Tez¶
Tez is Apache's latest open source computing framework that supports Directed Acyclic Graph (DAG) jobs. It can convert multiple dependent jobs into one job, greatly improving the performance of DAG jobs. If projects like Hive use Tez instead of MapReduce as the backbone of data processing, response time will be significantly reduced. Tez is built on YARN and can run MapReduce jobs without any modification.
MRS uses Tez as the default execution engine of Hive. Tez remarkably surpasses the original MapReduce computing engine in terms of execution efficiency.
For details about Tez, see https://tez.apache.org/.
Relationship Between Tez and MapReduce¶
Tez uses a DAG to organize MapReduce tasks. In the DAG, a node is an RDD, and an edge indicates an operation on the RDD. The core idea is to further split Map tasks and Reduce tasks. A Map task is split into the Input-Processor-Sort-Merge-Output tasks, and the Reduce task is split into the Input-Shuffle-Sort-Merge-Process-output tasks. Tez flexibly regroups several small tasks to form a large DAG job.
Figure 1 Processes for submitting tasks using Hive on MapReduce and Hive on Tez¶
A Hive on MapReduce task contains multiple MapReduce tasks. Each task stores intermediate results to HDFS. The reducer in the previous step provides data for the mapper in the next step. A Hive on Tez task can complete the same processing process in only one task, and HDFS does not need to be accessed between tasks.
Relationship Between Tez and Yarn¶
Tez is a computing framework running on Yarn. The runtime environment consists of ResourceManager and ApplicationMaster of Yarn. 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. In addition, TezUI depends on TimelineServer provided by Yarn to display the running process of Tez tasks.