顺序、消费组与回放（Kafka）

本页结论：Kafka 的顺序只在分区内成立——同 key 进同分区、组内瓜分分区、新消费组可从 earliest 全量回放；本页用三个实验分别验证，并给出与 RabbitMQ 的语义对照。

适用场景

需要「同 orderId 事件按序处理」的业务流。
验证消费组并行度与分区的关系。
验证回放能力（新消费组/位点重置从头读）。

拓扑

实验一：同 key 有序（ordering-replay）

bash

npm run lab -- kafka ordering-replay

步骤：Producer 用同一 key（order-1001）发送 6 条带序号消息 → 断言 6 条落在同一分区（samePartitionOnProduce=1）→ 消费组 g1 按序接收（observedOrder=[1..6]）→ 新消费组 g2 以 auto.offset.reset=earliest 从 offset 0 全量回放（replayed=6、replayFromOffset0=0）。

verifiedkafka / ordering-replaybroker 4.3.1 · java-kafka-clients-4.3.1

镜像	apache/kafka:4.3.1@sha256:77e3df9054047a88b520d0cc46e16696d3b22022e1d580aeccd2632df6532837
捕获时间	2026-08-19T09:29:57.825Z
耗时 / 退出码	23875 ms / exit 0

断言

produced	6
samePartitionOnProduce	1
samePartitionOnConsume	1
observedOrder	[ 1, 2, 3, 4, 5, 6 ]
replayed	6
replayFromOffset0	0
replayUniqueMessageIds	6

