Before we meet the Google Cloud service, we need to understand the kind of work it is built for.

Two kinds of programs

Most programs you use every day are interactive. You tap a button and get an answer in a blink. A website, a chat app, and a map are all interactive. Somebody is waiting for each answer.

A batch program is different. Nobody is staring at the screen. You hand it a large amount of work, it chews through the work for minutes or hours, and it leaves the results somewhere for you to pick up later. Examples include turning a movie scene into finished video frames, scanning millions of photos, analyzing DNA, running a weather forecast, and processing a whole month of bank transactions.

What is a “job”?

In batch computing, one complete piece of work you want done is called a job. A job usually breaks into many small pieces that can run at the same time. Each small piece is a task. Running many tasks together is called parallel processing, and it is how huge jobs finish quickly.

So what is Cloud Batch?

Batch is a Google Cloud service that lets you describe a job, then it does the boring heavy lifting for you. It creates virtual machines (VMs) on Compute Engine, starts your program on them, retries things that fail, collects the logs, and deletes the machines when the job ends. You never log in to a server to set up a cluster.

Every good tool is born from a pain. Here is the pain that Batch removes.

The old way: build your own cluster

Before managed batch services, a team that needed to process a huge workload had to build a mini data center. They installed a job scheduler, set up a head node, created a pool of worker machines, wrote scripts to add and remove machines, and watched everything day and night. If one worker died halfway, somebody had to notice and restart the work.

This is slow to build, hard to keep healthy, and wasteful. Machines sat idle between jobs but you still paid for them. Or the cluster was too small at the critical moment and work waited in line for days.

The Batch way: describe it, submit it, forget it

With Batch, you write down what you want: what program to run, how many copies, and how big the machines should be. You submit the job. Google Cloud finds capacity, builds the machines, runs the work, and shuts everything down. The machines exist only while your job needs them.

Problems Batch solves

Learn these five words and the rest of the service will feel easy.

Job

A job is the top-level thing you submit. It has a name, a location (a Google Cloud region), and a description of the work. Once submitted, it moves through states such as queued, scheduled, running, succeeded, or failed.

Task group

A task group is a set of identical tasks inside a job. All tasks in one group run the same instructions and use the same kind of machine. You set how many tasks the group has. A job can hold several task groups, but a simple job has just one.

Task

A task is the smallest unit of work. Each task gets its own number, starting at zero. Your program can read that number, so task 0 can handle the first slice of data, task 1 the second slice, and so on.

Runnable

A runnable is the actual instruction a task performs. Each task runs a list of runnables in order. There are three main kinds:

• Script runnable: a block of shell commands, or a script file, that runs directly on the VM.
• Container runnable: a container image that is pulled and run. This is the best choice when you want your program and all its libraries packaged together.
• Barrier runnable: a “wait here” marker. Every task must reach it before any task continues. It is used when tasks must stay in step with each other.

Environment variables

Batch gives every task a few helpful built-in values, such as the task’s own index number, the total number of tasks, and which retry attempt this is. Your program uses them to know “which slice is mine?”. You can also add your own values, and you can load secrets from Secret Manager so passwords never sit in plain text.

Let us look at the big pieces and how they talk to each other.

The main pieces

When you use Batch, several parts of Google Cloud work together. Here they are in plain words.

• You and your tools: the Google Cloud console, the gcloud command line, the API, client libraries, or tools such as Terraform. They all send the same job description.
• The Batch service: the brain. It accepts your job, keeps a queue, decides when and where it can run, and tracks its state.
• Compute Engine: the muscle. Batch asks Compute Engine to create the VMs that run your tasks. Behind the scenes it uses managed groups of VMs.
• The Batch agent: a small helper program that runs on every VM. It starts your runnables, watches them, and reports progress back to the service.
• Storage: places to read input and write results, such as Cloud Storage buckets, shared network file systems, and disks.
• Cloud Logging and Pub/Sub: where your program’s output goes, and where state-change messages can be sent.

flowchart LR
  U["You: Console, gcloud, API, Terraform"] --> B["Batch Service: queue and scheduler"]
  B --> CE["Compute Engine: creates VMs"]
  CE --> VM1["VM 1 with Batch Agent"]
  CE --> VM2["VM 2 with Batch Agent"]
  CE --> VM3["VM 3 with Batch Agent"]
  VM1 --> ST["Storage: Cloud Storage, Filestore, Disks"]
  VM2 --> ST
  VM3 --> ST
  VM1 --> LG["Cloud Logging"]
  VM2 --> LG
  VM3 --> LG
  B --> PS["Pub/Sub notifications"]
    

Read the picture from left to right. You submit a job. The Batch service queues it and asks Compute Engine for machines. Each machine runs the Batch agent, which starts your tasks. Tasks read and write storage, send logs to Cloud Logging, and the service can announce progress through Pub/Sub.

