# Consumer Groups and Committed Offsets — Apache Kafka: Event Streaming from Basics to Production

Source: https://www.geekswithgeeks.com/en/kafka/kc-groups-offsets

> Explain how a group shares partitions and how committed offsets and lag are tracked.

## One partition, one consumer per group

Consumers that share the same **`group.id`** form a **consumer group**. Kafka assigns each partition to **exactly one consumer in the group**, so the group processes the topic in parallel, while a different group reads the full topic independently. As a consumer works it **commits** the offset of the next record to read, stored in an internal topic (`__consumer_offsets`). **Lag** is the log-end offset minus the committed offset per partition: a growing lag means consumers are falling behind. A group cannot use more consumers than partitions; extra consumers sit idle.

## Share the work, track the position

Consumers in a group split a topic's partitions; Kafka remembers each group's committed offsets.

![Four ideas: group, assignment, offset, rebalance.](assets/figures/kafka/section-3-map.svg) — Figure 3.1 — Group, assignment, offset and rebalance.

## Group lag from a real broker, run

I read 4 of the 7 records with group `billing`, then ran `kafka-consumer-groups.sh --describe`. Partitions 1 and 2 are fully read (lag 0); partitions 4 and 5 still have 1 and 2 unread records (lag 3 in total). The group has no active members because the console consumer exited.

```bash
kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic orders \
  --group billing --from-beginning --max-messages 4
kafka-consumer-groups.sh --bootstrap-server localhost:9092 --describe --group billing
```

Output:

```
Consumer group 'billing' has no active members.

GROUP   TOPIC   PARTITION  CURRENT-OFFSET  LOG-END-OFFSET  LAG
billing orders  0          0               0               0
billing orders  1          1               1               0
billing orders  2          3               3               0
billing orders  3          0               0               0
billing orders  4          0               1               1
billing orders  5          0               2               2
```

## Lag arithmetic, run

Total lag is the sum over partitions; here 0 + 80 + 420 = 500 records behind.

```python
log_end   = {0: 1500, 1: 1480, 2: 1520}
committed = {0: 1500, 1: 1400, 2: 1100}
lag = {p: log_end[p] - committed[p] for p in log_end}
print(lag, sum(lag.values()))
```

Output:

```
{0: 0, 1: 80, 2: 420} 500
```

**Quiz:** What is consumer lag?

- [ ] Network latency to the broker
- [ ] The age of the topic
- [x] Log-end offset minus the committed offset
- [ ] The number of partitions

*Answer:* Log-end offset minus the committed offset. It counts how many records are written but not yet processed by the group.