SparkSQL vs Hive on Spark - Difference and pros an

Question

SparkSQL CLI internally uses HiveQL and in case Hive on spark(HIVE-7292) , hive uses spark as backend engine. Can somebody throw some more light, how exactly these two scenarios are different and pros and cons of both approaches?

Answer 1

1. When SparkSQL uses hive

   SparkSQL can use HiveMetastore to get the metadata of the data stored in HDFS. This metadata enables SparkSQL to do better optimization of the queries that it executes. Here Spark is the query processor.

2. When Hive uses Spark See the JIRA entry: HIVE-7292

   Here the the data is accessed via spark. And Hive is the Query processor. So we have all the deign features of Spark Core to take advantage of. But this is a Major Improvement for Hive and is still "in progress" as of Feb 2 2016.

3. There is a third option to process data with SparkSQL

   Use SparkSQL without using Hive. Here SparkSQL does not have access to the metadata from the Hive Metastore. And the queries run slower. I have done some performance tests comparing options 1 and 3. The results are here.

Answer 2

SparkSQL vs Spark API you can simply imagine you are in RDBMS world:

SparkSQL is pure SQL, and Spark API is language for writing stored procedure

Hive on Spark is similar to SparkSQL, it is a pure SQL interface that use spark as execution engine, SparkSQL uses Hive's syntax, so as a language, i would say they are almost the same.

but Hive on Spark has a much better support for hive features, especially hiveserver2 and security features, hive features in SparkSQL is really buggy, there is a hiveserver2 impl in SparkSQL, but in latest release version (1.6.x), hiveserver2 in SparkSQL doesn't work with hivevar and hiveconf argument anymore, and the username for login via jdbc doesn't work either...

see https://issues.apache.org/jira/browse/SPARK-13983

i believe hive support in spark project is really very low priority stuff...

sadly Hive on spark integration is not that easy, there are a lot of dependency conflicts... such as https://issues.apache.org/jira/browse/HIVE-13301

and, when i'm trying hive with spark integration, for debug purpose, i'm always starting hive cli like this:

export HADOOP_USER_CLASSPATH_FIRST=true
bin/hive --hiveconf hive.root.logger=DEBUG,console

our requirement is using spark with hiveserver2 in a secure way (with authentication and authorization), currently SparkSQL alone can not provide this, we are using ranger/sentry + Hive on Spark.

hope this can help you to get a better idea which direction you should go.

Answer 3

here is related answer I find in the hive official site:
1.3 Comparison with Shark and Spark SQL  There are two related projects in the Spark ecosystem that provide Hive QL support on Spark: Shark and Spark SQL.  ●The Shark project translates query plans generated by Hive into its own representation and executes them over Spark.   ●Spark SQL is a feature in Spark. It uses Hive’s parser as the frontend to provide Hive QL support. Spark application developers can easily express their data processing logic in SQL, as well as the other Spark operators, in their code. Spark SQL supports a different use case than Hive. 

Compared with Shark and Spark SQL, our approach by design supports all existing Hive features, including Hive QL (and any future extension), and Hive’s integration with authorization, monitoring, auditing, and other operational tools. 

3. Hive­-Level Design  As noted in the introduction, this project takes a different approach from that of Shark or Spark SQL in the sense that we are not going to implement SQL semantics using Spark's primitives. On the contrary, we will implement it using MapReduce primitives. The only new thing here is that these MapReduce primitives will be executed in Spark. In fact, only a few of Spark's primitives will be used in this design. 

The approach of executing Hive’s MapReduce primitives on Spark that is different from what Shark or Spark SQL does has the following direct advantages:  1.Spark users will automatically get the whole set of Hive’s rich features, including any new features that Hive might introduce in the future.  2.This approach avoids or reduces the necessity of any customization work in Hive’s Spark execution engine.
3.It will also limit the scope of the project and reduce longterm maintenance by keeping Hive­-on­-Spark congruent to Hive MapReduce and Tez.