In our previous article, we use the TPC-DS benchmark to compare the performance of five SQL-on-Hadoop systems: Hive-LLAP, Presto, SparkSQL, Hive on Tez, and Hive on MR3. As it uses both sequential tests and concurrency tests across three separate clusters, we believe that the performance evaluation is thorough and comprehensive enough to closely reflect the current state in the SQL-on-Hadoop landscape.
We often ask questions on the performance of SQL-on-Hadoop systems:
Hive running on top of MR3 0.2, or Hive-MR3 henceforth, supports LLAP (Low Latency Analytical Processing) I/O. In conjunction with the ability to execute multiple TaskAttempts concurrently inside a single ContainerWorker, the support for LLAP I/O makes Hive-MR3 functionally equivalent to Hive-LLAP. Hence Hive-MR3 can now serve as a substitute for Hive-LLAP in typical use cases.
In order to check if Hive running on top of MR3, or Hive-MR3 henceforth, is ready for production environments, we should test it for performance, stability, and scalability in multi-user environments in which many queries run concurrently. While Hive-on-Tez does a good job in multi-user environments, an analysis of the architecture of Tez reveals that we can further improve its support for multi-user environments by allowing a single DAGAppMaster to manage multiple concurrent DAGs. One of the design goals of MR3 is to overcome this limitation of Tez so as to better support multi-user environments as a new execution engine of Hive.
Since Hive running on top of MR3, or Hive-MR3 henceforth, uses MR3 as its execution engine and borrows runtime environments from Tez, a natural question arises as to whether the use of MR3 results in performance improvement in terms of execution time, turnaround time, or overall throughput at all. While it is difficult to accurately quantify the performance of MR3 over Tez as an execution engine, we can compare Hive-MR3 and Hive-on-Tez under identical conditions to see if there is any benefit of using MR3 in place of Tez.