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To trigger processing when a file is uploaded to Cloudflare R2, connect four pieces: an R2 source bucket, an R2 event-notification rule, a Cloudflare Queue, and a consumer. The notification rule sends matching object changes to the queue; a Worker then receives message batches and performs the downstream task, such as writing an audit record to another bucket. If your processing system runs elsewhere, an external service can pull and acknowledge queue messages over HTTP instead.
This design separates storage from processing. R2 records the object change, Queues provides the handoff, and your consumer owns the business logic. The steps below build an upload-log pipeline and show how to adapt it for filtering, deletion, copies, external consumers, and higher event rates.
What the R2-to-Queue pipeline does
An R2 event notification is a message generated when data in a bucket changes. You select the event type and, optionally, key-prefix and key-suffix filters. Cloudflare routes each matching notification to a Queue. The queue is not the transformation itself: it buffers the handoff, while a consumer decides what to parse, store, call, or discard.
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- A client uploads, copies, or completes a multipart upload in the source bucket.
- An
object-createnotification rule matches the change. - Cloudflare places an event message on the selected Queue.
- A consumer Worker receives a batch and runs your processing code.
- The Worker writes a result, calls another service, or records an audit event.
For a simple demonstration, the consumer serializes the received batch into a second R2 bucket. Replace that write with thumbnail generation, metadata indexing, validation, a webhook, or another application-specific action.
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Components and decisions to make first
Source bucket
This is where uploaded objects arrive. Event rules belong to a specific bucket, so decide whether one bucket should emit all events or whether separate buckets should represent separate workflows.
Queue
The Queue is the handoff between the R2 change and processing. A queue can feed a Worker consumer or be read by an external HTTP pull consumer. Choose a queue layout that matches your event volume and the ownership of the processing code.
Consumer Worker
A push consumer Worker is managed within Cloudflare. Cloudflare delivers message batches to the Worker’s queue handler, where your code parses the payload and performs the follow-on task.
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Destination bucket or service
The tutorial-style pipeline uses a second R2 bucket as a log sink. A destination binding gives the Worker a runtime handle for that bucket. The R2 Workers API is accessed through the binding and has semantic differences from the S3 API, so do not assume every S3 operation behaves identically.
Event scope
Select events deliberately. object-create covers PutObject, CopyObject, and CompleteMultipartUpload. object-delete covers DeleteObject and deletion caused by an R2 lifecycle rule. Prefix and suffix filters restrict notifications to matching object keys.
Build the upload-log pipeline
1. Create the resources
Create a source R2 bucket for uploaded files, a second R2 bucket for log output, and a Queue for event messages. Keep the names available for your Worker configuration. You can create these resources with Cloudflare’s current dashboard or Wrangler workflows; the exact command set can change, so use the syntax shown by the version of Wrangler installed in your project.
Give the Worker permission to consume the Queue and to write to the destination bucket. The notification rule will be attached to the source bucket after the consumer is deployed.
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2. Create a Worker project
Initialize a Worker project with Wrangler and place the queue handler in its entry module. The handler receives a batch object. In the tutorial pattern, the entire batch.messages array is serialized and written as one JSON object to the log bucket.
3. Add the queue-consumer and R2 bindings
Your Wrangler configuration needs two distinct bindings:
- A queue consumer binding registers this Worker as the consumer for the event Queue.
- An R2 bucket binding exposes the destination bucket to the Worker at runtime.
The following is a representative configuration based on the tutorial’s example values. The batch size of 100 and five-second timeout are example settings, not universal recommendations; tune them after measuring your handler’s work and execution limits.
name = "r2-upload-log"
main = "src/index.js"
compatibility_date = "2026-10-16"
[[queues.consumers]]
queue = "r2-upload-events"
max_batch_size = 100
max_batch_timeout = 5
[[r2_buckets]]
binding = "LOG_BUCKET"
bucket_name = "r2-upload-logs"Use your actual Worker name, Queue name, and destination bucket name. The compatibility date in this sample is an example for a project created on October 16, 2026; choose a date appropriate for your deployment rather than copying it blindly.