归一化日志

[producer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay messageId=mid-1 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-1 correlationId=order-1001 destination=orders.ordering partitionOrQueue=0 offset=0 seq=1 durationMs=<ms> status=produced
[producer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay messageId=mid-2 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-2 correlationId=order-1001 destination=orders.ordering partitionOrQueue=0 offset=1 seq=2 durationMs=<ms> status=produced
[producer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay messageId=mid-3 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-3 correlationId=order-1001 destination=orders.ordering partitionOrQueue=0 offset=2 seq=3 durationMs=<ms> status=produced
[producer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay messageId=mid-4 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-4 correlationId=order-1001 destination=orders.ordering partitionOrQueue=0 offset=3 seq=4 durationMs=<ms> status=produced
[producer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay messageId=mid-5 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-5 correlationId=order-1001 destination=orders.ordering partitionOrQueue=0 offset=4 seq=5 durationMs=<ms> status=produced
[producer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay messageId=mid-6 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-6 correlationId=order-1001 destination=orders.ordering partitionOrQueue=0 offset=5 seq=6 durationMs=<ms> status=produced
[producer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay destination=orders.ordering produced=6 status=done
[assert] produced=6 PASS
[assert] samePartitionOnProduce=1 PASS
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay destination=orders.ordering consumerGroup=orders-ordering-g1 consumer=g1 partitions=0,1,2 status=assigned
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay messageId=mid-1 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-1 correlationId=order-1001 destination=orders.ordering partitionOrQueue=0 offset=0 seq=1 consumerGroup=orders-ordering-g1 consumer=g1 attempt=1 redelivered=false status=received
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay messageId=mid-1 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-1 correlationId=order-1001 destination=orders.ordering partitionOrQueue=0 seq=1 consumerGroup=orders-ordering-g1 attempt=1 status=business_committed
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay messageId=mid-2 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-2 correlationId=order-1001 destination=orders.ordering partitionOrQueue=0 offset=1 seq=2 consumerGroup=orders-ordering-g1 consumer=g1 attempt=1 redelivered=false status=received
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay messageId=mid-2 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-2 correlationId=order-1001 destination=orders.ordering partitionOrQueue=0 seq=2 consumerGroup=orders-ordering-g1 attempt=1 status=duplicate_skipped
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay messageId=mid-3 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-3 correlationId=order-1001 destination=orders.ordering partitionOrQueue=0 offset=2 seq=3 consumerGroup=orders-ordering-g1 consumer=g1 attempt=1 redelivered=false status=received
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay messageId=mid-3 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-3 correlationId=order-1001 destination=orders.ordering partitionOrQueue=0 seq=3 consumerGroup=orders-ordering-g1 attempt=1 status=duplicate_skipped
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay messageId=mid-4 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-4 correlationId=order-1001 destination=orders.ordering partitionOrQueue=0 offset=3 seq=4 consumerGroup=orders-ordering-g1 consumer=g1 attempt=1 redelivered=false status=received
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay messageId=mid-4 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-4 correlationId=order-1001 destination=orders.ordering partitionOrQueue=0 seq=4 consumerGroup=orders-ordering-g1 attempt=1 status=duplicate_skipped
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay messageId=mid-5 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-5 correlationId=order-1001 destination=orders.ordering partitionOrQueue=0 offset=4 seq=5 consumerGroup=orders-ordering-g1 consumer=g1 attempt=1 redelivered=false status=received
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay messageId=mid-5 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-5 correlationId=order-1001 destination=orders.ordering partitionOrQueue=0 seq=5 consumerGroup=orders-ordering-g1 attempt=1 status=duplicate_skipped
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay messageId=mid-6 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-6 correlationId=order-1001 destination=orders.ordering partitionOrQueue=0 offset=5 seq=6 consumerGroup=orders-ordering-g1 consumer=g1 attempt=1 redelivered=false status=received
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay messageId=mid-6 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-6 correlationId=order-1001 destination=orders.ordering partitionOrQueue=0 seq=6 consumerGroup=orders-ordering-g1 attempt=1 status=duplicate_skipped
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay destination=orders.ordering consumer=g1 received=6 status=done
[assert] samePartitionOnConsume=1 PASS
[assert] observedOrder=1,2,3,4,5,6 PASS
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay destination=orders.ordering consumerGroup=orders-ordering-g2 consumer=g2 partitions=0,1,2 status=assigned
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay messageId=mid-1 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-1 correlationId=order-1001 destination=orders.ordering partitionOrQueue=0 offset=0 seq=1 consumerGroup=orders-ordering-g2 consumer=g2 attempt=1 redelivered=false status=received
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay messageId=mid-1 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-1 correlationId=order-1001 destination=orders.ordering partitionOrQueue=0 seq=1 consumerGroup=orders-ordering-g2 attempt=1 status=business_committed
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay messageId=mid-2 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-2 correlationId=order-1001 destination=orders.ordering partitionOrQueue=0 offset=1 seq=2 consumerGroup=orders-ordering-g2 consumer=g2 attempt=1 redelivered=false status=received
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay messageId=mid-2 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-2 correlationId=order-1001 destination=orders.ordering partitionOrQueue=0 seq=2 consumerGroup=orders-ordering-g2 attempt=1 status=duplicate_skipped
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay messageId=mid-3 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-3 correlationId=order-1001 destination=orders.ordering partitionOrQueue=0 offset=2 seq=3 consumerGroup=orders-ordering-g2 consumer=g2 attempt=1 redelivered=false status=received
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay messageId=mid-3 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-3 correlationId=order-1001 destination=orders.ordering partitionOrQueue=0 seq=3 consumerGroup=orders-ordering-g2 attempt=1 status=duplicate_skipped
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay messageId=mid-4 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-4 correlationId=order-1001 destination=orders.ordering partitionOrQueue=0 offset=3 seq=4 consumerGroup=orders-ordering-g2 consumer=g2 attempt=1 redelivered=false status=received
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay messageId=mid-4 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-4 correlationId=order-1001 destination=orders.ordering partitionOrQueue=0 seq=4 consumerGroup=orders-ordering-g2 attempt=1 status=duplicate_skipped
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay messageId=mid-5 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-5 correlationId=order-1001 destination=orders.ordering partitionOrQueue=0 offset=4 seq=5 consumerGroup=orders-ordering-g2 consumer=g2 attempt=1 redelivered=false status=received
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay messageId=mid-5 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-5 correlationId=order-1001 destination=orders.ordering partitionOrQueue=0 seq=5 consumerGroup=orders-ordering-g2 attempt=1 status=duplicate_skipped
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay messageId=mid-6 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-6 correlationId=order-1001 destination=orders.ordering partitionOrQueue=0 offset=5 seq=6 consumerGroup=orders-ordering-g2 consumer=g2 attempt=1 redelivered=false status=received
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay messageId=mid-6 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-6 correlationId=order-1001 destination=orders.ordering partitionOrQueue=0 seq=6 consumerGroup=orders-ordering-g2 attempt=1 status=duplicate_skipped
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=ordering-replay destination=orders.ordering consumer=g2 received=6 status=done
[assert] replayed=6 PASS
[assert] replayFromOffset0=0 PASS
[assert] replayUniqueMessageIds=6 PASS

npm run lab -- kafka ordering-replay

结论与边界：

同 key → 同分区 → 分区内写入有序；消费端单线程读分区即保序。
回放不改变日志本身：g1 的消费不影响 g2，offset 是组级位点。
跨分区无顺序可言；消费端多线程处理同一分区会打乱顺序（见分区与分发）。

