- Why you should run Hive on Kubernetes, even in a Hadoop cluster
- Testing MR3 - Principle and Practice
- Hive vs Spark SQL: Hive-LLAP, Hive on MR3, Spark SQL 2.3.2
- Hive Performance: Hive-LLAP in HDP 3.1.4 vs Hive 3/4 on MR3 0.10
- Presto vs Hive on MR3 (Presto 317 vs Hive on MR3 0.10)
- Correctness of Hive on MR3, Presto, and Impala
- Performance Evaluation of Impala, Presto, and Hive on MR3
- Performance Evaluation of SQL-on-Hadoop Systems using the TPC-DS Benchmark
- Performance Comparison of HDP LLAP, Presto, SparkSQL, Hive on Tez, and Hive on MR3 using the TPC-DS Benchmark
- Hive on MR3 0.2 vs Hive-LLAP
- All posts ...
Correctness of Hive on MR3, Presto, and Impala
Jun 26, 2019 • Sungwoo Park
MR3 is now released by DataMonad.
Do you trust Hive? Do you trust Presto? Do you trust Impala? Do you trust your SQL system?
We usually trust popular SQL systems such as Hive, Presto, and Impala. All these systems have been battle tested in production environments for many years, which makes it reasonable to assume that typical queries (almost) always return correct results. Occasionally users find new bugs that produce wrong results, but more often than not, such a correctness bug manifests itself only on a small class of queries under special circumstances. Thus the discovery of new correctness bugs is no threat at all to the soundness of such mature systems, and we keep running common queries without questioning the correctness of their results.
On the other hand, it can be a truly daunting task to manually verify the result of running a query. How can we check for correctness the result of running a complex query on a multi-terabyte dataset that would produce millions of rows if not accompanied by the LIMIT clause? It may be that even finding the correct number of rows is practically impossible, let alone the correctness of their contents, because of the sheer size of the dataset. Ironically we may be more inclined to trust our SQL system precisely because there is no formal way to verify its soundness.
Although it is hard to verify the soundness of an individual SQL system, we can compare several SQL systems together so as to gain (or lose) confidence on their correctness. For comparing three SQL systems, the idea goes as follows (which was inspired by the notion of relative soundness in the field of mathematical logic):
- We run a query on target SQL systems using a sufficiently large dataset.
- If all the results are identical (modulo rounding errors), all the systems are sound with an extremely high probability, i.e., they return a correct answer to the query.
- If only two systems agree on the result, the remaining system is unsound with an extremely high probability, i.e., it returns a wrong answer to the query.
- If all the systems differ in the result, we cannot draw any conclusion about their soundness – either all of them are unsound, or only one of them is sound.
The rationale is that all the systems under comparison have been developed independently of each other. Thus, unless two systems process a query in a correct manner, the chance of accidentally producing the same result is infinitesimal. By running many separate queries, we can further gain (or lose) confidence on their correctness.
This article reports the result of crosschecking Hive on MR3, Presto, and Impala using a variant of the TPC-DS benchmark (consisting of 99 queries) on a 10TB dataset. We take into account rounding errors, and discuss a few queries that produce different results.
We compare the following SQL-on-Hadoop systems using the TPC-DS benchmark.
- Hive 3.1.1 on MR3 0.7
- Presto 0.217
- Impala 2.12.0+cdh5.15.2+0 in Cloudera CDH 5.15.2
We run the experiment in a 13-node cluster using the same setting as in our previous article. The scale factor for the TPC-DS benchmark is 10TB. For Hive on MR3 and Presto, we generate the dataset in ORC. For Impala, we generate the dataset in Parquet. Both datasets are created from the same raw dataset and thus contain exactly the same collection of rows.
In order to increase the chance that different results from the same query translate to different numbers of rows, we modify TPC-DS queries by replacing an existing LIMIT clause with a new SELECT clause. For example, we extend query 12 with a new SELECT clause as follows:
with result as ( --- --- --- *** original query 12 without the LIMIT clause *** --- ---) select i_item_desc ,i_category ,i_class ,i_current_price ,i_item_id, itemrevenue, revenueratio from result where itemrevenue >= 120700.0 * 1.5;
Here the constant in the WHERE clause
itemrevenue >= 120700.0 * 1.5 is chosen arbitrarily.
(The modified query 12 returns 60152 rows instead of 100 rows.)
Because of the use of arbitrary constants in the WHERE clause,
two systems are now unlikely to produce the same number of rows unless both systems agree on the result.
For Presto which uses slightly different SQL syntax, we use another set of queries which are equivalent to the set for Impala and Hive on MR3 down to the level of constants. The reader can find the set of modified TPC-DS queries in the GitHub repository.
For the reader's perusal,
we attach the table containing the results of the experiment.
For each query, we report the number of rows.
If the result contains a single row,
we report the sum of all numerical values in it (marked in the column
If a query does not compile (which occurs in Impala) or fails (which occurs only in Presto),
we leave the cell blank (40 queries for Impala and 4 queries for Presto).
Here is a link to [Google Docs].
For most queries, the three systems return the same result and are thus sound with an extremely high probability. We assume that small differences in the number of rows are due to rounding errors (e.g., 692540 rows vs 690186 rows from query 11). Below we analyze those queries that produce clearly different results.
Hive on MR3 reports about 10 percent fewer rows than Presto, and Impala fails to compile the query. Assuming that the discrepancy is not due to rounding errors, we conclude that at least one of Hive on MR3 and Presto is certainly unsound with respect to query 21.
Hive on MR3 and Presto both report 249 rows whereas Impala reports 170 rows. As the difference in the number of rows is rather large (about 30 percent of 249 rows), we conclude that Impala is probably unsound with respect to query 31.
Query 46, 53, 68, 81
For each query, Hive on MR3 and Presto return the same result whereas Impala returns a smaller number of rows (up to 8 percent smaller). Thus we conclude that Impala is unsound with respect to these queries with a high probability.
In theory, unsoundness implies that a system is totally useless. This is because even if a system successfully returns an answer, we run the risk of using a wrong answer as it is unknown whether the answer is correct or not. Even in practice, there are many areas in which unsoundness is simply not acceptable. For example, nobody would use an unsound electronic calculator returning wrong answers occasionally, even if it returned correct answers most of the times.
Due to their complexity, SQL systems are a good example of software systems whose unsoundness is well accepted. In this article, we have crosschecked three SQL systems for soundness, and found that all of them have a reasonable chance of being unsound with respect to the TPC-DS benchmark. For Impala, we have shown that it is probably unsound with respect to query 31.
For developing Hive on MR3, checking its soundness against other SQL systems is an important part of testing and debugging. It helped us to find subtle errors that would be nearly impossible to detect through system testing only. Thus users of Hive on MR3 may assume that it guarantees at least the same level of correctness as Presto and Impala provide.
Proceed to a new article: Presto vs Hive on MR3 (Presto 317 vs Hive on MR3 0.10).