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This design separates storage from processing. R2 records the object change, Queues provides the handoff, and your Worker or external HTTP consumer decides what to do with each event. The sections below show how to configure the pipeline, choose event filters, handle payload differences, and plan for current service limits.
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The architecture: R2 change to processing job
An R2 event-driven pipeline has a clear sequence:
- An object is created, copied, completed after multipart upload, deleted, or removed by a lifecycle action in a source bucket.
- An event-notification rule matches that change and publishes a message to a Cloudflare Queue.
- A consumer receives one or more queued messages. The consumer can be a Worker running in Cloudflare or an external service using HTTP pull.
- The consumer parses the event and performs the business action: indexing metadata, generating a derivative, invoking another API, or recording an audit log in a destination bucket.
The queue is the handoff between the storage event and your processing code. Creating a notification rule does not transform an object or run application logic by itself; it only routes the change to the queue.
Why use a queue instead of calling a Worker directly?
R2 notifications and processing have different responsibilities. R2 can emit a compact description of a change, while the queue buffers messages until a consumer is ready. Your processing code can then work in batches and remain independent of the upload request. This is useful when uploads arrive in bursts, when processing takes longer than an object write, or when the destination system is not always available.
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Choose the event types before creating rules
Cloudflare documents two event types for R2 notifications. Select only the changes your pipeline needs.
| Event type | Operations covered | Typical use |
|---|---|---|
object-create |
PutObject, CopyObject, and CompleteMultipartUpload |
Start processing when a new object is available. |
object-delete |
DeleteObject and lifecycle deletion |
Remove an index entry, invalidate a cache, or record a deletion. |
Use prefix and suffix filters to constrain a rule to matching object keys. For example, a prefix can limit events to incoming/, while a suffix can limit them to .json. Filtering at the notification layer prevents unrelated objects from entering the queue.
Plan filters to avoid conflicting rules
Cloudflare rejects overlapping or conflicting rules that could produce more than one notification for the same event. A broad create rule for an entire bucket combined with a narrower create rule for a prefix can therefore fail validation. Design the keyspace first: divide prefixes or suffixes into mutually exclusive ranges, or route all matching objects through one rule and branch inside the consumer.
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Prepare these resources before wiring them together:
- Source bucket: receives the files whose changes should emit events.
- Queue: receives notification messages from the source bucket.
- Consumer Worker or external consumer: reads messages and executes the follow-on work.
- Destination bucket (optional): stores logs, transformed objects, or other output. The Cloudflare tutorial uses a second R2 bucket for this purpose.
- Wrangler project: the Worker source and configuration used to deploy the consumer.
Create the buckets and queue in your Cloudflare account using the dashboard or Wrangler, then keep the names consistent in the Worker configuration and notification command. The source and destination may be in the same account; the destination is simply another binding made available to the Worker.
Configure the consumer Worker
A queue consumer binding registers the Worker as the queue consumer. An R2 bucket binding gives the Worker a runtime handle to the destination bucket. These bindings are separate: the first delivers messages, while the second lets code read or write objects in R2.
Example Wrangler configuration
The following is the shape used by Cloudflare’s upload-log tutorial. Replace the names with your own resources.
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name = 'r2-event-logger'
main = 'src/index.js'
compatibility_date = '2026-10-16'
[[queues.consumers]]
queue = 'r2-events'
max_batch_size = 100
max_batch_timeout = 5
[[r2_buckets]]
binding = 'LOG_BUCKET'
bucket_name = 'r2-event-log'The tutorial’s values of 100 messages per batch and a five-second maximum batch timeout are example settings, not universal recommendations. Tune them after considering event size, processing time, memory use, and the latency your application can tolerate. Keep the compatibility date explicit so that the Worker uses the intended runtime behavior.
Implement a queue handler
A Worker queue handler receives a batch object. The tutorial serializes the batch messages and writes them to the destination bucket:
export default {
async queue(batch, env) {
const body = JSON.stringify(batch.messages);
const key = `batches/${Date.now()}.json`;
await env.LOG_BUCKET.put(key, body, {
httpMetadata: { contentType: 'application/json' }
});
}
};This example is intentionally simple: it creates one log object for each delivered batch. A production handler would usually validate the message, derive a deterministic record key, perform the required downstream action, and record enough context to diagnose failures. If you need one output object per event, iterate over batch.messages instead of serializing the whole array.
The R2 Workers API is exposed through the bucket binding. Cloudflare notes that this API has semantic differences from the S3 API, so do not assume that every S3 client behavior or option maps directly to a Workers binding. Use the Workers API methods and types documented for your runtime.