实验二：消费组瓜分分区（consumer-group）

bash

npm run lab -- kafka consumer-group

步骤：先发 3 条到 3 分区 Topic → 组 A 两个消费者并行消费（空闲超时退出后合并统计：每条消息恰被组内一个消费者收到一次，两个消费者都观察到分区分配）→ 组 B 独立消费，同样收到全量 3 条。

verifiedkafka / consumer-groupbroker 4.3.1 · java-kafka-clients-4.3.1

镜像	apache/kafka:4.3.1@sha256:77e3df9054047a88b520d0cc46e16696d3b22022e1d580aeccd2632df6532837
捕获时间	2026-08-19T09:29:21.932Z
耗时 / 退出码	135508 ms / exit 0

断言

produced	3
groupAReceived	3
groupAUnique	3
a1Assigned	true
a2Assigned	true
a1ExitCode	0
a2ExitCode	0
groupBReceived	3
groupBBusinessRows	3
groupALag	0

归一化日志

[producer] timestamp=<ts> level=INFO service=order-service product=kafka lab=consumer-group messageId=mid-1 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-1 correlationId=order-1001 destination=orders.group partitionOrQueue=0 offset=0 seq=1 durationMs=<ms> status=produced
[producer] timestamp=<ts> level=INFO service=order-service product=kafka lab=consumer-group messageId=mid-2 eventType=order.created schemaVersion=1 aggregateId=order-1002 traceId=trace-2 correlationId=order-1002 destination=orders.group partitionOrQueue=0 offset=1 seq=2 durationMs=<ms> status=produced
[producer] timestamp=<ts> level=INFO service=order-service product=kafka lab=consumer-group messageId=mid-3 eventType=order.created schemaVersion=1 aggregateId=order-1003 traceId=trace-3 correlationId=order-1003 destination=orders.group partitionOrQueue=1 offset=0 seq=3 durationMs=<ms> status=produced
[producer] timestamp=<ts> level=INFO service=order-service product=kafka lab=consumer-group destination=orders.group produced=3 status=done
[assert] produced=3 PASS
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=consumer-group destination=orders.group consumerGroup=orders-group-a consumer=a-1 partitions=0,1,2 status=assigned
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=consumer-group messageId=mid-1 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-1 correlationId=order-1001 destination=orders.group partitionOrQueue=0 offset=0 seq=1 consumerGroup=orders-group-a consumer=a-1 attempt=1 redelivered=false status=received
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=consumer-group messageId=mid-1 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-1 correlationId=order-1001 destination=orders.group partitionOrQueue=0 seq=1 consumerGroup=orders-group-a attempt=1 status=business_committed
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=consumer-group messageId=mid-2 eventType=order.created schemaVersion=1 aggregateId=order-1002 traceId=trace-2 correlationId=order-1002 destination=orders.group partitionOrQueue=0 offset=1 seq=2 consumerGroup=orders-group-a consumer=a-1 attempt=1 redelivered=false status=received
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=consumer-group messageId=mid-2 eventType=order.created schemaVersion=1 aggregateId=order-1002 traceId=trace-2 correlationId=order-1002 destination=orders.group partitionOrQueue=0 seq=2 consumerGroup=orders-group-a attempt=1 status=business_committed
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=consumer-group messageId=mid-3 eventType=order.created schemaVersion=1 aggregateId=order-1003 traceId=trace-3 correlationId=order-1003 destination=orders.group partitionOrQueue=1 offset=0 seq=3 consumerGroup=orders-group-a consumer=a-1 attempt=1 redelivered=false status=received
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=consumer-group messageId=mid-3 eventType=order.created schemaVersion=1 aggregateId=order-1003 traceId=trace-3 correlationId=order-1003 destination=orders.group partitionOrQueue=1 seq=3 consumerGroup=orders-group-a attempt=1 status=business_committed
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=consumer-group destination=orders.group consumerGroup=orders-group-a consumer=a-1 partitions=2 status=assigned
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=consumer-group destination=orders.group consumer=a-1 received=3 status=done
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=consumer-group destination=orders.group consumerGroup=orders-group-a consumer=a-2 partitions=0,1 status=assigned
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=consumer-group destination=orders.group consumerGroup=orders-group-a consumer=a-2 partitions=0,1,2 status=assigned
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=consumer-group destination=orders.group consumer=a-2 received=0 status=done
[assert] groupAReceived=3 PASS
[assert] groupAUnique=3 PASS
[assert] a1Assigned=true PASS
[assert] a2Assigned=true PASS
[assert] a1ExitCode=0 PASS
[assert] a2ExitCode=0 PASS
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=consumer-group destination=orders.group consumerGroup=orders-group-b consumer=b-1 partitions=0,1,2 status=assigned
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=consumer-group messageId=mid-1 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-1 correlationId=order-1001 destination=orders.group partitionOrQueue=0 offset=0 seq=1 consumerGroup=orders-group-b consumer=b-1 attempt=1 redelivered=false status=received
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=consumer-group messageId=mid-1 eventType=order.created schemaVersion=1 aggregateId=order-1001 traceId=trace-1 correlationId=order-1001 destination=orders.group partitionOrQueue=0 seq=1 consumerGroup=orders-group-b attempt=1 status=business_committed
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=consumer-group messageId=mid-2 eventType=order.created schemaVersion=1 aggregateId=order-1002 traceId=trace-2 correlationId=order-1002 destination=orders.group partitionOrQueue=0 offset=1 seq=2 consumerGroup=orders-group-b consumer=b-1 attempt=1 redelivered=false status=received
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=consumer-group messageId=mid-2 eventType=order.created schemaVersion=1 aggregateId=order-1002 traceId=trace-2 correlationId=order-1002 destination=orders.group partitionOrQueue=0 seq=2 consumerGroup=orders-group-b attempt=1 status=business_committed
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=consumer-group messageId=mid-3 eventType=order.created schemaVersion=1 aggregateId=order-1003 traceId=trace-3 correlationId=order-1003 destination=orders.group partitionOrQueue=1 offset=0 seq=3 consumerGroup=orders-group-b consumer=b-1 attempt=1 redelivered=false status=received
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=consumer-group messageId=mid-3 eventType=order.created schemaVersion=1 aggregateId=order-1003 traceId=trace-3 correlationId=order-1003 destination=orders.group partitionOrQueue=1 seq=3 consumerGroup=orders-group-b attempt=1 status=business_committed
[consumer] timestamp=<ts> level=INFO service=order-service product=kafka lab=consumer-group destination=orders.group consumer=b-1 received=3 status=done
[inspect] timestamp=<ts> level=INFO service=order-service product=kafka lab=consumer-group business_rows=3 processed_rows=3 status=snapshot
[assert] groupBReceived=3 PASS
[assert] groupBBusinessRows=3 PASS
[assert] groupALag=0 PASS

