What Are Batch Jobs? A Developer's Guide to How They Work

You have 10,000 records to process, and nobody needs the answer this second. Sending them one at a time is slow, and it wastes calls. A batch job is a common way to handle this kind of work.

This guide explains what batch jobs are, how they move from submission to results, and how they compare to real-time processing, cron jobs and queues. It also covers the modern batch API pattern and how to keep large jobs reliable.

What Is a Batch Job?

A batch job is a program that processes a group of items, such as records, files, API requests or URLs, as one unit of work, without a person waiting on each result. You submit the work once, the system runs it in the background, and you collect the results when it finishes.

A "unit of work" is a task the system tracks as a single thing, even if it contains thousands of items. "Asynchronous" means you don't wait for the work to finish before moving on. You get a receipt now and the results later.

This is different from an interactive request, where one input goes in and one answer comes back while you wait. Batch jobs are common in data platforms. Microsoft's Azure big data architectures guide notes that "a big data solution often processes data files by using long-running batch jobs to filter, aggregate, and otherwise prepare data for analysis."

A Simple Batch Job Example

Picture an e-commerce team that tracks competitor prices. Every night, a batch job takes a list of 10,000 product URLs, collects each page, extracts the price and writes the results to a database. By morning, the pricing dashboard is up to date.

Instead of making one call per URL, the job sends the whole list at once. That is what batch web scraping is: one request for many pages, processed in parallel.

Other everyday batch jobs follow the same shape:

• Generating monthly invoices for every customer
• Building end-of-day sales reports
• Running nightly database backups
• Loading data from one system into another (often called ETL: extract, transform, load)

How Do Batch Jobs Work?

Every batch job follows a lifecycle. It starts as a list of inputs, waits its turn, runs, and ends in a final state you can check.

The Batch Job Lifecycle

Most batch systems move a job through the same basic steps:

1. Define inputs: You prepare the list of items to process, such as rows, files or URLs.
2. Submit: You send the job to the system and get back a job ID.
3. Queue: The job waits until compute resources are free.
4. Run: Workers process the items, often many at the same time.
5. Finish: The job ends as succeeded or failed.
6. Collect results: You download or read the output.

Managed platforms name these states explicitly. According to the AWS Batch job states guide, "When you submit a job to an AWS Batch job queue, the job enters the SUBMITTED state." From there it can move through PENDING (if it depends on other jobs), RUNNABLE, STARTING and RUNNING, and ends as SUCCEEDED or FAILED.

Job ID, Polling and Webhooks

The job ID is your receipt. You use it to check progress and fetch results later.

There are two ways to learn that a job is done:

• Polling: Your code asks "is it done yet?" every few seconds or minutes.
• Webhook: The system sends an HTTP request to a URL you provide when the job finishes.

Polling is simple to build, but it makes extra calls. Webhooks avoid that. Olostep's batch endpoint documentation describes both: you can poll the batch status, or pass a webhook URL and receive a POST when the batch finishes, with all items completed or failed.

Batch Jobs vs Real-Time Processing

Real-time processing (often built on stream processing) handles each event as it arrives. Batch processing waits, collects data and processes it together. The trade-off is speed of response versus efficiency.

The table below gives general rules of thumb, not fixed benchmarks.

There is also a middle ground called micro-batching. It groups events into very small batches, every few seconds, to get near-real-time results with batch-style efficiency.

Batch Jobs vs Cron Jobs vs Queues

These three terms often get mixed up because they work together. A cron job decides when work starts. A batch job decides how work is grouped. A queue decides how individual tasks are handed to workers.

In practice, they stack. A cron schedule triggers a batch job, and the batch system may use a queue internally to spread items across workers.

Kubernetes makes this split explicit. A Job runs work to completion, and a CronJob creates Jobs on a schedule. The Kubernetes Jobs documentation also shows that retries are built in: "The .spec.backoffLimit is set by default to 6."

Batch APIs: Batch Jobs as a Service

Batch jobs used to mean scripts running on your own servers. Today, many APIs let you submit a batch, walk away and come back for the results. The provider typically handles queuing, parallel execution and retries.

AI Model Batch APIs

AI providers offer batch endpoints for work that doesn't need an instant answer, such as classifying documents or generating embeddings. The OpenAI Batch API guide lists a "50% cost discount compared to synchronous APIs," and states, "Each batch completes within 24 hours (and often more quickly)."

Batches have size limits too. The Anthropic Message Batches docs state, "A Message Batch is limited to either 100,000 Message requests or 256 MB in size, whichever is reached first." These numbers are set by each vendor and can change, so check the current docs before you plan around them.

Web Data Batch APIs

Collecting web data at scale is a natural batch workload. Instead of sending 10,000 separate scrape requests, you send one batch with 10,000 URLs. The Olostep Batch endpoint is built for this. Olostep's docs say a single batch can hold up to 10k URLs and takes around 5–8 minutes, though new accounts start at 100 items per batch until the limit is lifted.

