For log file processing in Hadoop, the TextInputFormat is commonly used. It treats each line in the input file as a separate record, making it suitable for scenarios where log entries are present in a line-by-line format.

Data replication is crucial in Hadoop administration for ensuring data availability and system reliability. Hadoop replicates data across multiple nodes in the cluster to provide fault tolerance. If a node fails, the data can still be retrieved from its replicated copies on other nodes.

In scenarios involving the joining of large datasets, the optimization features of Apache Pig, such as query optimization and parallel execution, significantly improve data processing efficiency. These optimization techniques help in handling large-scale data transformations more effectively, ensuring better performance in complex processing tasks.

Hive is the component in the Hadoop ecosystem primarily used for data warehousing and SQL queries. It provides a high-level language, HiveQL, for querying data stored in Hadoop's distributed storage, making it accessible to analysts familiar with SQL.

When tuning a Hadoop cluster, optimizing the Input Split Size is crucial for MapReduce job performance. It determines the amount of data each mapper processes, and an appropriate split size helps in achieving better parallelism and efficiency in job execution.

The MapReduce Shuffle phase contributes to data processing efficiency by performing data sorting. During this phase, the output of the Map tasks is sorted and partitioned based on keys, ensuring that the data is grouped appropriately before reaching the Reduce tasks. Sorting facilitates faster data processing during the subsequent Reduce phase.

For high-speed data processing in a Java-based Hadoop application, the combination of Apache Spark with Apache Kafka is most effective. Spark provides fast in-memory data processing, and Kafka ensures high-throughput, fault-tolerant data streaming.

UDFs (User Defined Functions) in Apache Pig play a crucial role in data transformation. They allow users to define their custom functions to process and transform data within Pig scripts, providing flexibility and extensibility in data processing operations.

Sqoop is commonly used in Hadoop for importing data from relational databases into HDFS. It provides a command-line interface and supports the transfer of data between Hadoop and relational databases like MySQL, Oracle, and others.

Apache Storm integrates with the Hadoop ecosystem by running on Hadoop YARN. YARN (Yet Another Resource Negotiator) allows Storm to utilize Hadoop's resource management capabilities, making it easier to deploy and manage real-time processing applications alongside batch processing in a Hadoop cluster.

Compaction is the process in HBase that involves combining smaller files into larger ones for efficiency. It helps in reducing the number of files and improving read and write performance in HBase.

The format of input data is crucial in Hadoop application development as it directly impacts job performance. Choosing the right input format, such as Hadoop's preferred formats like SequenceFile or Avro, can enhance data processing efficiency.