LV2 · Toolooo Standard
Async Messaging Decision Lab
Compare Queue, Pub/Sub and Event Stream against delivery, replay, ordering and operational requirements.
Choose delivery and history needs first. Fit scores use editorial rules and are not probabilities or vendor rankings.
Compare your requirements to see ranked fits, the strongest signals and where the recommendation is weak.
Pattern comparison
| Dimension | Queue | Pub/Sub | Event Stream |
|---|---|---|---|
| Delivery | One worker per work item | One copy per subscription | Append to retained log |
| Consumption | Shared backlog and acknowledgements | Independent subscriptions | Independent group positions |
| Replay | Usually unavailable after acknowledgement | Implementation-dependent | Within retained history |
| Retention | Until acknowledgement / expiry | Subscription policy-dependent | Time / size / compaction policy |
| Ordering | Requires explicit queue and processing rules | Implementation-dependent | Usually per partition / key |
| Scaling | Competing workers | Scale each subscriber group | Partitions and consumer groups |
| Fan-out | Separate queues / routing needed | Core delivery model | Multiple independent consumer groups |
| Independence | Workers share backlog | Subscribers have own delivery | Groups have own positions |
| Complexity | Retries, duplicates and poison messages | Subscription delivery policies | Retention, partitioning and lag |
These patterns overlap. Durable Pub/Sub can offer replay; a stream group can distribute work. None alone guarantees exactly-once effects. Design idempotency and failure handling.
What this tool does and why
Before you start
What
Compare Queue, Pub/Sub and Event Stream against delivery, replay, ordering and operational requirements.
Why
Work distribution, independent subscribers and retained replay are different needs; exposing their trade-offs avoids selecting a broker before deciding the pattern.
Your Result and Action update in the tool as you change its inputs.
Model note
This browser-side model explains the inputs you choose. Check an important decision against the real system, brief, or production constraints.
Next
How this tool stays honest
How it works
Rank asynchronous messaging patterns with a visible weighted fit model. Inspect the strongest signals, requirement conflicts, winner weaknesses and the delivery, retention and consumer-independence trade-offs before choosing an implementation.
Assumptions
The tool runs in your browser and models the information you provide. It does not know facts outside that model.
Limitations
Fit percentages are weighted editorial scores, not measured performance or probabilities. Patterns overlap and delivery, ordering, replay and durability guarantees depend on implementation and configuration.
Understand Async Messaging Decision Lab
What this tool helps you understand
Rank asynchronous messaging patterns with a visible weighted fit model. Inspect the strongest signals, requirement conflicts, winner weaknesses and the delivery, retention and consumer-independence trade-offs before choosing an implementation.
When to use it
When choosing between work distribution, subscribers and retained replay.
Try a scenario
Compare a background image-processing job with an event consumed by several independent services. Review delivery guarantees in the eventual implementation.
How to read the result
Inspect earned weighted points and conflicting requirements. Fit percentages describe this editorial model, not probabilities or implementation guarantees.
Assumptions and limits
Fit percentages are weighted editorial scores, not measured performance or probabilities. Patterns overlap and delivery, ordering, replay and durability guarantees depend on implementation and configuration.
Toolooo is part of Khizooology, created byKhizar Imtiaz (khizooo).