The StickyAssignor tries to minimize partition movement by preserving existing assignments as much as possible while still achieving a balanced assignment. This improves consumer stability and reduces rebalances.
From the Kafka Consumer Assignor Documentation:
"The StickyAssignor attempts to preserve as many existing assignments as possible, which helps minimize partition movement between rebalances." RoundRobinAssignor focuses on even distribution, not stability.
RangeAssignor groups partitions by topic and assigns them consecutively, but can lead to imbalances.
PartitionAssignor is an abstract base class, not an assignor used directly.
Reference: Kafka Consumer Assignor Docs

According to the official Apache Kafka consumer documentation, the auto.offset.reset configuration is only applied when a consumer has no valid committed offset for a partition. In this scenario, the consumer last committed offset 217, but the topic's earliest available offset is now 318. This means the previously committed offset is out of range, most likely due to topic retention cleanup.
When a committed offset is out of range, Kafka treats it as invalid and applies the auto.offset.reset policy.
Since the consumer is configured with auto.offset.reset=latest, it will start reading from the end of the log, not from the earliest available offset.
Kafka defines the "latest" offset as the log end offset, which is the offset of the next message to be written.
Given that the topic's offsets end at 588, the next offset is 589.
Therefore, the consumer will start reading from offset 589, skipping all existing records in the topic, which aligns with the documented Kafka consumer behavior.

Kafka Connect can run in standalone mode or distributed mode. For simple tasks like reading logs from a file on a single host, standalone mode is recommended.
From Kafka Connect User Guide:
"Standalone mode is useful when running connectors on a single machine (e.g., for development or simple deployments like log collection from a local file)." Distributed mode is preferred for scalability and fault tolerance but overkill for this use case.
Reference: Kafka Connect User Guide > Deployment Modes

Increasing partitions increases parallelism, but also means:
Consumers in a group may have to handle more partitions, especially if the number of consumers is lower than the number of partitions.
This can result in increased lag, especially under high load.
From Kafka Topic Management Docs:
"Increasing the number of partitions increases consumer work, and if consumers can't keep up, lag can accumulate." A is false: existing data is not redistributed.
B is false: records with the same key always map to the same partition based on hash.
D is not directly impacted by the partition count.
Reference: Kafka Topic Management > Adding Partitions

According to the Apache Kafka Consumer API documentation, the assign() method allows a consumer to manually assign specific partitions to itself, bypassing Kafka's consumer group coordination and rebalancing mechanism. When a consumer uses assign() with all partitions of a topic, that consumer will read from every partition, regardless of how many other consumers exist.
This behavior is required when every consumer instance must independently read the full dataset, such as in auditing, monitoring, or replication use cases. In contrast, subscribe() is designed for consumer group usage, where partitions are distributed among consumers, ensuring that each partition is consumed by only one consumer in the group.
Option B is invalid because assign() requires a collection of TopicPartition objects, not just a topic name.
Option C still uses group coordination and does not guarantee all partitions per consumer. Option D is invalid because an empty group ID is not supported by Kafka.
Thus, explicitly calling assign() with all topic partitions is the only correct and documented way to ensure each consumer reads from all partitions.