The High Availability feature in Hadoop ensures that there is a backup or standby NameNode that takes over in case the primary NameNode fails. This ensures continuous functioning and fault tolerance in the Hadoop system.

The replication factor in Hadoop cluster configuration is crucial for fault tolerance. It determines the number of copies of each data block stored across the cluster. By replicating data, Hadoop ensures that even if a DataNode fails, there are backup copies available, enhancing the system's resilience to node failures.

Apache Arrow is a cross-language development platform that plays a significant role in optimizing data serialization in Hadoop ecosystems. It provides a standardized in-memory format for efficient data interchange between different data processing frameworks.

The initial step in debugging unexpected output in a Hadoop job should focus on reviewing the Mapper Logic. Analyzing how data is processed in the mapping phase helps identify issues that may affect the final output, such as incorrect data transformations or filtering.

In the event of a NameNode failure, the system can be recovered by restoring it from a backup. Regular backups of the NameNode metadata are essential for quick and efficient recovery in case of a NameNode failure.

A Combiner in MapReduce optimizes data processing by aggregating intermediate outputs from the Mapper before sending them to the Reducer. This reduces the volume of data transferred over the network, improving overall performance by minimizing data movement.

Spark achieves fault tolerance through checkpointing. Periodically, Spark saves the state of the distributed computation to a reliable distributed file system, allowing it to recover lost data and continue processing in the event of a node failure.

YARN's ResourceManager handles large-scale applications differently from Hadoop 1.x's JobTracker by employing dynamic resource allocation. It dynamically allocates resources to applications based on their needs, optimizing resource utilization and improving scalability compared to the fixed assignment in Hadoop 1.x.

Rack Awareness in a Hadoop cluster helps balance the load and improve data locality. It ensures that data blocks are distributed across nodes in a way that considers the physical location of nodes in different racks, reducing network traffic and enhancing performance.

The Custom KeyValue InputFormat in Hadoop is used when standard InputFormats do not meet specific data processing needs. It allows for custom parsing of key-value pairs, providing flexibility in handling various data formats.