npm run lab -- kafka consumer-group

结论与边界：

组内瓜分：每个分区至多一个消费者；消费者数超过分区数时多余者空转。
组间广播：位点按组独立，组 B 的接收与组 A 互不影响。
分配的具体归属由协议决定（本实验断言「两者都有分配」而非具体归属，因为分配结果不保证可复现）。

与 RabbitMQ / Pulsar 的顺序语义对照

维度	Kafka	RabbitMQ	Pulsar
顺序单位	Partition（key 决定归属）	单个 Queue（binding 决定归属）	分区（key 路由）+ 订阅类型共同决定
同键有序怎么做	key=orderId → 同分区	routing key=orderId → 专属队列 + 单消费者	key=orderId → 分区 + Key_Shared 订阅（同 key 粘连同一消费者）
并行与顺序的冲突	分区数 = 并行上限，全局顺序需单分区	队列数类似；单队列多消费者会竞争乱序	分区数类似；Shared 订阅换并行但放弃顺序
失败后顺序	无 requeue；失败消息通常转发 DLQ Topic，原分区继续	NACK+requeue 会把消息送回队列，顺序可能抖动	negativeAck/ack 超时触发重投，同分区内重试消息与后续消息的顺序会被打乱
回放	原生：位点重置/新组 earliest	不适用（ACK 即删；Streams 除外）	原生：reset-cursor 到 earliest/时间戳（redelivery-replay 实验验证）
多订阅	多消费组各自位点	多队列各自绑定	同一 topic 可并存多个订阅（Exclusive/Shared/Failover/Key_Shared），各自独立游标

统一结论（对应顺序语义）：三家都只提供局部顺序；「全局顺序」需要牺牲并行度，且都要在消费端保持单线程处理同一顺序单位。Pulsar 的额外维度是订阅类型：Shared 换吞吐但无跨消费者顺序，Key_Shared 才能同时兼得同键有序与并行（见 subscriptions 实验）。

断言汇总

实验	关键断言	期望
ordering-replay	samePartitionOnProduce / observedOrder / replayed / replayFromOffset0	1 / [1..6] / 6 / 0
consumer-group	groupAReceived / groupAUnique / a1Assigned+a2Assigned / groupBReceived / groupALag	3 / 3 / 均有 / 3 / 0

官方资料与版本说明

Kafka 4.3.1（镜像 digest 锁定，见 .env.versions），客户端 kafka-clients 4.3.1。
The Producer（分区选择）：https://kafka.apache.org/documentation/#theproducer（checkedAt: 2026-08-19）
The Consumer（组与位点）：https://kafka.apache.org/documentation/#theconsumer（checkedAt: 2026-08-19）

顺序、消费组与回放（Kafka） ​

适用场景 ​

拓扑 ​

实验一：同 key 有序（ordering-replay） ​

实验二：消费组瓜分分区（consumer-group） ​

与 RabbitMQ / Pulsar 的顺序语义对照 ​

断言汇总 ​

官方资料与版本说明 ​