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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsChoose Kafka when you need durable, partition-based ordering with parallel processing across partitions. Choose a JetStream ordered consumer when a Go client should read a stream sequentially for inspection or replay. If multiple Go workers must share tracked JetStream work, use a regular pull consumer instead: ordered consumers are single-threaded and unacknowledged, while regular pull consumers support acknowledgments and work distribution.
The key design question is not simply “Kafka or NATS?” It is which events must stay in order, how much parallelism that boundary permits, and what your application should do after a worker fails.
How ordering works in Kafka and JetStream
Neither system provides one unrestricted ordering guarantee across every message in a distributed workload. Kafka orders records within a partition. JetStream assigns sequence numbers to messages stored in a stream, and consumers track their own positions. The way you partition or consume the data determines which order your application can rely on.
Kafka: order within a partition
Kafka’s ordering boundary is the partition: records in one partition have a total order, but records in different partitions do not have a defined order relative to one another. The Apache Kafka 2.0 documentation states that Kafka provides a total order within a partition, not between partitions in a topic.
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For per-entity order, use a Kafka key that routes all events for that entity to the same partition. For example, if an account’s events must be applied in sequence, the account identifier is a natural candidate key. Other partitions can still be processed concurrently. If the requirement is one total order for the entire topic, use one partition; that limits the topic’s consumer group to one active consumer at a time.
JetStream: stream sequence and consumer position
A JetStream stream captures messages by subject and assigns them stream sequence numbers. Consumers maintain their own positions in that stored stream. This gives a client a sequence to read, but the consumer type determines whether the read is sequential inspection or shared, acknowledged work. See the JetStream concepts documentation.
Which JetStream consumer fits ordered Go processing?
Use an ordered consumer for sequential reading
The nats.go OrderedConsumer is a client-managed, ephemeral, pull-based consumer intended to read stream storage order. It reads single-threaded, does not acknowledge messages, and is not supported for push delivery. If it detects lost order, it recreates its underlying consumer. These properties make it useful for deterministic inspection or replay, not a durable work queue whose progress is tracked through acknowledgments. The nats.go JetStream API documents the ordered consumer, while the JetStream consumer documentation describes consumer behavior.
Use a regular pull consumer for shared work
If several Go workers need to share a JetStream workload, use a regular pull consumer and design around its acknowledgments and redelivery behavior. A message that is not acknowledged can be delivered again, so a worker may repeat an operation after a failure or retry. Make side effects idempotent where duplicate execution would be harmful, and choose acknowledgment timing to match when the work is actually complete. NATS recommends pull consumers for new projects when scalability, flow control, or error handling matters; see its consumer guidance and JetStream development guide.
Comparison for a Go application
| Decision point | Kafka | NATS JetStream |
|---|---|---|
| Ordering boundary | Within a partition; a key can keep an entity’s events together. Global topic order requires one partition. Kafka 2.0 documentation. | Stored stream sequence; a consumer tracks its own position. OrderedConsumer reads that sequence sequentially. JetStream concepts; nats.go API. |
| Parallel processing | A consumer group assigns partitions among its members; the partition count and assignment bound parallelism for that topic. Kafka 2.0 documentation. | OrderedConsumer is single-threaded. Regular pull consumers are the option to evaluate for shared, scalable work. JetStream consumer documentation. |
| Progress tracking | Consumers track offsets for their partitions; consumer-group membership changes can trigger partition reassignment. Confluent Go client guide. | Streams assign sequence numbers, and consumers maintain positions. OrderedConsumer is ephemeral rather than a durable acknowledged work cursor. JetStream concepts; nats.go API. |
| Failure and retry behavior | Go consumers participate in group assignment and rebalance as membership changes; application offset commits and processing must be coordinated with that lifecycle. Confluent Go client guide. | Regular consumers use acknowledgments; unacknowledged messages can be redelivered. OrderedConsumer does not acknowledge messages. JetStream consumer documentation. |
| Go client | confluent-kafka-go provides producers and consumers and wraps librdkafka. Confluent Go client guide. |
The nats.go JetStream package exposes both ordered and regular consumer approaches. nats.go JetStream API. |
What to account for in Go
Kafka: preserve the key boundary through processing
The confluent-kafka-go consumer joins a group, polls for messages, and responds to partition assignment or revocation events as group membership changes. The group assigns partitions among members, so related keys must land in the same partition for per-key order. Your Go application must also avoid undoing that order after fetch: concurrently dispatching records from the same partition to handlers can let later work finish first. The Confluent Go client guide covers its consumer model.
Plan offset commits alongside side effects. If a process commits progress before its database or external operation is safely complete, a failure can leave the side effect unapplied even though the consumer has advanced. If the operation completes but the process fails before progress is committed, the record can be processed again. The exact handling depends on the application’s storage and failure model; broker order alone does not make external effects atomic.
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JetStream: distinguish ordered reading from acknowledged work
Use nats.go’s ordered consumer when one client should read in stream order and its ephemeral, single-threaded, no-ack behavior fits the task. Use a regular pull consumer when workers need tracked progress and acknowledgment-based handling. In either design, decide whether Go handlers may run concurrently and what ordering boundary that concurrency must preserve.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose by the required ordering boundary
- Per-entity order with parallelism across entities: Kafka is a natural fit when the entity key consistently routes related events to one partition. Keep processing for that entity ordered in the application.
- One total order for a Kafka topic: a single partition provides that boundary, with the trade-off that only one group member can actively consume that topic’s partition at a time.
- Sequential read or replay of stored JetStream messages: choose OrderedConsumer if ephemeral position, no acknowledgments, and single-threaded reading are acceptable.
- Multiple JetStream workers sharing tracked work: evaluate a regular pull consumer, and make duplicate delivery safe through idempotent or otherwise duplicate-aware side effects.
Before choosing, write down the unit that must remain ordered (topic, partition, stream, or entity), the maximum concurrency allowed inside that unit, what counts as completed work, and what should happen after a worker fails. Those requirements determine whether a partitioned Kafka group, an ordered JetStream reader, or an acknowledged JetStream pull consumer is appropriate.
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What the documentation does not settle
There is no apples-to-apples throughput, latency, or total-cost winner established here. Results depend on workload shape, message size, replication and retention settings, network, hardware, client and server versions, and concurrency. Compare the exact versions and deployment settings your team will run rather than treating a general benchmark as a verdict.
The cited Kafka ordering reference is specifically the Kafka 2.0 documentation. The nats.go API page and NATS documentation paths ending in master are moving references rather than pinned release specifications. Verify behavior against the versions actually deployed.
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