Consumer Group Fundamentals A consumer group is how Kafka distributes work across multiple consumers. Each partition in a topic is assigned to exactly one consumer instance in the group at any given time. flowchart TB subgraph Topic["Topic: orders — 6 partitions"] P0["P0"] P1["P1"] P2["P2"] P3["P3"] P4["P4"] P5["P5"] end subgraph CG1["Group: inventory-service (3 instances)"] I1["Instance 1\nP0, P1"] I2["Instance 2\nP2, P3"] I3["Instance 3\nP4, P5"] end subgraph CG2["Group: notification-service (1 instance)"] N1["Instance 1\nP0,P1,P2,P3,P4,P5"] end P0 & P1 --> I1 P2 & P3 --> I2 P4 & P5 --> I3 P0 & P1 & P2 & P3 & P4 & P5 --> N1 Both groups receive all events — they are independent.

Why Partitioning Strategy Matters How you route messages to partitions determines: Ordering: only messages in the same partition are ordered relative to each other Parallelism: how evenly work is distributed across consumers Hot spots: if one key generates 90% of traffic, one partition (and one consumer) gets 90% of the load flowchart TD subgraph Routing["Message Routing Decision"] Msg["Message"] HasKey{Has key?} HasPartition{Explicit partition?} KeyHash["hash(key) % numPartitions\n→ deterministic, same partition always"] RoundRobin["Sticky partitioning\n(batch to same partition,\nthen round-robin)"