Abstract: A method for processing shared data, an apparatus, and a server are provided, and relate to the field of communications technologies, so that shared data can be cached across application programs, which facilitates data sharing across applications programs, can reduce a quantity of repeated computations, and helps accelerate a computing speed of a Spark architecture. The method includes: receiving a first instruction; starting a first Spark context for a first application program, to create a DAG of the first application program, and caching the DAG of the first application program in a first area of a first server; receiving a second instruction; starting a second Spark context for a second application program reading m DAGs from the first area; and caching the to-be-cached shareable RDDs in a main memory of a second server, where the shareable RDD is an RDD included in at least two DAGs of the m DAGs.

Abstract: A method for processing shared data, an apparatus, and a server are provided, and relate to the field of communications technologies, so that shared data can be cached across application programs, which facilitates data sharing across applications programs, can reduce a quantity of repeated computations, and helps accelerate a computing speed of a Spark architecture. The method includes: receiving a first instruction; starting a first Spark context for a first application program, to create a DAG of the first application program, and caching the DAG of the first application program in a first area of a first server; receiving a second instruction; starting a second Spark context for a second application program reading m DAGs from the first area; and caching the to-be-cached shareable RDDs in a main memory of a second server, where the shareable RDD is an RDD included in at least two DAGs of the m DAGs.