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This handler writes each delivered batch to a uniquely named object in the log bucket:
export default {
async queue(batch, env) {
const key = `events/${Date.now()}-${crypto.randomUUID()}.json`;
await env.LOG_BUCKET.put(
key,
JSON.stringify(batch.messages),
{
httpMetadata: {
contentType: "application/json"
}
}
);
}
};The tutorial’s essential operation is JSON.stringify(batch.messages) followed by a write to the destination R2 binding. In a production handler, parse and validate each message before acting on it, and make downstream writes safe to repeat if your design requires that property. The documentation covered here does not establish ordering, exactly-once delivery, or a particular retry policy, so do not promise any of those behaviors without checking the current Queue documentation.
5. Deploy the Worker
Deploy with your project’s Wrangler command. Confirm that the deployment reports both the queue consumer and the R2 bucket binding. A successful Worker deployment alone does not create an R2 notification rule; the source bucket still needs an explicit rule.
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6. Create the object-create notification
Attach an upload notification to the source bucket with Wrangler:
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7. Upload a test object
Upload a file whose key matches the rule. The tutorial example reports that a log object appears in the sink bucket a few seconds after a test upload. Treat that as the tutorial’s observed example, not a general latency guarantee. Inspect the destination bucket and verify that the stored JSON contains the message batch you expect.
8. Add only the rules you need
Before adding more notifications, map each workflow to a non-overlapping key space. Cloudflare rejects overlapping or conflicting rules that could generate more than one notification for the same event. For example, a broad create rule for every key and a second create rule for the images/ prefix can conflict. Use disjoint prefixes or suffixes, or route all matching events to one consumer that branches in code.
Understand the notification payload
Write handlers against the documented shape rather than assuming every event has the same fields. A message includes the account identifier, an action name, the bucket name, an object record, and the event time. Object-create records can include object size and an eTag. Delete records do not include size or eTag. A copy event can include a copySource record.
That means a parser should treat event-dependent properties as optional. For example, a delete audit record can store the key, action, bucket, and event time without attempting to read a size field that is not present. A copy-aware workflow can inspect copySource when it exists and ignore it for ordinary uploads.
function normalizeEvent(message) {
const event = message.body;
const object = event.object || {};
return {
account: event.account,
action: event.action,
bucket: event.bucket,
key: object.key,
eventTime: event.eventTime,
size: object.size,
eTag: object.eTag,
copySource: event.copySource
};
}Keep this normalization tolerant: missing values should remain absent or undefined rather than being replaced with invented defaults. If your consumer receives a batch containing different event types, branch on the action before applying create-only logic.
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Choose push consumption or external HTTP pull
| Model | How it works | Best fit | Control point | Processing location |
|---|---|---|---|---|
| Consumer Worker (push) | The Queue delivers message batches to a Worker consumer. | Processing that naturally belongs in Cloudflare Workers and benefits from a managed event consumer. | Cloudflare invokes the consumer according to the Queue consumer configuration. | A Cloudflare Worker. |
| External HTTP pull consumer | An external system explicitly requests messages and acknowledges receipt. | Existing infrastructure that must decide when it is ready to fetch and how quickly it consumes. | The external system controls when it pulls and acknowledges. | Your external service or application. |
Neither model should be declared universally faster, cheaper, or more reliable on the documented evidence. Select the Worker model when keeping the processing code in Cloudflare simplifies deployment. Select HTTP pull when an existing service needs explicit control over its consumption schedule or already owns the integration logic.
If you choose HTTP pull, verify the current endpoint, authentication, batch, acknowledgement, and failure semantics in the Queue documentation before implementing a production client. The facts established here confirm that external pull is supported, but do not define a universal retry or ordering contract.
Plan filters without creating duplicate rules
Prefix filters
Use a prefix when the object key namespace identifies the workflow, such as incoming/ or media/. A prefix filter matches keys beginning with that string.
Suffix filters
Use a suffix when the file type identifies the workflow, such as .json or .parquet. A suffix filter matches keys ending with that string.
Combined planning
Write down every rule as a set of keys and check whether any one object could match two rules for the same event type. Do not rely on the consumer to deduplicate a configuration that Cloudflare will reject. If several file classes need different treatment but cannot be partitioned cleanly, use one notification rule and dispatch inside the Worker.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Throughput, limits, and scaling
Cloudflare currently documents up to 100 event-notification rules per R2 bucket and a per-Queue throughput of 5,000 messages per second. These are volatile service limits; verify them against the current documentation before committing to an architecture.
