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What Kafka ordering guarantees—and what it does not
Kafka stores records in each partition as an ordered log. A consumer instance sees records from that partition in the order stored in the log, as Apache Kafka’s introduction documentation explains. A topic with multiple partitions has no single total sequence across all of its records.
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“Session order” is therefore an application-level requirement: define which events belong to a session, then make sure they share an ordering domain. Kafka’s consumer group distributes partition assignments among members; in the classic consumer-group model described in the documentation, a partition is assigned to one member at a time. Adding goroutines does not create more Kafka ordering guarantees.
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At production time, use a stable session identifier as the record key. Configure every producer that writes these events to use a compatible key-to-partition strategy. Kafka clients determine that mapping; Kafka does not infer that two records belong to the same session. The Kafka protocol documentation describes partitioning by a semantic key such as user ID so that a user’s records go to one consumer.
- Choose a key that matches the required ordering scope, such as a session ID or customer ID.
- Use the same key consistently for all events whose relative order matters.
- Ensure producers use compatible partitioning behavior; inconsistent mapping can send one session’s records to different partitions.
This provides an ordering domain per key, provided the key continues to map consistently. It does not order unrelated sessions against one another.
Choose how Go processes records within a partition
Serial processing per partition
The simplest strict-order approach is to handle a record’s work to completion before starting the next record from that partition. This keeps processing in partition sequence, but a slow operation delays later records in that partition.
Concurrent work in per-key ordered lanes
If independent sessions need to progress concurrently, dispatch records into bounded lanes keyed by session. Each lane must process its records FIFO, while other lanes can work in parallel. This is an application design pattern, not a Kafka feature: the dispatcher, retries, failure handling, and completion tracking must all preserve the intended order.
Do not launch an unconstrained goroutine for every fetched message and assume the application will retain Kafka’s ordering. Later work can finish before earlier work, even when the records were fetched in order.
Commit only a contiguous completed prefix
For each partition, track which records have completed successfully and advance the committed position only through the highest contiguous completed offset. An offset is not an independent acknowledgement of just one record. The kafka-go Reader documentation and source warn that committing a higher message offset for a partition also commits preceding offsets. Committing past unfinished earlier work can prevent that work from being replayed after a restart, depending on the failure and processing outcome.
For example, if records at offsets 10, 11, and 12 are being processed concurrently, and 12 finishes before 11, do not advance the committed position beyond 10 until 11 also succeeds. Once 11 and 12 have both completed, the contiguous completed prefix can advance through 12. Apply the same rule when retrying a failed record: later completion must not move the commit past the gap.
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Compare the main designs
| Design | Ordering property | Throughput and complexity |
|---|---|---|
| Serial work per partition | Preserves partition sequence when the consumer waits for each record’s work to finish. | Straightforward; slow work blocks later records in that partition. |
| Per-key ordered lanes within a partition | Can preserve order for each key while different keys run concurrently, if dispatch and completion tracking are correct. | More potential concurrency; requires bounded queues, retry handling, and contiguous offset tracking. |
| One topic partition | Provides a single topic-wide partition sequence. | Limits consumption parallelism for that topic partition to one group member and may bottleneck throughput. |
Handle cancellation and rebalances without committing out of order
When a partition is revoked or the consumer is shutting down, stop admitting new work for that partition and prevent outstanding work from committing progress past an earlier unfinished record. The exact callbacks, ownership rules, and shutdown sequence depend on the Go client and its pinned version. Consult the documentation for the client version in use; the Confluent Go client documentation describes its own consumer APIs and guidance, which should not be assumed to apply unchanged to every Go client.
When you need a topic-wide order
If every event in the topic must have one total order, use one partition: the partition log supplies that sequence. The tradeoff is that the topic cannot gain partition-level consumption parallelism across a consumer group. If only per-session order matters, a stable session key and per-partition or per-key ordered processing avoid imposing that global bottleneck.
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