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Kafka and JetStream use different ordering boundaries
| Question | Kafka | NATS JetStream |
|---|---|---|
| What is ordered? | Records within one partition; a key can route related records to the same partition. A topic-wide total order requires one partition. (Apache Kafka 2.0 documentation) | Messages stored in a stream receive stream sequence numbers; consumers track their own positions. (JetStream concepts) |
| What fits sequential reading? | A consumer assigned the relevant partition reads its records in partition order. | The nats.go OrderedConsumer reads stream storage order, but is ephemeral, pull-based, single-threaded, and unacknowledged; it is not supported for push delivery. (nats.go JetStream API) |
| What fits shared work? | Consumer groups distribute topic partitions among group members; partition assignment bounds parallelism. (Confluent Go client guide) | Regular pull consumers support application-controlled work distribution and acknowledgments. Unacknowledged messages can be redelivered. (JetStream consumers) |
These are not interchangeable meanings of “ordered.” Kafka’s guarantee is partition-local. JetStream’s ordered-consumer feature is a sequential reading mode for stream storage, with different tracking and failure behavior from a durable work consumer.
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Choose the ordering boundary your application needs
Per-entity order with parallelism: partition Kafka by key
For event streams where each account, order, device, or other entity must be processed in sequence, use a stable entity key so related records route to the same Kafka partition. Other partitions can be processed concurrently. The partition is the ordering boundary—not the whole topic—and scaling a consumer group is constrained by the partitions available to assign.
That guarantee covers the order in which records are stored and read from a partition. It does not force your Go application to finish database writes in the same order if it dispatches fetched records to concurrent handlers. Keep side effects serialized within the required key boundary, or use another design that preserves that order.
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Topic-wide order: one Kafka partition
If every event in a topic must have one total order, use one partition. That removes parallel consumption of that topic across members of the same consumer group: only one group member can own that partition at a time. This can be the right trade-off for a genuinely global sequence, but it is not a way to get both global ordering and partition-level parallelism.
Sequential inspection or replay: JetStream ordered consumer
The nats.go OrderedConsumer is intended for reading stored stream messages in sequence. It is client-managed and ephemeral rather than a durable, acknowledged worker assignment. The client recreates its underlying consumer if it detects lost order. That behavior makes it useful when the goal is ordered inspection or replay, not when several workers must share durable progress and coordinate acknowledgments.
Shared JetStream work: regular pull consumer
For work that needs tracked progress, acknowledgments, or multiple workers, choose a regular consumer rather than the ordered-consumer convenience mode. NATS recommends pull consumers for new projects when scalability, detailed flow control, or error handling matters. Pull workers can share work, but redelivery means a handler may see a message again after a failure or missing acknowledgment. Make retryable side effects idempotent or otherwise safe to repeat.
What “ordered” means in a Go service
Define the exact point at which order must hold: broker delivery, handler execution, database commit, or an externally visible effect. A broker can provide an ordered sequence while a Go service breaks that sequence by processing messages concurrently. If later work must not overtake earlier work, constrain concurrency at that boundary. Where failures can cause retries or redelivery, make side effects duplicate-safe as well.
For Kafka, confluent-kafka-go wraps librdkafka. Its consumer joins a group, polls messages, and handles partition assignment or revocation events as group membership changes. The group assigns partitions to members; your code must still avoid reordering records after fetch if per-partition processing order matters. See the Confluent Go client guide.
For JetStream, decide whether the service needs sequential reading or durable work tracking. The former fits the ordered consumer’s unacknowledged, single-threaded model; the latter calls for a regular consumer with acknowledgment and redelivery behavior suited to the application’s retry policy. The JetStream consumer documentation describes these consumer behaviors.
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Replay and failure handling are part of the design
Kafka consumers track progress with offsets associated with partitions, and group membership changes can trigger partition reassignment. Plan for a consumer to resume from its tracked position after restart and for ownership to change as group membership changes. Offset progress and external side effects are separate concerns: if a process performs an effect and fails before its progress is safely recorded, the event may be processed again, depending on the application’s commit and recovery behavior.
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Best Value
Make the choice against your workload, not a presumed speed winner
- Choose Kafka when partition-based scaling and per-key ordering align with your event model, or when you need a topic-wide sequence and can accept a single partition’s consumption limit.
- Choose JetStream ordered consumption when one client needs to read or inspect stream history sequentially and ephemeral, unacknowledged, single-threaded behavior is acceptable.
- Choose a regular JetStream pull consumer when multiple workers need to share tracked work with acknowledgments and application-controlled pulling.
- Compare deployment and operations separately: pin the Kafka and NATS server versions and Go client versions you will run, then assess packaging, topology, retention and replication configuration, observability, and the team’s operational experience.
The cited documentation does not establish an apples-to-apples throughput, latency, or total-cost winner. Those outcomes depend on workload shape, message size, retention and replication settings, hardware, network, concurrency, and the deployed client/server versions. Benchmark the intended configuration if those figures will decide the architecture.
Check version-specific documentation before implementation
The Kafka ordering reference cited here is explicitly the Apache Kafka 2.0 documentation, while the Confluent Go client guide is current and the NATS API and main-branch documentation can change. Treat the links as behavioral guidance, not a pinned compatibility matrix: verify the documentation for the exact broker and client releases in your deployment before relying on a specific API or operational detail.
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