Regions matter

Batch is a regional service. You choose where the job runs. Pick a region close to your data, because moving huge files across the world is slow and can cost money. You can also limit which zones inside the region may be used.

Follow one job from the moment you press submit to the moment it finishes.

stateDiagram-v2
  [*] --> QUEUED
  QUEUED --> SCHEDULED
  SCHEDULED --> RUNNING
  RUNNING --> SUCCEEDED
  RUNNING --> FAILED
  SUCCEEDED --> [*]
  FAILED --> [*]
    

The states in Figure 2 are exactly what you will see in the console. Most waiting time happens in the first two states. Most of your money is spent in the running state.

What happens inside one task?

On each VM, the agent walks through the list of runnables in order. Normally, if one runnable fails, the task stops and is marked failed. You can change this. A runnable can be marked to run no matter what happened before, which is perfect for cleanup steps. Another can be marked to run in the background, such as a helper program that must stay alive while the main work happens. You can also say that a certain failure should simply be ignored.

Compute is useless without data. Here is how data gets in and out.

The rule of the temporary machine

The VMs that Batch creates are temporary. When the job ends, they are deleted, and anything stored only on their own disks disappears. So the golden rule is: read your input from permanent storage and write your results to permanent storage.

Storage choices

A typical data path

flowchart TD
  IN["Input bucket in Cloud Storage"] --> T0["Task 0 reads slice 0"]
  IN --> T1["Task 1 reads slice 1"]
  IN --> T2["Task 2 reads slice 2"]
  T0 --> OUT["Output bucket in Cloud Storage"]
  T1 --> OUT
  T2 --> OUT
  OUT --> NEXT["Next step: BigQuery, dashboard, or another job"]
    

Because each task knows its own number, the split is simple. With 1,000 input files and 100 tasks, task 0 takes files 0 to 9, task 1 takes files 10 to 19, and so on. No two tasks fight over the same file.

Different jobs need different machines. Batch lets you pick.

Machine types

A machine type is the size and shape of a VM: how many CPUs, how much memory. A job that handles lots of numbers may want many CPUs. A job that loads a giant file into memory wants lots of RAM. You choose a machine type per task group, or you let Batch pick from a list of allowed types.

GPUs and special hardware

A GPU is a chip with thousands of tiny workers that is very good at math on large blocks of numbers. Machine learning training, video processing, and scientific simulation often use GPUs. Batch can attach GPUs to the VMs it creates, so you only pay for them while the job runs.

Standard versus Spot

Google Cloud sells spare computer capacity at a big discount. These are called Spot VMs. The catch is that Google may take them back with short notice when it needs the capacity. Standard VMs cost more but stay until your job is done.

Where tasks land on machines

You can control how many tasks share one VM. If each task needs one CPU and your VM has eight, you can run up to eight tasks at once on that VM. You can also set a limit on how many tasks run at the same time across the whole job, which protects other systems your tasks may be calling, such as a database.

Images and networking

The VM needs an operating system image. Batch offers images prepared for batch work, and you can use your own custom image if your software is complicated to install. For networking, you can attach the VMs to your own private network. If you do not want the VMs to have public internet addresses, you can switch that off and rely on private access to Google services.

How to make a job faster, and how to survive things going wrong.

Two shapes of parallel work

Embarrassingly parallel work is the easy kind. Every task is independent and never talks to the others. Rendering frames of a film is a classic case, because frame 500 does not need to know about frame 501. Batch is excellent at this.

Tightly coupled work is the harder kind. Tasks must exchange messages constantly while they run, as in weather simulation or physics modeling. These often use a communication standard called MPI. For this you can ask Batch to place all required hosts together, and you can use a barrier so every task waits for the others at key points.

Retries and failure rules

Machines break, networks hiccup, and Spot VMs get reclaimed. Batch lets you set a maximum retry count for each task, so a failed task is simply started again. You can also write lifecycle policies. A lifecycle policy says: “if the task ends with this exit code, take this action.” For example, an exit code that means “temporary network error” can trigger a retry, while a code that means “bad input data” can fail the task immediately so you do not waste money retrying something that will never work.

