The process of commissioning or decommissioning nodes in a Hadoop cluster involves adding or removing data nodes. This dynamic adjustment helps in optimizing the cluster's capacity and resource utilization.

Kafka's Hash-based partitioning mechanism ensures that data with the same key is sent to the same partition, ensuring order and consistency in the distributed system. This is crucial for scalable and reliable data ingestion in Hadoop using Kafka.

To ensure minimal latency in data delivery for a real-time analytics application, configuring Flume to use a Memory Channel is crucial. Memory Channel stores events in memory, providing low-latency data transfer for real-time processing.

The Rack Awareness feature in Hadoop ensures that data blocks are stored on multiple racks, enhancing fault tolerance. This strategy reduces the risk of data loss in case an entire rack or network segment goes down, improving the overall reliability of the Hadoop cluster's data storage.

The primary role of Apache Oozie in the Hadoop ecosystem is workflow coordination. Oozie is a job scheduler that helps in managing and orchestrating workflows of Hadoop jobs, allowing users to define a series of tasks and their dependencies to execute complex data processing jobs.

Scalability is a key consideration in capacity planning for a rapidly expanding Hadoop environment. The architecture should be designed to scale horizontally, allowing the addition of nodes to accommodate growing data and processing needs seamlessly.

In optimizing MapReduce performance, the Shuffle phase plays a key role in managing memory and reducing disk I/O. It involves the exchange of data between the Map and Reduce tasks, and efficient shuffling contributes to overall job efficiency.

Avro is an ideal choice when schema evolution is frequent and critical. Its schema is stored along with the data, allowing for flexible changes over time without requiring all consumers to be updated simultaneously.

Hive uses SQL (Structured Query Language) for querying and managing large datasets. This allows users familiar with traditional relational database querying to work with big data stored in Hadoop without needing to learn complex programming languages like Java or MapReduce.

In a complex MapReduce job, the Partitioner plays a crucial role in data distribution. It determines how the key-value pairs outputted by the Map tasks are distributed to the Reducer tasks. An effective Partitioner ensures that similar keys end up in the same partition, optimizing data processing efficiency during the Reduce phase.