Performance Evaluation of Hive-MR3 0.1 (Part I)

Apr 2, 2018 • Sungwoo Park

Since Hive-MR3 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.

In this article, we report the result of testing Hive-MR3 and Hive-on-Tez on the TPC-DS benchmark in a sequential execution setting. We use exactly the same configuration files for both Hive-MR3 and Hive-on-Tez so that any discrepancy in performance can be attributed to the choice of the execution engine. We use the following 7 combinations of Hive-MR3 and Tez runtime:

  1. Hive-MR3 based on Hive 1.2.2 + Tez 0.7.0
  2. Hive-MR3 based on Hive 1.2.2 + Tez 0.8.4
  3. Hive-MR3 based on Hive 1.2.2 + Tez 0.9.1
  4. Hive-MR3 based on Hive 2.1.1 + Tez 0.8.4
  5. Hive-MR3 based on Hive 2.1.1 + Tez 0.9.1
  6. Hive-MR3 based on Hive 2.3.2 + Tez 0.8.4
  7. Hive-MR3 based on Hive 2.3.2 + Tez 0.9.1

We run the experiment in two different clusters: Red and Gold. All the machines in both clusters are identical:

  • 2 x Intel(R) Xeon(R) X5650 CPUs
  • 96GB memory
  • 6 x 500GB HDDs
  • 10 Gigabit network connection

The Red cluster is configured as follows:

  • One master and 10 slaves
  • Hadoop 2.7.1
  • HDFS replication factor of 3
  • Security with Kerberos
  • Dataset with a scale factor of 1TB for the TPC-DS benchmark
  • 80GB memory for Yarn on every slave machine

The Gold cluster is configured as follows:

  • Two masters (with a secondary namenode) and 40 slaves
  • Hadoop 2.7.1
  • HDFS replication factor of 3
  • No security
  • Dataset with a scale factor of 10TB for the TPC-DS benchmark
  • 80GB memory for Yarn on every slave machine

For each combination of Hive-MR3 and Tez runtime, we try MR3 and Tez as the execution engine (by setting hive.execution.engine to mr3 or tez in hive-site.xml). For Hive-MR3 based on Hive 1.2.2, we use 58 queries from the TPC-DS benchmark (excluding query 64 and query 85). For Hive-MR3 based on Hive 2.x, we use 60 queries from the TPC-DS benchmark. For each run, we execute all the queries sequentially, starting from query 3 and ending with query 98, with a single Beeline connection. We reuse configuration settings in the directory conf/tpcds of the Hive-MR3 release for all runs, except that each container uses 4GB memory in the Red cluster and 5GB memory in the Gold cluster, respectively. For those runs in the Gold cluster, we run MR3 DAGAppMasters in LocalProcess mode. Since we are interested only in the relative performance of MR3 with respect to Tez, our experiment does not enable various optimization features found in Hive 2.x.

Experimental results in the Red cluster

The following chart shows the total execution time (in seconds) for each run in the Red cluster: tpcds.red.sequential.total.time

The following table shows: 1) the reduction in execution time (in percentage) when switching from Tez to MR3; 2) the number of queries for which Hive-MR3 runs faster than Hive-on-Tez (out of a total of 60 queries):

Hive-MR3 based on Tez runtime Reduction in execution time Number of faster queries
Hive 1.2.2 Tez 0.7.0 7.18% 45
  Tez 0.8.4 8.94% 49
  Tez 0.9.1 12.00% 45
Hive 2.1.1 Tez 0.8.4 10.43% 44
  Tez 0.9.1 10.40% 43
Hive 2.3.2 Tez 0.8.4 11.51% 46
  Tez 0.9.1 9.21% 44

Experimental results in the Gold cluster

The following chart shows the total execution time (in seconds) for each run in the Gold cluster: tpcds.gold.sequential.total.time

The following table shows: 1) the reduction in execution time (in percentage) when switching from Tez to MR3; 2) the number of queries for which Hive-MR3 runs faster than Hive-on-Tez (out of a total of 60 queries):

Hive-MR3 based on Tez runtime Reduction in execution time Number of faster queries
Hive 1.2.2 Tez 0.7.0 1.74% 42
  Tez 0.8.4 3.93% 46
  Tez 0.9.1 4.53% 45
Hive 2.1.1 Tez 0.8.4 15.41% 46
  Tez 0.9.1 13.50% 44
Hive 2.3.2 Tez 0.8.4 9.34% 43
  Tez 0.9.1 12.27% 42

Analysis of experimental results

We observe that across all the combinations of Hive-MR3 and Tez runtime in both clusters, Hive-MR3 consistently outperforms Hive-on-Tez in terms of the total execution time of all queries. For example, Hive-MR3 based on Hive 2.1.1 reduces the total execution time by 15.41% in the Gold cluster. On average, Hive-MR3 reduces the total execution time by 9.31% in comparison with Hive-on-Tez. Overall we conclude that MR3 has a clear advantage over Tez with respect to performance by virtue of simplicity in the design as well as efficiency in the implementation.

Note that a reduction of 9.31% in the total execution time does mean that MR3 is merely 9.31% faster than Tez on average. In fact, MR3 is much faster than Tez because the majority of the execution time is spent not in the execution engine itself (DAGAppMaster in both MR3 and Tez) but in containers (ContainerWorker in MR3 and TezChild in Tez), which share the same runtime environment in both Hive-MR3 and Hive-on-Tez. As an example, for a hypothetical query whose execution alternates between the execution engine in 20% of the execution time and containers in 80% of the execution time, a reduction of 5% in the total execution time requires another execution execution engine that is twice faster (provided that the behavior of containers remains the same). Thus the reduction in the total execution time up to 15.41% implies that MR3 is indeed much faster than Tez as an execution engine.

An average reduction of 9.31% in the total execution time is something nice to have, but not a dramatic improvement that could easily persuade one to switch to Hive-MR3. After all, end users are usually concerned only with the wall-clock execution time of queries and not with efficiency in the implementation of the execution engine. Moreover it is practically impossible for Hive-MR3 in its current form to achieve a significantly higher reduction in the execution time because Hive-MR3 shares the same runtime environment with Hive-on-Tez. The user experience, however, is radically different in multi-user environments in which many queries run concurrently while competing for resources. In the next article, we will demonstrate the advantage offered by Hive-MR3 in a concurrent execution setting.