Deploy before enabling notifications
Deploy the Worker and confirm that its queue consumer binding and destination bucket binding are valid before you create the notification rule. Enabling the rule first can send events to a consumer that is not yet configured, making early troubleshooting harder.
Create the R2 notification rule
After the Worker is deployed, create an object-create rule on the source bucket. The documented Wrangler command is:
npx wrangler r2 bucket notification create <BUCKET> --event-type object-create --queue <QUEUE>Replace <BUCKET> and <QUEUE> with the actual source-bucket and queue names. Wrangler also accepts optional prefix, suffix, and jurisdiction arguments. A filtered rule has the same form with the relevant options added, for example:
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npx wrangler r2 bucket notification create uploads --event-type object-create --queue r2-events --prefix incoming/ --suffix .jsonUse one rule for a deliberately defined key range. If you need separate processing paths, make their filters non-overlapping and send them to separate queues or branch on the event inside one consumer.
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Test the complete upload path
Test each link in the chain rather than checking only that an object exists in R2.
- Upload a small test object whose key matches the rule, such as
incoming/health-check.json. - Confirm that the object appears in the source bucket.
- Inspect Worker logs to verify that the queue handler is invoked and that a batch contains the expected message.
- Check the destination bucket for the output object created by the handler.
- Upload an object outside the configured prefix or suffix and verify that it does not produce a message for that rule.
- If you configured delete notifications, delete a test object and inspect the resulting event separately from the create event.
Cloudflare’s tutorial reports that its log object appears in the sink bucket a few seconds after a test upload. Treat that as the tutorial’s example observation, not as a general latency guarantee for every account, region, or workload.
Understand the notification payload
Write the handler against the documented fields and tolerate event-specific omissions. A message includes account information, an action name, the bucket, an object record, and an event time. For object-create events, the object record can include the object key, size, and eTag. Delete events do not include size or eTag. Copy events can include a copySource record.
A representative shape is:
{
"account": "account-id",
"action": "PutObject",
"bucket": "uploads",
"object": {
"key": "incoming/health-check.json",
"size": 42,
"eTag": "example-etag"
},
"eventTime": "2026-10-16T12:00:00Z"
}Do not require size or eTag when handling deletes, and do not assume copySource exists for ordinary uploads. Branch on the action and test each event shape your rule can produce. Preserve the event time and action in your own records so that later processing can distinguish a copy from a direct upload.
Validate before doing expensive work
Check that the message has the bucket, object key, action, and event time your workflow needs. Reject or quarantine malformed records rather than constructing a destination key from an undefined field. For create events, you can then fetch the object or start a transformation; for delete events, use the key to remove or mark downstream state.
Consumer choices: Worker push or external HTTP pull
Cloudflare documents two consumption models. Both use the queue as the handoff, but control of consumption is different.
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| Decision axis | Consumer Worker (push) | External HTTP pull consumer |
|---|---|---|
| Where code runs | In a Cloudflare Worker registered as the queue consumer. | In infrastructure outside Workers that requests messages over HTTP. |
| Who controls consume rate | The managed queue-to-Worker delivery invokes the consumer with batches. | The external system decides when to request messages and when it is ready to acknowledge them. |
| Best fit | Processing that naturally belongs in Workers and benefits from a managed event consumer. | Existing services that need direct control over readiness, pacing, or deployment location. |
| Integration work | Configure a queue consumer binding and deploy the Worker. | Implement HTTP polling, message handling, and acknowledgement in the external service. |
Choose the Worker model when the transformation, indexing, or notification logic can run in the Workers environment and you want configuration close to R2. Choose HTTP pull when an established platform must decide when it can consume, or when processing belongs in infrastructure that is already operated outside Cloudflare. The available documentation does not establish that either model is universally faster, cheaper, or more reliable.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Throughput and rule limits
Cloudflare’s current R2 documentation states that each bucket supports up to 100 event-notification rules. It also documents throughput of up to 5,000 messages per second per queue. These are service limits recorded by Cloudflare in 2026 and can change; check the current documentation before sizing a new deployment.
For workloads above the documented per-queue throughput, Cloudflare recommends distributing notification rules across multiple queues. That requires an intentional partition, such as separate non-overlapping prefixes, so that each event is routed once and each queue has a manageable rate. A larger number of queues does not fix an overlapping-rule design; conflicting rules can be rejected before delivery begins.
Estimate the pressure on a queue
- Count the maximum object-change rate, including multipart completions and copies if they are enabled in your rule.
