A 'Channel Selector' in Apache Flume is responsible for routing events to specific channels based on defined criteria. It enables the selective forwarding of events to different channels, allowing for customized handling and distribution of data within the Flume agent.

For in-depth analysis of Hadoop job performance, JProfiler is a tool that can be used to profile Java applications. It provides detailed insights into the behavior and performance of Java code, helping developers optimize their Hadoop jobs for better efficiency.

The DAG Scheduler in Spark plays a crucial role in task execution by performing dependency analysis. It organizes tasks into stages based on their dependencies, optimizing the execution order and minimizing data shuffling. This is essential for efficient and parallel execution of tasks in Spark.

When integrating Python with Hadoop, Hadoop Streaming is commonly used. It allows Python scripts to be used as mappers and reducers in a MapReduce job, enabling Python developers to leverage Hadoop's distributed processing capabilities.

Sqoop is a tool in the Hadoop ecosystem specifically designed for efficiently transferring bulk data between Apache Hadoop and structured datastores such as relational databases. It simplifies the process of importing and exporting data, bridging the gap between Hadoop and traditional databases.

Kafka is the preferred tool for ingesting streaming data from various sources in Hadoop when near-real-time data processing is required. It acts as a distributed, fault-tolerant, and scalable messaging system, efficiently handling real-time data streams.

When a MapReduce job is running slower than expected, the first debugging approach should prioritize examining the Mapper Code. Issues in the mapping phase can significantly impact job performance, and optimizing the mapper logic can lead to performance improvements.

In HiveQL, the EXPLAIN command is used to show the query execution plan. It provides insights into how Hive intends to execute the given query, including the sequence of tasks and operations involved. Analyzing the execution plan helps optimize queries for better performance.

Data Serialization in Hadoop is crucial for optimizing read/write operations on large datasets. Serialization is the process of converting complex data structures into a format that can be easily transmitted or stored. In Hadoop, this optimization helps in efficient data transfer and storage.

In Flume, the Channel Selector mechanism allows for dynamic data routing and transformation. It helps in directing incoming data to different channels based on specified criteria, enabling flexibility in data processing and handling.