflow.new()
|> Workflow.add(:a, WorkerA.new(%{}))
|> Workflow.add(:b, WorkerB.new(%{}), deps: [:a])
|> Workflow.add(:c, WorkerC.new(%{}), deps: [:b])
|> run_workflow()

For testing recorded values between workers, insert predecessor jobs with pre-filled metadata.


Red Flags - STOP and Reconsider

Non-Pro:

  • Pattern matching on atom keys in perform/1
  • Catching all errors and returning {:ok, _}
  • Wrapping job logic in try/rescue
  • Creating one job per item when processing millions of records

Pro:

  • Using add_workflow when you need to wait for completion
  • Coupling notifications/emails to domain workflows
  • Not using recorded: true when you need output from grafted workflows
  • Using Workflows for simple linear job chains
  • Testing workflows with inline mode

Any of these? Re-read the serialization rules.

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