Time limits

You can set a maximum run time for a task. If a program gets stuck in an endless loop, the time limit stops it before it burns through your budget.

flowchart TD
  S["Task starts"] --> R["Runs the runnables"]
  R --> OK{"Finished well?"}
  OK -- "Yes" --> D["Task succeeded"]
  OK -- "No" --> P{"Lifecycle policy and retries left?"}
  P -- "Retry allowed" --> S
  P -- "Not allowed or no retries left" --> F["Task failed"]
    

Powerful machines running on your behalf must be locked down properly.

Who can do what: IAM

Google Cloud uses IAM (Identity and Access Management) to decide who may do what. For Batch there are roles for people who create jobs, people who only view jobs, and administrators. Give each person the smallest role that lets them do their work. This idea is called least privilege.

The job’s own identity: service accounts

The VMs run as a service account, which is an identity for programs rather than people. Your tasks use this identity to read buckets, call APIs, or write to a database. Create a dedicated service account for each kind of job and give it only the permissions that job needs, never broad owner-level power. The Batch agent on the VM also needs a role that lets it report progress back to the service.

Network safety

• Run VMs inside your own VPC network so firewall rules apply.
• Turn off external IP addresses when the tasks only need Google services.
• Allow only the network paths that the job truly needs.

Secrets and data protection

Never put passwords or API keys inside your job description or container image. Store them in Secret Manager and let Batch hand them to your task as environment variables at run time. Data at rest in Google Cloud is encrypted by default, and you can use your own encryption keys if your rules require it. Use trusted container images, and scan them for known weaknesses before you run them at scale.

A job that nobody watches can fail quietly. Here is how to keep an eye on it.

Logs

Anything your program prints to its normal output can be sent to Cloud Logging. You turn this on with a logs setting in the job. Each log line is labeled with the job and the task, so you can filter to see only task 742 when it misbehaves. Without this setting, you may have no way to see why a task died, so switch it on for every job.

Job and task states

The console and the API show the state of the job and of every task. You can see how many tasks are pending, running, succeeded, and failed. This gives you a quick view of progress, such as “812 of 1,000 tasks done”.

Metrics and alerts

The VMs report normal Compute Engine metrics, such as CPU use and memory. If CPU is almost zero, your tasks may be waiting on slow storage. If memory is maxed out, the machine type is too small. You can build dashboards and alerts in Cloud Monitoring around these numbers.

Notifications with Pub/Sub

You can ask Batch to publish a message to a Pub/Sub topic whenever the job or a task changes state. Another program can listen to these messages, then send a chat alert, start the next job, or update a status page. This turns Batch into a building block of larger automated pipelines.

No tool is perfect. Here is an honest look.

How does it compare to its neighbors?

The main trade-off is control versus convenience. Batch gives you more control over machines than Cloud Run jobs, and less operational work than running your own Kubernetes cluster.

Proven shapes to copy, and traps to avoid.

Good patterns

flowchart LR
  SCH["Cloud Scheduler: nightly trigger"] --> WF["Workflows: orchestrator"]
  WF --> J1["Batch Job A: process data in many tasks"]
  J1 --> PS["Pub/Sub: job finished message"]
  PS --> WF
  WF --> J2["Batch Job B: merge results"]
  J2 --> BQ["BigQuery: final tables"]
    

Anti-patterns

A checklist you can follow on day one.

Best practices

• Test small first. Run a job with two or three tasks before you launch thousands.
• Right-size the machines. Watch CPU and memory in a test run, then choose the smallest type that keeps tasks comfortable.
• Use containers for repeatable environments, and pin the image to a specific version instead of a floating “latest” label.
• Keep tasks short and idempotent so retries and Spot VMs are harmless.
• Set time limits and retry counts on every task.
• Limit parallelism when your tasks call a database or an outside service that cannot handle a flood.
• Turn on Cloud Logging for every job.
• Use labels on jobs so your bill can be split by team or project.
• Use a dedicated service account with minimum permissions.
• Check quotas early for CPUs, GPUs, and IP addresses in your chosen region.

Common mistakes

Where batch computing shows up in real industries.

The common thread is clear: the work is large, can be divided, does not need an instant answer, and needs a lot of power for a short time.

You started this guide with a bakery that needed 500 ovens for one night. You now know that Cloud Batch is the Google Cloud version of that idea: rent exactly the computing power you need, use it hard, and give it back.