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For workloads above the documented 5,000-message-per-second Queue throughput, Cloudflare recommends distributing notification rules across multiple Queues. That requires a deliberate partitioning scheme, such as separate non-overlapping prefixes, so that each event has one intended destination. Splitting queues without partitioning rules can create conflicting notifications or make operations harder to reason about.
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Throughput is not the same as end-to-end processing capacity. Your Worker’s batch size, timeout, downstream API limits, object size, and write rate can become the practical bottleneck. The tutorial’s batch size of 100 and timeout of five seconds are starting examples only; they do not establish a recommended setting for every workload.
Production design cautions
Do not assume delivery semantics
The material for this implementation does not establish delivery ordering, exactly-once behavior, or a specific retry policy. Before making guarantees to users, read the current Queue delivery documentation. If a downstream action must not happen twice, define an idempotency strategy based on an event identifier or a deterministic object key, and confirm which identifiers are actually present in the payload you receive.
Make event-dependent fields optional
Create, delete, and copy events do not carry identical object metadata. Validate the action first, then read size, eTag, or copySource only when present.
Separate configuration from business logic
Keep bucket names, Queue names, and filter choices in deployment configuration. Keep parsing and downstream behavior in code. This makes it possible to move a workflow to another bucket or Queue without silently changing what the handler does.
Observe both sides of the handoff
When diagnosing a pipeline, check the source event rule, the Queue, the Worker invocation, and the destination write independently. A missing log object can result from a rule that did not match, a consumer binding that points at another Queue, a handler exception, or a destination binding with the wrong bucket name.
Account for R2 API differences
The bucket binding exposes the R2 Workers API, which has semantic differences from the S3 API. Test the exact operations your Worker uses rather than assuming an S3 code sample can be copied unchanged.
Troubleshooting checklist
No message appears after an upload
- Confirm the upload went to the bucket named in the notification rule.
- Check that the event is one of the actions covered by
object-create; a lifecycle deletion requiresobject-delete. - Check the object key against every prefix and suffix filter.
- Verify that the rule targets the Queue consumed by this Worker.
- Ensure the Worker deployment includes the queue consumer binding.
The rule cannot be created
- List existing rules and look for a broad rule that overlaps the proposed prefix or suffix.
- Remove or redesign conflicting rules so one event maps to one notification.
- Keep the bucket below the documented rule limit of 100.
The Worker runs but no log object is written
- Verify that the R2 destination binding name in code matches the binding name in Wrangler configuration.
- Confirm that the configured bucket is the log sink, not the source bucket.
- Log the batch shape temporarily and check for event-dependent fields that your code treats as mandatory.
- Test the destination write with a minimal object before adding downstream processing.
An external consumer falls behind
- Measure the external service’s pull and acknowledgement cycle rather than assuming the Queue is empty.
- Reduce the work done per message or partition processing across multiple consumers where the current Queue and API limits allow it.
- Review current Queue documentation for delivery, retry, and acknowledgement behavior before changing failure handling.
When this architecture is the right fit
Use R2 notifications and Queues when object changes should initiate asynchronous work and the producer should not wait for the complete downstream operation. The pattern is especially useful for upload validation, metadata extraction, audit logging, and fan-out to services that can process events independently.
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Quick Recap
Implementation checklist
- Create the source bucket, destination bucket if needed, Queue, and Worker.
- Bind the Queue consumer and destination R2 bucket in Wrangler.
- Implement a
queuehandler that validates the batch and performs one clearly defined action. - Deploy the Worker before attaching the notification rule.
- Create an
object-createorobject-deleterule with non-overlapping filters. - Test uploads, copies, multipart completion, and deletions separately when those actions matter to your workflow.
- Handle absent delete-event metadata and optional copy-source data.
- Review the current 100-rule-per-bucket and 5,000-message-per-second-per-Queue limits before scaling.
- Verify delivery, retry, ordering, and acknowledgement semantics before promising production guarantees.
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