8 posts tagged with "Spark"

In this article, we report the results of evaluating the performance of the following systems using the 10TB TPC-DS Benchmark.

1. Trino 468 (released in December 2024)
2. Spark 4.0.0-RC2 (released in March 2025)
3. Hive 4.0.0 on Tez (built in February 2025)
4. Hive 4.0.0 on MR3 2.0 (released in April 2025)

Trino is an MPP-style query engine and is not fault-tolerant. The other three systems are fully fault-tolerant.

Introduction​

In our previous article, we have shown that Hive on MR3 1.7 runs much faster than Spark 3.4.0 on the TPC-DS benchmark with a scale factor of 10TB (7415 seconds vs 19669 seconds). The performance gap is expected to widen further due to improvements in Hive on MR3 1.8 (6867 seconds vs 7415 seconds). Still, however, there is a category of queries on which Hive on MR3 seems noticeably slower than Spark: single-table queries with no joins.

Introduction​

In this article, we evaluate the performance of the following systems.

1. Trino 418 (released on May 17, 2023)
2. Spark 3.4.0 (released on Apr 13, 2023)
3. Hive 3.1.3 on MR3 1.7 (released on May 15, 2023)

Introduction​

In this article, we evaluate the performance of the following systems.

1. Spark 2.3.8
2. Spark 3.2.1
3. Hive 3.1.2 on MR3 1.4
4. Hive-LLAP in HDP 3.1.4 (3.1.0.3.1.4.0-315)

Introduction​

MR3 is a general purpose execution engine that provides native support for Hadoop and Kubernetes. While Hive on MR3 is its main application, MR3 can easily execute MapReduce/Tez jobs as well. For example, Hive on MR3 supports compaction without relying on MapReduce because MapReduce jobs performing compaction are directly sent to MR3.

Introduction​

In our previous article published in October 2018, we use the TPC-DS benchmark to compare the performance of Hive-LLAP and SparkSQL 2.3.1 included in HDP 3.0.1 along with Hive 3.1.0 on MR3 0.4. In this article, we update the result by testing SparkSQL 2.3.2 included in HDP 3.1.4. As in the previous experiment, we use the TPC-DS benchmark.

Introduction​

We often ask questions on the performance of SQL-on-Hadoop systems:

• How fast is Hive-LLAP in comparison with Presto, SparkSQL, or Hive on Tez?
• As it is an MPP-style system, does Presto run the fastest if it successfully executes a query?
• As it stores intermediate data in memory, does SparkSQL run much faster than Hive on Tez in general?
• What is the best system for running concurrent queries?
• ...

NOTE: This article is superceded by a new article expanded with results of concurrency tests on newer versions of SQL-on-Hadoop systems.​