Within the Huawei Big Data platform architecture, the Spark service is composed of several key resident processes (daemons) that provide its core functionality. The JobHistory server is a persistent web UI that displays information about completed Spark applications from event logs. The JDBCServer, also known as the Spark Thrift Server, is a long-running process that exposes Spark SQL's query capabilities to external clients through standard JDBC/ODBC interfaces. Both are integral, continuously running components of the Spark service.

C. SparkResource: This is not a standard process name for a daemon within the Apache Spark framework or its implementation in Huawei's platform.

D. NodeManager: The NodeManager is a core resident process of the YARN (Yet Another Resource Negotiator) service, which manages resources on individual cluster nodes. It is not a process of the Spark service itself.

1. Huawei FusionInsight HD Product Documentation: In typical administrator guides for platforms like FusionInsight HD, the component management section details the roles for each service. The Spark service is listed with roles including JobHistoryServer and ThriftServer (JDBCServer). The YARN service is listed separately with roles including ResourceManager and NodeManager. (e.g., FusionInsight HD 6.5.1 Administrator Guide, Section: "Managing Components, Services, and Roles").

2. Official Apache Spark Documentation: The architecture of Spark, upon which Huawei's service is based, explicitly defines these components.

JobHistory: "Spark provides a History Server to display the details of completed applications... It is a standalone daemon..." (Reference: Apache Spark 3.1.2 Documentation, "Monitoring and Instrumentation", Section: "Spark History Server").

JDBCServer: "Spark SQL can also act as a distributed query engine using its JDBC/ODBC or command-line interface. In this mode, end-users or applications can interact with Spark SQL directly to run SQL queries... The Thrift JDBC/ODBC server corresponds to the start-thriftserver.sh script." (Reference: Apache Spark 3.1.2 Documentation, "Spark SQL, DataFrames and Datasets Guide", Section: "Distributed SQL Engine").