Each item gets a custom_id, a unique identifier you choose. It lets you match every result back to its input. This matters because some batch APIs, such as OpenAI's and Anthropic's, can return results in a different order than you submitted them.

import time
import requests

API = "https://api.olostep.com/v1/batches"
HEADERS = {"Authorization": "Bearer <YOUR_API_KEY>"}

urls = ["https://example.com/product/1", "https://example.com/product/2"]
items = [{"custom_id": f"product-{i}", "url": u} for i, u in enumerate(urls)]

# 1. Submit the batch and keep the job ID
batch = requests.post(API, headers=HEADERS, json={"items": items}).json()
batch_id = batch["id"]

# 2. Poll until the batch finishes (or pass a webhook instead)
while requests.get(f"{API}/{batch_id}", headers=HEADERS).json()["status"] != "completed":
    time.sleep(10)

# 3. List item outcomes and match them to inputs by custom_id
results = requests.get(f"{API}/{batch_id}/items", headers=HEADERS).json()

If you don't have a URL list yet, you can discover pages first with the Olostep Crawl endpoint, filter the URLs you need, and then send them as a batch.

Why Batch Jobs Fail (and How to Make Them Reliable)

A batch job can report "completed" while some of its items failed. The real unit of success is the item, not the job. If you only check the job status, missing data can go unnoticed until a report or model breaks downstream.

Olostep's guide to making batch scraping reliable frames this as an orchestration problem: retries create duplicates, and partial runs hide missing pages.

Common Failure Modes

• Silent failure: The job never ran, or ran and did nothing, and no one was alerted.
• Partial failure: Most items succeeded, but some failed or timed out.
• Duplicate work: A retry or restart processed the same items twice.
• Out-of-order results: Outputs come back in a different order than inputs, so position-based matching breaks.
• Rate limits: The target service returns HTTP 429 ("too many requests") and rejects work.
• Duplicate submissions: Submitting the same job twice can create two jobs. The Gemini Batch API docs warn, "The creation of a batch job is not idempotent."

A Reliability Checklist

Use this checklist before you trust a batch job in production:

• Give every item a stable ID: A deterministic ID per input lets you detect overlap and match results.
• Make work idempotent: Idempotent means running the same item twice gives the same result as running it once. This makes retries safe.
• Retry with backoff and limits: Backoff means waiting longer between each retry. Set a cap. In the AWS Batch job retries guide, "By default, each job is given one attempt," and you can configure between 1 and 10 attempts.
• Respect rate limits: Slow down when you see 429 errors. Olostep's explainer on rate limits and API quotas explains how exponential backoff works.
• Reconcile outputs against inputs: Compare the list of finished items with the list you submitted, so gaps are visible.
• Retry only what is missing: Re-run failed items by ID instead of re-running the whole batch.
• Alert on a missing "done" signal: If a job doesn't report completion on time, treat that as a failure.

How to Stop a Batch Job

To stop a batch job, use the platform's cancel command or API with the job ID. The exact command depends on where the job runs:

• AWS Batch: Cancel or terminate the job by its ID. The AWS retries guide linked above also notes that "Jobs that are cancelled or terminated aren't retried."
• Kubernetes: Delete the Job (for example, kubectl delete job <name>), or set a deadline so it stops automatically.
• AI batch APIs: Call the provider's cancel endpoint with the batch ID.

Cancellation is often not instant. The OpenAI guide says a batch stays in a cancelling state "until in-flight requests are complete (up to 10 minutes)." After cancelling, check which items finished so you only re-run the rest.

When Should You Use a Batch Job?

Batch jobs fit work where volume matters more than instant answers. Use this quick test:

• Use a batch job when: results can wait minutes or hours, you have many similar items, cost per item matters, and the work repeats on a schedule.
• Use real-time processing when: a user or system is waiting on the answer right now, or each event needs an immediate reaction.

If your batch workload involves web data, start with a small batch of URLs, check item-level outcomes, and scale up once your reconciliation step is in place.

FAQ

What Is a Batch Job Example?

A common example is a nightly job that generates invoices for every customer. Another is collecting thousands of product pages in one request to update a pricing database.

What Is the Difference Between a Cron Job and a Batch Job?

A cron job is a timer that decides when work starts. A batch job is the grouped work itself, and cron is often used to trigger it.

How Long Does a Batch Job Take?

It depends on the job size and the platform. For example, OpenAI says its batches complete within 24 hours, while Olostep's docs say a web data batch takes around 5–8 minutes.

Can a Batch Job Run Twice?

Yes, retries, restarts or duplicate submissions can repeat work. Designing each item to be idempotent makes repeated runs safe.

Is Batch Processing Cheaper Than Real-Time Processing?

Often, yes, because work is grouped and run when resources are free. Some AI providers, such as OpenAI, price batch requests at a 50% discount.