Relationship Between MapReduce and Other Components¶
Relationship Between MapReduce and HDFS¶
HDFS features high fault tolerance and high throughput, and can be deployed on low-cost hardware for storing data of applications with massive data sets.
MapReduce is a programming model used for parallel computation of large data sets (larger than 1 TB). Data computed by MapReduce comes from multiple data sources, such as Local FileSystem, HDFS, and databases. Most data comes from the HDFS. The high throughput of HDFS can be used to read massive data. After being computed, data can be stored in HDFS.
Relationship Between MapReduce and Yarn¶
MapReduce is a computing framework running on Yarn, which is used for batch processing. MRv1 is implemented based on MapReduce in Hadoop 1.0, which is composed of programming models (new and old programming APIs), running environment (JobTracker and TaskTracker), and data processing engine (MapTask and ReduceTask). This framework is still weak in scalability, fault tolerance (JobTracker SPOF), and compatibility with multiple frameworks. (Currently, only the MapReduce computing framework is supported.) MRv2 is implemented based on MapReduce in Hadoop 2.0. The source code reuses MRv1 programming models and data processing engine implementation, and the running environment is composed of ResourceManager and ApplicationMaster. ResourceManager is a brand new resource manager system, and ApplicationMaster is responsible for cutting MapReduce job data, assigning tasks, applying for resources, scheduling tasks, and tolerating faults.