- Account for whether one upload produces one event or whether your workflow also emits delete or follow-on events.
- Compare the resulting peak with the documented 5,000-message-per-second queue throughput.
- If the peak exceeds that figure, partition notification rules across queues and deploy consumers that can process each partition.
Do not turn the throughput figure into a latency or completion promise. It is a documented current throughput value, not a benchmark for your handler or a guarantee that every batch will be delivered within a fixed interval.
Design for duplicates and safe reprocessing
Overlapping notification rules are explicitly disallowed when they could create more than one notification for the same event. Even with non-overlapping rules, your application should make processing safe to repeat. The reviewed Cloudflare material does not establish delivery ordering, exactly-once behavior, or a specific retry policy, so do not build a correctness argument on any of those assumptions.
Practical safeguards include:
- Use a stable identifier derived from the event’s bucket, action, object key, and event time, or retain the provider’s event identifier if one is present in the runtime payload.
- Write output under deterministic keys when the operation is naturally idempotent.
- Record completion state before triggering an expensive downstream action, or use a deduplication store appropriate to your workload.
- Keep delete handling separate from create handling so that a late or repeated delete cannot be mistaken for an upload.
- Make external API calls tolerant of retries and record the response needed to investigate a partial failure.
These are application design practices, not claims about Cloudflare’s delivery semantics. Verify the current queue delivery and retry documentation before choosing acknowledgement, retry, or dead-letter behavior for a production system.
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Common failure modes and fixes
No message arrives after an upload
- Confirm that the object key matches the rule’s prefix and suffix exactly.
- Check that the rule uses
object-create; a delete rule will not match an upload. - Verify that the queue name in the notification rule matches the queue consumer binding.
- Ensure the Worker deployment succeeded and the consumer binding is attached to the deployed version.
The rule cannot be created
Look for overlapping or conflicting rules. Remove the broad rule or redesign the prefixes and suffixes so that one event can match only one notification rule. Also check the event type and queue name supplied to Wrangler.
The handler crashes on a delete event
Delete payloads do not include size or eTag. Treat those fields as optional and branch on the action before reading them. Use the object key and event time, which are the fields your deletion workflow normally needs.
The destination write fails
Check that the destination bucket binding’s binding name matches the name used in code and that bucket_name names the intended bucket. Remember that the Workers R2 API is not identical to the S3 API; adjust code to the Workers API rather than copying an S3-only option or method.
Events arrive in unexpected batches
The queue consumer receives batches, not necessarily one invocation per object. Process every message in batch.messages, and keep one malformed record from silently discarding the rest. The tutorial’s batch size and timeout values are examples; adjust them only after measuring your own processing behavior.
Extending the basic upload logger
Image or document processing
Use an object-create rule filtered to an input prefix such as incoming/. The Worker can read the source object through an R2 binding, create a derivative, and write it under a separate output prefix. Keep input and output prefixes distinct so that a derivative does not trigger the same rule again.
Metadata indexing
For each create event, extract the key, event time, action, size, and eTag when present, then write a record to your index service. For delete events, use the key to remove or tombstone the index entry. Because fields differ by action, store optional values as nullable rather than rejecting a valid delete.
Audit logging
The tutorial’s second bucket is a straightforward audit sink: serialize each batch and write it as JSON. For long-lived audit trails, choose a key scheme that avoids accidental overwrites and includes enough time information to locate a batch. If you need one immutable record per event, write individual records instead of one timestamp-based batch object.
Operational checklist
- Source bucket created and named consistently across configuration and notification commands.
- Queue created and referenced by exactly one intended consumer path.
- Worker deployed with a queue consumer binding.
- Destination bucket binding added only when the handler needs R2 access.
- Notification event type selected deliberately: create, delete, or both.
- Prefix and suffix filters tested with matching and non-matching keys.
- Rules checked for overlap before adding another notification.
- Handler tolerates missing delete fields and optional copy metadata.
- Batch processing iterates over every message.
- Current limits reviewed: 100 rules per bucket and 5,000 messages per second per queue, subject to change.
- Delivery, ordering, acknowledgement, and retry behavior verified against the current Cloudflare queue documentation before production launch.
What this architecture does—and does not—guarantee
This pattern gives you a documented route from an R2 object change to queued processing and supports either a managed Worker consumer or an external HTTP pull consumer. It does not, by itself, define your transformation, indexing, or audit policy. It also does not justify assumptions about ordering, exactly-once execution, retries, or fixed latency. Those properties must be confirmed in the current service documentation and then reflected in an idempotent application design.
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